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Sample records for prognosis 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 Exponential Survival Trees And Their Application to Tooth Prognosis

    Science.gov (United States)

    Fan, Juanjuan; Nunn, Martha E.; Su, Xiaogang

    2009-01-01

    SUMMARY This paper is concerned with developing rules for assignment of tooth prognosis based on actual tooth loss in the VA Dental Longitudinal Study. It is also of interest to rank the relative importance of various clinical factors for tooth loss. A multivariate survival tree procedure is proposed. The procedure is built on a parametric exponential frailty model, which leads to greater computational efficiency. We adopted the goodness-of-split pruning algorithm of LeBlanc and Crowley (1993) to determine the best tree size. In addition, the variable importance method is extended to trees grown by goodness-of-fit using an algorithm similar to the random forest procedure in Breiman (2001). Simulation studies for assessing the proposed tree and variable importance methods are presented. To limit the final number of meaningful prognostic groups, an amalgamation algorithm is employed to merge terminal nodes that are homogenous in tooth survival. The resulting prognosis rules and variable importance rankings seem to offer simple yet clear and insightful interpretations. PMID:21709804

  6. 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.

  7. 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

  8. Multivariate data analysis

    Digital Repository Service at National Institute of Oceanography (India)

    Fernandes, A.A.; Antony, M.K.; Somayajulu, Y.K.; Sarma, Y.V.B.; Almeida, A.M.; Mahadevan, R.

    of Center for Space Research, University of Texas, Austin for downloading the global TOPEX/Poseidon sea level height anomaly data. We are grateful to Dr. D.P. Chambers of the Center for Space Research, for enabling us to download the revised and upto date...). Acknowledgements v I wish to express my deep gratitude to Dr. M. R. Rameshkumar, who provided the funds from award monies earned by him, for enabling me to present a paper ?Complex EOF analysis of sea level anomaly in the Indian Ocean at ENSO time scale...

  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 Generalized Multiscale Entropy Analysis

    Directory of Open Access Journals (Sweden)

    Anne Humeau-Heurtier

    2016-11-01

    Full Text Available Multiscale entropy (MSE was introduced in the 2000s to quantify systems’ complexity. MSE relies on (i a coarse-graining procedure to derive a set of time series representing the system dynamics on different time scales; (ii the computation of the sample entropy for each coarse-grained time series. A refined composite MSE (rcMSE—based on the same steps as MSE—also exists. Compared to MSE, rcMSE increases the accuracy of entropy estimation and reduces the probability of inducing undefined entropy for short time series. The multivariate versions of MSE (MMSE and rcMSE (MrcMSE have also been introduced. In the coarse-graining step used in MSE, rcMSE, MMSE, and MrcMSE, the mean value is used to derive representations of the original data at different resolutions. A generalization of MSE was recently published, using the computation of different moments in the coarse-graining procedure. However, so far, this generalization only exists for univariate signals. We therefore herein propose an extension of this generalized MSE to multivariate data. The multivariate generalized algorithms of MMSE and MrcMSE presented herein (MGMSE and MGrcMSE, respectively are first analyzed through the processing of synthetic signals. We reveal that MGrcMSE shows better performance than MGMSE for short multivariate data. We then study the performance of MGrcMSE on two sets of short multivariate electroencephalograms (EEG available in the public domain. We report that MGrcMSE may show better performance than MrcMSE in distinguishing different types of multivariate EEG data. MGrcMSE could therefore supplement MMSE or MrcMSE in the processing of multivariate datasets.

  12. 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

  13. Detrended fluctuation analysis of multivariate time series

    Science.gov (United States)

    Xiong, Hui; Shang, P.

    2017-01-01

    In this work, we generalize the detrended fluctuation analysis (DFA) to the multivariate case, named multivariate DFA (MVDFA). The validity of the proposed MVDFA is illustrated by numerical simulations on synthetic multivariate processes, where the cases that initial data are generated independently from the same system and from different systems as well as the correlated variate from one system are considered. Moreover, the proposed MVDFA works well when applied to the multi-scale analysis of the returns of stock indices in Chinese and US stock markets. Generally, connections between the multivariate system and the individual variate are uncovered, showing the solid performances of MVDFA and the multi-scale MVDFA.

  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 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

  17. 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.

  18. Analysis of multivariate social science data

    CERN Document Server

    Bartholomew, David J; Galbraith, Jane; Moustaki, Irini

    2008-01-01

    Drawing on the authors' varied experiences working and teaching in the field, Analysis of Multivariate Social Science Data, Second Editionenables a basic understanding of how to use key multivariate methods in the social sciences. With updates in every chapter, this edition expands its topics to include regression analysis, confirmatory factor analysis, structural equation models, and multilevel models. After emphasizing the summarization of data in the first several chapters, the authors focus on regression analysis. This chapter provides a link between the two halves of the book, signal

  19. 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.

  20. Software For Multivariable Frequency-Domain Analysis

    Science.gov (United States)

    Armstrong, Ernest S.; Giesy, Daniel P.

    1991-01-01

    FREQ (Multivariable Frequency Domain Singular Value Analysis Package) software package of subroutines performing frequency-domain analysis of: continuous- or discrete-multivariable linear systems; any continuous system for which one calculates transfer matrix at points on imaginary axis; or any discrete system for which one calculates transfer matrix at points on unit circle. Four different versions available. Single-precision brief version LAR-14119, single-precision complete version LAR-14120, double-precision brief version LAR-14121, and double-precision complete version LAR-14122. Written in ANSI standard FORTRAN 77.

  1. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

    Science.gov (United States)

    Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

    2015-01-07

    Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web based survey and revised during a three day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).To encourage dissemination of the TRIPOD Statement, this article is freely accessible on the Annals of Internal Medicine Web site (www.annals.org) and

  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. Multivariate Analysis for the Processing of Signals

    Directory of Open Access Journals (Sweden)

    Beattie J.R.

    2014-01-01

    Full Text Available Real-world experiments are becoming increasingly more complex, needing techniques capable of tracking this complexity. Signal based measurements are often used to capture this complexity, where a signal is a record of a sample’s response to a parameter (e.g. time, displacement, voltage, wavelength that is varied over a range of values. In signals the responses at each value of the varied parameter are related to each other, depending on the composition or state sample being measured. Since signals contain multiple information points, they have rich information content but are generally complex to comprehend. Multivariate Analysis (MA has profoundly transformed their analysis by allowing gross simplification of the tangled web of variation. In addition MA has also provided the advantage of being much more robust to the influence of noise than univariate methods of analysis. In recent years, there has been a growing awareness that the nature of the multivariate methods allows exploitation of its benefits for purposes other than data analysis, such as pre-processing of signals with the aim of eliminating irrelevant variations prior to analysis of the signal of interest. It has been shown that exploiting multivariate data reduction in an appropriate way can allow high fidelity denoising (removal of irreproducible non-signals, consistent and reproducible noise-insensitive correction of baseline distortions (removal of reproducible non-signals, accurate elimination of interfering signals (removal of reproducible but unwanted signals and the standardisation of signal amplitude fluctuations. At present, the field is relatively small but the possibilities for much wider application are considerable. Where signal properties are suitable for MA (such as the signal being stationary along the x-axis, these signal based corrections have the potential to be highly reproducible, and highly adaptable and are applicable in situations where the data is noisy or

  6. International Conference on Measurement and Multivariate Analysis

    CERN Document Server

    Baba, Yasumasa; Bozdogan, Hamparsum; Kanefuji, Koji; Measurement and Multivariate Analysis

    2002-01-01

    Diversity is characteristic of the information age and also of statistics. To date, the social sciences have contributed greatly to the development of handling data under the rubric of measurement, while the statistical sciences have made phenomenal advances in theory and algorithms. Measurement and Multivariate Analysis promotes an effective interplay between those two realms of research-diversity with unity. The union and the intersection of those two areas of interest are reflected in the papers in this book, drawn from an international conference in Banff, Canada, with participants from 15 countries. In five major categories - scaling, structural analysis, statistical inference, algorithms, and data analysis - readers will find a rich variety of topics of current interest in the extended statistical community.

  7. Classification of adulterated honeys by multivariate analysis.

    Science.gov (United States)

    Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad

    2017-06-01

    In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%). Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Multivariate analysis methods for spectroscopic blood analysis

    Science.gov (United States)

    Wood, Michael F. G.; Rohani, Arash; Ghazalah, Rashid; Vitkin, I. Alex; Pawluczyk, Romuald

    2012-01-01

    Blood tests are an essential tool in clinical medicine with the ability diagnosis or monitor various diseases and conditions; however, the complexities of these measurements currently restrict them to a laboratory setting. P&P Optica has developed and currently produces patented high performance spectrometers and is developing a spectrometer-based system for rapid reagent-free blood analysis. An important aspect of this analysis is the need to extract the analyte specific information from the measured signal such that the analyte concentrations can be determined. To this end, advanced chemometric methods are currently being investigated and have been tested using simulated spectra. A blood plasma model was used to generate Raman, near infrared, and optical rotatory dispersion spectra with glucose as the target analyte. The potential of combined chemometric techniques, where multiple spectroscopy modalities are used in a single regression model to improve the prediction ability was investigated using unfold partial least squares and multiblock partial least squares. Results show improvement in the predictions of glucose levels using the combined methods and demonstrate potential for multiblock chemometrics in spectroscopic blood analysis.

  9. 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.

  10. 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.

  11. Heritability of somatotype components: a multivariate analysis.

    Science.gov (United States)

    Peeters, M W; Thomis, M A; Loos, R J F; Derom, C A; Fagard, R; Claessens, A L; Vlietinck, R F; Beunen, G P

    2007-08-01

    To study the genetic and environmental determination of variation in Heath-Carter somatotype (ST) components (endomorphy, mesomorphy and ectomorphy). Multivariate path analysis on twin data. Eight hundred and three members of 424 adult Flemish twin pairs (18-34 years of age). The results indicate the significance of sex differences and the significance of the covariation between the three ST components. After age-regression, variation of the population in ST components and their covariation is explained by additive genetic sources of variance (A), shared (familial) environment (C) and unique environment (E). In men, additive genetic sources of variance explain 28.0% (CI 8.7-50.8%), 86.3% (71.6-90.2%) and 66.5% (37.4-85.1%) for endomorphy, mesomorphy and ectomorphy, respectively. For women, corresponding values are 32.3% (8.9-55.6%), 82.0% (67.7-87.7%) and 70.1% (48.9-81.8%). For all components in men and women, more than 70% of the total variation was explained by sources of variance shared between the three components, emphasising the importance of analysing the ST in a multivariate way. The findings suggest that the high heritabilities for mesomorphy and ectomorphy reported in earlier twin studies in adolescence are maintained in adulthood. For endomorphy, which represents a relative measure of subcutaneous adipose tissue, however, the results suggest heritability may be considerably lower than most values reported in earlier studies on adolescent twins. The heritability is also lower than values reported for, for example, body mass index (BMI), which next to the weight of organs and adipose tissue also includes muscle and bone tissue. Considering the differences in heritability between musculoskeletal robustness (mesomorphy) and subcutaneous adipose tissue (endomorphy) it may be questioned whether studying the genetics of BMI will eventually lead to a better understanding of the genetics of fatness, obesity and overweight.

  12. Multivariate statistical analysis of wildfires in Portugal

    Science.gov (United States)

    Costa, Ricardo; Caramelo, Liliana; Pereira, Mário

    2013-04-01

    Several studies demonstrate that wildfires in Portugal present high temporal and spatial variability as well as cluster behavior (Pereira et al., 2005, 2011). This study aims to contribute to the characterization of the fire regime in Portugal with the multivariate statistical analysis of the time series of number of fires and area burned in Portugal during the 1980 - 2009 period. The data used in the analysis is an extended version of the Rural Fire Portuguese Database (PRFD) (Pereira et al, 2011), provided by the National Forest Authority (Autoridade Florestal Nacional, AFN), the Portuguese Forest Service, which includes information for more than 500,000 fire records. There are many multiple advanced techniques for examining the relationships among multiple time series at the same time (e.g., canonical correlation analysis, principal components analysis, factor analysis, path analysis, multiple analyses of variance, clustering systems). This study compares and discusses the results obtained with these different techniques. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005: "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 This work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project FCOMP-01-0124-FEDER-022692, the project FLAIR (PTDC/AAC-AMB/104702/2008) and the EU 7th Framework Program through FUME (contract number 243888).

  13. Nonlinear Granger Causality: Guidelines for Multivariate Analysis

    NARCIS (Netherlands)

    Diks, C.; Wolski, M.

    2016-01-01

    We propose an extension of the bivariate nonparametric Diks–Panchenko Granger non-causality test to multivariate settings. We first show that the asymptotic theory for the bivariate test fails to apply to the multivariate case, because the kernel density estimator bias and variance cannot both tend

  14. [Risk factors for preeclampsia. Multivariate analysis].

    Science.gov (United States)

    González, A L; Ulloa Galván, G; Alpuche, G; Romero Arauz, J F

    2000-08-01

    To determine in multivariate analysis the clinical, social, and demographic factors for preeclampsia. A case-control study was designed. Three hundred patients were included, divided in two groups. 150 cases with criteria diagnosis for preeclampsia. 150 patients with normal pregnancy and deliveries. The main variables analyzed were age, schooling, marital status, employment, socioeconomic status, smoking and alcohol consumption, body mass index, familiar history of preeclampsia, history of preeclampsia in previous pregnancy, parity and type of pregnancy (single or multiple). For comparison of cases and controls on categorical variables, odds ratios and 95% confidence intervals were calculated, and multiple logistic regression analyses. Multiple logistic regression analysis showed that history of preeclampsia in previous pregnancy has OR 23.7, 95% p < 0.001, familiar history of preeclampsia OR 1.62, p < 0.08, high body mass has OR 1.60. The knowledge of the most important risk factors in our population could be useful for the clinical to pre-detect the patient who will develop preeclampsia.

  15. 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.

  16. Multivariate analysis of industrial scale fermentation data

    DEFF Research Database (Denmark)

    Mears, Lisa; Nørregård, Rasmus; Stocks, Stuart

    , and thereforeareas offocus for optimising the processoperation.This requires multivariate methods which canutilise the complexdatasetswhich areroutinely collected, containing online measured variables and offline sample data.Fermentation processes are highly sensitive to operational changes, as well as between...

  17. Method for statistical data analysis of multivariate observations

    CERN Document Server

    Gnanadesikan, R

    1997-01-01

    A practical guide for multivariate statistical techniques-- now updated and revised In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data-based interpretations relevant to multivariate analysis in a way that addresses real-world problems arising in many areas of inte

  18. Multivariate Model for Test Response Analysis

    NARCIS (Netherlands)

    Krishnan, Shaji; Krishnan, Shaji; Kerkhoff, Hans G.

    2010-01-01

    A systematic approach to construct an effective multivariate test response model for capturing manufacturing defects in electronic products is described. The effectiveness of the model is demonstrated by its capability in reducing the number of test-points, while achieving the maximal coverage

  19. Multivariate Analysis of Genotype?Phenotype Association

    OpenAIRE

    Mitteroecker, Philipp; Cheverud, James M.; Pavlicev, Mihaela

    2016-01-01

    With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent var...

  20. 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...

  1. Predictors of dating violence: a multivariate analysis.

    Science.gov (United States)

    Bookwala, J; Frieze, I H; Smith, C; Ryan, K

    1992-01-01

    A multivariate approach was used to determine the pattern of predictors associated with engaging in dating violence. Predictors were selected whose relationship to dating violence has been established by earlier research: attitudes toward violence, sex-role attitudes, romantic jealousy, general levels of interpersonal aggression, verbal aggression, and verbal and physical aggression received from one's partner. Participants included 305 introductory psychology student volunteers (227 females and 78 males) who completed a set of scales related to dating relationships. Expecting different patterns of predictors to emerge for men and women, we performed separate multiple regression analyses for each. Of the set of predictors employed, receipt of physical violence from one's partner emerged as the largest predictor of expressed violence for both men and women. In addition, higher scores on attitudes toward violence and verbal aggression, and less traditional sex-role attitudes emerged as significant predictors of expressed violence for men. For women, less accepting attitudes toward violence, more traditional sex-role attitudes, feelings of romantic jealousy, higher general levels of interpersonal aggression, and verbal aggression were predictive of expressed violence. The implications of our findings for future research are discussed.

  2. 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

  3. Treatment of oesophageal perforation: a multivariate analysis

    NARCIS (Netherlands)

    Tilanus, H. W.; Bossuyt, P.; Eeftinck Schattenkerk, M.; Obertop, H.

    1991-01-01

    Perforation of the oesophagus was retrospectively analysed in 59 patients. Cause and extent of perforation, localization, quality of the oesophageal wall and therapeutic modes were subjected to univariate analysis. The perforations of the intrathoracic oesophagus (39) were also subjected to

  4. 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......, although different subsets of protein spots were selected. The explorative approach of using multivariate data analysis and variable selection in the analyses of 2-DEs seems to be promising as a fast, reliable and convenient way of screening and transforming many gel images into spot quantities....

  5. 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 ...

  6. 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.

  7. 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.

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

    African Journals Online (AJOL)

    Abstract. Multivariate statistical analysis was used to investigate changes in water chemistry at 5 river sites in the Vaal Dam catch- ... analysis (CCA) showed that the environmental variables used in the analysis, discharge and month of sampling, explained ...... DINGENEN R, WILD O and ZENG G (2006) The global atmos-.

  9. 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.

  10. [A comparable analysis of IPSS and IPSS-R for evaluating prognosis of myelodysplastic syndrome].

    Science.gov (United States)

    Lei, Ye; Xu, Xiaoqian; Yang, Jianmin; Zhang, Weiping; Song, Xianmin; Cheng, Hui; Gong, Shenglan; Wang, Jianmin

    2014-12-01

    To investigate the patients' characteristics and efficacy of prognosis evaluation by International Prognosis Scoring System (IPSS) and Revised International Prognosis Scoring System (IPSS-R) in patients with myelodysplastic syndrome (MDS). Prognostic value of IPSS and IPSS-R was evaluated on clinical data from 159 MDS patients, according to WHO classification. With a median age of 44 years (range:15-80 years), MDS patients had the frequency of 38.56% with abnormal karyotype, including the most common abnormality +8 (20/153, 12.6%). 34 of 142 patients transformed into leukemia. Age and the level of β2 micro-globulin were the prognostic factors by multivariate analysis and IPSS-R had a better prognostic significance. The differences in cumulative survival between IPSS subgroups were significant (P0.05). There were statistical differences for IPSS-R low risk group vs high or very high risk group, and intermediate risk group vs high or very high risk group (Pcompared with those in Europe and America. The proportion of higher risk (worse than good karyotype) in IPSS-R was higher than that in Europe and America. Age and the level of β2 micro-globulin were prognostic factors. Both IPSS and IPSS-R were applicable in Chinese MDS patients and the latter performed better. Applying IPSS-R to re-stratify IPSS subgroups helps evaluate prognosis more accurately and improve treatment outcomes.

  11. Estimating combining ability in popcorn lines using multivariate analysis

    Directory of Open Access Journals (Sweden)

    Leandro Simôes Azeredo Gonçalves

    2014-03-01

    Full Text Available Aiming to estimate the combining ability in tropical and temperate popcorn (Zea mays L. var. everta Sturt. lines using multivariate analysis, ten popcorn lines were crossed in a complete diallel without reciprocals and the lines and hybrids were tested in two randomized complete block experiments with three replicates. Data were subjected to univariate and multivariate ANOVA, principal component analysis, and univariate and multivariate diallel analysis. For multivariate diallel analysis, variables were divided into group I (grain yield, mean weight of ears with grains, popping expansion, mean number of ears per plant, and final stand and group II (days to silking, plant height, first ear height, and lodged or broken plants. The P2 line had positive values for agronomic traits related to yield and popping expansion for group I, whereas the P4 line had fewer days to silking and lodged or broken plants for group II. Regarding the hybrids, P2 x P7 exhibited favorable values for most of the analyzed variables and had potential for recommendation. The multivariate diallel analysis can be useful in popcorn genetic improvement programs, particularly when directed toward the best cross combinations, where the objective is to simultaneously obtain genetic gains in multiple traits.

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

    African Journals Online (AJOL)

    Multivariate cluster analysis of some major and trace elements distribution in an unsaturated zone profile, Densu river basin, Ghana. ... to human activities. Cluster analysis of the samples shows only one sample is needed from depths characterised by similar physical properties of texture and colour. Key words: Unsaturated ...

  13. 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

  14. 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 ...

  15. 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

  16. Multivariate statistical analysis of a multi-step industrial processes

    DEFF Research Database (Denmark)

    Reinikainen, S.P.; Høskuldsson, Agnar

    2007-01-01

    multivariate multi-step processes, where results from each step are used to evaluate future results, is presented. The methods presented are based on Priority PLS Regression. The basic idea is to compute the weights in the regression analysis for given steps, but adjust all data by the resulting score vectors...

  17. 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…

  18. Using Multivariate Statistical Analysis for Grouping of State Forest Enterprises

    Directory of Open Access Journals (Sweden)

    Atakan Öztürk

    2010-11-01

    Full Text Available The purpose of this study was to investigate the use possibilities of multivariate statistical analysis methods for grouping of Forest Enterprises. This study involved 24 Forest Enterprises in Eastern Black Sea Region. A total 69 variables, classified as physical, economic, social, rural settlements, technical-managerial, and functional variables, were developed. Multivariate statistics such as factor, cluster and discriminate analyses were used to classify 24 Forest Enterpprises. These enterprises classified into 2 groups. 22 enterprises were in first group and while remained 2 enterprises in second group.

  19. 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...... determine the result. This article reviews the most commonly used robust multivariate regression and exploratory methods that have appeared since 1996 in the field of chemometrics. Special emphasis is put on the robust versions of chemometric standard tools like PCA and PLS and the corresponding robust...

  20. An Object-Oriented Framework for Robust Multivariate Analysis

    Directory of Open Access Journals (Sweden)

    Valentin Todorov

    2009-10-01

    Full Text Available Taking advantage of the S4 class system of the programming environment R, which facilitates the creation and maintenance of reusable and modular components, an object-oriented framework for robust multivariate analysis was developed. The framework resides in the packages robustbase and rrcov and includes an almost complete set of algorithms for computing robust multivariate location and scatter, various robust methods for principal component analysis as well as robust linear and quadratic discriminant analysis. The design of these methods follows common patterns which we call statistical design patterns in analogy to the design patterns widely used in software engineering. The application of the framework to data analysis as well as possible extensions by the development of new methods is demonstrated on examples which themselves are part of the package rrcov.

  1. 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.

  2. Temporal MDS Plots for Analysis of Multivariate Data.

    Science.gov (United States)

    Jäckle, Dominik; Fischer, Fabian; Schreck, Tobias; Keim, Daniel A

    2016-01-01

    Multivariate time series data can be found in many application domains. Examples include data from computer networks, healthcare, social networks, or financial markets. Often, patterns in such data evolve over time among multiple dimensions and are hard to detect. Dimensionality reduction methods such as PCA and MDS allow analysis and visualization of multivariate data, but per se do not provide means to explore multivariate patterns over time. We propose Temporal Multidimensional Scaling (TMDS), a novel visualization technique that computes temporal one-dimensional MDS plots for multivariate data which evolve over time. Using a sliding window approach, MDS is computed for each data window separately, and the results are plotted sequentially along the time axis, taking care of plot alignment. Our TMDS plots enable visual identification of patterns based on multidimensional similarity of the data evolving over time. We demonstrate the usefulness of our approach in the field of network security and show in two case studies how users can iteratively explore the data to identify previously unknown, temporally evolving patterns.

  3. Breast cancer prognosis by combinatorial analysis of gene expression data.

    Science.gov (United States)

    Alexe, Gabriela; Alexe, Sorin; Axelrod, David E; Bonates, Tibérius O; Lozina, Irina I; Reiss, Michael; Hammer, Peter L

    2006-01-01

    The potential of applying data analysis tools to microarray data for diagnosis and prognosis is illustrated on the recent breast cancer dataset of van 't Veer and coworkers. We re-examine that dataset using the novel technique of logical analysis of data (LAD), with the double objective of discovering patterns characteristic for cases with good or poor outcome, using them for accurate and justifiable predictions; and deriving novel information about the role of genes, the existence of special classes of cases, and other factors. Data were analyzed using the combinatorics and optimization-based method of LAD, recently shown to provide highly accurate diagnostic and prognostic systems in cardiology, cancer proteomics, hematology, pulmonology, and other disciplines. LAD identified a subset of 17 of the 25,000 genes, capable of fully distinguishing between patients with poor, respectively good prognoses. An extensive list of 'patterns' or 'combinatorial biomarkers' (that is, combinations of genes and limitations on their expression levels) was generated, and 40 patterns were used to create a prognostic system, shown to have 100% and 92.9% weighted accuracy on the training and test sets, respectively. The prognostic system uses fewer genes than other methods, and has similar or better accuracy than those reported in other studies. Out of the 17 genes identified by LAD, three (respectively, five) were shown to play a significant role in determining poor (respectively, good) prognosis. Two new classes of patients (described by similar sets of covering patterns, gene expression ranges, and clinical features) were discovered. As a by-product of the study, it is shown that the training and the test sets of van 't Veer have differing characteristics. The study shows that LAD provides an accurate and fully explanatory prognostic system for breast cancer using genomic data (that is, a system that, in addition to predicting good or poor prognosis, provides an individualized

  4. Multivariant design and multiple criteria analysis of building refurbishments

    Energy Technology Data Exchange (ETDEWEB)

    Kaklauskas, A.; Zavadskas, E. K.; Raslanas, S. [Faculty of Civil Engineering, Vilnius Gediminas Technical University, Vilnius (Lithuania)

    2005-07-01

    In order to design and realize an efficient building refurbishment, it is necessary to carry out an exhaustive investigation of all solutions that form it. The efficiency level of the considered building's refurbishment depends on a great many of factors, including: cost of refurbishment, annual fuel economy after refurbishment, tentative pay-back time, harmfulness to health of the materials used, aesthetics, maintenance properties, functionality, comfort, sound insulation and longevity, etc. Solutions of an alternative character allow for a more rational and realistic assessment of economic, ecological, legislative, climatic, social and political conditions, traditions and for better the satisfaction of customer requirements. They also enable one to cut down on refurbishment costs. In carrying out the multivariant design and multiple criteria analysis of a building refurbishment much data was processed and evaluated. Feasible alternatives could be as many as 100,000. How to perform a multivariant design and multiple criteria analysis of alternate alternatives based on the enormous amount of information became the problem. Method of multivariant design and multiple criteria of a building refurbishment's analysis were developed by the authors to solve the above problems. In order to demonstrate the developed method, a practical example is presented in this paper. (author)

  5. 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.

    2016-01-01

    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 that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. PMID:26099484

  6. Multivariate statistical analysis of atom probe tomography data.

    Science.gov (United States)

    Parish, Chad M; Miller, Michael K

    2010-10-01

    The application of spectrum imaging multivariate statistical analysis methods, specifically principal component analysis (PCA), to atom probe tomography (APT) data has been investigated. The mathematical method of analysis is described and the results for two example datasets are analyzed and presented. The first dataset is from the analysis of a PM 2000 Fe-Cr-Al-Ti steel containing two different ultrafine precipitate populations. PCA properly describes the matrix and precipitate phases in a simple and intuitive manner. A second APT example is from the analysis of an irradiated reactor pressure vessel steel. Fine, nm-scale Cu-enriched precipitates having a core-shell structure were identified and qualitatively described by PCA. Advantages, disadvantages, and future prospects for implementing these data analysis methodologies for APT datasets, particularly with regard to quantitative analysis, are also discussed. Copyright 2010 Elsevier B.V. All rights reserved.

  7. 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 ...

  8. Discrete Multivariate Analysis Theory and Practice Theory and Practice

    CERN Document Server

    Bishop, Yvonne M M; Holland, Paul W

    2007-01-01

    The scientist searching for structure in large systems of data finds inspiration in his own discipline, support from modern computing, and guidance from statistical models. Because large sets of data are likely to be complicated, and because so many approaches suggest themselves, a codification of techniques of analysis, regarded as attractive paths rather than as straitjackets, offers the scientist valuable directions to try. The literature on discrete multivariate analysis, although extensive, is widely scattered. This book brings that literature together in an organized way

  9. 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

  10. Jelly pineapple syneresis assessment via univariate and multivariate analysis

    OpenAIRE

    Carlos Alberto da Silva Ledo; Rossana Catie Bueno de Godoy; Arislete Dantas de Aquino; Silvana Licodiedoff

    2010-01-01

    The evaluation of the pineapple jelly is intended to analyze the occurrence of syneresis by univariate and multivariate analysis. The jelly of the pineapple presents low concentration pectin, therefore, it was added high methoxyl pectin in the following concentrations: 0.50%, 0.75% and 1.00% corresponding to slow, medium and fast speed of gel formation process. In this study it was checked the pH, acidity, brix and the syneresis of jelly. The highest concentration of pectin in the jelly showe...

  11. 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

  12. Bayesian Analysis of Multivariate Nominal Measures Using Multivariate Multinomial Probit Models.

    Science.gov (United States)

    Zhang, Xiao; Boscardin, W John; Belin, Thomas R

    2008-03-15

    The multinomial probit model has emerged as a useful framework for modeling nominal categorical data, but extending such models to multivariate measures presents computational challenges. Following a Bayesian paradigm, we use a Markov chain Monte Carlo (MCMC) method to analyze multivariate nominal measures through multivariate multinomial probit models. As with a univariate version of the model, identification of model parameters requires restrictions on the covariance matrix of the latent variables that are introduced to define the probit specification. To sample the covariance matrix with restrictions within the MCMC procedure, we use a parameter-extended Metropolis-Hastings algorithm that incorporates artificial variance parameters to transform the problem into a set of simpler tasks including sampling an unrestricted covariance matrix. The parameter-extended algorithm also allows for flexible prior distributions on covariance matrices. The prior specification in the method described here generalizes earlier approaches to analyzing univariate nominal data, and the multivariate correlation structure in the method described here generalizes the autoregressive structure proposed in previous multiperiod multinomial probit models. Our methodology is illustrated through a simulated example and an application to a cancer-control study aiming to achieve early detection of breast cancer.

  13. 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.

  14. Multivariate analysis of quantitative traits can effectively classify rapeseed germplasm

    Directory of Open Access Journals (Sweden)

    Jankulovska Mirjana

    2014-01-01

    Full Text Available In this study, the use of different multivariate approaches to classify rapeseed genotypes based on quantitative traits has been presented. Tree regression analysis, PCA analysis and two-way cluster analysis were applied in order todescribe and understand the extent of genetic variability in spring rapeseed genotype by trait data. The traits which highly influenced seed and oil yield in rapeseed were successfully identified by the tree regression analysis. Principal predictor for both response variables was number of pods per plant (NP. NP and 1000 seed weight could help in the selection of high yielding genotypes. High values for both traits and oil content could lead to high oil yielding genotypes. These traits may serve as indirect selection criteria and can lead to improvement of seed and oil yield in rapeseed. Quantitative traits that explained most of the variability in the studied germplasm were classified using principal component analysis. In this data set, five PCs were identified, out of which the first three PCs explained 63% of the total variance. It helped in facilitating the choice of variables based on which the genotypes’ clustering could be performed. The two-way cluster analysissimultaneously clustered genotypes and quantitative traits. The final number of clusters was determined using bootstrapping technique. This approach provided clear overview on the variability of the analyzed genotypes. The genotypes that have similar performance regarding the traits included in this study can be easily detected on the heatmap. Genotypes grouped in the clusters 1 and 8 had high values for seed and oil yield, and relatively short vegetative growth duration period and those in cluster 9, combined moderate to low values for vegetative growth duration and moderate to high seed and oil yield. These genotypes should be further exploited and implemented in the rapeseed breeding program. The combined application of these multivariate methods

  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 meta-analysis with an increasing number of parameters.

    Science.gov (United States)

    Boca, Simina M; Pfeiffer, Ruth M; Sampson, Joshua N

    2017-05-01

    Meta-analysis can average estimates of multiple parameters, such as a treatment's effect on multiple outcomes, across studies. Univariate meta-analysis (UVMA) considers each parameter individually, while multivariate meta-analysis (MVMA) considers the parameters jointly and accounts for the correlation between their estimates. The performance of MVMA and UVMA has been extensively compared in scenarios with two parameters. Our objective is to compare the performance of MVMA and UVMA as the number of parameters, p, increases. Specifically, we show that (i) for fixed-effect (FE) meta-analysis, the benefit from using MVMA can substantially increase as p increases; (ii) for random effects (RE) meta-analysis, the benefit from MVMA can increase as p increases, but the potential improvement is modest in the presence of high between-study variability and the actual improvement is further reduced by the need to estimate an increasingly large between study covariance matrix; and (iii) when there is little to no between-study variability, the loss of efficiency due to choosing RE MVMA over FE MVMA increases as p increases. We demonstrate these three features through theory, simulation, and a meta-analysis of risk factors for non-Hodgkin lymphoma. © Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

  17. Sedimentary chemofacies characterization by means of multivariate analysis

    Science.gov (United States)

    Montero-Serrano, Jean Carlos; Palarea-Albaladejo, Javier; Martín-Fernández, Josep A.; Martínez-Santana, Manuel; Gutiérrez-Martín, José Vicente

    2010-07-01

    Multivariate statistical analysis is applied to geochemical data from three sections forming part of the stratigraphic record of the Cerro Pelado Formation (Oligocene-Miocene), in the central region of the Falcón Basin, northwestern Venezuela. Our main goal is introducing and testing a statistical protocol for the identification of chemofacies in the studied sections. The first step involves data preparation and cleaning: selection of relevant components, convenient replacement of values below the detection limit and determination of outliers. Second, a biplot analysis allows us to infer geochemical processes that can be interpreted from a paleoenvironmental point of view: detrital association, redox-organic matter association and carbonatic association. Considering such geochemical associations, a constrained cluster analysis is then carried out to determine the chemofacies for each section. According to the compositional nature of geochemical data, all statistical analysis is conducted within a log-ratio analysis framework. In addition, robust statistical methods are considered for outlier detection and biplot representation in order to smooth the influence of potential outliers on the estimates.

  18. Multivariate Statistical Analysis of the Tularosa-Hueco Basin

    Science.gov (United States)

    Agrawala, G.; Walton, J. C.

    2006-12-01

    The border region is growing rapidly and experiencing a sharp decline both in water quality and availability putting a strain on the quickly diminishing resource. Since water is used primarily for agricultural, domestic, commercial, livestock, mining and power generation, its rapid depletion is of major concern in the region. Tools such as Principal Component Analysis (PCA), Correspondence Analysis and Cluster Analysis have the potential to present new insight into this problem. The Tularosa-Hueco Basin is analyzed here using some of these Multivariate Analysis methods. PCA is applied to geo-chemical data from the region and a Cluster Analysis is applied to the results in order to group wells with similar characteristics. The derived Principal Axis and well groups are presented as biplots and overlaid on a digital elevation map of the region providing a visualization of potential interactions and flow path between surface water and ground water. Simulation by this modeling technique give a valuable insight to the water chemistry and the potential pollution threats to the already water diminishing resources.

  19. Influence analysis for skew-normal semiparametric joint models of multivariate longitudinal and multivariate survival data.

    Science.gov (United States)

    Tang, An-Min; Tang, Nian-Sheng; Zhu, Hongtu

    2017-04-30

    The normality assumption of measurement error is a widely used distribution in joint models of longitudinal and survival data, but it may lead to unreasonable or even misleading results when longitudinal data reveal skewness feature. This paper proposes a new joint model for multivariate longitudinal and multivariate survival data by incorporating a nonparametric function into the trajectory function and hazard function and assuming that measurement errors in longitudinal measurement models follow a skew-normal distribution. A Monte Carlo Expectation-Maximization (EM) algorithm together with the penalized-splines technique and the Metropolis-Hastings algorithm within the Gibbs sampler is developed to estimate parameters and nonparametric functions in the considered joint models. Case deletion diagnostic measures are proposed to identify the potential influential observations, and an extended local influence method is presented to assess local influence of minor perturbations. Simulation studies and a real example from a clinical trial are presented to illustrate the proposed methodologies. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  20. Jelly pineapple syneresis assessment via univariate and multivariate analysis

    Directory of Open Access Journals (Sweden)

    Carlos Alberto da Silva Ledo

    2010-09-01

    Full Text Available The evaluation of the pineapple jelly is intended to analyze the occurrence of syneresis by univariate and multivariate analysis. The jelly of the pineapple presents low concentration pectin, therefore, it was added high methoxyl pectin in the following concentrations: 0.50%, 0.75% and 1.00% corresponding to slow, medium and fast speed of gel formation process. In this study it was checked the pH, acidity, brix and the syneresis of jelly. The highest concentration of pectin in the jelly showed a decrease in the release of the water, syneresis. This result showed that the percentage of 1.00% of pectin in jelly is necessary to form the gel and to obtain a suitable texture.

  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. A Multivariate Analysis of Extratropical Cyclone Environmental Sensitivity

    Science.gov (United States)

    Tierney, G.; Posselt, D. J.; Booth, J. F.

    2015-12-01

    The implications of a changing climate system include more than a simple temperature increase. A changing climate also modifies atmospheric conditions responsible for shaping the genesis and evolution of atmospheric circulations. In the mid-latitudes, the effects of climate change on extratropical cyclones (ETCs) can be expressed through changes in bulk temperature, horizontal and vertical temperature gradients (leading to changes in mean state winds) as well as atmospheric moisture content. Understanding how these changes impact ETC evolution and dynamics will help to inform climate mitigation and adaptation strategies, and allow for better informed weather emergency planning. However, our understanding is complicated by the complex interplay between a variety of environmental influences, and their potentially opposing effects on extratropical cyclone strength. Attempting to untangle competing influences from a theoretical or observational standpoint is complicated by nonlinear responses to environmental perturbations and a lack of data. As such, numerical models can serve as a useful tool for examining this complex issue. We present results from an analysis framework that combines the computational power of idealized modeling with the statistical robustness of multivariate sensitivity analysis. We first establish control variables, such as baroclinicity, bulk temperature, and moisture content, and specify a range of values that simulate possible changes in a future climate. The Weather Research and Forecasting (WRF) model serves as the link between changes in climate state and ETC relevant outcomes. A diverse set of output metrics (e.g., sea level pressure, average precipitation rates, eddy kinetic energy, and latent heat release) facilitates examination of storm dynamics, thermodynamic properties, and hydrologic cycles. Exploration of the multivariate sensitivity of ETCs to changes in control parameters space is performed via an ensemble of WRF runs coupled with

  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. [Methods of the multivariate statistical analysis of so-called polyetiological diseases using the example of coronary heart disease].

    Science.gov (United States)

    Lifshits, A M

    1979-01-01

    General characteristics of the multivariate statistical analysis (MSA) is given. Methodical premises and criteria for the selection of an adequate MSA method applicable to pathoanatomic investigations of the epidemiology of multicausal diseases are presented. The experience of using MSA with computors and standard computing programs in studies of coronary arteries aterosclerosis on the materials of 2060 autopsies is described. The combined use of 4 MSA methods: sequential, correlational, regressional, and discriminant permitted to quantitate the contribution of each of the 8 examined risk factors in the development of aterosclerosis. The most important factors were found to be the age, arterial hypertension, and heredity. Occupational hypodynamia and increased fatness were more important in men, whereas diabetes melitus--in women. The registration of this combination of risk factors by MSA methods provides for more reliable prognosis of the likelihood of coronary heart disease with a fatal outcome than prognosis of the degree of coronary aterosclerosis.

  5. Classification of Malaysia aromatic rice using multivariate statistical analysis

    Science.gov (United States)

    Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

    2015-05-01

    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.

  6. 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.

  7. COMMUNITY VERSUS BIOCOENOSIS IN MULTIVARIATE ANALYSIS OF BENTHIC MOLLUSCAN THANATOCOENOSES

    Directory of Open Access Journals (Sweden)

    DANIELA BASSO

    2002-03-01

    Full Text Available Community and biocoenosis as descriptive units for benthic ecology are not perfectly interchangeable. Although the conceptual framework based on communities, originally defined by a statistical quantitative approach, appears to be the most suitable in the statistical treatment of thanatocoenoses data, this framework appears to oversimplify the picture of the most important ecological units in the Mediterranean benthos. On the contrary, the benthic bionomy with the biocoenoses, identified by a group of characteristic species (disregarding their abundance derives from a qualitative approach which has been more successfully adopted for the research in the Mediterranean area. A group of twelve thanatocoenoses from the Tyrrhenian Sea has been treated with both approaches with the aim to identify a practical strategy for analysing multispecies distribution patterns in benthic paleoecology, trying to combine the advantages of both quantitative and qualitative approaches. When dealing with large-sized data matrices of benthic thanatocoenoses, it is recommended to use a qualitative approach for data reduction, on the basis of their significance in benthic bionomy, prior to perform the quantitative multivariate analysis (classification, ordination, similarity and dissimilarity analysis. This procedure appears to be the most suitable for the identification of “natural” grouping of biotopes, since the results are not obscured by the diffuse occurrence of the most common and ubiquitous species. 

  8. Multivariate analysis of morphostructural characteristics in Nigerian indigenous sheep

    Directory of Open Access Journals (Sweden)

    Abdulmojeed Yakubu

    2011-04-01

    Full Text Available The population variability of three breeds of Nigerian sheep was investigated using multivariate discriminant analyses. The sampled populations comprised mature 331 Yankasa, 296 Uda and 166 Balami sheep kept by traditional farmers in northern Nigerian. A total of ten morphological traits (withers height, rump height, body length, face length, rump length, tail length, chest circumference, head width, shoulder width and rump width were collected on each animal. The body measures of Balami sheep were significantly higher (P<0.05 than the others with the exception of tail length. Uda sheep also had comparative advantage over their Yankasa counterparts in all the morphological traits analysed. The stepwise discriminant analysis revealed that head width chronologically followed by tail length, chest circumference and body length were more discriminating in separating the three populations. The Mahalanobis distance between Yankasa and Balami sheep was highest (4.83 while the least differentiation was observed between Uda and Yankasa sheep (1.79. Nearest neighbour discriminant analysis showed that most Balami sheep (61.45% were classified into their source genetic group. While 41.22% of Uda sheep were misclassified as Yankasa sheep, 35.35% of Yankasa were wrongly assigned as Uda sheep, showing the level of genetic exchange that has taken place between the two breeds overtime. The present information could be complemented with genetic analyses geared towards designing appropriate breeding and selection strategies.

  9. 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.

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

    Science.gov (United States)

    Coble, Jamie; Orton, Christopher; Schwantes, Jon

    2017-04-01

    The Multi-Isotope Process (MIP) Monitor provides an efficient means to monitor 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 key stages in the reprocessing stream 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; PWR and BWR, respectively), 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 for the three PWR and three BWR reactor designs studied. Locally weighted PLS models were fitted on-the-fly to estimate the remaining fuel characteristics. For the simulated gamma spectra considered, burn up was predicted with 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment with approximately 2% RMSPE. This approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.

  11. Multivariate analysis of prognostic factors in patients with glioblastoma

    Energy Technology Data Exchange (ETDEWEB)

    Lutterbach, J.; Guttenberger, R. [Dept. of Radiotherapy, Radiologic Univ. Hospital, Freiburg i.Br. (Germany); Sauerbrei, W. [Inst. of Medical Biometry and Medical Informatics, Univ. Hospital, Freiburg i.Br. (Germany)

    2003-01-01

    Background: To identify prognostic factors for overall survival in patients with newly diagnosed glioblastoma undergoing radiation therapy. Patients and Methods: From January 1980 to June 2000, we treated 432 consecutive patients with glioblastoma at our institution. 17 patients were excluded from the analysis for various reasons. Mean age of the 415 patients who were included in the study was 59 years (19-81 years), Karnofsky performance status (KPS) was {>=} 70 in 280 patients. 343 patients underwent resection, 72 had a biopsy. Various fractionation schemes were used (conventional fractionation, n = 112; hypofractionation, n = 94; accelerated hyperfractionation, n = 209). Survival probabilities were estimated using the method of Kaplan and Meier. Multivariate analysis was done with a Cox regression model. Results: By July 2001, 406 patients had died. Median overall survival was 8.2 months. Of ten factors considered in a proportional hazards model stratified for treatment (fractionation scheme and type of surgery), significant variables in a multivariate model were age (50-64 years vs < 50 years [RR 1.35; 95% CI 1.02-1.78], {>=} 65 years vs < 50 years [RR 2.08; 95% CI 1.54-2.81]), performance status (KPS < 70 vs {>=} 70 [RR 1.53; 95% CI 1.23-1.90]), and central tumor location (yes vs no [RR 1.39; 95% CI 1.04-1.87]). Blood hemoglobin (Hb) values were available in 318 patients and serum lactate dehydrogenase (LDH) levels in 234 patients. 89 patients were anemic (Hb men < 13 g/dl, women < 12 g/dl), in 80 patients the LDH level was raised beyond the upper limit of the normal range (> 240 U/l). By including the three significant variables, both parameters had an additional significant effect with an estimated relative risk of about 1.4 in their corresponding subgroups. Conclusion: Besides established prognostic factors, anemia and raised serum LDH levels may negatively influence outcome in glioblastoma patients. Our results from data-dependent modeling have to be

  12. Prognosis of the comorbid heart failure and Anemia: A systematic review and meta-analysis

    Directory of Open Access Journals (Sweden)

    M. Kyriakou

    2016-04-01

    Conclusion: The meta-analysis gives an outline profile of patients with the co-morbidity HF and anemia in terms of clinical outcomes. The results point out worse prognosis in HF patients with anemia. Nevertheless, the available data did not allow the extraction of a conclusion in which exact Hb levels anemia becomes a negative predictor of prognosis.

  13. 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.

  14. 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 of

  15. Gini s ideas: new perspectives for modern multivariate statistical analysis

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    Angela Montanari

    2013-05-01

    Full Text Available Corrado Gini (1884-1964 may be considered the greatest Italian statistician. We believe that his important contributions to statistics, however mainly limited to the univariate context, may be profitably employed in modern multivariate statistical methods, aimed at overcoming the curse of dimensionality by decomposing multivariate problems into a series of suitably posed univariate ones.In this paper we critically summarize Gini’s proposals and consider their impact on multivariate statistical methods, both reviewing already well established applications and suggesting new perspectives.Particular attention will be devoted to classification and regression trees, multiple linear regression, linear dimension reduction methods and transvariation based discrimination.

  16. Gravitational Wave Detection of Compact Binaries Through Multivariate Analysis

    Science.gov (United States)

    Atallah, Dany Victor; Dorrington, Iain; Sutton, Patrick

    2017-01-01

    The first detection of gravitational waves (GW), GW150914, as produced by a binary black hole merger, has ushered in the era of GW astronomy. The detection technique used to find GW150914 considered only a fraction of the information available describing the candidate event: mainly the detector signal to noise ratios and chi-squared values. In hopes of greatly increasing detection rates, we want to take advantage of all the information available about candidate events. We employ a technique called Multivariate Analysis (MVA) to improve LIGO sensitivity to GW signals. MVA techniques are efficient ways to scan high dimensional data spaces for signal/noise classification. Our goal is to use MVA to classify compact-object binary coalescence (CBC) events composed of any combination of black holes and neutron stars. CBC waveforms are modeled through numerical relativity. Templates of the modeled waveforms are used to search for CBCs and quantify candidate events. Different MVA pipelines are under investigation to look for CBC signals and un-modelled signals, with promising results. One such MVA pipeline used for the un-modelled search can theoretically analyze far more data than the MVA pipelines currently explored for CBCs, potentially making a more powerful classifier. In principle, this extra information could improve the sensitivity to GW signals. We will present the results from our efforts to adapt an MVA pipeline used in the un-modelled search to classify candidate events from the CBC search.

  17. 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.

  18. Determining the Metabolic Footprints of Hydrocarbon Degradation Using Multivariate Analysis

    Science.gov (United States)

    Smith, Renee. J.; Jeffries, Thomas C.; Adetutu, Eric M.; Fairweather, Peter G.; Mitchell, James G.

    2013-01-01

    The functional dynamics of microbial communities are largely responsible for the clean-up of hydrocarbons in the environment. However, knowledge of the distinguishing functional genes, known as the metabolic footprint, present in hydrocarbon-impacted sites is still scarcely understood. Here, we conducted several multivariate analyses to characterise the metabolic footprints present in a variety of hydrocarbon-impacted and non-impacted sediments. Non-metric multi-dimensional scaling (NMDS) and canonical analysis of principal coordinates (CAP) showed a clear distinction between the two groups. A high relative abundance of genes associated with cofactors, virulence, phages and fatty acids were present in the non-impacted sediments, accounting for 45.7 % of the overall dissimilarity. In the hydrocarbon-impacted sites, a high relative abundance of genes associated with iron acquisition and metabolism, dormancy and sporulation, motility, metabolism of aromatic compounds and cell signalling were observed, accounting for 22.3 % of the overall dissimilarity. These results suggest a major shift in functionality has occurred with pathways essential to the degradation of hydrocarbons becoming overrepresented at the expense of other, less essential metabolisms. PMID:24282619

  19. 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.

  20. Cell culture tracking by multivariate analysis of raw LCMS data.

    Science.gov (United States)

    Michaud, François-Thomas; Havugimana, Pierre Claver; Duchesne, Carl; Sanschagrin, François; Bernier, Alice; Lévesque, Roger C; Garnier, Alain

    2012-06-01

    Liquid chromatography mass spectrometry (LCMS) is a powerful technique that could serve to rapidly characterize cell culture protein expression profile and be used as a process monitoring and control tool. However, this application is often hampered by both the sample proteome and the LCMS signal complexities as well as the variability of this signal. To alleviate this problem, culture samples are usually extensively fractionated and pretreated before being analyzed by top-end instruments. Such an approach precludes LCMS usage for routine on-line or at-line application. In this work, by applying multivariate analysis (MA) directly on raw LCMS signals, we were able to extract relevant information from cell culture samples that were simply lyzed. By using the recombinant adenovirus production process as a model, we were able to follow the accumulation of the three major proteins produced, identified their accumulation dynamics, and draw useful conclusions from these results. The combination of LCMS and MA provides a simple, rapid, and precise means to monitor cell culture.

  1. 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.

  2. Atlantoaxial Langerhans cell histiocytosis radiographic characteristics and corresponding prognosis analysis

    Directory of Open Access Journals (Sweden)

    Lihua Zhang

    2017-01-01

    Conclusions: The atlas and axis were affected by LCH, mainly in children. The lateral mass was easily affected and compressed, destruction of the atlas and axis could lead to atlantoaxial joint instability. The prognosis was good in most of the patients.

  3. 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)

  4. 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.

  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. Deeper Insights into the Circumgalactic Medium using Multivariate Analysis Methods

    Science.gov (United States)

    Lewis, James; Churchill, Christopher W.; Nielsen, Nikole M.; Kacprzak, Glenn

    2017-01-01

    Drawing from a database of galaxies whose surrounding gas has absorption from MgII, called the MgII-Absorbing Galaxy Catalog (MAGIICAT, Neilsen et al 2013), we studied the circumgalactic medium (CGM) for a sample of 47 galaxies. Using multivariate analysis, in particular the k-means clustering algorithm, we determined that simultaneously examining column density (N), rest-frame B-K color, virial mass, and azimuthal angle (the projected angle between the galaxy major axis and the quasar line of sight) yields two distinct populations: (1) bluer, lower mass galaxies with higher column density along the minor axis, and (2) redder, higher mass galaxies with lower column density along the major axis. We support this grouping by running (i) two-sample, two-dimensional Kolmogorov-Smirnov (KS) tests on each of the six bivariate planes and (ii) two-sample KS tests on each of the four variables to show that the galaxies significantly cluster into two independent populations. To account for the fact that 16 of our 47 galaxies have upper limits on N, we performed Monte-Carlo tests whereby we replaced upper limits with random deviates drawn from a Schechter distribution fit, f(N). These tests strengthen the results of the KS tests. We examined the behavior of the MgII λ2796 absorption line equivalent width and velocity width for each galaxy population. We find that equivalent width and velocity width do not show similar characteristic distinctions between the two galaxy populations. We discuss the k-means clustering algorithm for optimizing the analysis of populations within datasets as opposed to using arbitrary bivariate subsample cuts. We also discuss the power of the k-means clustering algorithm in extracting deeper physical insight into the CGM in relationship to host galaxies.

  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

    correlation analysis (CCA), and orthonormalized PLS (OPLS), as well as their nonlinear extensions derived by means of the theory of reproducing kernel Hilbert spaces (RKHSs). We also review their connections to other methods for classification and statistical dependence estimation and introduce some recent...... developments to deal with the extreme cases of large-scale and low-sized problems. To illustrate the wide applicability of these methods in both classification and regression problems, we analyze their performance in a benchmark of publicly available data sets and pay special attention to specific real...

  8. Multivariate cluster analysis of forest fire events in Portugal

    Science.gov (United States)

    Tonini, Marj; Pereira, Mario; Vega Orozco, Carmen; Parente, Joana

    2015-04-01

    Portugal is one of the major fire-prone European countries, mainly due to its favourable climatic, topographic and vegetation conditions. Compared to the other Mediterranean countries, the number of events registered here from 1980 up to nowadays is the highest one; likewise, with respect to the burnt area, Portugal is the third most affected country. Portuguese mapped burnt areas are available from the website of the Institute for the Conservation of Nature and Forests (ICNF). This official geodatabase is the result of satellite measurements starting from the year 1990. The spatial information, delivered in shapefile format, provides a detailed description of the shape and the size of area burnt by each fire, while the date/time information relate to the ignition fire is restricted to the year of occurrence. In terms of a statistical formalism wildfires can be associated to a stochastic point process, where events are analysed as a set of geographical coordinates corresponding, for example, to the centroid of each burnt area. The spatio/temporal pattern of stochastic point processes, including the cluster analysis, is a basic procedure to discover predisposing factorsas well as for prevention and forecasting purposes. These kinds of studies are primarily focused on investigating the spatial cluster behaviour of environmental data sequences and/or mapping their distribution at different times. To include both the two dimensions (space and time) a comprehensive spatio-temporal analysis is needful. In the present study authors attempt to verify if, in the case of wildfires in Portugal, space and time act independently or if, conversely, neighbouring events are also closer in time. We present an application of the spatio-temporal K-function to a long dataset (1990-2012) of mapped burnt areas. Moreover, the multivariate K-function allowed checking for an eventual different distribution between small and large fires. The final objective is to elaborate a 3D

  9. 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....... 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...... models. The models include a dispersion parameter, which is essential for obtaining a decomposition of the variance of the trait of interest as a sum of parcels representing the additive genetic effects, environmental effects and unspecified sources of variability; as required in quantitative genetic...

  10. 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...... models. The models include a dispersion parameter, which is essential for obtaining a decomposition of the variance of the trait of interest as a sum of parcels representing the additive genetic effects, environmental effects and unspecified sources of variability; as required in quantitative genetic...

  11. Does pregnancy influence melanoma prognosis? A meta-analysis.

    Science.gov (United States)

    Kyrgidis, Athanassios; Lallas, Aimilios; Moscarella, Elvira; Longo, Caterina; Alfano, Roberto; Argenziano, Giuseppe

    2017-08-01

    The literature has not been able to conclude whether pregnancy influences the prognosis of melanoma. The aim of this study was to explore the prognosis of melanoma diagnosed during pregnancy or post partum [pregnancy-associated melanoma (PAM)] compared with melanoma in female patients who were not pregnant. We systematically searched for studies of female patients with melanoma that reported outcomes related to survival. Fifteen eligible studies were found. Overall, PAM was associated with a 17% higher mortality compared with melanoma diagnosed in female patients who were not pregnant (hazard ratio=1.17, 95% confidence interval: 1.03-1.33, P=0.02). The heterogeneity associated with this test was moderate (P=0.07; I=38%). PAM was also associated with a 50% higher recurrence rate compared with melanoma not associated with pregnancy (hazard ratio=1.50, 95% confidence interval: 1.19-1.90, Pdefinition of PAM, which is not unanimous among the studies included. Our results indicate that PAM is associated with a worse prognosis than melanoma not related to pregnancy, both in terms of overall survival and disease-free survival. On the basis of our data, we anticipate that the survival difference we report here will be further amplified with the addition of future well-carried out studies. We suggest that detection of PAM requires particular awareness by healthcare professionals.

  12. Bayesian Analysis of Multivariate Probit Models with Surrogate Outcome Data

    Science.gov (United States)

    Poon, Wai-Yin; Wang, Hai-Bin

    2010-01-01

    A new class of parametric models that generalize the multivariate probit model and the errors-in-variables model is developed to model and analyze ordinal data. A general model structure is assumed to accommodate the information that is obtained via surrogate variables. A hybrid Gibbs sampler is developed to estimate the model parameters. To…

  13. 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, ...

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

    African Journals Online (AJOL)

    FIRST LADY

    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.

  15. 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

  16. Decoding cognitive concepts from neuroimaging data using multivariate pattern analysis.

    Science.gov (United States)

    Alizadeh, Sarah; Jamalabadi, Hamidreza; Schönauer, Monika; Leibold, Christian; Gais, Steffen

    2017-10-01

    Multivariate pattern analysis (MVPA) methods are now widely used in life-science research. They have great potential but their complexity also bears unexpected pitfalls. In this paper, we explore the possibilities that arise from the high sensitivity of MVPA for stimulus-related differences, which may confound estimations of class differences during decoding of cognitive concepts. We propose a method that takes advantage of concept-unrelated grouping factors, uses blocked permutation tests, and gradually manipulates the proportion of concept-related information in data while the stimulus-related, concept-irrelevant factors are held constant. This results in a concept-response curve, which shows the relative contribution of these two components, i.e. how much of the decoding performance is specific to higher-order category processing and to lower order stimulus processing. It also allows separating stimulus-related from concept-related neuronal processing, which cannot be achieved experimentally. We applied our method to three different EEG data sets with different levels of stimulus-related confound to decode concepts of digits vs. letters, faces vs. houses, and animals vs. fruits based on event-related potentials at the single trial level. We show that exemplar-specific differences between stimuli can drive classification accuracy to above chance levels even in the absence of conceptual information. By looking into time-resolved windows of brain activity, concept-response curves can help characterize the time-course of lower-level and higher-level neural information processing and detect the corresponding temporal and spatial signatures of the corresponding cognitive processes. In particular, our results show that perceptual information is decoded earlier in time than conceptual information specific to processing digits and letters. In addition, compared to the stimulus-level predictive sites, concept-related topographies are spread more widely and, at later time

  17. Multivariate analysis of elemental chemistry as a robust biosignature

    Science.gov (United States)

    Storrie-Lombardi, M.; Nealson, K.

    2003-04-01

    The robotic detection of life in extraterrestrial settings (i.e., Mars, Europa, etc.) would be greatly simplified if analysis could be accomplished in the absence of direct mechanical manipulation of a sample. It would also be preferable to employ a fundamental physico-chemical phenomenon as a biosignature and depend less on the particular manifestations of life on Earth (i.e. to employ non-earthcentric methods). One such approach, which we put forward here, is that of elemental composition, a reflection of the use of specific chemical elements for the construction of living systems. Using appropriate analyses (over the proper spatial scales), it should be possible to see deviations from the geological background (mineral and geochemical composition of the crust), and identify anomalies that would indicate sufficient deviation from the norm as to indicate a possible living system. To this end, over the past four decades elemental distributions have been determined for the sun, the interstellar medium, seawater, the crust of the Earth, carbonaceous chondrite meteorites, bacteria, plants, animals, and human beings. Such data can be relatively easily obtained for samples of a variety of types using a technique known as laser-induced breakdown spectroscopy (LIBS), which employs a high energy laser to ablate a portion of a sample, and then determine elemental composition using remote optical spectroscopy. However, the elements commonly associated with living systems (H, C, O, and N), while useful for detecting extant life, are relatively volatile and are not easily constrained across geological time scales. This minimizes their utility as fossil markers of ancient life. We have investigated the possibility of distinguishing the distributions of less volatile elements in a variety of biological materials from the distributions found in carbonaceous chondrites and the Earth’s crust using principal component analysis (PCA), a classical multivariate analysis technique

  18. 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...

  19. Multitask Gaussian processes for multivariate physiological time-series analysis.

    Science.gov (United States)

    Dürichen, Robert; Pimentel, Marco A F; Clifton, Lei; Schweikard, Achim; Clifton, David A

    2015-01-01

    Gaussian process (GP) models are a flexible means of performing nonparametric Bayesian regression. However, GP models in healthcare are often only used to model a single univariate output time series, denoted as single-task GPs (STGP). Due to an increasing prevalence of sensors in healthcare settings, there is an urgent need for robust multivariate time-series tools. Here, we propose a method using multitask GPs (MTGPs) which can model multiple correlated multivariate physiological time series simultaneously. The flexible MTGP framework can learn the correlation between multiple signals even though they might be sampled at different frequencies and have training sets available for different intervals. Furthermore, prior knowledge of any relationship between the time series such as delays and temporal behavior can be easily integrated. A novel normalization is proposed to allow interpretation of the various hyperparameters used in the MTGP. We investigate MTGPs for physiological monitoring with synthetic data sets and two real-world problems from the field of patient monitoring and radiotherapy. The results are compared with standard Gaussian processes and other existing methods in the respective biomedical application areas. In both cases, we show that our framework learned the correlation between physiological time series efficiently, outperforming the existing state of the art.

  20. Integrative analysis of cancer prognosis data with multiple subtypes using regularized gradient descent.

    Science.gov (United States)

    Ma, Shuangge; Zhang, Yawei; Huang, Jian; Huang, Yuan; Lan, Qing; Rothman, Nathaniel; Zheng, Tongzhang

    2012-12-01

    In cancer research, high-throughput profiling studies have been extensively conducted, searching for genes/single nucleotide polymorphisms (SNPs) associated with prognosis. Despite seemingly significant differences, different subtypes of the same cancer (or different types of cancers) may share common susceptibility genes. In this study, we analyze prognosis data on multiple subtypes of the same cancer but note that the proposed approach is directly applicable to the analysis of data on multiple types of cancers. We describe the genetic basis of multiple subtypes using the heterogeneity model that allows overlapping but different sets of susceptibility genes/SNPs for different subtypes. An accelerated failure time (AFT) model is adopted to describe prognosis. We develop a regularized gradient descent approach that conducts gene-level analysis and identifies genes that contain important SNPs associated with prognosis. The proposed approach belongs to the family of gradient descent approaches, is intuitively reasonable, and has affordable computational cost. Simulation study shows that when prognosis-associated SNPs are clustered in a small number of genes, the proposed approach outperforms alternatives with significantly more true positives and fewer false positives. We analyze an NHL (non-Hodgkin lymphoma) prognosis study with SNP measurements and identify genes associated with the three major subtypes of NHL, namely, DLBCL, FL, and CLL/SLL. The proposed approach identifies genes different from using alternative approaches and has the best prediction performance. © 2012 Wiley Periodicals, Inc.

  1. Identification of Homogeneous Hydrological Regions through Multivariate Analysis

    Directory of Open Access Journals (Sweden)

    Álvarez-Olguín G.

    2011-07-01

    Full Text Available Hydrological regionalization is used to transfer information from gauged catchments to ungauged river basins. However, to obtain reliable results, the basins involved must have a similar hydrological behavior. The objective of this research was to identify hydrologically homogeneous regions in the Mixteca Oaxaqueña and surrounding areas. The area of study included 17 basins for which 20 climate and physiographic variables potentially useful in the prediction of flow were quantified. The applications of multivariate statistics techniques allowed us to identify three groups of basins hydrologically associated. A regional model was obtained to predict mean annual fl ow, which determined that the best predictive variables are the area and the average annual precipitation.

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

    DEFF Research Database (Denmark)

    Ljungqvist, Martin Georg

    Aquaculture is today one of the fastest growing food producing sectors in the world. Access to good and effective fish feed is a condition for optimised and sustainable aquaculture activity. In the aquaculture industry it is of utmost importance that the fish get feed of proper size and nutrition....... 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...

  3. 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.

  4. User's Guide To CHEAP0 II-Economic Analysis of Stand Prognosis Model Outputs

    Science.gov (United States)

    Joseph E. Horn; E. Lee Medema; Ervin G. Schuster

    1986-01-01

    CHEAP0 II provides supplemental economic analysis capability for users of version 5.1 of the Stand Prognosis Model, including recent regeneration and insect outbreak extensions. Although patterned after the old CHEAP0 model, CHEAP0 II has more features and analytic capabilities, especially for analysis of existing and uneven-aged stands....

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

    African Journals Online (AJOL)

    Canonical correspondence analysis (CCA) showed that the environmental variables used in the analysis, discharge and month of sampling, explained a small proportion of the total variance in the data set – less than 10% at each site. However, the total data set variance, explained by the 4 hypothetical axes generated by ...

  6. HRMAS-NMR spectroscopy and multivariate analysis meat characterisation.

    Science.gov (United States)

    Ritota, Mena; Casciani, Lorena; Failla, Sebastiana; Valentini, Massimiliano

    2012-12-01

    ¹H-High resolution magic angle spinning-nuclear magnetic resonance spectroscopy was employed to gain the metabolic profile of longissimus dorsi and semitendinosus muscles of four different breeds: Chianina, Holstein Friesian, Maremmana and Buffalo. Principal component analysis, partial least squares projection to latent structure - discriminant analysis and orthogonal partial least squares projection to latent structure - discriminant analysis were used to build models capable of discriminating the muscle type according to the breed. Data analysis led to an excellent classification for Buffalo and Chianina, while for Holstein Friesian the separation was lower. In the case of Maremmana the use of intelligent bucketing was necessary due to some resonances shifting allowed improvement of the discrimination ability. Finally, by using the Variable Importance in Projection values the metabolites relevant for the classification were identified. Copyright © 2012 Elsevier Ltd. All rights reserved.

  7. Metabolic profiling of body fluids and multivariate data analysis.

    Science.gov (United States)

    Trezzi, Jean-Pierre; Jäger, Christian; Galozzi, Sara; Barkovits, Katalin; Marcus, Katrin; Mollenhauer, Brit; Hiller, Karsten

    2017-01-01

    Metabolome analyses of body fluids are challenging due pre-analytical variations, such as pre-processing delay and temperature, and constant dynamical changes of biochemical processes within the samples. Therefore, proper sample handling starting from the time of collection up to the analysis is crucial to obtain high quality samples and reproducible results. A metabolomics analysis is divided into 4 main steps: 1) Sample collection, 2) Metabolite extraction, 3) Data acquisition and 4) Data analysis. Here, we describe a protocol for gas chromatography coupled to mass spectrometry (GC-MS) based metabolic analysis for biological matrices, especially body fluids. This protocol can be applied on blood serum/plasma, saliva and cerebrospinal fluid (CSF) samples of humans and other vertebrates. It covers sample collection, sample pre-processing, metabolite extraction, GC-MS measurement and guidelines for the subsequent data analysis. Advantages of this protocol include: •Robust and reproducible metabolomics results, taking into account pre-analytical variations that may occur during the sampling process•Small sample volume required•Rapid and cost-effective processing of biological samples•Logistic regression based determination of biomarker signatures for in-depth data analysis.

  8. Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data

    Directory of Open Access Journals (Sweden)

    Lijun Wang

    2013-01-01

    Full Text Available Brain decoding with functional magnetic resonance imaging (fMRI requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.

  9. Multivariate multifractal detrended fluctuation analysis of 3D wind field signals

    Science.gov (United States)

    Zhang, Xiaonei; Zeng, Ming; Meng, Qinghao

    2018-01-01

    Characterizing the dynamic behavior underlying wind field from experimental multivariate signals is a challenging problem of continuous interest. In this work, we propose the multivariate multifractal detrended fluctuation analysis (MV-MFDFA) method to directly study the fractal dynamics of multichannel data in a complex system. By conducting several simulations on synthetic multivariate series, the validity of the proposed MV-MFDFA is illustrated. Then we utilize MV-MFDFA to analyze the 3D wind field signals collected at two different airflow environments, i.e., indoor and outdoor environments. Results show that the indoor and outdoor three wind vectors show multifractal properties, and the multifractal degrees of outdoor three wind vectors are stronger than those of corresponding indoor three wind vectors. By analyzing the indoor and outdoor multivariate wind speed, we find that the indoor and outdoor multivariate wind speed are antipersistent long-range correlation, and the indoor multivariate wind speed exhibits weaker multifractal properties than that of outdoor multivariate wind speed. Moreover, the multifractality of indoor multivariate wind speed depends mainly on the large fluctuations, while the multifractality of outdoor multivariate wind speed depends mainly on the small fluctuations. These findings indicate that the MV-MFDFA allows better understanding the dynamical mechanisms governing 3D wind variability.

  10. Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer

    Science.gov (United States)

    Zhang, Yucheng; Oikonomou, Anastasia; Wong, Alexander; Haider, Masoom A.; Khalvati, Farzad

    2017-04-01

    Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative features from radiological images. Radiomic features have been shown to provide prognostic value in predicting clinical outcomes in several studies. However, several challenges including feature redundancy, unbalanced data, and small sample sizes have led to relatively low predictive accuracy. In this study, we explore different strategies for overcoming these challenges and improving predictive performance of radiomics-based prognosis for non-small cell lung cancer (NSCLC). CT images of 112 patients (mean age 75 years) with NSCLC who underwent stereotactic body radiotherapy were used to predict recurrence, death, and recurrence-free survival using a comprehensive radiomics analysis. Different feature selection and predictive modeling techniques were used to determine the optimal configuration of prognosis analysis. To address feature redundancy, comprehensive analysis indicated that Random Forest models and Principal Component Analysis were optimum predictive modeling and feature selection methods, respectively, for achieving high prognosis performance. To address unbalanced data, Synthetic Minority Over-sampling technique was found to significantly increase predictive accuracy. A full analysis of variance showed that data endpoints, feature selection techniques, and classifiers were significant factors in affecting predictive accuracy, suggesting that these factors must be investigated when building radiomics-based predictive models for cancer prognosis.

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

    African Journals Online (AJOL)

    FIRST LADY

    Error Analysis for Forecasts of 2008-2014 to Establish Model out of. Control. Let us consider forecasting production for the first quarter of years ahead ie. JAN productions in order to establish when the model will be reviewed when much error has been accumulated (See table 7). The predictions of the Table 7 shows that at ...

  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 Genetic Analysis of Learning and Early Reading Development

    Science.gov (United States)

    Byrne, Brian; Wadsworth, Sally; Boehme, Kristi; Talk, Andrew C.; Coventry, William L.; Olson, Richard K.; Samuelsson, Stefan; Corley, Robin

    2013-01-01

    The genetic factor structure of a range of learning measures was explored in twin children, recruited in preschool and followed to Grade 2 ("N"?=?2,084). Measures of orthographic learning and word reading were included in the analyses to determine how these patterned with the learning processes. An exploratory factor analysis of the…

  14. A Multivariate analysis of adloscent sexual behaviour in South ...

    African Journals Online (AJOL)

    This study examines the spatial variation in adolescent sexual behaviour and the underlying socio-economic determinants in South-Western Nigeria. Data collected with the aid of a structured questionnaire administered to 1,670 adolescents were analysed using Multiple Analysis of Variance. Results show among other ...

  15. 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

  16. 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...

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

    African Journals Online (AJOL)

    Hence, germination ability and salt stress tolerance of Agropyron desertorum were evaluated using ten genotypes originally collected from different areas of Iran in greenhouse condition. Five different concentrations of NaCl solution were used in this experiment. Analysis of variance showed considerable variation in all the ...

  18. 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 ...

  19. Provenance Study of Archaeological Ceramics from Syria Using XRF Multivariate Statistical Analysis and Thermoluminescence Dating

    National Research Council Canada - National Science Library

    Bakraji, Elias Hanna; Abboud, Rana; Issa, Haissm

    2014-01-01

    Thermoluminescence (TL) dating and multivariate statistical methods based on radioisotope X-ray fluorescence analysis have been utilized to date and classify Syrian archaeological ceramics fragment from Tel Jamous site...

  20. Characterization of plasma metal profiles in Alzheimer's disease using multivariate statistical analysis

    National Research Council Canada - National Science Library

    Chunmei Guan; Rui Dang; Yu Cui; Liyan Liu; Xiaobei Chen; Xiaoyu Wang; Jingli Zhu; Donggang Li; Junwei Li; Decai Wang

    .... We have used an analytical approach, based on inductively coupled plasma mass spectrometry coupled with multivariate statistical analysis, to study the profiles of a wide range of metals in AD...

  1. Characterization of plasma metal profiles in Alzheimer’s disease using multivariate statistical analysis

    National Research Council Canada - National Science Library

    Chunmei Guan; Rui Dang; Yu Cui; Liyan Liu; Xiaobei Chen; Xiaoyu Wang; Jingli Zhu; Donggang Li; Junwei Li; Decai Wang

    2017-01-01

    .... We have used an analytical approach, based on inductively coupled plasma mass spectrometry coupled with multivariate statistical analysis, to study the profiles of a wide range of metals in AD...

  2. [Application of multivariate statistical analysis and thinking in quality control of Chinese medicine].

    Science.gov (United States)

    Liu, Na; Li, Jun; Li, Bao-Guo

    2014-11-01

    The study of quality control of Chinese medicine has always been the hot and the difficulty spot of the development of traditional Chinese medicine (TCM), which is also one of the key problems restricting the modernization and internationalization of Chinese medicine. Multivariate statistical analysis is an analytical method which is suitable for the analysis of characteristics of TCM. It has been used widely in the study of quality control of TCM. Multivariate Statistical analysis was used for multivariate indicators and variables that appeared in the study of quality control and had certain correlation between each other, to find out the hidden law or the relationship between the data can be found,.which could apply to serve the decision-making and realize the effective quality evaluation of TCM. In this paper, the application of multivariate statistical analysis in the quality control of Chinese medicine was summarized, which could provided the basis for its further study.

  3. Multivariate Analysis Techniques for Optimal Vision System Design

    DEFF Research Database (Denmark)

    Sharifzadeh, Sara

    (SSPCA) and DCT based characterization of the spectral diffused reflectance images for wavelength selection and discrimination. These methods together with some other state-of-the-art statistical and mathematical analysis techniques are applied on datasets of different food items; meat, diaries, fruits......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...... based on the existing sparse regression methods (EN and lasso) and one unsupervised feature selection strategy based on the local maxima of the spectral 1D/2D signals of food items are proposed. In addition, two novel feature extraction and selection strategies are introduced; sparse supervised PCA...

  4. 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...

  5. 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.

  6. 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.

  7. Using the Regression Model in multivariate data analysis

    Directory of Open Access Journals (Sweden)

    Constantin Cristinel

    2017-07-01

    Full Text Available This paper is about an instrumental research regarding the using of Linear Regression Model for data analysis. The research uses a model based on real data and stress the necessity of a correct utilisation of such models in order to obtain accurate information for the decision makers. The main scope is to help practitioners and researchers in their efforts to build prediction models based on linear regressions. The conclusion reveals the necessity to use quantitative data for a correct model specification and to validate the model according to the assumptions of the least squares method.

  8. CONFERENCE ON CLUSTER ANALYSIS OF MULTIVARIATE DATA, NEW ORLEANS, LA., DECEMBER 9, 10 AND 11

    Science.gov (United States)

    Contents: Some critical issues and problems in cluster analysis ; Methods of cluster or typological analysis; Review of clustering methods in...mathematical basis of the taxonome computer program; Comparative cluster analysis of variables and individuals (Holzinger Abilities and the MMPI); Comparison... Cluster analysis and the search for structure underlying individual differences in psychological phenomena; the MAXOF clustering model; Multivariate

  9. Multivariate analysis of some economic characters in flax.

    Science.gov (United States)

    Kandil, A A; Shareif, A E; Abo-Zaied, T A; Moussa, A G T

    2012-01-15

    Twenty one parent flax genotypes and twenty F1 hybrids using principal components analysis based on 16 quantitative charismas were used to study the genetic relationship. Analysis of variance exposed high significant differences for all studied charismas among genotypes. High Genotypic Coefficient of Variation (GCV) values were observed with high Phenotypic Coefficient of Variation (PCV) for seed yield/plant, number of capsules/plant, fruiting zone length, main stem diameter and seed index which designated that variation for these characters substantively donates to the total variability moderate to low PCV and GCV were perceived for fiber characters, earliness and growth characters, respectively. Most characters, showed high heritability estimated in broad sense (> 70%) indicated that selection based on these characters would be effective as they are likely to be controlled by additive genes. The first five principal components were significant and accounted 78.2% of a total variance of all characters. The maximal amount of difference is shown in the first PC axis were 25.3%. Stem diameter, seed yield/plant, number of capsules/plant, straw yield/plant, fruiting zone length, number of apical branches and number of seed/capsules were a primary source of variation of the first PC axes and give high coefficients, respectively. While, the biggest coefficient in PC2 were earliness characters, plant height and fiber length. The other rest PC axes deals with seed index, fiber fineness and oil contented. The flax genotypes were plotted according to the first two PC axis. The most earlier parents Gowhar and L6 were separated according to PC2 since this axis deals with earliness characters.

  10. 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....

  11. 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.

  12. Integrated analysis of tropical trees growth: a multivariate approach.

    Science.gov (United States)

    Yáñez-Espinosa, Laura; Terrazas, Teresa; López-Mata, Lauro

    2006-09-01

    One of the problems analysing cause-effect relationships of growth and environmental factors is that a single factor could be correlated with other ones directly influencing growth. One attempt to understand tropical trees' growth cause-effect relationships is integrating research about anatomical, physiological and environmental factors that influence growth in order to develop mathematical models. The relevance is to understand the nature of the process of growth and to model this as a function of the environment. The relationships of Aphananthe monoica, Pleuranthodendron lindenii and Psychotria costivenia radial growth and phenology with environmental factors (local climate, vertical strata microclimate and physical and chemical soil variables) were evaluated from April 2000 to September 2001. The association among these groups of variables was determined by generalized canonical correlation analysis (GCCA), which considers the probable associations of three or more data groups and the selection of the most important variables for each data group. The GCCA allowed determination of a general model of relationships among tree phenology and radial growth with climate, microclimate and soil factors. A strong influence of climate in phenology and radial growth existed. Leaf initiation and cambial activity periods were associated with maximum temperature and day length, and vascular tissue differentiation with soil moisture and rainfall. The analyses of individual species detected different relationships for the three species. The analyses of the individual species suggest that each one takes advantage in a different way of the environment in which they are growing, allowing them to coexist.

  13. 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.

  14. MUMAL: Multivariate analysis in shotgun proteomics using machine learning techniques

    Directory of Open Access Journals (Sweden)

    Cerqueira Fabio R

    2012-10-01

    Full Text Available Abstract Background The shotgun strategy (liquid chromatography coupled with tandem mass spectrometry is widely applied for identification of proteins in complex mixtures. This method gives rise to thousands of spectra in a single run, which are interpreted by computational tools. Such tools normally use a protein database from which peptide sequences are extracted for matching with experimentally derived mass spectral data. After the database search, the correctness of obtained peptide-spectrum matches (PSMs needs to be evaluated also by algorithms, as a manual curation of these huge datasets would be impractical. The target-decoy database strategy is largely used to perform spectrum evaluation. Nonetheless, this method has been applied without considering sensitivity, i.e., only error estimation is taken into account. A recently proposed method termed MUDE treats the target-decoy analysis as an optimization problem, where sensitivity is maximized. This method demonstrates a significant increase in the retrieved number of PSMs for a fixed error rate. However, the MUDE model is constructed in such a way that linear decision boundaries are established to separate correct from incorrect PSMs. Besides, the described heuristic for solving the optimization problem has to be executed many times to achieve a significant augmentation in sensitivity. Results Here, we propose a new method, termed MUMAL, for PSM assessment that is based on machine learning techniques. Our method can establish nonlinear decision boundaries, leading to a higher chance to retrieve more true positives. Furthermore, we need few iterations to achieve high sensitivities, strikingly shortening the running time of the whole process. Experiments show that our method achieves a considerably higher number of PSMs compared with standard tools such as MUDE, PeptideProphet, and typical target-decoy approaches. Conclusion Our approach not only enhances the computational performance, and

  15. 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.

  16. Spatial analysis of hydrological and phytoplanktonic data of the Bay of Tunis. Multivariate cartography

    OpenAIRE

    HAMADOU, R.B.; Ibanez, F; SOUISSI, S.; A.C. CATHELINEAU

    2001-01-01

    A method of cartography originally used in geology was adapted to generate regionalization and to obtain 2-D maps of multivariate marine data. The ecological purpose of the method is to divide the studied area through homogeneous regions presenting common multivariate characteristics. Firstly, transformation was applied to the original matrix of hydrological parameters in order to satisfy the condition of multinormality. Then, associative analysis was used in order to produce an easy to inter...

  17. 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.

  18. 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

    packing did not lead to distinct changes in protein pattern. Applying DPLSR to the 2-DE data enabled the selection of protein spots critical for differentiation between 3 and 6months frozen storage with 12months frozen storage. Some of these protein spots have been identified by MS/MS, revealing myosin...... 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...

  19. 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

  20. Integrative analysis of multiple cancer prognosis studies with gene expression measurements

    Science.gov (United States)

    Ma, Shuangge; Huang, Jian; Wei, Fengrong; Xie, Yang; Fang, Kuangnan

    2012-01-01

    Although in cancer research microarray gene profiling studies have been successful in identifying genetic variants predisposing to the development and progression of cancer, the identified markers from analysis of single datasets often suffer low reproducibility. Among multiple possible causes, the most important one is the small sample size hence the lack of power of single studies. Integrative analysis jointly considers multiple heterogeneous studies, has a significantly larger sample size, and can improve reproducibility. In this article, we focus on cancer prognosis studies, where the response variables are progression-free, overall, or other types of survival. A group minimax concave penalty (GMCP) penalized integrative analysis approach is proposed for analyzing multiple heterogeneous cancer prognosis studies with microarray gene expression measurements. An efficient group coordinate descent algorithm is developed. The GMCP can automatically accommodate the heterogeneity across multiple datasets, and the identified markers have consistent effects across multiple studies. Simulation studies show that the GMCP provides significantly improved selection results as compared with the existing meta-analysis approaches, intensity approaches, and group Lasso penalized integrative analysis. We apply the GMCP to four microarray studies and identify genes associated with the prognosis of breast cancer. PMID:22105693

  1. Volatility Spillover and Multivariate Volatility Impulse Response Analysis of GFC News Events

    NARCIS (Netherlands)

    D.E. Allen (David); M.J. McAleer (Michael); R.J. Powell (Robert); A.K. Singh (Abhay)

    2016-01-01

    textabstractThis paper applies two measures to assess spillovers across markets: the Diebold Yilmaz (2012) Spillover Index and the Hafner and Herwartz (2006) analysis of multivariate GARCH models using volatility impulse response analysis. We use two sets of data, daily realized volatility estimates

  2. 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...

  3. A Study of Effects of MultiCollinearity in the Multivariable Analysis.

    Science.gov (United States)

    Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; Peter He, Qinghua; Lillard, James W

    2014-10-01

    A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables.

  4. Ripening of salami: assessment of colour and aspect evolution using image analysis and multivariate image analysis.

    Science.gov (United States)

    Fongaro, Lorenzo; Alamprese, Cristina; Casiraghi, Ernestina

    2015-03-01

    During ripening of salami, colour changes occur due to oxidation phenomena involving myoglobin. Moreover, shrinkage due to dehydration results in aspect modifications, mainly ascribable to fat aggregation. The aim of this work was the application of image analysis (IA) and multivariate image analysis (MIA) techniques to the study of colour and aspect changes occurring in salami during ripening. IA results showed that red, green, blue, and intensity parameters decreased due to the development of a global darker colour, while Heterogeneity increased due to fat aggregation. By applying MIA, different salami slice areas corresponding to fat and three different degrees of oxidised meat were identified and quantified. It was thus possible to study the trend of these different areas as a function of ripening, making objective an evaluation usually performed by subjective visual inspection. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. 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.

  6. 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...... analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii......) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation...

  7. Multivariate Analysis of Prognostic Factors Among 2,313 Patients With Stage III Melanoma: Comparison of Nodal Micrometastases Versus Macrometastases

    Science.gov (United States)

    Balch, Charles M.; Gershenwald, Jeffrey E.; Soong, Seng-jaw; Thompson, John F.; Ding, Shouluan; Byrd, David R.; Cascinelli, Natale; Cochran, Alistair J.; Coit, Daniel G.; Eggermont, Alexander M.; Johnson, Timothy; Kirkwood, John M.; Leong, Stanley P.; McMasters, Kelly M.; Mihm, Martin C.; Morton, Donald L.; Ross, Merrick I.; Sondak, Vernon K.

    2010-01-01

    Purpose To determine the survival rates and independent predictors of survival using a contemporary international cohort of patients with stage III melanoma. Patients and Methods Complete clinicopathologic and follow-up data were available for 2,313 patients with stage III disease in an updated and expanded American Joint Committee on Cancer (AJCC) melanoma staging database. Kaplan-Meier and Cox multivariate survival analyses were performed. Results Among all 2,313 patients with stage III disease, 81% had micrometastases, and 19% had clinically detectable macrometastases. The 5-year overall survival was 63%; it was 67% for patients with nodal micrometastases, and it was 43% for those with nodal macrometastases (P < .001). Tremendous heterogeneity in survival was observed, particularly in the microscopically detected nodal metastasis subset (from 23% to 87% for 5-year survival). Multivariate analysis demonstrated that in patients with nodal micrometastases, number of tumor-containing lymph nodes, primary tumor thickness, patient age, ulceration, and anatomic site of the primary independently predicted survival (all P < .01). When added to the model, primary tumor mitotic rate was the second-most powerful predictor of survival after the number of tumor-containing nodes. In contrast, for patients with nodal macrometastases, the number of tumor-containing nodes, primary ulceration, and patient age independently predicted survival (P < .01). Conclusion In this multi-institutional analysis, we demonstrated remarkable heterogeneity of prognosis among patients with stage III melanoma, especially among those with nodal micrometastases. These results should be incorporated into the design and interpretation of future clinical trials involving patients with stage III melanoma. PMID:20368546

  8. Multivariate Statistical Analysis: a Strategic Tool for Quality and Processes Control in Food Industry

    Directory of Open Access Journals (Sweden)

    Carlos Mario Zuluaga Dominguez

    2011-04-01

    Full Text Available The use of multivariate statistical techniques for quality and process control in the food industry has been growing significantly since the mid-seventies, as a result of the informatics revolution which facilitated the analysis of large data sets. Unlike univariate methods of data exploration, multivariate statistics uses as a major pillar the analysis of information described by three or more variables that can be simultaneously studied and understood in a fast, efficient and easy way. Thanks to the extraordinary advance in computing machines, it is now possible to apply these methodologies to solve extremely complex problems. This article presents the most recognized multivariate statistical techniques, as well as the compilation of some papers that serve as a demonstration of its applicability in the field of foods.

  9. Multivariate and univariate analysis of continuous arterial spin labeling perfusion MRI in Alzheimer's disease.

    Science.gov (United States)

    Asllani, Iris; Habeck, Christian; Scarmeas, Nikolaos; Borogovac, Ajna; Brown, Truman R; Stern, Yaakov

    2008-04-01

    Continuous arterial spin labeling (CASL) magnetic resonance imaging (MRI) was combined with multivariate analysis for detection of an Alzheimer's disease (AD)-related cerebral blood flow (CBF) covariance pattern. Whole-brain resting CBF maps were obtained using spin echo, echo planar imaging (SE-EPI) CASL in patients with mild AD (n=12, age=70.7+/-8.7 years, 7 males, modified Mini-Mental State Examination (mMMS)=38.7/57+/-11.1) and age-matched healthy controls (HC) (n=20; age=72.1+/-6.5 years, 8 males). A covariance pattern for which the mean expression was significantly higher (Pmap out the replicability of both multivariate and univariate approaches, the expression of the pattern from multivariate analysis was superior to that of the univariate.

  10. Multivariate analysis of progressive thermal desorption coupled gas chromatography-mass spectrometry.

    Energy Technology Data Exchange (ETDEWEB)

    Van Benthem, Mark Hilary; Mowry, Curtis Dale; Kotula, Paul Gabriel; Borek, Theodore Thaddeus, III

    2010-09-01

    Thermal decomposition of poly dimethyl siloxane compounds, Sylgard{reg_sign} 184 and 186, were examined using thermal desorption coupled gas chromatography-mass spectrometry (TD/GC-MS) and multivariate analysis. This work describes a method of producing multiway data using a stepped thermal desorption. The technique involves sequentially heating a sample of the material of interest with subsequent analysis in a commercial GC/MS system. The decomposition chromatograms were analyzed using multivariate analysis tools including principal component analysis (PCA), factor rotation employing the varimax criterion, and multivariate curve resolution. The results of the analysis show seven components related to offgassing of various fractions of siloxanes that vary as a function of temperature. Thermal desorption coupled with gas chromatography-mass spectrometry (TD/GC-MS) is a powerful analytical technique for analyzing chemical mixtures. It has great potential in numerous analytic areas including materials analysis, sports medicine, in the detection of designer drugs; and biological research for metabolomics. Data analysis is complicated, far from automated and can result in high false positive or false negative rates. We have demonstrated a step-wise TD/GC-MS technique that removes more volatile compounds from a sample before extracting the less volatile compounds. This creates an additional dimension of separation before the GC column, while simultaneously generating three-way data. Sandia's proven multivariate analysis methods, when applied to these data, have several advantages over current commercial options. It also has demonstrated potential for success in finding and enabling identification of trace compounds. Several challenges remain, however, including understanding the sources of noise in the data, outlier detection, improving the data pretreatment and analysis methods, developing a software tool for ease of use by the chemist, and demonstrating our belief

  11. Metabolomics of medicinal plants: the importance of multivariate analysis of analytical chemistry data.

    Science.gov (United States)

    Okada, Taketo; Afendi, Farit Mochamad; Altaf-Ul-Amin, Md; Takahashi, Hiroki; Nakamura, Kensuke; Kanaya, Shigehiko

    2010-09-01

    Metabolomics, the comprehensive and global analysis of diverse metabolites produced in cells and organisms, has greatly expanded metabolite fingerprinting and profiling as well as the selection and identification of marker metabolites. The methodology typically employs multivariate analysis to statistically process the massive amount of analytical chemistry data resulting from high-throughput and simultaneous metabolite analysis. Although the technology of plant metabolomics has mainly developed with other post-genomics in systems biology and functional genomics, it is independently applied to the evaluation of the qualities of medicinal plants, based on the diversity of metabolite fingerprints resulting from multivariate analysis of non-targeted or widely targeted metabolite analysis. One advantage of applying metabolomics is that medicinal plants are evaluated based not only on the limited number of metabolites that are pharmacologically important chemicals, but also on the fingerprints of minor metabolites and bioactive chemicals. In particular, score plot and loading plot analyses e.g. principal component analysis (PCA), partial-least-squares discriminant analysis (PLS-DA), and discrimination map analysis such as batch-learning self-organizing map (BL-SOM) analysis, are often employed for the reduction of a metabolite fingerprint and the classification of analyzed samples. Based on recent studies, we now understand that metabolomics can be an effective approach for comprehensive evaluation of the qualities of medicinal plants. In this review, we describe practical cases in which metabolomic study was performed on medicinal plants, and discuss the utility of metabolomics for this research field, with focus on multivariate analysis.

  12. Hyponatremia and short-term prognosis of patients with acute pulmonary embolism: A meta-analysis.

    Science.gov (United States)

    Zhou, Xiao-Yu; Chen, Hong-Lin; Ni, Song-Shi

    2017-01-15

    The aim of this study was to assess the relationship between hyponatremia and the short-term prognosis of patients with acute pulmonary embolism (PE). Searches of MEDLINE (1966-) and ISI Databases (1965-) were performed for English language studies. Odds ratio (OR) and adjusted hazard ratio (HR) for short-term prognosis were calculated for PE patients with or without hyponatremia. Meta-analysis was carried out following Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Eight studies with 18,616 patients were included in this meta-analysis. The mean in-hospital mortality was 12.9% in hyponatremia group, compared with 2.3% in normonatremia group. Meta-analysis showed the summary OR was 5.586 (95% CI 3.424 to 9.112). The mean 30-day mortality was 15.9% in hyponatremia group, compared with 7.4% in normonatremia group. The summary OR was 3.091 (95% CI 1.650 to 5.788). No significant publication bias was found for the meta-analysis. Sensitivity analyses by only pooled the adjusted HRs showed the summary HR was 0.924 (95% CI 0.897 to 0.951), which indicted the mortality risk will be decrease to 0.924 times for per-1mmol/L sodium increase in hyponatremia patients. Our meta-analysis indicates that hyponatremia was related with poor short-term prognosis in patients with acute PE. Hyponatremia is a simple, cheap, powerful marker of mortality, which should be used routinely tested in the PE prognostic assessment. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  13. Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study.

    Directory of Open Access Journals (Sweden)

    Binod Neupane

    Full Text Available In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when

  14. 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

  15. 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...

  16. Principal response curves: analysis of time-dependent multivariate responses of biological community to stress

    NARCIS (Netherlands)

    Brink, van den P.J.; Braak, ter C.J.F.

    1999-01-01

    In this paper a novel multivariate method is proposed for the analysis of community response data from designed experiments repeatedly sampled in time. The long-term effects of the insecticide chlorpyrifos on the invertebrate community and the dissolved oxygen (DO)–pH–alkalinity–conductivity

  17. 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…

  18. Why Do Principals Change Schools? A Multivariate Analysis of Principal Retention

    Science.gov (United States)

    Papa, Frank, Jr.

    2007-01-01

    This study uses multivariate analysis of a large panel dataset to examine the determinants of principal retention (and, thus, the determinants of attracting a principal away from her current position). The empirical model incorporates measures of a principal's traits and of the organizational structure, culture, and situational context within a…

  19. Study of ionically modified water performance in carbonate reservoir system by multivariate data analysis

    DEFF Research Database (Denmark)

    Sohal, Muhammad Adeel Nassar; Kucheryavskiy, Sergey V.; Thyne, Geoffrey

    2017-01-01

    in other cases. Most of the published results attributed EOR to improved water wetness in initially oil-wet carbonates. Nevertheless, in a few studies EOR was observed without apparent wettability alteration. We undertake the analysis of a large set of published recovery experiments to try to identify...... the critical mechanisms at the pore scale. Better pore scale physico-chemical understanding will guide to formulate accurate reservoir-scale models. This paper presents a comprehensive meta-analysis of the proposed mechanisms using multivariate data analysis. Detailed review of the subject, including...... mechanisms with supporting and contradictory evidence has been presented by Sohal et al. (2016). In this study, the significance of each contributing factor to EOR was quantified and subjected to rigorous multivariate statistical analysis. The analysis was limited because there is no uniform methodology...

  20. Diagnosis and prognosis of Ostheoarthritis by texture analysis using sparse linear models

    DEFF Research Database (Denmark)

    Marques, Joselene; Clemmensen, Line Katrine Harder; Dam, Erik

    We present a texture analysis methodology that combines uncommitted machine-learning techniques and sparse feature transformation methods in a fully automatic framework. We compare the performances of a partial least squares (PLS) forward feature selection strategy to a hard threshold sparse PLS...... algorithm and a sparse linear discriminant model. The texture analysis framework was applied to diagnosis of knee osteoarthritis (OA) and prognosis of cartilage loss. For this investigation, a generic texture feature bank was extracted from magnetic resonance images of tibial knee bone. The features were...

  1. An Empirical Bayes Method for Multivariate Meta-analysis with an Application in Clinical Trials.

    Science.gov (United States)

    Chen, Yong; Luo, Sheng; Chu, Haitao; Su, Xiao; Nie, Lei

    2014-07-29

    We propose an empirical Bayes method for evaluating overall and study-specific treatment effects in multivariate meta-analysis with binary outcome. Instead of modeling transformed proportions or risks via commonly used multivariate general or generalized linear models, we directly model the risks without any transformation. The exact posterior distribution of the study-specific relative risk is derived. The hyperparameters in the posterior distribution can be inferred through an empirical Bayes procedure. As our method does not rely on the choice of transformation, it provides a flexible alternative to the existing methods and in addition, the correlation parameter can be intuitively interpreted as the correlation coefficient between risks.

  2. 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.

  3. Structure determination of nanocomposites through 3D imaging using laboratory XPS and multivariate analysis

    Energy Technology Data Exchange (ETDEWEB)

    Artyushkova, K., E-mail: kartyush@unm.ed [Chemical and Nuclear Engineering Department, University of New Mexico, Albuquerque, NM 87131 (United States)

    2010-05-15

    The purpose of this review is to introduce current trends and future directions in efforts to obtain 3D images of materials both destructively and non-destructively by means of X-ray photoelectron spectroscopy. Non-destructive methods for creating a 3D volume of the material include peak shape analysis, image fusion of angle-resolved images, combination of ARXPS and mapping and multivariate analysis of ARXPS data. Destructive sputtering of nanocomposite samples with ion beams followed by analysis with X-ray photoelectron spectroscopy represents a powerful strategy for in-depth characterization of complex materials. The combination of photoelectron imaging with depth profiling to create 3D images is essential for accurate structure determination of laterally and vertically heterogeneous materials. There are only a few reports in the scientific literature, however, describing this approach. Advances towards realization of these experiments with assistance of multivariate analysis will be discussed.

  4. Evaluation of antibiotic effects on Pseudomonas aeruginosa biofilm using Raman spectroscopy and multivariate analysis

    OpenAIRE

    Jung, Gyeong Bok; Nam, Seong Won; Choi, Samjin; Lee, Gi-Ja; Park, Hun-Kuk

    2014-01-01

    We investigate the mode of action and classification of antibiotic agents (ceftazidime, patulin, and epigallocatechin gallate; EGCG) on Pseudomonas aeruginosa (P. aeruginosa) biofilm using Raman spectroscopy with multivariate analysis, including support vector machine (SVM) and principal component analysis (PCA). This method allows for quantitative, label-free, non-invasive and rapid monitoring of biochemical changes in complex biofilm matrices with high sensitivity and specificity. In this s...

  5. Multivariate meta-analysis for non-linear and other multi-parameter associations

    Science.gov (United States)

    Gasparrini, A; Armstrong, B; Kenward, M G

    2012-01-01

    In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22807043

  6. No impact of perioperative blood transfusion on prognosis after curative resection for hepatocellular carcinoma: a propensity score matching analysis.

    Science.gov (United States)

    Peng, T; Zhao, G; Wang, L; Wu, J; Cui, H; Liang, Y; Zhou, R; Liu, Z; Wang, Q

    2017-10-27

    The relationship between perioperative blood transfusion and long-term survival after curative resection for hepatocellular carcinoma (HCC) remains controversial. The aim of the present study was to investigate the impact of blood transfusion on the long-term prognosis of HCC patients. Patients with primary HCC who underwent a curative hepatectomy from 2003 to 2011 were enrolled and then retrospectively studied. The clinicopathologic characteristics between patients in the blood transfusion and non-transfusion groups were matched using a propensity score matching (PSM) analysis. Univariate and multivariate Cox regression analyses were used to identify whether perioperative blood transfusion affects long-term survival after resection for HCC. A total of 374 patients were enrolled and 113 patients received perioperative transfusions. The 1-, 3- and 5-year disease-free and overall survival rates of the entire cohort were 65.0, 37.3 and 23.9%, and 90.9, 70.7 and 57.5%, respectively. The disease-free and overall survival rates of the blood transfusion group were significantly worse than the disease-free and overall survival rates of the non-transfusion group in the entire cohort (p blood transfusion was not an independent predictor of disease-free and overall survival in the propensity-matched cohort (p = 0.154, p = 0.667). The present study demonstrates that perioperative blood transfusion has no impact on disease-free and overall survival after curative resection for HCC.

  7. Multivariate analysis of prognostic factors in early stage Hodgkin's disease

    Energy Technology Data Exchange (ETDEWEB)

    Tubiana, M.; Henry-Amar, M.; van der Werf-Messing, B.; Henry, J.; Abbatucci, J.; Burgers, M.; Hayat, M.; Somers, R.; Laugier, A.; Carde, P.

    1985-01-01

    A multivariate analysis of the prognostic factors was carried out with a Cox model on 1,139 patients with clinical Stage I + II Hodgkin's disease included in three controlled clinical trials. The following indicators had been prospectively registered: aged, sex, systemic symptoms, erythrocyte sedimentation, results of staging laparotomy when performed, as well as the date and type of treatment. A linear logistic analysis showed that most of the indicators are interrelated. This emphasizes the necessity of a multivariate analysis in order to assess the independent influence of each of them. The two main prognostic indicators for relapse-free survival are systemic symptoms and/or ESR and number of involved areas. The only significant factor for survival after relapse is age. Sex has a small but significant influence on relapse-free survival. The relative influence of each indicator varies with the type of treatment and these variations may help in understanding the biologic significance of the indicators.

  8. Dynamic molecular monitoring of retina inflammation by in vivo Raman spectroscopy coupled with multivariate analysis.

    Science.gov (United States)

    Marro, Monica; Taubes, Alice; Abernathy, Alice; Balint, Stephan; Moreno, Beatriz; Sanchez-Dalmau, Bernardo; Martínez-Lapiscina, Elena H; Amat-Roldan, Ivan; Petrov, Dmitri; Villoslada, Pablo

    2014-09-01

    Retinal tissue is damaged during inflammation in Multiple Sclerosis. We assessed molecular changes in inflamed murine retinal cultures by Raman spectroscopy. Partial Least Squares-Discriminant analysis (PLS-DA) was able to classify retina cultures as inflamed with high accuracy. Using Multivariate Curve Resolution (MCR) analysis, we deconvolved 6 molecular components suffering dynamic changes along inflammatory process. Those include the increase of immune mediators (Lipoxygenase, iNOS and TNFα), changes in molecules involved in energy production (Cytochrome C, phenylalanine and NADH/NAD+) and decrease of Phosphatidylcholine. Raman spectroscopy combined with multivariate analysis allows monitoring the evolution of retina inflammation. Copyright © 2014 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim.

  9. [Model of prognosis of outcome of burn trauma on the basis of probit-analysis].

    Science.gov (United States)

    Matveenko, A V; Plotnikov, S A; Shindiapin, S V

    2006-01-01

    On the basis of probit-analysis of results of treatment of 10,670 burned patients a prognostic model of the trauma outcome was created as a coordinate network. The model is very accurate, sensitive, specific and simple in use that allows it to be applied for prognosis of burn trauma outcomes in the early period after trauma, especially in organization of medical-evacuation work in places of appearance of mass burns. The model is a standard for the assessment of new protocols of treatment and can serve a criterion of the efficiency of work of intensive care units in burn centers.

  10. Correlations of Ezrin Expression with Pathological Characteristics and Prognosis of Osteosarcoma: A Meta-Analysis

    Directory of Open Access Journals (Sweden)

    Da-Hang Zhao

    2014-01-01

    Full Text Available We conducted a meta-analysis to comprehensively evaluate the correlations of ezrin expression with pathological characteristics and the prognosis of osteosarcoma. The MEDLINE (1966–2013, the Cochrane Library Database, EMBASE, CINAHL, Web of Science (1945–2013, and the Chinese Biomedical Database were searched without language restrictions. Meta-analyses conducted using STATA software were calculated. Ten studies met the inclusion criteria, including 459 patients with osteosarcoma. Meta-analysis results illustrated that ezrin expression may be closely associated with the recurrence of osteosarcoma or metastasis in osteosarcoma. Our findings also demonstrated that patients with grade III-IV osteosarcoma showed a higher frequency of ezrin expression than those with histological grade I-II osteosarcoma. Furthermore, we found that patients with positive expression of ezrin exhibited a shorter overall survival than those with negative ezrin expression. The results also indicated that positive ezrin expression was strongly correlated with poorer metastasis-free survival. Nevertheless, no significant relationships were observed between ezrin expression and clinical variables (age and gender. In the current meta-analysis, our results illustrated significant relationships of ezrin expression with pathological characteristics and prognosis of osteosarcoma. Thus, ezrin expression could be a promising marker in predicting the clinical outcome of patients with osteosarcoma.

  11. 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.

  12. 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.

  13. Prediction of chemical, physical and sensory data from process parameters for frozen cod using multivariate analysis

    DEFF Research Database (Denmark)

    Bechmann, Iben Ellegaard; Jensen, H.S.; Bøknæs, Niels

    1998-01-01

    Physical, chemical and sensory quality parameters were determined for 115 cod (Gadus morhua) samples stored under varying frozen storage conditions. Five different process parameters (period of frozen storage, frozen storage. temperature, place of catch, season for catching and state of rigor) we...... by ones and zeroes only. These results illustrate the application of multivariate analysis as an effective strategy for improving the quality of frozen fish products. (C) 1998 Society of Chemical Industry......Physical, chemical and sensory quality parameters were determined for 115 cod (Gadus morhua) samples stored under varying frozen storage conditions. Five different process parameters (period of frozen storage, frozen storage. temperature, place of catch, season for catching and state of rigor) were...... varied systematically at two levels. The data obtained were evaluated using the multivariate methods, principal component analysis (PCA) and partial least squares (PLS) regression. The PCA models were used to identify which process parameters were actually most important for the quality of the frozen cod...

  14. Fluorescence measurements for evaluating the application of multivariate analysis techniques to optically thick environments.

    Energy Technology Data Exchange (ETDEWEB)

    Reichardt, Thomas A.; Timlin, Jerilyn Ann; Jones, Howland D. T.; Sickafoose, Shane M.; Schmitt, Randal L.

    2010-09-01

    Laser-induced fluorescence measurements of cuvette-contained laser dye mixtures are made for evaluation of multivariate analysis techniques to optically thick environments. Nine mixtures of Coumarin 500 and Rhodamine 610 are analyzed, as well as the pure dyes. For each sample, the cuvette is positioned on a two-axis translation stage to allow the interrogation at different spatial locations, allowing the examination of both primary (absorption of the laser light) and secondary (absorption of the fluorescence) inner filter effects. In addition to these expected inner filter effects, we find evidence that a portion of the absorbed fluorescence is re-emitted. A total of 688 spectra are acquired for the evaluation of multivariate analysis approaches to account for nonlinear effects.

  15. 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

    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......: 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...

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

    Science.gov (United States)

    Labbé, Nicole; Harper, David; Rials, Timothy; Elder, Thomas

    2006-05-17

    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 multivariate models of charcoal were able to distinguish between species and wood thermal treatments, revealing that the characteristics of the wood charcoal depend not only on the wood species, but also on the carbonization temperature. This work demonstrates the potential of mid infrared spectroscopy in the whiskey industry, from the identification and classification of the wood species for the mellowing process to the chemical characterization of the barrels after the toasting and charring process.

  17. 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.

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

    OpenAIRE

    Richard Mather

    2015-01-01

    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 ‘...

  19. 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.

  20. Dissolving pulp : Multivariate Characterisation and Analysis of Reactivity and Spectroscopic Properties

    OpenAIRE

    Elg Christoffersson, Kristina

    2004-01-01

    Various chemical properties can be used to characterise dissolving pulp. The quality of the pulp must be carefully controlled to ensure that it meets the requirements for its intended use and the further processes to be applied. If it is to be used to prepare viscose, or other cellulose derivatives, the key prop-erties of the pulp are its accessibility and reactivity. The studies described in this thesis investigated the potential utility of multivariate analysis of chemi-cal and spectral dat...

  1. Multivariate Analysis of ToF-SIMS Data from Multicomponent Systems: The Why, When, and How

    OpenAIRE

    Graham, Daniel J.; Castner, David G.

    2012-01-01

    The use of multivariate analysis (MVA) methods in the processing of time-of-flight secondary ion mass spectrometry (ToF-SIMS) data has become increasingly more common. MVA presents a powerful set of tools to aid the user in processing data from complex, multicomponent surfaces such as biological materials and biosensors. When properly used, MVA can help the user identify the major sources of differences within a sample or between samples, determine where certain compounds exist on a sample, o...

  2. Pleiotropy Analysis of Quantitative Traits at Gene Level by Multivariate Functional Linear Models

    OpenAIRE

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

    2015-01-01

    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 te...

  3. Spectral compression algorithms for the analysis of very large multivariate images

    Science.gov (United States)

    Keenan, Michael R.

    2007-10-16

    A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.

  4. Long noncoding RNA CCAT2 can predict metastasis and poor prognosis: A meta-analysis.

    Science.gov (United States)

    Fan, Yang-Hua; Fang, Hua; Ji, Chen-Xing; Xie, Huan; Xiao, Bing; Zhu, Xin-Gen

    2017-03-01

    It has been reported that Colon cancer-associated transcript 2 (CCAT2) is dysregulated in various cancers. We performed this meta-analysis to clarify its promising functions as a prognosis marker in malignant tumors. Electronic databases, including PubMed, Medline, OVID, Cochrane Library, and Web of Science, were searched from inception to October 20, 2016. The hazard ratio (HR) and 95% confidence interval (CI) were calculated to explore the relationship between CCAT2 expression and survival, which were extracted from the eligible studies. The odds ratio (OR) was calculated to assess the association between CCAT2 expression and pathological parameters using RevMan5.3 software. Six original studies were included in this meta-analysis including 725 cancer patients. The pooled HR suggested that high CCAT2 expression was significantly correlated with overall survival (OS) (HR=2.30, 95% CI: 1.62-3.25, panalysis revealed a significant association between CCAT2 and OS in urogenital system (HR=1.70, 95% CI: 1.27-2.26, panalysis demonstrated that high CCAT2 expression significantly predicts poor OS, poor PFS, LNM, DM and tumor stage, suggesting that high CCAT2 expression may serve as a novel biomarker for poor prognosis and metastasis in cancers. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. APPLICATION OF MONITORING, DIAGNOSIS, AND PROGNOSIS IN THERMAL PERFORMANCE ANALYSIS FOR NUCLEAR POWER PLANTS

    Directory of Open Access Journals (Sweden)

    HYEONMIN KIM

    2014-12-01

    Although thermal performance tests implemented using industrial codes and standards can provide officially trustworthy results, they are essentially resource-consuming and maybe even a hind-sighted technique rather than a foresighted one, considering their periodicity. Therefore, if more accurate performance monitoring can be achieved using advanced data analysis techniques, we can expect more optimized operations and maintenance. This paper proposes a framework and describes associated methodologies for in-situ thermal performance analysis, which differs from conventional performance monitoring. The methodologies are effective for monitoring, diagnosis, and prognosis in pursuit of CBM. Our enabling techniques cover the intelligent removal of random and systematic errors, deviation detection between a best condition and a currently measured condition, degradation diagnosis using a structured knowledge base, and prognosis for decision-making about maintenance tasks. We also discuss how our new methods can be incorporated with existing performance tests. We provide guidance and directions for developers and end-users interested in in-situ thermal performance management, particularly in NPPs with large steam turbines.

  6. 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.

  7. Coreferentiality: a new method for the hypothesis-based analysis of phenotypes characterized by multivariate data.

    Directory of Open Access Journals (Sweden)

    Constantin Fesel

    Full Text Available Many multifactorial biologic effects, particularly in the context of complex human diseases, are still poorly understood. At the same time, the systematic acquisition of multivariate data has become increasingly easy. The use of such data to analyze and model complex phenotypes, however, remains a challenge. Here, a new analytic approach is described, termed coreferentiality, together with an appropriate statistical test. Coreferentiality is the indirect relation of two variables of functional interest in respect to whether they parallel each other in their respective relatedness to multivariate reference data, which can be informative for a complex effect or phenotype. It is shown that the power of coreferentiality testing is comparable to multiple regression analysis, sufficient even when reference data are informative only to a relatively small extent of 2.5%, and clearly exceeding the power of simple bivariate correlation testing. Thus, coreferentiality testing uses the increased power of multivariate analysis, however, in order to address a more straightforward interpretable bivariate relatedness. Systematic application of this approach could substantially improve the analysis and modeling of complex phenotypes, particularly in the context of human study where addressing functional hypotheses by direct experimentation is often difficult.

  8. A review on tomato authenticity: quality control methods in conjunction with multivariate analysis (chemometrics).

    Science.gov (United States)

    Arvanitoyannis, Ioannis S; Vaitsi, Olga B

    2007-01-01

    Authenticity and traceability have been two of the most important issues in the food chain. Authenticity in particular, is closely related with both food quality and safety issues. Vegetables stand for a category of foods heavily affected by adulteration either in terms of geographic origin (national or international level) or production methods (organic or conventional production, fertilizers, pesticides, genetically modified vegetables). This review aims at addressing most of the currently applied methods for ensuring quality control of vegetables; a) instrumental: ion chromatography, high pressure liquid chromatography, atomic absorption spectrophotometry, electronic nose and mass spectroscopy and b) sensory analysis. The results of all the above mentioned methods were analyzed by means of multivariate analysis (principal component analysis, discriminant analysis, cluster analysis, canonical analysis, and factor analysis). All ensuing results and conclusions are summarized in eight comprehensive tables.

  9. Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Feng, E-mail: fwang@unu.edu [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Huisman, Jaco [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Stevels, Ab [Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Baldé, Cornelis Peter [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Statistics Netherlands, Henri Faasdreef 312, 2492 JP Den Haag (Netherlands)

    2013-11-15

    Highlights: • A multivariate Input–Output Analysis method for e-waste estimates is proposed. • Applying multivariate analysis to consolidate data can enhance e-waste estimates. • We examine the influence of model selection and data quality on e-waste estimates. • Datasets of all e-waste related variables in a Dutch case study have been provided. • Accurate modeling of time-variant lifespan distributions is critical for estimate. - Abstract: 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

  10. Younger age is an independent predictor of worse prognosis among Lebanese nonmetastatic breast cancer patients: analysis of a prospective cohort

    Directory of Open Access Journals (Sweden)

    El Chediak A

    2017-06-01

    Full Text Available Alissar El Chediak,1 Raafat S Alameddine,1 Ayman Hakim,1 Lara Hilal,2 Sarah Abdel Massih,1 Lana Hamieh,3 Deborah Mukherji,1 Sally Temraz,1 Maya Charafeddine,1 Ali Shamseddine1 1Division of Hematology/Oncology, Department of Internal Medicine, 2Department of Radiation Oncology, American University of Beirut Medical Center, Beirut, Lebanon; 3Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, USA Background: Several retrospective studies have reported that younger age at presentation is associated with a worse prognosis for nonmetastatic breast cancer patients. In this study, we prospectively assessed the association between different baseline characteristics (age, tumor characteristics, mode of treatment, etc and outcomes among newly diagnosed nonmetastatic Lebanese breast cancer patients.Methods: We recruited a sample of 123 women newly diagnosed with nonmetastatic breast cancer presenting to American University of Beirut Medical Center. Immunohistochemical, molecular (vitamin D receptor, methylene tetrahydrofolate reductase polymorphisms, and genetic assays were performed. Patient characteristics were compared by age group (<40 and ≥40 years. A Cox regression analysis was performed to evaluate the variables affecting the disease-free survival (DFS. Outcome data were obtained, and DFS was estimated.Results: Among the 123 patients, 47 were 40 years of age or younger, and 76 were older than 40 years. Median follow-up duration was 58 months. Nine out of 47 patients <40 years (19.1% experienced disease relapse in contrast to four out of 76 patients >40 years (5.2%. A wide immunohistochemical panel included Ki-67, cyclin B1, p53, platelet-derived growth factor receptor, and vascular endothelial growth factor receptor, and did not reveal any significant difference in these markers between the two age groups. Older patients had a larger percentage of Luminal A than younger patients. On multivariate analysis

  11. 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.

  12. Raman spectroscopy combined with multivariate analysis techniques as a potential tool for semen investigation

    Science.gov (United States)

    Huang, Zufang; Lin, Jinyong; Cao, Gang; Chen, Xiwen; Li, Yongzeng; Feng, Shangyuan; Lin, Juqiang; Wang, Jing; Lin, Hongxin; Chen, Rong

    2014-09-01

    Molecular characterization of semen that can be used to provide an objective diagnosis of semen quality is still lacking. Raman spectroscopy measures vibrational modes of molecules, thus can be utilized to characterize biological fluids. Here, we employed Raman spectroscopy to characterize and compare normal and abnormal semen samples in the fingerprint region (400-1800cm-1). Multivariate analysis methods including principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were used for spectral analysis to differentiate between normal and abnormal semen samples. Compared with PCA-LDA analysis, PLS-DA improved the diagnostic results, showing a sensitivity of 77% and specificity of 73%. Furthermore, our preliminary quantitative analysis based on PLS algorithm demonstrated that spermatozoa concentration were relatively well predicted (R2=0.825). In conclusion, this study demonstrated that micro-Raman spectroscopy combined with multivariate methods can provide as a new diagnostic technique for semen analysis and differentiation between normal and abnormal semen samples.

  13. Diagnosis and Prognosis of Neuroendocrine Tumours of the Lung by Means of High Resolution Image Analysis

    Directory of Open Access Journals (Sweden)

    Uta Jütting

    1999-01-01

    Full Text Available Neuroendocrine tumours (NET of the lung are divided in subtypes with different malignant potential. The first is the benign or low‐grade malignant tumours, well‐differentiated, called typical carcinoids (TC and the second is the high‐grade malignant tumours, poorly differentiated of small (SCLC or large cell type (LCLC. Between these tumour types lies the well‐differentiated carcinoma with a lower grade of malignancy (WDNEC. In clinical routine it is very important with regard to prognosis to distinguish patients with low malignant potential from those with higher ones. In this study 32 cases of SCLC, 13 of WDNEC and 14 of TC with a follow‐up time up to 7 years were collected. Sections 4 μm thick from paraffin embedded tissue were Feulgen stained. By means of high resolution image analysis 100 nuclei per case were randomly gathered to extract morphometric, densitometric and textural quantitative features. To investigate the ploidy status of the tumour the corrected DNA distribution was calculated. Stepwise linear discriminant analysis to differentiate the classes and Cox regression analysis for the survival time analysis were applied. Using chromatin textural and morphometric features in two two‐class discriminations, 11 of the 14 TC cases and 8 of the 13 WDNEC cases were correctly classified and 11/13 WDNEC cases and 28/32 SCLC cases, respectively. The WDNEC cases are more similar in chromatin structure to TC than to SCLC. For the survival analysis, only chromatin features were selected to differentiate patients with better and worse prognosis independent of staging and tumour type.

  14. Craniometrical estimation of the native Japanese Mishima cattle, using multivariate analysis.

    Science.gov (United States)

    Ogawa, Y; Daigo, M; Amasaki, H

    1989-01-01

    The present study on measurement of the skull of Mishima cattle, which has been postulated as the only pure representative breed of native Japanese cattle, was performed using craniometrical multivariate analysis. The data of the skull of Mishima cattle was compared with 17 breeds of cattle, i.e. Korean cattle (Hamhung, Pyongyang, Chinju Suwon, and Kwangju), Mongolian cattle, Hainan Tao cattle, northeastern Chinese cattle (Shuangliao, Shenyang, Tongliao, Lüta, and Chilin), Astatic Water Buffalo, Yak, Bos Banteng, American Bison, and Holstein-Friesian. The Mishima cattle was included in the group of Korean breeds, especially it was closed on the group of Pyongyang and Chinju breeds. The distance on the craniometrical multivariate analyzing co-ordinate between Mishima cattle and Hainan Tao breed of Zebu cattle was larger than the distance between Mishima cattle and Korean breeds. While result, as a above the present study was very important for the origin of "Wagyu" (native Japanese cattle). Since the northern route theory of the origin of Mishima cattle has been reported on the type of serum enzymes and hemotypes. It was suggested that the craniometrical multivariate analysis supported to the northern route theory of the origin of Mishima cattle.

  15. Bivariate functional principal components analysis: considerations for use with multivariate movement signatures in sports biomechanics.

    Science.gov (United States)

    Warmenhoven, John; Cobley, Stephen; Draper, Conny; Harrison, Andrew; Bargary, Norma; Smith, Richard

    2017-11-10

    Sporting performance is often investigated through graphical observation of key technical variables that are representative of whole movements. The presence of differences between athletes in such variables has led to terms such as movement signatures being used. These signatures can be multivariate (multiple time-series observed concurrently), and also be composed of variables measured relative to different scales. Analytical techniques from areas of statistics such as Functional Data Analysis (FDA) present a practical alternative for analysing multivariate signatures. When applied to concurrent bivariate time-series multivariate functional principal components analysis (referred to as bivariate fPCA or bfPCA in this paper) has demonstrated preliminary application in biomechanical contexts. Despite this, given the infancy of bfPCA in sports biomechanics there are still necessary considerations for its use with non-conventional or complex bivariate structures. This paper focuses on the application of bfPCA to the force-angle graph in on-water rowing, which is a bivariate structure composed of variables with different units. A normalisation approach is proposed to investigate and standardise differences in variability between the two variables. The results of bfPCA applied to the non-normalised data and normalised data are then compared. Considerations and recommendations for the application of bfPCA in this context are also provided.

  16. Assessing signal-to-noise in quantitative proteomics: multivariate statistical analysis in DIGE experiments.

    Science.gov (United States)

    Friedman, David B

    2012-01-01

    All quantitative proteomics experiments measure variation between samples. When performing large-scale experiments that involve multiple conditions or treatments, the experimental design should include the appropriate number of individual biological replicates from each condition to enable the distinction between a relevant biological signal from technical noise. Multivariate statistical analyses, such as principal component analysis (PCA), provide a global perspective on experimental variation, thereby enabling the assessment of whether the variation describes the expected biological signal or the unanticipated technical/biological noise inherent in the system. Examples will be shown from high-resolution multivariable DIGE experiments where PCA was instrumental in demonstrating biologically significant variation as well as sample outliers, fouled samples, and overriding technical variation that would not be readily observed using standard univariate tests.

  17. MULTIVARIATE MATHEMATICAL MORPHOLOGY FOR DCE-MRI IMAGE ANALYSIS IN ANGIOGENESIS STUDIES

    Directory of Open Access Journals (Sweden)

    Guillaume Noyel

    2014-05-01

    Full Text Available We propose a new computer aided detection framework for tumours acquired on DCE-MRI (Dynamic Contrast Enhanced Magnetic Resonance Imaging series on small animals. To perform this approach, we consider DCE-MRI series as multivariate images. A full multivariate segmentation method based on dimensionality reduction, noise filtering, supervised classification and stochastic watershed is explained and tested on several data sets. The two main key-points introduced in this paper are noise reduction preserving contours and spatio temporal segmentation by stochastic watershed. Noise reduction is performed in a special way to select factorial axes of Factor Correspondence Analysis in order to preserves contours. Then a spatio-temporal approach based on stochastic watershed is used to segment tumours. The results obtained are in accordance with the diagnosis of the medical doctors.

  18. Spatial compression algorithm for the analysis of very large multivariate images

    Science.gov (United States)

    Keenan, Michael R [Albuquerque, NM

    2008-07-15

    A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.

  19. Multivariate prognostic factors analysis for second-line chemotherapy in advanced biliary tract cancer

    Science.gov (United States)

    Fornaro, L; Cereda, S; Aprile, G; Di Girolamo, S; Santini, D; Silvestris, N; Lonardi, S; Leone, F; Milella, M; Vivaldi, C; Belli, C; Bergamo, F; Lutrino, S E; Filippi, R; Russano, M; Vaccaro, V; Brunetti, A E; Rotella, V; Falcone, A; Barbera, M A; Corbelli, J; Fasola, G; Aglietta, M; Zagonel, V; Reni, M; Vasile, E; Brandi, G

    2014-01-01

    Background: The role of second-line chemotherapy (CT) is not established in advanced biliary tract cancer (aBTC). We investigated the outcome of aBTC patients treated with second-line CT and devised a prognostic model. Methods: Baseline clinical and laboratory data of 300 consecutive aBTC patients were collected and association with overall survival (OS) was investigated by multivariable Cox models. Results: The following parameters resulted independently associated with longer OS: Eastern Cooperative Oncology Group performance status of 0 (P<0.001; hazard ratio (HR), 0.348; 95% confidence interval (CI) 0.215–0.562), CA19.9 lower than median (P=0.013; HR, 0.574; 95% CI 0.370–0.891), progression-free survival after first-line CT ⩾6 months (P=0.027; HR, 0.633; 95% CI 0.422–0.949) and previous surgery on primary tumour (P=0.027; HR, 0.609; 95% CI 0.392–0.945). We grouped the 249 patients with complete data available into three categories according to the number of fulfilled risk factors: median OS times for good-risk (zero to one factors), intermediate-risk (two factors) and poor-risk (three to four factors) groups were 13.1, 6.6 and 3.7 months, respectively (P<0.001). Conclusions: Easily available clinical and laboratory factors predict prognosis of aBTC patients undergoing second-line CT. This model allows individual patient-risk stratification and may help in treatment decision and trial design. PMID:24714745

  20. Multivariate analysis of the chemical properties of the eroded brown soils

    Directory of Open Access Journals (Sweden)

    Juan Alejandro Villazón Gómez

    2017-01-01

    Full Text Available The work was carried out with the data obtained of 30 profiles of Brown soils classified according to the effect of erosion. With the objective of determining, by means of a multivariate analysis, the effect of the erosion on the chemicals properties of the Brown soils was carried out a Discriminant and Principals Components Analysis. It was evaluated the chemicals variables pH in water, pH in KCl, organic matter, calcium, magnesium, potassium, sodium and S, T and V values. The Multivariate Analysis allowed establishing that magnesium is the only chemical property that evidence contraposition with the other variables, due to the harmful effect that this base exerts on the soil aggregates, which can accelerate or stressing the action of the erosive processes in the Brown soils. In the Principals Components Analysis, then components represented by the influence of the soil reaction, the absorbing complex and magnesium accumulate 78.75 % of the variance. The Discriminant Analysis explains the 97.06 % of the total of the variation in the two first axes, with the 93.33 % of good classification, with all the groups conformed by the categories of erosion well told apart among themselves.

  1. 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.

  2. Multivariate data analysis as a fast tool in evaluation of solid state phenomena

    DEFF Research Database (Denmark)

    Jørgensen, Anna Cecilia; Miroshnyk, Inna; Karjalainen, Milja

    2006-01-01

    of information generated can be overwhelming and the need for more effective data analysis tools is well recognized. The aim of this study was to investigate the use of multivariate data analysis, in particular principal component analysis (PCA), for fast analysis of solid state information. The data sets...... analyzed covered dehydration phenomena of a set of hydrates followed by variable temperature X-ray powder diffractometry and Raman spectroscopy and the crystallization of amorphous lactose monitored by Raman spectroscopy. Identification of different transitional states upon the dehydration enabled...... the molecular level interpretation of the structural changes related to the loss of water, as well as interpretation of the phenomena related to the crystallization. The critical temperatures or critical time points were identified easily using the principal component analysis. The variables (diffraction angles...

  3. 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.

  4. Combination of multivariate curve resolution and multivariate classification techniques for comprehensive high-performance liquid chromatography-diode array absorbance detection fingerprints analysis of Salvia reuterana extracts.

    Science.gov (United States)

    Hakimzadeh, Neda; Parastar, Hadi; Fattahi, Mohammad

    2014-01-24

    In this study, multivariate curve resolution (MCR) and multivariate classification methods are proposed to develop a new chemometric strategy for comprehensive analysis of high-performance liquid chromatography-diode array absorbance detection (HPLC-DAD) fingerprints of sixty Salvia reuterana samples from five different geographical regions. Different chromatographic problems occurred during HPLC-DAD analysis of S. reuterana samples, such as baseline/background contribution and noise, low signal-to-noise ratio (S/N), asymmetric peaks, elution time shifts, and peak overlap are handled using the proposed strategy. In this way, chromatographic fingerprints of sixty samples are properly segmented to ten common chromatographic regions using local rank analysis and then, the corresponding segments are column-wise augmented for subsequent MCR analysis. Extended multivariate curve resolution-alternating least squares (MCR-ALS) is used to obtain pure component profiles in each segment. In general, thirty-one chemical components were resolved using MCR-ALS in sixty S. reuterana samples and the lack of fit (LOF) values of MCR-ALS models were below 10.0% in all cases. Pure spectral profiles are considered for identification of chemical components by comparing their resolved spectra with the standard ones and twenty-four components out of thirty-one components were identified. Additionally, pure elution profiles are used to obtain relative concentrations of chemical components in different samples for multivariate classification analysis by principal component analysis (PCA) and k-nearest neighbors (kNN). Inspection of the PCA score plot (explaining 76.1% of variance accounted for three PCs) showed that S. reuterana samples belong to four clusters. The degree of class separation (DCS) which quantifies the distance separating clusters in relation to the scatter within each cluster is calculated for four clusters and it was in the range of 1.6-5.8. These results are then

  5. A comparison of fMRI adaptation and multivariate pattern classification analysis in visual cortex.

    Science.gov (United States)

    Sapountzis, Panagiotis; Schluppeck, Denis; Bowtell, Richard; Peirce, Jonathan W

    2010-01-15

    Functional magnetic resonance imaging (fMRI) has become a ubiquitous tool in cognitive neuroscience. The technique allows noninvasive measurements of cortical responses in the human brain, but only on the millimeter scale. Because a typical voxel contains many thousands of neurons with varied properties, establishing the selectivity of their responses directly is impossible. In recent years, two methods using fMRI aimed at studying the selectivity of neuronal populations on a 'subvoxel' scale have been heavily used. The first technique, fMRI adaptation, relies on the observation that the blood oxygen level-dependent (BOLD) response in a given voxel is reduced after prolonged presentation of a stimulus, and that this reduction is selective to the characteristics of the repeated stimuli (adapters). The second technique, multivariate pattern analysis (MVPA), makes use of multivariate statistics to recover small biases in individual voxels in their responses to different stimuli. It is thought that these biases arise due to the uneven distribution of neurons (with different properties) sampled by the many voxels in the imaged volume. These two techniques have not been compared explicitly, however, and little is known about their relative sensitivities. Here, we compared fMRI results from orientation-specific visual adaptation and orientation-classification by MVPA, using optimized experimental designs for each, and found that the multivariate pattern classification approach was more sensitive to small differences in stimulus orientation than the adaptation paradigm. Estimates of orientation selectivity obtained with the two methods were, however, very highly correlated across visual areas.

  6. Integrated environmental monitoring and multivariate data analysis-A case study.

    Science.gov (United States)

    Eide, Ingvar; Westad, Frank; Nilssen, Ingunn; de Freitas, Felipe Sales; Dos Santos, Natalia Gomes; Dos Santos, Francisco; Cabral, Marcelo Montenegro; Bicego, Marcia Caruso; Figueira, Rubens; Johnsen, Ståle

    2017-03-01

    The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate

  7. 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.

  8. THE PROGNOSIS OF RUSSIAN DEFENSE INDUSTRY DEVELOPMENT IMPLEMENTED THROUGH REGRESSION ANALYSIS

    Directory of Open Access Journals (Sweden)

    L.M. Kapustina

    2007-03-01

    Full Text Available The article illustrates the results of investigation the major internal and external factors which influence the development of the defense industry, as well as the results of regression analysis which quantitatively displays the factorial contribution in the growth rate of Russian defense industry. On the basis of calculated regression dependences the authors fulfilled the medium-term prognosis of defense industry. Optimistic and inertial versions of defense product growth rate for the period up to 2009 are based on scenario conditions in Russian economy worked out by the Ministry of economy and development. In conclusion authors point out which factors and conditions have the largest impact on successful and stable operation of Russian defense industry.

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

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    1994-01-01

    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...... maximize the variance represented by each component, MAFs maximize the spatial autocorrelation represented by each component, and MNFs maximize a measure of signal-to-noise ratio represented by each component. In the literature MAF/MNF analysis is described for regularly gridded data only. Here...... filtering of MAF/MNFs is suggested. One case study successfully shows the effect of the MNF Fourier restoration. Another case shows the superiority of the MAF/MNF analysis over ordinary non-spatial factor analysis of geochemical data in South Greenland (with a geologist's comment). Also, two examples of MAF...

  10. Blood loss in trochanteric fractures: multivariate analysis comparing dynamic hip screw and Gamma nail.

    Science.gov (United States)

    Ronga, Mario; Bonzini, Daniele; Valoroso, Marco; La Barbera, Giuseppe; Tamini, Jacopo; Cherubino, Mario; Cherubino, Paolo

    2017-10-01

    Anaemia in patients with trochanteric fracture is associated with increased morbidity and mortality and it is an independent risk factor for functional mobility of patients. Several authors have reported the blood loss following operative treatment comparing different fixation systems but few authors have evaluated many associated variables that could influence the perioperative blood loss. To evaluate the blood loss in patients that had their trochanteric fracture stabilized with dynamic hip screw (DHS) or Gamma nail. Multivariate analysis of different variables that can influence blood loss was carried out (type of fracture, antiaggregant or anticoagulant therapy, time to surgery). The hypothesis was that there is no difference in terms of blood loss in patients with trochanteric fracture treated with DHS or Gamma nail considering all these variables. Perioperative blood loss was evaluated in 417 consecutive patients treated for trochanteric fracture with DHS or Gamma nail between January 2010 and March 2013. The perioperative blood loss was calculated using the Lisander formula modified by Foss-Kehlet based on pre- and post-operative haemoglobin values and transfusion rates. Univariate and multivariate analysis were performed integrating the following variables: type of fracture (A1 vs A2), antiaggregant/anticoagulant therapy vs no therapy, time to surgery (24 hours from trauma), type of implant (DHS vs Gamma nail). A significant blood loss (p 24 hours from trauma (1584.4ml vs 1323.9ml), DHS and Gamma nail (894.7ml vs 1720.6ml). At multivariate analysis, in the A1 fracture groups the DHS showed a significant lower blood loss compared to Gamma nail (p loss, DHS should be used in A1 fractures while Gamma nail can be taking in account for the unstable A2 fractures. © 2017 Elsevier Ltd. All rights reserved.

  11. 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...... that could not otherwise be handled before. The different mathematical models are all exemplified by food related subjects to underline the generic use of the models within the food chain. Applications will be given from meat, storage, vegetable characterization, fish quality monitoring and industrial food...

  12. Detecting causal interdependence in simulated neural signals based on pairwise and multivariate analysis.

    Science.gov (United States)

    Yang, C; Le Bouquin Jeannes, R; Faucon, G; Wendling, F

    2010-01-01

    Our objective is to analyze EEG signals recorded with depth electrodes during seizures in patients with drug-resistant epilepsy. Usually, different phases are observed during the seizure process, including a fast onset activity (FOA). We aim to determine how cerebral structures get involved during this FOA, in particular whether some structure can "drive" some other structures. This paper focuses on a linear Granger causality based measure to detect causal relation of interdependence in multivariate signals generated by a physiology-based model of coupled neuronal populations. When coupling between signals exists, statistical analysis supports the relevance of this index for characterizing the information flow and its direction among neuronal populations.

  13. 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. 

  14. Multivariate analysis of patient satisfaction factors affecting the usage of removable partial dentures.

    Science.gov (United States)

    Koyama, Shigeto; Sasaki, Keiichi; Kawata, Tetsuo; Atsumi, Tomohiro; Watanabe, Makoto

    2008-01-01

    The purpose of this retrospective cohort study was to investigate patient satisfaction factors that affect the usage of removable partial dentures (RPDs) using a multivariate analysis. Sixty-seven patients, who had RPDs inserted at the Tohoku University Hospital between 1996 and 2001, participated in this study. Data were collected from patients' clinical records and a questionnaire. Of the 15 factors examined, significant associations were found between RPD usage and pain, color of the artificial teeth, and arrangement of the artificial teeth. These findings suggest that RPD usage is related to patient satisfaction with esthetics and an absence of pain.

  15. Water quality analysis of the Rapur area, Andhra Pradesh, South India using multivariate techniques

    Science.gov (United States)

    Nagaraju, A.; Sreedhar, Y.; Thejaswi, A.; Sayadi, Mohammad Hossein

    2017-10-01

    The groundwater samples from Rapur area were collected from different sites to evaluate the major ion chemistry. The large number of data can lead to difficulties in the integration, interpretation, and representation of the results. Two multivariate statistical methods, hierarchical cluster analysis (HCA) and factor analysis (FA), were applied to evaluate their usefulness to classify and identify geochemical processes controlling groundwater geochemistry. Four statistically significant clusters were obtained from 30 sampling stations. This has resulted two important clusters viz., cluster 1 (pH, Si, CO3, Mg, SO4, Ca, K, HCO3, alkalinity, Na, Na + K, Cl, and hardness) and cluster 2 (EC and TDS) which are released to the study area from different sources. The application of different multivariate statistical techniques, such as principal component analysis (PCA), assists in the interpretation of complex data matrices for a better understanding of water quality of a study area. From PCA, it is clear that the first factor (factor 1), accounted for 36.2% of the total variance, was high positive loading in EC, Mg, Cl, TDS, and hardness. Based on the PCA scores, four significant cluster groups of sampling locations were detected on the basis of similarity of their water quality.

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

    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.

  17. 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.

  18. 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.

  19. Spatial analysis of hydrological and phytoplanktonic data of the Bay of Tunis. Multivariate cartography

    Directory of Open Access Journals (Sweden)

    R.B. HAMADOU

    2001-12-01

    Full Text Available A method of cartography originally used in geology was adapted to generate regionalization and to obtain 2-D maps of multivariate marine data. The ecological purpose of the method is to divide the studied area through homogeneous regions presenting common multivariate characteristics. Firstly, transformation was applied to the original matrix of hydrological parameters in order to satisfy the condition of multinormality. Then, associative analysis was used in order to produce an easy to interpret partition of sites. The level of heterogeneity between each station and the properties of each group was assessed by measuring the Bayesian probabilities. These conditional probabilities measure the chance that each site has of belonging to a predefined group of sites. Based on the geographical positions of the stations, the probability values for each group of stations were mapped using kriging interpolation algorithm. The obtained maps of iso-probabilities for the different groups of stations were used to define homogenous zones on a single map. Including the phytoplanktonic dataset afterwards, the indicator species were identified for each zone. This multivariate analysis was applied to a hydrological and phytoplanktonic dataset of the Bay of Tunis. Measures at surface were made at 17 stations, sampled monthly over 2 years. The results illustrated a partition of the bay considering four groups, two coastal and two central groups of stations. The importance of the inshore influence was demonstrated in the setting up of such a regionalization through the inflow of alluvium and other products of coastal activities. The significant presence of the toxic phytoplanktonic community in the bay suggests the need to institute a monitoring program.

  20. 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

    . 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....... The analysis is carried out on the raw data and specifies a model for the mean and the covariance structure. Results suggest that cerebellar volume and intracranial space vary with age and sex. Brain volumes tend to decrease slightly with age, and males generally have a larger brain volume than females....... 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...

  1. A multivariate partial least squares approach to joint association analysis for multiple correlated traits

    Directory of Open Access Journals (Sweden)

    Yang Xu

    2016-02-01

    Full Text Available Many complex traits are highly correlated rather than independent. By taking the correlation structure of multiple traits into account, joint association analyses can achieve both higher statistical power and more accurate estimation. To develop a statistical approach to joint association analysis that includes allele detection and genetic effect estimation, we combined multivariate partial least squares regression with variable selection strategies and selected the optimal model using the Bayesian Information Criterion (BIC. We then performed extensive simulations under varying heritabilities and sample sizes to compare the performance achieved using our method with those obtained by single-trait multilocus methods. Joint association analysis has measurable advantages over single-trait methods, as it exhibits superior gene detection power, especially for pleiotropic genes. Sample size, heritability, polymorphic information content (PIC, and magnitude of gene effects influence the statistical power, accuracy and precision of effect estimation by the joint association analysis.

  2. An extended multivariate autoregressive framework for EEG-based information flow analysis of a brain network.

    Science.gov (United States)

    Hettiarachchi, Imali T; Mohamed, Shady; Nyhof, Luke; Nahavandi, Saeid

    2013-01-01

    Recently effective connectivity studies have gained significant attention among the neuroscience community as Electroencephalography (EEG) data with a high time resolution can give us a wider understanding of the information flow within the brain. Among other tools used in effective connectivity analysis Granger Causality (GC) has found a prominent place. The GC analysis, based on strictly causal multivariate autoregressive (MVAR) models does not account for the instantaneous interactions among the sources. If instantaneous interactions are present, GC based on strictly causal MVAR will lead to erroneous conclusions on the underlying information flow. Thus, the work presented in this paper applies an extended MVAR (eMVAR) model that accounts for the zero lag interactions. We propose a constrained adaptive Kalman filter (CAKF) approach for the eMVAR model identification and demonstrate that this approach performs better than the short time windowing-based adaptive estimation when applied to information flow analysis.

  3. 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.

  4. Early prognosis of metastasis risk in inflammatory breast cancer by texture analysis of tumour microscopic images.

    Science.gov (United States)

    Kolarevic, Daniela; Tomasevic, Zorica; Dzodic, Radan; Kanjer, Ksenija; Vukosavljevic, Dragica Nikolic; Radulovic, Marko

    2015-10-01

    Inflammatory breast cancer (IBC) is a rare and aggressive type of locally advanced breast cancer. The purpose of this study was to determine the value of microscopic tumour histomorphology texture for prognosis of local and systemic recurrence at the time of initial IBC diagnosis. This retrospective study included a group of 52 patients selected on the basis of non-metastatic IBC diagnosis, stage IIIB. Gray-Level-Co-Occurrence-Matrix (GLCM) texture analysis was performed on digital images of primary tumour tissue sections stained with haematoxylin/eosin. Obtained values were categorized by use of both data- and outcome-based methods. All five acquired GLCM texture features significantly associated with metastasis outcome. By accuracies of 69-81% and AUCs of 0.71-0.81, prognostic performance of GLCM parameters exceeded that of standard major IBC clinical prognosticators such as tumour grade and response to induction chemotherapy. Furthermore, a composite score consisting of tumour grade, contrast and correlation as independent features resulted in further enhancement of prognostic performance by accuracy of 89%, discrimination efficiency by AUC of 0.93 and an outstanding hazard ratio of 71.6 (95%CI, 41.7-148.4). Internal validation was successfully performed by bootstrap and split-sample cross-validation, suggesting that the model is generalizable. This study indicates for the first time the potential use of primary breast tumour histology texture as a highly accurate, simple and cost-effective prognostic indicator of metastasis risk in IBC. Clinical relevance of the obtained results rests on the role of prognosis in decisions on induction chemotherapy and the resulting impact on quality of life and survival.

  5. 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.

  6. 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.

  7. Decoding the infant mind: Multivariate pattern analysis (MVPA) using fNIRS.

    Science.gov (United States)

    Emberson, Lauren L; Zinszer, Benjamin D; Raizada, Rajeev D S; Aslin, Richard N

    2017-01-01

    The MRI environment restricts the types of populations and tasks that can be studied by cognitive neuroscientists (e.g., young infants, face-to-face communication). FNIRS is a neuroimaging modality that records the same physiological signal as fMRI but without the constraints of MRI, and with better spatial localization than EEG. However, research in the fNIRS community largely lacks the analytic sophistication of analogous fMRI work, restricting the application of this imaging technology. The current paper presents a method of multivariate pattern analysis for fNIRS that allows the authors to decode the infant mind (a key fNIRS population). Specifically, multivariate pattern analysis (MVPA) employs a correlation-based decoding method where a group model is constructed for all infants except one; both average patterns (i.e., infant-level) and single trial patterns (i.e., trial-level) of activation are decoded. Between subjects decoding is a particularly difficult task, because each infant has their own somewhat idiosyncratic patterns of neural activation. The fact that our method succeeds at across-subject decoding demonstrates the presence of group-level multi-channel regularities across infants. The code for implementing these analyses has been made readily available online to facilitate the quick adoption of this method to advance the methodological tools available to the fNIRS researcher.

  8. [Study of cardiovascular morbidity in nurses exposed to cytostatic drugs: Multivaried approach analysis].

    Science.gov (United States)

    Tigha Bouaziz, N; Tourab, D; Nezzal, A M

    2016-06-01

    To investigate the relationship between cardiovascular morbidity and exposure to cytostatic drugs. A descriptive analytical study was conducted with 74 nurses exposed to cytostatic drugs in oncology and 215 unexposed. A medical questionnaire was applied. Exposure to cytostatic drugs was estimated by the exposure time and the index of cytostatic contact (ICC). The statistical tests used are: relative risk, odds ratio, multivariate analysis: descriptive (ACM) and predictive (AIC system). It is a young population; the average age is 42±9.9years with a female predominance (81%). The average length was 18.4±11.11years. The average of the ICC ranged from 0.60 to 12.6 with a highly significant difference. For morbidity, there was no difference for most cardiovascular disease (RR, 1.03; 95% CI [0.59; 1.82]) outside of hypertension and venous thrombosis. ACM objectified separation between the terms and the comments of the two groups for HTA. The interpretation of results at alpha=0.05 showed an association with cardiovascular disease. The study of the association between cardiovascular morbidity and exposure to cytostatic objectified association with seniority and the ICC with a statistically significant difference (P=0.01). Multivariate analysis helped to eliminate confounding factors and retain the ICC and length of exposure to cytostatic in the onset of cardiovascular morbidity. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

  9. Multivariate analysis of wound complications after surgery for laryngeal and hypopharyngeal cancers.

    Science.gov (United States)

    Sakai, Akihiro; Okami, Kenji; Sugimoto, Ryousuke; Ebisumoto, Koji; Yamamoto, Hikaru; Furuya, Hiroyuki; Iida, Masahiro

    2011-01-01

    The aim of this study was to identify the factors leading to postoperative complications following surgical management of laryngeal and hypopharyngeal cancers. Between 2001 and 2008, the medical records of 107 laryngeal and hypopharyngeal cancer patients requiring laryngectomy or pharyngolaryngectomy at our hospital were reviewed. The incidence of wound complications and correlation of complications with clinicopathological factors were investigated by univariate and multivariate analysis. The overall incidence of wound complication was 33.6%. The complication incidence was 35.2, 21.7 and 46.2% for the primary surgery, radiation and chemoradiation groups, respectively. Diabetes mellitus and bilateral paratracheal node dissection were significantly correlated and were independent risk factors according to multivariate analysis. Bleeding from a large vessel occurred in 4 patients, and there were significant correlations with chemoradiation. Preoperative chemoradiation was not a significant risk factor for wound complication in this study. However, once postoperative wound complications occurred, they tended to produce lethal outcomes. Copyright © 2011 S. Karger AG, Basel.

  10. Cerebral cortical folding analysis with multivariate modeling and testing: Studies on gender differences and neonatal development.

    Science.gov (United States)

    Awate, Suyash P; Yushkevich, Paul A; Song, Zhuang; Licht, Daniel J; Gee, James C

    2010-11-01

    This paper presents a novel statistical framework for human cortical folding pattern analysis that relies on a rich multivariate descriptor of folding patterns in a region of interest (ROI). The ROI-based approach avoids problems faced by spatial normalization-based approaches stemming from the deficiency of homologous features between typical human cerebral cortices. Unlike typical ROI-based methods that summarize folding by a single number, the proposed descriptor unifies multiple characteristics of surface geometry in a high-dimensional space (hundreds/thousands of dimensions). In this way, the proposed framework couples the reliability of ROI-based analysis with the richness of the novel cortical folding pattern descriptor. This paper presents new mathematical insights into the relationship of cortical complexity with intra-cranial volume (ICV). It shows that conventional complexity descriptors implicitly handle ICV differences in different ways, thereby lending different meanings to "complexity". The paper proposes a new application of a nonparametric permutation-based approach for rigorous statistical hypothesis testing with multivariate cortical descriptors. The paper presents two cross-sectional studies applying the proposed framework to study folding differences between genders and in neonates with complex congenital heart disease. Both studies lead to novel interesting results. Copyright 2010 Elsevier Inc. All rights reserved.

  11. 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.

  12. Decoding the infant mind: Multivariate pattern analysis (MVPA using fNIRS.

    Directory of Open Access Journals (Sweden)

    Lauren L Emberson

    Full Text Available The MRI environment restricts the types of populations and tasks that can be studied by cognitive neuroscientists (e.g., young infants, face-to-face communication. FNIRS is a neuroimaging modality that records the same physiological signal as fMRI but without the constraints of MRI, and with better spatial localization than EEG. However, research in the fNIRS community largely lacks the analytic sophistication of analogous fMRI work, restricting the application of this imaging technology. The current paper presents a method of multivariate pattern analysis for fNIRS that allows the authors to decode the infant mind (a key fNIRS population. Specifically, multivariate pattern analysis (MVPA employs a correlation-based decoding method where a group model is constructed for all infants except one; both average patterns (i.e., infant-level and single trial patterns (i.e., trial-level of activation are decoded. Between subjects decoding is a particularly difficult task, because each infant has their own somewhat idiosyncratic patterns of neural activation. The fact that our method succeeds at across-subject decoding demonstrates the presence of group-level multi-channel regularities across infants. The code for implementing these analyses has been made readily available online to facilitate the quick adoption of this method to advance the methodological tools available to the fNIRS researcher.

  13. 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.

  14. Multivariate Copula Analysis Toolbox (MvCAT): Describing dependence and underlying uncertainty using a Bayesian framework

    Science.gov (United States)

    Sadegh, Mojtaba; Ragno, Elisa; AghaKouchak, Amir

    2017-06-01

    We present a newly developed Multivariate Copula Analysis Toolbox (MvCAT) which includes a wide range of copula families with different levels of complexity. MvCAT employs a Bayesian framework with a residual-based Gaussian likelihood function for inferring copula parameters and estimating the underlying uncertainties. The contribution of this paper is threefold: (a) providing a Bayesian framework to approximate the predictive uncertainties of fitted copulas, (b) introducing a hybrid-evolution Markov Chain Monte Carlo (MCMC) approach designed for numerical estimation of the posterior distribution of copula parameters, and (c) enabling the community to explore a wide range of copulas and evaluate them relative to the fitting uncertainties. We show that the commonly used local optimization methods for copula parameter estimation often get trapped in local minima. The proposed method, however, addresses this limitation and improves describing the dependence structure. MvCAT also enables evaluation of uncertainties relative to the length of record, which is fundamental to a wide range of applications such as multivariate frequency analysis.

  15. Sustainability Multivariate Analysis of the Energy Consumption of Ecuador Using MuSIASEM and BIPLOT Approach

    Directory of Open Access Journals (Sweden)

    Nathalia Tejedor-Flores

    2017-06-01

    Full Text Available Rapid economic growth, expanding populations and increasing prosperity are driving up demand for energy, water and food, especially in developing countries. To understand the energy consumption of a country, we used the Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM approach. The MuSIASEM is an innovative approach to accounting that integrates quantitative information generated by distinct types of conventional models based on different dimensions and scales of analysis. The main objective of this work is to enrich the MuSIASEM approach with information from multivariate methods in order to improve the efficiency of existing models of sustainability. The Biplot method permits the joint plotting, in a reduced dimension of the rows (individuals and columns (variables of a multivariate data matrix. We found, in the case study of Ecuador, that the highest values of the Exosomatic Metabolic Rate (EMR per economic sector and Economic Labor Productivity (ELP are located in the Productive Sector (PS. We conclude that the combination of the MuSIASEM variables with the HJ-Biplot allows us to easily know the detailed behavior of the labor productivity and energy consumption of a country.

  16. 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.

  17. Effect of Different Obturation Techniques on the Prognosis of Endodontic Therapy: A Retrospective Comparative Analysis.

    Science.gov (United States)

    Sarin, Anurag; Gupta, Priyanka; Sachdeva, Jyoti; Gupta, Ajai; Sachdeva, Shobhit; Nagpal, Ravi

    2016-07-01

    Success of root canal therapy (RCT) is largely dependent upon the quality of biomechanical preparation and obturation of the pulp canal. Improperly cleaned or shaped root canal, regardless of the type of obturation method and obturating material, cannot lead to the success of endodontic therapy. Hence, we conducted a clinical comparative analysis of two obturating techniques. A total of 140 patients receiving RCT at the department of Endodontic were included in the present study. The average follow-up time for the patients was 29 months (18-38 months). Patients were grouped into two depending on the type of obturating technique used. Evaluation of the clinical and radiographic follow-up records of the patients was done and analysis was made. One-way analysis of variance (ANOVA) was used for assessing the level of significance. The average age of the patients undergoing obturation with carrier-based obturation (CO) technique and lateral compaction (LC) technique was 43 and 48 years respectively. While comparing failure and success of the teeth at the time of follow-up, nonsignificant results were obtained. Significant difference was seen, while comparing the presence of voids and type of teeth in which endodontic therapy was performed using different obturating techniques. Endodontic therapy done with LC obturating technique or with CO technique shows prognostic difference on the outcome or quality of treatment therapy. Quality of obturation is more important rather than type while performing endodontic therapy for better prognosis.

  18. [Meta-analysis of relationship between extranodal tumor deposits and prognosis in patients with colorectal cancer].

    Science.gov (United States)

    Zhang, Xianxiang; Shao, Shihong; Gao, Yuan; Zhang, Maoshen; Lu, Yun

    2016-03-01

    To investigate the relationship between extranodal tumor deposits and prognosis in patients with colorectal cancer. The literatures on extranodal tumor deposits and postoperative survival rate in patients with colorectal cancer published at home and abroad from 1990 to 2014 were retrieved in 15 English literature databases such as MEDLINE/PubMed, Web of Science, Directory of Open Access Journals(DOAJ), SpringerLink and Chinese literature databases such as Chinese Biomedical Literature Database CD-ROM, China National Knowledge Infrastructure (CNKI) Database with the internet platform of Yonsei University Library. After screening for inclusion, data extraction and quality assessment, meta-analysis was conducted by the Review Manager 5.3 software. There were 10 studies meeting the inclusion criteria for meta-analysis. The total sample size of the studies was 4 068 cases with ENTD(+) 727 cases, while ENTD(-) 3 341 cases. Meta analysis showed that 5-year overall survival rate and 5-year relapse-free survival rate were significantly lower in ENTD(+) group than those in ENTD(-) group (OR 0.27, 0.23; 95% CI:0.18 to 0.43, 0.16 to 0.34 respectively, both P=0.000); the 5-year overall survival rates were both significantly lower in ENTD(+) group as compared to ENTD(-) group for patients with N0 and N(+) colorectal cancer (both P<0.05). Extranodal tumor deposits is a poor prognostic factor of patients with colorectal cancer.

  19. 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.

  20. 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

    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.......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...

  1. 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).

  2. Evaluation of the quality of Chinese and Vietnamese cassia using LC-MS and multivariate analysis.

    Science.gov (United States)

    Tanaka, Ken; Li, Feng; Tezuka, Yasuhiro; Watanabe, Shiro; Kawahara, Nobuo; Kida, Hiroaki

    2013-01-01

    In the present study, the chemical composition of water extracts of Chinese and Vietnamese cassia (Cinnamomum cassia) were compared using multivariate analysis of LC-MS data. By principal component analysis of the LC-MS data, 6 compounds, cinnzeylanine (1), cinnzeylanol (2), anhydrocinnzeylanol (3), cinncasinol A (4), epicatechin (5) and procyanidin B2 (6), were identified as the marker compounds to characterize Chinese and Vietnamese cassia. It was clarified that Chinese cassia contains relatively larger amounts of epicatechin and procyanidin B2. On the other hand, Vietnamese cassia is characterized by a relatively larger amount of diterpenes. As catechin derivatives and diterpenes have different types of activity, it is important to choose the cassia that best suits the product for which it is to be used, whether in food or in herbal medicine.

  3. Lung cancer associated hypercalcemia: An analysis of factors influencing survival and prognosis in 34 cases

    Directory of Open Access Journals (Sweden)

    Su-jie ZHANG

    2012-06-01

    Full Text Available Objectives  To explore the factors influencing survival time in lung cancer associated hypercalcemia patients. Methods  Thirty-four patients with pathologically confirmed lung cancer complicated with hypercalcemia, who were treated at the Department of Oncology in General Hospital of PLA from Jan. 2001 to Dec. 2010, were enrolled in this study. The clinical data analyzed included sex, age, pathological type of the malignancies, organ metastasis (bone, lung, liver, kidney, brain, number of distal metastatic site, mental status, interval between final diagnosis of lung cancer and of hypercalcemia, peak value of blood calcium during the disease course, treatment methods and so on. Survival analysis was performed with the Kaplan-Meier method and Cox analysis with statistic software SPSS 18.0 to identify the potential prognostic factors. Results  The highest blood calcium level ranged from 2.77 to 4.87mmol/L, and the median value was 2.94mmol/L. The patients' survival time after diagnosis of hypercalcemia varied from 1 day to 1067 days, and the median survival time was 92 days. With the log-rank test, age above 50 years old, hypercalcemia occurring over 90 days after diagnosis of cancer, central nervous system symptoms and renal metastasis were predictors for poor survival (P=0.048, P=0.001, P=0.000, P=0.003. In the COX proportional hazard model analysis, age above 50 years old, hypercalcemia occurring over 90 days after cancer diagnosis, central nervous system symptoms and renal metastasis were significant prognostic factors for poor survival (HR=11.483, P=0.006; HR=4.371, P=0.002; HR=6.064, P=0.026; HR=8.502, P=0.011. Conclusions  Patients with lung cancer associated hypercalcemia have a shorter survival time and poor prognosis. Age above 50 years old, hypercalcemia occurring over 90 days after cancer diagnosis, central nervous system symptoms and renal metastasis are significant factors of poor prognosis.

  4. Multivariate co-integration analysis of the Kaya factors in Ghana.

    Science.gov (United States)

    Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

    2016-05-01

    The fundamental goal of the Government of Ghana's development agenda as enshrined in the Growth and Poverty Reduction Strategy to grow the economy to a middle income status of US$1000 per capita by the end of 2015 could be met by increasing the labour force, increasing energy supplies and expanding the energy infrastructure in order to achieve the sustainable development targets. In this study, a multivariate co-integration analysis of the Kaya factors namely carbon dioxide, total primary energy consumption, population and GDP was investigated in Ghana using vector error correction model with data spanning from 1980 to 2012. Our research results show an existence of long-run causality running from population, GDP and total primary energy consumption to carbon dioxide emissions. However, there is evidence of short-run causality running from population to carbon dioxide emissions. There was a bi-directional causality running from carbon dioxide emissions to energy consumption and vice versa. In other words, decreasing the primary energy consumption in Ghana will directly reduce carbon dioxide emissions. In addition, a bi-directional causality running from GDP to energy consumption and vice versa exists in the multivariate model. It is plausible that access to energy has a relationship with increasing economic growth and productivity in Ghana.

  5. 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.

  6. Global Synchronization Measurement of Multivariate Neural Signals with Massively Parallel Nonlinear Interdependence Analysis.

    Science.gov (United States)

    Chen, Dan; Li, Xiaoli; Cui, Dong; Wang, Lizhe; Lu, Dongchuan

    2014-01-01

    The estimation of synchronization amongst multiple brain regions is a critical issue in understanding brain functions. There is a lack of an appropriate approach which is capable of 1) measuring the direction and strength of synchronization of activities of multiple brain regions, and 2) adapting to the quickly increasing sizes and scales of neural signals. Nonlinear Interdependence (NLI) analysis is an effective method for measuring synchronization direction and strength of bivariate neural signal. However, the method currently does not directly apply in handling multivariate signal. Its application in practice has also long been largely hampered by the ultra-high complexity of NLI algorithms. Aiming at these problems, this study 1) extends the conventional NLI to quantify the global synchronization of multivariate neural signals, and 2) develops a parallelized NLI method with general-purpose computing on the graphics processing unit (GPGPU), namely, G-NLI. The approach performs synchronization measurement in a massively parallel manner. The G-NLI has improved the runtime performance by more than 1000 times comparing to the original sequential NLI. Meanwhile, the G-NLI was employed to analyze 10-channel local field potential (LFP) recordings from a patient suffering from temporal lobe epilepsy. The results demonstrate that the proposed G-NLI method can support real-time global synchronization measurement and it could be successful in localization of epileptic focus.

  7. Geriatric patients are predisposed to strabismus following thyroid-related orbital decompression surgery: A multivariate analysis.

    Science.gov (United States)

    Wu, Chris Y; Kahana, Alon

    2017-04-01

    Geriatric patients (age ≥ 65) are prone to complications after surgery and are at risk for severe thyroid eye disease (TED). In this study, we aim to identify preoperative demographic and TED patterns associated with geriatric patients who underwent decompression surgery, to examine the effect of age on postoperative strabismus rates, and to identify factors that may contribute to postoperative strabismus in the geriatric subgroup. We retrospectively reviewed patients who underwent thyroid-related orbital decompression surgery at the Kellogg Eye Center, University of Michigan, between 1999 and 2014. Primary outcome was postoperative strabismus requiring palliation with prisms and/or strabismus surgery. Descriptive, univariate, and multivariable logistic regression analyses were used to define association of geriatric age with postoperative strabismus and determine predictors of postoperative strabismus. Of 241 patients, 41 (17.0%) were geriatric. They were less likely to undergo bilateral decompression (P = 0.012), less likely to be current smokers at time of decompression (P = 0.002), and more likely to have preoperative primary gaze diplopia (P = 0.001). Postoperative strabismus rates for geriatric patients (≥ 65 years of age), ages 50-65, 30-50, and geriatric age remained an independent risk factor for postoperative strabismus when compared to each age group (P ≤ 0.001). Among geriatric patients in subgroup multivariable analysis, balanced as opposed to lateral wall decompression (P = 0.038) and shorter TED duration (P = 0.031) were independently predictive of postoperative strabismus.

  8. Detection and Classification of Individual Airborne Microparticles using Laser Ablation Mass Spectroscopy and Multivariate Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Gieray, R.A.; Lazar, A.; Parker, E.P.; Ramsey, J. M.; Reilly, P.T.A.; Rosenthal, S.E.; Trahan, M.W.; Wagner, J.S.; Whitten, W.B.

    1999-04-27

    We are developing a method for the real-time analysis of airborne microparticles based on laser ablation mass spectroscopy. Airborne particles enter an ion trap mass spectrometer through a differentially-pumped inlet, are detected by light scattered from two CW laser beams, and sampled by a 10 ns excimer laser pulse at 308 nm as they pass through the center of the ion trap electrodes. After the laser pulse, the stored ions are separated by conventional ion trap methods. In this work thousands of positive and negative ion spectra were collected for eighteen different species: six bacteria, six pollen, and six particulate samples. The data were then averaged and analyzed using the Multivariate Patch Algorithm (MPA), a variant of traditional multivariate anal ysis. The MPA correctly identified all of the positive ion spectra and 17 of the 18 negative ion spectra. In addition, when the average positive and negative spectra were combined the MPA correctly identified all 18 species. Finally, the MPA is also able to identify the components of computer synthesized mixtures of the samples studied

  9. 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.

  10. Assessment of soil quality parameters using multivariate analysis in the Rawal Lake watershed.

    Science.gov (United States)

    Firdous, Shahana; Begum, Shaheen; Yasmin, Azra

    2016-09-01

    Soil providing a wide array of ecosystem services is subjected to quality deterioration due to natural and anthropogenic factors. Most of the soils in Pakistan have poor status of available plant nutrients and cannot support optimum levels of crop productivity. The present study statistically analyzed ten soil quality parameters in five subwatersheds (Bari Imam, Chattar, Rumli, Shahdra, and Shahpur) of the Rawal Lake. Analysis of variance (ANOVA), cluster analysis (CA), and principal component analysis (PCA) were performed to evaluate correlation in soil quality parameters on spatiotemporal and vertical scales. Soil organic matter, electrical conductivity, nitrates, and sulfates were found to be lower than that required for good quality soil. Soil pH showed significant difference (p analysis resulted in three major factors contributing 76 % of the total variance. For factor 1, temperature, sand, silt, clay, and nitrates had the highest factor loading values (>0.75) and indicated that these were the most influential parameters of first factor or component. Cluster analysis separated five sampling sites into three statistically significant clusters: I (Shahdra-Bari Imam), II (Chattar), and III (Shahpur-Rumli). Among the five sites, Shahdra was found to have good quality soil followed by Bari Imam. The present study illustrated the usefulness of multivariate statistical approaches for the analysis and interpretation of complex datasets to understand variations in soil quality for effective watershed management.

  11. Impact of surgical staging on prognosis in patients with borderline ovarian tumours: A meta-analysis.

    Science.gov (United States)

    Shim, Seung-Hyuk; Kim, Soo-Nyung; Jung, Phill-Seung; Dong, Meari; Kim, Jung Eun; Lee, Sun Joo

    2016-02-01

    To quantify the effect of complete surgical staging (CSS) on prognosis in borderline ovarian tumour (BOT) patients through a meta-analysis. We systematically reviewed published studies comparing CSS with incomplete surgical staging (ISS) in BOT patients through April 2015. End-points were recurrence and mortality rates. Study design features that possibly affected participant selection, recurrence/death detection, and manuscript publication were assessed. For pooled estimates of the effect of CSS on recurrence/death, random- or fixed-effects meta-analytical models were used after assessing cross-study heterogeneity. Eighteen observational studies (CSS, 1297 patients; ISS, 1473 patients) met our search criteria. Fixed-effects model-based meta-analysis indicated a reduced recurrence risk among CSS patients (odds ratio [OR]=0.64; 95% confidence interval [CI]: 0.47-0.87, P types (OR = 0.66; 95% CI: 0.48-0.91, P BOT patients. No survival impact was observed. Longer-term randomised controlled trials could verify this relationship but appear infeasible for this rare tumour. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Identifying candidates with favorable prognosis following liver transplantation for hepatocellular carcinoma: Data mining analysis.

    Science.gov (United States)

    Tanaka, Tomohiro; Kurosaki, Masayuki; Lilly, Leslie B; Izumi, Namiki; Sherman, Morris

    2015-07-01

    The optimal cutoff of each value in configuring selection criteria for pre-transplant assessment of hepatocellular carcinoma (HCC) remains uncertain. To build a predictive model for recurrent HCC, we performed data mining analysis on patients who underwent LT for HCC at University Health Network (n = 246). The model was externally validated using a cohort from the Scientific Registry of Transplant Recipients (SRTR) database (n = 9,769). Among 246 patients, 14.6% (n = 36) experienced recurrent HCC within 2.5 years post-LT. The risk prediction model for recurrent HCC identified two subgroups with low-risk (total tumor diameter [TTD] 4 cm and/or AFP >73 ng/ml, n = 111). The reproducibility of the model was validated through the SRTR database; overall patient survival rate was significantly better in low-risk group than high-risk group (P predict post-transplant survival independent of underlying characteristics (P data mining analysis efficiently classify patients according by the post-transplant prognosis. © 2015 Wiley Periodicals, Inc.

  13. Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia.

    Science.gov (United States)

    Mohamed, Ibrahim; Othman, Faridah; Ibrahim, Adriana I N; Alaa-Eldin, M E; Yunus, Rossita M

    2015-01-01

    This case study uses several univariate and multivariate statistical techniques to evaluate and interpret a water quality data set obtained from the Klang River basin located within the state of Selangor and the Federal Territory of Kuala Lumpur, Malaysia. The river drains an area of 1,288 km(2), from the steep mountain rainforests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, into the Straits of Malacca. Water quality was monitored at 20 stations, nine of which are situated along the main river and 11 along six tributaries. Data was collected from 1997 to 2007 for seven parameters used to evaluate the status of the water quality, namely dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, ammoniacal nitrogen, pH, and temperature. The data were first investigated using descriptive statistical tools, followed by two practical multivariate analyses that reduced the data dimensions for better interpretation. The analyses employed were factor analysis and principal component analysis, which explain 60 and 81.6% of the total variation in the data, respectively. We found that the resulting latent variables from the factor analysis are interpretable and beneficial for describing the water quality in the Klang River. This study presents the usefulness of several statistical methods in evaluating and interpreting water quality data for the purpose of monitoring the effectiveness of water resource management. The results should provide more straightforward data interpretation as well as valuable insight for managers to conceive optimum action plans for controlling pollution in river water.

  14. Multivariate analysis of mixed contaminants (PAHs and heavy metals) at manufactured gas plant site soils.

    Science.gov (United States)

    Thavamani, Palanisami; Megharaj, Mallavarapu; Naidu, Ravi

    2012-06-01

    Principal component analysis (PCA) was used to provide an overview of the distribution pattern of polycyclic aromatic hydrocarbons (PAHs) and heavy metals in former manufactured gas plant (MGP) site soils. PCA is the powerful multivariate method to identify the patterns in data and expressing their similarities and differences. Ten PAHs (naphthalene, acenapthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, chrysene, benzo[a]pyrene) and four toxic heavy metals - lead (Pb), cadmium (Cd), chromium (Cr) and zinc (Zn) - were detected in the site soils. PAH contamination was contributed equally by both low and high molecular weight PAHs. PCA was performed using the varimax rotation method in SPSS, 17.0. Two principal components accounting for 91.7% of the total variance was retained using scree test. Principle component 1 (PC1) substantially explained the dominance of PAH contamination in the MGP site soils. All PAHs, except anthracene, were positively correlated in PC1. There was a common thread in high molecular weight PAHs loadings, where the loadings were inversely proportional to the hydrophobicity and molecular weight of individual PAHs. Anthracene, which was less correlated with other individual PAHs, deviated well from the origin which can be ascribed to its lower toxicity and different origin than its isomer phenanthrene. Among the four major heavy metals studied in MGP sites, Pb, Cd and Cr were negatively correlated in PC1 but showed strong positive correlation in principle component 2 (PC2). Although metals may not have originated directly from gaswork processes, the correlation between PAHs and metals suggests that the materials used in these sites may have contributed to high concentrations of Pb, Cd, Cr and Zn. Thus, multivariate analysis helped to identify the sources of PAHs, heavy metals and their association in MGP site, and thereby better characterise the site risk, which would not be possible if one uses chemical analysis

  15. Decision Analysis and Validation of Value Focused Thinking Decision Models Using Multivariate Analysis Techniques

    Science.gov (United States)

    2011-02-24

    analysis tool in decision analysis . Journal of Multicriteria Decision Analysis , pp. 162-180. Chen, H., & Kocaoglu, D. F. (2008). A sensitivity... Multicriteria Optimization. Berlin: Springer Insua, D. R., & French, S. (1991). A framework for sensitivity analysis in discrete multi- objective decision...DECISION ANALYSIS AND VALIDATION OF VALUE FOCUSED

  16. Predictive analysis for prognosis of CD14+ monocyte HLA-DR in geriatric trauma patients

    Directory of Open Access Journals (Sweden)

    Jie-fu LU

    2016-08-01

    Full Text Available Objective  To evaluate the prognostic roles of HLA-DR+/CD14+ expression rate in peripheral blood monocytes in geriatric trauma sepsis. Methods  A retrospective study of clinical data was carried out. Clinical data of geriatric trauma patients (age≥60 years admitted to intensive care unit (ICU of Guangzhou General Hospital of Guangzhou Command from January 2011 to December 2015 were collected. The expressions of HLA-DR+/CD14+, procalcitonin (PCT and C-reactive protein (CRP were detected within 24 hours after admission. Spearman correlation analysis was adopted to analyze the correlation between the HLADR+/CD14+ and the length of ICU stay, and between the length of stay and APACHE Ⅱ. Receiver operating characteristic (ROC curve was used to evaluate the prognostic roles of HLA-DR+/CD14+ expression, PCT, CRP and APACHE Ⅱscore. Results  There were significant differences between survivors and nonsurvivors in APACHE Ⅱscore (17.49±6.25 vs 27.38±8.68, P<0.05 and the expressions of HLA-DR+/CD14+ (59.80±18.02 vs 37.70±13.96, P<0.01. There were significant differences between sepsis and non-sepsis in APACHEⅡscore (26.16±8.44 vs 17.90±7.04, P<0.01 and the expressions of HLA-DR+/CD14+ (38.61±14.48 vs 59.79±18.17, P<0.01, PCT (34.45±68.29 vs 4.25±8.26, P<0.01 and CRP (129.88±103.25 vs 76.04±73.48, P<0.011. There existed a negative relationship between the HLA-DR+/CD14+ and length of ICU stay (r=–0.304, P=0.008, and APACHE Ⅱ(r=–0.559, P=0.000. There was no significant relationship between the HLA-DR+/CD14+ and length of stay (r=0.188, P=0.106. By ROC for sepsis prognosis, the area under the curve (Mean±SE of HLA-DR+/CD14+ was 0.807±0.051 (95%CI 0.706-0.907, P=0.000, the AU-ROC (Mean±SE of PCT was 0.714±0.063 (95% CI:0.591-0.837, P=0.003. The best cut-off for HLA-DR+/ CD14+ was 40%, with the sensitivity of 88.0% and specificity of 60.0%.The best cut-off for PCT was 1.01, with the sensitivity of 84.0% and specificity of 65

  17. 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.

  18. Multivariate change point analysis in time series for volcano unrest detection

    Science.gov (United States)

    Aliotta, M. A.; Cassisi, C.; Fiumara, S.; Montalto, P.

    2016-12-01

    The detection of unrest in volcanic areas represents a key task for civil protection purposes. Nowadays, large networks for different kinds of measurements deployed in most of active volcanoes supply huge amount of data, mainly in the form of time series. Automatic techniques are needed to perform the analysis of such amount of data. In this sense, time series analysis techniques can contribute to exploit the information coming from the measurements to identify possible changes into volcanic behaviour. In particular, the change point analysis can be used to this aim. The change point analysis is the process of detecting distributional changes within time-ordered observations. Among the different techniques proposed for this kind of analysis, we chose to use the SeqDrift (Sakthithasan et al., 2013) technique for its ability to deal with real time data. The algorithm iteratively compares two consecutive sliding windows coming from the data stream to choose whether the boundary point (in the between of the two windows) is a change point. The check is carried out by a non-parametric statistical test. We applied the proposed approach to a test case on Mt. Etna using large multivariate dataset from 2011-2015. The results indicate that the technique is effective to detect volcanic state changes. Sakthithasan, S., Pears, R., Koh, Y. S. (2013). One Pass Concept Change Detection for Data Streams. PAKDD (2): 461-472.

  19. Multivariate analysis for performance evaluation of active-queue-management mechanisms in the Internet

    Science.gov (United States)

    Eguchi, Tomoya; Ohsaki, Hiroyuki; Murata, Masayuki

    2002-07-01

    AQM (Active Queue Management) mechanism, which performs congestion control at a router for assisting the end-to-end congestion control mechanism of TCP, has been actively studied by many researchers. For instance, RED (Random Early Detection) is a representative AQM mechanism, which drops arriving packets with a probability being proportional to its average queue length. The RED router has four control parameters, and its effectiveness heavily depends on a choice of these control parameters. This is why many researches on the parameter tuning of RED control parameters have been performed. However, most of those studies have investigated the effect of RED control parameters on its performance from a small number of simulation results. In this paper, we therefore statistically analyze a great number of simulation results using the multivariate analysis. We quantitatively show the relation between RED control parameters and its performance.

  20. Hyperspectral fluorescence imaging coupled with multivariate image analysis techniques for contaminant screening of leafy greens

    Science.gov (United States)

    Everard, Colm D.; Kim, Moon S.; Lee, Hoyoung

    2014-05-01

    The production of contaminant free fresh fruit and vegetables is needed to reduce foodborne illnesses and related costs. Leafy greens grown in the field can be susceptible to fecal matter contamination from uncontrolled livestock and wild animals entering the field. Pathogenic bacteria can be transferred via fecal matter and several outbreaks of E.coli O157:H7 have been associated with the consumption of leafy greens. This study examines the use of hyperspectral fluorescence imaging coupled with multivariate image analysis to detect fecal contamination on Spinach leaves (Spinacia oleracea). Hyperspectral fluorescence images from 464 to 800 nm were captured; ultraviolet excitation was supplied by two LED-based line light sources at 370 nm. Key wavelengths and algorithms useful for a contaminant screening optical imaging device were identified and developed, respectively. A non-invasive screening device has the potential to reduce the harmful consequences of foodborne illnesses.

  1. Multivariate Analysis of Risk Factors in the Development of a Lower-Pitched Voice After Thyroidectomy.

    Science.gov (United States)

    Park, Jun-Ook; Bae, Ja-Sung; Lee, So-Hee; Shim, Mi-Ran; Hwang, Yeon-Shin; Joo, Young-Hoon; Park, Young Hak; Sun, Dong-Il

    2017-02-01

    Thyroid surgeons frequently encounter outpatients with mobile vocal cords complaining of lower-pitched voices following thyroidectomy. This study investigated the clinical and pathological parameters affecting voice pitch following thyroid surgery. We analyzed the data of 393 patients with mobile vocal cords and who also underwent thyroid surgery. Speaking fundamental frequency (SFF) and fundamental frequency (F0) were compared before and after surgery. Approximately 26.7% of patients had significantly lowered SFFs (ΔSFF ≥ 12 Hz), and 30.2% exhibited significantly lower sustained vowel F0s (ΔF0 ≥ 12 Hz) following thyroid surgery. On multivariate analysis, only gender: female remained a significant predictor of a clinically significant change in SFF following thyroid surgery ( P pitched voice and related vocal symptoms early after thyroid surgery. Such problems develop more frequently in females who underwent total thyroidectomy.

  2. UV-vis absorption spectroscopy and multivariate analysis as a method to discriminate tequila

    Science.gov (United States)

    Barbosa-García, O.; Ramos-Ortíz, G.; Maldonado, J. L.; Pichardo-Molina, J. L.; Meneses-Nava, M. A.; Landgrave, J. E. A.; Cervantes-Martínez, J.

    2007-01-01

    Based on the UV-vis absorption spectra of commercially bottled tequilas, and with the aid of multivariate analysis, it is proved that different brands of white tequila can be identified from such spectra, and that 100% agave and mixed tequilas can be discriminated as well. Our study was done with 60 tequilas, 58 of them purchased at liquor stores in various Mexican cities, and two directly acquired from a distillery. All the tequilas were of the "white" type, that is, no aged spirits were considered. For the purposes of discrimination and quality control of tequilas, the spectroscopic method that we present here offers an attractive alternative to the traditional methods, like gas chromatography, which is expensive and time-consuming.

  3. [Multivariate autoregressive analysis of carotid artery blood flow waveform in a newborn with multicystic encephalomalacia].

    Science.gov (United States)

    Kojo, M; Ogawa, T; Fukushima, N; Yamada, K; Goto, K

    1995-05-01

    We analyzed the carotid artery blood flow waveform (CABFW) through multivariate autoregressive analysis in a case with multicystic encephalomalacia (MCE) after neonatal asphyxia and compared the result with those of 35 healthy newborns. The total power of CABFW was at the -2 SD level of the value for 35 healthy newborns, and the power, % power, bio-informing amounts and damping time of component 3 (damping frequency 11.15 Hz) were less than -2 SD of the values in 35 healthy newborns. The Pulsatility Index (PI) of anterior cerebral artery (ACA) was high (0.76). These results suggest that cerebral blood flow decreases because of cerebral vasoconstriction in MCE after neonatal asphyxia.

  4. 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...

  5. 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.

  6. 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.

  7. Determinants of opioid efficiency in cancer pain: a comprehensive multivariate analysis from a tertiary cancer centre.

    Science.gov (United States)

    Goksu, Sema Sezgin; Bozcuk, Hakan; Uysal, Mukremin; Ulukal, Ece; Ay, Seren; Karasu, Gaye; Soydas, Turker; Coskun, Hasan Senol; Ozdogan, Mustafa; Savas, Burhan

    2014-01-01

    Pain is one of the most terrifying symptoms for cancer patients. Although most patients with cancer pain need opioids, complete relief of pain is hard to achieve. This study investigated the factors influencing persistent pain-free survival (PPFS) and opioid efficiency. A prospective study was conducted on 100 patients with cancer pain, hospitalized at the medical oncology clinic of Akdeniz University. Patient records were collected including patient demographics, the disease, treatment characteristics, and details of opioid usage. Pain intensity was measured using a patient self-reported visual analogue scale (VAS). The area under the curve (AUC) reflecting the pain load was calculated from daily VAS tables. PPFS, the primary measure of opioid efficacy, was described as the duration for which a patient reported a greater than or equal to two-point decline in their VAS for pain. Predictors of opioid efficacy were analysed using a multivariate analysis. In the multivariate analysis, PPFS was associated with the AUC for pain (Exp (B)=0.39 (0.23-0.67), P=0.001), the cumulative opioid dosage used during hospitalisation (Exp (B)=1.00(0.99-1.00), P=0.003) and changes in the opioid dosage (Exp (B)=1.01 (1.00-1.01), P=0.016). The change in VAS score over the standard dosage of opioids was strongly associated with current cancer treatment (chemotherapy vs. others) (β=-0.31, T=-2.81, P=0.007) and the VAS for pain at the time of hospitalisation (β=-0.34, T=-3.07, P= 0.003). The pain load, opioid dosage, concurrent usage of chemotherapy and initial pain intensity correlate with the benefit received from opioids in cancer patients.

  8. Groundwater source contamination mechanisms: Physicochemical profile clustering, risk factor analysis and multivariate modelling

    Science.gov (United States)

    Hynds, Paul; Misstear, Bruce D.; Gill, Laurence W.; Murphy, Heather M.

    2014-04-01

    An integrated domestic well sampling and "susceptibility assessment" programme was undertaken in the Republic of Ireland from April 2008 to November 2010. Overall, 211 domestic wells were sampled, assessed and collated with local climate data. Based upon groundwater physicochemical profile, three clusters have been identified and characterised by source type (borehole or hand-dug well) and local geological setting. Statistical analysis indicates that cluster membership is significantly associated with the prevalence of bacteria (p = 0.001), with mean Escherichia coli presence within clusters ranging from 15.4% (Cluster-1) to 47.6% (Cluster-3). Bivariate risk factor analysis shows that on-site septic tank presence was the only risk factor significantly associated (p agriculture adjacency was significantly associated with both borehole-related clusters. Well design criteria were associated with hand-dug wells and boreholes in areas characterised by high permeability subsoils, while local geological setting was significant for hand-dug wells and boreholes in areas dominated by low/moderate permeability subsoils. Multivariate susceptibility models were developed for all clusters, with predictive accuracies of 84% (Cluster-1) to 91% (Cluster-2) achieved. Septic tank setback was a common variable within all multivariate models, while agricultural sources were also significant, albeit to a lesser degree. Furthermore, well liner clearance was a significant factor in all models, indicating that direct surface ingress is a significant well contamination mechanism. Identification and elucidation of cluster-specific contamination mechanisms may be used to develop improved overall risk management and wellhead protection strategies, while also informing future remediation and maintenance efforts.

  9. 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.

  10. Neural network analysis of lymphoma microarray data: prognosis and diagnosis near-perfect

    Directory of Open Access Journals (Sweden)

    Song Li

    2003-04-01

    Full Text Available Abstract Background Microarray chips are being rapidly deployed as a major tool in genomic research. To date most of the analysis of the enormous amount of information provided on these chips has relied on clustering techniques and other standard statistical procedures. These methods, particularly with regard to cancer patient prognosis, have generally been inadequate in providing the reduced gene subsets required for perfect classification. Results Networks trained on microarray data from DLBCL lymphoma patients have, for the first time, been able to predict the long-term survival of individual patients with 100% accuracy. Other networks were able to distinguish DLBCL lymphoma donors from other donors, including donors with other lymphomas, with 99% accuracy. Differentiating the trained network can narrow the gene profile to less than three dozen genes for each classification. Conclusions Here we show that artificial neural networks are a superior tool for digesting microarray data both with regard to making distinctions based on the data and with regard to providing very specific reference as to which genes were most important in making the correct distinction in each case.

  11. Intelligence after traumatic brain injury: meta-analysis of outcomes and prognosis.

    Science.gov (United States)

    Königs, M; Engenhorst, P J; Oosterlaan, J

    2016-01-01

    Worldwide, 54-60 million individuals sustain traumatic brain injury (TBI) each year. This meta-analysis aimed to quantify intelligence impairments after TBI and to determine the value of age and injury severity in the prognosis of TBI. An electronic database search identified 81 relevant peer-reviewed articles encompassing 3890 patients. Full-scale IQ (FSIQ), performance IQ (PIQ) and verbal IQ (VIQ) impairments were quantified (Cohen's d) for patients with mild, moderate and severe TBI in the subacute phase of recovery and the chronic phase. Meta-regressions explored prognostic values of age and injury severity measures for intelligence impairments. The results showed that, in the subacute phase, FSIQ impairments were absent for patients with mild TBI, medium-sized for patients with moderate TBI (d = -0.61, P value were observed. In conclusion, TBI causes persisting intelligence impairments, where children may have better recovery from mild TBI and poorer recovery from severe TBI than adults. Injury severity measures predict intelligence impairments and do not outperform one another. © 2015 EAN.

  12. Risk factors for the prognosis of pediatric medulloblastoma: a retrospective analysis of 40 cases.

    Science.gov (United States)

    Yu, Jianzhong; Zhao, Rui; Shi, Wei; Li, Hao

    2017-05-01

    In this study, we evaluated the association of molecular subtypes, clinical characteristics and pathological types with the prognosis of patients with medulloblastoma. We analyzed forty patients with medulloblastoma who underwent surgical resection at our center between January 2004 and June 2014. Risk factors associated with survival, disease progression and recurrence were analyzed with a univariate Cox regression analysis, and the identified significant risk factors were further analyzed by Kaplan-Meier survival curves. Factors associated with overall survival included M stage (p=0.014), calcification (p=0.012), postoperative treatment, postoperative Karnofsky Performance Scale (KPS) score (p=0.015), and molecular subtype (p=0.005 for WNT and p=0.008 for SHH). Number of symptoms (p=0.029), M stage (p2 and ≥M1 stage without postoperative radiotherapy. The risk of recurrence increased with advanced M stage. Protective factors for recurrence included M0 stage and a combination of chemotherapy and radiotherapy. We identified the risk factors associated with survival, disease progression and recurrence of medulloblastoma patients. This information is helpful for understanding the prognostic factors related to medulloblastoma.

  13. Texture analysis of {sup 18}F-FDG PET/CT to predict tumour response and prognosis of patients with esophageal cancer treated by chemoradiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Nakajo, Masatoyo; Jinguji, Megumi; Nakabeppu, Yoshiaki; Higashi, Ryutarou; Fukukura, Yoshihiko; Yoshiura, Takashi [Kagoshima University, Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima (Japan); Nakajo, Masayuki [Nanpuh Hospital, Department of Radiology, Kagoshima (Japan); Sasaki, Ken; Uchikado, Yasuto; Natsugoe, Shoji [Kagoshima University, Department of Digestive Surgery, Breast and Thyroid Surgery, Graduate School of Medical and Dental Sciences, Kagoshima (Japan)

    2017-02-15

    This retrospective study was done to examine whether the heterogeneity in primary tumour F-18-fluorodeoxyglucose ({sup 18}F-FDG) distribution can predict tumour response and prognosis of patients with esophageal cancer treated by chemoradiotherapy (CRT). The enrolled 52 patients with esophageal cancer underwent {sup 18}F-FDG-PET/CT studies before CRT. SUVmax, SUVmean, metabolic tumour volume (MTV, SUV ≥ 2.5), total lesion glycolysis (TLG) and six heterogeneity parameters assessed by texture analysis were obtained. Patients were classified as responders or non-responders according to Response Evaluation Criteria in Solid Tumors. Progression-free survival (PFS) and overall survival (OS) were calculated by the Kaplan-Meier method. Prognostic significance was assessed by Cox proportional hazards analysis. Thirty four non-responders showed significantly higher MTV (p = 0.006), TLG (p = 0.007), intensity variability (IV; p = 0.003) and size-zone variability (SZV; p = 0.004) than 18 responders. The positive and negative predictive values for non-responders were 77 % and 69 % in MTV, 76 % and 100 % in TLG, 78 % and 67 % in IV and 78 % and 82 % in SZV, respectively. Although PFS and OS were significantly shorter in patients with high MTV (PFS, p = 0.018; OS, p = 0.014), TLG (PFS, p = 0.009; OS, p = 0.025), IV (PFS, p = 0.013; OS, p = 0.007) and SZV (PFS, p = 0.010; OS, p = 0.007) at univariate analysis, none of them was an independent factor, while lymph node status, stage and tumour response status were independent factors at multivariate analysis. Texture features IV and SZV, and volumetric parameters MTV and TLG can predict tumour response, but all of them have limited value in prediction of prognosis of patients with esophageal cancer treated by CRT. (orig.)

  14. Citation analysis of the prognosis of Haux et al. for the year 2013.

    Science.gov (United States)

    Stausberg, Jürgen

    2014-07-01

    In 2002, Haux, Ammenwerth, Herzog, and Knaup published a prognosis about health care in the information society. In contrast to other prognoses, they underpinned their 30 theses with 71 quantitative statements that could be easily checked. A citation analysis was performed to assess the perception of this work in the medical informatics community. The ISI Web of Science was used for the citation search. From 55 hits, 38 articles were finally included in the metadata analysis, 33 articles in the qualitative analysis. The most prominent statement citing the paper of Haux et al. was identified in each article, divided into statements about the present and those about the future. Each statement was tagged with one keyword out of a convenient list. One article provided a statement about the present and the future. Most of the references were published in English as journal articles between 2006 and 2009. The majority of the first authors were from Europe. Twenty-two articles offered a statement about the present, 12 about the future. There was a shift from the present emphasis on electronic medical records and information and communication technologies to challenges in the future because of an aging population and the advent of personalized medicine. The citing papers seemed to be representative of medical informatics in terms of journals and the authors' countries of origin. The statements relating the citing literature with the paper of Haux et al. corresponded well with current notions about medical informatics. However, there was no debate about the concrete theses and prognoses offered in the cited paper. Therefore, the medical informatics community needs to rethink its own citation strategy.

  15. 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

  16. The prognosis and treatment of acquired hemophilia: a systematic review and meta-analysis.

    Science.gov (United States)

    Bitting, Rhonda L; Bent, Stephen; Li, Yongmei; Kohlwes, Jeffrey

    2009-10-01

    The inhibition of factor VIII by autoantibody development, or acquired hemophilia, occurs in approximately one person per million each year and can cause life-threatening bleeding. Due to the disease rarity, there are no randomized studies addressing prognostic features and treatment. The goal of this study is to identify prognostic indictors in acquired hemophilia to guide treatment choices. MEDLINE and EMBASE search from 1985-2008 retrieved 32 studies with detailed clinical information on five or more patients with acquired hemophilia. Univariate and multivariate analysis of the effects of age, sex, underlying condition, inhibitor titer, and treatment regimen were evaluated with regards to complete remission and death. A total of 32 studies containing 359 patients with acquired hemophilia were included in the analysis. The all-cause mortality rate in this cohort was 21%. Multivariate analyses revealed that patients more likely to die are the elderly [odds ratio (OR) 2.4, 95% confidence interval (CI) 1.32-4.36] and those with underlying malignancy (OR 2.76, CI 1.38-5.50). Early achievement of complete remission resulted in improved survival. Complete remission occurred in 94% of patients receiving combination chemotherapy, 82% receiving dual therapy, and 68% receiving steroids alone. Patients receiving immunosuppression had reduced odds of persistent hemophilia, with combination chemotherapy being the most efficacious (OR 0.04, CI 0.01-0.23) and steroid therapy alone being the least (OR 0.38, CI 0.14-0.94). In acquired hemophilia, increased age, underlying malignancy, and lack of complete remission are risk factors for death. Although the included studies were not randomized, patients treated with combination chemotherapy had the greatest odds of remission and the lowest odds of death.

  17. 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.

  18. An evaluation of multivariate statistical techniques for the analysis of yield from barley (Hordeum vulgare L.) breeding trials data

    OpenAIRE

    ABDULLAH, AHMED

    2007-01-01

    This project involved two locations (Breda and Tel Hadya) over two seasons (1993 and 1994). Yield was found to have been affected by many factors including environment, genotype and morphological characters. A genotype-environment interaction (GEl) was also discovered. To investigate the influence of morphological characters on yield parameters, multivariate statistical techniques (canonical analysis, factor analysis and multiple regression analysis (linear and exponentia...

  19. 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.

  20. [Phytoplankton assemblages and their relation to environmental factors by multivariate statistic analysis in Bohai Bay].

    Science.gov (United States)

    Zhou, Ran; Peng, Shi-Tao; Qin, Xue-Bo; Shi, Hong-Hua; Ding, De-Wen

    2013-03-01

    A detailed field survey of hydrological, chemical and biological resources was conducted in the Bohai Bay in spring and summer 2007. The distributions of phytoplankton and their relations to environmental factors were investigated with multivariate analysis techniques. Totally 17 and 23 taxa were identified in spring and summer, respectively. The abundance of phytoplankton in spring was 115 x 10(4) cells x m(-3), which was significantly higher than that in summer (3.1 x 10(4) cells x m(-3)). Characteristics of phytoplankton assemblages in the two seasons were identified using principal component analysis (PCA), while redundancy analysis (RDA) was used to examine the environmental variables that may explain the patterns of variation of the phytoplankton community. Based on PCA results, in the spring, the phytoplankton was mainly distributed in the center and northern water zone, where the nitrate nitrogen concentration was higher. However, in summer, phytoplankton was found distributed in all zones of Bohai Bay, while the dominant species was mainly distributed in the estuary. RDA indicated that the key environmental factors that influenced phytoplankton assemblages in the spring were nitrate nitrogen (NO3(-) -N), nitrite nitrogen (NO2(-) -N) and soluble reactive phosphorus (SRP), while ammonium nitrogen (NH4(+) -N) and water temperature (WT) played key roles in summer.

  1. The discrimination of honey origin using melissopalynology and Raman spectroscopy techniques coupled with multivariate analysis.

    Science.gov (United States)

    Corvucci, Francesca; Nobili, Lara; Melucci, Dora; Grillenzoni, Francesca-Vittoria

    2015-02-15

    Honey traceability to food quality is required by consumers and food control institutions. Melissopalynologists traditionally use percentages of nectariferous pollens to discriminate the botanical origin and the entire pollen spectrum (presence/absence, type and quantities and association of some pollen types) to determinate the geographical origin of honeys. To improve melissopalynological routine analysis, principal components analysis (PCA) was used. A remarkable and innovative result was that the most significant pollens for the traditional discrimination of the botanical and geographical origin of honeys were the same as those individuated with the chemometric model. The reliability of assignments of samples to honey classes was estimated through explained variance (85%). This confirms that the chemometric model properly describes the melissopalynological data. With the aim to improve honey discrimination, FT-microRaman spectrography and multivariate analysis were also applied. Well performing PCA models and good agreement with known classes were achieved. Encouraging results were obtained for botanical discrimination. Copyright © 2014 Elsevier Ltd. All rights reserved.

  2. 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.

  3. Development of a scale down cell culture model using multivariate analysis as a qualification tool.

    Science.gov (United States)

    Tsang, Valerie Liu; Wang, Angela X; Yusuf-Makagiansar, Helena; Ryll, Thomas

    2014-01-01

    In characterizing a cell culture process to support regulatory activities such as process validation and Quality by Design, developing a representative scale down model for design space definition is of great importance. The manufacturing bioreactor should ideally reproduce bench scale performance with respect to all measurable parameters. However, due to intrinsic geometric differences between scales, process performance at manufacturing scale often varies from bench scale performance, typically exhibiting differences in parameters such as cell growth, protein productivity, and/or dissolved carbon dioxide concentration. Here, we describe a case study in which a bench scale cell culture process model is developed to mimic historical manufacturing scale performance for a late stage CHO-based monoclonal antibody program. Using multivariate analysis (MVA) as primary data analysis tool in addition to traditional univariate analysis techniques to identify gaps between scales, process adjustments were implemented at bench scale resulting in an improved scale down cell culture process model. Finally we propose an approach for small scale model qualification including three main aspects: MVA, comparison of key physiological rates, and comparison of product quality attributes.

  4. 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.

  5. Population structure of the Korean gizzard shad, Konosirus punctatus (Clupeiformes, Clupeidae) using multivariate morphometric analysis

    Science.gov (United States)

    Myoung, Se Hun; Kim, Jin-Koo

    2016-03-01

    The gizzard shad, Konosirus punctatus, is one of the most important fish species in Korea, China, Japan and Taiwan, and therefore the implementation of an appropriate population structure analysis is both necessary and fitting. In order to clarify the current distribution range for the two lineages of the Korean gizzard shad (Myoung and Kim 2014), we conducted a multivariate morphometric analysis by locality and lineage. We analyzed 17 morphometric and 5 meristic characters of 173 individuals, which were sampled from eight localities in the East Sea, the Yellow Sea and the Korean Strait. Unlike population genetics studies, the canonical discriminant analysis (CDA) results showed that the two morphotypes were clearly segregated by the center value "0" of CAN1, of which morphotype A occurred from the Yellow Sea to the western Korean Strait with negative values, and morphotype B occurred from the East Sea to the eastern Korean Strait with positive values even though there exists an admixture zone in the eastern Korean Strait. Further studies using more sensitive markers such as microsatellite DNA are required in order to define the true relationship between the two lineages.

  6. 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.

  7. 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.

  8. Predictors of outcome after anterior cervical discectomy and fusion: a multivariate analysis.

    Science.gov (United States)

    Anderson, Paul A; Subach, Brian R; Riew, K Daniel

    2009-01-15

    Retrospective cohort study. Perform a multivariate analysis to identify important predictors of poor outcome following anterior cervical discectomy and fusion. Identifying prognostic factors is important to aid surgical decision-making and counseling of patients. Recent randomized control trials of disc arthroplasty devices have established a large cohort of patients treated with fusion and 2-year outcomes that allow analysis of prognostic factors. The patient cohort was the fusion control patients (n = 488) from 2 randomized controlled studies of disc replacements. Surgical indications were recalcitrant single-level subaxial radiculopathy or myelopathy. The surgery included anterior discectomy and fusion with allograft and plate. Patients were assessed by neck and arm pain, neck disability index (NDI), SF-36, neurologic examination, and return to work. Overall clinical success was defined based on meeting all 4 of these criteria: >15-point improvement in NDI; maintained or improved neurologic examination; no serious adverse event related to the procedure; and no revision of the plate or graft. Patient's outcomes were recorded, at 3, 6, 12, and 24 months, with 77% follow-up at 24 months.The outcome variables for this analysis were overall clinical success and >15-point improvement in NDI. We studied the relationship between each of the outcome variables and 26 potential important variables including demographics, medical conditions, socioeconomic factors, and disease state. Two statistical models were used to explore the association between outcome variables and baseline measures: multivariate logistical regression of the full model with every prognostic variable included and the model with the variables selected by the stepwise selection procedure. In the full-model logistic analysis for overall success, worker's compensation and weak narcotic use were negative predictors while higher preoperative NDI score and normal sensory function were positive predictors. For

  9. APPLICATION OF RESULTS OF WAVELET AND MULTIFRACTAL ANALYSIS OF METAL STRUCTURE FOR PROGNOSIS OF ITS QUALITY

    Directory of Open Access Journals (Sweden)

    VOLCHUK V. M.

    2015-10-01

    Full Text Available Problem statement. At present , to implement a deterministic method of assessment of the mechanical features is not possible based on the analysis of causalit links, because they are influenced with a large number of variables that are highly correlated with each other, and some part of them are changing in a wide range of unpredictable ways. Especially, this problem is in assessing the mechanical properties of metal constructions and products of special purpose in the process of their expluatation: oil pipes, carcasses of residential buildings, etc. In these cases, mechanical testing is the problem is not always technically feasible, and out of variety of express methods of non-destructive control are used often in practice in verbal or semiquantitative. The difficulty is that under the impact of various factors: temperature, corrosive environments, etc., structural changes occur far from thermodynamic equilibrium, and as result the mixed structures are got, including widmanshtatten structure. Use of classical methods of metallography is not always possible to quantify such structures with the precision that may be necessary for practical purposes. In this regard, considerable interest is the search for new approaches to assess the metal structure with a purpose of prognosis of its mechanical properties. Purpose. To obtain information about the possible application of wavelet-multifractal analysis to assess the mechanical properties of metal. Conclusion. Sensitiveness between strength properties and uniformity is set with regularity of structure elements of bainite-perlite group, and also between the viscous properties and uniformity, a regularity of element of the ferrite group. The results suggest that the realization of this method allows in the minimal and possible cost for the real tests to provide the necessary accuracy for practical purposes.

  10. Multivariate areal analysis of the impact and efficiency of the family planning programme in peninsular Malaysia.

    Science.gov (United States)

    Tan Boon Ann

    1987-06-01

    The findings of the final phase of a 3-phase multivariate areal analysis study undertaken by the Economic and Social Commission for Asia and the Pacific (ESCAP) in 5 countries of the Asian and Pacific Region, including Malaysia, to examine the impact of family planning programs on fertility and reproduction are reported. The study used Malaysia's administrative district as the unit of analysis because the administration and implementation of socioeconomic development activities, as well as the family planning program, depend to a large extent on the decisions of local organizations at the district or state level. In phase 1, existing program and nonprogram data were analyzed using the multivariate technique to separate the impact of the family planning program net of other developmental efforts. The methodology in the 2nd phase consisted of in-depth investigation of selected areas in order to discern the dynamics and determinants of efficiency. The insights gained in phase 2 regarding dynamics of performance were used in phase 3 to refine the input variables of the phase 1 model. Thereafter, the phase 1 analysis was repeated. Insignificant variables and factors were trimmed in order to present a simplified model for studying the impact of environmental, socioeconomic development, family planning programs, and related factors on fertility. The inclusion of a set of family planning program and development variables in phase 3 increased the predictive power of the impact model. THe explained variance for total fertility rate (TFR) of women under 30 years increased from 71% in phase 1 to 79%. It also raised the explained variance of the efficiency model from 34% to 70%. For women age 30 years and older, their TFR was affected directly by the ethnic composition variable (.76), secondary educational status (-.45), and modern nonagricultural occupation (.42), among others. When controlled for other socioeconomic development and environmental indicators, the

  11. 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...

  12. Multivariate Analysis for Quantification of Plutonium(IV) in Nitric Acid Based on Absorption Spectra

    Energy Technology Data Exchange (ETDEWEB)

    Lines, Amanda M. [Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States; Adami, Susan R. [Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States; Sinkov, Sergey I. [Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States; Lumetta, Gregg J. [Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States; Bryan, Samuel A. [Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States

    2017-08-09

    Development of more effective, reliable, and fast methods for monitoring process streams is a growing opportunity for analytical applications. Many fields can benefit from on-line monitoring, including the nuclear fuel cycle where improved methods for monitoring radioactive materials will facilitate maintenance of proper safeguards and ensure safe and efficient processing of materials. On-line process monitoring with a focus on optical spectroscopy can provide a fast, non-destructive method for monitoring chemical species. However, identification and quantification of species can be hindered by the complexity of the solutions if bands overlap or show condition-dependent spectral features. Plutonium (IV) is one example of a species which displays significant spectral variation with changing nitric acid concentration. Single variate analysis (i.e. Beer’s Law) is difficult to apply to the quantification of Pu(IV) unless the nitric acid concentration is known and separate calibration curves have been made for all possible acid strengths. Multivariate, or chemometric, analysis is an approach that allows for the accurate quantification of Pu(IV) without a priori knowledge of nitric acid concentration.

  13. Tools based on multivariate statistical analysis for classification of soil and groundwater in Apulian agricultural sites.

    Science.gov (United States)

    Ielpo, Pierina; Leardi, Riccardo; Pappagallo, Giuseppe; Uricchio, Vito Felice

    2017-06-01

    In this paper, the results obtained from multivariate statistical techniques such as PCA (Principal component analysis) and LDA (Linear discriminant analysis) applied to a wide soil data set are presented. The results have been compared with those obtained on a groundwater data set, whose samples were collected together with soil ones, within the project "Improvement of the Regional Agro-meteorological Monitoring Network (2004-2007)". LDA, applied to soil data, has allowed to distinguish the geographical origin of the sample from either one of the two macroaeras: Bari and Foggia provinces vs Brindisi, Lecce e Taranto provinces, with a percentage of correct prediction in cross validation of 87%. In the case of the groundwater data set, the best classification was obtained when the samples were grouped into three macroareas: Foggia province, Bari province and Brindisi, Lecce and Taranto provinces, by reaching a percentage of correct predictions in cross validation of 84%. The obtained information can be very useful in supporting soil and water resource management, such as the reduction of water consumption and the reduction of energy and chemical (nutrients and pesticides) inputs in agriculture.

  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. Risk factors for non-alcoholic fatty liver disease: a multivariate analysis

    Directory of Open Access Journals (Sweden)

    PANG Xueqin

    2014-09-01

    Full Text Available ObjectiveTo investigate the risk factors for non-alcoholic fatty liver disease (NAFLD and to provide a basis for the prevention of NAFLD. MethodsA total of 190 patients with NAFLD who visited the First Affiliated Hospital of Soochow University from January 2011 to January 2013 were included in the study. The investigated factors included sex, age, height, weight, dietary habit, smoking and alcohol consumption, educational level, occupation, intensity and duration of physical exercise, bedtime, previous history, and family history. Univariate and multivariate analyses were performed using SPSS 18.0 to determine the risk factors for NAFLD. ResultsThe univariate analysis showed that sex, age, dietary habit, occupation, body mass index (BMI, and educational level were associated with NAFLD (P<0.05. The logistic regression analysis showed that the risk factors for NAFLD were sex (OR=5.692, P=0.029, age (OR=0.423, P=0.041, occupation (OR=0.698, P=0.008, BMI (OR=3.939, P=0.003, educational level (OR=5.463, P=0.030, and dietary habit (OR=9.235, P=0.039. ConclusionNAFLD may be related to many factors, and corresponding preventive measures may reduce the development of NAFLD.

  16. 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.

  17. 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.

  18. Provenance Study of Archaeological Ceramics from Syria Using XRF Multivariate Statistical Analysis and Thermoluminescence Dating

    Directory of Open Access Journals (Sweden)

    Elias Hanna Bakraji

    2014-01-01

    Full Text Available Thermoluminescence (TL dating and multivariate statistical methods based on radioisotope X-ray fluorescence analysis have been utilized to date and classify Syrian archaeological ceramics fragment from Tel Jamous site. 54 samples were analyzed by radioisotope X-ray fluorescence; 51 of them come from Tel Jamous archaeological site in Sahel Akkar region, Syria, which fairly represent ceramics belonging to the Middle Bronze Age (2150 to 1600 B.C. and the remaining three samples come from Mar-Takla archaeological site fairly representative of the Byzantine ceramics. We have selected four fragments from Tel Jamous site to determinate their age using thermoluminescence (TL method; the results revealed that the date assigned by archaeologists was good. An annular 109Cd radioactive source was used to irradiate the samples in order to determine their chemical composition and the results were treated statistically using two methods, cluster and factor analysis. This treatment revealed two main groups; the first one contains only the three samples M52, M53, and M54 from Mar-Takla site, and the second one contains samples that belong to Tel Jamous site (local.

  19. 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.

  20. 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.

  1. Chemical Attribution of Fentanyl Using Multivariate Statistical Analysis of Orthogonal Mass Spectral Data.

    Science.gov (United States)

    Mayer, Brian P; DeHope, Alan J; Mew, Daniel A; Spackman, Paul E; Williams, Audrey M

    2016-04-19

    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. A total of 160 distinct compounds and inorganic species were identified using gas and liquid chromatographies combined with mass spectrometric methods (gas chromatography/mass spectrometry (GC/MS) and liquid chromatography-tandem mass spectrometry-time of-flight (LC-MS/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.

  2. 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.

  3. Application of Multivariate Statistical Analysis in Evaluation of Surface River Water Quality of a Tropical River

    Directory of Open Access Journals (Sweden)

    Teck-Yee Ling

    2017-01-01

    Full Text Available The present study evaluated the spatial variations of surface water quality in a tropical river using multivariate statistical techniques, including cluster analysis (CA and principal component analysis (PCA. Twenty physicochemical parameters were measured at 30 stations along the Batang Baram and its tributaries. The water quality of the Batang Baram was categorized as “slightly polluted” where the chemical oxygen demand and total suspended solids were the most deteriorated parameters. The CA grouped the 30 stations into four clusters which shared similar characteristics within the same cluster, representing the upstream, middle, and downstream regions of the main river and the tributaries from the middle to downstream regions of the river. The PCA has determined a reduced number of six principal components that explained 83.6% of the data set variance. The first PC indicated that the total suspended solids, turbidity, and hydrogen sulphide were the dominant polluting factors which is attributed to the logging activities, followed by the five-day biochemical oxygen demand, total phosphorus, organic nitrogen, and nitrate-nitrogen in the second PC which are related to the discharges from domestic wastewater. The components also imply that logging activities are the major anthropogenic activities responsible for water quality variations in the Batang Baram when compared to the domestic wastewater discharge.

  4. Assessing assumptions of multivariate linear regression framework implemented for directionality analysis of fMRI.

    Science.gov (United States)

    Dang, Shilpa; Chaudhury, Santanu; Lall, Brejesh; Roy, Prasun Kumar

    2015-08-01

    Directionality analysis of time-series, recorded from task-activated regions-of-interest (ROIs) during functional Magnetic Resonance Imaging (fMRI), has helped in gaining insights of complex human behavior and human brain functioning. The most widely used standard method of Granger Causality for evaluating directionality employ linear regression modeling of temporal processes. Such a parameter-driven approach rests on various underlying assumptions about the data. The short-comings can arise when misleading conclusions are reached after exploration of data for which the assumptions are getting violated. In this study, we assess assumptions of Multivariate Autoregressive (MAR) framework which is employed for evaluating directionality among fMRI time-series recorded during a Sensory-Motor (SM) task. The fMRI time-series here is an averaged time-series from a user-defined ROI of multiple voxels. The "aim" is to establish a step-by-step procedure using statistical methods in conjunction with graphical methods to seek the validity of MAR models, specifically in the context of directionality analysis of fMRI data which has not been done previously to the best of our knowledge. Here, in our case of SM task (block design paradigm) there is violation of assumptions, indicating the inadequacy of MAR models to find directional interactions among different task-activated regions of brain.

  5. 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.

  6. Multivariate analysis of variance of designed chromatographic data. A case study involving fermentation of rooibos tea.

    Science.gov (United States)

    Marini, Federico; de Beer, Dalene; Walters, Nico A; de Villiers, André; Joubert, Elizabeth; Walczak, Beata

    2017-03-17

    An ultimate goal of investigations of rooibos plant material subjected to different stages of fermentation is to identify the chemical changes taking place in the phenolic composition, using an untargeted approach and chromatographic fingerprints. Realization of this goal requires, among others, identification of the main components of the plant material involved in chemical reactions during the fermentation process. Quantitative chromatographic data for the compounds for extracts of green, semi-fermented and fermented rooibos form the basis of preliminary study following a targeted approach. The aim is to estimate whether treatment has a significant effect based on all quantified compounds and to identify the compounds, which contribute significantly to it. Analysis of variance is performed using modern multivariate methods such as ANOVA-Simultaneous Component Analysis, ANOVA - Target Projection and regularized MANOVA. This study is the first one in which all three approaches are compared and evaluated. For the data studied, all tree methods reveal the same significance of the fermentation effect on the extract compositions, but they lead to its different interpretation. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Genetic and clinical contributions to cerebral palsy: a multi-variable analysis.

    Science.gov (United States)

    O'Callaghan, Michael E; Maclennan, Alastair H; Gibson, Catherine S; McMichael, Gai L; Haan, Eric A; Broadbent, Jessica L; Baghurst, Peter A; Goldwater, Paul N; Dekker, Gustaaf A

    2013-07-01

    This study aims to examine single nucleotide polymorphism (SNP) associations with cerebral palsy in a multi-variable analysis adjusting for potential clinical confounders and to assess SNP-SNP and SNP-maternal infection interactions as contributors to cerebral palsy. A case control study including 587 children with cerebral palsy and 1154 control children without cerebral palsy. Thirty-nine candidate SNPs were genotyped in both mother and child. Data linkage to perinatal notes and cerebral palsy registers was performed with a supplementary maternal pregnancy questionnaire. History of known maternal infection during pregnancy was extracted from perinatal databases. Both maternal and fetal carriage of inducible nitric oxide synthase SNP rs1137933 were significantly negatively associated with cerebral palsy in infants born at less than 32 weeks gestation after adjustment for potential clinical confounders and correction for multiple testing (odds ratio 0.55, 95% confidence interval 0.38-0.79; odds ratio 0.57, 95% confidence interval 0.4-0.82, respectively). Analysis did not show any statistically significant SNP-SNP or SNP-maternal infection interactions after correction for multiple testing. Maternal and child inducible nitric oxide synthase SNPs are associated with reduced risk of cerebral palsy in infants born very preterm. There was no evidence for statistically significant SNP-SNP or SNP-maternal infection interactions as modulators of cerebral palsy risk. © 2013 The Authors. Journal of Paediatrics and Child Health © 2013 Paediatrics and Child Health Division (Royal Australasian College of Physicians).

  8. Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework.

    Science.gov (United States)

    Looney, David; Hemakom, Apit; Mandic, Danilo P

    2015-01-08

    A novel multi-scale approach for quantifying both inter- and intra-component dependence of a complex system is introduced. This is achieved using empirical mode decomposition (EMD), which, unlike conventional scale-estimation methods, obtains a set of scales reflecting the underlying oscillations at the intrinsic scale level. This enables the data-driven operation of several standard data-association measures (intrinsic correlation, intrinsic sample entropy (SE), intrinsic phase synchrony) and, at the same time, preserves the physical meaning of the analysis. The utility of multi-variate extensions of EMD is highlighted, both in terms of robust scale alignment between system components, a pre-requisite for inter-component measures, and in the estimation of feature relevance. We also illuminate that the properties of EMD scales can be used to decouple amplitude and phase information, a necessary step in order to accurately quantify signal dynamics through correlation and SE analysis which are otherwise not possible. Finally, the proposed multi-scale framework is applied to detect directionality, and higher order features such as coupling and regularity, in both synthetic and biological systems.

  9. Heavy metal enrichment in the seagrasses of Lakshadweep group of islands--a multivariate statistical analysis.

    Science.gov (United States)

    Thangaradjou, T; Raja, S; Subhashini, Pon; Nobi, E P; Dilipan, E

    2013-01-01

    An assessment on heavy metal (Al, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb and Zn) accumulation by seven seagrass species of Lakshadweep group of islands was carried out using multivariate statistical tools like principal component analysis (PCA) and cluster analysis (CA). Among all the metals, Mg and Al were determined in higher concentration in all the seagrasses, and their values varied with respect to different seagrass species. The concentration of the four toxic heavy metals (Cd, Pb, Zn and Cu) was found higher in all the seagrasses when compared with the background values of seagrasses from Flores Sea, Indonesia. The contamination factor of these four heavy metals ranged as Cd (1.97-12.5), Cu (0.73-4.40), Pb (2.3-8.89) and Zn (1.27-2.787). In general, the Pollution Load Index (PLI) calculated was found to be maximum for Halophila decipiens (58.2). Results revealed that Halophila decipiens is a strong accumulator of heavy metals, followed by Halodule uninervis and Halodule pinifolia, among all the tested seagrasses. Interestingly, the small-leaved seagrasses were found to be efficient in heavy metal accumulation than the large-leaved seagrass species. Thus, seagrasses can better be used for biomonitoring, and seagrasses can be used as the heavy metal sink as the biomass take usually long term to get remineralize in nature.

  10. [Anatomo-clinical prognostic factors of papillary carcinoma of the thyroid. Multivariate analysis: report of a series of 52 cases].

    Science.gov (United States)

    Patey, M; Menzies, D; Theobald, S; Delisle, M J; Flament, J B; Pluot, M

    1998-02-01

    A retrospective study about 52 cases of papillary thyroid carcinomas was carried out with emphasis on histopathological features. The mean follow up period was 10 years. The survival curves were estimated using the Kaplan-Meier method and compared using the log rank test. The multivariate analysis was performed using the Cox's regression model. In univariate analysis, age, Tp (histopathological extension of the tumor), histological differentiation, VAN score (Vascular invasion nuclear Atypia tumor Necrosis) of Akslen and the LeuM1 expression were significant prognostic factors. In multivariate analysis, the Tp and histological differentiation were associated with high risks of poor outcome.

  11. Investigating the provenance of thermal groundwater using compositional multivariate statistical analysis: a hydrogeochemical study from Ireland

    Science.gov (United States)

    Blake, Sarah; Henry, Tiernan; Murray, John; Flood, Rory; Muller, Mark R.; Jones, Alan G.; Rath, Volker

    2016-04-01

    The geothermal energy of thermal groundwater is currently being exploited for district-scale heating in many locations world-wide. The chemical compositions of these thermal waters reflect the provenance and hydrothermal circulation patterns of the groundwater, which are controlled by recharge, rock type and geological structure. Exploring the provenance of these waters using multivariate statistical analysis (MSA) techniques increases our understanding of the hydrothermal circulation systems, and provides a reliable tool for assessing these resources. Hydrochemical data from thermal springs situated in the Carboniferous Dublin Basin in east-central Ireland were explored using MSA, including hierarchical cluster analysis (HCA) and principal component analysis (PCA), to investigate the source aquifers of the thermal groundwaters. To take into account the compositional nature of the hydrochemical data, compositional data analysis (CoDa) techniques were used to process the data prior to the MSA. The results of the MSA were examined alongside detailed time-lapse temperature measurements from several of the springs, and indicate the influence of three important hydrogeological processes on the hydrochemistry of the thermal waters: 1) increased salinity due to evaporite dissolution and increased water-rock-interaction; 2) dissolution of carbonates; and 3) dissolution of metal sulfides and oxides associated with mineral deposits. The use of MSA within the CoDa framework identified subtle temporal variations in the hydrochemistry of the thermal springs, which could not be identified with more traditional graphing methods (e.g., Piper diagrams), or with a standard statistical approach. The MSA was successful in distinguishing different geological settings and different annual behaviours within the group of springs. This study demonstrates the usefulness of the application of MSA within the CoDa framework in order to better understand the underlying controlling processes

  12. Studying variations in the PCDD/PCDF profile across various food products using multivariate statistical analysis

    Energy Technology Data Exchange (ETDEWEB)

    Antignac, Jean-Philippe [Ecole Nationale Veterinaire de Nantes (ENVN), Laboratoire d' Etude des Residus et Contaminants dans les Aliments (LABERCA), Nantes (France); LABERCA-ENVN, Nantes (France); Marchand, Philippe; Gade, Christel; Matayron, Gilles; Bizec, Bruno Le; Andre, Francois [Ecole Nationale Veterinaire de Nantes (ENVN), Laboratoire d' Etude des Residus et Contaminants dans les Aliments (LABERCA), Nantes (France); Qannari, El Mostafa [Ecole Nationale d' Ingenieurs des Techniques des Industries Agricoles et Alimentaires (ENITIAA), Unite de Sensometrie et de Chimiometrie, La Geraudiere, Nantes (France)

    2006-01-01

    Polychlorinated dibenzo-p-dioxins (PCDD) and polychlorinated dibenzofurans (PCDF) are widely recognized by the scientific community as persistent organic pollutants due to their toxicity and adverse effects on wildlife and human health. The actual regulation dedicated to the monitoring of dioxins in food is based on the measurement of 17 congener concentrations. The final result is reported as a toxic equivalent value that takes into account the relative toxicity of each congener. This procedure can minimize the qualitative information available from the abundances of each PCDD/PCDF congener: the characteristic contamination profile of the sample. Multivariate statistical techniques, such as principal component analysis (PCA) or linear discriminant analysis (LDA), represent an interesting way to investigate this qualitative information. Nevertheless, they have only been applied to the analysis of contamination data from food products and biological matrices infrequently. The objective of the present study was to analyze a large data set from dioxin analyses performed on various food products of animal origin. The results demonstrate the existence of differences in congener-specific patterns between the analyzed samples. Variability was first demonstrated in terms of the food type (fish, meat, milk, fatty products). Then a variability was observed that was related to the specific animal species for meat and milk samples (bovine, ovine, porcine, caprine and poultry). Some practical applications of these results are discussed. The origin(s) of the observed differences, as well as their significance, now remain to be investigated, both in terms of environmental factors and transfer through living organisms. A better knowledge of the relation between a contamination profile and its specific source and/or food product should be of great interest to scientists working in the fields of contaminant analysis, toxicology and metabolism, as well as to regulatory bodies and

  13. PTEN gene mutations correlate to poor prognosis in glioma patients: a meta-analysis

    Directory of Open Access Journals (Sweden)

    Han F

    2016-06-01

    Full Text Available Feng Han,1,* Rong Hu,2,* Hua Yang,1 Jian Liu,1 Jianmei Sui,1 Xin Xiang,1 Fan Wang,1 Liangzhao Chu,1 Shibin Song1 1Department of Neurosurgery, Affiliated Hospital of Guizhou Medical University, 2Department of Histology and Embryology, College of Basic Medical Sciences, Guizhou Medical University, Guiyang, Guizhou, People’s Republic of China *These authors contributed equally to this work Background: We conducted this meta-analysis based on eligible trials to investigate the relationship between phosphatase and tensin homolog (PTEN genetic mutation and glioma patients’ survival. Methods: PubMed, Web of Science, and EMBASE were searched for eligible studies regarding the relationship between PTEN genetic mutation and glioma patients’ survival. The primary outcome was the overall survival of glioma patient with or without PTEN genetic mutation, and second outcome was prognostic factors for the survival of glioma patient. A fixed-effects or random-effects model was used to pool the estimates according to the heterogeneity among the included studies. Results: Nine cohort studies, involving 1,173 patients, were included in this meta-analysis. Pooled results suggested that glioma patients with PTEN genetic mutation had a significant shorter overall survival than those without PTEN genetic mutation (hazard ratio [HR] =2.23, 95% confidence interval [CI]: 1.35, 3.67; P=0.002. Furthermore, subgroup analysis indicated that this association was only observed in American patients (HR =2.19, 95% CI: 1.23, 3.89; P=0.008, but not in Chinese patients (HR =1.44, 95% CI: 0.29, 7.26; P=0.657. Histopathological grade (HR =1.42, 95% CI: 0.07, 28.41; P=0.818, age (HR =0.94, 95% CI: 0.43, 2.04; P=0.877, and sex (HR =1.28, 95% CI: 0.55, 2.98; P=0.564 were not significant prognostic factors for the survival of patients with glioma. Conclusion: Current evidence indicates that PTEN genetic mutation is associated with poor prognosis in glioma patients. However, this

  14. Serological diagnosis and prognosis of severe acute pancreatitis by analysis of serum glycoprotein 2.

    Science.gov (United States)

    Roggenbuck, Dirk; Goihl, Alexander; Hanack, Katja; Holzlöhner, Pamela; Hentschel, Christian; Veiczi, Miklos; Schierack, Peter; Reinhold, Dirk; Schulz, Hans-Ulrich

    2017-05-01

    Glycoprotein 2 (GP2), the pancreatic major zymogen granule membrane glycoprotein, was reported to be elevated in acute pancreatitis in animal models. Enzyme-linked immunosorbent assays (ELISAs) were developed to evaluate human glycoprotein 2 isoform alpha (GP2a) and total GP2 (GP2t) as specific markers for acute pancreatitis in sera of 153 patients with acute pancreatitis, 26 with chronic pancreatitis, 125 with pancreatic neoplasms, 324 with non-pancreatic neoplasms, 109 patients with liver/biliary disease, 67 with gastrointestinal disease, and 101 healthy subjects. GP2a and GP2t levels were correlated with procalcitonin and C-reactive protein in 152 and 146 follow-up samples of acute pancreatitis patients, respectively. The GP2a ELISA revealed a significantly higher assay accuracy in contrast to the GP2t assay (sensitivity ≤3 disease days: 91.7%, specificity: 96.7%, positive likelihood ratio [LR+]: 24.6, LR-: 0.09). GP2a and GP2t levels as well as prevalences were significantly elevated in early acute pancreatitis (≤3 disease days) compared to all control cohorts (ppancreatitis at admission compared with mild cases (ppancreatitis with lethal outcome was 7.8 on admission (p=0.0222). GP2a and GP2t levels were significantly correlated with procalcitonin [Spearman's rank coefficient of correlation (ρ)=0.21, 0.26; p=0.0110, 0.0012; respectively] and C-reactive protein (ρ=0.37, 0.40; ppancreatitis and analysis of GP2a can aid in the differential diagnosis of acute upper abdominal pain and prognosis of severe acute pancreatitis.

  15. Environmental controls on microbial abundance and activity on the greenland ice sheet: a multivariate analysis approach.

    Science.gov (United States)

    Stibal, Marek; Telling, Jon; Cook, Joe; Mak, Ka Man; Hodson, Andy; Anesio, Alexandre M

    2012-01-01

    Microbes in supraglacial ecosystems have been proposed to be significant contributors to regional and possibly global carbon cycling, and quantifying the biogeochemical cycling of carbon in glacial ecosystems is of great significance for global carbon flow estimations. Here we present data on microbial abundance and productivity, collected along a transect across the ablation zone of the Greenland ice sheet (GrIS) in summer 2010. We analyse the relationships between the physical, chemical and biological variables using multivariate statistical analysis. Concentrations of debris-bound nutrients increased with distance from the ice sheet margin, as did both cell numbers and activity rates before reaching a peak (photosynthesis) or a plateau (respiration, abundance) between 10 and 20 km from the margin. The results of productivity measurements suggest an overall net autotrophy on the GrIS and support the proposed role of ice sheet ecosystems in carbon cycling as regional sinks of CO(2) and places of production of organic matter that can be a potential source of nutrients for downstream ecosystems. Principal component analysis based on chemical and biological data revealed three clusters of sites, corresponding to three 'glacier ecological zones', confirmed by a redundancy analysis (RDA) using physical data as predictors. RDA using data from the largest 'bare ice zone' showed that glacier surface slope, a proxy for melt water flow, accounted for most of the variation in the data. Variation in the chemical data was fully explainable by the determined physical variables. Abundance of phototrophic microbes and their proportion in the community were identified as significant controls of the carbon cycling-related microbial processes.

  16. 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

  17. DYS19 and DYS390 Y-STR polymorphism in the Iberian Peninsula: a multivariate analysis.

    Science.gov (United States)

    Carril, J C; Llamas, P; Luis, J R; Dios, S; Caeiro, B

    2003-03-01

    Genetic polymorphism of two Y-specific short tandem repeats (DYS19 and DYS390) was investigated in six populations from the Iberian Peninsula (Andalusia, Castilla-La Mancha, Castilla-Leon, Extremadura, Galicia and South East Spain) comprising a total of 895 unrelated and native individuals, and a complete database of DYS19 and DYS390 allele frequency distributions in 34 world-wide populations collected from literature was analysed. DYS19 and DYS390 polymorphism was screened by automated fluorescence analysis of PCR-amplified labelled sample fragments performed with and ABI PRISM 377 Genetic Analyser. The degree of population differentiation was analysed using the STP Test to calculate G Statistic values. Correspondence Analysis based on the allelic frequencies of each locus and combining both was performed using the NTSYS-PC version 1.70 computer package. The diversity of the genetic profiles of gene frequencies suggests an important population heterogeneity in the Iberian Peninsula as a whole (DYS390 being particularly evident), which is corroborated after statistical analyses (G = 139.8457, p = 1.7822 x 10(-14) for DYS19, G = 116.0293, p = 4.6845 x 10(-12) for DYS390). However, multivariate analysis indicates a well defined cluster of the populations of the Central region, and sets them apart from the positions within which peripheral Iberian Peninsula populations are distributed. The Galician population shows trends which bring it closer to the positions throughout which European Atlantic populations are distributed. The results shown by the Central Iberian Peninsula seem to lend support to a model of settlement population stocks which came from the region of Castilla-Leon after the Islam invasions, whereas in the South-East populations the genetic record of Middle Eastern populations is still present, a consequence of the expansion of Islam in Southern Europe in the Middle Ages.

  18. Risk management and statistical multivariate analysis approach for design and optimization of satranidazole nanoparticles.

    Science.gov (United States)

    Dhat, Shalaka; Pund, Swati; Kokare, Chandrakant; Sharma, Pankaj; Shrivastava, Birendra

    2017-01-01

    Rapidly evolving technical and regulatory landscapes of the pharmaceutical product development necessitates risk management with application of multivariate analysis using Process Analytical Technology (PAT) and Quality by Design (QbD). Poorly soluble, high dose drug, Satranidazole was optimally nanoprecipitated (SAT-NP) employing principles of Formulation by Design (FbD). The potential risk factors influencing the critical quality attributes (CQA) of SAT-NP were identified using Ishikawa diagram. Plackett-Burman screening design was adopted to screen the eight critical formulation and process parameters influencing the mean particle size, zeta potential and dissolution efficiency at 30min in pH7.4 dissolution medium. Pareto charts (individual and cumulative) revealed three most critical factors influencing CQA of SAT-NP viz. aqueous stabilizer (Polyvinyl alcohol), release modifier (Eudragit® S 100) and volume of aqueous phase. The levels of these three critical formulation attributes were optimized by FbD within established design space to minimize mean particle size, poly dispersity index, and maximize encapsulation efficiency of SAT-NP. Lenth's and Bayesian analysis along with mathematical modeling of results allowed identification and quantification of critical formulation attributes significantly active on the selected CQAs. The optimized SAT-NP exhibited mean particle size; 216nm, polydispersity index; 0.250, zeta potential; -3.75mV and encapsulation efficiency; 78.3%. The product was lyophilized using mannitol to form readily redispersible powder. X-ray diffraction analysis confirmed the conversion of crystalline SAT to amorphous form. In vitro release of SAT-NP in gradually pH changing media showed 95%) in pH7.4 in next 3h, indicative of burst release after a lag time. This investigation demonstrated effective application of risk management and QbD tools in developing site-specific release SAT-NP by nanoprecipitation. Copyright © 2016 Elsevier B.V. All

  19. Evaluation of antibiotic effects on Pseudomonas aeruginosa biofilm using Raman spectroscopy and multivariate analysis.

    Science.gov (United States)

    Jung, Gyeong Bok; Nam, Seong Won; Choi, Samjin; Lee, Gi-Ja; Park, Hun-Kuk

    2014-09-01

    We investigate the mode of action and classification of antibiotic agents (ceftazidime, patulin, and epigallocatechin gallate; EGCG) on Pseudomonas aeruginosa (P. aeruginosa) biofilm using Raman spectroscopy with multivariate analysis, including support vector machine (SVM) and principal component analysis (PCA). This method allows for quantitative, label-free, non-invasive and rapid monitoring of biochemical changes in complex biofilm matrices with high sensitivity and specificity. In this study, the biofilms were grown and treated with various agents in the microfluidic device, and then transferred onto gold-coated substrates for Raman measurement. Here, we show changes in biochemical properties, and this technology can be used to distinguish between changes induced in P. aeruginosa biofilms using three antibiotic agents. The Raman band intensities associated with DNA and proteins were decreased, compared to control biofilms, when the biofilms were treated with antibiotics. Unlike with exposure to ceftazidime and patulin, the Raman spectrum of biofilms exposed to EGCG showed a shift in the spectral position of the CH deformation stretch band from 1313 cm(-1) to 1333 cm(-1), and there was no difference in the band intensity at 1530 cm(-1) (C = C stretching, carotenoids). The PCA-SVM analysis results show that antibiotic-treated biofilms can be detected with high sensitivity of 93.33%, a specificity of 100% and an accuracy of 98.33%. This method also discriminated the three antibiotic agents based on the cellular biochemical and structural changes induced by antibiotics with high sensitivity and specificity of 100%. This study suggests that Raman spectroscopy with PCA-SVM is potentially useful for the rapid identification and classification of clinically-relevant antibiotics of bacteria biofilm. Furthermore, this method could be a powerful approach for the development and screening of new antibiotics.

  20. Multivariate Statistical Analysis Software Technologies for Astrophysical Research Involving Large Data Bases

    Science.gov (United States)

    Djorgovski, S. G.

    1994-01-01

    We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complex database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects of the SKICAT system, and of some of the scientific results achieved to date. We also developed a user-friendly package for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications and has

  1. 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.

  2. Expert Involvement Predicts mHealth App Downloads: Multivariate Regression Analysis of Urology Apps.

    Science.gov (United States)

    Pereira-Azevedo, Nuno; Osório, Luís; Cavadas, Vitor; Fraga, Avelino; Carrasquinho, Eduardo; Cardoso de Oliveira, Eduardo; Castelo-Branco, Miguel; Roobol, Monique J

    2016-07-15

    Urological mobile medical (mHealth) apps are gaining popularity with both clinicians and patients. mHealth is a rapidly evolving and heterogeneous field, with some urology apps being downloaded over 10,000 times and others not at all. The factors that contribute to medical app downloads have yet to be identified, including the hypothetical influence of expert involvement in app development. The objective of our study was to identify predictors of the number of urology app downloads. We reviewed urology apps available in the Google Play Store and collected publicly available data. Multivariate ordinal logistic regression evaluated the effect of publicly available app variables on the number of apps being downloaded. Of 129 urology apps eligible for study, only 2 (1.6%) had >10,000 downloads, with half having ≤100 downloads and 4 (3.1%) having none at all. Apps developed with expert urologist involvement (P=.003), optional in-app purchases (P=.01), higher user rating (PApp cost was inversely related to the number of downloads (Pdevelopers' websites, but not other platforms, were publicly available for analysis, and the level and nature of expert involvement was not documented. The explicit participation of urologists in app development is likely to enhance its chances to have a higher number of downloads. This finding should help in the design of better apps and further promote urologist involvement in mHealth. Official certification processes are required to ensure app quality and user safety.

  3. Analysis of longitudinal multivariate outcome data from couples cohort studies: application to HPV transmission dynamics.

    Science.gov (United States)

    Kong, Xiangrong; Wang, Mei-Cheng; Gray, Ronald

    2015-06-01

    We consider a specific situation of correlated data where multiple outcomes are repeatedly measured on each member of a couple. Such multivariate longitudinal data from couples may exhibit multi-faceted correlations which can be further complicated if there are polygamous partnerships. An example is data from cohort studies on human papillomavirus (HPV) transmission dynamics in heterosexual couples. HPV is a common sexually transmitted disease with 14 known oncogenic types causing anogenital cancers. The binary outcomes on the multiple types measured in couples over time may introduce inter-type, intra-couple, and temporal correlations. Simple analysis using generalized estimating equations or random effects models lacks interpretability and cannot fully utilize the available information. We developed a hybrid modeling strategy using Markov transition models together with pairwise composite likelihood for analyzing such data. The method can be used to identify risk factors associated with HPV transmission and persistence, estimate difference in risks between male-to-female and female-to-male HPV transmission, compare type-specific transmission risks within couples, and characterize the inter-type and intra-couple associations. Applying the method to HPV couple data collected in a Ugandan male circumcision (MC) trial, we assessed the effect of MC and the role of gender on risks of HPV transmission and persistence.

  4. Objective classification of ecological status in marine water bodies using ecotoxicological information and multivariate analysis.

    Science.gov (United States)

    Beiras, Ricardo; Durán, Iria

    2014-12-01

    Some relevant shortcomings have been identified in the current approach for the classification of ecological status in marine water bodies, leading to delays in the fulfillment of the Water Framework Directive objectives. Natural variability makes difficult to settle fixed reference values and boundary values for the Ecological Quality Ratios (EQR) for the biological quality elements. Biological responses to environmental degradation are frequently of nonmonotonic nature, hampering the EQR approach. Community structure traits respond only once ecological damage has already been done and do not provide early warning signals. An alternative methodology for the classification of ecological status integrating chemical measurements, ecotoxicological bioassays and community structure traits (species richness and diversity), and using multivariate analyses (multidimensional scaling and cluster analysis), is proposed. This approach does not depend on the arbitrary definition of fixed reference values and EQR boundary values, and it is suitable to integrate nonlinear, sensitive signals of ecological degradation. As a disadvantage, this approach demands the inclusion of sampling sites representing the full range of ecological status in each monitoring campaign. National or international agencies in charge of coastal pollution monitoring have comprehensive data sets available to overcome this limitation.

  5. 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.

  6. 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.

  7. 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.

  8. Forecasting daily source air quality using multivariate statistical analysis and radial basis function networks.

    Science.gov (United States)

    Sun, Gang; Hoff, Steven J; Zelle, Brian C; Nelson, Minda A

    2008-12-01

    It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.

  9. 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

  10. Design and evaluation of mucoadhesive oral films containing sodium hyaluronate using multivariate data analysis.

    Science.gov (United States)

    Walicová, Veronika; Gajdziok, Jan; Pavloková, Sylvie; Vetchý, David

    2017-03-01

    Mucoadhesive oral films, with their prolonged residence time at the site of application, offer a promising approach for protection of the oral lesion surface. The addition of sodium hyaluronate of different molecular weights as a second mucoadhesive polymer into the film matrix could positively influence the physico-mechanical and mucoadhesive properties of films. The aim of this study was to investigate the formulation of a monolayered film matrix containing varying amounts of sodium hyaluronate and to test the properties of such matrices by applying different characterization methods. Film matrix was composed of two mucoadhesive polymers, carmellose sodium and sodium hyaluronate, plasticized with glycerol. Resulting films were characterized with regard to their viscosity and physico-mechanical properties. Multivariate data analysis was employed to evaluate the influence of varying amounts of mucoadhesive polymers on the main mucoadhesive oral films' properties. The lower content of sodium hyaluronate caused improvements in mechanical properties and residence time on the artificial oral mucosa, both of which are the main characteristics that determine the quality of the final product. The best results were obtained by samples containing carmellose sodium with a small amount of sodium hyaluronate (about 0.5% in casting dispersion).

  11. 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.

  12. 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.

  13. Assessment of air pollutant sources in the deposit on monuments by multivariate analysis.

    Science.gov (United States)

    Ozga, Izabela; Ghedini, Nadia; Giosuè, Chiara; Sabbioni, Cristina; Tittarelli, Francesca; Bonazza, Alessandra

    2014-08-15

    A proper recognition of the pollutant sources in atmospheric deposit is a key problem for any action aiming at reducing their emission, being this an important issue with implications both on human health safeguard and on the cultural heritage conservation in urban sites. This work presents the results of a statistical approach application for the identification of pollutant sources in deposits and damage layers on monuments located in different European sites: Santa Maria del Fiore, Florence (Italy), Cologne Cathedral, Cologne (Germany), Ancient ramparts, Salè (Morocco), National Museum, Cracow (Poland) and National Gallery, Oslo (Norway). For this aim, the surface damage layers on monuments and historical buildings of the selected sites were collected and analyzed, in terms of ionic and elemental composition, through application of ion chromatography and induced coupled plasma-optical emission spectroscopy. The achieved results were processed by multivariate analyses such as correlation matrix and principal component analysis in order to identify the possible origin of pollutants affecting the state of conservation of the monuments. This allowed us to assume that in all case studies the traffic emission is the main pollutant source. In the case of Ancient ramparts, Salè (Morocco), and National Gallery, Oslo (Norway), the surfaces are also under influence of marine aerosols. Moreover, concerning the Cologne Cathedral, the strong impact of the pollutants emitted by railway station was also revealed. Copyright © 2014 Elsevier B.V. All rights reserved.

  14. 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.

  15. Tunable-Q Wavelet Transform Based Multivariate Sub-Band Fuzzy Entropy with Application to Focal EEG Signal Analysis

    Directory of Open Access Journals (Sweden)

    Abhijit Bhattacharyya

    2017-03-01

    Full Text Available This paper analyses the complexity of multivariate electroencephalogram (EEG signals in different frequency scales for the analysis and classification of focal and non-focal EEG signals. The proposed multivariate sub-band entropy measure has been built based on tunable-Q wavelet transform (TQWT. In the field of multivariate entropy analysis, recent studies have performed analysis of biomedical signals with a multi-level filtering approach. This approach has become a useful tool for measuring inherent complexity of the biomedical signals. However, these methods may not be well suited for quantifying the complexity of the individual multivariate sub-bands of the analysed signal. In this present study, we have tried to resolve this difficulty by employing TQWT for analysing the sub-band signals of the analysed multivariate signal. It should be noted that higher value of Q factor is suitable for analysing signals with oscillatory nature, whereas the lower value of Q factor is suitable for analysing signals with non-oscillatory transients in nature. Moreover, with an increased number of sub-bands and a higher value of Q-factor, a reasonably good resolution can be achieved simultaneously in high and low frequency regions of the considered signals. Finally, we have employed multivariate fuzzy entropy (mvFE to the multivariate sub-band signals obtained from the analysed signal. The proposed Q-based multivariate sub-band entropy has been studied on the publicly available bivariate Bern Barcelona focal and non-focal EEG signals database to investigate the statistical significance of the proposed features in different time segmented signals. Finally, the features are fed to random forest and least squares support vector machine (LS-SVM classifiers to select the best classifier. Our method has achieved the highest classification accuracy of 84.67% in classifying focal and non-focal EEG signals with LS-SVM classifier. The proposed multivariate sub-band fuzzy

  16. Multivariate Statistical Analysis: a Strategic Tool for Quality and Processes Control in Food Industry

    National Research Council Canada - National Science Library

    Carlos Mario Zuluaga Dominguez

    2011-01-01

    The use of multivariate statistical techniques for quality and process control in the food industry has been growing significantly since the mid-seventies, as a result of the informatics revolution...

  17. Bayesian Analysis of General Asymmetric Multivariate GARCH Models and News Impact Curves

    National Research Council Canada - National Science Library

    Asai, Manabu

    2015-01-01

    The BEKK model is a popular multivariate GARCH processes. The paper develops a new general asymmetric BEKK structure, which is based on recent empirical findings by semi-parametric news impact curves...

  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

    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. Multivariate analysis of flow cytometric data using decision trees

    Directory of Open Access Journals (Sweden)

    Svenja eSimon

    2012-04-01

    Full Text Available 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's quality, we used stratified 5-fold 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.

  20. 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.

  1. Multivariate statistical analysis as a tool for the segmentation of 3D spectral data.

    Science.gov (United States)

    Lucas, G; Burdet, P; Cantoni, M; Hébert, C

    2013-01-01

    Acquisition of three-dimensional (3D) spectral data is nowadays common using many different microanalytical techniques. In order to proceed to the 3D reconstruction, data processing is necessary not only to deal with noisy acquisitions but also to segment the data in term of chemical composition. In this article, we demonstrate the value of multivariate statistical analysis (MSA) methods for this purpose, allowing fast and reliable results. Using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) coupled with a focused ion beam (FIB), a stack of spectrum images have been acquired on a sample produced by laser welding of a nickel-titanium wire and a stainless steel wire presenting a complex microstructure. These data have been analyzed using principal component analysis (PCA) and factor rotations. PCA allows to significantly improve the overall quality of the data, but produces abstract components. Here it is shown that rotated components can be used without prior knowledge of the sample to help the interpretation of the data, obtaining quickly qualitative mappings representative of elements or compounds found in the material. Such abundance maps can then be used to plot scatter diagrams and interactively identify the different domains in presence by defining clusters of voxels having similar compositions. Identified voxels are advantageously overlaid on secondary electron (SE) images with higher resolution in order to refine the segmentation. The 3D reconstruction can then be performed using available commercial softwares on the basis of the provided segmentation. To asses the quality of the segmentation, the results have been compared to an EDX quantification performed on the same data. Copyright © 2013 Elsevier Ltd. All rights reserved.

  2. A multivariate analysis of age-related differences in functional networks supporting conflict resolution.

    Science.gov (United States)

    Salami, Alireza; Rieckmann, Anna; Fischer, Håkan; Bäckman, Lars

    2014-02-01

    Functional neuroimaging studies demonstrate age-related differences in recruitment of a large-scale attentional network during interference resolution, especially within dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC). These alterations in functional responses have been frequently observed despite equivalent task performance, suggesting age-related reallocation of neural resources, although direct evidence for a facilitating effect in aging is sparse. We used the multi-source interference task and multivariate partial-least-squares to investigate age-related differences in the neuronal signature of conflict resolution, and their behavioral implications in younger and older adults. There were interference-related increases in activity, involving fronto-parietal and basal ganglia networks that generalized across age. In addition an age-by-task interaction was observed within a distributed network, including DLPFC and ACC, with greater activity during interference in the old. Next, we combined brain-behavior and functional connectivity analyses to investigate whether compensatory brain changes were present in older adults, using DLPFC and ACC as regions of interest (i.e. seed regions). This analysis revealed two networks differentially related to performance across age groups. A structural analysis revealed age-related gray-matter losses in regions facilitating performance in the young, suggesting that functional reorganization may partly reflect structural alterations in aging. Collectively, these findings suggest that age-related structural changes contribute to reductions in the efficient recruitment of a youth-like interference network, which cascades into instantiation of a different network facilitating conflict resolution in elderly people. © 2013. Published by Elsevier Inc. All rights reserved.

  3. Multivariate analysis of quaternary carbamazepine-saccharin mixtures by X-ray diffraction and infrared spectroscopy.

    Science.gov (United States)

    Caliandro, Rocco; Di Profio, Gianluca; Nicolotti, Orazio

    2013-05-05

    Co-crystallization brings new opportunities for improving the solubility and dissolution rate of drugs with the chance of finely tuning some relevant chemical-physical properties of mixtures containing bioactive compounds. As co-crystallization process involves several molecular species, which are generally solid at room conditions, its control requires accurate knowledge and monitoring of the different phase that might appear during the formulation stage. In the present study the suitability of X-ray powder diffraction (XRPD) and Fourier-transformed infrared (FTIR) spectroscopy in quantifying mixtures of carbamazepine polymorphs (forms I and III), saccharin, and carbamazepine-saccharin cocrystals (form I) is assessed. Quaternary crystalline mixtures typically produced in the process of co-crystal production were analyzed by multivariate methods. Principal component analysis (PCA) was used for the identification of the crystal phases, while unsupervised simultaneous fitting of the spectra from pure phases, or supervised partial least squares (PLS) methods were used for their quantitative determination. The performance of data analysis was enhanced by applying peculiar pre-processing methods, such as SNIP filtering in case of FTIR and PCA filtering in case of XRPD. It was found that, for XRPD data, the automatic multi-fitting procedures and PLS models developed in this study are able to quantify single phases in mixtures to an accuracy level comparable to that obtained by the widely used Rietveld method, which, however, requires knowledge of the crystal structures. For FTIR data the results here obtained prove that this technique can be used as a fast method for polymorph characterization. Copyright © 2013 Elsevier B.V. All rights reserved.

  4. 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

  5. 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

  6. 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)

  7. 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

  8. Analysis of Preoperative Metabolic Risk Factors Affecting the Prognosis of Patients with Esophageal Squamous Cell Carcinoma: The Fujian Prospective Investigation of Cancer (FIESTA Study

    Directory of Open Access Journals (Sweden)

    Feng Peng

    2017-02-01

    Full Text Available Some metabolic factors have been shown to be associated with an increased risk of esophageal cancer; however the association with its prognosis is rarely reported. Here, we assessed the prediction of preoperative metabolic syndrome and its single components for esophageal cancer mortality by analyzing a subset of data from the ongoing Fujian prospective investigation of cancer (FIESTA study. Between 2000 and 2010, patients who underwent three-field lymphadenectomy were eligible for inclusion. Blood/tissue specimens, demographic and clinicopathologic data were collected at baseline. Metabolic syndrome is defined by the criteria proposed by Chinese Diabetes Society. In this study, analysis was restricted to esophageal squamous cell carcinoma (ESCC due to the limited number of other histological types. The median follow-up in 2396 ESCC patients (males/females: 1822/574 was 38.2 months (range, 0.5–180 months. The multivariate-adjusted hazard ratio (HR of metabolic syndrome for ESCC mortality was statistically significant in males (HR, 95% confidence interval, P: 1.45, 1.14–1.83, 0.002, but not in females (1.46, 0.92–2.31, 0.107. For single metabolic components, the multivariate-adjusted HRs were significant for hyperglycemia (1.98, 1.68–2.33, <0.001 and dyslipidemia (1.41, 1.20–1.65, <0.001 in males and for hyperglycemia (1.76, 1.23–2.51, <0.001 in females, independent of clinicopathologic characteristics and obesity. In tree-structured survival analysis, the top splitting factor in both genders was tumor-node-metastasis stage, followed by regional lymph node metastasis. Taken together, our findings demonstrate that preoperative metabolic syndrome was a significant independent predictor of ESCC mortality in males, and this effect was largely mediated by glyeolipid metabolism disorder.

  9. Multivariate Analysis of Multi-tracer and Climatological Data in an Urbanizing, Drought-impacted Watershed

    Science.gov (United States)

    Creech, L. T.; Donahoe, R. J.

    2009-12-01

    This paper documents water quality conditions of the Lake Tuscaloosa, Alabama water-supply reservoir and its watershed under two end-members of hydrologic and climatic variability. These data afford the opportunity to view water quality in the context of both land use and drought, facilitating the development of coupled hydrologic and water-quality forecast models to guide watershed management decisions. This study demonstrates that even the region’s normal 10-year drought cycle holds the capacity to significantly impact water quality and should be incorporated into watershed models and decision-making. To accomplish the goals of this project, a multi-tracer approach has been adopted to assess solute sources and water-quality impairments induced by land use. The biogeochemical tracers include: Major- and minor-ions, trace metals, nutrient speciation and stable-isotope tracers at natural abundance levels. These tracers are also vital to understand the role of climate variability in the context of a heterogeneous landscape. Eight seasonal sampling events across 23 sample locations and two water years yield 184 discrete water-quality samples representative of a range of landscape variability and climatological conditions. Each sample was analyzed for 27 solute species and relevant indicators of water quality. Climatological data was obtained from public repositories (NCDC, USDA); hydrologic data from stream and precipitation gages within the watershed (USGS). Multivariate statistics are used to facilitate the numerical analysis and interpretation of the resulting data. Measurements of nitrogen speciation were collected to document patterns of nutrient loading and nitrogen cycling. These data are augmented by the analysis of nitrogen and oxygen isotopes of nitrate. These data clarify the extent to which nitrogen is being loaded in the non-growing season as well as the capacity of the lake to assimilate nutrients. Under drought conditions the lake becomes nitrogen

  10. 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.

  11. Determination of dominant biogeochemical processes in a contaminated aquifer-wetland system using multivariate statistical analysis

    Science.gov (United States)

    Baez-Cazull, S. E.; McGuire, J.T.; Cozzarelli, I.M.; Voytek, M.A.

    2008-01-01

    Determining the processes governing aqueous biogeochemistry in a wetland hydrologically linked to an underlying contaminated aquifer is challenging due to the complex exchange between the systems and their distinct responses to changes in precipitation, recharge, and biological activities. To evaluate temporal and spatial processes in the wetland-aquifer system, water samples were collected using cm-scale multichambered passive diffusion samplers (peepers) to span the wetland-aquifer interface over a period of 3 yr. Samples were analyzed for major cations and anions, methane, and a suite of organic acids resulting in a large dataset of over 8000 points, which was evaluated using multivariate statistics. Principal component analysis (PCA) was chosen with the purpose of exploring the sources of variation in the dataset to expose related variables and provide insight into the biogeochemical processes that control the water chemistry of the system. Factor scores computed from PCA were mapped by date and depth. Patterns observed suggest that (i) fermentation is the process controlling the greatest variability in the dataset and it peaks in May; (ii) iron and sulfate reduction were the dominant terminal electron-accepting processes in the system and were associated with fermentation but had more complex seasonal variability than fermentation; (iii) methanogenesis was also important and associated with bacterial utilization of minerals as a source of electron acceptors (e.g., barite BaSO4); and (iv) seasonal hydrological patterns (wet and dry periods) control the availability of electron acceptors through the reoxidation of reduced iron-sulfur species enhancing iron and sulfate reduction. Copyright ?? 2008 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.

  12. Transcriptome and Multivariable Data Analysis of Corynebacterium glutamicum under Different Dissolved Oxygen Conditions in Bioreactors.

    Science.gov (United States)

    Sun, Yang; Guo, Wenwen; Wang, Fen; Peng, Feng; Yang, Yankun; Dai, Xiaofeng; Liu, Xiuxia; Bai, Zhonghu

    2016-01-01

    Dissolved oxygen (DO) is an important factor in the fermentation process of Corynebacterium glutamicum, which is a widely used aerobic microbe in bio-industry. Herein, we described RNA-seq for C. glutamicum under different DO levels (50%, 30% and 0%) in 5 L bioreactors. Multivariate data analysis (MVDA) models were used to analyze the RNA-seq and metabolism data to investigate the global effect of DO on the transcriptional distinction of the substance and energy metabolism of C. glutamicum. The results showed that there were 39 and 236 differentially expressed genes (DEGs) under the 50% and 0% DO conditions, respectively, compared to the 30% DO condition. Key genes and pathways affected by DO were analyzed, and the result of the MVDA and RNA-seq revealed that different DO levels in the fermenter had large effects on the substance and energy metabolism and cellular redox balance of C. glutamicum. At low DO, the glycolysis pathway was up-regulated, and TCA was shunted by the up-regulation of the glyoxylate pathway and over-production of amino acids, including valine, cysteine and arginine. Due to the lack of electron-acceptor oxygen, 7 genes related to the electron transfer chain were changed, causing changes in the intracellular ATP content at 0% and 30% DO. The metabolic flux was changed to rebalance the cellular redox. This study applied deep sequencing to identify a wealth of genes and pathways that changed under different DO conditions and provided an overall comprehensive view of the metabolism of C. glutamicum. The results provide potential ways to improve the oxygen tolerance of C. glutamicum and to modify the metabolic flux for amino acid production and heterologous protein expression.

  13. Multivariate geometrical analysis of catalytic residues in the penicillin-binding proteins.

    Science.gov (United States)

    Bobba, Sudheer; Gutheil, William G

    2011-10-01

    Penicillin-binding proteins (PBPs) are bacterial enzymes involved in the final stages of cell wall biosynthesis, and are targets of the β-lactam antibiotics. They can be subdivided into essential high-molecular-mass (HMM) and non-essential low-molecular-mass (LMM) PBPs, and further divided into subclasses based on sequence homologies. PBPs can catalyze transpeptidase or hydrolase (carboxypeptidase and endopeptidase) reactions. The PBPs are of interest for their role in bacterial cell wall biosynthesis, and as mechanistically interesting enzymes which can catalyze alternative reaction pathways using the same catalytic machinery. A global catalytic residue comparison seemed likely to provide insight into structure-function correlations within the PBPs. More than 90 PBP structures were aligned, and a number (40) of active site geometrical parameters extracted. This dataset was analyzed using both univariate and multivariate statistical methods. Several interesting relationships were observed. (1) Distribution of the dihedral angle for the SXXK-motif Lys side chain (DA_1) was bimodal, and strongly correlated with HMM/transpeptidase vs LMM/hydrolase classification/activity (P<0.001). This structural feature may therefore be associated with the main functional difference between the HMM and LMM PBPs. (2) The distance between the SXXK-motif Lys-NZ atom and the Lys/His-nitrogen atom of the (K/H)T(S)G-motif was highly conserved, suggesting importance for PBP function, and a possibly conserved role in the catalytic mechanism of the PBPs. (3) Principal components-based cluster analysis revealed several distinct clusters, with the HMM Class A and B, LMM Class C, and LMM Class A K15 PBPs forming one "Main" cluster, and demonstrating a globally similar arrangement of catalytic residues within this group. Copyright © 2011 Elsevier Ltd. All rights reserved.

  14. Multivariate pattern analysis reveals anatomical connectivity differences between the left and right mesial temporal lobe epilepsy.

    Science.gov (United States)

    Fang, Peng; An, Jie; Zeng, Ling-Li; Shen, Hui; Chen, Fanglin; Wang, Wensheng; Qiu, Shijun; Hu, Dewen

    2015-01-01

    Previous studies have demonstrated differences of clinical signs and functional brain network organizations between the left and right mesial temporal lobe epilepsy (mTLE), but the anatomical connectivity differences underlying functional variance between the left and right mTLE remain uncharacterized. We examined 43 (22 left, 21 right) mTLE patients with hippocampal sclerosis and 39 healthy controls using diffusion tensor imaging. After the whole-brain anatomical networks were constructed for each subject, multivariate pattern analysis was applied to classify the left mTLE from the right mTLE and extract the anatomical connectivity differences between the left and right mTLE patients. The classification results reveal 93.0% accuracy for the left mTLE versus the right mTLE, 93.4% accuracy for the left mTLE versus controls and 90.0% accuracy for the right mTLE versus controls. Compared with the right mTLE, the left mTLE exhibited a different connectivity pattern in the cortical-limbic network and cerebellum. The majority of the most discriminating anatomical connections were located within or across the cortical-limbic network and cerebellum, thereby indicating that these disease-related anatomical network alterations may give rise to a portion of the complex of emotional and memory deficit between the left and right mTLE. Moreover, the orbitofrontal gyrus, cingulate cortex, hippocampus and parahippocampal gyrus, which exhibit high discriminative power in classification, may play critical roles in the pathophysiology of mTLE. The current study demonstrated that anatomical connectivity differences between the left mTLE and the right mTLE may have the potential to serve as a neuroimaging biomarker to guide personalized diagnosis of the left and right mTLE.

  15. Transcriptome and Multivariable Data Analysis of Corynebacterium glutamicum under Different Dissolved Oxygen Conditions in Bioreactors.

    Directory of Open Access Journals (Sweden)

    Yang Sun

    Full Text Available Dissolved oxygen (DO is an important factor in the fermentation process of Corynebacterium glutamicum, which is a widely used aerobic microbe in bio-industry. Herein, we described RNA-seq for C. glutamicum under different DO levels (50%, 30% and 0% in 5 L bioreactors. Multivariate data analysis (MVDA models were used to analyze the RNA-seq and metabolism data to investigate the global effect of DO on the transcriptional distinction of the substance and energy metabolism of C. glutamicum. The results showed that there were 39 and 236 differentially expressed genes (DEGs under the 50% and 0% DO conditions, respectively, compared to the 30% DO condition. Key genes and pathways affected by DO were analyzed, and the result of the MVDA and RNA-seq revealed that different DO levels in the fermenter had large effects on the substance and energy metabolism and cellular redox balance of C. glutamicum. At low DO, the glycolysis pathway was up-regulated, and TCA was shunted by the up-regulation of the glyoxylate pathway and over-production of amino acids, including valine, cysteine and arginine. Due to the lack of electron-acceptor oxygen, 7 genes related to the electron transfer chain were changed, causing changes in the intracellular ATP content at 0% and 30% DO. The metabolic flux was changed to rebalance the cellular redox. This study applied deep sequencing to identify a wealth of genes and pathways that changed under different DO conditions and provided an overall comprehensive view of the metabolism of C. glutamicum. The results provide potential ways to improve the oxygen tolerance of C. glutamicum and to modify the metabolic flux for amino acid production and heterologous protein expression.

  16. Multivariate Statistical Analysis of Cigarette Design Feature Influence on ISO TNCO Yields.

    Science.gov (United States)

    Agnew-Heard, Kimberly A; Lancaster, Vicki A; Bravo, Roberto; Watson, Clifford; Walters, Matthew J; Holman, Matthew R

    2016-06-20

    The aim of this study is to explore how differences in cigarette physical design parameters influence tar, nicotine, and carbon monoxide (TNCO) yields in mainstream smoke (MSS) using the International Organization of Standardization (ISO) smoking regimen. Standardized smoking methods were used to evaluate 50 U.S. domestic brand cigarettes and a reference cigarette representing a range of TNCO yields in MSS collected from linear smoking machines using a nonintense smoking regimen. Multivariate statistical methods were used to form clusters of cigarettes based on their ISO TNCO yields and then to explore the relationship between the ISO generated TNCO yields and the nine cigarette physical design parameters between and within each cluster simultaneously. The ISO generated TNCO yields in MSS are 1.1-17.0 mg tar/cigarette, 0.1-2.2 mg nicotine/cigarette, and 1.6-17.3 mg CO/cigarette. Cluster analysis divided the 51 cigarettes into five discrete clusters based on their ISO TNCO yields. No one physical parameter dominated across all clusters. Predicting ISO machine generated TNCO yields based on these nine physical design parameters is complex due to the correlation among and between the nine physical design parameters and TNCO yields. From these analyses, it is estimated that approximately 20% of the variability in the ISO generated TNCO yields comes from other parameters (e.g., filter material, filter type, inclusion of expanded or reconstituted tobacco, and tobacco blend composition, along with differences in tobacco leaf origin and stalk positions and added ingredients). A future article will examine the influence of these physical design parameters on TNCO yields under a Canadian Intense (CI) smoking regimen. Together, these papers will provide a more robust picture of the design features that contribute to TNCO exposure across the range of real world smoking patterns.

  17. Accelerating Policy Decisions to Adopt Haemophilus influenzae Type b Vaccine: A Global, Multivariable Analysis

    Science.gov (United States)

    Shearer, Jessica C.; Stack, Meghan L.; Richmond, Marcie R.; Bear, Allyson P.; Hajjeh, Rana A.; Bishai, David M.

    2010-01-01

    Background 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). Methods and Findings 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. Conclusions 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. Please see later in the article for the Editors' Summary PMID:20305714

  18. Comparative analysis of oncogenes identified by microarray and RNA-sequencing as biomarkers for clinical prognosis.

    Science.gov (United States)

    Liu, Yuan; Jing, Runyu; Xu, Junmei; Liu, Keqin; Xue, Jiwei; Wen, Zhining; Li, Menglong

    2015-01-01

    Although RNA-sequencing has been widely used to identify the differentially expressed genes (DEGs) as biomarkers to guide the therapeutic treatment, it is necessary to investigate the concordance of DEGs identified by microarray and RNA-sequencing for the clinical prognosis. By using The Cancer Genome Atlas data sets, we thoroughly investigated the concordance of DEGs identified from microarray and RNA-sequencing data and their molecular functions. The DEGs identified by both technologies averaged ~98.6% overlap. The cancer-related gene sets were significantly enriched with the DEGs and consistent between two technologies. The highly consistency of DEGs in their regulation directionality and molecular functions indicated the good reproducibility between microarray and RNA-sequencing in identifying potential oncogenes for clinical prognosis.

  19. Multivariate analysis of soil moisture and runoff dynamics for better understanding of catchment moisture state

    Science.gov (United States)

    Graeff, Thomas; Bronstert, Axel; Cunha Costa, Alexandre; Zehe, Erwin

    2010-05-01

    Soil moisture is a key state that controls runoff formation, infiltration and portioning of radiation into latent and sensible heat flux. The experimental characterisation of near surface soil moisture patterns and their controls on runoff formation is, however, still largely untapped. Using an intelligent sampling strategy of two TDR clusters installed in the head water of the Wilde Weißeritz catchment (Eastern Ore Mountains, Germany), we investigated how well "the catchment state" may be characterised by means of distributed soil moisture data observed at the field scale. A grassland site and a forested site both located on gentle slopes were instrumented with two Spatial TDR clusters (STDR) that consist of 39 and 32 coated TDR probes of 60 cm length. The interplay of soil moisture and runoff formation was interrogated using discharge data from three nested catchments: the Becherbach with a size of 2 km², the Rehefeld catchment (17 km²) and the superordinate Ammelsdorf catchment (49 km²). Multiple regression analysis and information theory including observations of groundwater levels, soil moisture and rainfall intensity were employed to predict stream flow. On the small scale we found a strong correlation between the average soil moisture and the runoff coefficients of rainfall-runoff events, which almost explains as much variability as the pre-event runoff. There was, furthermore, a strong correlation between surface soil moisture and subsurface wetness. With increasing catchment size, the explanatory power of soil moisture reduced, but it was still in a good accordance to the former results. Combining those results with a recession analysis of soil moisture and discharge we derived a first conceptual model of the dominant runoff mechanisms operating in these catchments, namely subsurface flow, but also by groundwater. The multivariate analysis indicated that the proposed sampling strategy of clustering TDR probes in typical functional units is a promising

  20. 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

  1. [ANALYSIS OF PRIMARY TREATMENT AND PROGNOSIS OF SPONTANEOUS URTICARIA IN A LOCAL CLINIC OF OFFICE DERMATOLOGY].

    Science.gov (United States)

    Tanaka, Toshihiko; Hiragun, Makiko; Hide, Michihiro; Hiragun, Takaaki

    2015-09-01

    Prognosis of spontaneous urticaria in association with early treatment remained unclear. In this study, we retrospectively studied the prognosis of acute spontaneous urticaria in relation to age and treatments in a local clinic of dermatology. Out of 5000 patients who visited an office dermatology clinic, clinical records of patients with spontaneous urticaria were extracted. Their prognosis and the relation to age and treatments were analyzed by the Kaplan-Meier method and generalized Wilcoxon test. Among 386 patients diagnosed as spontaneous urticaria, 284 patients (73.6%) had begun treatments within a week after the onset. The non-remission rates of them after one week, four week and one year from the onset were 26.8%, 15.0% and 6.7%, respectively. The non-remission rate of patients who were 20-years-old or younger by one year after the onset of urticaria, was significantly lower than that of patients older than 20-years-old. No apparent relations between the remission rate and sex or the use of steroids was detected. However, the non-remission rate of urticaria that was treated with a standard dose of antihistamine was lower than that treated with additional medications. Most patients who began treatments within one week from the onset remitted shortly. However approximately 7% of them continued to suffer from symptoms for more than a year. Such prolongation tends to be seen among patients who required other medications in addition to standard dose of antihistamine.

  2. [Primary Neuroendocrine Carcinoma of Thymus Caused Cushing Syndrome: Surgical Treatment and Prognosis Analysis].

    Science.gov (United States)

    Li, Li; Chen, Yeye; Li, Shanqing; Liu, Hongsheng; Huang, Cheng; Qin, Yingzhi

    2015-07-01

    Primary neuroendocrine carcinoma of thymus (pNECT) is a rare thymic neoplasm. Some pNECTs could produce an adrenocorticotropic hormone and cause Cushing syndrome (CS). The aim os this study is to discuss the diagnostic technique and surgical management of pNECT-caused CS and analyze prognosis factors to improve the clinical experience of the disease. The outcome of surgery and follow-up of 14 cases (eight males and six females) of pNECT-caused CS were retrospectively analyzed from November 1987 to June 2013. The median age of the patients was 29, and the median duration of the disease was four months (1 month-44 months). All cases exhibited clinical evidence for the diagnosis of CS, and thoracic computed tomography (CT) was used to detect thymic tumors. Surgical treatment significantly decreased the concentration of both serum cortisol and adrenocorticotropic hormone (P<0.01) but caused one death in the perioperative period. With multidisciplinary therapy, the median survival was 38 months. pNECT-caused CS is a rare disease with aggressive characteristics and unclear prognosis. Early diagnosis and therapy is a challenge for clinicians. Thoracic CT is important for disease location and preoperative evaluation and should be routinely applied to all CS patients to allow early surgery and improved prognosis.

  3. 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…

  4. 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

  5. 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.

  6. 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...

  7. A Framework for Establishing Standard Reference Scale of Texture by Multivariate Statistical Analysis Based on Instrumental Measurement and Sensory Evaluation.

    Science.gov (United States)

    Zhi, Ruicong; Zhao, Lei; Xie, Nan; Wang, Houyin; Shi, Bolin; Shi, Jingye

    2016-01-13

    A framework of establishing standard reference scale (texture) is proposed by multivariate statistical analysis according to instrumental measurement and sensory evaluation. Multivariate statistical analysis is conducted to rapidly select typical reference samples with characteristics of universality, representativeness, stability, substitutability, and traceability. The reasonableness of the framework method is verified by establishing standard reference scale of texture attribute (hardness) with Chinese well-known food. More than 100 food products in 16 categories were tested using instrumental measurement (TPA test), and the result was analyzed with clustering analysis, principal component analysis, relative standard deviation, and analysis of variance. As a result, nine kinds of foods were determined to construct the hardness standard reference scale. The results indicate that the regression coefficient between the estimated sensory value and the instrumentally measured value is significant (R(2) = 0.9765), which fits well with Stevens's theory. The research provides reliable a theoretical basis and practical guide for quantitative standard reference scale establishment on food texture characteristics.

  8. Likelihood Analysis of Multivariate Probit Models Using a Parameter Expanded MCEM Algorithm.

    Science.gov (United States)

    Xu, Huiping; Craig, Bruce A

    2010-08-01

    Multivariate binary data arise in a variety of settings. In this paper, we propose a practical and efficient computational framework for maximum likelihood estimation of multivariate probit regression models. This approach uses the Monte Carlo EM (MCEM) algorithm, with parameter expansion to complete the M-step, to avoid the direct evaluation of the intractable multivariate normal orthant probabilities. The parameter expansion not only enables a closed-form solution in the M-step but also improves efficiency. Using the simulation studies, we compare the performance of our approach with the MCEM algorithms developed by Chib and Greenberg (1998) and Song and Lee (2005), as well as the iterative approach proposed by Li and Schafer (2008). Our approach is further illustrated using a real-world example.

  9. Likelihood Analysis of Multivariate Probit Models Using a Parameter Expanded MCEM Algorithm

    Science.gov (United States)

    Xu, Huiping; Craig, Bruce A.

    2010-01-01

    Multivariate binary data arise in a variety of settings. In this paper, we propose a practical and efficient computational framework for maximum likelihood estimation of multivariate probit regression models. This approach uses the Monte Carlo EM (MCEM) algorithm, with parameter expansion to complete the M-step, to avoid the direct evaluation of the intractable multivariate normal orthant probabilities. The parameter expansion not only enables a closed-form solution in the M-step but also improves efficiency. Using the simulation studies, we compare the performance of our approach with the MCEM algorithms developed by Chib and Greenberg (1998) and Song and Lee (2005), as well as the iterative approach proposed by Li and Schafer (2008). Our approach is further illustrated using a real-world example. PMID:21042430

  10. Practical robustness measures in multivariable control system analysis. Ph.D. Thesis

    Science.gov (United States)

    Lehtomaki, N. A.

    1981-01-01

    The robustness of the stability of multivariable linear time invariant feedback control systems with respect to model uncertainty is considered using frequency domain criteria. Available robustness tests are unified under a common framework based on the nature and structure of model errors. These results are derived using a multivariable version of Nyquist's stability theorem in which the minimum singular value of the return difference transfer matrix is shown to be the multivariable generalization of the distance to the critical point on a single input, single output Nyquist diagram. Using the return difference transfer matrix, a very general robustness theorem is presented from which all of the robustness tests dealing with specific model errors may be derived. The robustness tests that explicitly utilized model error structure are able to guarantee feedback system stability in the face of model errors of larger magnitude than those robustness tests that do not. The robustness of linear quadratic Gaussian control systems are analyzed.

  11. Squamous cell carcinoma of the tongue: clinical and morphological analysis of 57 cases and correlation with prognosis

    Directory of Open Access Journals (Sweden)

    Marcelo Gadelha Vasconcelos

    2014-10-01

    Full Text Available Introduction: Oral squamous cell carcinoma (OSCC, which represents more than 90% of head and neck malignant neoplasms, has a poor prognosis due to its high frequency of lymph node metastasis and local invasion. Previous studies have investigated parameters related to the biological behavior of OSCC and its correlation with disease outcome (DO. Objective: To evaluate clinical and morphological data in cases of tongue squamous cell carcinoma (TSCC, correlating these findings with prognosis. Material and methods: Fifty-seven specimens of TSCC were obtained from patients undergoing surgical excision at a referral hospital in Natal, Brazil. Clinical data, such as tumor-node-metastasis (TNM stage and DO, were collected from medical records. Hematoxylin and eosin-stained sections were analyzed regarding histological grade of malignancy (HGM, based on the system proposed by Bryne (1998 Results: The majority of patients (38.6% were diagnosed as TNM stage III, and 57.9% developed metastases. Remission of the tumor occurred in 77.2% of the cases. The parameter “metastasis” exhibited a significant association with DO (p = 0 and TNM stage (p = 0.001, thus constituting a good indicator of tumor progression. Correlation of HGM and TNM stage with DO was not evidenced. Nevertheless, statistical analysis showed a significant association between HGM and TNM stage (p = 0.006. Conclusion: TNM clinical staging and HGM, evaluated in association, may be useful to estimate the prognosis of TSCC.

  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. The Effect of Diabetes Mellitus on Lung Cancer Prognosis: A PRISMA-compliant Meta-analysis of Cohort Studies.

    Science.gov (United States)

    Zhu, Linhai; Cao, Hongxin; Zhang, Tiehong; Shen, Hongchang; Dong, Wei; Wang, Liguang; Du, Jiajun

    2016-04-01

    Previous studies suggested that diabetes mellitus (DM) was associated with risk and mortality of cancer, but studies investigating the correlation between DM and lung cancer prognosis remain controversial. Herein, a meta-analysis was performed to derive a more precise estimate of the prognostic role of DM in lung cancer.Medline and Embase were searched for eligible articles from inception to October 25, 2015. The pooled hazard ratio (HR) with its 95% confidence interval (95% CI) was calculated to evaluate the correlation between DM and lung cancer prognosis. Subgroup meta-analysis was performed based on the histology and the treatment methods.A total of 20 cohort studies from 12 articles were included in the meta-analysis. Also, 16 studies investigated the overall survival (OS) and 4 studies investigated the progression-free survival (PFS). DM was significantly associated with the inferior OS of lung cancer with the pooled HR 1.28 (95% CI: 1.10-1.49, P = 0.001). The association was prominent in the nonsmall cell lung cancer (NSCLC) subgroup (HR 1.35, 95%CI: 1.14-1.60, P = 0.002), whereas the association was not significant in the small cell lung cancer (SCLC) subgroup (HR 1.33, 95% CI: 0.87-2.03, P = 0.18). When NSCLC patients were further stratified by treatment methods, DM had more influence on the surgically treated subgroup than the nonsurgically treated subgroup. There was no obvious evidence for publication bias by Begg's and Egger's test.The results of this meta-analysis exhibit an association of DM with inferior prognosis amongst lung cancer patients, especially the surgically treated NSCLC patients. Given the small number of studies included in this meta-analysis, the present conclusion should be consolidated with more high-quality prospective cohort studies or randomized controlled trials.

  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. 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.

  16. 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…

  17. Multivariate statistical study with a factor analysis of foraminiferal fauna from the Chilka Lake, India

    Digital Repository Service at National Institute of Oceanography (India)

    Jayalakshmy, K.V.; Rao, K.K.

    Harbour, En- gland: a reappraisal using multivariate tech- niques. J. Paleontol., 43 (3) : 660-675. Imbrie, J. and F.B. Phleger. 1963. Analisis por vectores de los foraminiferos bentonicos del area de San Diego, California. Soc. Geol. Mex., Bol., 26...

  18. 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

  19. 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...

  20. 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

  1. 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.

  2. 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

  3. 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

  4. Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer's disease.

    Directory of Open Access Journals (Sweden)

    Javier Rasero

    Full Text Available Alzheimer's disease (AD is the most common form of dementia among older people and increasing longevity ensures its prevalence will rise even further. Whether AD originates by disconnecting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. An important related challenge is to predict whether a given subject, with a mild cognitive impairment (MCI, will convert or not to AD. Here, our aim is to characterize the structural connectivity pattern of MCI and AD subjects using the multivariate distance matrix regression (MDMR analysis, and to compare it to those of healthy subjects. MDMR is a technique developed in genomics that has been recently applied to functional brain network data, and here applied to identify brain nodes with different connectivity patterns, in controls and patients, because of brain atrophy. We address this issue at the macroscale by looking to differences in individual structural MRI brain networks, obtained from MR images according to a recently proposed definition of connectivity which measures the image similarity between patches at different locations in the brain. In particular, using data from ADNI, we selected four groups of subjects (all of them matched by age and sex: HC (healthy control participants, ncMCI (mild cognitive impairment not converting to AD, cMCI (mild cognitive impairment converting to AD and AD. Next, we built structural MRI brain networks and performed group comparison for all the pairs of groups. Our results were three-fold: (i considering the comparison of HC with the three other groups, the number of significant brain regions was 4 for ncMCI, 290 for cMCI and 74 for AD, out of a total of 549 regions; hence, in terms of the structural MRI connectivity here adopted, cMCI subjects have the maximal altered pattern w.r.t. healthy conditions. (ii Eight and seven nodes were significant for the comparisons AD-ncMCI and AD

  5. Multivariate Analysis of Mixed Lipid Aggregate Phase Transitions Monitored Using Raman Spectroscopy.

    Science.gov (United States)

    Neal, Sharon L

    2018-01-01

    The phase behavior of aqueous 1,2-dimyristoyl-sn-glycero-3-phosphorylcholine (DMPC)/1,2-dihexanoyl-sn-glycero-3-phosphocholine (DHPC) mixtures between 8.0 ℃ and 41.0 ℃ were monitored using Raman spectroscopy. Temperature-dependent Raman matrices were assembled from series of spectra and subjected to multivariate analysis. The consensus of pseudo-rank estimation results is that seven to eight components account for the temperature-dependent changes observed in the spectra. The spectra and temperature response profiles of the mixture components were resolved by applying a variant of the non-negative matrix factorization (NMF) algorithm described by Lee and Seung (1999). The rotational ambiguity of the data matrix was reduced by augmenting the original temperature-dependent spectral matrix with its cumulative counterpart, i.e., the matrix formed by successive integration of the spectra across the temperature index (columns). Successive rounds of constrained NMF were used to isolate component spectra from a significant fluorescence background. Five major components exhibiting varying degrees of gel and liquid crystalline lipid character were resolved. Hydrogen-bonded water networks exhibiting varying degrees of organization are associated with the lipid components. Spectral parameters were computed to compare the chain conformation, packing, and hydration indicated by the resolved spectra. Based on spectral features and relative amounts of the components observed, four components reflect long chain lipid response. The fifth component could reflect the response of the short chain lipid, DHPC, but there were no definitive spectral features confirming this assignment. A minor component of uncertain assignment that exhibits a striking response to the DMPC pre-transition and chain melting transition also was recovered. While none of the spectra resolved exhibit features unequivocally attributable to a specific aggregate morphology or step in the gelation process

  6. Discrimination between Alzheimer’s Disease and Late Onset Bipolar Disorder using multivariate analysis

    Directory of Open Access Journals (Sweden)

    Ariadna eBesga

    2015-12-01

    Full Text Available textbf{Background} Late Onset Bipolar Disorder (LOBD is often difficultto distinguish from degenerative dementias, such as Alzheimer Disease(AD, due to comorbidities and common cognitive symptoms. Moreover,LOBD prevalence in the elder population is not negligible and it isincreasing. Both pathologies share pathophysiological features relatedto neuroinflammation. Improved means to differentiate between LOBDand AD in elder subjects will help to select the best personalizedtreatment. textbf{Objective} The aim of this study textcolor{red}{was}to assess the relative significance of clinical observations, neuropsychologicaltests, and textcolor{red}{specific} textcolor{red}{blood plasma}biomarkers (inflammatory and neurotrophic, separately and combined,in the textcolor{red}{differential diagnosis} of LOBD versus AD.The textcolor{red}{significance} assessment textcolor{red}{was}carried out evaluating the accuracy achieved by classification basedcomputer aided diagnosis (CAD systems based on these variables. textbf{Materials}A sample of healthy controls (HC (n=26, AD patients (n=37, andLOBD patients (n=32 textcolor{red}{was} recruited at the Alava UniversityHospital. Clinical observations, neuropsychological tests, and plasmabiomarkers textcolor{red}{were} obtained at recruitment time. textbf{Methods}We appltextcolor{red}{ied} multivariate machine learning classificationmethods to discriminate subjects from HC, AD and LOBD populationsin the study. We analyzetextcolor{red}{d} of feature sets textcolor{red}{combining}clinical observations, neuropshycological measures, and biologicalmarkers, including inflammation biomarkers. textcolor{red}{A featureset containing variables showing significative differences for eachclassification contrast was tested also.} Furthermore, a battery ofclassifier approaches textcolor{red}{were} applied, encompassinglinear and non-linear Support Vector Machines (SVM, Random Forests(RF, Classification and regression trees (CART

  7. 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

  8. 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; de Cássia de Souza Schneider, Rosana

    2013-01-01

    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. PMID:23539030

  9. 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.

  10. 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.

  11. [Timing of tracheotomy on the prognosis of patients with prolonged mechanical ventilation: a meta-analysis of randomized controlled trials].

    Science.gov (United States)

    Lu, Yuan-hua; Qiu, Xiao-Hua; Guo, Feng-mei; Yang, Yi; Qiu, Hai-bo

    2011-02-01

    To evaluate the effect of timing of tracheotomy on the prognosis of prolonged mechanically ventilated patients. Randomized controlled trials (RCTs) that studied the effect of timing of tracheotomy on the prognosis of prolonged mechanically ventilated patients were searched from Pubmed, Embase, The Cochrane Library, CBM during January 1990 to June 2010. The quality of the RCTs was evaluated. Meta-analysis of timing of tracheotomy on the prognosis of prolonged mechanically ventilated patients were conducted using the methods recommended by the Cochrane Collaboration. Definition of early tracheotomy was the patients performed tracheotomy during 10 days after admission to hospital or ICU, mechanical ventilation or intubation. Late tracheotomy was defined tracheotomy performed beyond 10 days of admission to hospital or ICU, mechanical ventilation or intubation; or those mechanically ventilated through intubation all the time. Eight hundred and twenty eight patients, 411 in early tracheotomy group and 417 in late tracheotomy group, from 6 RCTs were included in the analysis of data. The meta-analysis showed that early tracheotomy could reduce mortality of patients (RR: 0.81, 95%CI: 0.66 - 0.99, P = 0.04); but it didn't significantly alter the incidence of pneumonia (RR:0.89, 95%CI: 0.68 - 1.17, P = 0.41), mechanical ventilation days (mean difference: -2.19, 95%CI: -9.86 - 5.49, P = 0.58) and length of ICU stay (mean difference: -5.65, 95%CI: -17.11 - 5.81, P = 0.33). In critically ill adult patients who require prolonged mechanical ventilation, early tracheotomy performed at an earlier stage reduces the mortality, but doesn't reduce the incidence of pneumonia and shorten the mechanical ventilation days and ICU length of stay. But more high quality RCTs are required to confirm it.

  12. 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...

  13. 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.

  14. 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.

  15. Tumor necrosis factor-308 polymorphism with the risk and prognosis of non-Hodgkin lymphomas: a meta-analysis study

    Directory of Open Access Journals (Sweden)

    Gao S

    2016-03-01

    Full Text Available Sicheng Gao,1,* Guoqing Zhu,2,* Yan Lin,1 Xingliang Fan,1 Pingan Qian,1 Junfeng Zhu,3 Yongchun Yu1 1Central Laboratory, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 2Department of Clinical Laboratory Medicine, Shanghai Tenth People’s Hospital, Tongji University, 3Department of Hepatology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China *These authors contributed equally to this work Background: Tumor necrosis factor-308 (TNF-308 was implied to be associated with the development of non-Hodgkin lymphoma (NHL. The aim of this meta-analysis study was to investigate the association of TNF-308A polymorphism with the susceptibility to, and prognosis of, NHL. Methods: PubMed, Web of Science, Elsevier, HighWire, Scopus, and Google Scholar were searched up to May 2015. The association of TNF-308 polymorphism with the risk of NHL and prognosis was assessed by odds ratio and hazard ratio, respectively. Results: Overall, TNF-308G>A polymorphism increased the risk of NHL, B-cell lymphomas (BCL, and T-cell lymphomas and decreased the risk of follicular lymphomas. In stratified analysis, increased risk of BCL and diffuse large B-cell lymphomas (DLBCL were observed in Caucasians and population-based studies, whereas decreased risk of NHL, BCL, and DLBCL were detected in Asians and hospital-based studies. Furthermore, pooled results of 1,192 patients with NHL from five studies suggested that TNF-308A was correlated with shorter progression-free survival and overall survival in patients with NHL, BCL, and DLBCL. Conclusion: Current evidence indicated that TNF-308A polymorphism was significantly associated with the risk and prognosis of NHL. Future studies should further confirm these associations in other NHL subtypes and ethnicities. Keywords: tumor necrosis factor, polymorphism, rs1800629

  16. 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

  17. 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.

  18. Analysis of the Role of PET/CT SUVmax in Prognosis and Its Correlation with 
Clinicopathological Characteristics in Resectable Lung Squamous Cell Carcinoma

    Directory of Open Access Journals (Sweden)

    Hongliang REN

    2016-04-01

    Full Text Available Background and objective Lung cancer is the leading cause of cancer death in men and women in the world, more than one-half of cases are diagnosed at a advanced stage, and the overall 5-year survival rate for lung cancer is 18%. Lung cancer is divided into non-small cell lung carcinoma (NSCLC and small cell lung carcinoma (SCLC. Approximately 80%-85% of cases are NSCLC which includes three main types: adenocarcinoma (40%, squamous cell carcinoma (SCC (20%-30%, and large cell carcinoma (10%. Although therapies that target driver mutations in adenocarcinomas are showing some promise, they are proving ineffective in smoking-related SCC. We need pay more attention to the diagnosis and treatment of SCC. 18F-FDG positron emission tomography (PET/computed tomography (CT has emerged as an accurate staging modality in lung cancer diagnosis. The aim of this study is to investigate the role of maximum standardized uptake value (SUVmax on PET-CT in prognosis and its correlation with clinicopathological characteristics in resectable SCC. Methods One hundred and eighty-two resectable SCC patients who underwent PET/CT imaging between May 2005 and October 2014 were enrolled into this retrospectively study. All the enrolled patients had underwent pulmonary resection with mediastinal lymph node dissection without preoperative chemotherapy or radiotherapy. Survival outcomes were analyzed using the Kaplan-Meier method and multivariate Cox proportional hazards model. Correlation between SUVmax and clinicopathological factors was analysed using Pearson correlation analysis and Spearman rank correlation analysis. Results The patients were divided into two groups on the basis of SUVmax 13.0 as cutoff value, and patients with SUVmax more than 13.0 had shorter median overall survival than patients less than 13.0 in univariate analysis (56 months vs 87 months; P=0.022. There was remarkable correlation between SUVmax and gender, tumor size, tumor-node-metastasis (TNM stage

  19. Multivariate analysis in the evaluation of the antinociceptive activity of irradiated essential oil of nutmeg

    Energy Technology Data Exchange (ETDEWEB)

    Santos, Marcelo C.; Lima, Keila S.C.; Oliveira, Sergio E.M.; Lima, Antonio L.S., E-mail: marcelocdossantos@yahoo.com.br [Instituto Militar de Engenharia (IME), Rio de Janeiro, RJ (Brazil); Silva, Jose C.C., E-mail: pinto@peq.coppe.ufrj.br [Coordenacao do Programas de Pos-Graduacao em Engenharia (COPPE/UFRJ), Rio de Janeiro, RJ (Brazil); Silva, Otniel F., E-mail: otniel.freitas@embrapa.br [Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA), Rio de Janeiro, RJ (Brazil)

    2013-07-01

    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)

  20. 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

  1. 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.

  2. A multivariate tobit analysis of highway accident-injury-severity rates.

    Science.gov (United States)

    Anastasopoulos, Panagiotis Ch; Shankar, Venky N; Haddock, John E; Mannering, Fred L

    2012-03-01

    Relatively recent research has illustrated the potential that tobit regression has in studying factors that affect vehicle accident rates (accidents per distance traveled) on specific roadway segments. Tobit regression has been used because accident rates on specific roadway segments are continuous data that are left-censored at zero (they are censored because accidents may not be observed on all roadway segments during the period over which data are collected). This censoring may arise from a number of sources, one of which being the possibility that less severe crashes may be under-reported and thus may be less likely to appear in crash databases. Traditional tobit-regression analyses have dealt with the overall accident rate (all crashes regardless of injury severity), so the issue of censoring by the severity of crashes has not been addressed. However, a tobit-regression approach that considers accident rates by injury-severity level, such as the rate of no-injury, possible injury and injury accidents per distance traveled (as opposed to all accidents regardless of injury-severity), can potentially provide new insights, and address the possibility that censoring may vary by crash-injury severity. Using five-year data from highways in Washington State, this paper estimates a multivariate tobit model of accident-injury-severity rates that addresses the possibility of differential censoring across injury-severity levels, while also accounting for the possible contemporaneous error correlation resulting from commonly shared unobserved characteristics across roadway segments. The empirical results show that the multivariate tobit model outperforms its univariate counterpart, is practically equivalent to the multivariate negative binomial model, and has the potential to provide a fuller understanding of the factors determining accident-injury-severity rates on specific roadway segments. Published by Elsevier Ltd.

  3. 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...

  4. 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.

  5. The Covariance Adjustment Approaches for Combining Incomparable Cox Regressions Caused by Unbalanced Covariates Adjustment: A Multivariate Meta-Analysis Study

    Directory of Open Access Journals (Sweden)

    Tania Dehesh

    2015-01-01

    Full Text Available Background. Univariate meta-analysis (UM procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS method as a multivariate meta-analysis approach. Methods. We evaluated the efficiency of four new approaches including zero correlation (ZC, common correlation (CC, estimated correlation (EC, and multivariate multilevel correlation (MMC on the estimation bias, mean square error (MSE, and 95% probability coverage of the confidence interval (CI in the synthesis of Cox proportional hazard models coefficients in a simulation study. Result. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. Conclusion. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.

  6. Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition.

    Science.gov (United States)

    Alegre-Cortés, J; Soto-Sánchez, C; Pizá, Á G; Albarracín, A L; Farfán, F D; Felice, C J; Fernández, E

    2016-07-15

    Linear analysis has classically provided powerful tools for understanding the behavior of neural populations, but the neuron responses to real-world stimulation are nonlinear under some conditions, and many neuronal components demonstrate strong nonlinear behavior. In spite of this, temporal and frequency dynamics of neural populations to sensory stimulation have been usually analyzed with linear approaches. In this paper, we propose the use of Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD), a data-driven template-free algorithm, plus the Hilbert transform as a suitable tool for analyzing population oscillatory dynamics in a multi-dimensional space with instantaneous frequency (IF) resolution. The proposed approach was able to extract oscillatory information of neurophysiological data of deep vibrissal nerve and visual cortex multiunit recordings that were not evidenced using linear approaches with fixed bases such as the Fourier analysis. Texture discrimination analysis performance was increased when Noise-Assisted Multivariate Empirical Mode plus Hilbert transform was implemented, compared to linear techniques. Cortical oscillatory population activity was analyzed with precise time-frequency resolution. Similarly, NA-MEMD provided increased time-frequency resolution of cortical oscillatory population activity. Noise-Assisted Multivariate Empirical Mode Decomposition plus Hilbert transform is an improved method to analyze neuronal population oscillatory dynamics overcoming linear and stationary assumptions of classical methods. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. The Prognostic Role of STEAP1 Expression Determined via Immunohistochemistry Staining in Predicting Prognosis of Primary Colorectal Cancer: A Survival Analysis

    Directory of Open Access Journals (Sweden)

    Ching-Hsiao Lee

    2016-04-01

    Full Text Available STEAP1 (six transmembrane epithelial antigen of the prostate 1 is a transmembrane protein that functions as a potential channel or transporter protein. It is overexpressed in certain cancers and is viewed as a promising therapeutic target. However, the prognostic role of STEAP1 is still controversial, and no role for STEAP1 has yet been indicated in colorectal cancer. The aim of this study was to investigate the possible association of STEAP1 expression with colorectal cancer prognosis. STEAP1 expression was analyzed by immunohistochemical staining of a tissue array of 165 cancer specimens from primary colorectal cancer patients. The mean and medium follow-up times after surgery were 5.1 and 3.9 years, respectively. A total of 139 patients died during the 13 years of follow-up in the survey period. The prognostic value of STEAP1 with respect to overall survival was analyzed by Kaplan-Meier analysis and Cox proportional hazard models. In total, 164 samples displayed detectable STEAP1 expression in the cytoplasm and membrane. Low STEAP1 expression was correlated with poor overall survival (five-year survival: 33.7% vs. 57.0%, low expression vs. high expression, p = 0.020. Accordingly, multivariate analysis identified low STEAP1 expression as an independent risk factor (hazard ratio = 1.500, p = 0.018, especially in elderly patients or those with late stage cancers, late T values, and early N values. We suggest that analysis of STEAP1 expression by immunohistochemical staining could serve as an independent prognostic marker for colorectal patients. This finding should be validated by other investigative groups.

  8. Linking Forest Cover to Water Quality: A Multivariate Analysis of Large Monitoring Datasets

    Directory of Open Access Journals (Sweden)

    Delphine Brogna

    2017-03-01

    Full Text Available Forested catchments are generally assumed to provide higher quality water. However, this hypothesis must be validated in various contexts as interactions between multiple land use and land cover (LULC types, ecological variables and water quality variables render this relationship highly complex. This paper applies a straightforward multivariate approach on a typical large monitoring dataset of a highly managed and densely populated area (Wallonia, Belgium; 10-year dataset, quantifying forest cover effects on nine physico-chemical water quality variables. Results show that forest cover explains about one third of the variability of water quality and is positively correlated with higher quality water. When controlling for spatial autocorrelation, forest cover still explains 9% of water quality. Unlike needle-leaved forest cover, broad-leaved forest cover presents an independent effect from ecological variables and explains independently 4.8% of water quality variability while it shares 5.8% with cropland cover. This study demonstrates clear independent effects of forest cover on water quality, and presents a method to tease out independent LULC effects from typical large multivariate monitoring datasets. Further research on explanatory variables, spatial distribution effects and water quality datasets could lead to effective strategies to mitigate pollution and reach legal targets.

  9. Determinants in the number of staff in hospitals' maintenance departments: a multivariate regression analysis approach.

    Science.gov (United States)

    Miguel Cruz, Antonio; Guarín, Mayra R

    2017-02-01

    To date, there are no broadly accepted or accurate models to determine appropriate staffing [levels] for clinical engineering departments (CEDs). The purpose of this study is to determine what the determinants of the staffing levels are (total number of full time equivalents (FTEs)) in CEDs in healthcare organisations. In doing so, we used a cross-sectional exploratory approach by using a multivariate regression model over a secondary source of data information from the AAMI Benchmarking Solutions-Healthcare Technology Management database. Two hundred and one healthcare organisations were included in our study. Our study revealed that on average, there are almost 14 biomedical technicians (BMETs) per clinical engineer and one FTE per 1083.72 devices (SD 545.69). The results of this study also revealed that the total number of devices and the total technology management hours devoted to these devices positively affects the number of FTEs in a CED, whereas the hospital complexity, measured by healthcare organisation patient discharges matters inversely. The most important factor that matters in the number of FTEs in CEDs was the total technology management hours devoted to devices. A value of explained variance (i.e. R2) of 85% was obtained, indicating the strong power of the prediction accuracy of our multivariate regression model.

  10. Electrolyte Disturbances Are Associated with Non-Survival in Dogs—A Multivariable Analysis

    Directory of Open Access Journals (Sweden)

    Robert Goggs

    2017-08-01

    Full Text Available Electrolyte disorders have been individually associated with mortality in small populations of dogs and cats with specific conditions, but the associations and interactions between electrolyte disturbances and outcome have not been evaluated in a large, heterogeneous population. It was hypothesized that abnormalities of sodium, chloride, potassium, and calcium concentrations would be independently and proportionately associated with death from natural causes and with all-cause mortality in dogs. An electronic database containing 33,117 electrolyte profiles was constructed to retrospectively assess the association between disorders of sodium, potassium, corrected chloride, and ionized calcium concentrations with non-survival and with death excluding euthanasia by multivariable modeling. A second database containing 11,249 records was used to validate the models constructed from the first database. All four electrolytes assessed had non-linear U-shaped associations with case fatality rates, wherein concentrations clustered around the reference interval had the lowest case fatality rates, while progressively abnormal concentrations were associated with proportionately increased risk of non-survival (AUROC 0.624 or death (AUROC 0.678. Multivariable modeling suggested that these electrolyte disturbances were associated with non-survival and with death from natural causes independent of each other. This study suggests that measurement of electrolyte concentrations is an important component of the assessment of dogs in emergency rooms or intensive care units. Future studies should focus on confirming these associations in a prospective manner accounting for disease severity.

  11. Multivariate Statistical Analysis of Labile Trace Elements in H Chondrites: Evidence for Meteoroid Streams

    Science.gov (United States)

    Wolf, S. F.; Lipschutz, M. E.

    1992-07-01

    Differences have been observed between meteorite populations with vastly different terrestrial ages, i.e. Antarctic and non-Antarctic meteorite populations (Koeberl and Cassidy, 1991 and references therein). Comparisons of labile trace element contents (Wolf and Lipschutz, 1992) and induced TL parameters (Benoit and Sears, 1992) in samples from Victoria Land and Queen Maud Land, populations which also differ in mean terrestrial age (Nishiizumi et al, 1989), show significant differences consistent with different average thermal histories. These differences are consistent with the proposition that the flux of meteoritic material to Earth varied temporally. Variations in the flux of meteoritic material over time scales of 10^5 10^6 y require the existence of undispersed streams of meteoroids of asteroidal origin which were initially disputed by Wetherill ( 1986) but have since been observed (Olsson-Steele, 1988; Oberst, 1989; Halliday et al. 1990). Orbital evidence for meteoroid and asteroid streams has been independently obtained by others, particularly Halliday et al.(1990) and Drummond (1991). A group of H chondrites of various petrographic types and diverse CRE ages that yielded 16 falls from 1855 until 1895 in the month of May has been proposed to be two co-orbital meteoroid streams with a common source (R. T. Dodd, personal communication). Compositional evidence of a preterrestrial association of the proposed stream members, if it exists, might be observed in the most sensitive indicators of genetic thermal history, the labile trace elements. We report RNAA data for the concentrations of 14 trace elements, mostly labile ones, (Ag, Au, Bi, Cd, Cs, Co, Ga, In, Rb, Sb, Se, Te, Tl, and Zn) in H4-6 ordinary chondrites. Variance of elemental concentrations within a subpopulation, the members of a proposed co-orbital meteorite stream for example, could be expected to be smaller than the variance for the entire population. We utilize multivariate linear regression and

  12. Path analysis and multi-criteria decision making: an approach for multivariate model selection and analysis in health.

    Science.gov (United States)

    Vasconcelos, A G; Almeida, R M; Nobre, F F

    2001-08-01

    This paper introduces an approach that includes non-quantitative factors for the selection and assessment of multivariate complex models in health. A goodness-of-fit based methodology combined with fuzzy multi-criteria decision-making approach is proposed for model selection. Models were obtained using the Path Analysis (PA) methodology in order to explain the interrelationship between health determinants and the post-neonatal component of infant mortality in 59 municipalities of Brazil in the year 1991. Socioeconomic and demographic factors were used as exogenous variables, and environmental, health service and agglomeration as endogenous variables. Five PA models were developed and accepted by statistical criteria of goodness-of fit. These models were then submitted to a group of experts, seeking to characterize their preferences, according to predefined criteria that tried to evaluate model relevance and plausibility. Fuzzy set techniques were used to rank the alternative models according to the number of times a model was superior to ("dominated") the others. The best-ranked model explained above 90% of the endogenous variables variation, and showed the favorable influences of income and education levels on post-neonatal mortality. It also showed the unfavorable effect on mortality of fast population growth, through precarious dwelling conditions and decreased access to sanitation. It was possible to aggregate expert opinions in model evaluation. The proposed procedure for model selection allowed the inclusion of subjective information in a clear and systematic manner.

  13. 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.

  14. [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.

  15. Multivariate Analysis in the Reconstruction of Photon/Electron Energies in the CMS

    CERN Document Server

    Raclariu, Ana-Maria

    2013-01-01

    A new semi-parametric multivariate regression was used to improve the energy reconstruction in the CMS electromagnetic calorimeter. The method is based on the generation of boosted decision trees by optimizing the parameters of the double crystal ball function fitted to the ratio of the raw to generated energies of simulated photons and electrons. The full training was done on half the electrons with generated transverse momenta p$_{T}\\geq$ 16 GeV in the barrel and corrections were applied to subsets of the remaining events. The dependence of the means and widths of the resulting distributions on p$_{T}$ was deduced. The corrected reconstructed energies peak close 1 for p$_{T}$ values down to 16 GeV. It was found that fixing $\\alpha$ of the double crystal ball function in the training improves its performance.

  16. A multivariate analysis for evaluating the environmental and economical aspects of agroecosystem sustainability in central Italy.

    Science.gov (United States)

    Di Felice, Vincenzo; Mancinelli, Roberto; Proulx, Raphaël; Campiglia, Enio

    2012-05-15

    Over the past century farming activity has intensified worldwide, characterized by an increasing dependence on external inputs and on land conversion. Although the intensification of agriculture has increased productivity, the sustainability of agroecosystems has also been compromised. The objective of this study is to build multivariate relationships between farm structural characteristics and farm performance to highlight the relative costs and benefits of four main farming systems in Central Italy: organic, conventional, mixed and non-mixed farms. Results show that the relationship between cropping diversity and agroecological sustainability is associated to a mixed versus non-mixed farm management dichotomy, not to organic or conventional farming practices. The presence of livestock appears to have played an important role as an economic lever for diversifying the farm cropping system. Copyright © 2011 Elsevier Ltd. All rights reserved.

  17. [An optimal selection method of samples of calibration set and validation set for spectral multivariate analysis].

    Science.gov (United States)

    Liu, Wei; Zhao, Zhong; Yuan, Hong-Fu; Song, Chun-Feng; Li, Xiao-Yu

    2014-04-01

    The side effects in spectral multivariate modeling caused by the uneven distribution of sample numbers in the region of the calibration set and validation set were analyzed, and the "average" phenomenon that samples with small property values are predicted with larger values, and those with large property values are predicted with less values in spectral multivariate calibration is showed in this paper. Considering the distribution feature of spectral space and property space simultaneously, a new method of optimal sample selection named Rank-KS is proposed. Rank-KS aims at improving the uniformity of calibration set and validation set. Y-space was divided into some regions uniformly, samples of calibration set and validation set were extracted by Kennard-Stone (KS) and Random-Select (RS) algorithm respectively in every region, so the calibration set was distributed evenly and had a strong presentation. The proposed method were applied to the prediction of dimethylcarbonate (DMC) content in gasoline with infrared spectra and dimethylsulfoxide in its aqueous solution with near infrared spectra. The "average" phenomenon showed in the prediction of multiple linear regression (MLR) model of dimethylsulfoxide was weakened effectively by Rank-KS. For comparison, the MLR models and PLS1 models of MDC and dimethylsulfoxide were constructed by using RS, KS, Rank-Select, sample set partitioning based on joint X- and Y-blocks (SPXY) and proposed Rank-KS algorithms to select the calibration set, respectively. Application results verified that the best prediction was achieved by using Rank-KS. Especially, for the distribution of sample set with more in the middle and less on the boundaries, or none in the local, prediction of the model constructed by calibration set selected using Rank-KS can be improved obviously.

  18. Papillary Thyroid Cancer, Macrofollicular Variant: The Follow-Up and Analysis of Prognosis of 5 Patients

    Directory of Open Access Journals (Sweden)

    Varlık Erol

    2014-01-01

    Full Text Available Objective. The main aim of this study was to comparatively analyze the recurrence and prognosis of this rare variant with the literature by analyzing the follow-up data of 5 patients diagnosed with papillary cancer macrofollicular variant. Methods. The demographic data, radiological and pathological data, and prognostic data of 5 patients who underwent surgery for thyroid cancer and were diagnosed with papillary cancer macrofollicular variant pathologically were retrospectively analyzed. Results. The mean age of patients whose mean follow-up period was determined as 7.2 years was 41, and the male/female ratio was 4/1. All patients underwent total thyroidectomy. The pathology report of 2 patients (40% revealed macrofollicular variant of papillary microcancer, and 3 patients papillary cancer macrofollicular variant. Central dissection was performed in one patient (20% due to macroscopic pathologic lymph node and 4 metastatic lymph nodes were reported. Also, locoregional recurrence was present in 3 out of 5 patients (60%. Conclusions. Although an impression of earlier and increased risk of recurrence in papillary carcinoma with macrofollicular variant has been documented, more studies with extensive follow-up times and large populations are required.

  19. 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)

  20. 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.

  1. Chemotherapy-induced neutropenia and the prognosis of colorectal cancer: a meta-analysis of cohort studies.

    Science.gov (United States)

    Tan, XiangZhou; Wen, QiaoCheng; Wang, Ran; Chen, ZhiKang

    2017-11-01

    Recently, there has been a controversial discussion about the prognostic value of chemotherapy-induced neutropenia (CIN) in colorectal cancer patients. Thus, a meta-analysis was conducted to determine the relationship between CIN and the prognosis of colorectal cancer patients. We searched the PubMed, EMBASE, and Cochrane library databases to identify studies evaluating the association between CIN and colorectal cancer prognosis. Pooled random/fixed effect models were used to calculate pooled hazard ratios (HRs) and 95% confidence intervals (CIs) to assess the association. Eight studies were selected for the meta-analysis, for a total of 2,745 patients. There was significant improved survival among colorectal cancer patients with CIN (HR = 0.62, 95% CI = 0.47-0.76). However, significant heterogeneity was found (p = 0.000, Ι2 = 75.0%). Through subgroup analysis, we could greatly eliminate the heterogeneity and found that neutropenia was associated with better survival in stage IV colorectal cancer patients, no matter the HR calculated by overall survival (OS) or progression-free survival (PFS). Meanwhile, the prognostic value of neutropenia in stage II/III colorectal cancer can be found when the HR is calculated by disease-free survival (DFS). Additionally, we observed significant differences after stratification according to various tumor stages, endpoints, and the use of G-CSF. Our results which, based on a cohort study, indicate that CIN is associated with improved survival in patients with colorectal cancer. However, further randomized controlled trials are warranted.

  2. Quality-by-Design Case Study: Investigation of the Role of Poloxamer in Immediate-Release Tablets by Experimental Design and Multivariate Data Analysis

    National Research Council Canada - National Science Library

    Kaul, Goldi; Huang, Jun; Chatlapalli, Ramarao; Ghosh, Krishnendu; Nagi, Arwinder

    2011-01-01

    ...) combined with design of experiments (DOE). While the DOE analysis yielded important clues into the cause-and-effect relationship between the responses and design factors, multivariate data analysis of the 40...

  3. Multivariate approach in popcorn genotypes using the Ward-MLM strategy: morpho-agronomic analysis and incidence of Fusarium spp.

    Science.gov (United States)

    Kurosawa, R N F; do Amaral Junior, A T; Silva, F H L; Dos Santos, A; Vivas, M; Kamphorst, S H; Pena, G F

    2017-02-08

    The multivariate analyses are useful tools to estimate the genetic variability between accessions. In the breeding programs, the Ward-Modified Location Model (MLM) multivariate method has been a powerful strategy to quantify variability using quantitative and qualitative variables simultaneously. The present study was proposed in view of the dearth of information about popcorn breeding programs under a multivariate approach using the Ward-MLM methodology. The objective of this study was thus to estimate the genetic diversity among 37 genotypes of popcorn aiming to identify divergent groups associated with morpho-agronomic traits and traits related to resistance to Fusarium spp. To this end, 7 qualitative and 17 quantitative variables were analyzed. The experiment was conducted in 2014, at Universidade Estadual do Norte Fluminense, located in Campos dos Goytacazes, RJ, Brazil. The Ward-MLM strategy allowed the identification of four groups as follows: Group I with 10 genotypes, Group II with 11 genotypes, Group III with 9 genotypes, and Group IV with 7 genotypes. Group IV was distant in relation to the other groups, while groups I, II, and III were near. The crosses between genotypes from the other groups with those of group IV allow an exploitation of heterosis. The Ward-MLM strategy provided an appropriate grouping of genotypes; ear weight, ear diameter, and grain yield were the traits that most contributed to the analysis of genetic diversity.

  4. 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.

  5. A comparison between multivariate Slash, Student's t and probit threshold models for analysis of clinical mastitis in first lactation cows.

    Science.gov (United States)

    Chang, Y-M; Gianola, D; Heringstad, B; Klemetsdal, G

    2006-10-01

    Robust threshold models with multivariate Student's t or multivariate Slash link functions were employed to infer genetic parameters of clinical mastitis at different stages of lactation, with each cow defining a cluster of records. The robust fits were compared with that from a multivariate probit model via a pseudo-Bayes factor and an analysis of residuals. Clinical mastitis records on 36 178 first-lactation Norwegian Red cows from 5286 herds, daughters of 245 sires, were analysed. The opportunity for infection interval, going from 30 days pre-calving to 300 days postpartum, was divided into four periods: (i) -30 to 0 days pre-calving; (ii) 1-30 days; (iii) 31-120 days; and (iv) 121-300 days of lactation. Within each period, absence or presence of clinical mastitis was scored as 0 or 1 respectively. Markov chain Monte Carlo methods were used to draw samples from posterior distributions of interest. Pseudo-Bayes factors strongly favoured the multivariate Slash and Student's t models over the probit model. The posterior mean of the degrees of freedom parameter for the Slash model was 2.2, indicating heavy tails of the liability distribution. The posterior mean of the degrees of freedom for the Student's t model was 8.5, also pointing away from a normal liability for clinical mastitis. A residual was the observed phenotype (0 or 1) minus the posterior mean of the probability of mastitis. The Slash and Student's t models tended to have smaller residuals than the probit model in cows that contracted mastitis. Heritability of liability to clinical mastitis was 0.13-0.14 before calving, and ranged from 0.05 to 0.08 after calving in the robust models. Genetic correlations were between 0.50 and 0.73, suggesting that clinical mastitis resistance is not the same trait across periods, corroborating earlier findings with probit models.

  6. Prognosis of cirrhotic patients admitted to intensive care unit: a meta-analysis.

    Science.gov (United States)

    Weil, Delphine; Levesque, Eric; McPhail, Marc; Cavallazzi, Rodrigo; Theocharidou, Eleni; Cholongitas, Evangelos; Galbois, Arnaud; Pan, Heng Chih; Karvellas, Constantine J; Sauneuf, Bertrand; Robert, René; Fichet, Jérome; Piton, Gaël; Thevenot, Thierry; Capellier, Gilles; Di Martino, Vincent

    2017-12-01

    The best predictors of short- and medium-term mortality of cirrhotic patients receiving intensive care support are unknown. We conducted meta-analyses from 13 studies (2523 cirrhotics) after selection of original articles and response to a standardized questionnaire by the corresponding authors. End-points were in-ICU, in-hospital, and 6-month mortality in ICU survivors. A total of 301 pooled analyses, including 95 analyses restricted to 6-month mortality among ICU survivors, were conducted considering 249 variables (including reason for admission, organ replacement therapy, and composite prognostic scores). In-ICU, in-hospital, and 6-month mortality was 42.7, 54.1, and 75.1%, respectively. Forty-eight patients (3.8%) underwent liver transplantation during follow-up. In-ICU mortality was lower in patients admitted for variceal bleeding (OR 0.46; 95% CI 0.36-0.59; p  19 at baseline (OR 8.54; 95% CI 2.09-34.91; p  26 (OR 3.97; 95% CI 1.92-8.22; p < 0.0001; PPV = 0.75), and hepatorenal syndrome (OR 4.67; 95% CI 1.24-17.64; p = 0.022; PPV = 0.88). Prognosis of cirrhotic patients admitted to ICU is poor since only a minority undergo liver transplant. The prognostic performance of general ICU scores decreases over time, unlike the Child-Pugh and MELD scores, even recorded in the context of organ failure. Infection-related parameters had a short-term impact, whereas liver and renal failure had a sustained impact on mortality.

  7. Analysis of four scoring systems for the prognosis of patients with metastasis of the vertebral column.

    Science.gov (United States)

    Pollner, Péter; Horváth, Anna; Mezei, Tamás; Banczerowski, Péter; Czigléczki, Gábor

    2018-01-31

    The metastatic spinal diseases are common health problems and there is no consensus on the appropriate treatment of metastases in several conditions. Using clinical measures (e.g. survival time, functional status), the prognosis prediction systems advise on the appropriate interventions. The aim of this article is to assess and compare four widely used scoring systems (revised Tokuhashi, Tomita, Van der Linden and modified Bauer scores) on a single center cohort. A retrospective study of 329 patients was designed who were subjected to surgeries because of metastatic spinal diseases. Subpopulations according to the classifications of four scoring systems were identified. The overall survival was calculated with the Kaplan-Meier formula. The difference between the survival curves of subpopulations was analyzed with the log-rank tests. The consistency rates for the four scoring systems are calculated as well. The follow up period was 8 years. The median survival time was 222 days. The overall survival of prognostic categories in three scoring systems was significantly differing from each other, but we found no differences between the categories of the van der Linden system. In this cohort the revised Tokuhashi system gave the best approximation for the survival time with a mean predictive capability 60.5%. The evaluation of four standard scoring systems showed that three of them were self-consistent although, none of systems was able to predict the survival rates in our cohort. Based on the predictive capability, the revised Tokuhashi system may provide the best predictions with careful examination of individual cases. Copyright © 2018 Elsevier Inc. All rights reserved.

  8. Comprehensive Analysis of Cancer-Proteogenome to Identify Biomarkers for the Early Diagnosis and Prognosis of Cancer

    Directory of Open Access Journals (Sweden)

    Hem D. Shukla

    2017-10-01

    Full Text Available During the past century, our understanding of cancer diagnosis and treatment has been based on a monogenic approach, and as a consequence our knowledge of the clinical genetic underpinnings of cancer is incomplete. Since the completion of the human genome in 2003, it has steered us into therapeutic target discovery, enabling us to mine the genome using cutting edge proteogenomics tools. A number of novel and promising cancer targets have emerged from the genome project for diagnostics, therapeutics, and prognostic markers, which are being used to monitor response to cancer treatment. The heterogeneous nature of cancer has hindered progress in understanding the underlying mechanisms that lead to abnormal cellular growth. Since, the start of The Cancer Genome Atlas (TCGA, and the International Genome consortium projects, there has been tremendous progress in genome sequencing and immense numbers of cancer genomes have been completed, and this approach has transformed our understanding of the diagnosis and treatment of different types of cancers. By employing Genomics and proteomics technologies, an immense amount of genomic data is being generated on clinical tumors, which has transformed the cancer landscape and has the potential to transform cancer diagnosis and prognosis. A complete molecular view of the cancer landscape is necessary for understanding the underlying mechanisms of cancer initiation to improve diagnosis and prognosis, which ultimately will lead to personalized treatment. Interestingly, cancer proteome analysis has also allowed us to identify biomarkers to monitor drug and radiation resistance in patients undergoing cancer treatment. Further, TCGA-funded studies have allowed for the genomic and transcriptomic characterization of targeted cancers, this analysis aiding the development of targeted therapies for highly lethal malignancy. High-throughput technologies, such as complete proteome, epigenome, protein–protein interaction

  9. Application of multivariate data analysis for identification and successful resolution of a root cause for a bioprocessing application.

    Science.gov (United States)

    Kirdar, Alime Ozlem; Green, Ken D; Rathore, Anurag S

    2008-01-01

    Multivariate Data Analysis (MVDA) can be used for supporting key activities required for successful bioprocessing. These activities include process characterization, process scale-up, process monitoring, fault diagnosis and root cause analysis. This paper examines an application of MVDA towards root cause analysis for identifying scale-up differences and parameter interactions that adversely impact cell culture process performance. Multivariate data analysis and modeling were performed using data from small-scale (2 L), pilot-scale (2,000 L) and commercial-scale (15,000 L) batches. The input parameters examined included bioreactor pCO2, glucose, lactate, ammonium, raw materials and seed inocula. The output parameters included product attributes, product titer, viable cell density, cell viability and osmolality. Time course performance variables (daily, initial, peak and end point) were also evaluated. Application of MVDA as a diagnostic tool was successful in identifying the root cause and designing experimental conditions to demonstrate and correct it. Process parameters and their interactions that adversely impact cell culture performance and product attributes were successfully identified. MVDA was successfully used as an effective tool for collating process knowledge and increasing process understanding.

  10. 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.

  11. Transmission of prices and price volatility in Australian electricity spot markets. A multivariate GARCH analysis

    Energy Technology Data Exchange (ETDEWEB)

    Worthington, Andrew; Kay-Spratley, Adam; Higgs, Helen [School of Economics and Finance, Queensland University of Technology, G.P.O. Box 2434, Brisbane, Qld 4001 (Australia)

    2005-03-15

    This paper examines the transmission of spot electricity prices and price volatility among the five regional electricity markets in the Australian National Electricity Market: namely, New South Wales, Queensland, South Australia, the Snowy Mountains Hydroelectric Scheme and Victoria. A multivariate generalised autoregressive conditional heteroskedasticity model is used to identify the source and magnitude of price and price volatility spillovers. The results indicate the presence of positive own mean spillovers in only a small number of markets and no mean spillovers between any of the markets. This appears to be directly related to the physical transfer limitations of the present system of regional interconnection. Nevertheless, the large number of significant own-volatility and cross-volatility spillovers in all five markets indicates the presence of strong autoregressive conditional heteroskedasticity and generalised autoregressive conditional heteroskedasticity effects. This indicates that shocks in some markets will affect price volatility in others. Finally, and contrary to evidence from studies in North American electricity markets, the results also indicate that Australian electricity spot prices are stationary.

  12. Transmission of prices and price volatility in Australian electricity spot markets: a multivariate GARCH analysis

    Energy Technology Data Exchange (ETDEWEB)

    Worthington, A.; Kay-Spratley, A.; Higgs, H. [Queensland University of Technology, Brisbane (Australia). School of Economics and Finance

    2005-03-01

    This paper examines the transmission of spot electricity prices and price volatility among the five regional electricity markets in the Australian National Electricity Market: namely, New South Wales, Queensland, South Australia, the Snowy Mountains Hydroelectric Scheme and Victoria. A multivariate generalised autoregressive conditional heteroskedasticity model is used to identify the source and magnitude of price and price volatility spillovers. The results indicate the presence of positive own mean spillovers in only a small number of markets and no mean spillovers between any of the markets. This appears to be directly related to the physical transfer limitations of the present system of regional interconnection. Nevertheless, the large number of significant own-volatility and cross-volatility spillovers in all five markets indicates the presence of strong autoregressive conditional heteroskedasticity and generalised autoregressive conditional heteroskedasticity effects. This indicates that shocks in some markets will affect price volatility in others. Finally, and contrary to evidence from studies in North American electricity markets, the results also indicate that Australian electricity spot prices are stationary. (author)

  13. 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.

  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. Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

    Science.gov (United States)

    Ulbrich, Norbert Manfred

    2013-01-01

    A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.

  16. Analysis of pelagic species decline in the upper San Francisco Estuary using multivariate autoregressive modeling (MAR)

    Science.gov (United States)

    Mac Nally, Ralph; Thomson, James R.; Kimmerer, Wim J.; Feyrer, Frederick; Newman, Ken B.; Sih, Andy; Bennett, William A.; Brown, Larry; Fleishman, Erica; Culberson, Steven D.; Castillo, Gonzalo

    2010-01-01

    Four species of pelagic fish of particular management concern in the upper San Francisco Estuary, California, USA, have declined precipitously since ca. 2002: delta smelt (Hypomesus transpacificus), longfin smelt (Spirinchus thaleichthys), striped bass (Morone saxatilis), and threadfin shad (Dorosoma petenense). The estuary has been monitored since the late 1960s with extensive collection of data on the fishes, their pelagic prey, phytoplankton biomass, invasive species, and physical factors. We used multivariate autoregressive (MAR) modeling to discern the main factors responsible for the declines. An expert-elicited model was built to describe the system. Fifty-four relationships were built into the model, only one of which was of uncertain direction a priori. Twenty-eight of the proposed relationships were strongly supported by or consistent with the data, while 26 were close to zero (not supported by the data but not contrary to expectations). The position of the 2‰ isohaline (a measure of the physical response of the estuary to freshwater flow) and increased water clarity over the period of analyses were two factors affecting multiple declining taxa (including fishes and the fishes' main zooplankton prey). Our results were relatively robust with respect to the form of stock–recruitment model used and to inclusion of subsidiary covariates but may be enhanced by using detailed state–space models that describe more fully the life-history dynamics of the declining species.

  17. 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.

  18. Perspectives of family medicine physicians on the importance of adolescent preventive care: a multivariate analysis.

    Science.gov (United States)

    Taylor, Jaime L; Aalsma, Matthew C; Gilbert, Amy L; Hensel, Devon J; Rickert, Vaughn I

    2016-01-20

    The study objective was to identify commonalities amongst family medicine physicians who endorse annual adolescent visits. A nationally weighted representative on-line survey was used to explore pediatrician (N = 204) and family medicine physicians (N = 221) beliefs and behaviors surrounding adolescent wellness. Our primary outcome was endorsement that adolescents should receive annual preventive care visits. Pediatricians were significantly more likely (p family medicine physicians, bivariate comparisons were conducted between those who endorsed an annual visit (N = 164) compared to those who did not (N = 57) with significant predictors combined into two multivariate logistic regression models. Model 1 controlled for: patient race, proportion of 13-17 year olds in provider's practice, discussion beliefs scale and discussion behaviors with parents scale. Model 2 controlled for the same first three variables as well as discussion behaviors with adolescents scale. Model 1 showed for each discussion beliefs scale topic selected, family medicine physicians had 1.14 increased odds of endorsing annual visits (p family medicine physicians had 1.15 increased odds of also endorsing the importance of annual visits (p Family medicine physicians that endorse annual visits are significantly more likely to affirm they hold strong beliefs about topics that should be discussed during the annual exam. They also act on these beliefs by talking to parents of teens about these topics. This group appears to focus on quality of care in thought and deed.

  19. Measuring connectivity in linear multivariate processes: definitions, interpretation, and practical analysis.

    Science.gov (United States)

    Faes, Luca; Erla, Silvia; Nollo, Giandomenico

    2012-01-01

    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.

  20. 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.

  1. MR Imaging Analysis of Non-Measurable Enhancing Lesions Newly Appearing after Concomitant Chemoradiotherapy in Glioblastoma Patients for Prognosis Prediction.

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    Bo Ram Kim

    Full Text Available To analyze the enhancement patterns and apparent diffusion coefficient (ADC values of non-measurable surgical cavity wall enhancement pattern, newly appearing after completion of standard concurrent chemoradiotherapy (CCRT with temozolomide in glioblastoma patients for the prognosis prediction.From January 2010 to April 2014, among 190 patients with histopathologically confirmed glioblastoma, a total of 33 patients with non-measurable wall enhancement on post-CCRT MR imaging were enrolled and divided into two subgroups: non-progression (n = 18 and progression groups (n = 15. We analyzed the wall enhancement patterns, which were categorized into three patterns: thin, thick and nodular enhancement. ADC values were measured in the enhancing portions of the walls. The progression-free survival (PFS related to the wall enhancement was analyzed by Kaplan-Meier analysis, and survival curves were compared using the log-rank test.Statistically significant differences in the surgical cavity wall enhancement patterns was shown between the progression and non-progression groups (P = 0.0032. Thin wall enhancement was more frequently observed in the non-progression group, and thick or nodular wall enhancement were observed in the progression group (P = 0.0016. There was no statistically significant difference in the mean ADC values between the progression and non-progression groups. The mean PFS was longer in patients with thin wall enhancement than in those with nodular or thick wall enhancement (35.5 months vs. 15.8 months, P = 0.008.Pattern analysis of non-measurable surgical cavity wall enhancement on post-CCRT MR imaging might be useful tool for predicting prognosis of GBM patient before clear progression of non-measurable disease.

  2. 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.

  3. Discrimination of wild Paris based on near infrared spectroscopy and high performance liquid chromatography combined with multivariate analysis.

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    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.

  4. Discrimination of wild Paris based on near infrared spectroscopy and high performance liquid chromatography combined with multivariate analysis.

    Science.gov (United States)

    Zhao, Yanli; Zhang, Ji; Yuan, Tianjun; Shen, Tao; Li, Wei; Yang, Shihua; Hou, Ying; Wang, Yuanzhong; Jin, Hang

    2014-01-01

    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(2)X and Q(2)Y 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.

  5. Correlation of aqueous solubility of salts of benzylamine with experimentally and theoretically derived parameters. A multivariate data analysis approach

    DEFF Research Database (Denmark)

    Parshad, Henrik; Frydenvang, Karla Andrea; Liljefors, Tommy

    2002-01-01

    Twenty two salts of benzylamine and p-substituted benzoic acids were prepared and characterized. The p-substituent was varied with regard to electronic, hydrophobic, and steric effects as well as hydrogen bonding potential. A multivariate data analysis was used to describe the relationship between...... intrinsic dissolution rate, intrinsic solubility of the unionized acids (S(0)), Hansch's hydrophobic parameter, Charton's steric parameter and molecular weight (MW). Statistically good models for predicting solubility of a selected test set were obtained by using simple models consisting of a few...

  6. Impact of Serum Apolipoprotein A-I on Prognosis and Bevacizumab Efficacy in Patients with Metastatic Colorectal Cancer: a Propensity Score-Matched Analysis

    Directory of Open Access Journals (Sweden)

    Qi Quan

    2017-04-01

    Full Text Available PURPOSE: We aimed to investigate the role of apolipoprotein A-I (ApoA-I as a predictor of prognosis and treatment efficacy of bevacizumab in patients with metastatic colorectal cancer (mCRC treated with first-line chemotherapy with or without bevacizumab. METHODS: We conducted a retrospective study on consecutive patients who were diagnosed with mCRC at Sun Yat-sen University Cancer Center. According to their pretreatment ApoA-I level, patients were divided into low– and high–ApoA-I groups. Propensity score-matched method was performed to balance baseline characteristics between two groups. Based on whether they accepted bevacizumab as a first-line therapy, patients were further divided into the chemo + bevacizumab group and the chemo group. Overall survival (OS and progression-free survival (PFS were assessed with Kaplan-Meier method, log-rank test, and Cox regression. RESULTS: The optimal cutoff value for the ApoA-I level was determined to be 1.105 g/l. In the propensity-matched cohort of 508 patients, low ApoA-I was significantly associated with inferior OS (P < .001 and PFS (P < .001 than high ApoA-I. Multivariate analysis showed that ApoA-I level was an independent prognostic maker of OS (P < .001 and PFS (P = .001. PFS (P < .001 in either the high– or low–ApoA-I groups could be extended significantly after the administration of bevacizumab, and patients with a high ApoA-I level also had a better OS in the chemo + bevacizumab group than the chemo group (P = .049. CONCLUSIONS: Patients with a low ApoA-I level have poor prognoses, and they did not display an OS benefit from bevacizumab.

  7. [Retrospective analysis of treatment strategy and prognosis in childhood myelodysplastic syndromes from China and Japan].

    Science.gov (United States)

    Chen, Xiao-Juan; Manabe, Atsushi; Yang, Wen-Yu; Zhang, Pei-Hong; Wang, Shu-Chun; Guo, Ye; Liu, Fang; Chang, Li-Xian; Wei, Wei; Wan, Yang; Zhu, Xiao-Fan

    2013-08-01

    This study was aimed to retrospectively analyse the prognosis of childhood myelodysplastic syndromes (MDS-RCC ) from China and Japan. Two hematologists and one pathologist from China and Japan constituted a diagnosis group. According to the criteria of 2008 WHO, 33 children with MDS-RCC from 50 chinese children diagnosed as acquired bone marrow failure syndrome from 2009 to 2011, and 74 Japanese children with MDS-RCC in a prospective registration group conducted by the Japanese Society of Pediatric Hematology were enrolled in this study. The outcome of total 107 childhood MDS-RCC treated with different treatment strategies was analyzed retrospectively. The results indicated that: (1) the 3 and 5-year overall survival rates (OS) for all patients were 93.8% and 79.7% respectively. (2) All 107 patients with MDS-RCC were further subclassified into 2 groups: transfusion dependent group and transfusion independent group. The 3 and 5-year overall survival rates (OS) for transfusion dependent group were 89.9% and for transfusion independent group were 70.6% respectively, the 5-year overall survival rate (OS) for transfusion independent group was 100.0%. (3) Treatment strategy: patients from transfusion dependent group were treated with immunosuppression therapy (IST), in which CsA combined with or without ATG and patients were treated with HSCT. The 5-year overall survival rate (OS) was 100% for IST group. The 3 and 5-year overall survival rates (OS) for HSCT patients were 82.9% and 30.6%, respectively. All the patients from transfusion independent group were alive till the last follow up. (4) Compared with patients from our hospital and Japan, the 5-year overall survival rate (OS) for patients in our hospital was 100.0%, the 3-and 5-year overall survival rates (OS) for patients in Japan were 93.7% and 75.0%, respectively. It is concluded that children with MDS-RCC are seldom progressive. The observation and wait strategy is applicable for patients with MDS-RCC who have no

  8. 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.

  9. 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.

  10. Multivariate hydrological frequency analysis for extreme events using Archimedean copula. Case study: Lower Tunjuelo River basin (Colombia)

    Science.gov (United States)

    Gómez, Wilmar

    2017-04-01

    By analyzing the spatial and temporal variability of extreme precipitation events we can prevent or reduce the threat and risk. Many water resources projects require joint probability distributions of random variables such as precipitation intensity and duration, which can not be independent with each other. The problem of defining a probability model for observations of several dependent variables is greatly simplified by the joint distribution in terms of their marginal by taking copulas. This document presents a general framework set frequency analysis bivariate and multivariate using Archimedean copulas for extreme events of hydroclimatological nature such as severe storms. This analysis was conducted in the lower Tunjuelo River basin in Colombia for precipitation events. The results obtained show that for a joint study of the intensity-duration-frequency, IDF curves can be obtained through copulas and thus establish more accurate and reliable information from design storms and associated risks. It shows how the use of copulas greatly simplifies the study of multivariate distributions that introduce the concept of joint return period used to represent the needs of hydrological designs properly in frequency analysis.

  11. Multivariate statistical analysis of water chemistry conditions in three wastewater stabilization ponds with algae blooms and pH fluctuations.

    Science.gov (United States)

    Wallace, Jack; Champagne, Pascale; Hall, Geof

    2016-06-01

    The wastewater stabilization ponds (WSPs) at a wastewater treatment facility in eastern Ontario, Canada, have experienced excessive algae growth and high pH levels in the summer months. A full range of parameters were sampled from the system and the chemical dynamics in the three WSPs were assessed through multivariate statistical analysis. The study presents a novel approach for exploratory analysis of a comprehensive water chemistry dataset, incorporating principal components analysis (PCA) and principal components (PC) and partial least squares (PLS) regressions. The analyses showed strong correlations between chl-a and sunlight, temperature, organic matter, and nutrients, and weak and negative correlations between chl-a and pH and chl-a and DO. PCA reduced the data from 19 to 8 variables, with a good fit to the original data matrix (similarity measure of 0.73). Multivariate regressions to model system pH in terms of these key parameters were performed on the reduced variable set and the PCs generated, for which strong fits (R(2) > 0.79 with all data) were observed. The methodologies presented in this study are applicable to a wide range of natural and engineered systems where a large number of water chemistry parameters are monitored resulting in the generation of large data sets. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. Multivariate approach to quantitative analysis of Aphis gossypii Glover (Hemiptera: Aphididae) and their natural enemy populations at different cotton spacings

    Science.gov (United States)

    Malaquias, José B.; Ramalho, Francisco S.; Dos S. Dias, Carlos T.; Brugger, Bruno P.; S. Lira, Aline Cristina; Wilcken, Carlos F.; Pachú, Jéssica K. S.; Zanuncio, José C.

    2017-02-01

    The relationship between pests and natural enemies using multivariate analysis on cotton in different spacing has not been documented yet. Using multivariate approaches is possible to optimize strategies to control Aphis gossypii at different crop spacings because the possibility of a better use of the aphid sampling strategies as well as the conservation and release of its natural enemies. The aims of the study were (i) to characterize the temporal abundance data of aphids and its natural enemies using principal components, (ii) to analyze the degree of correlation between the insects and between groups of variables (pests and natural enemies), (iii) to identify the main natural enemies responsible for regulating A. gossypii populations, and (iv) to investigate the similarities in arthropod occurrence patterns at different spacings of cotton crops over two seasons. High correlations in the occurrence of Scymnus rubicundus with aphids are shown through principal component analysis and through the important role the species plays in canonical correlation analysis. Clustering the presence of apterous aphids matches the pattern verified for Chrysoperla externa at the three different spacings between rows. Our results indicate that S. rubicundus is the main candidate to regulate the aphid populations in all spacings studied.

  13. Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging.

    Science.gov (United States)

    Falahati, Farshad; Westman, Eric; Simmons, Andrew

    2014-01-01

    Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Advances in medical imaging and medical image analysis have provided a means to generate and extract valuable neuroimaging information. Automatic classification techniques provide tools to analyze this information and observe inherent disease-related patterns in the data. In particular, these classifiers have been used to discriminate AD patients from healthy control subjects and to predict conversion from mild cognitive impairment to AD. In this paper, recent studies are reviewed that have used machine learning and multivariate analysis in the field of AD research. The main focus is on studies that used structural magnetic resonance imaging (MRI), but studies that included positron emission tomography and cerebrospinal fluid biomarkers in addition to MRI are also considered. A wide variety of materials and methods has been employed in different studies, resulting in a range of different outcomes. Influential factors such as classifiers, feature extraction algorithms, feature selection methods, validation approaches, and cohort properties are reviewed, as well as key MRI-based and multi-modal based studies. Current and future trends are discussed.

  14. Integration of multivariate empirical mode decomposition and independent component analysis for fetal ECG separation from abdominal signals.

    Science.gov (United States)

    Thanaraj, Palani; Roshini, Mable; Balasubramanian, Parvathavarthini

    2016-11-14

    The fetal electrocardiogram (FECG) signals are essential to monitor the health condition of the baby. Fetal heart rate (FHR) is commonly used for diagnosing certain abnormalities in the formation of the heart. Usually, non-invasive abdominal electrocardiogram (AbECG) signals are obtained by placing surface electrodes in the abdomen region of the pregnant woman. AbECG signals are often not suitable for the direct analysis of fetal heart activity. Moreover, the strength and magnitude of the FECG signals are low compared to the maternal electrocardiogram (MECG) signals. The MECG signals are often superimposed with the FECG signals that make the monitoring of FECG signals a difficult task. Primary goal of the paper is to separate the fetal electrocardiogram (FECG) signals from the unwanted maternal electrocardiogram (MECG) signals. A multivariate signal processing procedure is proposed here that combines the Multivariate Empirical Mode Decomposition (MEMD) and Independent Component Analysis (ICA). The proposed method is evaluated with clinical abdominal signals taken from three pregnant women (N= 3) recorded during the 38-41 weeks of the gestation period. The number of fetal R-wave detected (NEFQRS), the number of unwanted maternal peaks (NMQRS), the number of undetected fetal R-wave (NUFQRS) and the FHR detection accuracy quantifies the performance of our method. Clinical investigation with three test subjects shows an overall detection accuracy of 92.8%. Comparative analysis with benchmark signal processing method such as ICA suggests the noteworthy performance of our method.

  15. 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

  16. 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...

  17. 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

  18. missMDA: A Package for Handling Missing Values in Multivariate Data Analysis

    Directory of Open Access Journals (Sweden)

    Julie Josse

    2016-04-01

    Full Text Available We present the R package missMDA which performs principal component methods on incomplete data sets, aiming to obtain scores, loadings and graphical representations despite missing values. Package methods include principal component analysis for continuous variables, multiple correspondence analysis for categorical variables, factorial analysis on mixed data for both continuous and categorical variables, and multiple factor analysis for multi-table data. Furthermore, missMDA can be used to perform single imputation to complete data involving continuous, categorical and mixed variables. A multiple imputation method is also available. In the principal component analysis framework, variability across different imputations is represented by confidence areas around the row and column positions on the graphical outputs. This allows assessment of the credibility of results obtained from incomplete data sets.

  19. Detecting phase separation of freeze-dried binary amorphous systems using pair-wise distribution function and multivariate data analysis

    DEFF Research Database (Denmark)

    Chieng, Norman; Trnka, Hjalte; Boetker, Johan

    2013-01-01

    The purpose of this study is to investigate the use of multivariate data analysis for powder X-ray diffraction-pair-wise distribution function (PXRD-PDF) data to detect phase separation in freeze-dried binary amorphous systems. Polymer-polymer and polymer-sugar binary systems at various ratios were...... freeze-dried. All samples were analyzed by PXRD, transformed to PDF and analyzed by principal component analysis (PCA). These results were validated by differential scanning calorimetry (DSC) through characterization of glass transition of the maximally freeze-concentrate solute (Tg'). Analysis of PXRD......-PDF data using PCA provides a more clear 'miscible' or 'phase separated' interpretation through the distribution pattern of samples on a score plot presentation compared to residual plot method. In a phase separated system, samples were found to be evenly distributed around the theoretical PDF profile...

  20. Multivariate statistical analysis of the polyphenolic constituents in kiwifruit juices to trace fruit varieties and geographical origins.

    Science.gov (United States)

    Guo, Jing; Yuan, Yahong; Dou, Pei; Yue, Tianli

    2017-10-01

    Fifty-one kiwifruit juice samples of seven kiwifruit varieties from five regions in China were analyzed to determine their polyphenols contents and to trace fruit varieties and geographical origins by multivariate statistical analysis. Twenty-one polyphenols belonging to four compound classes were determined by ultra-high-performance liquid chromatography coupled with ultra-high-resolution TOF mass spectrometry. (-)-Epicatechin, (+)-catechin, procyanidin B1 and caffeic acid derivatives were the predominant phenolic compounds in the juices. Principal component analysis (PCA) allowed a clear separation of the juices according to kiwifruit varieties. Stepwise linear discriminant analysis (SLDA) yielded satisfactory categorization of samples, provided 100% success rate according to kiwifruit varieties and 92.2% success rate according to geographical origins. The result showed that polyphenolic profiles of kiwifruit juices contain enough information to trace fruit varieties and geographical origins. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Development of methodology for identification the nature of the polyphenolic extracts by FTIR associated with multivariate analysis

    Science.gov (United States)

    Grasel, Fábio dos Santos; Ferrão, Marco Flôres; Wolf, Carlos Rodolfo

    2016-01-01

    Tannins are polyphenolic compounds of complex structures formed by secondary metabolism in several plants. These polyphenolic compounds have different applications, such as drugs, anti-corrosion agents, flocculants, and tanning agents. This study analyses six different type of polyphenolic extracts by Fourier transform infrared spectroscopy (FTIR) combined with multivariate analysis. Through both principal component analysis (PCA) and hierarchical cluster analysis (HCA), we observed well-defined separation between condensed (quebracho and black wattle) and hydrolysable (valonea, chestnut, myrobalan, and tara) tannins. For hydrolysable tannins, it was also possible to observe the formation of two different subgroups between samples of chestnut and valonea and between samples of tara and myrobalan. Among all samples analysed, the chestnut and valonea showed the greatest similarity, indicating that these extracts contain equivalent chemical compositions and structure and, therefore, similar properties.

  2. Understanding Cancer Prognosis

    Medline Plus

    Full Text Available ... your chances of survival. The estimate of how the disease will go for you is called prognosis. It ... to discuss cancer prognosis (the likely course of the disease). Learn key points about prognosis and how to ...

  3. Understanding Cancer Prognosis

    Medline Plus

    Full Text Available ... disease will go for you is called prognosis. It can be hard to understand what prognosis means ... prognosis include: The type of cancer and where it is in your body The stage of the ...

  4. Quality-by-Design Case Study: Investigation of the Role of Poloxamer in Immediate-Release Tablets by Experimental Design and Multivariate Data Analysis

    OpenAIRE

    Kaul, Goldi; Huang, Jun; Chatlapalli, Ramarao; Ghosh, Krishnendu; Nagi, Arwinder

    2011-01-01

    The role of poloxamer 188, water and binder addition rate, on retarding dissolution in immediate-release tablets of a model drug from BCS class II was investigated by means of multivariate data analysis (MVDA) combined with design of experiments (DOE). While the DOE analysis yielded important clues into the cause-and-effect relationship between the responses and design factors, multivariate data analysis of the 40+ variables provided additional information on slowdown in tablet dissolution. A...

  5. Gray-Level Co-Occurrence Matrix Texture Analysis of Breast Tumor Images in Prognosis of Distant Metastasis Risk.

    Science.gov (United States)

    Vujasinovic, Tijana; Pribic, Jelena; Kanjer, Ksenija; Milosevic, Nebojsa T; Tomasevic, Zorica; Milovanovic, Zorka; Nikolic-Vukosavljevic, Dragica; Radulovic, Marko

    2015-06-01

    Owing to exceptional heterogeneity in the outcome of invasive breast cancer it is essential to develop highly accurate prognostic tools for effective therapeutic management. Based on this pressing need, we aimed to improve breast cancer prognosis by exploring the prognostic value of tumor histology image analysis. Patient group (n=78) selection was based on invasive breast cancer diagnosis without systemic treatment with a median follow-up of 147 months. Gray-level co-occurrence matrix texture analysis was performed retrospectively on primary tumor tissue section digital images stained either nonspecifically with hematoxylin and eosin or specifically with a pan-cytokeratin antibody cocktail for epithelial malignant cells. Univariate analysis revealed stronger association with metastasis risk by texture analysis when compared with clinicopathological parameters. The combination of individual clinicopathological and texture variables into composite scores resulted in further powerful enhancement of prognostic performance, with an accuracy of up to 90%, discrimination efficiency by the area under the curve [95% confidence interval (CI)] of 0.94 (0.87-0.99) and hazard ratio (95% CI) of 20.1 (7.5-109.4). Internal validation was successfully performed by bootstrap and split-sample cross-validation, suggesting that the models are generalizable. Whereas further validation is needed on an external set of patients, this preliminary study indicates the potential use of primary breast tumor histology texture as a highly accurate, simple, and cost-effective prognostic indicator of distant metastasis risk.

  6. Copeptin as a biomarker for prediction of prognosis of acute ischemic stroke and transient ischemic attack: a meta-analysis.

    Science.gov (United States)

    Xu, Qian; Tian, Yunfan; Peng, Hao; Li, Hongmei

    2017-05-01

    This meta-analysis aimed to investigate the predictive effect of copeptin as a biomarker for the prognosis of acute ischemic stroke and transient ischemic attack. Electronic databases including PubMed, Medline, EMBASE, Web of Science and Cochrane Central were searched for studies assessing the association of copeptin level on admission with prognosis of acute ischemic stroke and transient ischemic attack. The Newcastle-Ottawa Quality assessment scale for cohort study was used to evaluate quality. A total of 1976 acute ischemic stroke patients from 6 studies were included, and 59% of patients were male. Patients with poor outcomes and nonsurvivors had a higher copeptin level at admission (PCopeptin combined with an admission National Institutes of Health Stroke Scale score significantly improved the discriminatory accuracy of functional outcome and mortality compared with the National Institutes of Health Stroke Scale alone. Elevation in plasma copeptin level carried a higher risk of all-cause mortality (odds ratio=4.16; 95% CI: 2.77-6.25) and poor functional outcome (odds ratio=2.56; 95% CI: 1.97-3.32) after acute ischemic stroke. In addition, copeptin improved the prognostic value of the ABCD2 (age, blood pressure, clinical features of transient ischemic attack, duration of symptoms and presence of diabetes mellitus) score for a recurrent cerebrovascular event in transient ischemic attack. Copeptin seems to be a promising independent biomarker for predicting the functional outcome and all-cause mortality within 3 months or 1 year after acute ischemic stroke, and it could also be a powerful tool for early risk stratification for patients with transient ischemic attack.

  7. Geographical variation of unmet medical needs in Italy: a multivariate logistic regression analysis.

    Science.gov (United States)

    Cavalieri, Marina

    2013-05-12

    Unmet health needs should be, in theory, a minor issue in Italy where a publicly funded and universally accessible health system exists. This, however, does not seem to be the case. Moreover, in the last two decades responsibilities for health care have been progressively decentralized to regional governments, which have differently organized health service delivery within their territories. Regional decision-making has affected the use of health care services, further increasing the existing geographical disparities in the access to care across the country. This study aims at comparing self-perceived unmet needs across Italian regions and assessing how the reported reasons - grouped into the categories of availability, accessibility and acceptability - vary geographically. Data from the 2006 Italian component of the European Union Statistics on Income and Living Conditions are employed to explore reasons and predictors of self-reported unmet medical needs among 45,175 Italian respondents aged 18 and over. Multivariate logistic regression models are used to determine adjusted rates for overall unmet medical needs and for each of the three categories of reasons. Results show that, overall, 6.9% of the Italian population stated having experienced at least one unmet medical need during the last 12 months. The unadjusted rates vary markedly across regions, thus resulting in a clear-cut north-south divide (4.6% in the North-East vs. 10.6% in the South). Among those reporting unmet medical needs, the leading reason was problems of accessibility related to cost or transportation (45.5%), followed by acceptability (26.4%) and availability due to the presence of too long waiting lists (21.4%). In the South, more than one out of two individuals with an unmet need refrained from seeing a physician due to economic reasons. In the northern regions, working and family responsibilities contribute relatively more to the underutilization of medical services. Logistic regression

  8. 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

  9. 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.

  10. Brief Report: Some Possible Uses Of Factor Analysis In Multivariate Studies.

    Science.gov (United States)

    Stogdill, R M

    1966-07-01

    Factor analysis is used in item selection in the hopes of producing a small number of factors each of which will represent a unidimensional sub- scale. If item analysis has been successful in producing truly independent subscales, it might be hoped that the number of factors would equal the number of subscales and that each factor would be highly defined by a single subscale. Factor analysis when used in studies of organization, is not assumed to produce factors that represent unidimensional scales. Rather, factor analysis is used to reveal various substructures that exist within an organization. If several variables are loaded on a single factor, the variables can be regarded as nodes of interaction between measured dimensions of organization.

  11. MethSurv: a web tool to perform multivariable survival analysis using DNA methylation data.

    Science.gov (United States)

    Modhukur, Vijayachitra; Iljasenko, Tatjana; Metsalu, Tauno; Lokk, Kaie; Laisk-Podar, Triin; Vilo, Jaak

    2017-12-21

    To develop a web tool for survival analysis based on CpG methylation patterns. We utilized methylome data from 'The Cancer Genome Atlas' and used the Cox proportional-hazards model to develop an interactive web interface for survival analysis. MethSurv enables survival analysis for a CpG located in or around the proximity of a query gene. For further mining, cluster analysis for a query gene to associate methylation patterns with clinical characteristics and browsing of top biomarkers for each cancer type are provided. MethSurv includes 7358 methylomes from 25 different human cancers. The MethSurv tool is a valuable platform for the researchers without programming skills to perform the initial assessment of methylation-based cancer biomarkers.

  12. 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

    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...

  13. MULTIVARIATE ANALYSIS OF MACROINVERTEBRATE ASSEMBLAGES TO DETERMINE IMPACTS ON ROCKY MOUNTAIN STREAM ECOSYSTEMS

    Science.gov (United States)

    Using reduncancy (RDA) and canonical correlation analysis (CCA) we assessed relationships between chemical and physical characteristics and periphyton at 105 stream sites sampled by REMAP in the mineral belt of the southern Rockies ecoregion in Colorado. We contrasted results ob...

  14. Lipid mapping of colonic mucosa by cluster TOF-SIMS imaging and multivariate analysis in cftr knockout mice[S

    Science.gov (United States)

    Brulet, Marc; Seyer, Alexandre; Edelman, Aleksander; Brunelle, Alain; Fritsch, Janine; Ollero, Mario; Laprévote, Olivier

    2010-01-01

    The cftr knockout mouse model of cystic fibrosis (CF) shows intestinal obstruction; malabsorption and inflammation; and a fatty acid imbalance in intestinal mucosa. We performed a lipid mapping of colon sections from CF and control (WT) mice by cluster time of flight secondary-ion mass spectrometry (TOF-SIMS) imaging to localize lipid alterations. Data were processed either manually or by multivariate statistical methods. TOF-SIMS analysis showed a particular localization for cholesteryl sulfate at the epithelial border, C16:1 fatty acid in Lieberkühn glands, and C18:0 fatty acid in lamina propria and submucosa. Significant increases in vitamin E (vE) and C16:0 fatty acid in the epithelial border of CF colon were detected. Principal component analysis (PCA) and partitioning clustering allowed us to characterize different structural regions of colonic mucosa according to variations in C14:0, C16:0, C16:1, C18:0, C18:1, C18:2, C20:3, C20:4, and C22:6 fatty acids; phosphatidylethanolamine, phosphatidylcholine, and phosphatidylinositol glycerolipids; cholesterol; vitamin E; and cholesteryl sulfate. PCA on spectra from Lieberkühn glands led to separation of CF and WT individuals. This study shows for the first time the spatial distrib