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Sample records for automatic multivariable analysis

  1. Automatic and objective oral cancer diagnosis by Raman spectroscopic detection of keratin with multivariate curve resolution analysis

    NASA Astrophysics Data System (ADS)

    Chen, Po-Hsiung; Shimada, Rintaro; Yabumoto, Sohshi; Okajima, Hajime; Ando, Masahiro; Chang, Chiou-Tzu; Lee, Li-Tzu; Wong, Yong-Kie; Chiou, Arthur; Hamaguchi, Hiro-O.

    2016-01-01

    We have developed an automatic and objective method for detecting human oral squamous cell carcinoma (OSCC) tissues with Raman microspectroscopy. We measure 196 independent Raman spectra from 196 different points of one oral tissue sample and globally analyze these spectra using a Multivariate Curve Resolution (MCR) analysis. Discrimination of OSCC tissues is automatically and objectively made by spectral matching comparison of the MCR decomposed Raman spectra and the standard Raman spectrum of keratin, a well-established molecular marker of OSCC. We use a total of 24 tissue samples, 10 OSCC and 10 normal tissues from the same 10 patients, 3 OSCC and 1 normal tissues from different patients. Following the newly developed protocol presented here, we have been able to detect OSCC tissues with 77 to 92% sensitivity (depending on how to define positivity) and 100% specificity. The present approach lends itself to a reliable clinical diagnosis of OSCC substantiated by the “molecular fingerprint” of keratin.

  2. Multivariate Quantitative Chemical Analysis

    NASA Technical Reports Server (NTRS)

    Kinchen, David G.; Capezza, Mary

    1995-01-01

    Technique of multivariate quantitative chemical analysis devised for use in determining relative proportions of two components mixed and sprayed together onto object to form thermally insulating foam. Potentially adaptable to other materials, especially in process-monitoring applications in which necessary to know and control critical properties of products via quantitative chemical analyses of products. In addition to chemical composition, also used to determine such physical properties as densities and strengths.

  3. Multivariate analysis in thoracic research

    PubMed Central

    Mengual-Macenlle, Noemí; Marcos, Pedro J.; Golpe, Rafael

    2015-01-01

    Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use. PMID:25922743

  4. Multivariate analysis in thoracic research.

    PubMed

    Mengual-Macenlle, Noemí; Marcos, Pedro J; Golpe, Rafael; González-Rivas, Diego

    2015-03-01

    Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use.

  5. Multivariate Data Analysis

    DTIC Science & Technology

    1975-02-03

    the anthropometrists, biologists, and psychologists of that era. Such initial contributors to modern statistics as Francis Galton and Karl Pearson...1159-78. [5] Galton , Francis (1888), "Co-relations and Their Measurements, Chiefly from Anthropometric Data," Proceedings of the...stem from that period. Galton seemed to be perpetually engaged in data analysis. He and his cousin, Darwin, and others revolved in an age of

  6. MvDAT: Multivariate Dependence Analysis Toolbox

    NASA Astrophysics Data System (ADS)

    Sadegh, M.; Ragno, E.; AghaKouchak, A.

    2016-12-01

    Hydrologic and climatic variables are interdependent, and it is often necessary to analyze association among variables using multivariate methods. Univariate marginal distributions may not be sufficient to describe hydrologic variables (or events) that bear intrinsic multivariate characteristics. The concept of copula is widely used to model the dependence structure of two (or more) random variables. Multivariate methods and copulas have been used in drought monitoring, frequency analysis, and extreme value analysis, among others. Here, we present a newly developed MultiVariate Dependence Analysis Toolbox (MvDAT) for assessing the dependence structure of target variables using 26 copulas. Copulas included in MvDAT differ in complexity with one to three tunable parameters. The Graphical User Interface (GUI) of this program enables users to conveniently browse the input data, select the desired copula family (one, multiple, or all), and finally choose the optimization approach (local/global) for dependence analysis. The program will automatically plot posterior parameter distributions of selected copula(s), if global optimization is selected, as well as fitted versus empirical probability isolines. Moreover, a summary report is automatically generated that rank the performance of selected copulas based on Maximum Likelihood, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Summary report also details on the best and 95% uncertainty ranges of parameters of each copula, and its best performance in terms of root mean square error (RMSE) and Nash-Sutcliff efficiency (NSE) criteria. This package is developed in MATLAB and enables the community to perform dependence analysis using a more rigorous and comprehensive approach.

  7. Multivariate image analysis in biomedicine.

    PubMed

    Nattkemper, Tim W

    2004-10-01

    In recent years, multivariate imaging techniques are developed and applied in biomedical research in an increasing degree. In research projects and in clinical studies as well m-dimensional multivariate images (MVI) are recorded and stored to databases for a subsequent analysis. The complexity of the m-dimensional data and the growing number of high throughput applications call for new strategies for the application of image processing and data mining to support the direct interactive analysis by human experts. This article provides an overview of proposed approaches for MVI analysis in biomedicine. After summarizing the biomedical MVI techniques the two level framework for MVI analysis is illustrated. Following this framework, the state-of-the-art solutions from the fields of image processing and data mining are reviewed and discussed. Motivations for MVI data mining in biology and medicine are characterized, followed by an overview of graphical and auditory approaches for interactive data exploration. The paper concludes with summarizing open problems in MVI analysis and remarks upon the future development of biomedical MVI analysis.

  8. Method of multivariate spectral analysis

    DOEpatents

    Keenan, Michael R.; Kotula, Paul G.

    2004-01-06

    A method of determining the properties of a sample from measured spectral data collected from the sample by performing a multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used to analyze X-ray spectral data generated by operating a Scanning Electron Microscope (SEM) with an attached Energy Dispersive Spectrometer (EDS).

  9. Multivariate Longitudinal Analysis with Bivariate Correlation Test.

    PubMed

    Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

    2016-01-01

    In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated.

  10. Multivariate meta-analysis: potential and promise.

    PubMed

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

    2011-09-10

    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.

  11. Detrended fluctuation analysis of multivariate time series

    NASA Astrophysics Data System (ADS)

    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.

  12. Basics of Multivariate Analysis in Neuroimaging Data

    PubMed Central

    Habeck, Christian Georg

    2010-01-01

    Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques1,5,6,7,8,9. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic

  13. Multivariate meta-analysis: Potential and promise

    PubMed Central

    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

  14. Cross-Modal Multivariate Pattern Analysis

    PubMed Central

    Meyer, Kaspar; Kaplan, Jonas T.

    2011-01-01

    Multivariate pattern analysis (MVPA) is an increasingly popular method of analyzing functional magnetic resonance imaging (fMRI) data1-4. Typically, the method is used to identify a subject's perceptual experience from neural activity in certain regions of the brain. For instance, it has been employed to predict the orientation of visual gratings a subject perceives from activity in early visual cortices5 or, analogously, the content of speech from activity in early auditory cortices6. Here, we present an extension of the classical MVPA paradigm, according to which perceptual stimuli are not predicted within, but across sensory systems. Specifically, the method we describe addresses the question of whether stimuli that evoke memory associations in modalities other than the one through which they are presented induce content-specific activity patterns in the sensory cortices of those other modalities. For instance, seeing a muted video clip of a glass vase shattering on the ground automatically triggers in most observers an auditory image of the associated sound; is the experience of this image in the "mind's ear" correlated with a specific neural activity pattern in early auditory cortices? Furthermore, is this activity pattern distinct from the pattern that could be observed if the subject were, instead, watching a video clip of a howling dog? In two previous studies7,8, we were able to predict sound- and touch-implying video clips based on neural activity in early auditory and somatosensory cortices, respectively. Our results are in line with a neuroarchitectural framework proposed by Damasio9,10, according to which the experience of mental images that are based on memories - such as hearing the shattering sound of a vase in the "mind's ear" upon seeing the corresponding video clip - is supported by the re-construction of content-specific neural activity patterns in early sensory cortices. PMID:22105246

  15. Automatic Microwave Network Analysis.

    DTIC Science & Technology

    A program and procedure are developed for the automatic measurement of microwave networks using a Hewlett-Packard network analyzer and programmable calculator . The program and procedure are used in the measurement of a simple microwave two port network. These measurements are evaluated by comparing with measurements on the same network using other techniques. The programs...in the programmable calculator are listed in Appendix 1. The step by step procedure used is listed in Appendix 2. (Author)

  16. Automatic cephalometric analysis.

    PubMed

    Leonardi, Rosalia; Giordano, Daniela; Maiorana, Francesco; Spampinato, Concetto

    2008-01-01

    To describe the techniques used for automatic landmarking of cephalograms, highlighting the strengths and weaknesses of each one and reviewing the percentage of success in locating each cephalometric point. The literature survey was performed by searching the Medline, the Institute of Electrical and Electronics Engineers, and the ISI Web of Science Citation Index databases. The survey covered the period from January 1966 to August 2006. Abstracts that appeared to fulfill the initial selection criteria were selected by consensus. The original articles were then retrieved. Their references were also hand-searched for possible missing articles. The search strategy resulted in 118 articles of which eight met the inclusion criteria. Many articles were rejected for different reasons; among these, the most frequent was that results of accuracy for automatic landmark recognition were presented as a percentage of success. A marked difference in results was found between the included studies consisting of heterogeneity in the performance of techniques to detect the same landmark. All in all, hybrid approaches detected cephalometric points with a higher accuracy in contrast to the results for the same points obtained by the model-based, image filtering plus knowledge-based landmark search and "soft-computing" approaches. The systems described in the literature are not accurate enough to allow their use for clinical purposes. Errors in landmark detection were greater than those expected with manual tracing and, therefore, the scientific evidence supporting the use of automatic landmarking is low.

  17. Multivariate Analysis in Personnel Selection

    ERIC Educational Resources Information Center

    Mays, Robert

    1976-01-01

    Variables used in applicant selection into the British Civil Service were analyzed for a sample using varimax factor analysis, multidimensional scaling and multiple regression. Results indicated that four dimensions or factors of applicant selection were consistent across a variety of settings and analyses. (JKS)

  18. Multivariate Longitudinal Analysis with Bivariate Correlation Test

    PubMed Central

    Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

    2016-01-01

    In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

  19. Multivariate analysis: A statistical approach for computations

    NASA Astrophysics Data System (ADS)

    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. Schmidt decomposition and multivariate statistical analysis

    NASA Astrophysics Data System (ADS)

    Bogdanov, Yu. I.; Bogdanova, N. A.; Fastovets, D. V.; Luckichev, V. F.

    2016-12-01

    The new method of multivariate data analysis based on the complements of classical probability distribution to quantum state and Schmidt decomposition is presented. We considered Schmidt formalism application to problems of statistical correlation analysis. Correlation of photons in the beam splitter output channels, when input photons statistics is given by compound Poisson distribution is examined. The developed formalism allows us to analyze multidimensional systems and we have obtained analytical formulas for Schmidt decomposition of multivariate Gaussian states. It is shown that mathematical tools of quantum mechanics can significantly improve the classical statistical analysis. The presented formalism is the natural approach for the analysis of both classical and quantum multivariate systems and can be applied in various tasks associated with research of dependences.

  1. Multivariate Analysis of Ladle Vibration

    NASA Astrophysics Data System (ADS)

    Yenus, Jaefer; Brooks, Geoffrey; Dunn, Michelle

    2016-08-01

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

  2. Adjustment of automatic control systems of production facilities at coal processing plants using multivariant physico- mathematical models

    NASA Astrophysics Data System (ADS)

    Evtushenko, V. F.; Myshlyaev, L. P.; Makarov, G. V.; Ivushkin, K. A.; Burkova, E. V.

    2016-10-01

    The structure of multi-variant physical and mathematical models of control system is offered as well as its application for adjustment of automatic control system (ACS) of production facilities on the example of coal processing plant.

  3. Multivariate analysis: greater insights into complex systems

    USDA-ARS?s Scientific Manuscript database

    Many agronomic researchers measure and collect multiple response variables in an effort to understand the more complex nature of the system being studied. Multivariate (MV) statistical methods encompass the simultaneous analysis of all random variables (RV) measured on each experimental or sampling ...

  4. Classical least squares multivariate spectral analysis

    DOEpatents

    Haaland, David M.

    2002-01-01

    An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.

  5. Automatic Error Analysis Using Intervals

    ERIC Educational Resources Information Center

    Rothwell, E. J.; Cloud, M. J.

    2012-01-01

    A technique for automatic error analysis using interval mathematics is introduced. A comparison to standard error propagation methods shows that in cases involving complicated formulas, the interval approach gives comparable error estimates with much less effort. Several examples are considered, and numerical errors are computed using the INTLAB…

  6. Automatic Error Analysis Using Intervals

    ERIC Educational Resources Information Center

    Rothwell, E. J.; Cloud, M. J.

    2012-01-01

    A technique for automatic error analysis using interval mathematics is introduced. A comparison to standard error propagation methods shows that in cases involving complicated formulas, the interval approach gives comparable error estimates with much less effort. Several examples are considered, and numerical errors are computed using the INTLAB…

  7. Automatic Linguistic Analysis.

    ERIC Educational Resources Information Center

    Coker, Pamela L.; Underwood, Mark A.

    Computer programs for linguistic analysis of language samples from bilingual children were surveyed in order to evaluate their usefulness. Eight programs which could be implemented on the UCLA IBM 370/3033 computer were considered. It was determined that the Computer Assisted Language Analysis System was the most promising in terms of capabilities…

  8. Hybrid least squares multivariate spectral analysis methods

    DOEpatents

    Haaland, David M.

    2002-01-01

    A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following estimation or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The "hybrid" method herein means a combination of an initial classical least squares analysis calibration step with subsequent analysis by an inverse multivariate analysis method. A "spectral shape" herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The "shape" can be continuous, discontinuous, or even discrete points illustrative of the particular effect.

  9. Multivariate analysis of longitudinal rates of change.

    PubMed

    Bryan, Matthew; Heagerty, Patrick J

    2016-12-10

    Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed in the literature. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, 'accelerated time' methods have been developed which assume that covariates rescale time in longitudinal models for disease progression. In this manuscript, we detail an alternative multivariate model formulation that directly structures longitudinal rates of change and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. Copyright © 2016 John Wiley & Sons, Ltd.

  10. Hybrid least squares multivariate spectral analysis methods

    DOEpatents

    Haaland, David M.

    2004-03-23

    A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following prediction or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The hybrid method herein means a combination of an initial calibration step with subsequent analysis by an inverse multivariate analysis method. A spectral shape herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The shape can be continuous, discontinuous, or even discrete points illustrative of the particular effect.

  11. Multivariate analysis of endometrial tissue fluorescence spectra

    NASA Astrophysics Data System (ADS)

    Vaitkuviene, Aurelija; Auksorius, E.; Fuchs, D.; Gavriushin, V.

    2002-10-01

    Background and Objective: The detailed multivariate analysis of endometrial tissue fluorescence spectra was done. Spectra underlying features and classification algorithm were analyzed. An effort has been made to determine the importance of neopterin component in endometrial premalignization. Study Design/Materials and Methods: Biomedical tissue fluorescence was measured by excitation with the Nd YAG laser third harmonic. Multivariate analysis techniques were used to analyze fluorescence spectra. Biomedical optics group at Vilnius University analyzed the neopterin substance supplied by the Institute of Medical Chemistry and Biochemistry of Innsbruck University. Results: Seven statistically significant spectral compounds were found. The classification algorithm classifying samples to histopathological categories was developed and resulted in sensitivity of 80% and specificity 93% for malignant vs. hyperplastic and normal. Conclusions: Fluorescence spectra could be classified with high accuracy. Spectral variation underlying features can be extracted. Neopterin component might play an important role in endometrial hyperplasia development.

  12. Toward automatic finite element analysis

    NASA Technical Reports Server (NTRS)

    Kela, Ajay; Perucchio, Renato; Voelcker, Herbert

    1987-01-01

    Two problems must be solved if the finite element method is to become a reliable and affordable blackbox engineering tool. Finite element meshes must be generated automatically from computer aided design databases and mesh analysis must be made self-adaptive. The experimental system described solves both problems in 2-D through spatial and analytical substructuring techniques that are now being extended into 3-D.

  13. PYCHEM: a multivariate analysis package for python.

    PubMed

    Jarvis, Roger M; Broadhurst, David; Johnson, Helen; O'Boyle, Noel M; Goodacre, Royston

    2006-10-15

    We have implemented a multivariate statistical analysis toolbox, with an optional standalone graphical user interface (GUI), using the Python scripting language. This is a free and open source project that addresses the need for a multivariate analysis toolbox in Python. Although the functionality provided does not cover the full range of multivariate tools that are available, it has a broad complement of methods that are widely used in the biological sciences. In contrast to tools like MATLAB, PyChem 2.0.0 is easily accessible and free, allows for rapid extension using a range of Python modules and is part of the growing amount of complementary and interoperable scientific software in Python based upon SciPy. One of the attractions of PyChem is that it is an open source project and so there is an opportunity, through collaboration, to increase the scope of the software and to continually evolve a user-friendly platform that has applicability across a wide range of analytical and post-genomic disciplines. http://sourceforge.net/projects/pychem

  14. Application of multi-variable control for automatic frequency controller of HVDC transmission system

    SciTech Connect

    Sanpei, Masatoshi ); Kakehi, Atsuyuki; Takeda, Hideo )

    1994-04-01

    In an HVDC transmission system that links two ac power systems, the automatic frequency controller (AFC) calculates power to be interchanged between the two ac systems according to their frequencies thereby improving the frequency characteristics of the two power systems. This paper introduces a newly developed dc AFC system, which applies a multi-variable control to the dc system-based frequency control. It is capable of controlling the frequencies of the two ac systems optimally while maintaining their stability. This system was developed for one of Japan's HVDC transmission facilities and produced good results in a combined test using a power system simulator. The field installation will be completed in March 1993, when the AFC system will enter service.

  15. Classification of adulterated honeys by multivariate analysis.

    PubMed

    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%).

  16. Tailored multivariate analysis for modulated enhanced diffraction

    SciTech Connect

    Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni; Tutuncu, Goknur; Hanson, Jonathan C.

    2015-10-21

    Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scores and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. Furthermore, the multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). Furthermore, when applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. In order to develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.

  17. Tailored multivariate analysis for modulated enhanced diffraction

    SciTech Connect

    Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni; Tutuncu, Goknur; Hanson, Jonathan C.

    2015-10-21

    Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scores and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. The multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). When applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. To develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.

  18. Tailored multivariate analysis for modulated enhanced diffraction

    DOE PAGES

    Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni; ...

    2015-10-21

    Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scoresmore » and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. Furthermore, the multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). Furthermore, when applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. In order to develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.« less

  19. Multivariate Analysis of Genotype–Phenotype Association

    PubMed Central

    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 variables) that are maximally associated—in terms of effect size—with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype–phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype–phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype–phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype–phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3—the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the

  20. Multivariate Analysis of Genotype-Phenotype Association.

    PubMed

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

    2016-04-01

    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 variables) that are maximally associated-in terms of effect size-with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype-phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype-phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype-phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype-phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3-the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the genotype-phenotype map

  1. Augmented classical least squares multivariate spectral analysis

    DOEpatents

    Haaland, David M.; Melgaard, David K.

    2004-02-03

    A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

  2. Augmented Classical Least Squares Multivariate Spectral Analysis

    DOEpatents

    Haaland, David M.; Melgaard, David K.

    2005-07-26

    A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

  3. Augmented Classical Least Squares Multivariate Spectral Analysis

    DOEpatents

    Haaland, David M.; Melgaard, David K.

    2005-01-11

    A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

  4. [Automatic Classification of Epileptic Electroencephalogram Signal Based on Improved Multivariate Multiscale Entropy].

    PubMed

    Xu, Yonghong; Cui, Jie; Hong, Wenxue; Liang, Huijuan

    2015-04-01

    Traditional sample entropy fails to quantify inherent long-range dependencies among real data. Multiscale sample entropy (MSE) can detect intrinsic correlations in data, but it is usually used in univariate data. To generalize this method for multichannel data, we introduced multivariate multiscale entropy into multiscale signals as a reflection of the nonlinear dynamic correlation. But traditional multivariate multiscale entropy has a large quantity of computation and costs a large period of time and space for more channel system, so that it can not reflect the correlation between variables timely and accurately. In this paper, therefore, an improved multivariate multiscale entropy embeds on all variables at the same time, instead of embedding on a single variable as in the traditional methods, to solve the memory overflow while the number of channels rise, and it is more suitable for the actual multivariate signal analysis. The method was tested in simulation data and Bonn epilepsy dataset. The simulation results showed that the proposed method had a good performance to distinguish correlation data. Bonn epilepsy dataset experiment also showed that the method had a better classification accuracy among the five data set, especially with an accuracy of 100% for data collection of Z and S.

  5. Multivariate analysis methods for spectroscopic blood analysis

    NASA Astrophysics Data System (ADS)

    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.

  6. The flyby anomaly: a multivariate analysis approach

    NASA Astrophysics Data System (ADS)

    Acedo, L.

    2017-02-01

    The flyby anomaly is the unexpected variation of the asymptotic post-encounter velocity of a spacecraft with respect to the pre-encounter velocity as it performs a slingshot manoeuvre. This effect has been detected in, at least, six flybys of the Earth but it has not appeared in other recent flybys. In order to find a pattern in these, apparently contradictory, data several phenomenological formulas have been proposed but all have failed to predict a new result in agreement with the observations. In this paper we use a multivariate dimensional analysis approach to propose a fitting of the data in terms of the local parameters at perigee, as it would occur if this anomaly comes from an unknown fifth force with latitude dependence. Under this assumption, we estimate the range of this force around 300 km.

  7. Multivariate analysis applied to tomato hybrid production.

    PubMed

    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.

  8. Hierarchical multivariate covariance analysis of metabolic connectivity.

    PubMed

    Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J

    2014-12-01

    Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).

  9. Hierarchical multivariate covariance analysis of metabolic connectivity

    PubMed Central

    Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J

    2014-01-01

    Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI). PMID:25294129

  10. Multivariate Visual Explanation for High Dimensional Datasets

    PubMed Central

    Barlowe, Scott; Zhang, Tianyi; Liu, Yujie; Yang, Jing; Jacobs, Donald

    2010-01-01

    Understanding multivariate relationships is an important task in multivariate data analysis. Unfortunately, existing multivariate visualization systems lose effectiveness when analyzing relationships among variables that span more than a few dimensions. We present a novel multivariate visual explanation approach that helps users interactively discover multivariate relationships among a large number of dimensions by integrating automatic numerical differentiation techniques and multidimensional visualization techniques. The result is an efficient workflow for multivariate analysis model construction, interactive dimension reduction, and multivariate knowledge discovery leveraging both automatic multivariate analysis and interactive multivariate data visual exploration. Case studies and a formal user study with a real dataset illustrate the effectiveness of this approach. PMID:20694164

  11. Apparatus and system for multivariate spectral analysis

    DOEpatents

    Keenan, Michael R.; Kotula, Paul G.

    2003-06-24

    An apparatus and system for determining the properties of a sample from measured spectral data collected from the sample by performing a method of multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used by a spectrum analyzer to process X-ray spectral data generated by a spectral analysis system that can include a Scanning Electron Microscope (SEM) with an Energy Dispersive Detector and Pulse Height Analyzer.

  12. Multivariate statistical analysis of wildfires in Portugal

    NASA Astrophysics Data System (ADS)

    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. Gravitational-wave detection using multivariate analysis

    NASA Astrophysics Data System (ADS)

    Adams, Thomas S.; Meacher, Duncan; Clark, James; Sutton, Patrick J.; Jones, Gareth; Minot, Ariana

    2013-09-01

    Searches for gravitational-wave bursts (transient signals, typically of unknown waveform) require identification of weak signals in background detector noise. The sensitivity of such searches is often critically limited by non-Gaussian noise fluctuations that are difficult to distinguish from real signals, posing a key problem for transient gravitational-wave astronomy. Current noise rejection tests are based on the analysis of a relatively small number of measured properties of the candidate signal, typically correlations between detectors. Multivariate analysis (MVA) techniques probe the full space of measured properties of events in an attempt to maximize the power to accurately classify events as signal or background. This is done by taking samples of known background events and (simulated) signal events to train the MVA classifier, which can then be applied to classify events of unknown type. We apply the boosted decision tree (BDT) MVA technique to the problem of detecting gravitational-wave bursts associated with gamma-ray bursts. We find that BDTs are able to increase the sensitive distance reach of the search by as much as 50%, corresponding to a factor of ˜3 increase in sensitive volume. This improvement is robust against trigger sky position, large sky localization error, poor data quality, and the simulated signal waveforms that are used. Critically, we find that the BDT analysis is able to detect signals that have different morphologies from those used in the classifier training and that this improvement extends to false alarm probabilities beyond the 3σ significance level. These findings indicate that MVA techniques may be used for the robust detection of gravitational-wave bursts with a priori unknown waveform.

  14. A Multivariate Analysis of Galaxy Cluster Properties

    NASA Astrophysics Data System (ADS)

    Ogle, P. M.; Djorgovski, S.

    1993-05-01

    We have assembled from the literature a data base on on 394 clusters of galaxies, with up to 16 parameters per cluster. They include optical and x-ray luminosities, x-ray temperatures, galaxy velocity dispersions, central galaxy and particle densities, optical and x-ray core radii and ellipticities, etc. In addition, derived quantities, such as the mass-to-light ratios and x-ray gas masses are included. Doubtful measurements have been identified, and deleted from the data base. Our goal is to explore the correlations between these parameters, and interpret them in the framework of our understanding of evolution of clusters and large-scale structure, such as the Gott-Rees scaling hierarchy. Among the simple, monovariate correlations we found, the most significant include those between the optical and x-ray luminosities, x-ray temperatures, cluster velocity dispersions, and central galaxy densities, in various mutual combinations. While some of these correlations have been discussed previously in the literature, generally smaller samples of objects have been used. We will also present the results of a multivariate statistical analysis of the data, including a principal component analysis (PCA). Such an approach has not been used previously for studies of cluster properties, even though it is much more powerful and complete than the simple monovariate techniques which are commonly employed. The observed correlations may lead to powerful constraints for theoretical models of formation and evolution of galaxy clusters. P.M.O. was supported by a Caltech graduate fellowship. S.D. acknowledges a partial support from the NASA contract NAS5-31348 and the NSF PYI award AST-9157412.

  15. Multivariate singular spectrum analysis and the road to phase synchronization

    NASA Astrophysics Data System (ADS)

    Groth, Andreas; Ghil, Michael

    2010-05-01

    Singular spectrum analysis (SSA) and multivariate SSA (M-SSA) are based on the classical work of Kosambi (1943), Loeve (1945) and Karhunen (1946) and are closely related to principal component analysis. They have been introduced into information theory by Bertero, Pike and co-workers (1982, 1984) and into dynamical systems analysis by Broomhead and King (1986a,b). Ghil, Vautard and associates have applied SSA and M-SSA to the temporal and spatio-temporal analysis of short and noisy time series in climate dynamics and other fields in the geosciences since the late 1980s. M-SSA provides insight into the unknown or partially known dynamics of the underlying system by decomposing the delay-coordinate phase space of a given multivariate time series into a set of data-adaptive orthonormal components. These components can be classified essentially into trends, oscillatory patterns and noise, and allow one to reconstruct a robust "skeleton" of the dynamical system's structure. For an overview we refer to Ghil et al. (Rev. Geophys., 2002). In this talk, we present M-SSA in the context of synchronization analysis and illustrate its ability to unveil information about the mechanisms behind the adjustment of rhythms in coupled dynamical systems. The focus of the talk is on the special case of phase synchronization between coupled chaotic oscillators (Rosenblum et al., PRL, 1996). Several ways of measuring phase synchronization are in use, and the robust definition of a reasonable phase for each oscillator is critical in each of them. We illustrate here the advantages of M-SSA in the automatic identification of oscillatory modes and in drawing conclusions about the transition to phase synchronization. Without using any a priori definition of a suitable phase, we show that M-SSA is able to detect phase synchronization in a chain of coupled chaotic oscillators (Osipov et al., PRE, 1996). Recently, Muller et al. (PRE, 2005) and Allefeld et al. (Intl. J. Bif. Chaos, 2007) have

  16. Integrating Automatic Genre Analysis into Digital Libraries.

    ERIC Educational Resources Information Center

    Rauber, Andreas; Muller-Kogler, Alexander

    With the number and types of documents in digital library systems increasing, tools for automatically organizing and presenting the content have to be found. While many approaches focus on topic-based organization and structuring, hardly any system incorporates automatic structural analysis and representation. Yet, genre information…

  17. Canonical Analysis as a Generalized Regression Technique for Multivariate Analysis.

    ERIC Educational Resources Information Center

    Williams, John D.

    The use of characteristic coding (dummy coding) is made in showing solutions to four multivariate problems using canonical analysis. The canonical variates can be themselves analyzed by the use of multiple linear regression. When the canonical variates are used as criteria in a multiple linear regression, the R2 values are equal to 0, where 0 is…

  18. Show me: automatic presentation for visual analysis.

    PubMed

    Mackinlay, Jock; Hanrahan, Pat; Stolte, Chris

    2007-01-01

    This paper describes Show Me, an integrated set of user interface commands and defaults that incorporate automatic presentation into a commercial visual analysis system called Tableau. A key aspect of Tableau is VizQL, a language for specifying views, which is used by Show Me to extend automatic presentation to the generation of tables of views (commonly called small multiple displays). A key research issue for the commercial application of automatic presentation is the user experience, which must support the flow of visual analysis. User experience has not been the focus of previous research on automatic presentation. The Show Me user experience includes the automatic selection of mark types, a command to add a single field to a view, and a pair of commands to build views for multiple fields. Although the use of these defaults and commands is optional, user interface logs indicate that Show Me is used by commercial users.

  19. Exploratory Multivariate Analysis of Variance: Contrasts and Variables.

    ERIC Educational Resources Information Center

    Barcikowski, Robert S.; Elliott, Ronald S.

    The contribution of individual variables to overall multivariate significance in a multivariate analysis of variance (MANOVA) is investigated using a combination of canonical discriminant analysis and Roy-Bose simultaneous confidence intervals. Difficulties with this procedure are discussed, and its advantages are illustrated using examples based…

  20. Multi-Variable Analysis and Design Techniques.

    DTIC Science & Technology

    1981-09-01

    by A.G.J.MacFarlane 2 MULTIVARIABLE DESIGN TECHNIQUES BASED ON SINGULAR VALUE GENERALIZATIONS OF CLASSICAL CONTROL by J.C. Doyle 3 LIMITATIONS ON...prototypes to complex mathematical representations. All of these assemblages of information or information generators can loosely be termed "models...non linearities (e.g., control saturation) I neglect of high frequency dynamics. T hese approximations are well understood and in general their impact

  1. The Method of Unweighted Means in Multivariate Analysis of Variance.

    ERIC Educational Resources Information Center

    Betz, M. Austin; Elliott, Steven D.

    1984-01-01

    The method of unweighted means in the multivariate analysis of variance with unequal sample sizes was investigated. By approximating the distribution of the hypothesis sums-of-squares-and-cross-products with a Wishart distribution, multivariate test statistics were derived. Monte Carlo methods and a numerical example illustrate the technique.…

  2. Exploratory Tobit Factor Analysis for Multivariate Censored Data.

    ERIC Educational Resources Information Center

    Kamakura, Wagner A.; Wedel, Michel

    2001-01-01

    Proposes a class of multivariate Tobit models with a factor structure on the covariance matrix. Such models are useful in the exploratory analysis of multivariate censored data and the identification of latent variables from behavioral data. The factor structure provides a parsimonious representation of the censored data. Models are estimated with…

  3. TaylUR 3, a multivariate arbitrary-order automatic differentiation package for Fortran 95

    NASA Astrophysics Data System (ADS)

    von Hippel, G. M.

    2010-03-01

    comments: This version of TaylUR is released under the second version of the GNU General Public License (GPLv2). Therefore anyone is free to use or modify the code for their own calculations. As part of the licensing, it is requested that any publications including results from the use of TaylUR or any modification derived from it cite Refs. [1,2] as well as this paper. Finally, users are also requested to communicate to the author details of such publications, as well as of any bugs found or of required or useful modifications made or desired by them. Running time: The running time of TaylUR operations grows rapidly with both the number of variables and the Taylor expansion order. Judicious use of the masking facility to drop unneeded higher derivatives can lead to significant accelerations, as can activation of the Diagonal_taylors variable whenever mixed partial derivatives are not needed. Acknowledgments: The author thanks Alistair Hart for helpful comments and suggestions. This work is supported by the Deutsche Forschungsgemeinschaft in the SFB/TR 09. References:G.M. von Hippel, TaylUR, an arbitrary-order diagonal automatic differentiation package for Fortran 95, Comput. Phys. Comm. 174 (2006) 569. G.M. von Hippel, New version announcement for TaylUR, an arbitrary-order diagonal automatic differentiation package for Fortran 95, Comput. Phys. Comm. 176 (2007) 710. G.M. Constantine, T.H. Savits, A multivariate Faa di Bruno formula with applications, Trans. Amer. Math. Soc. 348 (2) (1996) 503. A. Hart, G.M. von Hippel, R.R. Horgan, E.H. Müller, Automated generation of lattice QCD Feynman rules, Comput. Phys. Comm. 180 (2009) 2698, doi:10.1016/j.cpc.2009.04.021, arXiv:0904.0375.

  4. Cluster analysis using multivariate mixed effects models.

    PubMed

    Villarroel, Luis; Marshall, Guillermo; Barón, Anna E

    2009-09-10

    A common situation in the biological and social sciences is to have data on one or more variables measured longitudinally on a sample of individuals. A problem of growing interest in these areas is the grouping of individuals into one of two or more clusters according to their longitudinal behavior. Recently, methods have been proposed to deal with cases where individuals are classified into clusters through a linear model of mixed univariate effects deriving from a longitudinally measured variable. The method proposed in the current work deals with the case of clustering and then classification based on two or more variables measured longitudinally, through the fitting of non-linear multivariate mixed effect models, and with consideration given to parameter estimation for balanced and unbalanced data using an EM algorithm. The application of the method is illustrated with an example in which the clusters are identified and the classification into clusters is compared with the true membership of individuals in one of two groups, which is known at the end of the follow-up period.

  5. Fear of Crime in the United States: A Multivariate Analysis

    ERIC Educational Resources Information Center

    Clemente, Frank; Kleiman, Michael B.

    1977-01-01

    Multivariate Nominal Scale Analysis (MNA) was used to assess the independent ability of each variable to predict respondents who indicated a fear of crime (42 percent) and those who did not (58 percent). (Author/AM)

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

    NASA Astrophysics Data System (ADS)

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

    2004-06-01

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

  7. Classification Techniques for Multivariate Data Analysis.

    DTIC Science & Technology

    1980-03-28

    analysis among biologists, botanists, and ecologists, while some social scientists may refer "typology". Other frequently encountered terms are pattern...the determinantal equation: lB -XW 0 (42) 49 The solutions X. are the eigenvalues of the matrix W-1 B 1 as in discriminant analysis. There are t non...Statistical Package for Social Sciences (SPSS) (14) subprogram FACTOR was used for the principal components analysis. It is designed both for the factor

  8. Automatic emotional expression analysis from eye area

    NASA Astrophysics Data System (ADS)

    Akkoç, Betül; Arslan, Ahmet

    2015-02-01

    Eyes play an important role in expressing emotions in nonverbal communication. In the present study, emotional expression classification was performed based on the features that were automatically extracted from the eye area. Fırst, the face area and the eye area were automatically extracted from the captured image. Afterwards, the parameters to be used for the analysis through discrete wavelet transformation were obtained from the eye area. Using these parameters, emotional expression analysis was performed through artificial intelligence techniques. As the result of the experimental studies, 6 universal emotions consisting of expressions of happiness, sadness, surprise, disgust, anger and fear were classified at a success rate of 84% using artificial neural networks.

  9. Automatic tools for microprocessor failure analysis

    NASA Astrophysics Data System (ADS)

    Conard, Didier; Laurent, J.; Velazco, Raoul; Ziade, Haissam; Cabestany, J.; Sala, F.

    A new approach for fault location when testing microprocessors is presented. The startpoint for the backtracing analysis converging to the failure is constituted by the automatic localization of a reduced area. Automatic image comparison based on pattern recognition is performed by means of an electron beam tester. The developed hardware and software tools allow large circuit areas to be covered offering powerful diagnosis capabilities to the user. The validation of this technique was performed on faulty 68000 microprocessors. It shows the feasibility of the automation of the first and most important step of failure analysis: fault location at the chip surface.

  10. Multisample Analysis of Multivariate Ordinal Categorical Variables.

    ERIC Educational Resources Information Center

    Poon, Wai-Yin; Tang, Fung-Chu

    2002-01-01

    Studied a multiple group model with ordinal categorical observed variables that are manifestations of underlying normal variables. Proposed to apply across-group stochastic constraints on thresholds to identify the model and used a Bayesian approach to analyze the model. Simulation findings and the analysis of a real data set show the usefulness…

  11. MICROSCOPE: A Software System for Multivariate Analysis.

    DTIC Science & Technology

    1984-06-01

    Design Work Unit Number 3 (Numerical Analysis and Scientific Computing) Department of Mathematics, University of Utah, Salt Lake City, Utah 84112...2 where eps and aps are random numbers between -1 and +1. The addition of 1 2 the eps term is not standard but appropriate in the present context. 2...because in investigations with MICROSCOE small numbers are often due to taking differences between very close large numbers , leading to a cancellation

  12. The statistical analysis of multivariate serological frequency data.

    PubMed

    Reyment, Richard A

    2005-11-01

    Data occurring in the form of frequencies are common in genetics-for example, in serology. Examples are provided by the AB0 group, the Rhesus group, and also DNA data. The statistical analysis of tables of frequencies is carried out using the available methods of multivariate analysis with usually three principal aims. One of these is to seek meaningful relationships between the components of a data set, the second is to examine relationships between populations from which the data have been obtained, the third is to bring about a reduction in dimensionality. This latter aim is usually realized by means of bivariate scatter diagrams using scores computed from a multivariate analysis. The multivariate statistical analysis of tables of frequencies cannot safely be carried out by standard multivariate procedures because they represent compositions and are therefore embedded in simplex space, a subspace of full space. Appropriate procedures for simplex space are compared and contrasted with simple standard methods of multivariate analysis ("raw" principal component analysis). The study shows that the differences between a log-ratio model and a simple logarithmic transformation of proportions may not be very great, particularly as regards graphical ordinations, but important discrepancies do occur. The divergencies between logarithmically based analyses and raw data are, however, great. Published data on Rhesus alleles observed for Italian populations are used to exemplify the subject.

  13. Chemical equilibria studies using multivariate analysis methods.

    PubMed

    Jaumot, Joaquim; Eritja, Ramon; Gargallo, Raimundo

    2011-02-01

    Chemical multiequilibria systems can be monitored efficiently with the aid of spectroscopic techniques. Both hard- and soft-modeling are effective and powerful tools to extract chemical information from spectroscopic data. Recently, hybrid approaches that combine the flexibility of soft-modeling with the precise solutions provided by hard-modeling have been proposed. Here, we tested the performance of these three chemometric approaches for the analysis of several simulated data sets. In addition, experimental data recorded during the study of the acid-base equilibria of two DNA structures (G-quadruplex and i-motif) corresponding to two short sequences of the k-ras oncogene were studied. Finally, we also analyzed the interaction of the two DNA sequences with the model ligand TMPyP4. The results obtained from the analysis of these data sets may be useful to determine the most appropriate use of each approach. Whenever the presence of optically active interferences or unknown drifts can be neglected and a chemical model can easily be proposed and fitted, the hard-modeling method shows the best performance. If any of these conditions is not fulfilled, a hybrid-modeling approach may be a better option because all the contributions (chemical and unknown) can be modeled and the ambiguities inherent to soft-modeling methods show minor effects.

  14. Multivariate Analysis of Solar Spectral Irradiance Measurements

    NASA Technical Reports Server (NTRS)

    Pilewskie, P.; Rabbette, M.

    2001-01-01

    Principal component analysis is used to characterize approximately 7000 downwelling solar irradiance spectra retrieved at the Southern Great Plains site during an Atmospheric Radiation Measurement (ARM) shortwave intensive operating period. This analysis technique has proven to be very effective in reducing a large set of variables into a much smaller set of independent variables while retaining the information content. It is used to determine the minimum number of parameters necessary to characterize atmospheric spectral irradiance or the dimensionality of atmospheric variability. It was found that well over 99% of the spectral information was contained in the first six mutually orthogonal linear combinations of the observed variables (flux at various wavelengths). Rotation of the principal components was effective in separating various components by their independent physical influences. The majority of the variability in the downwelling solar irradiance (380-1000 nm) was explained by the following fundamental atmospheric parameters (in order of their importance): cloud scattering, water vapor absorption, molecular scattering, and ozone absorption. In contrast to what has been proposed as a resolution to a clear-sky absorption anomaly, no unexpected gaseous absorption signature was found in any of the significant components.

  15. Virulence of Bacillus cereus: a multivariate analysis.

    PubMed

    Minnaard, J; Delfederico, L; Vasseur, V; Hollmann, A; Rolny, I; Semorile, L; Pérez, P F

    2007-05-10

    Biological activity and presence of DNA sequences related to virulence genes were studied in 21 strains of the Bacillus cereus group. The activity of spent culture supernatants and the effect of infection by vegetative bacterial cells were assessed on cultured human enterocytes (Caco-2 cells). The effect of extracellular factors on the detachment, necrosis and mitochondrial dehydrogenase activity of cultured human enterocytes was studied. Hemolytic activity on rabbit red blood cells was also evaluated and the effect of direct procaryotic-eucaryotic interactions was assessed in infection assays with vegetative bacterial cells. Concerning virulence genes, presence of the DNA sequences corresponding to the genes entS, entFM, nhe (A, B and C), sph, hbl (A, B, C and D), piplC and bceT was assessed by PCR. Ribopatterns were determined by an automated riboprinting analysis after digestion of the DNA with EcoRI. Principal component analysis and biplots were used to address the relationship between variables. Results showed a wide range of biological activities: decrease in mitochondrial dehydrogenase activity, necrosis, cell detachment and hemolytic activity. These effects were strain-dependent. Concerning the occurrence of the DNA sequences tested, different patterns were found. In addition, ribotyping showed that strains under study grouped into two main clusters. One of these clusters includes all the strains that were positive for all the DNA sequences tested. Positive and negative correlations between variables under study were evidenced. Interestingly, high detaching strains were positively correlated with the presence of the sequences entS, nheC and sph. Within gene complexes, high correlation was found between sequences of the hbl complex. In contrast, sequences of the nhe complex were not correlated. Some strains clustered together in the biplots. These strains were positive for all the DNA sequences tested and they were able to detach enterocytes upon infection

  16. Nonlinear independent component analysis and multivariate time series analysis

    NASA Astrophysics Data System (ADS)

    Storck, Jan; Deco, Gustavo

    1997-02-01

    We derive an information-theory-based unsupervised learning paradigm for nonlinear independent component analysis (NICA) with neural networks. We demonstrate that under the constraint of bounded and invertible output transfer functions the two main goals of unsupervised learning, redundancy reduction and maximization of the transmitted information between input and output (Infomax-principle), are equivalent. No assumptions are made concerning the kind of input and output distributions, i.e. the kind of nonlinearity of correlations. An adapted version of the general NICA network is used for the modeling of multivariate time series by unsupervised learning. Given time series of various observables of a dynamical system, our net learns their evolution in time by extracting statistical dependencies between past and present elements of the time series. Multivariate modeling is obtained by making present value of each time series statistically independent not only from their own past but also from the past of the other series. Therefore, in contrast to univariate methods, the information lying in the couplings between the observables is also used and a detection of higher-order cross correlations is possible. We apply our method to time series of the two-dimensional Hénon map and to experimental time series obtained from the measurements of axial velocities in different locations in weakly turbulent Taylor-Couette flow.

  17. [Multivariate analysis of marital fertility in Japan].

    PubMed

    Nohara Atoh, M

    1981-05-01

    Drawing on the data from the 7th Japanese National Fertility Survey held by the Institute of Population Problems, JMHW, in 1977, multiple classification analysis (MCA) was done for the number of children ever born (NCEB) of currently married women to identify social determinants of marital fertility in contemporary Japan. These are the major findings. 1) Among all of the explanatory variables included in the MCA model, spouses' age at marriage has the largest explanatory power. The proportion of the total variance of NCEB explained by social variables is relatively small. 2) Although wife's work before marriage did not have any significant relation to NCEB, wife's work during the early reproductive years has the largest negative effect on NCEB among social variables. This relationship holds even after controlling for fecundity status. 3) Variables such as wife's education, nature of place of residence, and husband's occupation have a small but systematic effect on NCEB even after MCA adjustment. Higher education, urban place of residence, and husband's status as an employee are associated with lower NCEB. 4) Among such variables relevant to the family system as type of marriage, spouses' number of siblings, and birth order, and locality type, only locality type is significantly associated with NCEB. Married couples who cohabit with their parents at marriage have higher fertility than those who do not. 5) Wife's sex role norms and type of conjugal relationship role have been regarded as an important determinant of fertility. This does not hold true for Japan; a highly segregated role relationship between husband and wife coexists with low fertility. (author's modified)

  18. Association Analysis for Visual Exploration of Multivariate Scientific Data Sets.

    PubMed

    Liu, Xiaotong; Shen, Han-Wei

    2016-01-01

    The heterogeneity and complexity of multivariate characteristics poses a unique challenge to visual exploration of multivariate scientific data sets, as it requires investigating the usually hidden associations between different variables and specific scalar values to understand the data's multi-faceted properties. In this paper, we present a novel association analysis method that guides visual exploration of scalar-level associations in the multivariate context. We model the directional interactions between scalars of different variables as information flows based on association rules. We introduce the concepts of informativeness and uniqueness to describe how information flows between scalars of different variables and how they are associated with each other in the multivariate domain. Based on scalar-level associations represented by a probabilistic association graph, we propose the Multi-Scalar Informativeness-Uniqueness (MSIU) algorithm to evaluate the informativeness and uniqueness of scalars. We present an exploration framework with multiple interactive views to explore the scalars of interest with confident associations in the multivariate spatial domain, and provide guidelines for visual exploration using our framework. We demonstrate the effectiveness and usefulness of our approach through case studies using three representative multivariate scientific data sets.

  19. Automatic photointerpretation via texture and morphology analysis

    NASA Technical Reports Server (NTRS)

    Tou, J. T.

    1982-01-01

    Computer-based techniques for automatic photointerpretation based upon information derived from texture and morphology analysis of images are discussed. By automatic photointerpretation, is meant the determination of semantic descriptions of the content of the images by computer. To perform semantic analysis of morphology, a heirarchical structure of knowledge representation was developed. The simplest elements in a morphology are strokes, which are used to form alphabets. The alphabets are the elements for generating words, which are used to describe the function or property of an object or a region. The words are the elements for constructing sentences, which are used for semantic description of the content of the image. Photointerpretation based upon morphology is then augmented by textural information. Textural analysis is performed using a pixel-vector approach.

  20. Multivariate calibration applied to the quantitative analysis of infrared spectra

    SciTech Connect

    Haaland, D.M.

    1991-01-01

    Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in-situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mind- or near-infrared spectra of the blood. Progress toward the non-invasive determination of glucose levels in diabetics is an ultimate goal of this research. 13 refs., 4 figs.

  1. Automatic subsystem identification in statistical energy analysis

    NASA Astrophysics Data System (ADS)

    Díaz-Cereceda, Cristina; Poblet-Puig, Jordi; Rodríguez-Ferran, Antonio

    2015-03-01

    An automatic methodology for identifying SEA (statistical energy analysis) subsystems within a vibroacoustic system is presented. It consists in dividing the system into cells and grouping them into subsystems via a hierarchical cluster analysis based on the problem eigenmodes. The subsystem distribution corresponds to the optimal grouping of the cells, which is defined in terms of the correlation distance between them. The main advantages of this methodology are its automatic performance and its applicability both to vibratory and vibroacoustic systems. Moreover, the method allows the definition of more than one subsystem in the same geometrical region when required. This is the case of eigenmodes with a very different mechanical response (e.g. out-of-plane or in-plane vibration in shells).

  2. Automatic Prosodic Analysis to Identify Mild Dementia

    PubMed Central

    Gonzalez-Moreira, Eduardo; Torres-Boza, Diana; Kairuz, Héctor Arturo; Ferrer, Carlos; Garcia-Zamora, Marlene; Espinoza-Cuadros, Fernando; Hernandez-Gómez, Luis Alfonso

    2015-01-01

    This paper describes an exploratory technique to identify mild dementia by assessing the degree of speech deficits. A total of twenty participants were used for this experiment, ten patients with a diagnosis of mild dementia and ten participants like healthy control. The audio session for each subject was recorded following a methodology developed for the present study. Prosodic features in patients with mild dementia and healthy elderly controls were measured using automatic prosodic analysis on a reading task. A novel method was carried out to gather twelve prosodic features over speech samples. The best classification rate achieved was of 85% accuracy using four prosodic features. The results attained show that the proposed computational speech analysis offers a viable alternative for automatic identification of dementia features in elderly adults. PMID:26558287

  3. Univariate Analysis of Multivariate Outcomes in Educational Psychology.

    ERIC Educational Resources Information Center

    Hubble, L. M.

    1984-01-01

    The author examined the prevalence of multiple operational definitions of outcome constructs and an estimate of the incidence of Type I error rates when univariate procedures were applied to multiple variables in educational psychology. Multiple operational definitions of constructs were advocated and wider use of multivariate analysis was…

  4. Univariate Analysis of Multivariate Outcomes in Educational Psychology.

    ERIC Educational Resources Information Center

    Hubble, L. M.

    1984-01-01

    The author examined the prevalence of multiple operational definitions of outcome constructs and an estimate of the incidence of Type I error rates when univariate procedures were applied to multiple variables in educational psychology. Multiple operational definitions of constructs were advocated and wider use of multivariate analysis was…

  5. Multivariate Meta-Analysis Using Individual Participant Data

    ERIC Educational Resources Information Center

    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…

  6. Multivariate Meta-Analysis Using Individual Participant Data

    ERIC Educational Resources Information Center

    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…

  7. Evaluation of Meterorite Amono Acid Analysis Data Using Multivariate Techniques

    NASA Technical Reports Server (NTRS)

    McDonald, G.; Storrie-Lombardi, M.; Nealson, K.

    1999-01-01

    The amino acid distributions in the Murchison carbonaceous chondrite, Mars meteorite ALH84001, and ice from the Allan Hills region of Antarctica are shown, using a multivariate technique known as Principal Component Analysis (PCA), to be statistically distinct from the average amino acid compostion of 101 terrestrial protein superfamilies.

  8. Multivariate geometry as an approach to algal community analysis

    USGS Publications Warehouse

    Allen, T.F.H.; Skagen, S.

    1973-01-01

    Multivariate analyses are put in the context of more usual approaches to phycological investigations. The intuitive common-sense involved in methods of ordination, classification and discrimination are emphasised by simple geometric accounts which avoid jargon and matrix algebra. Warnings are given that artifacts result from technique abuses by the naive or over-enthusiastic. An analysis of a simple periphyton data set is presented as an example of the approach. Suggestions are made as to situations in phycological investigations, where the techniques could be appropriate. The discipline is reprimanded for its neglect of the multivariate approach.

  9. Automatic analysis of D-partition

    NASA Astrophysics Data System (ADS)

    Bogaevskaya, V. G.

    2017-01-01

    The paper is dedicated to automatization of D-partition analysis. D-partition is one of the most common methods for determination of solution stability in systems with time-delayed feedback control and its dependency on values of control parameters. A transition from analytical form of D-partition to plain graph has been investigated. An algorithm of graph faces determination and calculation of count of characteristic equation roots with positive real part for appropriate area of D-partition has been developed. The algorithm keeps an information about analytical formulas for edges of faces. It allows to make further analytical research based on the results of computer analysis.

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

    SciTech Connect

    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.

  11. Temporal MDS Plots for Analysis of Multivariate Data.

    PubMed

    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.

  12. Voxelwise multivariate analysis of multimodality magnetic resonance imaging

    PubMed Central

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

    2015-01-01

    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 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 remains 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. PMID:23408378

  13. Voxelwise multivariate analysis of multimodality magnetic resonance imaging.

    PubMed

    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.

  14. PIXE-quantified AXSIA : elemental mapping by multivariate spectral analysis.

    SciTech Connect

    Doyle, Barney Lee; Antolak, Arlyn J.; Campbell, J. L.; Ryan, C. G.; Provencio, Paula Polyak; Barrett, Keith E.; Kotula, Paul Gabriel

    2005-07-01

    Automated, nonbiased, multivariate statistical analysis techniques are useful for converting very large amounts of data into a smaller, more manageable number of chemical components (spectra and images) that are needed to describe the measurement. We report the first use of the multivariate spectral analysis program AXSIA (Automated eXpert Spectral Image Analysis) developed at Sandia National Laboratories to quantitatively analyze micro-PIXE data maps. AXSIA implements a multivariate curve resolution technique that reduces the spectral image data sets into a limited number of physically realizable and easily interpretable components (including both spectra and images). We show that the principal component spectra can be further analyzed using conventional PIXE programs to convert the weighting images into quantitative concentration maps. A common elemental data set has been analyzed using three different PIXE analysis codes and the results compared to the cases when each of these codes is used to separately analyze the associated AXSIA principal component spectral data. We find that these comparisons are in good quantitative agreement with each other.

  15. Research on automatic human chromosome image analysis

    NASA Astrophysics Data System (ADS)

    Ming, Delie; Tian, Jinwen; Liu, Jian

    2007-11-01

    Human chromosome karyotyping is one of the essential tasks in cytogenetics, especially in genetic syndrome diagnoses. In this thesis, an automatic procedure is introduced for human chromosome image analysis. According to different status of touching and overlapping chromosomes, several segmentation methods are proposed to achieve the best results. Medial axis is extracted by the middle point algorithm. Chromosome band is enhanced by the algorithm based on multiscale B-spline wavelets, extracted by average gray profile, gradient profile and shape profile, and calculated by the WDD (Weighted Density Distribution) descriptors. The multilayer classifier is used in classification. Experiment results demonstrate that the algorithms perform well.

  16. Multivariate meta-analysis using individual participant data

    PubMed Central

    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

  17. Multivariate meta-analysis using individual participant data.

    PubMed

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

    2015-06-01

    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.

  18. Multivariate pattern analysis of fMRI: the early beginnings.

    PubMed

    Haxby, James V

    2012-08-15

    In 2001, we published a paper on the representation of faces and objects in ventral temporal cortex that introduced a new method for fMRI analysis, which subsequently came to be called multivariate pattern analysis (MVPA). MVPA now refers to a diverse set of methods that analyze neural responses as patterns of activity that reflect the varying brain states that a cortical field or system can produce. This paper recounts the circumstances and events that led to the original study and later developments and innovations that have greatly expanded this approach to fMRI data analysis, leading to its widespread application. Copyright © 2012 Elsevier Inc. All rights reserved.

  19. Symbolic observability coefficients for univariate and multivariate analysis

    NASA Astrophysics Data System (ADS)

    Letellier, Christophe; Aguirre, Luis A.

    2009-06-01

    In practical problems, the observability of a system not only depends on the choice of observable(s) but also on the space which is reconstructed. In fact starting from a given set of observables, the reconstructed space is not unique, since the dimension can be varied and, in the case of multivariate measurement functions, there are various ways to combine the measured observables. Using a graphical approach recently introduced, we analytically compute symbolic observability coefficients which allow to choose from the system equations the best observable, in the case of scalar reconstructions, and the best way to combine the observables in the case of multivariate reconstructions. It is shown how the proposed coefficients are also helpful for analysis in higher dimension.

  20. Semi-automatic analysis of fire debris

    PubMed

    Touron; Malaquin; Gardebas; Nicolai

    2000-05-08

    Automated analysis of fire residues involves a strategy which deals with the wide variety of received criminalistic samples. Because of unknown concentration of accelerant in a sample and the wide range of flammable products, full attention from the analyst is required. Primary detection with a photoionisator resolves the first problem, determining the right method to use: the less responsive classical head-space determination or absorption on active charcoal tube, a better fitted method more adapted to low concentrations can thus be chosen. The latter method is suitable for automatic thermal desorption (ATD400), to avoid any risk of cross contamination. A PONA column (50 mx0.2 mm i.d.) allows the separation of volatile hydrocarbons from C(1) to C(15) and the update of a database. A specific second column is used for heavy hydrocarbons. Heavy products (C(13) to C(40)) were extracted from residues using a very small amount of pentane, concentrated to 1 ml at 50 degrees C and then placed on an automatic carousel. Comparison of flammables with referenced chromatograms provided expected identification, possibly using mass spectrometry. This analytical strategy belongs to the IRCGN quality program, resulting in analysis of 1500 samples per year by two technicians.

  1. A micropolariscope for automatic stress analysis

    NASA Astrophysics Data System (ADS)

    Fessler, H.; Marston, R. E.; Ollerton, E.

    1987-01-01

    A micropolariscope has been developed for the automatic analysis of photoelastic data. It will position frozen stress slices mounted on its stage to within + or - 0.002 mm and take readings of isoclinic angles and fractional fringe orders, repeatable to within + or - 0.08 degrees and + or - 0.001 fringes. A rectangular grid of up to 3 x 50 points can be read automatically, taking about 1.25 minutes per point; the readings are stored on a floppy disc and printed out. The original slice is itself sliced, and the subslice is viewed again in the orthogonal direction to produce a second set of readings. Software has been devised to analyze the two sets of readings. It makes use of Tesar's (1933) modification of Frocht and Guernsey (1952) shear difference method to calculate five Cartesian stresses, which may be plotted and printed in tabular form. Flexible facilities are provided for editing, correcting, plotting, and printing intermediate stages in the analysis, and for storing results in data files.

  2. Maize authentication: quality control methods and multivariate analysis (chemometrics).

    PubMed

    Arvanitoyannis, Ioannis S; Vlachos, Antonios

    2009-06-01

    Maize is one of the most important cereals because of its numerous applications in processed foods where it is the major or minor component. Apart from maize authenticity issues related to cultivar and geographical origin (national and/or international level), there is another important issue related to genetically modified maize. Various objective parameters such as fatty acids, phenolic compounds, pigments, heavy metals were determined in conjunction with subjective (sensory analysis) in order to identify the maize authenticity. However, the implementation of multivariate analysis (principal component analysis, cluster analysis, discriminant analysis, canonical analysis) is of great importance toward reaching valid conclusions on authenticity issues. This review summarized the most important finding of both objective and subjective evaluations of maize in five comprehensive tables in conjunction with the discussion.

  3. Asymmetric measures of association, closed data, and multivariate analysis

    USGS Publications Warehouse

    Hohn, M. Ed; Nuhfer, E.B.

    1980-01-01

    The association between constant-sum variables Xiand Xjexpressed as percentages can be calculated as a product-moment correlation between Xiand Xj/(100 - Xi) and a correlation between Xjand Xi/(100 - Xj). An asymmetric, square matrix may be formed from these coefficients, and multivariate analysis performed by two methods: singular value decomposition and canonical decomposition. Either analysis avoids problems in the interpretation of correlation coefficients determined from closed arrays, and provides information about dependencies among the variables beyond that obtained from the usual correlation coefficient between Xiand Xj. Two examples show the canonical decomposition to have the greater usefulness. ?? 1980 Plenum Publishing Corporation.

  4. Multivariate statistical analysis of low-voltage EDS spectrum images

    SciTech Connect

    Anderson, I.M.

    1998-03-01

    Whereas energy-dispersive X-ray spectrometry (EDS) has been used for compositional analysis in the scanning electron microscope for 30 years, the benefits of using low operating voltages for such analyses have been explored only during the last few years. This paper couples low-voltage EDS with two other emerging areas of characterization: spectrum imaging and multivariate statistical analysis. The specimen analyzed for this study was a finished Intel Pentium processor, with the polyimide protective coating stripped off to expose the final active layers.

  5. Automatic analysis of speckle photography fringes.

    PubMed

    Buendía, M; Cibrián, R; Salvador, R; Roldán, C; Iñesta, J M

    1997-04-10

    Speckle interferometry is a technique adequate to metrological problems such as the measurement of object deformation. An automatic system of analysis of such measurements is given; it consists of a motorized x-y plate positioner controlled by computer, a CCD video camera, and software for image analysis. A fringe-recognition algorithm determines the spacing and orientation of the fringes and permits the calculation of the magnitude and direction of the displacement of the analyzed object point in images with variable degrees of illumination. For a 256 x 256 pixel image resolution, the procedure allows one to analyze from three fringes to a number of fringes that corresponds to 3 pixels/fringe.

  6. Multivariate Analysis and Prediction of Dioxin-Furan ...

    EPA Pesticide Factsheets

    Peer Review Draft of Regional Methods Initiative Final Report Dioxins, which are bioaccumulative and environmentally persistent, pose an ongoing risk to human and ecosystem health. Fish constitute a significant source of dioxin exposure for humans and fish-eating wildlife. Current dioxin analytical methods are costly, time-consuming, and produce hazardous by-products. A Danish team developed a novel, multivariate statistical methodology based on the covariance of dioxin-furan congener Toxic Equivalences (TEQs) and fatty acid methyl esters (FAMEs) and applied it to North Atlantic Ocean fishmeal samples. The goal of the current study was to attempt to extend this Danish methodology to 77 whole and composite fish samples from three trophic groups: predator (whole largemouth bass), benthic (whole flathead and channel catfish) and forage fish (composite bluegill, pumpkinseed and green sunfish) from two dioxin contaminated rivers (Pocatalico R. and Kanawha R.) in West Virginia, USA. Multivariate statistical analyses, including, Principal Components Analysis (PCA), Hierarchical Clustering, and Partial Least Squares Regression (PLS), were used to assess the relationship between the FAMEs and TEQs in these dioxin contaminated freshwater fish from the Kanawha and Pocatalico Rivers. These three multivariate statistical methods all confirm that the pattern of Fatty Acid Methyl Esters (FAMEs) in these freshwater fish covaries with and is predictive of the WHO TE

  7. Automatic dirt trail analysis in dermoscopy images.

    PubMed

    Cheng, Beibei; Joe Stanley, R; Stoecker, William V; Osterwise, Christopher T P; Stricklin, Sherea M; Hinton, Kristen A; Moss, Randy H; Oliviero, Margaret; Rabinovitz, Harold S

    2013-02-01

    Basal cell carcinoma (BCC) is the most common cancer in the US. Dermatoscopes are devices used by physicians to facilitate the early detection of these cancers based on the identification of skin lesion structures often specific to BCCs. One new lesion structure, referred to as dirt trails, has the appearance of dark gray, brown or black dots and clods of varying sizes distributed in elongated clusters with indistinct borders, often appearing as curvilinear trails. In this research, we explore a dirt trail detection and analysis algorithm for extracting, measuring, and characterizing dirt trails based on size, distribution, and color in dermoscopic skin lesion images. These dirt trails are then used to automatically discriminate BCC from benign skin lesions. For an experimental data set of 35 BCC images with dirt trails and 79 benign lesion images, a neural network-based classifier achieved a 0.902 are under a receiver operating characteristic curve using a leave-one-out approach. Results obtained from this study show that automatic detection of dirt trails in dermoscopic images of BCC is feasible. This is important because of the large number of these skin cancers seen every year and the challenge of discovering these earlier with instrumentation. © 2011 John Wiley & Sons A/S.

  8. Advanced multivariate analysis to assess remediation of hydrocarbons in soils.

    PubMed

    Lin, Deborah S; Taylor, Peter; Tibbett, Mark

    2014-10-01

    Accurate monitoring of degradation levels in soils is essential in order to understand and achieve complete degradation of petroleum hydrocarbons in contaminated soils. We aimed to develop the use of multivariate methods for the monitoring of biodegradation of diesel in soils and to determine if diesel contaminated soils could be remediated to a chemical composition similar to that of an uncontaminated soil. An incubation experiment was set up with three contrasting soil types. Each soil was exposed to diesel at varying stages of degradation and then analysed for key hydrocarbons throughout 161 days of incubation. Hydrocarbon distributions were analysed by Principal Coordinate Analysis and similar samples grouped by cluster analysis. Variation and differences between samples were determined using permutational multivariate analysis of variance. It was found that all soils followed trajectories approaching the chemical composition of the unpolluted soil. Some contaminated soils were no longer significantly different to that of uncontaminated soil after 161 days of incubation. The use of cluster analysis allows the assignment of a percentage chemical similarity of a diesel contaminated soil to an uncontaminated soil sample. This will aid in the monitoring of hydrocarbon contaminated sites and the establishment of potential endpoints for successful remediation.

  9. [Neuroimaging in psychiatry: multivariate analysis techniques for diagnosis and prognosis].

    PubMed

    Kambeitz, J; Koutsouleris, N

    2014-06-01

    Multiple studies successfully applied multivariate analysis to neuroimaging data demonstrating the potential utility of neuroimaging for clinical diagnostic and prognostic purposes. Summary of the current state of research regarding the application of neuroimaging in the field of psychiatry. Literature review of current studies. Results of current studies indicate the potential application of neuroimaging data across various diagnoses, such as depression, schizophrenia, bipolar disorder and dementia. Potential applications include disease classification, differential diagnosis and prediction of disease course. The results of the studies are heterogeneous although some studies report promising findings. Further multicentre studies are needed with clearly specified patient populations to systematically investigate the potential utility of neuroimaging for the clinical routine.

  10. Multivariate survival analysis of the patients with recurrent endometrial cancer

    PubMed Central

    Odagiri, Tetsuji; Hosaka, Masayoshi; Mitamura, Takashi; Konno, Yousuke; Kato, Tatsuya; Kobayashi, Noriko; Sudo, Satoko; Takeda, Mahito; Kaneuchi, Masanori; Sakuragi, Noriaki

    2011-01-01

    Objective Few studies on the prognosticators of the patients with recurrent endometrial cancer after relapse have been reported in the literature. The aim of this study was to determine the prognosticators after relapse in patients with recurrent endometrial cancer who underwent primary complete cytoreductive surgery and adjuvant chemotherapy. Methods Thirty-five patients with recurrent endometrial cancer were included in this retrospective analysis. The prognostic significance of several clinicopathological factors including histologic type, risk for recurrence, time to relapse after primary surgery, number of relapse sites, site of relapse, treatment modality, and complete resection of recurrent tumors were evaluated. Survival analyses were performed by Kaplan-Meier curves and the log-rank test. Independent prognostic factors were determined by multivariate Cox regression analysis. Results Among the clinicopathological factors analyzed, histologic type (p=0.04), time to relapse after primary surgery (p=0.03), and the number of relapse sites (p=0.03) were significantly related to survival after relapse. Multivariate analysis revealed that time to relapse after primary surgery (hazard ratio, 6.8; p=0.004) and the number of relapse sites (hazard ratio, 11.1; p=0.002) were independent prognostic factors for survival after relapse. Survival after relapse could be stratified into three groups by the combination of two independent prognostic factors. Conclusion We conclude that time to relapse after primary surgery, and the number of relapse sites were independent prognostic factors for survival after relapse in patients with recurrent endometrial cancer. PMID:21607089

  11. Treatment and multivariate analysis of colorectal cancer with liver metastasis.

    PubMed

    Wang, Yue; Duan, Boshi; Shen, Chunjian; Wu, Bo; Luo, Ji; Zhao, Guohua

    2014-09-01

    The aim of this study was to identify the influencing factors related to outcome of patients of colorectal cancer with liver metastasis. From January 1999 to January 2009, 293 cases of colorectal cancer with liver metastasis undergoing surgery were analysised retrospectively. Relationships between survival and clinicopathological factors including patient demographics and tumor characteristics were evaluated using univariate and multivariate analysis. Results: The 1-, 3- and 5-year survival rates of patients after resection were 58.3%, 26.4%, and 11.3%, respectively. Univariate analysis showed that preoperative CEA level, degree of primary tumor differentiation, resection margin, number of liver metastases, resection of liver metastases were prognostic impacts. The difference was statistically significant (p<0.05). Cox multivariate analysis showed that preoperative CEA level, number of liver metastases, and resection of liver metastases are three separate prognostic factors. Racical resection is the key to improve the long-term survival rate of colorectal cancer with liver metastasis. Important predictive factors related to poor survival are preoperative CEA level and number of liver metastases.

  12. Automatic cortical thickness analysis on rodent brain

    NASA Astrophysics Data System (ADS)

    Lee, Joohwi; Ehlers, Cindy; Crews, Fulton; Niethammer, Marc; Budin, Francois; Paniagua, Beatriz; Sulik, Kathy; Johns, Josephine; Styner, Martin; Oguz, Ipek

    2011-03-01

    Localized difference in the cortex is one of the most useful morphometric traits in human and animal brain studies. There are many tools and methods already developed to automatically measure and analyze cortical thickness for the human brain. However, these tools cannot be directly applied to rodent brains due to the different scales; even adult rodent brains are 50 to 100 times smaller than humans. This paper describes an algorithm for automatically measuring the cortical thickness of mouse and rat brains. The algorithm consists of three steps: segmentation, thickness measurement, and statistical analysis among experimental groups. The segmentation step provides the neocortex separation from other brain structures and thus is a preprocessing step for the thickness measurement. In the thickness measurement step, the thickness is computed by solving a Laplacian PDE and a transport equation. The Laplacian PDE first creates streamlines as an analogy of cortical columns; the transport equation computes the length of the streamlines. The result is stored as a thickness map over the neocortex surface. For the statistical analysis, it is important to sample thickness at corresponding points. This is achieved by the particle correspondence algorithm which minimizes entropy between dynamically moving sample points called particles. Since the computational cost of the correspondence algorithm may limit the number of corresponding points, we use thin-plate spline based interpolation to increase the number of corresponding sample points. As a driving application, we measured the thickness difference to assess the effects of adolescent intermittent ethanol exposure that persist into adulthood and performed t-test between the control and exposed rat groups. We found significantly differing regions in both hemispheres.

  13. Forensic discrimination of dyed hair color: II. Multivariate statistical analysis.

    PubMed

    Barrett, Julie A; Siegel, Jay A; Goodpaster, John V

    2011-01-01

    This research is intended to assess the ability of UV-visible microspectrophotometry to successfully discriminate the color of dyed hair. Fifty-five red hair dyes were analyzed and evaluated using multivariate statistical techniques including agglomerative hierarchical clustering (AHC), principal component analysis (PCA), and discriminant analysis (DA). The spectra were grouped into three classes, which were visually consistent with different shades of red. A two-dimensional PCA observations plot was constructed, describing 78.6% of the overall variance. The wavelength regions associated with the absorbance of hair and dye were highly correlated. Principal components were selected to represent 95% of the overall variance for analysis with DA. A classification accuracy of 89% was observed for the comprehensive dye set, while external validation using 20 of the dyes resulted in a prediction accuracy of 75%. Significant color loss from successive washing of hair samples was estimated to occur within 3 weeks of dye application.

  14. Multivariate Analysis of Blood Transfusion Rates After Shoulder Arthroplasty.

    PubMed

    King, Joseph J; Patrick, Matthew R; Schnetzer, Ryan E; Farmer, Kevin W; Struk, Aimee M; Garvan, Cyndi; Wright, Thomas W

    A retrospective review was performed of all shoulder arthroplasties with patients grouped on the basis of transfusion protocol time period. Group 1 had transfusions if postoperative hematocrit was <30. Group 2 had transfusions based on symptomatic anemia. Bivariate analysis of transfusion factors and multivariate analysis of significant bivariate factors were performed. Protocol change decreased transfusion rates from 16% (group 1, 153 arthroplasties) to 8% (group 2, 149 arthroplasties). Reverse shoulder arthroplasty (RTSA) transfusion rate decreased dramatically (from 24% to 5%). Transfusion rates after total shoulder arthroplasty (TSA) were low (4%) and after revision arthroplasty were high (21% + 27%) in both groups. Age, gender, heart disease, preoperative hematocrit, diagnosis, and estimated blood loss (EBL) were risk factors on bivariate analysis. Failed arthroplasty and fracture diagnoses carried high transfusion rates (25% + 28%). Logistic regression showed that low preoperative hematocrit, increased EBL, revision arthroplasty, and heart disease were transfusion risk factors. Protocol based on symptomatic anemia results in low transfusion rates after primary TSA and RTSA.

  15. "Multimodal Contrast" from the Multivariate Analysis of Hyperspectral CARS Images

    NASA Astrophysics Data System (ADS)

    Tabarangao, Joel T.

    The typical contrast mechanism employed in multimodal CARS microscopy involves the use of other nonlinear imaging modalities such as two-photon excitation fluorescence (TPEF) microscopy and second harmonic generation (SHG) microscopy to produce a molecule-specific pseudocolor image. In this work, I explore the use of unsupervised multivariate statistical analysis tools such as Principal Component Analysis (PCA) and Vertex Component Analysis (VCA) to provide better contrast using the hyperspectral CARS data alone. Using simulated CARS images, I investigate the effects of the quadratic dependence of CARS signal on concentration on the pixel clustering and classification and I find that a normalization step is necessary to improve pixel color assignment. Using an atherosclerotic rabbit aorta test image, I show that the VCA algorithm provides pseudocolor contrast that is comparable to multimodal imaging, thus showing that much of the information gleaned from a multimodal approach can be sufficiently extracted from the CARS hyperspectral stack itself.

  16. Multivariate analysis of heavy metals concentrations in river estuary.

    PubMed

    Alkarkhi, Abbas F M; Ahmad, Anees; Ismail, Norli; Easa, Azhar Mat

    2008-08-01

    Multivariate statistical techniques such as multivariate analysis of variance (MANOVA) and discriminant analysis (DA) were applied for analyzing the data obtained from two rivers in the Penang State of Malaysia for the concentration of heavy metal ions (As, Cr, Cd, Zn, Cu, Pb, and Hg) using a flame atomic absorption spectrometry (F-AAS) for Cr, Cd, Zn, Cu, Pb, As and cold vapor atomic absorption spectrometry (CV-AAS) for Hg. The two locations of interest with 20 sampling points of each location were Kuala Juru (Juru River) and Bukit Tambun (Jejawi River). MANOVA showed a strong significant difference between the two rivers in terms of heavy metal concentrations in water samples. DA gave the best result to identify the relative contribution for all parameters in discriminating (distinguishing) the two rivers. It provided an important data reduction as it used four parameters (Zn, Pb, Cd and Cr) affording 100% correct assignations. Results indicated that the two rivers were different in terms of heavy metals concentrations in water, and the major difference was due to the contribution of Zn. A negative correlation was found between discriminate functions (DF) and Cr and As, whereas positive correlation was exhibited with other heavy metals. Therefore, DA allowed a reduction in the dimensionality of the data set, delineating a few indicator parameters responsible for large variations in heavy metal concentrations. Correlation matrix between the parameters exhibited a strong evidence of mutual dependence of these metals.

  17. Multivariate Granger causality analysis of fMRI data.

    PubMed

    Deshpande, Gopikrishna; LaConte, Stephan; James, George Andrew; Peltier, Scott; Hu, Xiaoping

    2009-04-01

    This article describes the combination of multivariate Granger causality analysis, temporal down-sampling of fMRI time series, and graph theoretic concepts for investigating causal brain networks and their dynamics. As a demonstration, this approach was applied to analyze epoch-to-epoch changes in a hand-gripping, muscle fatigue experiment. Causal influences between the activated regions were analyzed by applying the directed transfer function (DTF) analysis of multivariate Granger causality with the integrated epoch response as the input, allowing us to account for the effects of several relevant regions simultaneously. Integrated responses were used in lieu of originally sampled time points to remove the effect of the spatially varying hemodynamic response as a confounding factor; using integrated responses did not affect our ability to capture its slowly varying affects of fatigue. We separately modeled the early, middle, and late periods in the fatigue. We adopted graph theoretic concepts of clustering and eccentricity to facilitate the interpretation of the resultant complex networks. Our results reveal the temporal evolution of the network and demonstrate that motor fatigue leads to a disconnection in the related neural network.

  18. Stellar populations in ω Centauri: a multivariate analysis

    NASA Astrophysics Data System (ADS)

    Fraix-Burnet, D.; Davoust, E.

    2015-07-01

    We have performed multivariate statistical analyses of photometric and chemical abundance parameters of three large samples of stars in the globular cluster ω Centauri. The statistical analysis of a sample of 735 stars based on seven chemical abundances with the method of Maximum Parsimony (cladistics) yields the most promising results: seven groups are found, distributed along three branches with distinct chemical, spatial and kinematical properties. A progressive chemical evolution can be traced from one group to the next, but also within groups, suggestive of an inhomogeneous chemical enrichment of the initial interstellar matter. The adjustment of stellar evolution models shows that the groups with metallicities [Fe/H] > -1.5 are Helium enriched, thus presumably of second generation. The spatial concentration of the groups increases with chemical evolution, except for two groups, which stand out in their other properties as well. The amplitude of rotation decreases with chemical evolution, except for two of the three metal-rich groups, which rotate fastest, as predicted by recent hydrodynamical simulations. The properties of the groups are interpreted in terms of star formation in gas clouds of different origins. In conclusion, our multivariate analysis has shown that metallicity alone cannot segregate the different populations of ω Centauri.

  19. Processes and subdivisions in diogenites, a multivariate statistical analysis

    NASA Technical Reports Server (NTRS)

    Harriott, T. A.; Hewins, R. H.

    1984-01-01

    Multivariate statistical techniques used on diogenite orthopyroxene analyses show the relationships that occur within diogenites and the two orthopyroxenite components (class I and II) in the polymict diogenite Garland. Cluster analysis shows that only Peckelsheim is similar to Garland class I (Fe-rich) and the other diogenites resemble Garland class II. The unique diogenite Y 75032 may be related to type I by fractionation. Factor analysis confirms the subdivision and shows that Fe does not correlate with the weakly incompatible elements across the entire pyroxene composition range, indicating that igneous fractionation is not the process controlling total diogenite composition variation. The occurrence of two groups of diogenites is interpreted as the result of sampling or mixing of two main sequences of orthopyroxene cumulates with slightly different compositions.

  20. Multivariate space - time analysis of PRE-STORM precipitation

    NASA Technical Reports Server (NTRS)

    Polyak, Ilya; North, Gerald R.; Valdes, Juan B.

    1994-01-01

    This paper presents the methodologies and results of the multivariate modeling and two-dimensional spectral and correlation analysis of PRE-STORM rainfall gauge data. Estimated parameters of the models for the specific spatial averages clearly indicate the eastward and southeastward wave propagation of rainfall fluctuations. A relationship between the coefficients of the diffusion equation and the parameters of the stochastic model of rainfall fluctuations is derived that leads directly to the exclusive use of rainfall data to estimate advection speed (about 12 m/s) as well as other coefficients of the diffusion equation of the corresponding fields. The statistical methodology developed here can be used for confirmation of physical models by comparison of the corresponding second-moment statistics of the observed and simulated data, for generating multiple samples of any size, for solving the inverse problem of the hydrodynamic equations, and for application in some other areas of meteorological and climatological data analysis and modeling.

  1. Multivariate space - time analysis of PRE-STORM precipitation

    NASA Technical Reports Server (NTRS)

    Polyak, Ilya; North, Gerald R.; Valdes, Juan B.

    1994-01-01

    This paper presents the methodologies and results of the multivariate modeling and two-dimensional spectral and correlation analysis of PRE-STORM rainfall gauge data. Estimated parameters of the models for the specific spatial averages clearly indicate the eastward and southeastward wave propagation of rainfall fluctuations. A relationship between the coefficients of the diffusion equation and the parameters of the stochastic model of rainfall fluctuations is derived that leads directly to the exclusive use of rainfall data to estimate advection speed (about 12 m/s) as well as other coefficients of the diffusion equation of the corresponding fields. The statistical methodology developed here can be used for confirmation of physical models by comparison of the corresponding second-moment statistics of the observed and simulated data, for generating multiple samples of any size, for solving the inverse problem of the hydrodynamic equations, and for application in some other areas of meteorological and climatological data analysis and modeling.

  2. Oil sludge depository assessment using multivariate data analysis.

    PubMed

    Ermakov, V V; Bogomolov, A; Bykov, D E

    2012-08-30

    Oil-containing industrial wastes tend to accumulate and present a growing environmental danger. This is of particular concern in certain areas of Russia. For effective processing of depositories, the wastes' physico-chemical properties and depository characteristics should both be taken into account. Representative sample sets were collected from fifty four depositories of different age, origin, and location in Samara region and analyzed using multivariate data analysis: Principal Component Analysis (PCA) and Partial Least-Squares (PLS) regression. PCA results provide a better understanding of the internal data structure, i.e. variable correlations and groupings. Based on the PCA results, a new approach to the classification of oil sludge depositories has been suggested. Another practically important task of site assessment has been solved by PLS regression modeling. The method has been successfully applied to the accurate estimation of the depository processing profitability for a specific site. Copyright © 2012 Elsevier Ltd. All rights reserved.

  3. Processes and subdivisions in diogenites, a multivariate statistical analysis

    NASA Technical Reports Server (NTRS)

    Harriott, T. A.; Hewins, R. H.

    1984-01-01

    Multivariate statistical techniques used on diogenite orthopyroxene analyses show the relationships that occur within diogenites and the two orthopyroxenite components (class I and II) in the polymict diogenite Garland. Cluster analysis shows that only Peckelsheim is similar to Garland class I (Fe-rich) and the other diogenites resemble Garland class II. The unique diogenite Y 75032 may be related to type I by fractionation. Factor analysis confirms the subdivision and shows that Fe does not correlate with the weakly incompatible elements across the entire pyroxene composition range, indicating that igneous fractionation is not the process controlling total diogenite composition variation. The occurrence of two groups of diogenites is interpreted as the result of sampling or mixing of two main sequences of orthopyroxene cumulates with slightly different compositions.

  4. Application of automatic image analysis in wood science

    Treesearch

    Charles W. McMillin

    1982-01-01

    In this paper I describe an image analysis system and illustrate with examples the application of automatic quantitative measurement to wood science. Automatic image analysis, a powerful and relatively new technology, uses optical, video, electronic, and computer components to rapidly derive information from images with minimal operator interaction. Such instruments...

  5. Multivariate statistical analysis software technologies for astrophysical research involving large data bases

    NASA Technical Reports Server (NTRS)

    Djorgovski, George

    1993-01-01

    The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multiparameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resource.

  6. Multivariate statistical analysis software technologies for astrophysical research involving large data bases

    NASA Technical Reports Server (NTRS)

    Djorgovski, Stanislav

    1992-01-01

    The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multi parameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resources.

  7. Multivariate statistical analysis software technologies for astrophysical research involving large data bases

    NASA Technical Reports Server (NTRS)

    Djorgovski, George

    1993-01-01

    The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multiparameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resource.

  8. Multivariate or Multivariable Regression?

    PubMed Central

    Goodman, Melody

    2013-01-01

    The terms multivariate and multivariable are often used interchangeably in the public health literature. However, these terms actually represent 2 very distinct types of analyses. We define the 2 types of analysis and assess the prevalence of use of the statistical term multivariate in a 1-year span of articles published in the American Journal of Public Health. Our goal is to make a clear distinction and to identify the nuances that make these types of analyses so distinct from one another. PMID:23153131

  9. Multivariate meta-analysis with an increasing number of parameters.

    PubMed

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

    2017-02-14

    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.

  10. Is standard multivariate analysis sufficient in clinical and epidemiological studies?

    PubMed

    Cova, Tânia F G G; Pereira, Jorge L G F S C; Pais, Alberto A C C

    2013-02-01

    Clinical tests and epidemiological studies often produce large amounts of data, being multivariate in nature. The respective analysis is, in most cases, of importance comparable to the clinical and sampling tasks. Simple, easily interpretable techniques from chemometrics provide most of the ingredients to carry out this analysis. We have selected available data from different sources pertaining to cancer diagnosis and incidence: (1) cytological diagnosis of breast cancer, (2) classification of breast tissues through parameters obtained from impedance spectra and (3) distribution of new cancer cases in the United States. Hierarchical cluster analysis (HCA) is needed especially in cases where there is no a priori identification of classes, suggesting a structure of the data based on clusters. These clusters or the classes, are then further detailed and rationalized by principal component analysis (PCA). Partial least squares (PLS) and linear discriminant analysis (LDA) provide further insight into the systems. An additional step for understanding the data set is the removal of less characteristic data (NR) using a density-based approach, so as to make it more clearly defined. Results clearly reveal that breast cytology diagnosis relies on variables conveying mostly the same type of information, being thus interchangeable in nature. In the study on tissue characterization by electrical measurements, the distribution of the different types of tissues can be easily constructed. Finally, the distribution of new cancer cases possesses clear, easily unravelled, geographical patterns. Copyright © 2012. Published by Elsevier Inc.

  11. Sedimentary chemofacies characterization by means of multivariate analysis

    NASA Astrophysics Data System (ADS)

    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.

  12. Multivariate Granger Causality Analysis of Obesity Related Variables.

    PubMed

    Mukhopadhyay, Nitai D; Wheeler, David; Sabo, Roy; Sun, Shumei S

    Obesity is a complex health outcome that is a combination of multiple health indicators. Here we attempt to explore the dependence network among multiple aspects of obesity. Two longitudinal cohort studies across multiple decades have been used. The concept of causality is defined similar to Granger causality among multiple time series, however, modified to accommodate multivariate time series as the nodes of the network. Our analysis reveals relatively central position of physical measurements and blood chemistry measures in the overall network across both genders. Also there are some patterns specific to only male or female population. The geometry of the causality network is expected to help in our strategy to control the increasing trend of obesity rate.

  13. Ordinary chondrites - Multivariate statistical analysis of trace element contents

    NASA Technical Reports Server (NTRS)

    Lipschutz, Michael E.; Samuels, Stephen M.

    1991-01-01

    The contents of mobile trace elements (Co, Au, Sb, Ga, Se, Rb, Cs, Te, Bi, Ag, In, Tl, Zn, and Cd) in Antarctic and non-Antarctic populations of H4-6 and L4-6 chondrites, were compared using standard multivariate discriminant functions borrowed from linear discriminant analysis and logistic regression. A nonstandard randomization-simulation method was developed, making it possible to carry out probability assignments on a distribution-free basis. Compositional differences were found both between the Antarctic and non-Antarctic H4-6 chondrite populations and between two L4-6 chondrite populations. It is shown that, for various types of meteorites (in particular, for the H4-6 chondrites), the Antarctic/non-Antarctic compositional difference is due to preterrestrial differences in the genesis of their parent materials.

  14. Ordinary chondrites - Multivariate statistical analysis of trace element contents

    NASA Technical Reports Server (NTRS)

    Lipschutz, Michael E.; Samuels, Stephen M.

    1991-01-01

    The contents of mobile trace elements (Co, Au, Sb, Ga, Se, Rb, Cs, Te, Bi, Ag, In, Tl, Zn, and Cd) in Antarctic and non-Antarctic populations of H4-6 and L4-6 chondrites, were compared using standard multivariate discriminant functions borrowed from linear discriminant analysis and logistic regression. A nonstandard randomization-simulation method was developed, making it possible to carry out probability assignments on a distribution-free basis. Compositional differences were found both between the Antarctic and non-Antarctic H4-6 chondrite populations and between two L4-6 chondrite populations. It is shown that, for various types of meteorites (in particular, for the H4-6 chondrites), the Antarctic/non-Antarctic compositional difference is due to preterrestrial differences in the genesis of their parent materials.

  15. Multivariate Statistical Analysis of MSL APXS Bulk Geochemical Data

    NASA Astrophysics Data System (ADS)

    Hamilton, V. E.; Edwards, C. S.; Thompson, L. M.; Schmidt, M. E.

    2014-12-01

    We apply cluster and factor analyses to bulk chemical data of 130 soil and rock samples measured by the Alpha Particle X-ray Spectrometer (APXS) on the Mars Science Laboratory (MSL) rover Curiosity through sol 650. Multivariate approaches such as principal components analysis (PCA), cluster analysis, and factor analysis compliment more traditional approaches (e.g., Harker diagrams), with the advantage of simultaneously examining the relationships between multiple variables for large numbers of samples. Principal components analysis has been applied with success to APXS, Pancam, and Mössbauer data from the Mars Exploration Rovers. Factor analysis and cluster analysis have been applied with success to thermal infrared (TIR) spectral data of Mars. Cluster analyses group the input data by similarity, where there are a number of different methods for defining similarity (hierarchical, density, distribution, etc.). For example, without any assumptions about the chemical contributions of surface dust, preliminary hierarchical and K-means cluster analyses clearly distinguish the physically adjacent rock targets Windjana and Stephen as being distinctly different than lithologies observed prior to Curiosity's arrival at The Kimberley. In addition, they are separated from each other, consistent with chemical trends observed in variation diagrams but without requiring assumptions about chemical relationships. We will discuss the variation in cluster analysis results as a function of clustering method and pre-processing (e.g., log transformation, correction for dust cover) and implications for interpreting chemical data. Factor analysis shares some similarities with PCA, and examines the variability among observed components of a dataset so as to reveal variations attributable to unobserved components. Factor analysis has been used to extract the TIR spectra of components that are typically observed in mixtures and only rarely in isolation; there is the potential for similar

  16. A Multivariate Analysis of Extratropical Cyclone Environmental Sensitivity

    NASA Astrophysics Data System (ADS)

    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

  17. Acoustic wave network and multivariate analysis for biosensing in space

    NASA Astrophysics Data System (ADS)

    Jayarajah, Christine N.; Thompson, Michael

    2005-03-01

    Bioanalytical techniques play an important role in monitoring the effects of environmental stress factors on fundamental life processes. In terms of space flight and extraterrestrial research, radiation, altered and microgravity are known to induce changes in gene expression. We report the use of an on-line transverse shear mode (TSM) acoustic wave biosensor to detect the initiation of gene transcription and DNA — drug binding. Since this biosensor offers real-time, label free monitoring of biological processes, it is possible to detect sequential binding steps as demonstrated in this paper. Furthermore, this sensor responds to several factors in the liquid phase such as viscosity, elasticity, surface tension, charge distribution and mass loading, which can in turn be influenced by specific gravity. The sensing device is a piezoelectric quartz crystal onto which the probe molecule (DNA in this case) is immobilized. Change in resonance frequency of the crystal in response to the binding of the target molecule(s), RNA polymerase and actinomycin-D, is fit to an equivalent circuit model from which multidimensional data is extracted. By performing multivariate analysis on this data we are able to observe interactions between several of these data series representing parameters such as motional resistance and capacitance. As well, we are able to observe the dominating parameters (for instance, frequency vs. motional resistance, which in turn can correspond to mass loading vs. energy dissipation) during the course of the experiment, as they vary between the different steps. Such advantages offered by the TSM sensor along with multivariate analysis are indispensable for biotechnological work under the influence of microgravity as several variables come into play.

  18. AUTOMATIC DIRT TRAIL ANALYSIS IN DERMOSCOPY IMAGES

    PubMed Central

    Cheng, Beibei; Stanley, R. Joe; Stoecker, William V.; Osterwise, Christopher T.P.; Stricklin, Sherea M.; Hinton, Kristen A.; Moss, Randy H.; Oliviero, Margaret; Rabinovitz, Harold S.

    2011-01-01

    Basal cell carcinoma (BCC) is the most common cancer in the U.S. Dermatoscopes are devices used by physicians to facilitate the early detection of these cancers based on the identification of skin lesion structures often specific to BCCs. One new lesion structure, referred to as dirt trails, has the appearance of dark gray, brown or black dots and clods of varying sizes distributed in elongated clusters with indistinct borders, often appearing as curvilinear trails. In this research, we explore a dirt trail detection and analysis algorithm for extracting, measuring, and characterizing dirt trails based on size, distribution, and color in dermoscopic skin lesion images. These dirt trails are then used to automatically discriminate BCC from benign skin lesions. For an experimental data set of 35 BCC images with dirt trails and 79 benign lesion images, a neural network-based classifier achieved a 0.902 area under a receiver operating characteristic curve using a leave-one-out approach, demonstrating the potential of dirt trails for BCC lesion discrimination. PMID:22233099

  19. Multivariate Comparative Analysis of Stock Exchanges: The European Perspective

    NASA Astrophysics Data System (ADS)

    Koralun-Bereźnicka, Julia

    The aim of the research is to perform a multivariate comparative analysis of 20 European stock exchanges in order to identify the main similarities between the objects. Due to the convergence process of capital markets in Europe the similarities between stock exchanges could be expected to increase over time. The research is meant to show whether and how these similarities change. Consequently, the distances between clusters of similar stock exchanges should become less significant, which the analysis also aims at verifying. The basis of comparison is a set of 48 monthly variables from the period January, 2003 to December, 2006. The variables are classified into three categories: size of the market, equity trading and bonds. The paper aims at identifying the clusters of alike stock exchanges and at finding the characteristic features of each of the distinguished groups. The obtained categorization to some extent corresponds with the division of the European Union into “new” and “old” member countries. Clustering method, performed for each quarter separately, also reveals that the classification is fairly stable in time. The factor analysis, which was carried out to reduce the number of variables, reveals three major factors behind the data, which are related with the earlier mentioned categories of variables.

  20. A multivariate analysis of fatness and relative fat patterning.

    PubMed

    Mueller, W H; Reid, R M

    1979-02-01

    Skinfold measurements (triceps, subscapular, suprailiac and medial calf) in four samples (376 boys, 352 girs, 338 men and 380 women from rural Colombia) were subjected to principal components analysis to identify components of obesity and relative fat patterning. Three components emerged which were similar in the four samples: a first component of fatness explaining 70-80% of the variance and two fat pattern components each explaining 10-15% of the variance: trunk-extremity and upper-lower body. Fatness and the trunk-extremity pattern components changed with age in children (7-12 years), but none of the components changed with age in adults (25-60+). The fatter tended to be more patterned in both age groups. Canonical correlation analysis revealed that socioeconomic status was more related to fatness than to patterning. With the exception of brothers, all first degree relatives (sib, parent-off-spring) and spouses were correlated in fatness. Some of the correlations between relatives--usually sibs, but not spouses--were also significant for the pattern components, suggesting a genetic basis for the known stability of this characteristic (Garn, '55a). Principal components analysis is a useful multivariate alternative for quantitative studies of anthropometric patterning.

  1. Applications of Multivariate Statistical Analysis (MSA) in Microanalysis

    SciTech Connect

    Anderson, I.M.

    1999-02-16

    Recent improvements in computer hardware and software for the acquisition, storage and analysis of series of spectra and images allow for a change in strategy for quantitative microanalysis. For example, in the area of X-ray microanalysis, whereas compositional analysis and elemental distributions have been traditionally performed using point microanalysis and simple intensity mapping from a ROI, respectively, the two tasks are now routinely performed simultaneously through X-ray spectrum-imaging, where full spectra are acquired from pixels in a two-dimensional array of points on the specimen. Commercially available software now allows for the acquisition and storage of such spectrum-images, perhaps comprising as much as 100 MBytes of data or more. A variety of post-acquisition processing tools are provided by the developer to allow the extraction of both X-ray intensity maps, with or without rudimentary background subtraction, or full spectra from pixels of interest. In order to maximize the extraction of information from these large data sets, a number of linear and nonlinear methods are currently being explored that identify statistically significant variations among the series of spectra without a priori assumptions about the content of the data set. Among these methods, linear multivariate statistical analysis (MSA) has a number of significant advantages, including its comprehensiveness, since all spectral variations distinct from the Poisson noise level are identified, and its broad applicability to a variety of microanalytical techniques.

  2. A multivariate analysis of childhood abdominal pain in Trinidad.

    PubMed

    Anatol, T I; Holder, Y

    1995-04-01

    This is a multivariate analysis of the data recorded in assessing 1158 consecutive admissions presenting to a children's surgical ward with acute abdominal pain. There were 56 binary variables available for entry into the analysis. A statistical software package was used to perform a stepwise discriminant analysis on the data. The program selected 18 variables as having discriminating power in assigning patients to the six diagnostic groups. In order of discriminating power these were, mainly, a positive urine culture, the bowel history, the findings on rectal examination, the location of abdominal tenderness, the presence of a mass, and the white cell count. Lesser discriminating potential was assigned to the presence of dehydration; fluid levels on erect abdominal films, a rise in temperature, an increased pulse rate, the presence of urinary symptoms, and the general appearance of the child. Use of these data led to an overall correct classification of 80.7% of cases. It is concluded that these variables should be included in the assessment of children with acute abdominal pain.

  3. Multivariate Analysis of the Globular Clusters in M87

    NASA Astrophysics Data System (ADS)

    Das, Sukanta; Chattopadhayay, Tanuka; Davoust, Emmanuel

    2015-11-01

    An objective classification of 147 globular clusters (GCs) in the inner region of the giant elliptical galaxy M87 is carried out with the help of two methods of multivariate analysis. First, independent component analysis (ICA) is used to determine a set of independent variables that are linear combinations of various observed parameters (mostly Lick indices) of the GCs. Next, K-means cluster analysis (CA) is applied on the independent components (ICs), to find the optimum number of homogeneous groups having an underlying structure. The properties of the four groups of GCs thus uncovered are used to explain the formation mechanism of the host galaxy. It is suggested that M87 formed in two successive phases. First a monolithic collapse, which gave rise to an inner group of metal-rich clusters with little systematic rotation and an outer group of metal-poor clusters in eccentric orbits. In a second phase, the galaxy accreted low-mass satellites in a dissipationless fashion, from the gas of which the two other groups of GCs formed. Evidence is given for a blue stellar population in the more metal rich clusters, which we interpret by Helium enrichment. Finally, it is found that the clusters of M87 differ in some of their chemical properties (NaD, TiO1, light-element abundances) from GCs in our Galaxy and M31.

  4. Causal diagrams and multivariate analysis II: precision work.

    PubMed

    Jupiter, Daniel C

    2014-01-01

    In this Investigators' Corner, I continue my discussion of when and why we researchers should include variables in multivariate regression. My examination focuses on studies comparing treatment groups and situations for which we can either exclude variables from multivariate analyses or include them for reasons of precision. Copyright © 2014 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

  5. Classification of Malaysia aromatic rice using multivariate statistical analysis

    SciTech Connect

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

    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.

  6. Classification of Malaysia aromatic rice using multivariate statistical analysis

    NASA Astrophysics Data System (ADS)

    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.

  7. Multivariate analysis of dim elves from ISUAL observations

    NASA Astrophysics Data System (ADS)

    Offroy, Marc; Farges, Thomas; Gaillard, Pierre; Kuo, Cheng Ling; Chen, Alfred Bing-Chih; Hsu, Rue-Ron; Takahashi, Yukihiro

    2015-08-01

    The Imager of Sprites and Upper Atmospheric Lightning (ISUAL) on the FORMOSAT-2 satellite, launched in 2004, records Transient Luminous Events (TLEs). ISUAL has an imager and a spectrophotometer that observe TLEs all over the globe. Among these phenomena, elves are particularly difficult to detect. ISUAL often records events that correspond to significant far ultraviolet (FUV) emissions in the spectrophotometer but have no discernible TLEs in the imager. These FUV events are called "dim" elves. Therefore, it is important to develop mathematical tools to analyze the data to obtain a better evaluation of the number of elves and their occurrence. Multivariate approaches are applied to characterize the unlabeled events. The first approach is the principal component analysis which distinguishes two different groups, one including elves and dim elves. The second approach is the PARallel FACtor analysis which provides a waveform model for each group. These methodologies confirm that FUV signal is the evidence of TLE presence. A crude classification method was then suggested taking into account these results. The proportion of elves, relatively to the considered ISUAL data set, is found to be about 40%. It is similar to previous results and confirms that relatively weak lightning peak current is sufficient to produce elves. This new strategy demonstrates the potential for discriminating between lightning and TLEs without prior knowledge within the selectivity of the FUV spectral band.

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

    NASA Astrophysics Data System (ADS)

    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.

  9. Multivariate image analysis for process monitoring and control

    NASA Astrophysics Data System (ADS)

    MacGregor, John F.; Bharati, Manish H.; Yu, Honglu

    2001-02-01

    Information from on-line imaging sensors has great potential for the monitoring and control of quality in spatially distributed systems. The major difficulty lies in the efficient extraction of information from the images, information such as the frequencies of occurrence of specific and often subtle features, and their locations in the product or process space. This paper presents an overview of multivariate image analysis methods based on Principal Component Analysis and Partial Least Squares for decomposing the highly correlated data present in multi-spectral images. The frequencies of occurrence of certain features in the image, regardless of their spatial locations, can be easily monitored in the space of the principal components. The spatial locations of these features can then be obtained by transposing highlighted pixels from the PC score space into the original image space. In this manner it is possible to easily detect and locate even very subtle features from on-line imaging sensors for the purpose of statistical process control or feedback control of spatial processes. The concepts and potential of the approach are illustrated using a sequence of LANDSAT satellite multispectral images, depicting a pass over a certain region of the earth's surface. Potential applications in industrial process monitoring using these methods will be discussed from a variety of areas such as pulp and paper sheet products, lumber and polymer films.

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

    NASA Astrophysics Data System (ADS)

    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.

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

    NASA Astrophysics Data System (ADS)

    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.

  12. Gravitational Wave Detection of Compact Binaries Through Multivariate Analysis

    NASA Astrophysics Data System (ADS)

    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.

  13. Multivariate analysis in provenance studies: Cerrillos obsidians case, Peru

    NASA Astrophysics Data System (ADS)

    Bustamante, A.; Delgado, M.; Latini, R. M.; Bellido, A. V. B.

    2007-02-01

    We present the preliminary results of a provenance study of obsidians samples from Cerrillos (ca. 800 100 b.c.) using Mössbauer Spectroscopy. The Cerrillos archaeological site, located in the Upper Ica Valley, Peru, is the only Paracas ceremonial center excavated so far. The archaeological data collected suggest the existence of a complex social and economic organization on the south coast of Peru. Provenance research of obsidian provides valuable information about the selection of lithic resources by our ancestors and eventually about the existence of communication routes and exchange networks. We characterized 18 obsidian artifacts samples by Mössbauer spectroscopy from Cerrillos. The spectra, recorded at room temperature using different velocities, are mainly composed of broad asymmetric doublets due to the superposition of at least two quadrupole doublets corresponding to Fe2+ in two different sites (species A and B), one weak Fe3+ doublet (specie C) and magnetic components associated to the presence of small particles of magnetite. Multivariate statistical analysis of the Mössbauer data (hyperfine parameters) allows to defined two main groups of obsidians, reflecting different geographical origins.

  14. Multivariate wavelet texture analysis for pharmaceutical solid product characterization.

    PubMed

    García-Muñoz, Salvador; Carmody, Alan

    2010-10-15

    The application of multivariate wavelet texture analysis (MWTA) is presented and discussed as it is applied to three different types of pharmaceutical materials: (a) tablet cores, (b) wet granules and (c) controlled release tablets. The application of MWTA is initially proposed as a quantitative replacement to the human visual judgment of the textural appearance of the different materials. In all cases, the metrics obtained with MWTA agree with visual assessment on the progression of textural features such as erosion and surface roughness. This work further demonstrates that MWTA also represents a useful tool to increase the understanding of the manufacturing process, as it provides diagnostics to relate process parameters with textural features of the material that are difficult or costly to measure otherwise (such as granule size for wet material or surface appearance for a controlled release product). MWTA is also presented as a potential tool for real-time release for those cases where the textural features can be proven to provide accurate enough predictions of the final product performance; as shown here with the obtained prediction of dissolution from the controlled release tablet using the texture of the product as an input. Copyright 2010 Elsevier B.V. All rights reserved.

  15. Determining the metabolic footprints of hydrocarbon degradation using multivariate analysis.

    PubMed

    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.

  16. A multivariate analysis of arctic climate in GCMs

    SciTech Connect

    McGinnis, D.L.; Crane, R.G. )

    1994-08-01

    A multivariate analysis of Arctic climate is performed comparing the observed climate with that simulated by four different global climate models (GCMs). The focus is on the patterns of temporal and spatial variability in several climate parameters (sea level pressure, temperature, specific humidity, and precipitation). There are broad similarities between the observed data and all the GCM climates. There are, however, several major differences. The observed data show the Arctic climate to be dominated by the summertime pattern of temperature and humidity, which is decoupled from the atmospheric circulation. The winter patterns explain less of the observed variance but show a much closer association between temperature and the large-scale circulation. The GCMs, in contrast, overemphasize the winter season and show more of a large-scale advective control on summertime temperature patterns. Possible reasons for these differences are suggested, and their implications for GCM climate studies are discussed. The shortcomings in the GCMs point to the need for improvements in boundary layer rendition, in the treatment of Arctic stratus, and in sea ice simulations through coupled ocean models and the inclusion of ice dynamics. 25 refs., 7 figs., 2 tabs.

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

    PubMed Central

    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

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

    PubMed

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

    2012-07-01

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

  19. Estimation and Psychometric Analysis of Component Profile Scores via Multivariate Generalizability Theory

    ERIC Educational Resources Information Center

    Grochowalski, Joseph H.

    2015-01-01

    Component Universe Score Profile analysis (CUSP) is introduced in this paper as a psychometric alternative to multivariate profile analysis. The theoretical foundations of CUSP analysis are reviewed, which include multivariate generalizability theory and constrained principal components analysis. Because CUSP is a combination of generalizability…

  20. SERS spectroscopy and multivariate analysis of globulin in human blood

    NASA Astrophysics Data System (ADS)

    Wang, J.; Zeng, Y. Y.; Lin, J. Q.; Lin, L.; Wang, X. C.; Chen, G. N.; Huang, Z. F.; Li, B. H.; Zeng, H. S.; Chen, R.

    2014-06-01

    Globulin plays a significant role in body processes, acts as an important marker for disease diagnosis and determines blood type. Moreover, recent reports about the strong association between cancer risk and blood type imply that further studying these relationships may yield new findings on the biological mechanisms of tumorigenesis. In this paper, we propose and evaluate the efficacy of surface-enhanced Raman scattering (SERS) for the determination of this important globulin derived from human blood. Comparing globulins from different blood types by utilizing SERS spectroscopy and multivariate analysis, we show that primary structures of globulins from different blood types are similar to each other, but subtle differences in structures which may be vital for determining blood type are still observed. The abilities of globulins from different blood types to approach silver surfaces seem to differ, which also indicates that there are structural differences in blood type related globulins. Furthermore, this method differentiates blood type A from type B, type A from type O, type B from type O, type AB from type A, type AB from type B, and type AB from type O with sensitivities and specificities as follows: (90.0%, 95.0%), (80.0%, 83.9%), (95.0%, 90.3%), (97.3%, 96.7%), (94.6%, 95.5%), (100%, 100%), suggesting a potential feasibility for use in blood type identification. Our method sheds new light on blood type analysis, paves the way for the study of relationships between cancer risk and blood types, and expands the flexibility of SERS for useful applications in the life sciences.

  1. Deeper Insights into the Circumgalactic Medium using Multivariate Analysis Methods

    NASA Astrophysics Data System (ADS)

    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.

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

    DOE PAGES

    Coble, Jamie; Orton, Christopher; Schwantes, Jon

    2017-01-17

    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 gammamore » spectra were used in this paper 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. Finally, 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.« less

  3. Quantitative Remote Laser-Induced Breakdown Spectroscopy by Multivariate Analysis

    NASA Astrophysics Data System (ADS)

    Clegg, S. M.; Sklute, E. C.; Dyar, M. D.; Barefield, J. E.; Wiens, R. C.

    2007-12-01

    The ChemCam instrument selected for the Mars Science Laboratory (MSL) rover includes a remote Laser- Induced Breakdown Spectrometer (LIBS) that will quantitatively probe samples up to 9m from the rover mast. LIBS is fundamentally an elemental analysis technique. LIBS involves focusing a Nd:YAG laser operating at 1064 nm onto the surface of the sample. The laser ablates material from the surface, generating an expanding plasma containing electronically excited ions, atoms, and small molecules. As these electronically excited species relax back to the ground state, they emit light at wavelengths characteristic of the species present in the sample. Some of this emission is directed into one of three dispersive spectrometers. In this paper, we studied a suite of 18 igneous and highly-metamorphosed samples from a wide variety of parageneses for which chemical analyses by XRF were already available. Rocks were chosen to represent a range of chemical composition from basalt to rhyolite, thus providing significant variations in all of the major element contents (Si, Fe, Al, Ca, Na, K, O, Ti, Mg, and Mn). These samples were probed at a 9m standoff distance under experimental conditions that are similar to ChemCam. Extracting quantitative elemental concentrations from LIBS spectra is complicated by the chemical matrix effects. Conventional methods for obtaining quantitative chemical data from LIBS analyses are compared with new multivariate analysis (MVA) techniques that appear to compensate for these chemical matrix effects. The traditional analyses use specific elemental peak heights or areas, which compared with calibration curves for each element at one or more emission lines for a series of standard samples. Because of matrix effects, the calibration standards generally must have similar chemistries to the unknown samples, and thus this conventional approach imposes severe limitations on application of the technique to remote analyses. In this suite of samples, the use

  4. Automatic analysis of the corneal ulcer

    NASA Astrophysics Data System (ADS)

    Ventura, Liliane; Chiaradia, Caio; Faria de Sousa, Sidney J.

    1999-06-01

    A very common disease in agricultural countries is the corneal ulcer. Particularly in the public hospitals, several patients come every week presenting this kind of pathology. One of the most important features to diagnose the regression of the disease is the determination of the vanishing of the affected area. An automatic system (optical system and software), attached to a Slit Lamp, has been developed to determine automatically the area of the ulcer and to follow up its regression. The clinical procedure to isolate the ulcer is still done, but the measuring time is fast enough to not cause discomfort to the patient as the traditional evaluation does. The system has been used in the last 6 months in a hospital that has about 80 patients per week presenting corneal ulcer. The patients follow up (which is an indispensable criteria for the cure of the disease) has been improved by the system and has guaranteed the treatment success.

  5. Automatic basal slice detection for cardiac analysis

    NASA Astrophysics Data System (ADS)

    Paknezhad, Mahsa; Marchesseau, Stephanie; Brown, Michael S.

    2016-03-01

    Identification of the basal slice in cardiac imaging is a key step to measuring the ejection fraction (EF) of the left ventricle (LV). Despite research on cardiac segmentation, basal slice identification is routinely performed manually. Manual identification, however, has been shown to have high inter-observer variability, with a variation of the EF by up to 8%. Therefore, an automatic way of identifying the basal slice is still required. Prior published methods operate by automatically tracking the mitral valve points from the long-axis view of the LV. These approaches assumed that the basal slice is the first short-axis slice below the mitral valve. However, guidelines published in 2013 by the society for cardiovascular magnetic resonance indicate that the basal slice is the uppermost short-axis slice with more than 50% myocardium surrounding the blood cavity. Consequently, these existing methods are at times identifying the incorrect short-axis slice. Correct identification of the basal slice under these guidelines is challenging due to the poor image quality and blood movement during image acquisition. This paper proposes an automatic tool that focuses on the two-chamber slice to find the basal slice. To this end, an active shape model is trained to automatically segment the two-chamber view for 51 samples using the leave-one-out strategy. The basal slice was detected using temporal binary profiles created for each short-axis slice from the segmented two-chamber slice. From the 51 successfully tested samples, 92% and 84% of detection results were accurate at the end-systolic and the end-diastolic phases of the cardiac cycle, respectively.

  6. Semi-automatic aortic aneurysm analysis

    NASA Astrophysics Data System (ADS)

    Bodur, Osman; Grady, Leo; Stillman, Arthur; Setser, Randolph; Funka-Lea, Gareth; O'Donnell, Thomas

    2007-03-01

    Aortic aneurysms are the 13 th leading cause of death in the United States. In standard clinical practice, assessing the progression of disease in the aorta, as well as the risk of aneurysm rupture, is based on measurements of aortic diameter. We propose a method for automatically segmenting the aortic vessel border allowing the calculation of aortic diameters on CTA acquisitions which is accurate and fast, allowing clinicians more time for their evaluations. While segmentation of aortic lumen is straightforward in CTA, segmentation of the outer vessel wall (epithelial layer) in a diseased aorta is difficult; furthermore, no clinical tool currently exists to perform this task. The difficulties are due to the similarities in intensity of surrounding tissue (and thrombus due to lack of contrast agent uptake), as well as the complications from bright calcium deposits. Our overall method makes use of a centerline for the purpose of resampling the image volume into slices orthogonal to the vessel path. This centerline is computed semi-automatically via a distance transform. The difficult task of automatically segmenting the aortic border on the orthogonal slices is performed via a novel variation of the isoperimetric algorithm which incorporates circular constraints (priors). Our method is embodied in a prototype which allows the loading and registration of two datasets simultaneously, facilitating longitudinal comparisons. Both the centerline and border segmentation algorithms were evaluated on four patients, each with two volumes acquired 6 months to 1.5 years apart, for a total of eight datasets. Results showed good agreement with clinicians' findings.

  7. Multivariate meta-analysis of mixed outcomes: a Bayesian approach

    PubMed Central

    Bujkiewicz, Sylwia; Thompson, John R; Sutton, Alex J; Cooper, Nicola J; Harrison, Mark J; Symmons, Deborah PM; Abrams, Keith R

    2013-01-01

    Multivariate random effects meta-analysis (MRMA) is an appropriate way for synthesizing data from studies reporting multiple correlated outcomes. In a Bayesian framework, it has great potential for integrating evidence from a variety of sources. In this paper, we propose a Bayesian model for MRMA of mixed outcomes, which extends previously developed bivariate models to the trivariate case and also allows for combination of multiple outcomes that are both continuous and binary. We have constructed informative prior distributions for the correlations by using external evidence. Prior distributions for the within-study correlations were constructed by employing external individual patent data and using a double bootstrap method to obtain the correlations between mixed outcomes. The between-study model of MRMA was parameterized in the form of a product of a series of univariate conditional normal distributions. This allowed us to place explicit prior distributions on the between-study correlations, which were constructed using external summary data. Traditionally, independent ‘vague’ prior distributions are placed on all parameters of the model. In contrast to this approach, we constructed prior distributions for the between-study model parameters in a way that takes into account the inter-relationship between them. This is a flexible method that can be extended to incorporate mixed outcomes other than continuous and binary and beyond the trivariate case. We have applied this model to a motivating example in rheumatoid arthritis with the aim of incorporating all available evidence in the synthesis and potentially reducing uncertainty around the estimate of interest. © 2013 The Authors. Statistics inMedicine Published by John Wiley & Sons, Ltd. PMID:23630081

  8. Multivariate Analysis for Animal Selection in Experimental Research

    PubMed Central

    Pinto, Renan Mercuri; de Campos, Dijon Henrique Salomé; Tomasi, Loreta Casquel; Cicogna, Antonio Carlos; Okoshi, Katashi; Padovani, Carlos Roberto

    2015-01-01

    Background Several researchers seek methods for the selection of homogeneous groups of animals in experimental studies, a fact justified because homogeneity is an indispensable prerequisite for casualization of treatments. The lack of robust methods that comply with statistical and biological principles is the reason why researchers use empirical or subjective methods, influencing their results. Objective To develop a multivariate statistical model for the selection of a homogeneous group of animals for experimental research and to elaborate a computational package to use it. Methods The set of echocardiographic data of 115 male Wistar rats with supravalvular aortic stenosis (AoS) was used as an example of model development. Initially, the data were standardized, and became dimensionless. Then, the variance matrix of the set was submitted to principal components analysis (PCA), aiming at reducing the parametric space and at retaining the relevant variability. That technique established a new Cartesian system into which the animals were allocated, and finally the confidence region (ellipsoid) was built for the profile of the animals’ homogeneous responses. The animals located inside the ellipsoid were considered as belonging to the homogeneous batch; those outside the ellipsoid were considered spurious. Results The PCA established eight descriptive axes that represented the accumulated variance of the data set in 88.71%. The allocation of the animals in the new system and the construction of the confidence region revealed six spurious animals as compared to the homogeneous batch of 109 animals. Conclusion The biometric criterion presented proved to be effective, because it considers the animal as a whole, analyzing jointly all parameters measured, in addition to having a small discard rate. PMID:25651342

  9. Multivariate analysis for animal selection in experimental research.

    PubMed

    Pinto, Renan Mercuri; Campos, Dijon Henrique Salomé de; Tomasi, Loreta Casquel; Cicogna, Antonio Carlos; Okoshi, Katashi; Padovani, Carlos Roberto

    2015-02-01

    Several researchers seek methods for the selection of homogeneous groups of animals in experimental studies, a fact justified because homogeneity is an indispensable prerequisite for casualization of treatments. The lack of robust methods that comply with statistical and biological principles is the reason why researchers use empirical or subjective methods, influencing their results. To develop a multivariate statistical model for the selection of a homogeneous group of animals for experimental research and to elaborate a computational package to use it. The set of echocardiographic data of 115 male Wistar rats with supravalvular aortic stenosis (AoS) was used as an example of model development. Initially, the data were standardized, and became dimensionless. Then, the variance matrix of the set was submitted to principal components analysis (PCA), aiming at reducing the parametric space and at retaining the relevant variability. That technique established a new Cartesian system into which the animals were allocated, and finally the confidence region (ellipsoid) was built for the profile of the animals' homogeneous responses. The animals located inside the ellipsoid were considered as belonging to the homogeneous batch; those outside the ellipsoid were considered spurious. The PCA established eight descriptive axes that represented the accumulated variance of the data set in 88.71%. The allocation of the animals in the new system and the construction of the confidence region revealed six spurious animals as compared to the homogeneous batch of 109 animals. The biometric criterion presented proved to be effective, because it considers the animal as a whole, analyzing jointly all parameters measured, in addition to having a small discard rate.

  10. Multivariate cluster analysis of forest fire events in Portugal

    NASA Astrophysics Data System (ADS)

    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

  11. Functional analysis screening for problem behavior maintained by automatic reinforcement.

    PubMed

    Querim, Angie C; Iwata, Brian A; Roscoe, Eileen M; Schlichenmeyer, Kevin J; Ortega, Javier Virués; Hurl, Kylee E

    2013-01-01

    A common finding in previous research is that problem behavior maintained by automatic reinforcement continues to occur in the alone condition of a functional analysis (FA), whereas behavior maintained by social reinforcement typically is extinguished. Thus, the alone condition may represent an efficient screening procedure when maintenance by automatic reinforcement is suspected. We conducted a series of 5-min alone (or no-interaction) probes for 30 cases of problem behavior and compared initial predictions of maintenance or extinction to outcomes obtained in subsequent FAs. Results indicated that data from the screening procedure accurately predicted that problem behavior was maintained by automatic reinforcement in 21 of 22 cases and by social reinforcement in 7 of 8 cases. Thus, results of the screening accurately predicted the function of problem behavior (social vs. automatic reinforcement) in 28 of 30 cases. © Society for the Experimental Analysis of Behavior.

  12. Statistical analysis of multivariate atmospheric variables. [cloud cover

    NASA Technical Reports Server (NTRS)

    Tubbs, J. D.

    1979-01-01

    Topics covered include: (1) estimation in discrete multivariate distributions; (2) a procedure to predict cloud cover frequencies in the bivariate case; (3) a program to compute conditional bivariate normal parameters; (4) the transformation of nonnormal multivariate to near-normal; (5) test of fit for the extreme value distribution based upon the generalized minimum chi-square; (6) test of fit for continuous distributions based upon the generalized minimum chi-square; (7) effect of correlated observations on confidence sets based upon chi-square statistics; and (8) generation of random variates from specified distributions.

  13. Automatism

    PubMed Central

    McCaldon, R. J.

    1964-01-01

    Individuals can carry out complex activity while in a state of impaired consciousness, a condition termed “automatism”. Consciousness must be considered from both an organic and a psychological aspect, because impairment of consciousness may occur in both ways. Automatism may be classified as normal (hypnosis), organic (temporal lobe epilepsy), psychogenic (dissociative fugue) or feigned. Often painstaking clinical investigation is necessary to clarify the diagnosis. There is legal precedent for assuming that all crimes must embody both consciousness and will. Jurists are loath to apply this principle without reservation, as this would necessitate acquittal and release of potentially dangerous individuals. However, with the sole exception of the defence of insanity, there is at present no legislation to prohibit release without further investigation of anyone acquitted of a crime on the grounds of “automatism”. PMID:14199824

  14. Application of multivariate statistical analysis to STEM X-ray spectral images: interfacial analysis in microelectronics.

    PubMed

    Kotula, Paul G; Keenan, Michael R

    2006-12-01

    Multivariate statistical analysis methods have been applied to scanning transmission electron microscopy (STEM) energy-dispersive X-ray spectral images. The particular application of the multivariate curve resolution (MCR) technique provides a high spectral contrast view of the raw spectral image. The power of this approach is demonstrated with a microelectronics failure analysis. Specifically, an unexpected component describing a chemical contaminant was found, as well as a component consistent with a foil thickness change associated with the focused ion beam specimen preparation process. The MCR solution is compared with a conventional analysis of the same spectral image data set.

  15. Turnover intention in new graduate nurses: a multivariate analysis

    PubMed Central

    Beecroft, Pauline C; Dorey, Frederick; Wenten, Madé

    2008-01-01

    Title Turnover intention in new graduate nurses: a multivariate analysis Aim This paper is a report of a study to determine the relationship of new nurse turnover intent with individual characteristics, work environment variables and organizational factors and to compare new nurse turnover with actual turnover in the 18 months of employment following completion of a residency. Background Because of their influence on patient safety and health outcomes nurse turnover and turnover intent have received considerable attention worldwide. When nurse staffing is inadequate, especially during nursing shortages, unfavourable clinical outcomes have been documented. Method Prospective data collection took place from 1999 to 2006 with 889 new paediatric nurses who completed the same residency. Scores on study instruments were related to likelihood of turnover intent using logistic regression analysis models. Relationships between turnover intent and actual turnover were compared using Kaplan–Meier survivorship. Results The final model demonstrated that older respondents were more likely to have turnover intent if they did not get their ward choice. Also higher scores on work environment and organizational characteristics contributed to likelihood that the new nurse would not be in the turnover intent group. These factors distinguish a new nurse with turnover intent from one without 79% of the time. Increased seeking of social support was related to turnover intent and older new graduates were more likely to be in the turnover intent group if they did not get their ward choice. Conclusion When new graduate nurses are satisfied with their jobs and pay and feel committed to the organization, the odds against turnover intent decrease. What is already known about this topic There is concern in many countries about nurse turnover and the resulting effects on patient safety and quality of care. Decreasing ability to recruit experienced nurses has increased the emphasis on

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

    ERIC Educational Resources Information Center

    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…

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

    ERIC Educational Resources Information Center

    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…

  18. A Multivariate Generalizability Analysis of the Multistate Bar Examination

    ERIC Educational Resources Information Center

    Yin, Ping

    2005-01-01

    The main purpose of this study is to examine the content structure of the Multistate Bar Examination (MBE) using the "table of specifications" model from the perspective of multivariate generalizability theory. Specifically, using MBE data collected over different years (six administrations: three from the February test and three from July test),…

  19. Multivariate analysis of full-term neonatal polysomnographic data.

    PubMed

    Gerla, V; Paul, K; Lhotska, L; Krajca, V

    2009-01-01

    Polysomnography (PSG) is one of the most important noninvasive methods for studying maturation of the child brain. Sleep in infants is significantly different from sleep in adults. This paper addresses the problem of computer analysis of neonatal polygraphic signals. We applied methods designed for differentiating three important neonatal behavioral states: quiet sleep, active sleep, and wakefulness. The proportion of these states is a significant indicator of the maturity of the newborn brain in clinical practice. In this study, we used data provided by the Institute for Care of Mother and Child, Prague (12 newborn infants of similar postconceptional age). The data were scored by an experienced physician to four states (wake, quiet sleep, active sleep, movement artifact). For accurate classification, it was necessary to determine the most informative features. We used a method based on power spectral density (PSD) applied to each EEG channel. We also used features derived from electrooculogram (EOG), electromyogram (EMG), ECG, and respiration [pneumogram (PNG)] signals. The most informative feature was the measure of regularity of respiration from the PNG signal. We designed an algorithm for interpreting these characteristics. This algorithm was based on Markov models. The results of automatic detection of sleep states were compared to the "sleep profiles" determined visually. We evaluated both the success rate and the true positive rate of the classification, and statistically significant agreement of the two scorings was found. Two variants, for learning and for testing, were applied, namely learning from the data of all 12 newborns and tenfold cross-validation, and learning from the data of 11 newborns and testing on the data from the 12th newborn. We utilized information obtained from several biological signals (EEG, ECG, PNG, EMG, EOG) for our final classification. We reached the final success rate of 82.5%. The true positive rate was 81.8% and the false

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

    NASA Astrophysics Data System (ADS)

    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

  1. Multivariate analysis of elemental chemistry as a robust biosignature

    NASA Astrophysics Data System (ADS)

    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

  2. Automatic ionospheric layers detection: Algorithms analysis

    NASA Astrophysics Data System (ADS)

    Molina, María G.; Zuccheretti, Enrico; Cabrera, Miguel A.; Bianchi, Cesidio; Sciacca, Umberto; Baskaradas, James

    2016-03-01

    Vertical sounding is a widely used technique to obtain ionosphere measurements, such as an estimation of virtual height versus frequency scanning. It is performed by high frequency radar for geophysical applications called ;ionospheric sounder; (or ;ionosonde;). Radar detection depends mainly on targets characteristics. While several targets behavior and correspondent echo detection algorithms have been studied, a survey to address a suitable algorithm for ionospheric sounder has to be carried out. This paper is focused on automatic echo detection algorithms implemented in particular for an ionospheric sounder, target specific characteristics were studied as well. Adaptive threshold detection algorithms are proposed, compared to the current implemented algorithm, and tested using actual data obtained from the Advanced Ionospheric Sounder (AIS-INGV) at Rome Ionospheric Observatory. Different cases of study have been selected according typical ionospheric and detection conditions.

  3. Causal diagrams and multivariate analysis III: confound it!

    PubMed

    Jupiter, Daniel C

    2015-01-01

    This commentary concludes my series concerning inclusion of variables in multivariate analyses. We take up the issues of confounding and effect modification and summarize the work we have thus far done. Finally, we provide a rough algorithm to help guide us through the maze of possibilities that we have outlined. Copyright © 2015 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

  4. Analysis of Forest Foliage Using a Multivariate Mixture Model

    NASA Technical Reports Server (NTRS)

    Hlavka, C. A.; Peterson, David L.; Johnson, L. F.; Ganapol, B.

    1997-01-01

    Data with wet chemical measurements and near infrared spectra of ground leaf samples were analyzed to test a multivariate regression technique for estimating component spectra which is based on a linear mixture model for absorbance. The resulting unmixed spectra for carbohydrates, lignin, and protein resemble the spectra of extracted plant starches, cellulose, lignin, and protein. The unmixed protein spectrum has prominent absorption spectra at wavelengths which have been associated with nitrogen bonds.

  5. Correlative and multivariate analysis of increased radon concentration in underground laboratory.

    PubMed

    Maletić, Dimitrije M; Udovičić, Vladimir I; Banjanac, Radomir M; Joković, Dejan R; Dragić, Aleksandar L; Veselinović, Nikola B; Filipović, Jelena

    2014-11-01

    The results of analysis using correlative and multivariate methods, as developed for data analysis in high-energy physics and implemented in the Toolkit for Multivariate Analysis software package, of the relations of the variation of increased radon concentration with climate variables in shallow underground laboratory is presented. Multivariate regression analysis identified a number of multivariate methods which can give a good evaluation of increased radon concentrations based on climate variables. The use of the multivariate regression methods will enable the investigation of the relations of specific climate variable with increased radon concentrations by analysis of regression methods resulting in 'mapped' underlying functional behaviour of radon concentrations depending on a wide spectrum of climate variables.

  6. Automatic analysis of microscopic images of red blood cell aggregates

    NASA Astrophysics Data System (ADS)

    Menichini, Pablo A.; Larese, Mónica G.; Riquelme, Bibiana D.

    2015-06-01

    Red blood cell aggregation is one of the most important factors in blood viscosity at stasis or at very low rates of flow. The basic structure of aggregates is a linear array of cell commonly termed as rouleaux. Enhanced or abnormal aggregation is seen in clinical conditions, such as diabetes and hypertension, producing alterations in the microcirculation, some of which can be analyzed through the characterization of aggregated cells. Frequently, image processing and analysis for the characterization of RBC aggregation were done manually or semi-automatically using interactive tools. We propose a system that processes images of RBC aggregation and automatically obtains the characterization and quantification of the different types of RBC aggregates. Present technique could be interesting to perform the adaptation as a routine used in hemorheological and Clinical Biochemistry Laboratories because this automatic method is rapid, efficient and economical, and at the same time independent of the user performing the analysis (repeatability of the analysis).

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

    NASA Astrophysics Data System (ADS)

    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.

  8. Multivariate singular spectrum analysis and phase synchronization: An application to U.S. business cycles

    NASA Astrophysics Data System (ADS)

    Groth, Andreas; Ghil, Michael; Hallegatte, Stephane; Dumas, Patrice

    2010-05-01

    Over the last two decades, singular spectrum analysis (SSA) and multivariate SSA (M-SSA) have proven their power in the temporal and spatio-temporal analysis of short and noisy time series in numerous fields of the geosciences and of other disciplines. M-SSA provides insight into the unknown or partially known dynamics of the underlying system by decomposing the delay-coordinate phase space of a given multivariate time series into a set of data-adaptive orthonormal components. These components can be classified essentially into trends, oscillatory patterns and noise, and allow one to reconstruct a robust "skeleton" of the dynamical system's structure. For an overview we refer to Ghil et al. (Rev. Geophys., 2002). We first present M-SSA in the context of synchronization analysis and illustrate its ability to unveil information about the mechanisms behind the adjustment of rhythms in coupled dynamical systems. This poster deals with the special case of phase synchronization between coupled chaotic oscillators (Rosenblum et al., PRL, 1996). Several ways of measuring phase synchronization are in use, and the robust definition of a reasonable phase for each oscillator is critical in each of them. We illustrate here the advantages of M-SSA in the automatic identification of oscillatory modes and in drawing conclusions about the transition to phase synchronization. Without using any a priori definition of a suitable phase, we show that M-SSA is able to detect phase synchronization in a chain of coupled chaotic oscillators (Osipov et al., PRE, 1996). The key application of these theoretical results in this poster is to U.S. macroeconomic data for 1954--2005. M-SSA helps us draw conclusions about the cyclical behavior of the U.S. economy and its underlying dynamical properties. The recurrence of expansions and recessions, at approximately 5--6-year intervals, is referred to as business cycles; their origin is still a matter of considerable controversy. Our analysis sheds

  9. A hierarchical structure for automatic meshing and adaptive FEM analysis

    NASA Technical Reports Server (NTRS)

    Kela, Ajay; Saxena, Mukul; Perucchio, Renato

    1987-01-01

    A new algorithm for generating automatically, from solid models of mechanical parts, finite element meshes that are organized as spatially addressable quaternary trees (for 2-D work) or octal trees (for 3-D work) is discussed. Because such meshes are inherently hierarchical as well as spatially addressable, they permit efficient substructuring techniques to be used for both global analysis and incremental remeshing and reanalysis. The global and incremental techniques are summarized and some results from an experimental closed loop 2-D system in which meshing, analysis, error evaluation, and remeshing and reanalysis are done automatically and adaptively are presented. The implementation of 3-D work is briefly discussed.

  10. Environment for the automatic manipulation and analysis of morphological expressions

    NASA Astrophysics Data System (ADS)

    Richardson, Craig H.; Schafer, Ronald W.

    1990-11-01

    This paper describes a LISP based environment for the automatic manipulation and analysis of morphological expressions. The foundation of this environment is an aggregation of morphological knowledge that includes signal and system property information rule bases for representing morphological relationships and inferencing mechanisms for using this collection of knowledge. The layers surrounding this foundation include representations of abstract signal and structuring element classes as well as actual structuring elements implementations of the morphological operators and the ability to optimally decompose structels. The representational requirements for automatically manipulating expressions and determining the computational cost are described and the capabilities of the environment are illustrated by examples of symbolic manipulations and expression analysis.

  11. Project Report: Automatic Sequence Processor Software Analysis

    NASA Technical Reports Server (NTRS)

    Benjamin, Brandon

    2011-01-01

    The Mission Planning and Sequencing (MPS) element of Multi-Mission Ground System and Services (MGSS) provides space missions with multi-purpose software to plan spacecraft activities, sequence spacecraft commands, and then integrate these products and execute them on spacecraft. Jet Propulsion Laboratory (JPL) is currently is flying many missions. The processes for building, integrating, and testing the multi-mission uplink software need to be improved to meet the needs of the missions and the operations teams that command the spacecraft. The Multi-Mission Sequencing Team is responsible for collecting and processing the observations, experiments and engineering activities that are to be performed on a selected spacecraft. The collection of these activities is called a sequence and ultimately a sequence becomes a sequence of spacecraft commands. The operations teams check the sequence to make sure that no constraints are violated. The workflow process involves sending a program start command, which activates the Automatic Sequence Processor (ASP). The ASP is currently a file-based system that is comprised of scripts written in perl, c-shell and awk. Once this start process is complete, the system checks for errors and aborts if there are any; otherwise the system converts the commands to binary, and then sends the resultant information to be radiated to the spacecraft.

  12. Neural network architecture for automatic chromosome analysis

    NASA Astrophysics Data System (ADS)

    Diez-Higuera, Jose F.; Diaz-Pernas, F. J.; Lopez-Coronado, Juan

    1996-03-01

    We are interested in designing a neural network system for automatic chromosome. The goal of this approach is to make the chromosome regions more salient and more interpretable to human skilled technicians than they are in the original imagery. The proposed segmentation model is based upon the biologically derived boundary contour system (BCS) of Grossberg and Mingolla. The practical application of the model to real images raises an important problem. The boundaries generated by BCS have a sizable thickness that is a function of the contrast gradient between two adjacent regions. In order to solve this problem we propose the use of a feedback diffusion. The image resultant of the diffusion is fed back to the simple cell layer. Furthermore, the boundary representation is also fed back to the boundary segmentation stage. In this way, the boundaries are adapted to the variations produced by the feedback diffusion, achieving a gradual boundary thinning. We also propose a modificated diffusive filling-in equation for obtaining better results in homogeneous regions. The behavior of the Grossberg-Todorovic's equation reduces the homogenizing of the regions contained inside the boundaries. In order to solve this problem we introduce a new parameter, rho, called recovery parameter. This parameter regulates the activity variation margin of a node with respect to its initial value. With regard to the improvement in homogenizing, with a value for parameter rho near to zero, the resulting regions present a plain surface, making easy the chromosome bands separation.

  13. Publishing nutrition research: a review of multivariate techniques--part 2: analysis of variance.

    PubMed

    Harris, Jeffrey E; Sheean, Patricia M; Gleason, Philip M; Bruemmer, Barbara; Boushey, Carol

    2012-01-01

    This article is the eighth in a series exploring the importance of research design, statistical analysis, and epidemiology in nutrition and dietetics research, and the second in a series focused on multivariate statistical analytical techniques. The purpose of this review is to examine the statistical technique, analysis of variance (ANOVA), from its simplest to multivariate applications. Many dietetics practitioners are familiar with basic ANOVA, but less informed of the multivariate applications such as multiway ANOVA, repeated-measures ANOVA, analysis of covariance, multiple ANOVA, and multiple analysis of covariance. The article addresses all these applications and includes hypothetical and real examples from the field of dietetics.

  14. Profiling School Shooters: Automatic Text-Based Analysis

    PubMed Central

    Neuman, Yair; Assaf, Dan; Cohen, Yochai; Knoll, James L.

    2015-01-01

    School shooters present a challenge to both forensic psychiatry and law enforcement agencies. The relatively small number of school shooters, their various characteristics, and the lack of in-depth analysis of all of the shooters prior to the shooting add complexity to our understanding of this problem. In this short paper, we introduce a new methodology for automatically profiling school shooters. The methodology involves automatic analysis of texts and the production of several measures relevant for the identification of the shooters. Comparing texts written by 6 school shooters to 6056 texts written by a comparison group of male subjects, we found that the shooters’ texts scored significantly higher on the Narcissistic Personality dimension as well as on the Humilated and Revengeful dimensions. Using a ranking/prioritization procedure, similar to the one used for the automatic identification of sexual predators, we provide support for the validity and relevance of the proposed methodology. PMID:26089804

  15. Systematic wavelength selection for improved multivariate spectral analysis

    DOEpatents

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

    1995-01-01

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

  16. Automatic functional analysis of left ventricle in cardiac cine MRI.

    PubMed

    Lu, Ying-Li; Connelly, Kim A; Dick, Alexander J; Wright, Graham A; Radau, Perry E

    2013-08-01

    A fully automated left ventricle segmentation method for the functional analysis of cine short axis (SAX) magnetic resonance (MR) images was developed, and its performance evaluated with 133 studies of subjects with diverse pathology: ischemic heart failure (n=34), non-ischemic heart failure (n=30), hypertrophy (n=32), and healthy (n=37). The proposed automatic method locates the left ventricle (LV), then for each image detects the contours of the endocardium, epicardium, papillary muscles and trabeculations. Manually and automatically determined contours and functional parameters were compared quantitatively. There was no significant difference between automatically and manually determined end systolic volume (ESV), end diastolic volume (EDV), ejection fraction (EF) and left ventricular mass (LVM) for each of the four groups (paired sample t-test, α=0.05). The automatically determined functional parameters showed high correlations with those derived from manual contours, and the Bland-Altman analysis biases were small (1.51 mL, 1.69 mL, -0.02%, -0.66 g for ESV, EDV, EF and LVM, respectively). The proposed technique automatically and rapidly detects endocardial, epicardial, papillary muscles' and trabeculations' contours providing accurate and reproducible quantitative MRI parameters, including LV mass and EF.

  17. Automatic functional analysis of left ventricle in cardiac cine MRI

    PubMed Central

    Lu, Ying-Li; Connelly, Kim A.; Dick, Alexander J.; Wright, Graham A.

    2013-01-01

    Rationale and objectives A fully automated left ventricle segmentation method for the functional analysis of cine short axis (SAX) magnetic resonance (MR) images was developed, and its performance evaluated with 133 studies of subjects with diverse pathology: ischemic heart failure (n=34), non-ischemic heart failure (n=30), hypertrophy (n=32), and healthy (n=37). Materials and methods The proposed automatic method locates the left ventricle (LV), then for each image detects the contours of the endocardium, epicardium, papillary muscles and trabeculations. Manually and automatically determined contours and functional parameters were compared quantitatively. Results There was no significant difference between automatically and manually determined end systolic volume (ESV), end diastolic volume (EDV), ejection fraction (EF) and left ventricular mass (LVM) for each of the four groups (paired sample t-test, α=0.05). The automatically determined functional parameters showed high correlations with those derived from manual contours, and the Bland-Altman analysis biases were small (1.51 mL, 1.69 mL, –0.02%, –0.66 g for ESV, EDV, EF and LVM, respectively). Conclusions The proposed technique automatically and rapidly detects endocardial, epicardial, papillary muscles’ and trabeculations’ contours providing accurate and reproducible quantitative MRI parameters, including LV mass and EF. PMID:24040616

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

    ERIC Educational Resources Information Center

    Warne, Russell T.

    2014-01-01

    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…

  19. Estimating an Effect Size in One-Way Multivariate Analysis of Variance (MANOVA)

    ERIC Educational Resources Information Center

    Steyn, H. S., Jr.; Ellis, S. M.

    2009-01-01

    When two or more univariate population means are compared, the proportion of variation in the dependent variable accounted for by population group membership is eta-squared. This effect size can be generalized by using multivariate measures of association, based on the multivariate analysis of variance (MANOVA) statistics, to establish whether…

  20. Estimating an Effect Size in One-Way Multivariate Analysis of Variance (MANOVA)

    ERIC Educational Resources Information Center

    Steyn, H. S., Jr.; Ellis, S. M.

    2009-01-01

    When two or more univariate population means are compared, the proportion of variation in the dependent variable accounted for by population group membership is eta-squared. This effect size can be generalized by using multivariate measures of association, based on the multivariate analysis of variance (MANOVA) statistics, to establish whether…

  1. Analysis techniques for multivariate root loci. [a tool in linear control systems

    NASA Technical Reports Server (NTRS)

    Thompson, P. M.; Stein, G.; Laub, A. J.

    1980-01-01

    Analysis and techniques are developed for the multivariable root locus and the multivariable optimal root locus. The generalized eigenvalue problem is used to compute angles and sensitivities for both types of loci, and an algorithm is presented that determines the asymptotic properties of the optimal root locus.

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

    ERIC Educational Resources Information Center

    Warne, Russell T.

    2014-01-01

    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…

  3. Automatic identification of reticular pseudodrusen using multimodal retinal image analysis.

    PubMed

    van Grinsven, Mark J J P; Buitendijk, Gabriëlle H S; Brussee, Corina; van Ginneken, Bram; Hoyng, Carel B; Theelen, Thomas; Klaver, Caroline C W; Sánchez, Clara I

    2015-01-08

    To examine human performance and agreement on reticular pseudodrusen (RPD) detection and quantification by using single- and multimodality grading protocols and to describe and evaluate a machine learning system for the automatic detection and quantification of reticular pseudodrusen by using single- and multimodality information. Color fundus, fundus autofluoresence, and near-infrared images of 278 eyes from 230 patients with or without presence of RPD were used in this study. All eyes were scored for presence of RPD during single- and multimodality setups by two experienced observers and a developed machine learning system. Furthermore, automatic quantification of RPD area was performed by the proposed system and compared with human delineations. Observers obtained a higher performance and better interobserver agreement for RPD detection with multimodality grading, achieving areas under the receiver operating characteristic (ROC) curve of 0.940 and 0.958, and a κ agreement of 0.911. The proposed automatic system achieved an area under the ROC of 0.941 with a multimodality setup. Automatic RPD quantification resulted in an intraclass correlation (ICC) value of 0.704, which was comparable with ICC values obtained between single-modality manual delineations. Observer performance and agreement for RPD identification improved significantly by using a multimodality grading approach. The developed automatic system showed similar performance as observers, and automatic RPD area quantification was in concordance with manual delineations. The proposed automatic system allows for a fast and accurate identification and quantification of RPD, opening the way for efficient quantitative imaging biomarkers in large data set analysis. Copyright 2015 The Association for Research in Vision and Ophthalmology, Inc.

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

    PubMed

    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.

  5. Application of software technology to automatic test data analysis

    NASA Technical Reports Server (NTRS)

    Stagner, J. R.

    1991-01-01

    The verification process for a major software subsystem was partially automated as part of a feasibility demonstration. The methods employed are generally useful and applicable to other types of subsystems. The effort resulted in substantial savings in test engineer analysis time and offers a method for inclusion of automatic verification as a part of regression testing.

  6. Trends of Science Education Research: An Automatic Content Analysis

    ERIC Educational Resources Information Center

    Chang, Yueh-Hsia; Chang, Chun-Yen; Tseng, Yuen-Hsien

    2010-01-01

    This study used scientometric methods to conduct an automatic content analysis on the development trends of science education research from the published articles in the four journals of "International Journal of Science Education, Journal of Research in Science Teaching, Research in Science Education, and Science Education" from 1990 to 2007. The…

  7. Trends of Science Education Research: An Automatic Content Analysis

    ERIC Educational Resources Information Center

    Chang, Yueh-Hsia; Chang, Chun-Yen; Tseng, Yuen-Hsien

    2010-01-01

    This study used scientometric methods to conduct an automatic content analysis on the development trends of science education research from the published articles in the four journals of "International Journal of Science Education, Journal of Research in Science Teaching, Research in Science Education, and Science Education" from 1990 to 2007. The…

  8. Automatic zebrafish heartbeat detection and analysis for zebrafish embryos.

    PubMed

    Pylatiuk, Christian; Sanchez, Daniela; Mikut, Ralf; Alshut, Rüdiger; Reischl, Markus; Hirth, Sofia; Rottbauer, Wolfgang; Just, Steffen

    2014-08-01

    A fully automatic detection and analysis method of heartbeats in videos of nonfixed and nonanesthetized zebrafish embryos is presented. This method reduces the manual workload and time needed for preparation and imaging of the zebrafish embryos, as well as for evaluating heartbeat parameters such as frequency, beat-to-beat intervals, and arrhythmicity. The method is validated by a comparison of the results from automatic and manual detection of the heart rates of wild-type zebrafish embryos 36-120 h postfertilization and of embryonic hearts with bradycardia and pauses in the cardiac contraction.

  9. Effectiveness of an Automatic Tracking Software in Underwater Motion Analysis

    PubMed Central

    Magalhaes, Fabrício A.; Sawacha, Zimi; Di Michele, Rocco; Cortesi, Matteo; Gatta, Giorgio; Fantozzi, Silvia

    2013-01-01

    Tracking of markers placed on anatomical landmarks is a common practice in sports science to perform the kinematic analysis that interests both athletes and coaches. Although different software programs have been developed to automatically track markers and/or features, none of them was specifically designed to analyze underwater motion. Hence, this study aimed to evaluate the effectiveness of a software developed for automatic tracking of underwater movements (DVP), based on the Kanade-Lucas-Tomasi feature tracker. Twenty-one video recordings of different aquatic exercises (n = 2940 markers’ positions) were manually tracked to determine the markers’ center coordinates. Then, the videos were automatically tracked using DVP and a commercially available software (COM). Since tracking techniques may produce false targets, an operator was instructed to stop the automatic procedure and to correct the position of the cursor when the distance between the calculated marker’s coordinate and the reference one was higher than 4 pixels. The proportion of manual interventions required by the software was used as a measure of the degree of automation. Overall, manual interventions were 10.4% lower for DVP (7.4%) than for COM (17.8%). Moreover, when examining the different exercise modes separately, the percentage of manual interventions was 5.6% to 29.3% lower for DVP than for COM. Similar results were observed when analyzing the type of marker rather than the type of exercise, with 9.9% less manual interventions for DVP than for COM. In conclusion, based on these results, the developed automatic tracking software presented can be used as a valid and useful tool for underwater motion analysis. Key Points The availability of effective software for automatic tracking would represent a significant advance for the practical use of kinematic analysis in swimming and other aquatic sports. An important feature of automatic tracking software is to require limited human

  10. Automatic analysis of double coronal mass ejections from coronagraph images

    NASA Astrophysics Data System (ADS)

    Jacobs, Matthew; Chang, Lin-Ching; Pulkkinen, Antti; Romano, Michelangelo

    2015-11-01

    Coronal mass ejections (CMEs) can have major impacts on man-made technology and humans, both in space and on Earth. These impacts have created a high interest in the study of CMEs in an effort to detect and track events and forecast the CME arrival time to provide time for proper mitigation. A robust automatic real-time CME processing pipeline is greatly desired to avoid laborious and subjective manual processing. Automatic methods have been proposed to segment CMEs from coronagraph images and estimate CME parameters such as their heliocentric location and velocity. However, existing methods suffered from several shortcomings such as the use of hard thresholding and an inability to handle two or more CMEs occurring within the same coronagraph image. Double-CME analysis is a necessity for forecasting the many CME events that occur within short time frames. Robust forecasts for all CME events are required to fully understand space weather impacts. This paper presents a new method to segment CME masses and pattern recognition approaches to differentiate two CMEs in a single coronagraph image. The proposed method is validated on a data set of 30 halo CMEs, with results showing comparable ability in transient arrival time prediction accuracy and the new ability to automatically predict the arrival time of a double-CME event. The proposed method is the first automatic method to successfully calculate CME parameters from double-CME events, making this automatic method applicable to a wider range of CME events.

  11. A Bayesian Semiparametric Multivariate Causal Model, with Automatic Covariate Selection and for Possibly-Nonignorable Missing Data

    ERIC Educational Resources Information Center

    Karabatsos, G.; Walker, S.G.

    2010-01-01

    Causal inference is central to educational research, where in data analysis the aim is to learn the causal effects of educational treatments on academic achievement, to evaluate educational policies and practice. Compared to a correlational analysis, a causal analysis enables policymakers to make more meaningful statements about the efficacy of…

  12. Development of an automatic identification algorithm for antibiogram analysis.

    PubMed

    Costa, Luan F R; da Silva, Eduardo S; Noronha, Victor T; Vaz-Moreira, Ivone; Nunes, Olga C; Andrade, Marcelino M de

    2015-12-01

    Routinely, diagnostic and microbiology laboratories perform antibiogram analysis which can present some difficulties leading to misreadings and intra and inter-reader deviations. An Automatic Identification Algorithm (AIA) has been proposed as a solution to overcome some issues associated with the disc diffusion method, which is the main goal of this work. AIA allows automatic scanning of inhibition zones obtained by antibiograms. More than 60 environmental isolates were tested using susceptibility tests which were performed for 12 different antibiotics for a total of 756 readings. Plate images were acquired and classified as standard or oddity. The inhibition zones were measured using the AIA and results were compared with reference method (human reading), using weighted kappa index and statistical analysis to evaluate, respectively, inter-reader agreement and correlation between AIA-based and human-based reading. Agreements were observed in 88% cases and 89% of the tests showed no difference or a <4mm difference between AIA and human analysis, exhibiting a correlation index of 0.85 for all images, 0.90 for standards and 0.80 for oddities with no significant difference between automatic and manual method. AIA resolved some reading problems such as overlapping inhibition zones, imperfect microorganism seeding, non-homogeneity of the circumference, partial action of the antimicrobial, and formation of a second halo of inhibition. Furthermore, AIA proved to overcome some of the limitations observed in other automatic methods. Therefore, AIA may be a practical tool for automated reading of antibiograms in diagnostic and microbiology laboratories.

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

    PubMed

    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.

  14. Automatic movie skimming with general tempo analysis

    NASA Astrophysics Data System (ADS)

    Lee, Shih-Hung; Yeh, Chia-Hung; Kuo, C. C. J.

    2003-11-01

    Story units are extracted by general tempo analysis including tempos analysis including tempos of audio and visual information in this research. Although many schemes have been proposed to successfully segment video data into shots using basic low-level features, how to group shots into meaningful units called story units is still a challenging problem. By focusing on a certain type of video such as sport or news, we can explore models with the specific application domain knowledge. For movie contents, many heuristic rules based on audiovisual clues have been proposed with limited success. We propose a method to extract story units using general tempo analysis. Experimental results are given to demonstrate the feasibility and efficiency of the proposed technique.

  15. Suicidal ideation among Canadian youth: a multivariate analysis.

    PubMed

    Peter, Tracey; Roberts, Lance W; Buzdugan, Raluca

    2008-01-01

    A multivariate model was developed incorporating various socio-demographic, social-environmental, and social-psychological factors in an attempt to predict suicidal ideation among Canadian youth. The main research objective sought to determine what socially based factors elevate or reduce suicidal ideation within this population. Using data from the National Longitudinal Study of Children and Youth-Cycle 5 (2003), a cross-sectional sample of 1,032 was used to empirically identify various social determinants of suicidal ideation among youth between the ages of 12 and 15. Results reveal statistically significant correlations between suicide ideation and some lesser examined socially based measures. In particular, ability to communicate feelings, negative attachment to parents/guardians, taunting/bullying or abuse, and presence of deviant peers were significant predictors of suicidal ideation. As expected, depression/anxiety, gender, and age were also correlated with thoughts of suicide. Research findings should help foster a better understanding toward the social elements of suicide and provide insight into how suicide prevention strategies may be improved through an increased emphasis on substance use education, direct targeting of dysfunctional families and deviant peer groups, and exploring more avenues of self-expression for youth.

  16. Prognosis in equine colic patients using multivariable analysis.

    PubMed Central

    Reeves, M J; Curtis, C R; Salman, M D; Hilbert, B J

    1989-01-01

    Multiple logistic regression was used to investigate prognosis in 308 horses referred to the University of Minnesota veterinary teaching hospital with colic. Bivariate results identified the following significant individual parameters: absent or hypomotile abdominal sounds, medical or surgical classification, peritoneal fluid total protein, anion gap, serum glucose, capillary refill time, blood pH, heart rate, packed cell volume, base excess, serum chloride, plasma bicarbonate, serum urinary nitrogen and age. Two multivariable prognostic models were developed using logistic regression. Model I (based on 257 cases with a mortality rate of 39%) included age, sex, medical or surgical classification, capillary refill time, packed cell volume and heart rate. Model II (based on 138 cases with a mortality rate of 48%) included age, sex, medical or surgical classification, capillary refill time, serum bicarbonate, serum chloride and respiratory rate. Predictive performance of the models was evaluated by treating the calculated probability of death for each horse as a continuous test result. The influence of varying the probability cutoff point for death on test characteristics (sensitivity, specificity and positive and negative predictive values) was determined. These models have not been validated and thus their performance in a different population is uncertain. PMID:2914230

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

    PubMed

    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.

  18. Analysis/forecast experiments with a multivariate statistical analysis scheme using FGGE data

    NASA Technical Reports Server (NTRS)

    Baker, W. E.; Bloom, S. C.; Nestler, M. S.

    1985-01-01

    A three-dimensional, multivariate, statistical analysis method, optimal interpolation (OI) is described for modeling meteorological data from widely dispersed sites. The model was developed to analyze FGGE data at the NASA-Goddard Laboratory of Atmospherics. The model features a multivariate surface analysis over the oceans, including maintenance of the Ekman balance and a geographically dependent correlation function. Preliminary comparisons are made between the OI model and similar schemes employed at the European Center for Medium Range Weather Forecasts and the National Meteorological Center. The OI scheme is used to provide input to a GCM, and model error correlations are calculated for forecasts of 500 mb vertical water mixing ratios and the wind profiles. Comparisons are made between the predictions and measured data. The model is shown to be as accurate as a successive corrections model out to 4.5 days.

  19. Mytoe: automatic analysis of mitochondrial dynamics.

    PubMed

    Lihavainen, Eero; Mäkelä, Jarno; Spelbrink, Johannes N; Ribeiro, Andre S

    2012-04-01

    We present Mytoe, a tool for analyzing mitochondrial morphology and dynamics from fluorescence microscope images. The tool provides automated quantitative analysis of mitochondrial motion by optical flow estimation and of morphology by segmentation of individual branches of the network-like structure of the organelles. Mytoe quantifies several features of individual branches, such as length, tortuosity and speed, and of the macroscopic structure, such as mitochondrial area and degree of clustering. We validate the methods and apply them to the analysis of sequences of images of U2OS human cells with fluorescently labeled mitochondria. Source code, Windows software and Manual available at http://www.cs.tut.fi/%7Esanchesr/mito Supplementary data are available at Bioinformatics online. eero.lihavainen@tut.fi; andre.ribeiro@tut.fi.

  20. The method of quantitative automatic metallographic analysis

    NASA Astrophysics Data System (ADS)

    Martyushev, N. V.; Skeeba, V. Yu

    2017-01-01

    A brief analysis of the existing softwares for computer processing of microstructure photographs is presented. The descriptions of the the software package developed by the author are demonstrated. This software product is intended for quantitative metallographic analysis of digital photographs of the microstructure of materials. It allows calculating the volume fraction and the average size of particles of the structure by several hundred secants (depending on the photographs resolution) in one vision field. Besides, a special module is built in the software allowing assessing the degree of deviation of the shape of different particles and impurities from the spherical one. The article presents the main algorithms, used during the creation of the software product, and formulae according to which the software calculates the parameters of the microstructure. It is shown that the reliability of calculations depends on the quality of preparation of the microstructure.

  1. Multivariate processing strategies for enhancing qualitative and quantitative analysis based on infrared spectroscopy

    NASA Astrophysics Data System (ADS)

    Wan, Boyong

    2007-12-01

    Airborne passive Fourier transform infrared spectrometry is gaining increased attention in environmental applications because of its great flexibility. Usually, pattern recognition techniques are used for automatic analysis of large amount of collected data. However, challenging problems are the constantly changing background and high calibration cost. As aircraft is flying, background is always changing. Also, considering the great variety of backgrounds and high expense of data collection from aircraft, cost of collecting representative training data is formidable. Instead of using airborne data, data generated from simulation strategies can be used for training purposes. Training data collected under controlled conditions on the ground or synthesized from real backgrounds can be both options. With both strategies, classifiers may be developed with much lower cost. For both strategies, signal processing techniques need to be used to extract analyte features. In this dissertation, signal processing methods are applied either in interferogram or spectral domain for features extraction. Then, pattern recognition methods are applied to develop binary classifiers for automated detection of air-collected methanol and ethanol vapors. The results demonstrate, with optimized signal processing methods and training set composition, classifiers trained from ground-collected or synthetic data can give good classification on real air-collected data. Near-infrared (NIR) spectrometry is emerging as a promising tool for noninvasive blood glucose detection. In combination with multivariate calibration techniques, NIR spectroscopy can give quick quantitative determinations of many species with minimal sample preparation. However, one main problem with NIR calibrations is degradation of calibration model over time. The varying background information will worsen the prediction precision and complicate the multivariate models. To mitigate the needs for frequent recalibration and

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

    PubMed

    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.

  3. Seasonal foods of coyotes in southeastern Idaho: a multivariate analysis

    SciTech Connect

    MacCracken, J.G.; Hansen, R.M.

    1982-03-01

    Seasonal foods of coyotes (Canis latrans) inhabiting the Idaho National Engineering Laboratory site were examined using step-wise discriminant analysis. Significant differences (P<0.01) were detected among seasons in food consumption by coyotes, where univariate statistical analysis failed to recognize differences. Recognition of seasonal changes in foods consumed by coyotes is essential to understanding coyote feeding strategies. The role opportunistic behavior plays in coyote food selection on the study area is questioned.

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

    PubMed

    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.

  5. Water quality analysis of Godavari river basin using multivariate analysis techniques.

    PubMed

    Gupta, Indrani; Salunkhe, Abhaysinh; Rohra, Nanda; Kumar, Rakesh

    2013-01-01

    Multivariate statistical techniques, including cluster analysis, principal component analysis factor analysis and discriminant analysis, have been used to evaluate spatial variations and to interpret a large and complex water quality data set collected from the Godavari river basin. The data sets, containing 7 parameters, were generated during the 3-years (2007-2009) at 78 different sites along the river and its tributaries. Water quality indices based on four parameters (pH, DO, BOD and FC) calculated for all the sites were found to be medium to good, good to excellent and bad using modified NSF index. Three significant groups (cleaner, slightly and moderately polluted sites) were detected by CA method, and three latent factors were identified by PCA method. The results of DA revealed that only two parameters (i.e. pH and BOD) were necessary for analysis in spatial variation. 83.3% of the original sites were correctly.classified using discriminant function developed from the analysis.

  6. Multivariate Genetic Analysis of Learning and Early Reading Development

    ERIC Educational Resources Information Center

    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…

  7. Multivariate Genetic Analysis of Learning and Early Reading Development

    ERIC Educational Resources Information Center

    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…

  8. Automatic recognition and analysis of synapses. [in brain tissue

    NASA Technical Reports Server (NTRS)

    Ungerleider, J. A.; Ledley, R. S.; Bloom, F. E.

    1976-01-01

    An automatic system for recognizing synaptic junctions would allow analysis of large samples of tissue for the possible classification of specific well-defined sets of synapses based upon structural morphometric indices. In this paper the three steps of our system are described: (1) cytochemical tissue preparation to allow easy recognition of the synaptic junctions; (2) transmitting the tissue information to a computer; and (3) analyzing each field to recognize the synapses and make measurements on them.

  9. Automatic recognition and analysis of synapses. [in brain tissue

    NASA Technical Reports Server (NTRS)

    Ungerleider, J. A.; Ledley, R. S.; Bloom, F. E.

    1976-01-01

    An automatic system for recognizing synaptic junctions would allow analysis of large samples of tissue for the possible classification of specific well-defined sets of synapses based upon structural morphometric indices. In this paper the three steps of our system are described: (1) cytochemical tissue preparation to allow easy recognition of the synaptic junctions; (2) transmitting the tissue information to a computer; and (3) analyzing each field to recognize the synapses and make measurements on them.

  10. Automatic Spot Identification for High Throughput Microarray Analysis

    PubMed Central

    Wu, Eunice; Su, Yan A.; Billings, Eric; Brooks, Bernard R.; Wu, Xiongwu

    2013-01-01

    High throughput microarray analysis has great potential in scientific research, disease diagnosis, and drug discovery. A major hurdle toward high throughput microarray analysis is the time and effort needed to accurately locate gene spots in microarray images. An automatic microarray image processor will allow accurate and efficient determination of spot locations and sizes so that gene expression information can be reliably extracted in a high throughput manner. Current microarray image processing tools require intensive manual operations in addition to the input of grid parameters to correctly and accurately identify gene spots. This work developed a method, herein called auto-spot, to automate the spot identification process. Through a series of correlation and convolution operations, as well as pixel manipulations, this method makes spot identification an automatic and accurate process. Testing with real microarray images has demonstrated that this method is capable of automatically extracting subgrids from microarray images and determining spot locations and sizes within each subgrid, regardless of variations in array patterns and background noises. With this method, we are one step closer to the goal of high throughput microarray analysis. PMID:24298393

  11. Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students

    ERIC Educational Resources Information Center

    Valero-Mora, Pedro M.; Ledesma, Ruben D.

    2011-01-01

    This paper discusses the use of interactive graphics to teach multivariate data analysis to Psychology students. Three techniques are explored through separate activities: parallel coordinates/boxplots; principal components/exploratory factor analysis; and cluster analysis. With interactive graphics, students may perform important parts of the…

  12. The Potential of Multivariate Analysis in Assessing Students' Attitude to Curriculum Subjects

    ERIC Educational Resources Information Center

    Gaotlhobogwe, Michael; Laugharne, Janet; Durance, Isabelle

    2011-01-01

    Background: Understanding student attitudes to curriculum subjects is central to providing evidence-based options to policy makers in education. Purpose: We illustrate how quantitative approaches used in the social sciences and based on multivariate analysis (categorical Principal Components Analysis, Clustering Analysis and General Linear…

  13. The Potential of Multivariate Analysis in Assessing Students' Attitude to Curriculum Subjects

    ERIC Educational Resources Information Center

    Gaotlhobogwe, Michael; Laugharne, Janet; Durance, Isabelle

    2011-01-01

    Background: Understanding student attitudes to curriculum subjects is central to providing evidence-based options to policy makers in education. Purpose: We illustrate how quantitative approaches used in the social sciences and based on multivariate analysis (categorical Principal Components Analysis, Clustering Analysis and General Linear…

  14. Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students

    ERIC Educational Resources Information Center

    Valero-Mora, Pedro M.; Ledesma, Ruben D.

    2011-01-01

    This paper discusses the use of interactive graphics to teach multivariate data analysis to Psychology students. Three techniques are explored through separate activities: parallel coordinates/boxplots; principal components/exploratory factor analysis; and cluster analysis. With interactive graphics, students may perform important parts of the…

  15. Stalked protozoa identification by image analysis and multivariable statistical techniques.

    PubMed

    Amaral, A L; Ginoris, Y P; Nicolau, A; Coelho, M A Z; Ferreira, E C

    2008-06-01

    Protozoa are considered good indicators of the treatment quality in activated sludge systems as they are sensitive to physical, chemical and operational processes. Therefore, it is possible to correlate the predominance of certain species or groups and several operational parameters of the plant. This work presents a semiautomatic image analysis procedure for the recognition of the stalked protozoa species most frequently found in wastewater treatment plants by determining the geometrical, morphological and signature data and subsequent processing by discriminant analysis and neural network techniques. Geometrical descriptors were found to be responsible for the best identification ability and the identification of the crucial Opercularia and Vorticella microstoma microorganisms provided some degree of confidence to establish their presence in wastewater treatment plants.

  16. Characterization of Nuclear Fuel using Multivariate Statistical Analysis

    SciTech Connect

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

    2007-11-27

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

  17. Multivariate analysis of Buteo nest site selection in Washington

    SciTech Connect

    Smith, D.G.; Bechard, M.; Knight, R.L.; Fitzner, R.E.

    1983-03-01

    Raptor breeding populations of grasslands and semi-arid grasslands of western North America include varying densities of three Buteo species, the Red-tailed Hawk (Buteo jamaicensis), Ferruginous Hawk (Buteo regalis) and Swainson's Hawk (Buteo swainsoni). These three species are behaviorially rather similar in their diurnal activity patterns and foraging habitats and exhibit similar diets comprised of small mammals and birds. Principal component analysis and discriminant function analysis were used to describe key similarities and differences in nest site selection of the three Buteo species. Differences in nest site selection may offer an explanation of how the three Buteo species may nest sympatrically and at times successfully at distances of less than 0.5 km despite apparent similarities in use of other resources. 15 refs., 1 fig., 9 tabs.

  18. Tomographic spectral imaging: Data acquisition and analysis via multivariate statistical analysis

    NASA Astrophysics Data System (ADS)

    Kotula, Paul G.; Sorensen, N. R.

    2011-07-01

    Tomographic spectral imaging is a powerful technique for the three-dimensional (3-D) analysis of materials. Using a focused ion-beam/scanning electron microscope equipped with an x-ray spectrometer, 3-D microanalysis can be performed on individual regions of a sample, such as defects, with microanalytical spatial resolution of better than 300 nm typically. The focused ion-beam can serially section at comparable thicknesses to sequentially reveal new analytical surfaces within the specimen. After each slice a full 2-spatial dimension spectral image, consisting of a complete spectrum at each point in the 2-D array, is acquired with the scanning electron microscope/energy-dispersive x-ray spectrometer on the same platform. The process is repeated multiple times to result in a 3-D or tomographic spectral image. The challenge is to effectively and efficiently analyze the tomographic spectral image to extract chemical phase distributions. Therefore, automated multivariate statistical analysis methods were developed and applied to these images. Sandia's Automated eXpert Spectral Image Analysis multivariate statistical analysis software requires no a priori information to find even very weak signals hidden in the data sets. The result of the analysis is a small number of chemical components which describe the 3-D phase distribution in the volume of material sampled. These 3-D phases can then be effectively visualized with off-the-shelf 3-D rendering software.

  19. Multivariate statistical analysis of Raman images of a pharmaceutical tablet.

    PubMed

    Lin, Haisheng; Marjanović, Ognjen; Lennox, Barry; Šašić, Slobodan; Clegg, Ian M

    2012-03-01

    This paper describes the application of principal component analysis (PCA) and independent component analysis (ICA) to identify the reference spectra of a pharmaceutical tablet's constituent compounds from Raman spectroscopic data. The analysis shows, first with a simulated data set and then with data collected from a pharmaceutical tablet, that both PCA and ICA are able to identify most of the features present in the reference spectra of the constituent compounds. However, the results suggest that the ICA method may be more appropriate when attempting to identify unknown reference spectra from a sample. The resulting PCA and ICA models are subsequently used to estimate the relative concentrations of the constituent compounds and to produce spatial distribution images of the analyzed tablet. These images provide a visual representation of the spatial distribution of the constituent compounds throughout the tablet. Images associated with the ICA scores are found to be more informative and not as affected by measurement noise as the PCA based score images. The paper concludes with a discussion of the future work that needs to be undertaken for ICA to gain wider acceptance in the applied spectroscopy community.

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

    PubMed Central

    Urrechaga, Eloísa; Aguirre, Urko; Izquierdo, Silvia

    2013-01-01

    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. PMID:24093062

  1. Automatic generation of user material subroutines for biomechanical growth analysis.

    PubMed

    Young, Jonathan M; Yao, Jiang; Ramasubramanian, Ashok; Taber, Larry A; Perucchio, Renato

    2010-10-01

    The analysis of the biomechanics of growth and remodeling in soft tissues requires the formulation of specialized pseudoelastic constitutive relations. The nonlinear finite element analysis package ABAQUS allows the user to implement such specialized material responses through the coding of a user material subroutine called UMAT. However, hand coding UMAT subroutines is a challenge even for simple pseudoelastic materials and requires substantial time to debug and test the code. To resolve this issue, we develop an automatic UMAT code generation procedure for pseudoelastic materials using the symbolic mathematics package MATHEMATICA and extend the UMAT generator to include continuum growth. The performance of the automatically coded UMAT is tested by simulating the stress-stretch response of a material defined by a Fung-orthotropic strain energy function, subject to uniaxial stretching, equibiaxial stretching, and simple shear in ABAQUS. The MATHEMATICA UMAT generator is then extended to include continuum growth by adding a growth subroutine to the automatically generated UMAT. The MATHEMATICA UMAT generator correctly derives the variables required in the UMAT code, quickly providing a ready-to-use UMAT. In turn, the UMAT accurately simulates the pseudoelastic response. In order to test the growth UMAT, we simulate the growth-based bending of a bilayered bar with differing fiber directions in a nongrowing passive layer. The anisotropic passive layer, being topologically tied to the growing isotropic layer, causes the bending bar to twist laterally. The results of simulations demonstrate the validity of the automatically coded UMAT, used in both standardized tests of hyperelastic materials and for a biomechanical growth analysis.

  2. A multivariate analysis of long-term care nursing services.

    PubMed

    Segal, M N

    1992-01-01

    Marketing, as a useful conceptual framework, has been extended to a variety of nonprofit sectors including the health care industry. Despite ever growing literature devoted to general health care marketing, there appears to be a death of specific application-oriented studies. This paper illustrates the development and application of a multiple discriminant analysis model in the context of long-term care (LTC) facilities. Empirical findings are presented and factors affecting the occupancy rates are discussed with implications for marketers, managers and administrators of skilled LTC nursing homes.

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

    NASA Astrophysics Data System (ADS)

    Heinrich, Durwood J.

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

  4. Quantitative multivariate analysis of dynamic multicellular morphogenic trajectories.

    PubMed

    White, Douglas E; Sylvester, Jonathan B; Levario, Thomas J; Lu, Hang; Streelman, J Todd; McDevitt, Todd C; Kemp, Melissa L

    2015-07-01

    Interrogating fundamental cell biology principles that govern tissue morphogenesis is critical to better understanding of developmental biology and engineering novel multicellular systems. Recently, functional micro-tissues derived from pluripotent embryonic stem cell (ESC) aggregates have provided novel platforms for experimental investigation; however elucidating the factors directing emergent spatial phenotypic patterns remains a significant challenge. Computational modelling techniques offer a unique complementary approach to probe mechanisms regulating morphogenic processes and provide a wealth of spatio-temporal data, but quantitative analysis of simulations and comparison to experimental data is extremely difficult. Quantitative descriptions of spatial phenomena across multiple systems and scales would enable unprecedented comparisons of computational simulations with experimental systems, thereby leveraging the inherent power of computational methods to interrogate the mechanisms governing emergent properties of multicellular biology. To address these challenges, we developed a portable pattern recognition pipeline consisting of: the conversion of cellular images into networks, extraction of novel features via network analysis, and generation of morphogenic trajectories. This novel methodology enabled the quantitative description of morphogenic pattern trajectories that could be compared across diverse systems: computational modelling of multicellular structures, differentiation of stem cell aggregates, and gastrulation of cichlid fish. Moreover, this method identified novel spatio-temporal features associated with different stages of embryo gastrulation, and elucidated a complex paracrine mechanism capable of explaining spatiotemporal pattern kinetic differences in ESC aggregates of different sizes.

  5. Application of multivariate analysis to optimize function of cultured hepatocytes.

    PubMed

    Chan, Christina; Hwang, Daehee; Stephanopoulos, Gregory N; Yarmush, Martin L; Stephanopoulos, George

    2003-01-01

    Understanding the metabolic and regulatory pathways of hepatocytes is important for biotechnological applications involving liver cells, including the development of bioartificial liver (BAL) devices. To characterize intermediary metabolism in the hepatocytes, metabolic flux analysis (MFA) was applied to elucidate the changes in intracellular pathway fluxes of primary rat hepatocytes exposed to human plasma and to provide a comprehensive snapshot of the hepatic metabolic profile. In the current study, the combination of preconditioning and plasma supplementation produced distinct metabolic states. Combining the metabolic flux distribution obtained by MFA with methodologies such as Fisher discriminant analysis (FDA) and partial least squares or projection to latent structures (PLS) provided insights into the underlying structure and causal relationship within the data. With the aid of these analyses, patterns in the cellular response of the hepatocytes that contributed to the separation of the different hepatic states were identified. Of particular interest was the recognition of distal pathways that strongly correlated with a particular hepatic function. The hepatic functions investigated were intracellular triglyceride accumulation and urea production. This study illustrates a framework for optimizing hepatic function and a possibility of identifying potential targets for improving hepatic functions.

  6. Multivariate analysis of parameters related to lake acidification in Quebec

    SciTech Connect

    Bobee, B.; Lachance, M.

    1984-08-01

    Physico-chemical data from 234 lakes were collected during the spring and summer of 1980 by the Quebec Ministry of the Environment, the Quebec Ministry of Recreation, Hunting and Fishing and the Canadian Wildlife Service. A statistical method, based on the joint use of factorial correspondence analysis and cluster analysis, was applied to these data to obtain a general picture of the spatial variability of a member of physico-chemical parameters related to the sensitivity or acidification of lakewaters. This method was first applied to the entire Quebec territory, and showed that the part of Quebec lying on the Canadian shield is especially vulnerable to acidification. The method also showed that the southwestern portion of this area of Quebec was more substantially affected by acid fallout. A detailed study of spatial variability over the shield area revealed the existence of greater spatial heterogeneity. More precisely, it was possible to pinpoint zones which are highly vulnerable to acid precipitation and zones whose lakes show clear signs of acidification resulting from such precipitation. These two statistical analyses led to a first general diagnosis on lake acidification in Quebec. They contribute to the rationalization of data acquisition in Quebec by delimitating zones where network density needs to be increased.

  7. Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials.

    PubMed

    Chen, Yong; Luo, Sheng; Chu, Haitao; Wei, Peng

    2013-05-01

    Multivariate meta-analysis is useful in combining evidence from independent studies which involve several comparisons among groups based on a single outcome. For binary outcomes, the commonly used statistical models for multivariate meta-analysis are multivariate generalized linear mixed effects models which assume risks, after some transformation, follow a multivariate normal distribution with possible correlations. In this article, we consider an alternative model for multivariate meta-analysis where the risks are modeled by the multivariate beta distribution proposed by Sarmanov (1966). This model have several attractive features compared to the conventional multivariate generalized linear mixed effects models, including simplicity of likelihood function, no need to specify a link function, and has a closed-form expression of distribution functions for study-specific risk differences. We investigate the finite sample performance of this model by simulation studies and illustrate its use with an application to multivariate meta-analysis of adverse events of tricyclic antidepressants treatment in clinical trials.

  8. Machine processing for remotely acquired data. [using multivariate statistical analysis

    NASA Technical Reports Server (NTRS)

    Landgrebe, D. A.

    1974-01-01

    This paper is a general discussion of earth resources information systems which utilize airborne and spaceborne sensors. It points out that information may be derived by sensing and analyzing the spectral, spatial and temporal variations of electromagnetic fields emanating from the earth surface. After giving an overview system organization, the two broad categories of system types are discussed. These are systems in which high quality imagery is essential and those more numerically oriented. Sensors are also discussed with this categorization of systems in mind. The multispectral approach and pattern recognition are described as an example data analysis procedure for numerically-oriented systems. The steps necessary in using a pattern recognition scheme are described and illustrated with data obtained from aircraft and the Earth Resources Technology Satellite (ERTS-1).

  9. Multivariate Data Analysis for Neuroimaging Data: Overview and Application to Alzheimer’s Disease

    PubMed Central

    Stern, Yaakov

    2010-01-01

    As clinical and cognitive neuroscience mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address functional connectivity in the brain. The covariance approach can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent, and often overly conservative, corrections for voxel-wise multiple comparisons. Multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The following article attempts to provide a basic introduction with sample

  10. Facilitator control as automatic behavior: A verbal behavior analysis

    PubMed Central

    Hall, Genae A.

    1993-01-01

    Several studies of facilitated communication have demonstrated that the facilitators were controlling and directing the typing, although they appeared to be unaware of doing so. Such results shift the focus of analysis to the facilitator's behavior and raise questions regarding the controlling variables for that behavior. This paper analyzes facilitator behavior as an instance of automatic verbal behavior, from the perspective of Skinner's (1957) book Verbal Behavior. Verbal behavior is automatic when the speaker or writer is not stimulated by the behavior at the time of emission, the behavior is not edited, the products of behavior differ from what the person would produce normally, and the behavior is attributed to an outside source. All of these characteristics appear to be present in facilitator behavior. Other variables seem to account for the thematic content of the typed messages. These variables also are discussed. PMID:22477083

  11. Multivariate Analysis and Geovisualization with an Integrated Geographic Knowledge Discovery Approach

    PubMed Central

    Guo, Diansheng; Gahegan, Mark; MacEachren, Alan M.; Zhou, Biliang

    2009-01-01

    The discovery, interpretation, and presentation of multivariate spatial patterns are important for scientific understanding of complex geographic problems. This research integrates computational, visual, and cartographic methods together to detect and visualize multivariate spatial patterns. The integrated approach is able to: (1) perform multivariate analysis, dimensional reduction, and data reduction (summarizing a large number of input data items in a moderate number of clusters) with the Self-Organizing Map (SOM); (2) encode the SOM result with a systematically designed color scheme; (3) visualize the multivariate patterns with a modified Parallel Coordinate Plot (PCP) display and a geographic map (GeoMap); and (4) support human interactions to explore and examine patterns. The research shows that such “mixed initiative” methods (computational and visual) can mitigate each other’s weakness and collaboratively discover complex patterns in large geographic datasets, in an effective and efficient way. PMID:19960118

  12. A non-iterative extension of the multivariate random effects meta-analysis.

    PubMed

    Makambi, Kepher H; Seung, Hyunuk

    2015-01-01

    Multivariate methods in meta-analysis are becoming popular and more accepted in biomedical research despite computational issues in some of the techniques. A number of approaches, both iterative and non-iterative, have been proposed including the multivariate DerSimonian and Laird method by Jackson et al. (2010), which is non-iterative. In this study, we propose an extension of the method by Hartung and Makambi (2002) and Makambi (2001) to multivariate situations. A comparison of the bias and mean square error from a simulation study indicates that, in some circumstances, the proposed approach perform better than the multivariate DerSimonian-Laird approach. An example is presented to demonstrate the application of the proposed approach.

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

    PubMed

    Jackson, Daniel; Riley, Richard D

    2014-02-20

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

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

    SciTech Connect

    Clegg, Samuel M; Barefield, James E; Wiens, Roger C; Sklute, Elizabeth; Dyare, Melinda D

    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.

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

    USGS Publications Warehouse

    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.

  16. Multivariate genetic analysis of learning and early reading development

    PubMed Central

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

    2011-01-01

    The genetic factor structure of a range of learning measures was explored in twin children, recruited in preschool and followed to Grade 2 (total N = 2084). 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 genetic correlations among the variables indicated a three-factor model. Vocabulary tests loaded on the first factor, the Grade 2 measures of word reading and orthographic learning, plus preschool letter knowledge, loaded on the second, and the third was characterized by tests of verbal short-term memory. The three genetic factors correlated, with the second (print) factor showing the most specificity. We conclude that genetically-influenced learning processes underlying print-speech integration, foreshadowed by preschool letter knowledge, have a degree of independence from genetic factors affecting spoken language. We also argue that the psychology and genetics of associative learning be afforded a more central place in studies of reading (dis)ability, and suggest some links to molecular studies of the genetics of learning. PMID:23626456

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

    PubMed Central

    McKeever, Tricia M; Lewis, Sarah A; Smit, Henriette A; Burney, Peter; Cassano, Patricia A; Britton, John

    2008-01-01

    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. PMID:18823528

  18. A multivariate analysis of kinanthropometric profiles of elite female orienteers.

    PubMed

    Creagh, U; Reilly, T

    1995-03-01

    The purpose of this study was to examine the relevance of various kinanthropometric features of elite female orienteers in terms of success at the Student World Orienteering Championships (SWOC)(1992). Participants at this competition were divided into successful (final placing in top 35; n = 12) and less successful (final placing below 35; n = 11) competitors. A non-orienteering reference group was used to enable the identification of any sport specific factors. Measurements of the orienteering group were taken during the week of the SWOC and included 5 skinfold thicknesses, 2 limb girths, 2 bone breadths, 3 proportional lengths and 3 physical tests (grip strength, leg strength, flexibility). Using the results of these, percent adiposity, somatotypes and various anthropometric indices were also obtained. Age and adiposity variables were found to correlate significantly with competitive performance (p < 0.01). Discriminant function analysis successfully distinguished between the successful and less successful orienteers (Wilks' Lambda = 0.603; p < 0.01). There was no difference (p > 0.05) between the orienteering group and the reference group for the majority of the proportionality measures and similarily in the results of the physical tests. Adiposity values were higher in the non-orienteers. In conclusion, body composition measures discriminated between successful and less successful orienteers at a major event at an elite level. The primary difference between the orienteers and the reference group was in body composition rather than linear or proportional measures.

  19. The heterogeneity of bone disease in cirrhosis: a multivariate analysis.

    PubMed

    Crawford, Bronwyn A L; Kam, C; Donaghy, A J; McCaughan, G W

    2003-12-01

    This study aimed to assess the clinical, biochemical and hormonal factors contributing to low bone density in a large ambulatory group of patients with cirrhosis of diverse aetiology. Bone density of the lumbar spine, neck of femur, total hip, total body, as well as total body fat, was measured by dual X-ray (DEXA) absorptiometry in 81 men and 32 women (average age 50.3 years). Morning blood and urine samples were taken for hormonal and biochemical analysis. Viral hepatitis was the most common cause of cirrhosis (54%) and the severity of cirrhosis ranged from Child-Pugh A5-C14. Osteoporosis was most common in the lumbar spine but was present at any site in 31% of women and 22% of men, with osteopenia present in another 40% of both genders. Urinary deoxypyridinoline, a marker of bone resorption, was elevated in 56% of patients and was associated with increasing severity of cirrhosis and a higher prevalence of osteoporosis, particularly of the lumbar spine. Hip-bone density was primarily affected by low 25-hydroxyvitamin D levels and was associated with secondary hyperparathyroidism in one third of these patients. Additional important predictors for low bone density at all sites were age in women and testosterone in men. These findings indicate that, although the pathophysiology of osteoporosis in chronic liver disease is heterogeneous, high bone turnover may be the underlying pathophysiological mechanism in a significant subgroup of cirrhotic patients and may reflect metabolic effects of hypogonadism or secondary hyperparathyroidism on bone.

  20. Localization of genes involved in the metabolic syndrome using multivariate linkage analysis

    PubMed Central

    Olswold, Curtis; Andrade, Mariza de

    2003-01-01

    There are no well accepted criteria for the diagnosis of the metabolic syndrome. However, the metabolic syndrome is identified clinically by the presence of three or more of these five variables: larger waist circumference, higher triglyceride levels, lower HDL-cholesterol concentrations, hypertension, and impaired fasting glucose. We use sets of two or three variables, which are available in the Framingham Heart Study data set, to localize genes responsible for this syndrome using multivariate quantitative linkage analysis. This analysis demonstrates the applicability of using multivariate linkage analysis and how its use increases the power to detect linkage when genes are involved in the same disease mechanism. PMID:14975125

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

    PubMed

    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.

  2. Multivariate Analysis, Retrieval, and Storage System (MARS). Volume 1: MARS System and Analysis Techniques

    NASA Technical Reports Server (NTRS)

    Hague, D. S.; Vanderberg, J. D.; Woodbury, N. W.

    1974-01-01

    A method for rapidly examining the probable applicability of weight estimating formulae to a specific aerospace vehicle design is presented. The Multivariate Analysis Retrieval and Storage System (MARS) is comprised of three computer programs which sequentially operate on the weight and geometry characteristics of past aerospace vehicles designs. Weight and geometric characteristics are stored in a set of data bases which are fully computerized. Additional data bases are readily added to the MARS system and/or the existing data bases may be easily expanded to include additional vehicles or vehicle characteristics.

  3. Integrated Analysis of Tropical Trees Growth: A Multivariate Approach

    PubMed Central

    YÁÑEZ-ESPINOSA, LAURA; TERRAZAS, TERESA; LÓPEZ-MATA, LAURO

    2006-01-01

    • Background and Aims 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. • Methods 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. • Key Results 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. • Conclusions 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. PMID:16822807

  4. Classification of lyophilised mixtures using multivariate analysis of NIR spectra.

    PubMed

    Grohganz, Holger; Fonteyne, Margot; Skibsted, Erik; Falck, Thomas; Palmqvist, Bent; Rantanen, Jukka

    2010-02-01

    Excipient selection is critically affecting the processing and the stability of a lyophilised product. Near infra-red (NIR) spectroscopy was applied to investigate freeze-dried samples containing varying ratios of the commonly used excipients mannitol and sucrose. Further variation in the formulation was achieved by adding NaCl, CaCl(2) and histidine and by exposing the samples to different conditions. Untreated NIR spectra are strongly affected by the physical nature of samples and can thus be useful for detecting production outliers. Applying standard normal variate (SNV) transformation highlights chemical information. The obtained NIR spectra of the freeze-dried samples were clustered by principal component analysis (PCA) after applying SNV correction in the range from 4200 to 7400cm(-1) (1350-2380nm). Relative humidity under storage and the mannitol/sucrose ratio were clearly represented in the first two principal components, while influence of other excipients was observed in the 3rd and 4th principal component. It was investigated whether this could be due to an influence of the excipients on the mannitol crystallisation behavior. Performing PCA with two principal components of SNV-corrected spectra in the range 4200-4500cm(-1) (2220-1380nm) led to the following observation: while the 1st principal component closely resembled the spectra of beta-mannitol, the 2nd principal component contained additional features that were not attributable to beta-mannitol but correlated well to the main absorbance band of delta-mannitol and mannitol hemihydrate. Therefore, it seems feasible that NIR can analyse versatile freeze-dried samples and classify these according to composition, water content and solid-state properties. Copyright (c) 2009 Elsevier B.V. All rights reserved.

  5. A Multivariate Analysis of Freshwater Variability over West Africa

    NASA Astrophysics Data System (ADS)

    Andam-Akorful, S. A.; He, X.; Ferreira, V. G.; Quaye-Ballard, J. A.

    2015-12-01

    As one of the most vulnerable regions to climate change, West Africa (WA) has since the 1970s suffered sustained reduction in rainfall amounts, leading to droughts and associated negative impacts on its water resources. Although rainfall rates have been reported to have experienced a degree of recovery, dry conditions persist. Additionally, the region faces perennial flooding, thus resulting in a highly variable hydrologic regime due to the extreme climate conditions. This therefore necessitates routine monitoring of the WA's freshwater reserves and its response to climate variations at the short and long term scales to aid sustainable use and management. However, this monitoring is hampered by data deficiency issues within the region. Consequently, dynamics leading to changes in water availability over the region are not completely understood. In this work, the recent flux and state of freshwater availability over WA from 1979 to 2013 is assessed by investigating the coupled variability of GRACE-derived terrestrial water storage (TWS) and its changes (TWSC) estimates with rainfall, evapotranspiration, and land surface air temperature (LSAT), as well as, major global and regional teleconnection indices using complex principal component analysis and wavelet transforms. Since GRACE covers a relatively short period, and thereby present challenges for long to medium term analyses, Artificial Neural Network (ANN) is employed to extend the GRACE series to 1979. The results from the ANN proved to be robust upon evaluation; spatially-averaged series for major basins and sub-climatic zones, as well as, the whole of WA presented RMSE, Nash-Sutcliffe efficient, and coefficient of determination (R2) of 11.83 mm, 0.76 and 0.89 respectively. Overall, the results obtained from this study indicate that, sustained increase in water flux, in terms of TWSC, contributed to a resurgence in freshwater reserves in the 21st century over WA from the low levels in the late 20th century

  6. An Investigation of Multivariate Adaptive Regression Splines for Modeling and Analysis of Univariate and Semi-Multivariate Time Series Systems

    DTIC Science & Technology

    1991-09-01

    GRAFSTAT from IBM Research; I am grateful to Dr . Peter Welch for supplying GRAFSTAT. To P.A.W. Lewis, Thank you for your support, confidence and...34Multivariate Adaptive Regression Splines", Annals of Statistics, v. 19, no. 2, pp. 1-142, 1991. Geib , A., Applied Optimal Estimation, M.I.T. Press, Cambridge

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

    Treesearch

    Nicole Labbe; David Harper; Timothy Rials; Thomas Elder

    2006-01-01

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

  8. The application of multivariate data analysis in the interpretation of engineering geological parameters

    NASA Astrophysics Data System (ADS)

    Kovács, József; Bodnár, Nikolett; Török, Ákos

    2016-01-01

    The paper presents the evaluation of engineering geological laboratory test results of core drillings along the new metro line (line 4) in Budapest by using a multivariate data analysis. A data set of 30 core drillings with a total coring length of over 1500 meters was studied. Of the eleven engineering geological parameters considered in this study, only the five most reliable (void ratio, dry bulk density, angle of internal friction, cohesion and compressive strength) representing 1260 data points were used for multivariate (cluster and discriminant) analyses. To test the results of the cluster analysis discriminant analysis was used. The results suggest that the use of multivariate analyses allows the identification of different groups of sediments even when the data sets are overlapping and contain several uncertainties. The tests also prove that the use of these methods for seemingly very scattered parameters is crucial in obtaining reliable engineering geological data for design.

  9. Intraoperative imaging of cortical cerebral perfusion by time-resolved thermography and multivariate data analysis.

    PubMed

    Steiner, Gerald; Sobottka, Stephan B; Koch, Edmund; Schackert, Gabriele; Kirsch, Matthias

    2011-01-01

    A new approach to cortical perfusion imaging is demonstrated using high-sensitivity thermography in conjunction with multivariate statistical data analysis. Local temperature changes caused by a cold bolus are imaged and transferred to a false color image. A cold bolus of 10 ml saline at ice temperature is injected systemically via a central venous access. During the injection, a sequence of 735 thermographic images are recorded within 2 min. The recorded data cube is subjected to a principal component analysis (PCA) to select slight changes of the cortical temperature caused by the cold bolus. PCA reveals that 11 s after injection the temperature of blood vessels is shortly decreased followed by an increase to the temperature before the cold bolus is injected. We demonstrate the potential of intraoperative thermography in combination with multivariate data analysis to image cortical cerebral perfusion without any markers. We provide the first in vivo application of multivariate thermographic imaging.

  10. Spectral analysis methods for automatic speech recognition applications

    NASA Astrophysics Data System (ADS)

    Parinam, Venkata Neelima Devi

    In this thesis, we evaluate the front-end of Automatic Speech Recognition (ASR) systems, with respect to different types of spectral processing methods that are extensively used. A filter bank approach for front end spectral analysis is one of the common methods used for spectral analysis. In this work we describe and evaluate spectral analysis based on Mel and Gammatone filter banks. These filtering methods are derived from auditory models and are thought to have some advantages for automatic speech recognition work. Experimentally, however, we show that direct use of FFT spectral values is just as effective as using either Mel or Gammatone filter banks, provided that the features extracted from the FFT spectral values take into account a Mel or Mel-like frequency scale. It is also shown that trajectory features based on sliding block of spectral features, computed using either FFT or filter bank spectral analysis are considerably more effective, in terms of ASR accuracy, than are delta and delta-delta terms often used for ASR. Although there is no major performance disadvantage to using a filter bank, simplicity of analysis is a reason to eliminate this step in speech processing. These assertions hold for both clean and noisy speech.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-01-01

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

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

    PubMed

    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.

  14. DMET-Analyzer: automatic analysis of Affymetrix DMET Data

    PubMed Central

    2012-01-01

    Background Clinical Bioinformatics is currently growing and is based on the integration of clinical and omics data aiming at the development of personalized medicine. Thus the introduction of novel technologies able to investigate the relationship among clinical states and biological machineries may help the development of this field. For instance the Affymetrix DMET platform (drug metabolism enzymes and transporters) is able to study the relationship among the variation of the genome of patients and drug metabolism, detecting SNPs (Single Nucleotide Polymorphism) on genes related to drug metabolism. This may allow for instance to find genetic variants in patients which present different drug responses, in pharmacogenomics and clinical studies. Despite this, there is currently a lack in the development of open-source algorithms and tools for the analysis of DMET data. Existing software tools for DMET data generally allow only the preprocessing of binary data (e.g. the DMET-Console provided by Affymetrix) and simple data analysis operations, but do not allow to test the association of the presence of SNPs with the response to drugs. Results We developed DMET-Analyzer a tool for the automatic association analysis among the variation of the patient genomes and the clinical conditions of patients, i.e. the different response to drugs. The proposed system allows: (i) to automatize the workflow of analysis of DMET-SNP data avoiding the use of multiple tools; (ii) the automatic annotation of DMET-SNP data and the search in existing databases of SNPs (e.g. dbSNP), (iii) the association of SNP with pathway through the search in PharmaGKB, a major knowledge base for pharmacogenomic studies. DMET-Analyzer has a simple graphical user interface that allows users (doctors/biologists) to upload and analyse DMET files produced by Affymetrix DMET-Console in an interactive way. The effectiveness and easy use of DMET Analyzer is demonstrated through different case studies regarding

  15. Multivariate statistical analysis: Principles and applications to coorbital streams of meteorite falls

    NASA Technical Reports Server (NTRS)

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

    1993-01-01

    Multivariate statistical analysis techniques (linear discriminant analysis and logistic regression) can provide powerful discrimination tools which are generally unfamiliar to the planetary science community. Fall parameters were used to identify a group of 17 H chondrites (Cluster 1) that were part of a coorbital stream which intersected Earth's orbit in May, from 1855 - 1895, and can be distinguished from all other H chondrite falls. Using multivariate statistical techniques, it was demonstrated that a totally different criterion, labile trace element contents - hence thermal histories - or 13 Cluster 1 meteorites are distinguishable from those of 45 non-Cluster 1 H chondrites. Here, we focus upon the principles of multivariate statistical techniques and illustrate their application using non-meteoritic and meteoritic examples.

  16. Multivariate statistical analysis: Principles and applications to coorbital streams of meteorite falls

    NASA Technical Reports Server (NTRS)

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

    1993-01-01

    Multivariate statistical analysis techniques (linear discriminant analysis and logistic regression) can provide powerful discrimination tools which are generally unfamiliar to the planetary science community. Fall parameters were used to identify a group of 17 H chondrites (Cluster 1) that were part of a coorbital stream which intersected Earth's orbit in May, from 1855 - 1895, and can be distinguished from all other H chondrite falls. Using multivariate statistical techniques, it was demonstrated that a totally different criterion, labile trace element contents - hence thermal histories - or 13 Cluster 1 meteorites are distinguishable from those of 45 non-Cluster 1 H chondrites. Here, we focus upon the principles of multivariate statistical techniques and illustrate their application using non-meteoritic and meteoritic examples.

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

    PubMed

    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.

  18. Rapid automatic keyword extraction for information retrieval and analysis

    DOEpatents

    Rose, Stuart J [Richland, WA; Cowley,; E, Wendy [Richland, WA; Crow, Vernon L [Richland, WA; Cramer, Nicholas O [Richland, WA

    2012-03-06

    Methods and systems for rapid automatic keyword extraction for information retrieval and analysis. Embodiments can include parsing words in an individual document by delimiters, stop words, or both in order to identify candidate keywords. Word scores for each word within the candidate keywords are then calculated based on a function of co-occurrence degree, co-occurrence frequency, or both. Based on a function of the word scores for words within the candidate keyword, a keyword score is calculated for each of the candidate keywords. A portion of the candidate keywords are then extracted as keywords based, at least in part, on the candidate keywords having the highest keyword scores.

  19. Corpus analysis and automatic detection of emotion-including keywords

    NASA Astrophysics Data System (ADS)

    Yuan, Bo; He, Xiangqing; Liu, Ying

    2013-12-01

    Emotion words play a vital role in many sentiment analysis tasks. Previous research uses sentiment dictionary to detect the subjectivity or polarity of words. In this paper, we dive into Emotion-Inducing Keywords (EIK), which refers to the words in use that convey emotion. We first analyze an emotion corpus to explore the pragmatic aspects of EIK. Then we design an effective framework for automatically detecting EIK in sentences by utilizing linguistic features and context information. Our system outperforms traditional dictionary-based methods dramatically in increasing Precision, Recall and F1-score.

  20. Multivariate analysis of TOF-SIMS spectra from self-assembled monolayers

    NASA Astrophysics Data System (ADS)

    Graham, Daniel Jay

    Recently the concept of engineered biomaterial surfaces has started a revolution in the biomaterials community. These biomaterial surfaces are designed using knowledge from cell biology to produce a healing response that will integrate the biomaterials into the body. These surfaces will require specific, complex chemistries that will elicit the desired responses. Such complex surfaces will require an equally detailed surface characterization method. Due to its molecular specificity and high sensitivity, TOF-SIMS appears to be an ideal method for this challenge. Nevertheless TOF-SIMS spectra are complex and difficult to interpret. This complexity results from the shear number of peaks within the spectra, the inter-related nature of the peaks, and lack of fundamental understanding of TOF-SIMS fragmentation mechanisms. This work approaches addressing these problems through use of multivariate analysis. Multivariate analysis enables detailed spectral interpretation and provides insight into fragmentation mechanisms by extracting the salient information from within the complex spectral data set. Multivariate spectral interpretation was explored using a series of self-assembled monolayers that varied in surface order, surface functionality, formation method, and chain length. A multivariate SAM ratio was developed that correlates with thermodynamic properties of the surface. This ratio is the first to demonstrate a direct relationship between TOF-SIMS data and surface thermodynamic parameters. A model for TOF-SIMS fragmentation of SAMs was created and explored using multivariate analysis of a thiol containing a hydroxyl end group. This model explains the emission of fragments from the surface over a time course experiment. This is the first use of multivariate analysis with TOF-SIMS data to provide mechanistic information about the TOF-SIMS process. This methodology provides a technique for studying TOF-SIMS fragmentation using actual data without the need for molecular

  1. Multivariate analysis of the modifications induced by an environmental acoustic cue on rat exploratory behavior.

    PubMed

    Casarrubea, Maurizio; Sorbera, Filippina; Crescimanno, Giuseppe

    2008-03-18

    The aim of the present paper is to study by means of a multivariate analysis the modifications induced by an environmental acoustic cue on the structure of rat exploratory behavior. Adult male Wistar rats were observed during the exploration of a soundproof observation box. Each rat was acoustically stimulated after 150 s from the beginning of the experimental session, lasting 300 s, and recorded through a digital videocamera. A frame by frame analysis was thereafter carried out using a professional video-recording system. Thirteen behavioral patterns were selected: immobility, immobile-sniffing, walking, rearing, climbing, chewing, paw-licking, face-grooming, body-grooming, head-turning, tuning, oriented-sniffing, focusing. Both descriptive and multivariate analyses (cluster, stochastic, adjusted residuals) were carried out. Through descriptive statistical analysis, latencies and per cent distribution of each pattern were studied. A multivariate cluster analysis revealed the presence of three main behavioral clusters, an additional one being identified following acoustic stimulation. Multivariate stochastic analysis showed that all the patterns converged on immobile-sniffing which could represent a key component in behavioral switching processes related to environmental exploration. Moreover, through adjusted residuals, the degree of relationship among different patterns was shown according to statistic Z-distribution. Our data assign new ethological meanings to different behavioral patterns. Notably, head-turning is suggested to be considered as a generic directional search and tuning as a subtle activity of stimulus localization.

  2. Use of multivariate analysis to compare antimicrobial agents on the basis of in vitro activity data.

    PubMed Central

    Hernández, J M; Conforti, P

    1994-01-01

    Multivariate techniques such as principal component analysis or similar factor analysis help in analyses of the simultaneous interrelationships among several variables. A comparative multivariate analysis on the in vitro activities of eight antimicrobial agents, including the novel molecule daptomycin, is presented. Multivariate analysis detects components or factors and establishes connections among antimicrobial agents on the basis of their different levels of participation in each factor. The first principal component was dominated by vancomycin, teicoplanin, and rifampin (0.94344, 0.92792, and 0.72127, respectively). The second principal component showed strong effects from imipenem, gentamicin, and cephalothin (0.87922, 0.86126, and 0.68870, respectively). Daptomycin stood out alone in the third principal component (0.83983). The first three components defined 81.5% of the total variance and could easily be represented graphically in a three-dimensional scatter plot. In this graphic representation, the eight antimicrobial agents clustered in three different spatial regions; daptomycin occupied a separate spatial position. The use of multivariate analysis offers a different approach to determination of the in vitro activities of new antimicrobial agents and adds some new data on the relationships among different classes. Notwithstanding its limitations, the application of these methods in microbiology and drug development could be an additional tool for use in processing information. PMID:8192440

  3. Automatic analysis of attack data from distributed honeypot network

    NASA Astrophysics Data System (ADS)

    Safarik, Jakub; Voznak, MIroslav; Rezac, Filip; Partila, Pavol; Tomala, Karel

    2013-05-01

    There are many ways of getting real data about malicious activity in a network. One of them relies on masquerading monitoring servers as a production one. These servers are called honeypots and data about attacks on them brings us valuable information about actual attacks and techniques used by hackers. The article describes distributed topology of honeypots, which was developed with a strong orientation on monitoring of IP telephony traffic. IP telephony servers can be easily exposed to various types of attacks, and without protection, this situation can lead to loss of money and other unpleasant consequences. Using a distributed topology with honeypots placed in different geological locations and networks provides more valuable and independent results. With automatic system of gathering information from all honeypots, it is possible to work with all information on one centralized point. Communication between honeypots and centralized data store use secure SSH tunnels and server communicates only with authorized honeypots. The centralized server also automatically analyses data from each honeypot. Results of this analysis and also other statistical data about malicious activity are simply accessible through a built-in web server. All statistical and analysis reports serve as information basis for an algorithm which classifies different types of used VoIP attacks. The web interface then brings a tool for quick comparison and evaluation of actual attacks in all monitored networks. The article describes both, the honeypots nodes in distributed architecture, which monitor suspicious activity, and also methods and algorithms used on the server side for analysis of gathered data.

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

    SciTech Connect

    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 that

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

    PubMed

    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.

  6. Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool.

    PubMed

    Clark, Neil R; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D; Jones, Matthew R; Ma'ayan, Avi

    2015-11-01

    Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community.

  7. A multivariate analysis approach for the Imaging Atmospheric Cerenkov Telescopes System H.E.S.S

    SciTech Connect

    Dubois, F.; Lamanna, G.

    2008-12-24

    We present a multivariate classification approach applied to the analysis of data from the H.E.S.S. Very High Energy (VHE){gamma}-ray IACT stereoscopic system. This approach combines three complementary analysis methods already successfully applied in the H.E.S.S. data analysis. The proposed approach, with the combined effective estimator X{sub eff}, is conceived to improve the signal-to-background ratio and therefore particularly relevant to the morphological studies of faint extended sources.

  8. Multivariable and Multigroup Receiver Operating Characteristics Curve Analyses for Qualitative and Quantitative Analysis

    DTIC Science & Technology

    2012-01-01

    presents the original experimental data collected by Fisher and includes the lengths and widths of the sepals and petals of the three iris flower ...more than two experimental groups in a systematic fashion. The classic Fisher iris flower data set is treated as one variable and two cases at a time...enhanced computer efficiency and information-rich analysis. 15. SUBJECT TERMS Multivariate analysis Sepal Univariate analysis Petal Frequency

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

    PubMed Central

    Neupane, Binod; Beyene, Joseph

    2015-01-01

    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 the missing data

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

    PubMed

    Neupane, Binod; Beyene, Joseph

    2015-01-01

    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 the missing data

  11. Entropy analysis of automatic sequences revisited: An entropy diagnostic for automaticity

    NASA Astrophysics Data System (ADS)

    Karamanos, Kostas

    2001-06-01

    We give a necessary entropy condition, valid for all automatic sequences read by lumping. We next establish new entropic decimation schemes for the Thue-Morse, the Rudin-Shapiro and the paperfolding sequences read by lumping.

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

    ERIC Educational Resources Information Center

    Finch, W. Holmes

    2016-01-01

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

  13. Genetic and Environmental Components of Adolescent Adjustment and Parental Behavior: A Multivariate Analysis

    ERIC Educational Resources Information Center

    Loehlin, John C.; Neiderhiser, Jenae M.; Reiss, David

    2005-01-01

    Adolescent adjustment measures may be related to each other and to the social environment in various ways. Are these relationships similar in genetic and environmental sources of covariation, or different? A multivariate behaviorgenetic analysis was made of 6 adjustment and 3 treatment composites from the study Nonshared Environment in Adolescent…

  14. A Multivariate Model for the Meta-Analysis of Study Level Survival Data at Multiple Times

    ERIC Educational Resources Information Center

    Jackson, Dan; Rollins, Katie; Coughlin, Patrick

    2014-01-01

    Motivated by our meta-analytic dataset involving survival rates after treatment for critical leg ischemia, we develop and apply a new multivariate model for the meta-analysis of study level survival data at multiple times. Our data set involves 50 studies that provide mortality rates at up to seven time points, which we model simultaneously, and…

  15. Multivariate geomorphic analysis of forest streams: Implications for assessment of land use impacts on channel condition

    Treesearch

    Richard. D. Wood-Smith; John M. Buffington

    1996-01-01

    Multivariate statistical analyses of geomorphic variables from 23 forest stream reaches in southeast Alaska result in successful discrimination between pristine streams and those disturbed by land management, specifically timber harvesting and associated road building. Results of discriminant function analysis indicate that a three-variable model discriminates 10...

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

    ERIC Educational Resources Information Center

    Finch, W. Holmes

    2016-01-01

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

  17. Structure of Hierarchic Clusterings: Implications for Information Retrieval and for Multivariate Data Analysis.

    ERIC Educational Resources Information Center

    Murtagh, F.

    1984-01-01

    Using examples of data from the areas of information retrieval and of multivariate data analysis, six hierarchic clustering algorithms (single link, median, centroid, group average, complete link, Wards's) are examined and evaluated by using three proposed coefficients of hierarchic structure. Nine references are cited. (EJS)

  18. Web-Based Tools for Modelling and Analysis of Multivariate Data: California Ozone Pollution Activity

    ERIC Educational Resources Information Center

    Dinov, Ivo D.; Christou, Nicolas

    2011-01-01

    This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting…

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

    ERIC Educational Resources Information Center

    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…

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

    USDA-ARS?s Scientific Manuscript database

    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...

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

    ERIC Educational Resources Information Center

    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…

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

    ERIC Educational Resources Information Center

    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…

  3. Web-Based Tools for Modelling and Analysis of Multivariate Data: California Ozone Pollution Activity

    ERIC Educational Resources Information Center

    Dinov, Ivo D.; Christou, Nicolas

    2011-01-01

    This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting…

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

    PubMed

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

    2014-04-01

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

  5. Comparison of pure laparoscopic versus open left hemihepatectomy by multivariate analysis: a retrospective cohort study.

    PubMed

    Cho, Hwui-Dong; Kim, Ki-Hun; Hwang, Shin; Ahn, Chul-Soo; Moon, Deok-Bog; Ha, Tae-Yong; Song, Gi-Won; Jung, Dong-Hwan; Park, Gil-Chun; Lee, Sung-Gyu

    2017-07-21

    To compare the outcomes of pure laparoscopic left hemihepatectomy (LLH) versus open left hemihepatectomy (OLH) for benign and malignant conditions using multivariate analysis. All consecutive cases of LLH and OLH between October 2007 and December 2013 in a tertiary referral hospital were enrolled in this retrospective cohort study. All surgical procedures were performed by one surgeon. The LLH and OLH groups were compared in terms of patient demographics, preoperative data, clinical perioperative outcomes, and tumor characteristics in patients with malignancy. Multivariate analysis of the prognostic factors associated with severe complications was then performed. The LLH group (n = 62) had a significantly shorter postoperative hospital stay than the OLH group (n = 118) (9.53 ± 3.30 vs 14.88 ± 11.36 days, p < 0.001). Multivariate analysis revealed that the OLH group had >4 times the risk of the LLH group in terms of developing severe complications (Clavien-Dindo grade ≥III) (odds ratio 4.294, 95% confidence intervals 1.165-15.832, p = 0.029). LLH was a safe and feasible procedure for selected patients. LLH required shorter hospital stay and resulted in less operative blood loss. Multivariate analysis revealed that LLH was associated with a lower risk of severe complications compared to OLH. The authors suggest that LLH could be a reasonable treatment option for selected patients.

  6. The Multivariate Reality of Educational Research: Detecting Interaction Effects Using Canonical Correlation Analysis.

    ERIC Educational Resources Information Center

    Kirby, Peggy C.; Abernathy, Mari W.

    Canonical correlation analysis is the best technique to employ when the research problem has multiple predictor and multiple criterion (outcome) variables, which is usually the case in the "real" world of education. A hypothetical data set is presented to illustrate how this particular multivariate method can be used to detect effects of…

  7. Entropy analysis of OCT signal for automatic tissue characterization

    NASA Astrophysics Data System (ADS)

    Wang, Yahui; Qiu, Yi; Zaki, Farzana; Xu, Yiqing; Hubbi, Basil; Belfield, Kevin D.; Liu, Xuan

    2016-03-01

    Optical coherence tomography (OCT) signal can provide microscopic characterization of biological tissue and assist clinical decision making in real-time. However, raw OCT data is noisy and complicated. It is challenging to extract information that is directly related to the pathological status of tissue through visual inspection on huge volume of OCT signal streaming from the high speed OCT engine. Therefore, it is critical to discover concise, comprehensible information from massive OCT data through novel strategies for signal analysis. In this study, we perform Shannon entropy analysis on OCT signal for automatic tissue characterization, which can be applied in intraoperative tumor margin delineation for surgical excision of cancer. The principle of this technique is based on the fact that normal tissue is usually more structured with higher entropy value, compared to pathological tissue such as cancer tissue. In this study, we develop high-speed software based on graphic processing units (GPU) for real-time entropy analysis of OCT signal.

  8. Independent component analysis for automatic note extraction from musical trills

    NASA Astrophysics Data System (ADS)

    Brown, Judith C.; Smaragdis, Paris

    2004-05-01

    The method of principal component analysis, which is based on second-order statistics (or linear independence), has long been used for redundancy reduction of audio data. The more recent technique of independent component analysis, enforcing much stricter statistical criteria based on higher-order statistical independence, is introduced and shown to be far superior in separating independent musical sources. This theory has been applied to piano trills and a database of trill rates was assembled from experiments with a computer-driven piano, recordings of a professional pianist, and commercially available compact disks. The method of independent component analysis has thus been shown to be an outstanding, effective means of automatically extracting interesting musical information from a sea of redundant data.

  9. Independent component analysis for automatic note extraction from musical trills.

    PubMed

    Brown, Judith C; Smaragdis, Paris

    2004-05-01

    The method of principal component analysis, which is based on second-order statistics (or linear independence), has long been used for redundancy reduction of audio data. The more recent technique of independent component analysis, enforcing much stricter statistical criteria based on higher-order statistical independence, is introduced and shown to be far superior in separating independent musical sources. This theory has been applied to piano trills and a database of trill rates was assembled from experiments with a computer-driven piano, recordings of a professional pianist, and commercially available compact disks. The method of independent component analysis has thus been shown to be an outstanding, effective means of automatically extracting interesting musical information from a sea of redundant data.

  10. Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

    PubMed

    Ma, Yan; Mazumdar, Madhu

    2011-10-30

    Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting.

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

    PubMed Central

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

    2013-01-01

    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. PMID:25089070

  12. Spectral saliency via automatic adaptive amplitude spectrum analysis

    NASA Astrophysics Data System (ADS)

    Wang, Xiaodong; Dai, Jialun; Zhu, Yafei; Zheng, Haiyong; Qiao, Xiaoyan

    2016-03-01

    Suppressing nonsalient patterns by smoothing the amplitude spectrum at an appropriate scale has been shown to effectively detect the visual saliency in the frequency domain. Different filter scales are required for different types of salient objects. We observe that the optimal scale for smoothing amplitude spectrum shares a specific relation with the size of the salient region. Based on this observation and the bottom-up saliency detection characterized by spectrum scale-space analysis for natural images, we propose to detect visual saliency, especially with salient objects of different sizes and locations via automatic adaptive amplitude spectrum analysis. We not only provide a new criterion for automatic optimal scale selection but also reserve the saliency maps corresponding to different salient objects with meaningful saliency information by adaptive weighted combination. The performance of quantitative and qualitative comparisons is evaluated by three different kinds of metrics on the four most widely used datasets and one up-to-date large-scale dataset. The experimental results validate that our method outperforms the existing state-of-the-art saliency models for predicting human eye fixations in terms of accuracy and robustness.

  13. Analysis and differentiation of paper samples by capillary electrophoresis and multivariate analysis.

    PubMed

    Fernández de la Ossa, Ma Ángeles; Ortega-Ojeda, Fernando; García-Ruiz, Carmen

    2014-11-01

    This work reports an investigation for the analysis of different paper samples using CE with laser-induced detection. Papers from four different manufactures (white-copy paper) and four different paper sources (white and recycled-copy papers, adhesive yellow paper notes and restaurant serviettes) were pulverized by scratching with a surgical scalpel prior to their derivatization with a fluorescent labeling agent, 8-aminopyrene-1,3,6-trisulfonic acid. Methodological conditions were evaluated, specifically the derivatization conditions with the aim to achieve the best S/N signals and the separation conditions in order to obtain optimum values of sensitivity and reproducibility. The best conditions, in terms of fastest, and easiest sample preparation procedure, minimal sample consumption, as well as the use of the simplest and fastest CE-procedure for obtaining the best analytical parameters, were applied to the analysis of the different paper samples. The registered electropherograms were pretreated (normalized and aligned) and subjected to multivariate analysis (principal component analysis). A successful discrimination among paper samples without entanglements was achieved. To the best of our knowledge, this work presents the first approach to achieve a successful differentiation among visually similar white-copy paper samples produced by different manufactures and paper from different paper sources through their direct analysis by CE-LIF and subsequent comparative study of the complete cellulose electropherogram by chemometric tools. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. Plasma metabolic profiling analysis of nephrotoxicity induced by acyclovir using metabonomics coupled with multivariate data analysis.

    PubMed

    Zhang, Xiuxiu; Li, Yubo; Zhou, Huifang; Fan, Simiao; Zhang, Zhenzhu; Wang, Lei; Zhang, Yanjun

    2014-08-01

    Acyclovir (ACV) is an antiviral agent. However, its use is limited by adverse side effect, particularly by its nephrotoxicity. Metabonomics technology can provide essential information on the metabolic profiles of biofluids and organs upon drug administration. Therefore, in this study, mass spectrometry-based metabonomics coupled with multivariate data analysis was used to identify the plasma metabolites and metabolic pathways related to nephrotoxicity caused by intraperitoneal injection of low (50mg/kg) and high (100mg/kg) doses of acyclovir. Sixteen biomarkers were identified by metabonomics and nephrotoxicity results revealed the dose-dependent effect of acyclovir on kidney tissues. The present study showed that the top four metabolic pathways interrupted by acyclovir included the metabolisms of arachidonic acid, tryptophan, arginine and proline, and glycerophospholipid. This research proves the established metabonomic approach can provide information on changes in metabolites and metabolic pathways, which can be applied to in-depth research on the mechanism of acyclovir-induced kidney injury.

  15. A heuristic algorithm for pattern identification in large multivariate analysis of geophysical data sets

    NASA Astrophysics Data System (ADS)

    da Silva Pereira, João Eduardo; Strieder, Adelir José; Amador, Janete Pereira; da Silva, José Luiz Silvério; Volcato Descovi Filho, Leônidas Luiz

    2010-01-01

    This paper aims to present a heuristic algorithm with factor analysis and a local search optimization system for pattern identification problems as applied to large and multivariate aero-geophysical data. The algorithm was developed in MATLAB code using both multivariate and univariate methodologies. Two main analysis steps are detailed in the MATLAB code: the first deals with multivariate factor analysis to reduce the problem of dimension, and to orient the variables in an independent and orthogonal structure; and the second with the application of a novel local research optimization system based on univariate structure. The process of local search is simple and consistent because it solves a multivariate problem by summing up univariate and independent problems. Thus, it can reduce computational time and render the efficiency of estimates independent of the data bank. The aero-geophysical data include the results of the magnetometric and gammaspectrometric (TC, K, Th, and U channels) surveys for the Santa Maria region (RS, Brazil). After the classification, when the observations are superimposed on the regional map, one can see that data belonging to the same subspace appear closer to each other revealing some physical law governing area pattern distribution. The analysis of variance for the original variables as functions of the subspaces obtained results in different mean behaviors for all the variables. This result shows that the use of factor transformation captures the discriminative capacity of the original variables. The proposed algorithm for multivariate factor analysis and the local search system open up new challenges in aero-geophysical data handling and processing techniques.

  16. Application of multivariate data-analysis techniques to biomedical diagnostics based on mid-infrared spectroscopy.

    PubMed

    Wang, Liqun; Mizaikoff, Boris

    2008-07-01

    The objective of this contribution is to review the application of advanced multivariate data-analysis techniques in the field of mid-infrared (MIR) spectroscopic biomedical diagnosis. MIR spectroscopy is a powerful chemical analysis tool for detecting biomedically relevant constituents such as DNA/RNA, proteins, carbohydrates, lipids, etc., and even diseases or disease progression that may induce changes in the chemical composition or structure of biological systems including cells, tissues, and bio-fluids. However, MIR spectra of multiple constituents are usually characterized by strongly overlapping spectral features reflecting the complexity of biological samples. Consequently, MIR spectra of biological samples are frequently difficult to interpret by simple data-analysis techniques. Hence, with increasing complexity of the sample matrix more sophisticated mathematical and statistical data analysis routines are required for deconvoluting spectroscopic data and for providing useful results from information-rich spectroscopic signals. A large body of work relates to the combination of multivariate data-analysis techniques with MIR spectroscopy, and has been applied by a variety of research groups to biomedically relevant areas such as cancer detection and analysis, artery diseases, biomarkers, and other pathologies. The reported results indeed reveal a promising perspective for more widespread application of multivariate data analysis in assisting MIR spectroscopy as a screening or diagnostic tool in biomedical research and clinical studies. While the authors do not mean to ignore any relevant contributions to biomedical analysis across the entire electromagnetic spectrum, they confine the discussion in this contribution to the mid-infrared spectral range as a potentially very useful, yet underutilized frequency region. Selected representative examples without claiming completeness will demonstrate a range of biomedical diagnostic applications with particular

  17. [Automatic analysis pipeline of next-generation sequencing data].

    PubMed

    Wenke, Li; Fengyu, Li; Siyao, Zhang; Bin, Cai; Na, Zheng; Yu, Nie; Dao, Zhou; Qian, Zhao

    2014-06-01

    The development of next-generation sequencing has generated high demand for data processing and analysis. Although there are a lot of software for analyzing next-generation sequencing data, most of them are designed for one specific function (e.g., alignment, variant calling or annotation). Therefore, it is necessary to combine them together for data analysis and to generate interpretable results for biologists. This study designed a pipeline to process Illumina sequencing data based on Perl programming language and SGE system. The pipeline takes original sequence data (fastq format) as input, calls the standard data processing software (e.g., BWA, Samtools, GATK, and Annovar), and finally outputs a list of annotated variants that researchers can further analyze. The pipeline simplifies the manual operation and improves the efficiency by automatization and parallel computation. Users can easily run the pipeline by editing the configuration file or clicking the graphical interface. Our work will facilitate the research projects using the sequencing technology.

  18. DWT features performance analysis for automatic speech recognition of Urdu.

    PubMed

    Ali, Hazrat; Ahmad, Nasir; Zhou, Xianwei; Iqbal, Khalid; Ali, Sahibzada Muhammad

    2014-01-01

    This paper presents the work on Automatic Speech Recognition of Urdu language, using a comparative analysis for Discrete Wavelets Transform (DWT) based features and Mel Frequency Cepstral Coefficients (MFCC). These features have been extracted for one hundred isolated words of Urdu, each word uttered by ten different speakers. The words have been selected from the most frequently used words of Urdu. A variety of age and dialect has been covered by using a balanced corpus approach. After extraction of features, the classification has been achieved by using Linear Discriminant Analysis. After the classification task, the confusion matrix obtained for the DWT features has been compared with the one obtained for Mel-Frequency Cepstral Coefficients based speech recognition. The framework has been trained and tested for speech data recorded under controlled environments. The experimental results are useful in determination of the optimum features for speech recognition task.

  19. MGAS: a powerful tool for multivariate gene-based genome-wide association analysis.

    PubMed

    Van der Sluis, Sophie; Dolan, Conor V; Li, Jiang; Song, Youqiang; Sham, Pak; Posthuma, Danielle; Li, Miao-Xin

    2015-04-01

    Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype-phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis. MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype-phenotype models. MGAS is freely available in KGG v3.0 (http://statgenpro.psychiatry.hku.hk/limx/kgg/download.php). Access to the metabolic dataset can be requested at dbGaP (https://dbgap.ncbi.nlm.nih.gov/). The R-simulation code is available from http://ctglab.nl/people/sophie_van_der_sluis. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

  20. Automatic analysis for neuron by confocal laser scanning microscope

    NASA Astrophysics Data System (ADS)

    Satou, Kouhei; Aoki, Yoshimitsu; Mataga, Nobuko; Hensh, Takao K.; Taki, Katuhiko

    2005-12-01

    The aim of this study is to develop a system that recognizes both the macro- and microscopic configurations of nerve cells and automatically performs the necessary 3-D measurements and functional classification of spines. The acquisition of 3-D images of cranial nerves has been enabled by the use of a confocal laser scanning microscope, although the highly accurate 3-D measurements of the microscopic structures of cranial nerves and their classification based on their configurations have not yet been accomplished. In this study, in order to obtain highly accurate measurements of the microscopic structures of cranial nerves, existing positions of spines were predicted by the 2-D image processing of tomographic images. Next, based on the positions that were predicted on the 2-D images, the positions and configurations of the spines were determined more accurately by 3-D image processing of the volume data. We report the successful construction of an automatic analysis system that uses a coarse-to-fine technique to analyze the microscopic structures of cranial nerves with high speed and accuracy by combining 2-D and 3-D image analyses.

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

    PubMed Central

    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

  2. Scalable Multivariate Volume Visualization and Analysis Based on Dimension Projection and Parallel Coordinates.

    PubMed

    Guo, Hanqi; Xiao, He; Yuan, Xiaoru

    2012-09-01

    In this paper, we present an effective and scalable system for multivariate volume data visualization and analysis with a novel transfer function interface design that tightly couples parallel coordinates plots (PCP) and MDS-based dimension projection plots. In our system, the PCP visualizes the data distribution of each variate (dimension) and the MDS plots project features. They are integrated seamlessly to provide flexible feature classification without context switching between different data presentations during the user interaction. The proposed interface enables users to identify relevant correlation clusters and assign optical properties with lassos, magic wand, and other tools. Furthermore, direct sketching on the volume rendered images has been implemented to probe and edit features. With our system, users can interactively analyze multivariate volumetric data sets by navigating and exploring feature spaces in unified PCP and MDS plots. To further support large-scale multivariate volume data visualization and analysis, Scalable Pivot MDS (SPMDS), parallel adaptive continuous PCP rendering, as well as parallel rendering techniques are developed and integrated into our visualization system. Our experiments show that the system is effective in multivariate volume data visualization and its performance is highly scalable for data sets with different sizes and number of variates.

  3. Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space.

    PubMed

    Cichy, Radoslaw Martin; Pantazis, Dimitrios

    2017-09-01

    Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research. Copyright © 2017 Elsevier Inc. All rights reserved.

  4. Extending Inferential Group Analysis in Type 2 Diabetic Patients with Multivariate GLM Implemented in SPM8

    PubMed Central

    Ferreira, Fábio S.; Pereira, João M.S.; Duarte, João V.; Castelo-Branco, Miguel

    2017-01-01

    Background: Although voxel based morphometry studies are still the standard for analyzing brain structure, their dependence on massive univariate inferential methods is a limiting factor. A better understanding of brain pathologies can be achieved by applying inferential multivariate methods, which allow the study of multiple dependent variables, e.g. different imaging modalities of the same subject. Objective: Given the widespread use of SPM software in the brain imaging community, the main aim of this work is the implementation of massive multivariate inferential analysis as a toolbox in this software package. applied to the use of T1 and T2 structural data from diabetic patients and controls. This implementation was compared with the traditional ANCOVA in SPM and a similar multivariate GLM toolbox (MRM). Method: We implemented the new toolbox and tested it by investigating brain alterations on a cohort of twenty-eight type 2 diabetes patients and twenty-six matched healthy controls, using information from both T1 and T2 weighted structural MRI scans, both separately – using standard univariate VBM - and simultaneously, with multivariate analyses. Results: Univariate VBM replicated predominantly bilateral changes in basal ganglia and insular regions in type 2 diabetes patients. On the other hand, multivariate analyses replicated key findings of univariate results, while also revealing the thalami as additional foci of pathology. Conclusion: While the presented algorithm must be further optimized, the proposed toolbox is the first implementation of multivariate statistics in SPM8 as a user-friendly toolbox, which shows great potential and is ready to be validated in other clinical cohorts and modalities. PMID:28761571

  5. Extending Inferential Group Analysis in Type 2 Diabetic Patients with Multivariate GLM Implemented in SPM8.

    PubMed

    Ferreira, Fábio S; Pereira, João M S; Duarte, João V; Castelo-Branco, Miguel

    2017-01-01

    Although voxel based morphometry studies are still the standard for analyzing brain structure, their dependence on massive univariate inferential methods is a limiting factor. A better understanding of brain pathologies can be achieved by applying inferential multivariate methods, which allow the study of multiple dependent variables, e.g. different imaging modalities of the same subject. Given the widespread use of SPM software in the brain imaging community, the main aim of this work is the implementation of massive multivariate inferential analysis as a toolbox in this software package. applied to the use of T1 and T2 structural data from diabetic patients and controls. This implementation was compared with the traditional ANCOVA in SPM and a similar multivariate GLM toolbox (MRM). We implemented the new toolbox and tested it by investigating brain alterations on a cohort of twenty-eight type 2 diabetes patients and twenty-six matched healthy controls, using information from both T1 and T2 weighted structural MRI scans, both separately - using standard univariate VBM - and simultaneously, with multivariate analyses. Univariate VBM replicated predominantly bilateral changes in basal ganglia and insular regions in type 2 diabetes patients. On the other hand, multivariate analyses replicated key findings of univariate results, while also revealing the thalami as additional foci of pathology. While the presented algorithm must be further optimized, the proposed toolbox is the first implementation of multivariate statistics in SPM8 as a user-friendly toolbox, which shows great potential and is ready to be validated in other clinical cohorts and modalities.

  6. Multivariate phenotype association analysis by marker-set kernel machine regression.

    PubMed

    Maity, Arnab; Sullivan, Patrick F; Tzeng, Jun-Ying

    2012-11-01

    Genetic studies of complex diseases often collect multiple phenotypes relevant to the disorders. As these phenotypes can be correlated and share common genetic mechanisms, jointly analyzing these traits may bring more power to detect genes influencing individual or multiple phenotypes. Given the advancement brought by the multivariate phenotype approaches and the multimarker kernel machine regression, we construct a multivariate regression based on kernel machine to facilitate the joint evaluation of multimarker effects on multiple phenotypes. The kernel machine serves as a powerful dimension-reduction tool to capture complex effects among markers. The multivariate framework incorporates the potentially correlated multidimensional phenotypic information and accommodates common or different environmental covariates for each trait. We derive the multivariate kernel machine test based on a score-like statistic, and conduct simulations to evaluate the validity and efficacy of the method. We also study the performance of the commonly adapted strategies for kernel machine analysis on multiple phenotypes, including the multiple univariate kernel machine tests with original phenotypes or with their principal components. Our results suggest that none of these approaches has the uniformly best power, and the optimal test depends on the magnitude of the phenotype correlation and the effect patterns. However, the multivariate test retains to be a reasonable approach when the multiple phenotypes have none or mild correlations, and gives the best power once the correlation becomes stronger or when there exist genes that affect more than one phenotype. We illustrate the utility of the multivariate kernel machine method through the Clinical Antipsychotic Trails of Intervention Effectiveness antibody study. © 2012 Wiley Periodicals, Inc.

  7. Multivariation calibration techniques applied to NIRA (near infrared reflectance analysis) and FTIR (Fourier transform infrared) data

    NASA Astrophysics Data System (ADS)

    Long, C. L.

    1991-02-01

    Multivariate calibration techniques can reduce the time required for routine testing and can provide new methods of analysis. Multivariate calibration is commonly used with near infrared reflectance analysis (NIRA) and Fourier transform infrared (FTIR) spectroscopy. Two feasibility studies were performed to determine the capability of NIRA, using multivariate calibration techniques, to perform analyses on the types of samples that are routinely analyzed at this laboratory. The first study performed included a variety of samples and indicated that NIRA would be well-suited to perform analyses on selected materials properties such as water content and hydroxyl number on polyol samples, epoxy content on epoxy resins, water content of desiccants, and the amine values of various amine cure agents. A second study was performed to assess the capability of NIRA to perform quantitative analysis of hydroxyl numbers and water contents of hydroxyl-containing materials. Hydroxyl number and water content were selected for determination because these tests are frequently run on polyol materials and the hydroxyl number determination is time consuming. This study pointed out the necessity of obtaining calibration standards identical to the samples being analyzed for each type of polyol or other material being analyzed. Multivariate calibration techniques are frequently used with FTIR data to determine the composition of a large variety of complex mixtures. A literature search indicated many applications of multivariate calibration to FTIR data. Areas identified where quantitation by FTIR would provide a new capability are quantitation of components in epoxy and silicone resins, polychlorinated biphenyls (PCBs) in oils, and additives to polymers.

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

    PubMed

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

    2016-01-01

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

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

    PubMed Central

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

    2017-01-01

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

  10. Analysis of worldwide earthquake mortality using multivariate demographic and seismic data.

    PubMed

    Gutiérrez, E; Taucer, F; De Groeve, T; Al-Khudhairy, D H A; Zaldivar, J M

    2005-06-15

    In this paper, mortality in the immediate aftermath of an earthquake is studied on a worldwide scale using multivariate analysis. A statistical method is presented that analyzes reported earthquake fatalities as a function of a heterogeneous set of parameters selected on the basis of their presumed influence on earthquake mortality. The ensemble was compiled from demographic, seismic, and reported fatality data culled from available records of past earthquakes organized in a geographic information system. The authors consider the statistical relation between earthquake mortality and the available data ensemble, analyze the validity of the results in view of the parametric uncertainties, and propose a multivariate mortality analysis prediction method. The analysis reveals that, although the highest mortality rates are expected in poorly developed rural areas, high fatality counts can result from a wide range of mortality ratios that depend on the effective population size.

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  12. Nonlinear Multivariate and Time Series Analysis by Neural Network Methods, with Applications to ENSO

    NASA Astrophysics Data System (ADS)

    Hsieh, W. W.

    2003-12-01

    Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data--- data from observational arrays, from satellites or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression (LR) at the base, followed by principal component analysis (PCA), and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression (NLR) and classification. More recently, multi-layer perceptron neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA) and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA and NLSSA techniques, and their applications to various datasets of the atmosphere and the ocean, especially in the nonlinear study of the El Niño-Southern Oscillation (ENSO) phenomenon.

  13. Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis

    NASA Astrophysics Data System (ADS)

    Feng, Shangyuan; Lin, Juqiang; Huang, Zufang; Chen, Guannan; Chen, Weisheng; Wang, Yue; Chen, Rong; Zeng, Haishan

    2013-01-01

    The capability of using silver nanoparticle based near-infrared surface enhanced Raman scattering (SERS) spectroscopy combined with principal component analysis (PCA) and linear discriminate analysis (LDA) to differentiate esophageal cancer tissue from normal tissue was presented. Significant differences in Raman intensities of prominent SERS bands were observed between normal and cancer tissues. PCA-LDA multivariate analysis of the measured tissue SERS spectra achieved diagnostic sensitivity of 90.9% and specificity of 97.8%. This exploratory study demonstrated great potential for developing label-free tissue SERS analysis into a clinical tool for esophageal cancer detection.

  14. Dynamic Analysis of AN Automatic Dynamic Balancer for Rotating Mechanisms

    NASA Astrophysics Data System (ADS)

    CHUNG, J.; RO, D. S.

    1999-12-01

    Dynamic stability and behavior of an automatic dynamic balance (ADB) are analyzed by a theoretical approach. Using Lagrange's equation, we derive the non-linear equations of motion for an autonomous system with respect to the polar co-ordinate system. From the equations of motion for the autonomous system, the equilibrium positions and the linear variational equations are obtained by the perturbation method. Based on the variational equations, the dynamic stability of the system in the neighborhood of the equilibrium positions is investigated by the Routh-Hurwitz criteria. The results of the stability analysis provide the design requirements for the ADB to achieve balancing of the system. In addition, in order to verify the stability of the system, time responses are computed by the generalized-α method. We also investigate the dynamic behavior of the system and the effects of damping on balancing.

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

    PubMed

    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.

  16. Baseline fetal heart rate analysis: eleven automatic methods versus expert consensus.

    PubMed

    de l'Aulnoit, Agathe Houze; Boudet, Samuel; Demailly, Romain; Peyrodie, Laurent; Beuscart, Regis; de l'Aulnoit, Denis Houze

    2016-08-01

    Visual analysis of fetal heart rate (FHR) during labor is subject to inter- and intra-observer variability that is particularly troublesome for anomalous recordings. Automatic FHR analysis has been proposed as a promising way to reduce this variability. The major difficulty with automatic analysis is to determine the baseline from which accelerations and decelerations will be detected. Eleven methods for automatic FHR analysis were reprogrammed using description from the literature and applied to 66 FHR recordings collected during the first stage of delivery. The FHR baselines produced by the automatic methods were compared with the baseline defined by agreement among a panel of three experts. The better performance of the automatic methods described by Mongelli, Lu, Wrobel and Pardey was noted despite their different approaches on signal processing. Nevertheless, for several recordings, none of the automatic studied methods produced a baseline similar to that defined by the experts.

  17. Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging

    PubMed Central

    Rosa, Maria J.; Mehta, Mitul A.; Pich, Emilio M.; Risterucci, Celine; Zelaya, Fernando; Reinders, Antje A. T. S.; Williams, Steve C. R.; Dazzan, Paola; Doyle, Orla M.; Marquand, Andre F.

    2015-01-01

    An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA). However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labeling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow. PMID:26528117

  18. Fresh Biomass Estimation in Heterogeneous Grassland Using Hyperspectral Measurements and Multivariate Statistical Analysis

    NASA Astrophysics Data System (ADS)

    Darvishzadeh, R.; Skidmore, A. K.; Mirzaie, M.; Atzberger, C.; Schlerf, M.

    2014-12-01

    Accurate estimation of grassland biomass at their peak productivity can provide crucial information regarding the functioning and productivity of the rangelands. Hyperspectral remote sensing has proved to be valuable for estimation of vegetation biophysical parameters such as biomass using different statistical techniques. However, in statistical analysis of hyperspectral data, multicollinearity is a common problem due to large amount of correlated hyper-spectral reflectance measurements. The aim of this study was to examine the prospect of above ground biomass estimation in a heterogeneous Mediterranean rangeland employing multivariate calibration methods. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of above ground biomass for 170 sample plots. Multivariate calibrations including partial least squares regression (PLSR), principal component regression (PCR), and Least-Squared Support Vector Machine (LS-SVM) were used to estimate the above ground biomass. The prediction accuracy of the multivariate calibration methods were assessed using cross validated R2 and RMSE. The best model performance was obtained using LS_SVM and then PLSR both calibrated with first derivative reflectance dataset with R2cv = 0.88 & 0.86 and RMSEcv= 1.15 & 1.07 respectively. The weakest prediction accuracy was appeared when PCR were used (R2cv = 0.31 and RMSEcv= 2.48). The obtained results highlight the importance of multivariate calibration methods for biomass estimation when hyperspectral data are used.

  19. Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

    PubMed Central

    Xu, Rui; Zhen, Zonglei; Liu, Jia

    2010-01-01

    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. PMID:21152081

  20. Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging.

    PubMed

    Rosa, Maria J; Mehta, Mitul A; Pich, Emilio M; Risterucci, Celine; Zelaya, Fernando; Reinders, Antje A T S; Williams, Steve C R; Dazzan, Paola; Doyle, Orla M; Marquand, Andre F

    2015-01-01

    An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA). However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labeling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow.

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

    PubMed Central

    Jackson, Daniel; Riley, Richard D

    2014-01-01

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

  2. A Signal Transmission Technique for Stability Analysis of Multivariable Non-Linear Control Systems

    NASA Technical Reports Server (NTRS)

    Jackson, Mark; Zimpfer, Doug; Adams, Neil; Lindsey, K. L. (Technical Monitor)

    2000-01-01

    Among the difficulties associated with multivariable, non-linear control systems is the problem of assessing closed-loop stability. Of particular interest is the class of non-linear systems controlled with on/off actuators, such as spacecraft thrusters or electrical relays. With such systems, standard describing function techniques are typically too conservative, and time-domain simulation analysis is prohibitively extensive, This paper presents an open-loop analysis technique for this class of non-linear systems. The technique is centered around an innovative use of multivariable signal transmission theory to quantify the plant response to worst case control commands. The technique has been applied to assess stability of thruster controlled flexible space structures. Examples are provided for Space Shuttle attitude control with attached flexible payloads.

  3. [Characterization of flowability of pharmaceutical powders based on multivariate analysis method].

    PubMed

    Du, Yan; Zhao, Li-Jie; Xiong, Yao-Kun; Li, Xiao-Hai; Wang, Song-Tao; Feng, Yi; Xu, De-Sheng

    2012-09-01

    The main methods of characterizing the flowability of pharmaceutical powders include repose angle method, HR method, Carr's index method, Jenike flow function method, fractal dimension method, and mass flow rate method, etc. Regarding powders with different flowabilities as the research subject, comprehensive features of pharmaceutical materials were investigated and characterized. The multivariate analysis method was employed to evaluate and analyze flowability values of the tested pharmaceutical materials. Comparing with the method of the mass flow rate, it was feasible to use multivariate analysis method to evaluate the flowability of powders. Simultaneously, the flowability of pharmaceutical materials could be ranked and definitely quantified, and critical values be determined according to the actual production, which has promoted the previous methods dependent only on the single parameter, i.e. repose angle and compression degree methods. A relatively objective standard method of evaluating flowability of powders is formed.

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

    SciTech Connect

    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.

  5. Authentication of Trappist beers by LC-MS fingerprints and multivariate data analysis.

    PubMed

    Mattarucchi, Elia; Stocchero, Matteo; Moreno-Rojas, José Manuel; Giordano, Giuseppe; Reniero, Fabiano; Guillou, Claude

    2010-12-08

    The aim of this study was to asses the applicability of LC-MS profiling to authenticate a selected Trappist beer as part of a program on traceability funded by the European Commission. A total of 232 beers were fingerprinted and classified through multivariate data analysis. The selected beer was clearly distinguished from beers of different brands, while only 3 samples (3.5% of the test set) were wrongly classified when compared with other types of beer of the same Trappist brewery. The fingerprints were further analyzed to extract the most discriminating variables, which proved to be sufficient for classification, even using a simplified unsupervised model. This reduced fingerprint allowed us to study the influence of batch-to-batch variability on the classification model. Our results can easily be applied to different matrices and they confirmed the effectiveness of LC-MS profiling in combination with multivariate data analysis for the characterization of food products.

  6. Automatic Beam Path Analysis of Laser Wakefield Particle Acceleration Data

    SciTech Connect

    Rubel, Oliver; Geddes, Cameron G.R.; Cormier-Michel, Estelle; Wu, Kesheng; Prabhat,; Weber, Gunther H.; Ushizima, Daniela M.; Messmer, Peter; Hagen, Hans; Hamann, Bernd; Bethel, E. Wes

    2009-10-19

    Numerical simulations of laser wakefield particle accelerators play a key role in the understanding of the complex acceleration process and in the design of expensive experimental facilities. As the size and complexity of simulation output grows, an increasingly acute challenge is the practical need for computational techniques that aid in scientific knowledge discovery. To that end, we present a set of data-understanding algorithms that work in concert in a pipeline fashion to automatically locate and analyze high energy particle bunches undergoing acceleration in very large simulation datasets. These techniques work cooperatively by first identifying features of interest in individual timesteps, then integrating features across timesteps, and based on the information derived perform analysis of temporally dynamic features. This combination of techniques supports accurate detection of particle beams enabling a deeper level of scientific understanding of physical phenomena than hasbeen possible before. By combining efficient data analysis algorithms and state-of-the-art data management we enable high-performance analysis of extremely large particle datasets in 3D. We demonstrate the usefulness of our methods for a variety of 2D and 3D datasets and discuss the performance of our analysis pipeline.

  7. Automatic traffic real-time analysis system based on video

    NASA Astrophysics Data System (ADS)

    Ding, Liya; Liu, Jilin; Zhou, Qubo; Wang, Rengrong

    2003-05-01

    Automatic traffic analysis is very important in the modern world with heavy traffic. It can be achieved in numerous ways, among them, detection and analysis through video system, being able to provide affluent information and having little disturbance to the traffic, is an ideal choice. The proposed traffic vision analysis system uses Image Acquisition Card to capture real time images of the traffic scene through video camera, and then exploits the sequence of traffic scene and the image processing and analysis technique to detect the presence and movement of vehicles. First getting rid of the complex traffic background, which is always changing, the system segment each vehicle in the region the user interested. The system extracts features from each vehicle and tracks them through the image sequence. Combined with calibration, the system calculates information of the traffic, such as the speed of the vehicles, their types, the volume of flow, the traffic density, the waiting length of the lanes, the turning information of the vehicles, and so on. Traffic congestion and vehicles" shadows are disturbing problems of the vehicle detection, segmentation and tracking. So we make great effort to investigate on methods to dealing with them.

  8. Causal diagrams and multivariate analysis I: a quiver full of arrows.

    PubMed

    Jupiter, Daniel C

    2014-01-01

    How do we know which variables we should include in our multivariate analyses? What role does each variable play in our understanding of the analysis? In this article I begin a discussion of these issues and describe 2 different types of studies for which this problem must be handled in different ways. Copyright © 2014 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

  9. Application of Maxent Multivariate Analysis to Define Reptile Species Distributions and Changes Related to Climate Change

    DTIC Science & Technology

    2016-06-01

    ER D C/ CE RL T R- 16 -6 Base Facilities Environmental Quality Application of Maxent Multivariate Analysis to Define Reptile Species ...Define Reptile Species Distributions and Changes Related to Climate Change Robert C. Lozar and James D. Westervelt Construction Engineering Research...ii Abstract The maximum entropy (Maxent) statistical technique was applied to de- termine the habitat extent of seven reptile species and to

  10. Structural analysis and design of multivariable control systems: An algebraic approach

    NASA Technical Reports Server (NTRS)

    Tsay, Yih Tsong; Shieh, Leang-San; Barnett, Stephen

    1988-01-01

    The application of algebraic system theory to the design of controllers for multivariable (MV) systems is explored analytically using an approach based on state-space representations and matrix-fraction descriptions. Chapters are devoted to characteristic lambda matrices and canonical descriptions of MIMO systems; spectral analysis, divisors, and spectral factors of nonsingular lambda matrices; feedback control of MV systems; and structural decomposition theories and their application to MV control systems.

  11. Differentiation of normal and disturbed sleep by automatic analysis.

    PubMed

    Hasan, J

    1983-01-01

    stage classification could be used for the differentiation between normal and disturbed sleep. In the present work only EEG waveform parameters and body movement activity were studied with this in mind. It was found that sleep can satisfactorily be classified in stages by automatic analysis if it is not markedly disturbed. The percentage agreement obtained for the three groups having practically normal sleep (young normals appr. 80%, older normals 77% and anonymous alcoholics 75%) was satisfactory and sufficient for clinical and experimental work.(ABSTRACT TRUNCATED AT 400 WORDS)

  12. Automatic analysis of the micronucleus test in primary human lymphocytes using image analysis.

    PubMed

    Frieauff, W; Martus, H J; Suter, W; Elhajouji, A

    2013-01-01

    The in vitro micronucleus test (MNT) is a well-established test for early screening of new chemical entities in industrial toxicology. For assessing the clastogenic or aneugenic potential of a test compound, micronucleus induction in cells has been shown repeatedly to be a sensitive and a specific parameter. Various automated systems to replace the tedious and time-consuming visual slide analysis procedure as well as flow cytometric approaches have been discussed. The ROBIAS (Robotic Image Analysis System) for both automatic cytotoxicity assessment and micronucleus detection in human lymphocytes was developed at Novartis where the assay has been used to validate positive results obtained in the MNT in TK6 cells, which serves as the primary screening system for genotoxicity profiling in early drug development. In addition, the in vitro MNT has become an accepted alternative to support clinical studies and will be used for regulatory purposes as well. The comparison of visual with automatic analysis results showed a high degree of concordance for 25 independent experiments conducted for the profiling of 12 compounds. For concentration series of cyclophosphamide and carbendazim, a very good correlation between automatic and visual analysis by two examiners could be established, both for the relative division index used as cytotoxicity parameter, as well as for micronuclei scoring in mono- and binucleated cells. Generally, false-positive micronucleus decisions could be controlled by fast and simple relocation of the automatically detected patterns. The possibility to analyse 24 slides within 65h by automatic analysis over the weekend and the high reproducibility of the results make automatic image processing a powerful tool for the micronucleus analysis in primary human lymphocytes. The automated slide analysis for the MNT in human lymphocytes complements the portfolio of image analysis applications on ROBIAS which is supporting various assays at Novartis.

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

    PubMed

    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.

  14. Coreferentiality: A New Method for the Hypothesis-Based Analysis of Phenotypes Characterized by Multivariate Data

    PubMed Central

    Fesel, Constantin

    2012-01-01

    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. PMID:22479494

  15. Epithelial cells in bone marrow of oesophageal cancer patients: a significant prognostic factor in multivariate analysis

    PubMed Central

    Thorban, S; Rosenberg, R; Busch, R; Roder, R J

    2000-01-01

    The detection of epithelial cells in bone marrow, blood or lymph nodes indicates a disseminatory potential of solid tumours. 225 patients with squamous cell carcinoma of the oesophagus were prospectively studied. Prior to any therapy, cytokeratin-positive (CK) cells in bone marrow were immunocytochemically detected in 75 patients with the monoclonal anti-epithelial-cell antibody A45-B/B3 and correlated with established histopathologic and patient-specific prognosis factors. The prognosis factors were assessed by multivariate analysis. Twenty-nine of 75 (38.7%) patients with oesophageal cancer showed CK-positive cells in bone marrow. The analyses of the mean and median overall survival time showed a significant difference between patients with and without epithelial cells in bone marrow (P< 0.001). Multivariate analysis in the total patient population and in patients with curative resection of the primary tumour confirmed the curative resection rate and the bone marrow status as the strongest independent prognostic factors, besides the T-category. The detection of epithelial cells in bone marrow of oesophageal cancer patients is a substantial prognostic factor proved by multivariate analysis and is helpful for exact preoperative staging, as well as monitoring of neoadjuvant therapy. © 2000 Cancer Research Campaign PMID:10883665

  16. The association between body mass index and severe biliary infections: a multivariate analysis.

    PubMed

    Stewart, Lygia; Griffiss, J McLeod; Jarvis, Gary A; Way, Lawrence W

    2012-11-01

    Obesity has been associated with worse infectious disease outcomes. It is a risk factor for cholesterol gallstones, but little is known about associations between body mass index (BMI) and biliary infections. We studied this using factors associated with biliary infections. A total of 427 patients with gallstones were studied. Gallstones, bile, and blood (as applicable) were cultured. Illness severity was classified as follows: none (no infection or inflammation), systemic inflammatory response syndrome (fever, leukocytosis), severe (abscess, cholangitis, empyema), or multi-organ dysfunction syndrome (bacteremia, hypotension, organ failure). Associations between BMI and biliary bacteria, bacteremia, gallstone type, and illness severity were examined using bivariate and multivariate analysis. BMI inversely correlated with pigment stones, biliary bacteria, bacteremia, and increased illness severity on bivariate and multivariate analysis. Obesity correlated with less severe biliary infections. BMI inversely correlated with pigment stones and biliary bacteria; multivariate analysis showed an independent correlation between lower BMI and illness severity. Most patients with severe biliary infections had a normal BMI, suggesting that obesity may be protective in biliary infections. This study examined the correlation between BMI and biliary infection severity. Published by Elsevier Inc.

  17. Risk factors for incidental durotomy during lumbar surgery: a retrospective study by multivariate analysis.

    PubMed

    Chen, Zhixiang; Shao, Peng; Sun, Qizhao; Zhao, Dong

    2015-03-01

    The purpose of the present study was to use a prospectively collected data to evaluate the rate of incidental durotomy (ID) during lumbar surgery and determine the associated risk factors by using univariate and multivariate analysis. We retrospectively reviewed 2184 patients who underwent lumbar surgery from January 1, 2009 to December 31, 2011 at a single hospital. Patients with ID (n=97) were compared with the patients without ID (n=2019). The influences of several potential risk factors that might affect the occurrence of ID were assessed using univariate and multivariate analyses. The overall incidence of ID was 4.62%. Univariate analysis demonstrated that older age, diabetes, lumbar central stenosis, posterior approach, revision surgery, prior lumber surgery and minimal invasive surgery are risk factors for ID during lumbar surgery. However, multivariate analysis identified older age, prior lumber surgery, revision surgery, and minimally invasive surgery as independent risk factors. Older age, prior lumber surgery, revision surgery, and minimal invasive surgery were independent risk factors for ID during lumbar surgery. These findings may guide clinicians making future surgical decisions regarding ID and aid in the patient counseling process to alleviate risks and complications. Copyright © 2015 Elsevier B.V. All rights reserved.

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

    DOEpatents

    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.

  19. Visualization analysis of multivariate spatial-temporal data of the Red Army Long March in China

    NASA Astrophysics Data System (ADS)

    Ma, Ding; Ma, Zhimin; Meng, Lumin; Li, Xia

    2009-10-01

    Recently, the visualization of spatial-temporal data in historic events is emphasized by more and more people. To provide an efficient and effective approach to meet this requirement is the duty of Geo-data modeling researchers. The aim of the paper is to ground on a new perspective to visualize the multivariate spatial-temporal data of the Red Army Long March, which is one of the most important events of the Chinese modem history. This research focuses on the extraction of relevant information from a 3-dimensional trajectory, which captures object locations in geographic space at specified temporal intervals. However, existing visualization methods cannot deal with the multivariate spatial-temporal data effectively. Thus there is a potential chance to represent and analyze this kind of data in the case study. The thesis combines two visualization methods, the Space-Time-Cube for spatial temporal data and Parallel Coordinates Plots (PCPs) for multivariable data, to develop conceptual GIS database model that facilitates the exploration and analysis of multivariate spatial-temporal data sets in the combination with 3D Space-Time-Path and 2D graphics. The designed model is supported by the geo-visualization environment and integrates diverse sets of multivariate spatial-temporal data and built-up the dynamic process and relationships. It is concluded that this way of geo-visualization can effectively manipulate a large amount of distributed data, realize the high efficient transmission of quantitative and qualitative information and also provide a new research mode in the field of the History of CPC and military affairs.

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

    PubMed

    Martin, David; Boyle, Fergal

    2015-09-01

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

  1. Analysis of the real EADGENE data set: Multivariate approaches and post analysis (Open Access publication)

    PubMed Central

    Sørensen, Peter; Bonnet, Agnès; Buitenhuis, Bart; Closset, Rodrigue; Déjean, Sébastien; Delmas, Céline; Duval, Mylène; Glass, Liz; Hedegaard, Jakob; Hornshøj, Henrik; Hulsegge, Ina; Jaffrézic, Florence; Jensen, Kirsty; Jiang, Li; de Koning, Dirk-Jan; Cao, Kim-Anh Lê; Nie, Haisheng; Petzl, Wolfram; Pool, Marco H; Robert-Granié, Christèle; San Cristobal, Magali; Lund, Mogens Sandø; van Schothorst, Evert M; Schuberth, Hans-Joachim; Seyfert, Hans-Martin; Tosser-Klopp, Gwenola; Waddington, David; Watson, Michael; Yang, Wei; Zerbe, Holm

    2007-01-01

    The aim of this paper was to describe, and when possible compare, the multivariate methods used by the participants in the EADGENE WP1.4 workshop. The first approach was for class discovery and class prediction using evidence from the data at hand. Several teams used hierarchical clustering (HC) or principal component analysis (PCA) to identify groups of differentially expressed genes with a similar expression pattern over time points and infective agent (E. coli or S. aureus). The main result from these analyses was that HC and PCA were able to separate tissue samples taken at 24 h following E. coli infection from the other samples. The second approach identified groups of differentially co-expressed genes, by identifying clusters of genes highly correlated when animals were infected with E. coli but not correlated more than expected by chance when the infective pathogen was S. aureus. The third approach looked at differential expression of predefined gene sets. Gene sets were defined based on information retrieved from biological databases such as Gene Ontology. Based on these annotation sources the teams used either the GlobalTest or the Fisher exact test to identify differentially expressed gene sets. The main result from these analyses was that gene sets involved in immune defence responses were differentially expressed. PMID:18053574

  2. Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment.

    PubMed

    Westman, Eric; Aguilar, Carlos; Muehlboeck, J-Sebastian; Simmons, Andrew

    2013-01-01

    Automated structural magnetic resonance imaging (MRI) processing pipelines are gaining popularity for Alzheimer's disease (AD) research. They generate regional volumes, cortical thickness measures and other measures, which can be used as input for multivariate analysis. It is not clear which combination of measures and normalization approach are most useful for AD classification and to predict mild cognitive impairment (MCI) conversion. The current study includes MRI scans from 699 subjects [AD, MCI and controls (CTL)] from the Alzheimer's disease Neuroimaging Initiative (ADNI). The Freesurfer pipeline was used to generate regional volume, cortical thickness, gray matter volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures. 259 variables were used for orthogonal partial least square to latent structures (OPLS) multivariate analysis. Normalisation approaches were explored and the optimal combination of measures determined. Results indicate that cortical thickness measures should not be normalized, while volumes should probably be normalized by intracranial volume (ICV). Combining regional cortical thickness measures (not normalized) with cortical and subcortical volumes (normalized with ICV) using OPLS gave a prediction accuracy of 91.5 % when distinguishing AD versus CTL. This model prospectively predicted future decline from MCI to AD with 75.9 % of converters correctly classified. Normalization strategy did not have a significant effect on the accuracies of multivariate models containing multiple MRI measures for this large dataset. The appropriate choice of input for multivariate analysis in AD and MCI is of great importance. The results support the use of un-normalised cortical thickness measures and volumes normalised by ICV.

  3. Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies.

    PubMed

    Kambeitz, Joseph; Cabral, Carlos; Sacchet, Matthew D; Gotlib, Ian H; Zahn, Roland; Serpa, Mauricio H; Walter, Martin; Falkai, Peter; Koutsouleris, Nikolaos

    2017-09-01

    Multiple studies have examined functional and structural brain alteration in patients diagnosed with major depressive disorder (MDD). The introduction of multivariate statistical methods allows investigators to utilize data concerning these brain alterations to generate diagnostic models that accurately differentiate patients with MDD from healthy control subjects (HCs). However, there is substantial heterogeneity in the reported results, the methodological approaches, and the clinical characteristics of participants in these studies. We conducted a meta-analysis of all studies using neuroimaging (volumetric measures derived from T1-weighted images, task-based functional magnetic resonance imaging [MRI], resting-state MRI, or diffusion tensor imaging) in combination with multivariate statistical methods to differentiate patients diagnosed with MDD from HCs. Thirty-three (k = 33) samples including 912 patients with MDD and 894 HCs were included in the meta-analysis. Across all studies, patients with MDD were separated from HCs with 77% sensitivity and 78% specificity. Classification based on resting-state MRI (85% sensitivity, 83% specificity) and on diffusion tensor imaging data (88% sensitivity, 92% specificity) outperformed classifications based on structural MRI (70% sensitivity, 71% specificity) and task-based functional MRI (74% sensitivity, 77% specificity). Our results demonstrate the high representational capacity of multivariate statistical methods to identify neuroimaging-based biomarkers of depression. Future studies are needed to elucidate whether multivariate neuroimaging analysis has the potential to generate clinically useful tools for the differential diagnosis of affective disorders and the prediction of both treatment response and functional outcome. Copyright © 2016 Society of Biological Psychiatry. All rights reserved.

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

    SciTech Connect

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

    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

  5. Automatic adventitious respiratory sound analysis: A systematic review.

    PubMed

    Pramono, Renard Xaviero Adhi; Bowyer, Stuart; Rodriguez-Villegas, Esther

    2017-01-01

    Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established. To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works. A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification. Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated. Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved. A total of 77 reports from the literature were included in this review. 55 (71.43%) of the studies focused on wheeze, 40 (51.95%) on crackle, 9 (11.69%) on stridor, 9 (11

  6. Automatic quantitative analysis of cardiac MR perfusion images

    NASA Astrophysics Data System (ADS)

    Breeuwer, Marcel M.; Spreeuwers, Luuk J.; Quist, Marcel J.

    2001-07-01

    Magnetic Resonance Imaging (MRI) is a powerful technique for imaging cardiovascular diseases. The introduction of cardiovascular MRI into clinical practice is however hampered by the lack of efficient and accurate image analysis methods. This paper focuses on the evaluation of blood perfusion in the myocardium (the heart muscle) from MR images, using contrast-enhanced ECG-triggered MRI. We have developed an automatic quantitative analysis method, which works as follows. First, image registration is used to compensate for translation and rotation of the myocardium over time. Next, the boundaries of the myocardium are detected and for each position within the myocardium a time-intensity profile is constructed. The time interval during which the contrast agent passes for the first time through the left ventricle and the myocardium is detected and various parameters are measured from the time-intensity profiles in this interval. The measured parameters are visualized as color overlays on the original images. Analysis results are stored, so that they can later on be compared for different stress levels of the heart. The method is described in detail in this paper and preliminary validation results are presented.

  7. Particle-verification for single-particle, reference-based reconstruction using multivariate data analysis and classification.

    PubMed

    Shaikh, Tanvir R; Trujillo, Ramon; LeBarron, Jamie S; Baxter, William T; Frank, Joachim

    2008-10-01

    As collection of electron microscopy data for single-particle reconstruction becomes more efficient, due to electronic image capture, one of the principal limiting steps in a reconstruction remains particle-verification, which is especially costly in terms of user input. Recently, some algorithms have been developed to window particles automatically, but the resulting particle sets typically need to be verified manually. Here we describe a procedure to speed up verification of windowed particles using multivariate data analysis and classification. In this procedure, the particle set is subjected to multi-reference alignment before the verification. The aligned particles are first binned according to orientation and are binned further by K-means classification. Rather than selection of particles individually, an entire class of particles can be selected, with an option to remove outliers. Since particles in the same class present the same view, distinction between good and bad images becomes more straightforward. We have also developed a graphical interface, written in Python/Tkinter, to facilitate this implementation of particle-verification. For the demonstration of the particle-verification scheme presented here, electron micrographs of ribosomes are used.

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

    PubMed

    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. Borrowing of strength and study weights in multivariate and network meta-analysis

    PubMed Central

    Jackson, Dan; White, Ian R; Price, Malcolm; Copas, John; Riley, Richard D

    2016-01-01

    Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of ‘borrowing of strength’. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis). PMID:26546254

  10. Influence of donor specific HLA antibodies detected by Luminex in kidney graft survival: a multivariate analysis.

    PubMed

    Caro-Oleas, J L; González-Escribano, M F; Gentil-Govantes, M A; Acevedo, M J; González-Roncero, F M; Bernal-Blanco, G; Núñez-Roldán, A

    2013-05-01

    Some studies have demonstrated the clinical relevance of a positive virtual crossmatch in graft survival; nevertheless, other donor and recipient variables influence the outcome of the transplant. The aim of this study was to investigate the relevance of a positive virtual crossmatch in the graft survival performing a multivariate analysis including other pretransplant variables. A total of 879 deceased kidney transplantations were included. Univariate and multivariate analyses were performed using Cox regression model. After performing the multivariate analysis, a positive virtual crossmatch against class I (adjusted HR 6.613; 95% CI 3.222-13.573), class II (adjusted HR 2.419; 95% CI 1.170-5.002) and class I+II (adjusted HR 5.717; 95% CI 1.925-16.975) detected by single antigen Luminex was the variable conferring the greatest relative risk of graft loss. A positive virtual crossmatch predicts a worse kidney graft survival even after correction by other variables and therefore, transplantation of patients with positive virtual crossmatches should be avoided. Copyright © 2013 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.

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

    PubMed

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

    2015-09-01

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

  12. Borrowing of strength and study weights in multivariate and network meta-analysis.

    PubMed

    Jackson, Dan; White, Ian R; Price, Malcolm; Copas, John; Riley, Richard D

    2015-11-06

    Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of 'borrowing of strength'. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis). © The Author(s) 2015.

  13. Multivariate regional frequency analysis: Two new methods to increase the accuracy of measures

    NASA Astrophysics Data System (ADS)

    Abdi, Amin; Hassanzadeh, Yousef; Talatahari, Siamak; Fakheri-Fard, Ahmad; Mirabbasi, Rasoul; Ouarda, Taha B. M. J.

    2017-09-01

    The accurate detection of discordant sites in a heterogeneous region and the estimation of the regional parameters of a statistical distribution are two important issues in multivariate regional frequency analysis. In this study, two new methods are proposed for increasing the accuracy of the multivariate L-moment approach. The first one, the optimization-based method (OBM) is utilized to estimate the best distribution parameters. The second one is the rank-based method (RBM), which is used in the robust discordancy measure for identifying discordant sites. In order to assess the performance of the proposed approaches on the heterogeneity measure, real and simulated regions of drought characteristics are considered. The results confirm the usefulness of the new methods in comparison with some well-established techniques.

  14. Quantitative analysis of directional strengths in jointly stationary linear multivariate processes.

    PubMed

    Gigi, S; Tangirala, A K

    2010-08-01

    Identification and analysis of directed influences in multivariate systems is an important problem in many scientific areas. Recent studies in neuroscience have provided measures to determine the network structure of the process and to quantify the total effect in terms of energy transfer. These measures are based on joint stationary representations of a multivariate process using vector auto-regressive (VAR) models. A few important issues remain unaddressed though. The primary outcomes of this study are (i) a theoretical proof that the total coupling strength consists of three components, namely, the direct, indirect, and the interference produced by the direct and indirect effects, (ii) expressions to estimate/calculate these effects, and (iii) a result which shows that the well-known directed measure for linear systems, partial directed coherence (PDC) only aids in structure determination but does not provide a normalized measure of the direct energy transfer. Simulation case studies are shown to illustrate the theoretical results.

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

    PubMed

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

    2015-06-01

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

  16. Multivariate reference technique for quantitative analysis of fiber-optic tissue Raman spectroscopy.

    PubMed

    Bergholt, Mads Sylvest; Duraipandian, Shiyamala; Zheng, Wei; Huang, Zhiwei

    2013-12-03

    We report a novel method making use of multivariate reference signals of fused silica and sapphire Raman signals generated from a ball-lens fiber-optic Raman probe for quantitative analysis of in vivo tissue Raman measurements in real time. Partial least-squares (PLS) regression modeling is applied to extract the characteristic internal reference Raman signals (e.g., shoulder of the prominent fused silica boson peak (~130 cm(-1)); distinct sapphire ball-lens peaks (380, 417, 646, and 751 cm(-1))) from the ball-lens fiber-optic Raman probe for quantitative analysis of fiber-optic Raman spectroscopy. To evaluate the analytical value of this novel multivariate reference technique, a rapid Raman spectroscopy system coupled with a ball-lens fiber-optic Raman probe is used for in vivo oral tissue Raman measurements (n = 25 subjects) under 785 nm laser excitation powers ranging from 5 to 65 mW. An accurate linear relationship (R(2) = 0.981) with a root-mean-square error of cross validation (RMSECV) of 2.5 mW can be obtained for predicting the laser excitation power changes based on a leave-one-subject-out cross-validation, which is superior to the normal univariate reference method (RMSE = 6.2 mW). A root-mean-square error of prediction (RMSEP) of 2.4 mW (R(2) = 0.985) can also be achieved for laser power prediction in real time when we applied the multivariate method independently on the five new subjects (n = 166 spectra). We further apply the multivariate reference technique for quantitative analysis of gelatin tissue phantoms that gives rise to an RMSEP of ~2.0% (R(2) = 0.998) independent of laser excitation power variations. This work demonstrates that multivariate reference technique can be advantageously used to monitor and correct the variations of laser excitation power and fiber coupling efficiency in situ for standardizing the tissue Raman intensity to realize quantitative analysis of tissue Raman measurements in vivo, which is particularly appealing in

  17. Multivariate analysis approach for correlations between material properties and tablet tensile strength of microcrystalline cellulose.

    PubMed

    Liao, Zhenggen; Zhang, Nan; Zhao, Guowei; Zhang, Jing; Liang, Xinli; Zhong, Shaojin; Wang, Guangfa; Chen, Xulong

    2012-09-01

    In this study we applied statistical multivariate analysis techniques to establish correlations between material properties and tablet tensile strength (TS) of microcrystalline cellulose (MCC) with different types and manufacturers. There were sixteen MCC samples included in this analysis described by 22 material parameters. For data analysis, principal component analysis (PCA) was used to model and evaluate the various relationships between the material properties and TS. Furthermore, partial least squares regression (PLS) analysis was performed to quantify the relationships between the material properties and TS and to predict the most influential MCC parameters contributing to the compactibility. The results showed that the moisture content, hygroscopicity and crystallinity did not exhibit significant impact on TS. The turgidity, maximum water uptake, degree of polymerization and molecular weight presented a strong positive influence on TS, while the density property, bulk and tap density, exhibited an obvious negative impact. The present work demonstrated that multivariate data analysis techniques (PCA and PLS) are useful for interpreting complex relations between 22 material properties and the tabletting properties of MCC. Furthermore, the method can be used for material classification.

  18. Severe pneumonia in the elderly: a multivariate analysis of risk factors

    PubMed Central

    Li, Wei; Ding, Cheng; Yin, Shaojun

    2015-01-01

    Pneumonia is the second leading reason for hospitalization of medicare beneficiaries. The mortality rate is high, especially in the elderly. In this study, we aimed to determine the risk factors associated with severe pneumonia in the elderly. Retrospective study was conducted and data of old patients with severe pneumonia were collected. They were divided into two groups: the experiment group (death group) and the control (living group). The general situation, underlying diseases, laboratory tests, types of etiology, imaging analysis and treatment situation of patients were analyzed and compared. Univariate analysis and logistic multivariate regression analysis were used to screen the related and independent risk factors for the diagnosis of severe pneumonia in the elderly. In univariate analysis, there were many factors had statistical significance including chronic kidney disease, electrolyte disturbance, low phosphorus and so on. Result of logistic multivariate regression analysis showed pro-BNP level and serum prealbumin were independent risk factors. In sputum culture, the relevance ratio of acinetobacter baumannii was the highest in gram negative bacteria followed by klebsiella pneumoniae. In gram positive bacteria, the relevance ratio of staphylococcus aureus was the highest. In conclusion, the analysis on risk factors for severe pneumonia has great clinical significance on improving the prognosis. PMID:26550157

  19. Multivariate analysis of heavy metal contamination using river sediment cores of Nankan River, northern Taiwan

    NASA Astrophysics Data System (ADS)

    Lee, An-Sheng; Lu, Wei-Li; Huang, Jyh-Jaan; Chang, Queenie; Wei, Kuo-Yen; Lin, Chin-Jung; Liou, Sofia Ya Hsuan

    2016-04-01

    Through the geology and climate characteristic in Taiwan, generally rivers carry a lot of suspended particles. After these particles settled, they become sediments which are good sorbent for heavy metals in river system. Consequently, sediments can be found recording contamination footprint at low flow energy region, such as estuary. Seven sediment cores were collected along Nankan River, northern Taiwan, which is seriously contaminated by factory, household and agriculture input. Physico-chemical properties of these cores were derived from Itrax-XRF Core Scanner and grain size analysis. In order to interpret these complex data matrices, the multivariate statistical techniques (cluster analysis, factor analysis and discriminant analysis) were introduced to this study. Through the statistical determination, the result indicates four types of sediment. One of them represents contamination event which shows high concentration of Cu, Zn, Pb, Ni and Fe, and low concentration of Si and Zr. Furthermore, three possible contamination sources of this type of sediment were revealed by Factor Analysis. The combination of sediment analysis and multivariate statistical techniques used provides new insights into the contamination depositional history of Nankan River and could be similarly applied to other river systems to determine the scale of anthropogenic contamination.

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  1. Severe pneumonia in the elderly: a multivariate analysis of risk factors.

    PubMed

    Li, Wei; Ding, Cheng; Yin, Shaojun

    2015-01-01

    Pneumonia is the second leading reason for hospitalization of medicare beneficiaries. The mortality rate is high, especially in the elderly. In this study, we aimed to determine the risk factors associated with severe pneumonia in the elderly. Retrospective study was conducted and data of old patients with severe pneumonia were collected. They were divided into two groups: the experiment group (death group) and the control (living group). The general situation, underlying diseases, laboratory tests, types of etiology, imaging analysis and treatment situation of patients were analyzed and compared. Univariate analysis and logistic multivariate regression analysis were used to screen the related and independent risk factors for the diagnosis of severe pneumonia in the elderly. In univariate analysis, there were many factors had statistical significance including chronic kidney disease, electrolyte disturbance, low phosphorus and so on. Result of logistic multivariate regression analysis showed pro-BNP level and serum prealbumin were independent risk factors. In sputum culture, the relevance ratio of acinetobacter baumannii was the highest in gram negative bacteria followed by klebsiella pneumoniae. In gram positive bacteria, the relevance ratio of staphylococcus aureus was the highest. In conclusion, the analysis on risk factors for severe pneumonia has great clinical significance on improving the prognosis.

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

    DOEpatents

    Keenan, Michael R.

    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.

  3. The MIDAS processor. [Multivariate Interactive Digital Analysis System for multispectral scanner data

    NASA Technical Reports Server (NTRS)

    Kriegler, F. J.; Gordon, M. F.; Mclaughlin, R. H.; Marshall, R. E.

    1975-01-01

    The MIDAS (Multivariate Interactive Digital Analysis System) processor is a high-speed processor designed to process multispectral scanner data (from Landsat, EOS, aircraft, etc.) quickly and cost-effectively to meet the requirements of users of remote sensor data, especially from very large areas. MIDAS consists of a fast multipipeline preprocessor and classifier, an interactive color display and color printer, and a medium scale computer system for analysis and control. The system is designed to process data having as many as 16 spectral bands per picture element at rates of 200,000 picture elements per second into as many as 17 classes using a maximum likelihood decision rule.

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

    DOEpatents

    Keenan, Michael R.

    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.

  5. GANALYZER: A TOOL FOR AUTOMATIC GALAXY IMAGE ANALYSIS

    SciTech Connect

    Shamir, Lior

    2011-08-01

    We describe Ganalyzer, a model-based tool that can automatically analyze and classify galaxy images. Ganalyzer works by separating the galaxy pixels from the background pixels, finding the center and radius of the galaxy, generating the radial intensity plot, and then computing the slopes of the peaks detected in the radial intensity plot to measure the spirality of the galaxy and determine its morphological class. Unlike algorithms that are based on machine learning, Ganalyzer is based on measuring the spirality of the galaxy, a task that is difficult to perform manually, and in many cases can provide a more accurate analysis compared to manual observation. Ganalyzer is simple to use, and can be easily embedded into other image analysis applications. Another advantage is its speed, which allows it to analyze {approx}10,000,000 galaxy images in five days using a standard modern desktop computer. These capabilities can make Ganalyzer a useful tool in analyzing large data sets of galaxy images collected by autonomous sky surveys such as SDSS, LSST, or DES. The software is available for free download at http://vfacstaff.ltu.edu/lshamir/downloads/ganalyzer, and the data used in the experiment are available at http://vfacstaff.ltu.edu/lshamir/downloads/ganalyzer/GalaxyImages.zip.

  6. Shape analysis for an automatic oyster grading system

    NASA Astrophysics Data System (ADS)

    Lee, Dah-Jye; Xu, Xiaoqian; Lane, Robert M.; Zhan, Pengcheng

    2004-12-01

    An overview of the oyster industry in the U. S. with emphasis in Virginia shows oyster grading occurs at harvest, wholesale and processing markets. Currently whole oysters, also called shellstock, are graded manually by screening and sorting based on diameter or weight. The majority of oysters harvested for the processing industry are divided into three to four main grades: small, medium, large, and selects. We have developed a shape analysis method for an automatic oyster grading system. The system first detects and removes poor quality oysters such as banana shape, broken shell, and irregular shapes. Good quality oysters move further into grades of small, medium and large. The contours of the oysters are extracted for shape analysis. Banana shape and broken shell have a specific shape flaw (or difference) compared to the ones with good quality. Global shape properties such as compactness, roughness, and elongation are suitable and useful to measure the shape flaw. Image projection area or length of the major axis measured as global properties for sizing. Incorporating a machine vision system for grading, sorting and counting oysters supports reduced operating costs. The savings produced from reducing labor, increasing accuracy in size, grade and count and providing real time accurate data for accounting and billing would contribute to the profit of the oysters industry.

  7. Difference image analysis: automatic kernel design using information criteria

    NASA Astrophysics Data System (ADS)

    Bramich, D. M.; Horne, Keith; Alsubai, K. A.; Bachelet, E.; Mislis, D.; Parley, N.

    2016-03-01

    We present a selection of methods for automatically constructing an optimal kernel model for difference image analysis which require very few external parameters to control the kernel design. Each method consists of two components; namely, a kernel design algorithm to generate a set of candidate kernel models, and a model selection criterion to select the simplest kernel model from the candidate models that provides a sufficiently good fit to the target image. We restricted our attention to the case of solving for a spatially invariant convolution kernel composed of delta basis functions, and we considered 19 different kernel solution methods including six employing kernel regularization. We tested these kernel solution methods by performing a comprehensive set of image simulations and investigating how their performance in terms of model error, fit quality, and photometric accuracy depends on the properties of the reference and target images. We find that the irregular kernel design algorithm employing unregularized delta basis functions, combined with either the Akaike or Takeuchi information criterion, is the best kernel solution method in terms of photometric accuracy. Our results are validated by tests performed on two independent sets of real data. Finally, we provide some important recommendations for software implementations of difference image analysis.

  8. A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis

    PubMed Central

    Liu, Zitao; Hauskrecht, Milos

    2015-01-01

    Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning Multivariate Time Series (MTS). However, in general, it is difficult to set the dimension of an LDS’s hidden state space. A small number of hidden states may not be able to model the complexities of a MTS, while a large number of hidden states can lead to overfitting. In this paper, we study learning methods that impose various regularization penalties on the transition matrix of the LDS model and propose a regularized LDS learning framework (rLDS) which aims to (1) automatically shut down LDSs’ spurious and unnecessary dimensions, and consequently, address the problem of choosing the optimal number of hidden states; (2) prevent the overfitting problem given a small amount of MTS data; and (3) support accurate MTS forecasting. To learn the regularized LDS from data we incorporate a second order cone program and a generalized gradient descent method into the Maximum a Posteriori framework and use Expectation Maximization to obtain a low-rank transition matrix of the LDS model. We propose two priors for modeling the matrix which lead to two instances of our rLDS. We show that our rLDS is able to recover well the intrinsic dimensionality of the time series dynamics and it improves the predictive performance when compared to baselines on both synthetic and real-world MTS datasets. PMID:25905027

  9. Identification of Novel Biomarkers in Seasonal Allergic Rhinitis by Combining Proteomic, Multivariate and Pathway Analysis

    PubMed Central

    Wang, Hui; Gottfries, Johan; Barrenäs, Fredrik; Benson, Mikael

    2011-01-01

    Background Glucocorticoids (GCs) play a key role in the treatment of seasonal allergic rhinitis (SAR). However, some patients show a low response to GC treatment. We hypothesized that proteins that correlated to discrimination between symptomatic high and low responders (HR and LR) to GC treatment might be regulated by GCs and therefore suitable as biomarkers for GC treatment. Methodology/Principal Findings We identified 953 nasal fluid proteins in symptomatic HR and LR with a LC MS/MS based-quantitative proteomics analysis and performed multivariate analysis to identify a combination of proteins that best separated symptomatic HR and LR. Pathway analysis showed that those proteins were most enriched in the acute phase response pathway. We prioritized candidate biomarkers for GC treatment based on the multivariate and pathway analysis. Next, we tested if those candidate biomarkers differed before and after GC treatment in nasal fluids from 40 patients with SAR using ELISA. Several proteins including ORM (P<0.0001), APOH (P<0.0001), FGA (P<0.01), CTSD (P<0.05) and SERPINB3 (P<0.05) differed significantly before and after GC treatment. Particularly, ORM (P<0.01), FGA (P<0.05) and APOH (P<0.01) that belonged to the acute phase response pathway decreased significantly in HR but not LR before and after GC treatment. Conclusions/Significance We identified several novel biomarkers for GC treatment response in SAR with combined proteomics, multivariate and pathway analysis. The analytical principles may be generally applicable to identify biomarkers in clinical studies of complex diseases. PMID:21887273

  10. Assessment of trace elements levels in patients with Type 2 diabetes using multivariate statistical analysis.

    PubMed

    Badran, M; Morsy, R; Soliman, H; Elnimr, T

    2016-01-01

    The trace elements metabolism has been reported to possess specific roles in the pathogenesis and progress of diabetes mellitus. Due to the continuous increase in the population of patients with Type 2 diabetes (T2D), this study aims to assess the levels and inter-relationships of fast blood glucose (FBG) and serum trace elements in Type 2 diabetic patients. This study was conducted on 40 Egyptian Type 2 diabetic patients and 36 healthy volunteers (Hospital of Tanta University, Tanta, Egypt). The blood serum was digested and then used to determine the levels of 24 trace elements using an inductive coupled plasma mass spectroscopy (ICP-MS). Multivariate statistical analysis depended on correlation coefficient, cluster analysis (CA) and principal component analysis (PCA), were used to analysis the data. The results exhibited significant changes in FBG and eight of trace elements, Zn, Cu, Se, Fe, Mn, Cr, Mg, and As, levels in the blood serum of Type 2 diabetic patients relative to those of healthy controls. The statistical analyses using multivariate statistical techniques were obvious in the reduction of the experimental variables, and grouping the trace elements in patients into three clusters. The application of PCA revealed a distinct difference in associations of trace elements and their clustering patterns in control and patients group in particular for Mg, Fe, Cu, and Zn that appeared to be the most crucial factors which related with Type 2 diabetes. Therefore, on the basis of this study, the contributors of trace elements content in Type 2 diabetic patients can be determine and specify with correlation relationship and multivariate statistical analysis, which confirm that the alteration of some essential trace metals may play a role in the development of diabetes mellitus.

  11. Automatic Video Analysis for Obstructive Sleep Apnea Diagnosis

    PubMed Central

    Abad, Jorge; Muñoz-Ferrer, Aida; Cervantes, Miguel Ángel; Esquinas, Cristina; Marin, Alicia; Martínez, Carlos; Morera, Josep; Ruiz, Juan

    2016-01-01

    Study Objectives: We investigated the diagnostic accuracy for the identification of obstructive sleep apnea (OSA) and its severity of a noninvasive technology based on image processing (SleepWise). Methods: This is an observational, prospective study to evaluate the degree of agreement between polysomnography (PSG) and SleepWise. We recruited 56 consecutive subjects with suspected OSA who were referred as outpatients to the Sleep Unit of the Hospital Universitari Germans Trias i Pujol (HUGTiP) from January 2013 to January 2014. All patients underwent laboratory PSG and image processing with SleepWise simultaneously the same night. Both PSG and SleepWise analyses were carried independently and blindly. Results: We analyzed 50 of the 56 patients recruited. OSA was diagnosed through PSG in a total of 44 patients (88%) with a median apnea-hypopnea index (AHI) of 25.35 (24.9). According to SleepWise, 45 patients (90%) met the criteria for a diagnosis of OSA, with a median AHI of 22.8 (22.03). An analysis of the ability of PSG and SleepWise to classify patients by severity on the basis of their AHI shows that the two diagnostic systems distribute the different groups similarly. According to PSG, 23 patients (46%) had a diagnosis of severe OSA, 11 patients (22%) moderate OSA, and 10 patients (20%) mild OSA. According to SleepWise, 20, 13, and 12 patients (40%, 26%, and 24%, respectively) had a diagnosis of severe, moderate, and mild OSA respectively. For OSA diagnosis, SleepWise was found to have sensitivity of 100% and specificity of 83% in relation to PSG. The positive predictive value was 97% and the negative predictive value was 100%. The Bland-Altman plot comparing the mean AHI values obtained through PSG and SleepWise shows very good agreement between the two diagnostic techniques, with a bias of −3.85, a standard error of 12.18, and a confidence interval of −0.39 to −7.31. Conclusions: SleepWise was reasonably accurate for noninvasive and automatic diagnosis

  12. Automatic analysis of the 2015 Gorkha earthquake aftershock sequence.

    NASA Astrophysics Data System (ADS)

    Baillard, C.; Lyon-Caen, H.; Bollinger, L.; Rietbrock, A.; Letort, J.; Adhikari, L. B.

    2016-12-01

    The Mw 7.8 Gorkha earthquake, that partially ruptured the Main Himalayan Thrust North of Kathmandu on the 25th April 2015, was the largest and most catastrophic earthquake striking Nepal since the great M8.4 1934 earthquake. This mainshock was followed by multiple aftershocks, among them, two notable events that occurred on the 12th May with magnitudes of 7.3 Mw and 6.3 Mw. Due to these recent events it became essential for the authorities and for the scientific community to better evaluate the seismic risk in the region through a detailed analysis of the earthquake catalog, amongst others, the spatio-temporal distribution of the Gorkha aftershock sequence. Here we complement this first study by doing a microseismic study using seismic data coming from the eastern part of the Nepalese Seismological Center network associated to one broadband station in Everest. Our primary goal is to deliver an accurate catalog of the aftershock sequence. Due to the exceptional number of events detected we performed an automatic picking/locating procedure which can be splitted in 4 steps: 1) Coarse picking of the onsets using a classical STA/LTA picker, 2) phase association of picked onsets to detect and declare seismic events, 3) Kurtosis pick refinement around theoretical arrival times to increase picking and location accuracy and, 4) local magnitude calculation based amplitude of waveforms. This procedure is time efficient ( 1 sec/event), reduces considerably the location uncertainties ( 2 to 5 km errors) and increases the number of events detected compared to manual processing. Indeed, the automatic detection rate is 10 times higher than the manual detection rate. By comparing to the USGS catalog we were able to give a new attenuation law to compute local magnitudes in the region. A detailed analysis of the seismicity shows a clear migration toward the east of the region and a sudden decrease of seismicity 100 km east of Kathmandu which may reveal the presence of a tectonic

  13. Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials

    PubMed Central

    Chen, Yong; Luo, Sheng; Chu, Haitao; Wei, Peng

    2013-01-01

    Multivariate meta-analysis is useful in combining evidence from independent studies which involve several comparisons among groups based on a single outcome. For binary outcomes, the commonly used statistical models for multivariate meta-analysis are multivariate generalized linear mixed effects models which assume risks, after some transformation, follow a multivariate normal distribution with possible correlations. In this article, we consider an alternative model for multivariate meta-analysis where the risks are modeled by the multivariate beta distribution proposed by Sarmanov (1966). This model have several attractive features compared to the conventional multivariate generalized linear mixed effects models, including simplicity of likelihood function, no need to specify a link function, and has a closed-form expression of distribution functions for study-specific risk differences. We investigate the finite sample performance of this model by simulation studies and illustrate its use with an application to multivariate meta-analysis of adverse events of tricyclic antidepressants treatment in clinical trials. PMID:23853700

  14. Characterizing the Moisture Content of Tea with Diffuse Reflectance Spectroscopy Using Wavelet Transform and Multivariate Analysis

    PubMed Central

    Li, Xiaoli; Xie, Chuanqi; He, Yong; Qiu, Zhengjun; Zhang, Yanchao

    2012-01-01

    Effects of the moisture content (MC) of tea on diffuse reflectance spectroscopy were investigated by integrated wavelet transform and multivariate analysis. A total of 738 representative samples, including fresh tea leaves, manufactured tea and partially processed tea were collected for spectral measurement in the 325–1,075 nm range with a field portable spectroradiometer. Then wavelet transform (WT) and multivariate analysis were adopted for quantitative determination of the relationship between MC and spectral data. Three feature extraction methods including WT, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of spectral data. Comparison of those three methods indicated that the variables generated by WT could efficiently discover structural information of spectral data. Calibration involving seeking the relationship between MC and spectral data was executed by using regression analysis, including partial least squares regression, multiple linear regression and least square support vector machine. Results showed that there was a significant correlation between MC and spectral data (r = 0.991, RMSEP = 0.034). Moreover, the effective wavelengths for MC measurement were detected at range of 888–1,007 nm by wavelet transform. The results indicated that the diffuse reflectance spectroscopy of tea is highly correlated with MC. PMID:23012574

  15. Characterizing the moisture content of tea with diffuse reflectance spectroscopy using wavelet transform and multivariate analysis.

    PubMed

    Li, Xiaoli; Xie, Chuanqi; He, Yong; Qiu, Zhengjun; Zhang, Yanchao

    2012-01-01

    Effects of the moisture content (MC) of tea on diffuse reflectance spectroscopy were investigated by integrated wavelet transform and multivariate analysis. A total of 738 representative samples, including fresh tea leaves, manufactured tea and partially processed tea were collected for spectral measurement in the 325-1,075 nm range with a field portable spectroradiometer. Then wavelet transform (WT) and multivariate analysis were adopted for quantitative determination of the relationship between MC and spectral data. Three feature extraction methods including WT, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of spectral data. Comparison of those three methods indicated that the variables generated by WT could efficiently discover structural information of spectral data. Calibration involving seeking the relationship between MC and spectral data was executed by using regression analysis, including partial least squares regression, multiple linear regression and least square support vector machine. Results showed that there was a significant correlation between MC and spectral data (r = 0.991, RMSEP = 0.034). Moreover, the effective wavelengths for MC measurement were detected at range of 888-1,007 nm by wavelet transform. The results indicated that the diffuse reflectance spectroscopy of tea is highly correlated with MC.

  16. Image analysis techniques associated with automatic data base generation.

    NASA Technical Reports Server (NTRS)

    Bond, A. D.; Ramapriyan, H. K.; Atkinson, R. J.; Hodges, B. C.; Thomas, D. T.

    1973-01-01

    This paper considers some basic problems relating to automatic data base generation from imagery, the primary emphasis being on fast and efficient automatic extraction of relevant pictorial information. Among the techniques discussed are recursive implementations of some particular types of filters which are much faster than FFT implementations, a 'sequential similarity detection' technique of implementing matched filters, and sequential linear classification of multispectral imagery. Several applications of the above techniques are presented including enhancement of underwater, aerial and radiographic imagery, detection and reconstruction of particular types of features in images, automatic picture registration and classification of multiband aerial photographs to generate thematic land use maps.

  17. 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.

    PubMed

    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

  18. Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis.

    PubMed

    Ponsoda, Vicente; Martínez, Kenia; Pineda-Pardo, José A; Abad, Francisco J; Olea, Julio; Román, Francisco J; Barbey, Aron K; Colom, Roberto

    2017-02-01

    Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp 38:803-816, 2017. © 2016 Wiley Periodicals, Inc.

  19. Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals

    NASA Astrophysics Data System (ADS)

    Azami, Hamed; Escudero, Javier

    2017-01-01

    Multiscale entropy (MSE) is an appealing tool to characterize the complexity of time series over multiple temporal scales. Recent developments in the field have tried to extend the MSE technique in different ways. Building on these trends, we propose the so-called refined composite multivariate multiscale fuzzy entropy (RCmvMFE) whose coarse-graining step uses variance (RCmvMFEσ2) or mean (RCmvMFEμ). We investigate the behavior of these multivariate methods on multichannel white Gaussian and 1/ f noise signals, and two publicly available biomedical recordings. Our simulations demonstrate that RCmvMFEσ2 and RCmvMFEμ lead to more stable results and are less sensitive to the signals' length in comparison with the other existing multivariate multiscale entropy-based methods. The classification results also show that using both the variance and mean in the coarse-graining step offers complexity profiles with complementary information for biomedical signal analysis. We also made freely available all the Matlab codes used in this paper.

  20. Classification of Ilex species based on metabolomic fingerprinting using nuclear magnetic resonance and multivariate data analysis.

    PubMed

    Choi, Young Hae; Sertic, Sarah; Kim, Hye Kyong; Wilson, Erica G; Michopoulos, Filippos; Lefeber, Alfons W M; Erkelens, Cornelis; Prat Kricun, Sergio D; Verpoorte, Robert

    2005-02-23

    The metabolomic analysis of 11 Ilex species, I. argentina, I. brasiliensis, I. brevicuspis, I. dumosavar. dumosa, I. dumosa var. guaranina, I. integerrima, I. microdonta, I. paraguariensis var. paraguariensis, I. pseudobuxus, I. taubertiana, and I. theezans, was carried out by NMR spectroscopy and multivariate data analysis. The analysis using principal component analysis and classification of the (1)H NMR spectra showed a clear discrimination of those samples based on the metabolites present in the organic and aqueous fractions. The major metabolites that contribute to the discrimination are arbutin, caffeine, phenylpropanoids, and theobromine. Among those metabolites, arbutin, which has not been reported yet as a constituent of Ilex species, was found to be a biomarker for I. argentina,I. brasiliensis, I. brevicuspis, I. integerrima, I. microdonta, I. pseudobuxus, I. taubertiana, and I. theezans. This reliable method based on the determination of a large number of metabolites makes the chemotaxonomical analysis of Ilex species possible.

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

    PubMed

    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.

  2. Graphical model based multivariate analysis (GAMMA): an open-source, cross-platform neuroimaging data analysis software package.

    PubMed

    Chen, Rong; Herskovits, Edward H

    2012-04-01

    The GAMMA suite is an open-source, cross-platform data-mining software package designed to analyze neuroimaging data. Analyzing brain image volumes is a very challenging problem, due to undersampling and the potential for multivariate nonlinear interactions among variables. The GAMMA suite provides a set of tools to facilitate the analysis of neuroimaging data.

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

    PubMed

    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

  4. Multivariate Statistical Modelling of Compound Events via Pair-Copula Constructions: Analysis of Floods in Ravenna

    NASA Astrophysics Data System (ADS)

    Bevacqua, Emanuele; Maraun, Douglas; Hobæk Haff, Ingrid; Widmann, Martin; Vrac, Mathieu

    2017-04-01

    Compound events are multivariate extreme events in which the individual contributing variables may not be extreme themselves, but their joint - dependent - occurrence causes an extreme impact. The conventional univariate statistical analysis cannot give accurate information regarding the multivariate nature of these events. We develop a conceptual model, implemented via pair-copula constructions, which allows for the quantification of the risk associated with compound events in present day and future climate, as well as the uncertainty estimates around such risk. The model includes meteorological predictors which provide insight into both the involved physical processes, and the temporal variability of CEs. Moreover, this model provides multivariate statistical downscaling of compound events. Downscaling of compound events is required to extend their risk assessment to the past or future climate, where climate models either do not simulate realistic values of the local variables driving the events, or do not simulate them at all. Based on the developed model, we study compound floods, i.e. joint storm surge and high river runoff, in Ravenna (Italy). To explicitly quantify the risk, we define the impact of compound floods as a function of sea and river levels. We use meteorological predictors to extend the analysis to the past, and get a more robust risk analysis. We quantify the uncertainties of the risk analysis observing that they are very large due to the shortness of the available data, though this may also be the case in other studies where they have not been estimated. Ignoring the dependence between sea and river levels would result in an underestimation of risk, in particular the expected return period of the highest compound flood observed increases from about 20 to 32 years when switching from the dependent to the independent case.

  5. Risk Factors for Medical Complication after Cervical Spine Surgery: a multivariate analysis of 582 patients

    PubMed Central

    Lee, Michael J.; Konodi, Mark A.; Cizik, Amy M.; Weinreich, Mark A.; Bransford, Richard J.; Bellabarba, Carlo; Chapman, Jens

    2012-01-01

    Study Design Multivariate analysis of prospectively collected registry data Objective Using multivariate analysis, to determine significant risk factors for medical complication after cervical spine surgery. Summary of Background Data Several studies have examined the occurrence of medical complication after spine surgery. However many of these studies have been done utilizing large national databases. While these allow for analysis of thousands of patients, potentially influential co-variates are not accounted for in these retrospective studies. Furthermore, the accuracy of these retrospective data collection in these databases has been called into question. Methods The Spine End Results Registry (2003–2004) is a collection prospectively collected data on all patients who underwent spine surgery at our two institutions. Extensive demographic and medical information were prospectively recorded as described previously by Mirza et al. Complications were defined in detail a priori and were prospectively recorded for at least 2 years after surgery. We analyzed risk factors for medical complication after lumbar spine surgery using univariate and multivariate analysis. Results We analayzed data from 582 patients who met out inclusion criteria. The cumulative incidences of complication after cervical spine surgery per organ system are as follows: cardiac – 8.4%, pulmonary – 13%, gastrointestinal – 3.9%, neurological – 7.4%, hematological – 10.8% and urologic complications – 9.2%. The occurrence of cardiac or respiratory complication after cervical spine surgery was significantly associated with death within 2 years (RR 4.32, 6.43 respectively). Relative risk values with 95% confidence intervals and p values are listed individually in Tables 2 and 3. Conclusion Risk factors identified in this study can be beneficial to clinicians and patients alike when considering surgical treatment of the cervical spine. Future analyses and models that predict the

  6. Risk factors for baclofen pump infection in children: a multivariate analysis.

    PubMed

    Spader, Heather S; Bollo, Robert J; Bowers, Christian A; Riva-Cambrin, Jay

    2016-06-01

    OBJECTIVE Intrathecal baclofen infusion systems to manage severe spasticity and dystonia are associated with higher infection rates in children than in adults. Factors unique to this population, such as poor nutrition and physical limitations for pump placement, have been hypothesized as the reasons for this disparity. The authors assessed potential risk factors for infection in a multivariate analysis. METHODS Patients who underwent implantation of a programmable pump and intrathecal catheter for baclofen infusion at a single center between January 1, 2000, and March 1, 2012, were identified in this retrospective cohort study. The primary end point was infection. Potential risk factors investigated included preoperative (i.e., demographics, body mass index [BMI], gastrostomy tube, tracheostomy, previous spinal fusion), intraoperative (i.e., surgeon, antibiotics, pump size, catheter location), and postoperative (i.e., wound dehiscence, CSF leak, and number of revisions) factors. Univariate analysis was performed, and a multivariate logistic regression model was created to identify independent risk factors for infection. RESULTS A total of 254 patients were evaluated. The overall infection rate was 9.8%. Univariate analysis identified young age, shorter height, lower weight, dehiscence, CSF leak, and number of revisions within 6 months of pump placement as significantly associated with infection. Multivariate analysis identified young age, dehiscence, and number of revisions as independent risk factors for infection. CONCLUSIONS Young age, wound dehiscence, and number of revisions were independent risk factors for infection in this pediatric cohort. A low BMI and the presence of either a gastrostomy or tracheostomy were not associated with infection and may not be contraindications for this procedure.

  7. Automatic analysis of ciliary beat frequency using optical flow

    NASA Astrophysics Data System (ADS)

    Figl, Michael; Lechner, Manuel; Werther, Tobias; Horak, Fritz; Hummel, Johann; Birkfellner, Wolfgang

    2012-02-01

    Ciliary beat frequency (CBF) can be a useful parameter for diagnosis of several diseases, as e.g. primary ciliary dyskinesia. (PCD). CBF computation is usually done using manual evaluation of high speed video sequences, a tedious, observer dependent, and not very accurate procedure. We used the OpenCV's pyramidal implementation of the Lukas-Kanade algorithm for optical flow computation and applied this to certain objects to follow the movements. The objects were chosen by their contrast applying the corner detection by Shi and Tomasi. Discrimination between background/noise and cilia by a frequency histogram allowed to compute the CBF. Frequency analysis was done using the Fourier transform in matlab. The correct number of Fourier summands was found by the slope in an approximation curve. The method showed to be usable to distinguish between healthy and diseased samples. However there remain difficulties in automatically identifying the cilia, and also in finding enough high contrast cilia in the image. Furthermore the some of the higher contrast cilia are lost (and sometimes found) by the method, an easy way to distinguish the correct sub-path of a point's path have yet to be found in the case where the slope methods doesn't work.

  8. Automatic comic page image understanding based on edge segment analysis

    NASA Astrophysics Data System (ADS)

    Liu, Dong; Wang, Yongtao; Tang, Zhi; Li, Luyuan; Gao, Liangcai

    2013-12-01

    Comic page image understanding aims to analyse the layout of the comic page images by detecting the storyboards and identifying the reading order automatically. It is the key technique to produce the digital comic documents suitable for reading on mobile devices. In this paper, we propose a novel comic page image understanding method based on edge segment analysis. First, we propose an efficient edge point chaining method to extract Canny edge segments (i.e., contiguous chains of Canny edge points) from the input comic page image; second, we propose a top-down scheme to detect line segments within each obtained edge segment; third, we develop a novel method to detect the storyboards by selecting the border lines and further identify the reading order of these storyboards. The proposed method is performed on a data set consisting of 2000 comic page images from ten printed comic series. The experimental results demonstrate that the proposed method achieves satisfactory results on different comics and outperforms the existing methods.

  9. Trends of Science Education Research: An Automatic Content Analysis

    NASA Astrophysics Data System (ADS)

    Chang, Yueh-Hsia; Chang, Chun-Yen; Tseng, Yuen-Hsien

    2010-08-01

    This study used scientometric methods to conduct an automatic content analysis on the development trends of science education research from the published articles in the four journals of International Journal of Science Education, Journal of Research in Science Teaching, Research in Science Education, and Science Education from 1990 to 2007. The multi-stage clustering technique was employed to investigate with what topics, to what development trends, and from whose contribution that the journal publications constructed as a science education research field. This study found that the research topic of Conceptual Change & Concept Mapping was the most studied topic, although the number of publications has slightly declined in the 2000's. The studies in the themes of Professional Development, Nature of Science and Socio-Scientific Issues, and Conceptual Chang and Analogy were found to be gaining attention over the years. This study also found that, embedded in the most cited references, the supporting disciplines and theories of science education research are constructivist learning, cognitive psychology, pedagogy, and philosophy of science.

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

    PubMed

    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.

  11. Groundwater quality in Imphal West district, Manipur, India, with multivariate statistical analysis of data.

    PubMed

    Singh, Elangbam J K; Gupta, Abhik; Singh, N R

    2013-04-01

    The aim of this paper was to analyze the groundwater quality of Imphal West district, Manipur, India, and assess its suitability for drinking, domestic, and agricultural use. Eighteen physico-chemical variables were analyzed in groundwater from 30 different hand-operated tube wells in urban, suburban, and rural areas in two seasons. The data were subjected to uni-, bi-, and multivariate statistical analysis, the latter comprising cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA). Arsenic concentrations exceed the Indian standard in 23.3% and the WHO limit in 73.3% of the groundwater sources with only 26.7% in the acceptable range. Several variables like iron, chloride, sodium, sulfate, total dissolved solids, and turbidity are also beyond their desirable limits for drinking water in a number of sites. Sodium concentrations and sodium absorption ratio (SAR) are both high to render the water from the majority of the sources unsuitable for agricultural use. Multivariate statistical techniques, especially varimax rotation of PCA data helped to bring to focus the hidden yet important variables and understand their roles in influencing groundwater quality. Widespread arsenic contamination and high sodium concentration of groundwater pose formidable constraints towards its exploitation for drinking and other domestic and agricultural use in the study area, although urban anthropogenic impacts are not yet pronounced.

  12. PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data

    PubMed Central

    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. PMID:19184561

  13. Lithology of gravel deposits of the Front Range urban corridor, Colorado: data and multivariate statistical analysis

    USGS Publications Warehouse

    Lindsey, David A.

    2001-01-01

    Pebble count data from Quaternary gravel deposits north of Denver, Colo., were analyzed by multivariate statistical methods to identify lithologic factors that might affect aggregate quality. The pebble count data used in this analysis were taken from the map by Colton and Fitch (1974) and are supplemented by data reported by the Front Range Infrastructure Resources Project. This report provides data tables and results of the statistical analysis. The multivariate statistical analysis used here consists of log-contrast principal components analysis (method of Reyment and Savazzi, 1999) followed by rotation of principal components and factor interpretation. Three lithologic factors that might affect aggregate quality were identified: 1) granite and gneiss versus pegmatite, 2) quartz + quartzite versus total volcanic rocks, and 3) total sedimentary rocks (mainly sandstone) versus granite. Factor 1 (grain size of igneous and metamorphic rocks) may represent destruction during weathering and transport or varying proportions of rocks in source areas. Factor 2 (resistant source rocks) represents the dispersion shadow of metaquartzite detritus, perhaps enhanced by resistance of quartz and quartzite during weathering and transport. Factor 3 (proximity to sandstone source) represents dilution of gravel by soft sedimentary rocks (mainly sandstone), which are exposed mainly in hogbacks near the mountain front. Factor 1 probably does not affect aggregate quality. Factor 2 would be expected to enhance aggregate quality as measured by the Los Angeles degradation test. Factor 3 may diminish aggregate quality.

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

    PubMed

    Ultsch, Alfred; Lötsch, Jörn

    2015-01-01

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

  15. Discrimination of base differences in oligonucleotides using mid-infrared spectroscopy and multivariate analysis.

    PubMed

    Kelly, Jemma G; Martin-Hirsch, Pierre L; Martin, Francis L

    2009-07-01

    Attenuated total reflection Fourier transform-infrared (ATR-FTIR) spectroscopy was employed to interrogate a panel of simple oligonucleotides designed to contain various base differences; combined with subsequent multivariate analysis, we set out to determine whether the specificity of this approach would point to a novel means for mutation detection. Oligonucleotides were designed that were 15 bases in length and contained various combinations of purines (adenine, guanine) or pyrimidines (cytosine, thymine). These were applied to 1 cm x 1 cm low-E reflective glass slides, and triplicate samples were interrogated using ATR-FTIR spectroscopy. Per oligonucleotide sample, 10 independent spectral acquisitions were obtained. Prior to multivariate analysis, infrared spectra were baseline-corrected and vector normalized over the 1750-760 cm(-1) region specific to the chemical bonds of organic molecules. Spectral categories were then analyzed using principal component analysis (PCA) followed by linear discriminant analysis (LDA). Scores plots revealed that PCA-LDA clearly segregated different oligonucleotide sequences, even in the presence of a single base difference. Loadings plots confirmed the chemical entities associated with distinguishing base differences. These results suggest that mid-IR spectroscopy might have future roles in interrogating polymorphic forms of a DNA template.

  16. The tall-cell variant of papillary thyroid carcinoma: a multivariate analysis of clinical risk factors.

    PubMed

    Machens, Andreas; Holzhausen, Hans-Jürgen; Lautenschläger, Christine; Dralle, Henning

    2004-08-01

    The biological behaviour of the tall-cell variant (TCV) of papillary thyroid carcinoma (PTC) remains to be clarified in a multivariate analysis that controls for all relevant clinicopathological parameters. A retrospective analysis was carried out of 332 consecutive PTC patients operated on at a university hospital between November 1994 and February 2003. A total of 16 TCV tumours (4.8%) was identified among the 332 PTC patients. Nodal and (predominantly pulmonary) distant metastases were identified at surgery in, respectively, 50% and 31% of TCV tumours. On univariate analysis, only the association between the TCV and distant metastasis retained statistical significance after adjustment for multiple testing. On multivariate logistic regression analysis, the presence of distant metastasis increased more than fourfold [odds ratio (OR) 4.2] the chance of having the TCV of PTC, with controls for extrathyroidal extension, nodal metastasis, operation status, patient gender, categorized patient age, and categorized primary tumour diameter. The increased risk of distant metastasis associated with the TCV morphology of PTC warrants an extensive post-operative search for distant metastasis to facilitate early diagnosis and treatment of tumour deposits in distant organs.

  17. Risk Factors for Hypertension After Living Donor Kidney Transplantation in Korea: A Multivariate Analysis.

    PubMed

    Yu, H; Kim, H S; Baek, C H; Shin, E H; Cho, H J; Han, D J; Park, S K

    2016-01-01

    Post-transplantation hypertension is very common and is associated with cardiovascular complications and poor graft survival in kidney transplant recipients. This study aimed to identify risk factors for hypertension after living donor kidney transplantation. We retrospectively analyzed patients who underwent renal transplantation between January 2009 and April 2012. Hypertension was defined as the use of antihypertensive medications at 12 months post-transplantation. Student t test and chi-squared test were performed for univariate analysis. Logistic regression analysis was performed for multivariate analysis. Five-hundred thirty-nine patients were enrolled in the analyses. The rate of antihypertensive medication use was 67% at 12 months. In multivariate analysis, male gender (odds ratio [OR], 2.68; 95% confidence interval [CI], 1.55-4.61), pretransplantation hypertension (OR, 4.65; 95% CI, 2.14-10.11), donor hypertension (OR, 3.23; 95% CI, 1.05-9.96), high body mass index (BMI; OR, 1.21; 95% CI, 1.12-1.29), and use of cyclosporine (OR, 2.05; 95% CI, 1.28-3.27) were associated with post-transplantation hypertension. These data show that male recipient, hypertension before transplantation, donor hypertension, high BMI, and cyclosporine use were independent factors associated with hypertension. It would be useful to predict and prevention the hypertension after kidney transplantation. Copyright © 2016 Elsevier Inc. All rights reserved.

  18. Joint analysis of multiple blood pressure phenotypes in GAW19 data by using a multivariate rare-variant association test.

    PubMed

    Sun, Jianping; Bhatnagar, Sahir R; Oualkacha, Karim; Ciampi, Antonio; Greenwood, Celia M T

    2016-01-01

    Large-scale sequencing studies often measure many related phenotypes in addition to the genetic variants. Joint analysis of multiple phenotypes in genetic association studies may increase power to detect disease-associated loci. We apply a recently developed multivariate rare-variant association test to the Genetic Analysis Workshop 19 data in order to test associations between genetic variants and multiple blood pressure phenotypes simultaneously. We also compare this multivariate test with a widely used univariate test that analyzes phenotypes separately. The multivariate test identified 2 genetic variants that have been previously reported as associated with hypertension or coronary artery disease. In addition, our region-based analyses also show that the multivariate test tends to give smaller p values than the univariate test. Hence, the multivariate test has potential to improve test power, especially when multiple phenotypes are correlated.

  19. Multivariate pattern analysis reveals subtle brain anomalies relevant to the cognitive phenotype in neurofibromatosis type 1.

    PubMed

    Duarte, João V; Ribeiro, Maria J; Violante, Inês R; Cunha, Gil; Silva, Eduardo; Castelo-Branco, Miguel

    2014-01-01

    Neurofibromatosis Type 1 (NF1) is a common genetic condition associated with cognitive dysfunction. However, the pathophysiology of the NF1 cognitive deficits is not well understood. Abnormal brain structure, including increased total brain volume, white matter (WM) and grey matter (GM) abnormalities have been reported in the NF1 brain. These previous studies employed univariate model-driven methods preventing detection of subtle and spatially distributed differences in brain anatomy. Multivariate pattern analysis allows the combination of information from multiple spatial locations yielding a discriminative power beyond that of single voxels. Here we investigated for the first time subtle anomalies in the NF1 brain, using a multivariate data-driven classification approach. We used support vector machines (SVM) to classify whole-brain GM and WM segments of structural T1 -weighted MRI scans from 39 participants with NF1 and 60 non-affected individuals, divided in children/adolescents and adults groups. We also employed voxel-based morphometry (VBM) as a univariate gold standard to study brain structural differences. SVM classifiers correctly classified 94% of cases (sensitivity 92%; specificity 96%) revealing the existence of brain structural anomalies that discriminate NF1 individuals from controls. Accordingly, VBM analysis revealed structural differences in agreement with the SVM weight maps representing the most relevant brain regions for group discrimination. These included the hippocampus, basal ganglia, thalamus, and visual cortex. This multivariate data-driven analysis thus identified subtle anomalies in brain structure in the absence of visible pathology. Our results provide further insight into the neuroanatomical correlates of known features of the cognitive phenotype of NF1. Copyright © 2012 Wiley Periodicals, Inc.

  20. Multivariate analysis of risk factors for postoperative complications after laparoscopic liver resection.

    PubMed

    Tranchart, Hadrien; Gaillard, Martin; Chirica, Mircea; Ferretti, Stefano; Perlemuter, Gabriel; Naveau, Sylvie; Dagher, Ibrahim

    2015-09-01

    The identification of modifiable perioperative risk factors in patients undergoing laparoscopic liver resection (LLR) should aid the selection of appropriate surgical procedures and thus improve further the outcomes associated with LLR. The aim of this retrospective study was to determine the risk factors for postoperative morbidity associated with laparoscopic liver surgery. All patients who underwent elective LLR between January 1999 and December 2012 were included. Demographic data, preoperative risk factors, operative variables, histological analysis, and postoperative course were recorded. Multivariate analysis was carried out using an unconditional logistic regression model. Between January 1999 and December 2012, 140 patients underwent LLR. There were 56 male patients (40%) and mean age was 57.8 ± 17 years. Postoperative complications were recorded in 30 patients (21.4%). Postoperative morbidity was significantly higher after LLR of malignant tumors [n = 26 (41.3%)] when compared to LLR of benign lesions [n = 4 (5.2%) (P < 0.0001)]. By multivariate analysis, operative time [OR = 1.008 (1.003-1.01), P = 0.001] and LLR performed for malignancy [OR = 9.8 (2.5-37.6); P = 0.01] were independent predictors of postoperative morbidity. In the subgroup of patients that underwent LLR for malignancy using the same multivariate model, operative time was the sole independent predictor of postoperative morbidity [OR = 1.008 (1.002-1.013); P = 0.004]. Postoperative complication rate increases by 60% with each additional operative hour during LLR. Therefore, expected operative time should be assessed before and during LLR, especially when dealing with malignant tumor.

  1. Characterisation of DNA methylation status using spectroscopy (mid-IR versus Raman) with multivariate analysis.

    PubMed

    Kelly, Jemma G; Najand, Ghazal M; Martin, Francis L

    2011-05-01

    Methylation status plays important roles in the regulation of gene expression and significantly influences the dynamics, bending and flexibility of DNA. The aim of this study was to determine whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) or Raman spectroscopy with subsequent multivariate analysis could determine methylation patterning in oligonucleotides variously containing 5-methylcytosine, cytosine and guanine bases. Applied to Low-E reflective glass slides, 10 independent spectral acquisitions were acquired per oligonucleotide sample. Resultant spectra were baseline-corrected and vector normalised over the 1750 cm(-1) -760 cm(-1) (for ATR-FTIR spectroscopy) or the 1750 cm(-1) -600 cm(-1) (for Raman spectroscopy) regions. Data were then analysed using principal component analysis (PCA) coupled with linear discriminant analysis (LDA). Exploiting this approach, biomolecular signatures enabling sensitive and specific discrimination of methylation patterning were derived. For DNA sequence and methylation analysis, this approach has the potential to be an important tool, especially when material is scarce.

  2. Flow Injection Mass Spectroscopic Fingerprinting and Multivariate Analysis for Differentiation of Three Panax Species

    PubMed Central

    Chen, Pei; Harnly, James M.; Harrington, Peter de B.

    2013-01-01

    This study describes the use of spectral fingerprints acquired by flow injection(FI)-MS and multivariate analysis to differentiate three Panax species: P. ginseng, P. quinquefolius, and P. notoginseng. Data were acquired using both high resolution and unit resolution MS, and were processed using principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), partial least squares-discriminant analysis (PLS-DA), and a fuzzy rule-building expert system (FuRES). Both high and unit resolution MS allowed discrimination among the three Panax species. PLS-DA and FuRES provided classification with 100% accuracy while SIMCA provided classification accuracies of 77 and 88% by high- and low-resolution MS, respectively. The method does not quantify any of the sample components. With FI-MS, the analysis time was less than 2 min. PMID:21391484

  3. Multivariate Voronoi Outlier Detection for Time Series.

    PubMed

    Zwilling, Chris E; Wang, Michelle Yongmei

    2014-10-01

    Outlier detection is a primary step in many data mining and analysis applications, including healthcare and medical research. This paper presents a general method to identify outliers in multivariate time series based on a Voronoi diagram, which we call Multivariate Voronoi Outlier Detection (MVOD). The approach copes with outliers in a multivariate framework, via designing and extracting effective attributes or features from the data that can take parametric or nonparametric forms. Voronoi diagrams allow for automatic configuration of the neighborhood relationship of the data points, which facilitates the differentiation of outliers and non-outliers. Experimental evaluation demonstrates that our MVOD is an accurate, sensitive, and robust method for detecting outliers in multivariate time series data.

  4. ToF-SIMS imaging of PE/PP polymer using multivariate analysis

    NASA Astrophysics Data System (ADS)

    Miyasaka, Toyomitsu; Ikemoto, Takashi; Kohno, Teiichiro

    2008-12-01

    The distribution of polyethylene (PE) and polypropylene (PP) in PE/PP blended-polymer film was determined by applying principal components analysis (PCA) and multivariate curve resolution (MCR) to time-of-flight secondary ion mass spectroscopy (ToF-SIMS) imaging, together with preprocessing by pixel binning, normalization, and autoscaling to increase image contrast by reducing topographic and charge-distribution effects. The PE/PP distribution was confirmed by MVA conducted on the image data over static limit. The MCR score with normalized-autoscaling was found to give the PE/PP distribution distinctly.

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

    PubMed

    Reyment, R A

    1983-01-01

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

  6. Automatic adventitious respiratory sound analysis: A systematic review

    PubMed Central

    Bowyer, Stuart; Rodriguez-Villegas, Esther

    2017-01-01

    Background Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established. Objective To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works. Data sources A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification. Study selection Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated. Data extraction Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved. Data synthesis A total of 77 reports from the literature were included in this review. 55 (71.43%) of the

  7. Analysis of mosses and topsoils for detecting sources of heavy metal pollution: multivariate and enrichment factor analysis.

    PubMed

    Dragović, S; Mihailović, N

    2009-10-01

    In order to assess the contribution of emission sources to the pollution of areas remote from industrial facilities, a combined approach of enrichment factor analysis and multivariate statistics was used for detecting the origin of heavy metal pollution in the Zlatibor ecosystem, in Serbia. Samples of moss (Pleurozium schreberi, Hylocomium splendens, Scleropodium purum, Hypnum cupressiforme and Thuidum delicatulum) and of topsoil (0-5 cm) were collected in 2005. The concentrations of seven heavy metals (Cd, Cr, Cu, Mn, Ni, Pb and Zn) were determined in moss and soil samples by atomic absorption spectrometry. The results obtained by enrichment factor analysis and two multivariate statistical methods, principal component analysis and cluster analysis, enabled discrimination of the lithologic and anthropogenic sources of heavy metals in the mosses. Enrichment factors, calculated to evaluate the contribution to the metal content in moss from anthropogenic sources, revealed pollution of the investigated area by Cd and Pb, originating from long-range transport and fossil fuel burning.

  8. Analysis of non-stationary turbulent flows using Multivariate EMD and Matching Pursuits

    NASA Astrophysics Data System (ADS)

    Mohan, Arvind; Agostini, Lionel; Gaitonde, Datta; Visbal, Miguel

    2016-11-01

    Time-series analysis of highly transient non-stationary turbulent flow is challenging. Traditional Fourier based techniques are generally difficult to apply because of the highly aperiodic nature of the data. Another significant obstacle is assimilating multivariate data, such as multiple variables at a location or from different sources in a flow-field. Such an analysis has the potential to identify sensitive events common among these sources. In this work, we explore two techniques to address these challenges - Multivariate Empirical Mode Decomposition and Matching Pursuits, on deep dynamic stall of a plunging airfoil in a mixed laminar-transitional-turbulent regime. Although primarily used for neuroscience applications, we use them in fluid mechanics and highlight their significant potential to overcome limitations of more traditional techniques. Application of these methods highlight different stages in the development of stall. A first stage shows development of 2-D boundary layer oscillations at frequencies similar to those associated with trailing edge vortices. Subsequently, new instabilities arise due to imminent separation. The separation bubble itself is characterized by relatively higher frequency content, and further analysis indicates its 3-D collapse.

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

    NASA Astrophysics Data System (ADS)

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

    2016-11-01

    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.

  10. Multivariate analysis of maize disease resistances suggests a pleiotropic genetic basis and implicates a GST gene

    PubMed Central

    Wisser, Randall J.; Kolkman, Judith M.; Patzoldt, Megan E.; Holland, James B.; Yu, Jianming; Krakowsky, Matthew; Nelson, Rebecca J.; Balint-Kurti, Peter J.

    2011-01-01

    Plants are attacked by pathogens representing diverse taxonomic groups, such that genes providing multiple disease resistance (MDR) are expected to be under positive selection pressure. To address the hypothesis that naturally occurring allelic variation conditions MDR, we extended the framework of structured association mapping to allow for the analysis of correlated complex traits and the identification of pleiotropic genes. The multivariate analytical approach used here is directly applicable to any species and set of traits exhibiting correlation. From our analysis of a diverse panel of maize inbred lines, we discovered high positive genetic correlations between resistances to three globally threatening fungal diseases. The maize panel studied exhibits rapidly decaying linkage disequilibrium that generally occurs within 1 or 2 kb, which is less than the average length of a maize gene. The positive correlations therefore suggested that functional allelic variation at specific genes for MDR exists in maize. Using a multivariate test statistic, a glutathione S-transferase (GST) gene was found to be associated with modest levels of resistance to all three diseases. Resequencing analysis pinpointed the association to a histidine (basic amino acid) for aspartic acid (acidic amino acid) substitution in the encoded protein domain that defines GST substrate specificity and biochemical activity. The known functions of GSTs suggested that variability in detoxification pathways underlie natural variation in maize MDR. PMID:21490302

  11. Application of multivariate analysis toward biotech processes: case study of a cell-culture unit operation.

    PubMed

    Kirdar, Alime Ozlem; Conner, Jeremy S; Baclaski, Jeffrey; Rathore, Anurag S

    2007-01-01

    This paper examines the feasibility of using multivariate data analysis (MVDA) for supporting some of the key activities that are required for successful manufacturing of biopharmaceutical products. These activities include scale-up, process comparability, process characterization, and fault diagnosis. Multivariate data analysis and modeling were performed using representative data from small-scale (2 L) and large-scale (2000 L) batches of a cell-culture process. Several input parameters (pCO2, pO2, glucose, pH, lactate, ammonium ions) and output parameters (purity, viable cell density, viability, osmolality) were evaluated in this analysis. Score plots, loadings plots, and VIP plots were utilized for assessing scale-up and comparability of the cell-culture process. Batch control charts were found to be useful for fault diagnosis during routine manufacturing. Finally, observations made from reviewing VIP plots were found to be in agreement with conclusions from process characterization studies demonstrating the effectiveness of MVDA as a tool for extracting process knowledge.

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

    NASA Astrophysics Data System (ADS)

    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.

  13. Metabolomic Fingerprinting of Romaneschi Globe Artichokes by NMR Spectroscopy and Multivariate Data Analysis.

    PubMed

    de Falco, Bruna; Incerti, Guido; Pepe, Rosa; Amato, Mariana; Lanzotti, Virginia

    2016-09-01

    Globe artichoke (Cynara cardunculus L. var. scolymus L. Fiori) and cardoon (Cynara cardunculus L. var. altilis DC) are sources of nutraceuticals and bioactive compounds. To apply a NMR metabolomic fingerprinting approach to Cynara cardunculus heads to obtain simultaneous identification and quantitation of the major classes of organic compounds. The edible part of 14 Globe artichoke populations, belonging to the Romaneschi varietal group, were extracted to obtain apolar and polar organic extracts. The analysis was also extended to one species of cultivated cardoon for comparison. The (1) H-NMR of the extracts allowed simultaneous identification of the bioactive metabolites whose quantitation have been obtained by spectral integration followed by principal component analysis (PCA). Apolar organic extracts were mainly based on highly unsaturated long chain lipids. Polar organic extracts contained organic acids, amino acids, sugars (mainly inulin), caffeoyl derivatives (mainly cynarin), flavonoids, and terpenes. The level of nutraceuticals was found to be highest in the Italian landraces Bianco di Pertosa zia E and Natalina while cardoon showed the lowest content of all metabolites thus confirming the genetic distance between artichokes and cardoon. Metabolomic approach coupling NMR spectroscopy with multivariate data analysis allowed for a detailed metabolite profile of artichoke and cardoon varieties to be obtained. Relevant differences in the relative content of the metabolites were observed for the species analysed. This work is the first application of (1) H-NMR with multivariate statistics to provide a metabolomic fingerprinting of Cynara scolymus. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

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

    PubMed Central

    Duarte, José Maurício Barbanti

    2016-01-01

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

  15. Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin

    NASA Astrophysics Data System (ADS)

    zhang, L.

    2011-12-01

    Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be

  16. Multivariate analysis for the optimization of polysaccharide-based nanoparticles prepared by self-assembly.

    PubMed

    Pistone, Sara; Qoragllu, Dafina; Smistad, Gro; Hiorth, Marianne

    2016-10-01

    Polysaccharide-based nanoparticles are promising carriers for drug delivery applications. The particle size influences the biodistribution of the nanoparticles; hence size distributions and polydispersity index (PDI) are critical characteristics. However, the preparation of stable particles with a low PDI is a challenging task and is usually based on empirical trials. In this study, we report the use of multivariate evaluation to optimize the formulation factors for the preparation of alginate-zinc nanoparticles by ionotropic gelation. The PDI was selected as the response variable. Particle size, size distributions, zeta potential and pH of the samples were also recorded. Two full factorial (mixed-level) designs were analyzed by partial least squares regression (PLS). In the first design, the influence of the polysaccharide and the crosslinker concentrations were studied. The results revealed that size distributions with a low PDI were obtained by using a low polysaccharide concentrations (0.03-0.05%) and a zinc concentration of 0.03% (w/w). However, a high polysaccharide concentration can be advantageous for drug delivery systems. Therefore, in the second design, a high alginate concentration was used (0.09%) and a reduction in the PDI was obtained by simultaneously increasing the ionic strength of the solvent and the zinc concentration. The multivariate analysis also revealed the interaction between the factors in terms of their effects on the PDI; hence, compared to traditional univariate analyses, the multivariate analysis allowed us to obtain a more complete understanding of the effects of the factors scrutinized. In addition, the results are considered useful in order to avoid extensive empirical tests for future formulation studies.

  17. Novel Multivariate Analysis for Soil Carbon Measurements Using Laser-Induced Breakdown Spectroscopy

    SciTech Connect

    Martin, Madhavi Z; Labbe, Nicole; Andre, Nicolas O; Wullschleger, Stan D; Harris, Ronny D; Ebinger, Michael H

    2010-01-01

    Laser-induced breakdown spectroscopy (LIBS), a rapid and potentially field-deployable technology for estimating total carbon in soil, represents a novel approach to address important issues in soil science and carbon management. Our study has shown that models relating LIBS signal intensity at 247.85 nm to percent total carbon determined by dry combustion vary as a function of elemental and textural characteristics of the soil, and, to a lesser extent, wavelength and excitation energy of the laser. To better quantify these sources of variation, two wavelengths and three excitation energies were used to analyze soils from various locations. The emission line of carbon at 247.85 nm was pronounced at an excitation wavelength of 532 nm and energy of 45 mJ, but it was largely obscured by the 248.9 nm Fe line at 1064 nm and excitation energies of 90 and 135 mJ. Univariate analysis revealed linear, but soil-specific correlations between signal intensity at 247.85 nm and total carbon concentration. A single calibration model correlating LIBS spectra to carbon concentration in all samples was obtained using a multivariate approach. Several emission lines in addition to the strong carbon line contributed significantly to the multivariate model. These results show that multivariate analysis can be used to construct a robust calibration model for LIBS spectra and therein provide a reliable estimate of total soil carbon. Such results must be confirmed for a broader range of soils, yet crop and soil scientists, carbon managers, and instrument developers should find these results encouraging.

  18. Multivariate analysis of chromatographic retention data and lipophilicity of phenylacetamide derivatives.

    PubMed

    Vastag, Gyöngyi; Apostolov, Suzana; Perišić-Janjić, Nada; Matijević, Borko

    2013-03-12

    One of the most important physicochemical parameters of a molecule that determines its bioactivity is its lipophilicity. Cluster analysis (CA), principal component analysis (PCA), and sum of ranking differences (SRD) were used to compare the lipophilic parameters of twenty phenylacetamide derivatives, obtained experimentally as chromatographic retention data in the presence of different solvents and calculated by different mathematical methods. All the applied methods of multivariate analysis gave approximately similar grouping of the studied lipophilic parameters. In the attempt to group the investigated compounds in respect of their lipophilicity, the obtained results appeared to be dependent on the applied chemometric method. The CA and PCA, grouped the compounds on the basis of the nature of the substituents R1 and R2, indicating that they determine to a great extent the lipophilicity of the investigated molecules. Unlike them, the SRD method could not be used to group the studied compounds on the basis of their lipophilic character.

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

    PubMed

    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.

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

    NASA Astrophysics Data System (ADS)

    Conceição, A. L. C.; Antoniassi, M.; Cunha, D. M.; Ribeiro-Silva, A.; Poletti, M. E.

    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 -1≤ q(=4 π sin( θ/2)/ λ)≤70.5 nm -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 κ, respectively, in correctly differentiating between healthy and pathological tissues.

  1. Characterization of bright tobaccos by multivariate analysis of 13C CPMAS NMR spectra.

    PubMed

    Wooten, Jan B; Kalengamaliro, Newton E; Axelson, David E

    2009-05-01

    Univariate and multivariate statistics were applied to characterize cured bright tobacco samples on the basis of their 13C CPMAS NMR spectra and leaf constituent analysis. NMR spectra were obtained for 55 samples selected from a set of 134 samples of graded bright tobacco leaves from crop year 1999. Historical leaf constituent analyses were available for total alkaloids, reducing sugars, total nitrogen, and insoluble ash. In addition, we applied HPLC to quantify the two abundant plant polyphenols, chlorogenic acid, and rutin. Principal component analysis (PCA) and partial least squares (PLS) of the NMR spectra revealed systematic relationships between groups of samples related to these substances and afforded predictive quantitative models for the analyzed constituents. Analysis of the PLS significant variables showed that leaf polysaccharides, alkaloids, and minerals are major determinants influencing the grading of cured bright tobacco leaves.

  2. Multivariate statistical analysis of flat vowel spectra with a view to characterizing dysphonic voices.

    PubMed

    Schoentgen, J; Bensaid, M; Bucella, F

    2000-12-01

    The aim of this article is to show how dysphonic voices can be characterized by means of a multivariate statistical analysis of flat vowel spectra. The spectral contour was obtained by means of a wavelet transform of the logarithmic magnitude spectrum, which was subsequently flattened to remove interspeaker variability related to the excitation and vocal tract filter functions. The results of the statistical analysis of flat spectra were the following. Firstly, principal components analysis produced markers that separated noisy from clean spectra. Secondly, the heuristic search for harmonic peaks or interharmonic dips could be omitted. Thirdly, conventional spectral markers of noise appeared as special instances of the markers that were derived statistically. Fourthly, the levels of visually assigned hoarseness and the first two principal components were significantly correlated. The assignment of different levels of (visual) hoarseness to different vowel timbres could be explained by the variability associated with the spectral contour.

  3. Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

    SciTech Connect

    Belianinov, Alex; Panchapakesan, G.; Lin, Wenzhi; Sales, Brian C.; Sefat, Athena Safa; Jesse, Stephen; Pan, Minghu; Kalinin, Sergei V.

    2014-12-02

    Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe0.55Se0.45 (Tc = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe1 x Sex structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.

  4. Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

    NASA Astrophysics Data System (ADS)

    Belianinov, Alex; Ganesh, Panchapakesan; Lin, Wenzhi; Sales, Brian C.; Sefat, Athena S.; Jesse, Stephen; Pan, Minghu; Kalinin, Sergei V.

    2014-12-01

    Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe0.55Se0.45 (Tc = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe1-xSex structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.

  5. Spatial Latent Class Analysis Model for Spatially Distributed Multivariate Binary Data

    PubMed Central

    Wall, Melanie M.; Liu, Xuan

    2009-01-01

    A spatial latent class analysis model that extends the classic latent class analysis model by adding spatial structure to the latent class distribution through the use of the multinomial probit model is introduced. Linear combinations of independent Gaussian spatial processes are used to develop multivariate spatial processes that are underlying the categorical latent classes. This allows the latent class membership to be correlated across spatially distributed sites and it allows correlation between the probabilities of particular types of classes at any one site. The number of latent classes is assumed fixed but is chosen by model comparison via cross-validation. An application of the spatial latent class analysis model is shown using soil pollution samples where 8 heavy metals were measured to be above or below government pollution limits across a 25 square kilometer region. Estimation is performed within a Bayesian framework using MCMC and is implemented using the OpenBUGS software. PMID:20161235

  6. MULTIVARIATE ANALYSIS ON LEVELS OF SELECTED METALS, PARTICULATE MATTER, VOC, AND HOUSEHOLD CHARACTERISTICS AND ACTIVITIES FROM THE MIDWESTERN STATES NHEXAS

    EPA Science Inventory

    Microenvironmental and biological/personal monitoring information were collected during the National Human Exposure Assessment Survey (NHEXAS), conducted in the six states comprising U.S. EPA Region Five. They have been analyzed by multivariate analysis techniques with general ...

  7. MULTIVARIATE ANALYSIS ON LEVELS OF SELECTED METALS, PARTICULATE MATTER, VOC, AND HOUSEHOLD CHARACTERISTICS AND ACTIVITIES FROM THE MIDWESTERN STATES NHEXAS

    EPA Science Inventory

    Microenvironmental and biological/personal monitoring information were collected during the National Human Exposure Assessment Survey (NHEXAS), conducted in the six states comprising U.S. EPA Region Five. They have been analyzed by multivariate analysis techniques with general ...

  8. Automatic analysis of stereoscopic satellite image pairs for determination of cloud-top height and structure

    NASA Technical Reports Server (NTRS)

    Hasler, A. F.; Strong, J.; Woodward, R. H.; Pierce, H.

    1991-01-01

    Results are presented on an automatic stereo analysis of cloud-top heights from nearly simultaneous satellite image pairs from the GOES and NOAA satellites, using a massively parallel processor computer. Comparisons of computer-derived height fields and manually analyzed fields show that the automatic analysis technique shows promise for performing routine stereo analysis in a real-time environment, providing a useful forecasting tool by augmenting observational data sets of severe thunderstorms and hurricanes. Simulations using synthetic stereo data show that it is possible to automatically resolve small-scale features such as 4000-m-diam clouds to about 1500 m in the vertical.

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

    PubMed Central

    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. PMID:28426802

  10. Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling.

    PubMed

    Korsgaard, Inge Riis; Lund, Mogens Sandø; Sorensen, Daniel; Gianola, Daniel; Madsen, Per; Jensen, Just

    2003-01-01

    A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories) or binary traits, where the grouping is determined via thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed.

  11. Cerebral Cortical Folding Analysis with Multivariate Modeling and Testing: Studies on Gender Differences and Neonatal Development

    PubMed Central

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

    2010-01-01

    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 non-parametric 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. PMID:20630489

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

    PubMed

    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.

  13. Integration of stochastic simulation with multivariate analysis: short-term facility fit prediction.

    PubMed

    Stonier, Adam; Pain, David; Westlake, Ashley; Hutchinson, Nicholas; Thornhill, Nina F; Farid, Suzanne S

    2013-01-01

    This article describes a decision-support tool to help pinpoint the potential root causes of sub-optimal short-term facility fit issues in biopharmaceutical facilities. This was achieved by creating a tool that integrated stochastic simulation with advanced multivariate statistical analysis. Process fluctuations in product titers in cell culture, step yields, and chromatography eluate volumes were mimicked using Monte Carlo simulation data derived using a stochastic discrete-event simulation model. The resulting stochastic datasets, with the computed consequences on key metrics such as product mass loss and cost of goods, were examined using advanced multivariate statistical techniques. Principal component analysis combined with clustering algorithms was used to analyze the complex datasets from complete industrial batch processes for biopharmaceuticals. The challenge of visualizing the multidimensional nature of the dataset was addressed using hierarchical and k-means clustering as well as stacked parallel co-ordinate plots to help identify process fingerprints and characteristics of clusters leading to sub-optimal facility fit issues. Industrially-relevant case studies are presented that focus on technology transfer challenges for therapeutic antibodies moving from early phase to late phase clinical trials. The case study details how sub-optimal facility fit can be alleviated by allocating alternative product pool tanks. The impact of this operational change is then assessed by reviewing an updated process fingerprint. Copyright © 2013 American Institute of Chemical Engineers.

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

    PubMed

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

    2017-03-22

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

  15. Automatic Line Network Extraction from Aerial Imagery of Urban Areas through Knowledge Based Image Analysis

    DTIC Science & Technology

    1989-08-01

    Automatic Line Network Extraction from Aerial Imangery of Urban Areas Sthrough KnowledghBased Image Analysis N 04 Final Technical ReportI December...Automatic Line Network Extraction from Aerial Imagery of Urban Areas through Knowledge Based Image Analysis Accesion For NTIS CRA&I DTIC TAB 0...paittern re’ognlition. blac’kboardl oriented symbollic processing, knowledge based image analysis , image understanding, aer’ial imsagery, urban area, 17

  16. Detecting Neuroimaging Biomarkers for Schizophrenia: A Meta-Analysis of Multivariate Pattern Recognition Studies

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-05-01

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

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

    PubMed Central

    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 tests based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks’s Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. PMID:25809955

  19. Improved MR-based characterization of engineered cartilage using multiexponential T2 relaxation and multivariate analysis.

    PubMed

    Reiter, David A; Irrechukwu, Onyi; Lin, Ping-Chang; Moghadam, Somaieh; Von Thaer, Sarah; Pleshko, Nancy; Spencer, Richard G

    2012-03-01

    Noninvasive monitoring of tissue quality would be of substantial use in the development of cartilage tissue engineering strategies. Conventional MR parameters provide noninvasive measures of biophysical tissue properties and are sensitive to changes in matrix development, but do not clearly distinguish between groups with different levels of matrix development. Furthermore, MR outcomes are nonspecific, with particular changes in matrix components resulting in changes in multiple MR parameters. To address these limitations, we present two new approaches for the evaluation of tissue engineered constructs using MR, and apply them to immature and mature engineered cartilage after 1 and 5 weeks of development, respectively. First, we applied multiexponential T(2) analysis for the quantification of matrix macromolecule-associated water compartments. Second, we applied multivariate support vector machine analysis using multiple MR parameters to improve detection of degree of matrix development. Monoexponential T(2) values decreased with maturation, but without further specificity. Much more specific information was provided by multiexponential analysis. The T(2) distribution in both immature and mature constructs was qualitatively comparable to that of native cartilage. The analysis showed that proteoglycan-bound water increased significantly during maturation, from a fraction of 0.05 ± 0.01 to 0.07 ± 0.01. Classification of samples based on individual MR parameters, T(1), T(2), k(m) or apparent diffusion coefficient, showed that the best classifiers were T(1) and k(m), with classification accuracies of 85% and 84%, respectively. Support vector machine analysis improved the accuracy to 98% using the combination (k(m), apparent diffusion coefficient). These approaches were validated using biochemical and Fourier transform infrared imaging spectroscopic analyses, which showed increased proteoglycan and collagen with maturation. In summary, multiexponential T(2) and

  20. Operational testing of system for automatic sleep analysis

    NASA Technical Reports Server (NTRS)

    Kellaway, P.

    1972-01-01

    Tables on the performance, under operational conditions, of an automatic sleep monitoring system are presented. Data are recorded from patients who were undergoing heart and great vessel surgery. This study resulted in cap, electrode, and preamplifier improvements. Children were used to test the sleep analyzer and medical console write out units. From these data, an automatic voltage control circuit for the analyzer was developed. A special circuitry for obviating the possibility of incorrect sleep staging due to the presence of a movement artifact was also developed as a result of the study.

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

    PubMed

    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.

  2. Surgical workflow analysis with Gaussian mixture multivariate autoregressive (GMMAR) models: a simulation study.

    PubMed

    Loukas, Constantinos; Georgiou, Evangelos

    2013-01-01

    There is currently great interest in analyzing the workflow of minimally invasive operations performed in a physical or simulation setting, with the aim of extracting important information that can be used for skills improvement, optimization of intraoperative processes, and comparison of different interventional strategies. The first step in achieving this goal is to segment the operation into its key interventional phases, which is currently approached by modeling a multivariate signal that describes the temporal usage of a predefined set of tools. Although this technique has shown promising results, it is challenged by the manual extraction of the tool usage sequence and the inability to simultaneously evaluate the surgeon's skills. In this paper we describe an alternative methodology for surgical phase segmentation and performance analysis based on Gaussian mixture multivariate autoregressive (GMMAR) models of the hand kinematics. Unlike previous work in this area, our technique employs signals from orientation sensors, attached to the endoscopic instruments of a virtual reality simulator, without considering which tools are employed at each time-step of the operation. First, based on pre-segmented hand motion signals, a training set of regression coefficients is created for each surgical phase using multivariate autoregressive (MAR) models. Then, a signal from a new operation is processed with GMMAR, wherein each phase is modeled by a Gaussian component of regression coefficients. These coefficients are compared to those of the training set. The operation is segmented according to the prior probabilities of the surgical phases estimated via GMMAR. The method also allows for the study of motor behavior and hand motion synchronization demonstrated in each phase, a quality that can be incorporated into modern laparoscopic simulators for skills assessment.

  3. Web-based tools for modelling and analysis of multivariate data: California ozone pollution activity

    NASA Astrophysics Data System (ADS)

    Dinov, Ivo D.; Christou, Nicolas

    2011-09-01

    This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting and statistical inference on these data are presented. All components of this case study (data, tools, activity) are freely available online at: http://wiki.stat.ucla.edu/socr/index.php/SOCR_MotionCharts_CAOzoneData. Several types of exploratory (motion charts, box-and-whisker plots, spider charts) and quantitative (inference, regression, analysis of variance (ANOVA)) data analyses tools are demonstrated. Two specific human health related questions (temporal and geographic effects of ozone pollution) are discussed as motivational challenges.

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

    PubMed

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

    2014-06-01

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

  5. [Multivariate analysis as a means of access to optimal pharmacochemical specificity and to molecular archetypes].

    PubMed

    Doré, J C; Viel, C; Lacroix, R; Lacroix, J

    1990-01-01

    For complex works as studies relationships structure-activity, in heterogeneous therapeutic families we have selected mathematical methods founded upon systemic approach rather analytic one, appealing to bibliographical data, taking into consideration a plurality of biological targets and envisaging structural extrapolations rather than interpolations. Compared with classical QSAR, multivariate analysis (factorial analysis and multidimentional data reduction) intend from structuration of whole complex items to definite spheres of correlations between structural parameters and biological ones to issue then on a symetric typology of this two groups of parameters. These approaches have not only a descriptive character but lead to operational conclusions through an interactive dialogue with data bank; for example: --to explore acting potentiality of others molecular families or/and particular sub-structures --to find chemical sequences of molecules synthesized for other aims but not again experimented for this property. The case of antiparasitic agents is here developed.

  6. Simultaneous analysis of riboflavin and aromatic amino acids in beer using fluorescence and multivariate calibration methods.

    PubMed

    Sikorska, Ewa; Gliszczyńska-Swigło, Anna; Insińska-Rak, Małgorzata; Khmelinskii, Igor; De Keukeleire, Denis; Sikorski, Marek

    2008-04-21

    The study demonstrates an application of the front-face fluorescence spectroscopy combined with multivariate regression methods to the analysis of fluorescent beer components. Partial least-squares regressions (PLS1, PLS2, and N-way PLS) were utilized to develop calibration models between synchronous fluorescence spectra and excitation-emission matrices of beers, on one hand, and analytical concentrations of riboflavin and aromatic amino acids, on the other hand. The best results were obtained in the analysis of excitation-emission matrices using the N-way PLS2 method. The respective correlation coefficients, and the values of the root mean-square error of cross-validation (RMSECV), expressed as percentages of the respective mean analytic concentrations, were: 0.963 and 14% for riboflavin, 0.974 and 4% for tryptophan, 0.980 and 4% for tyrosine, and 0.982 and 19% for phenylalanine.

  7. Distinguishing Monosaccharide Stereo- and Structural Isomers with ToF-SIMS and Multivariate Statistical Analysis

    SciTech Connect

    Berman, E F; Kulp, K S; Knize, M G; Wu, L; Nelson, E J; Nelson, D O; Wu, K J

    2006-05-04

    Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) is utilized to examine the mass spectra and fragmentation patterns of seven isomeric monosaccharides. Multivariate statistical analysis techniques, including principal component analysis (PCA), allow discrimination of the extremely similar mass spectra of stereoisomers. Furthermore, PCA identifies those fragment peaks which vary significantly between spectra. Heavy isotope studies confirm that these peaks are indeed sugar fragments, allow identification of the fragments, and provide clues to the fragmentation pathways. Excellent reproducibility is shown by multiple experiments performed over time and on separate samples. This study demonstrates the combined selectivity and discrimination power of ToF-SIMS and PCA, and suggests new applications of the technique including differentiation of subtle chemical changes in biological samples that may provide insights into cellular processes, disease progress, and disease diagnosis.

  8. MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information.

    PubMed

    Lee, S H; van der Werf, J H J

    2016-05-01

    We have developed an algorithm for genetic analysis of complex traits using genome-wide SNPs in a linear mixed model framework. Compared to current standard REML software based on the mixed model equation, our method is substantially faster. The advantage is largest when there is only a single genetic covariance structure. The method is particularly useful for multivariate analysis, including multi-trait models and random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss the advantages and limitations. MTG2 is available in https://sites.google.com/site/honglee0707/mtg2 CONTACT: hong.lee@une.edu.au Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

  9. Web-based tools for modelling and analysis of multivariate data: California ozone pollution activity

    PubMed Central

    Dinov, Ivo D.; Christou, Nicolas

    2014-01-01

    This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting and statistical inference on these data are presented. All components of this case study (data, tools, activity) are freely available online at: http://wiki.stat.ucla.edu/socr/index.php/SOCR_MotionCharts_CAOzoneData. Several types of exploratory (motion charts, box-and-whisker plots, spider charts) and quantitative (inference, regression, analysis of variance (ANOVA)) data analyses tools are demonstrated. Two specific human health related questions (temporal and geographic effects of ozone pollution) are discussed as motivational challenges. PMID:24465054

  10. Classification of ancient Etruscan ceramics using statistical multivariate analysis of data

    NASA Astrophysics Data System (ADS)

    Fermo, P.; Cariati, F.; Ballabio, D.; Consonni, V.; Bagnasco Gianni, G.

    About one hundred Etruscan ceramic shards dating from the VIII to the IV century BC and coming from the archaeological excavation at Pian di Civita in Tarquinia (central Italy) have been analyzed by inductively coupled plasma optical emission spectrometry and flame atomic emission spectrometry in order to settle their provenance and to acquire knowledge about the ceramic production technology. The examined shards belong to the class of the depurata pottery, a fine ware produced in Tarquinia over a long period, and are representative of different sub-classes. The samples have been analyzed for fifteen elements (Ca, Al, Mg, Fe, Ti, Cr, Cu, Ni, Zn, Mn, Zr, Sr, Na, K and Rb). The data acquired have been treated by multivariate analysis techniques such as principal component analysis and Kohonen artificial neural networks. Most of the analyzed shards have been locally produced as belonging to a unique large group. A continuity in usage of both choice of materials and technology has been recognized.

  11. Multivariate analysis of groundwater resources in Ganga-Yamuna basin (India).

    PubMed

    Sargaonkar, Aabha P; Gupta, Apurba; Devotta, Sukumar

    2008-07-01

    Groundwater quality data on physico-chemical, bacteriological and heavy metal concentrations in three cities (Faridabad, Allahabad and Varanasi) in Ganga-Yamuna basin was subjected to multivariate analysis (MVA) using SPSS. The factors extracted showed high loading (> 0.3) of various parameters, such as Cl, conductivity, TDS, hardness, Na, Mg, and SO4, indicating contamination due to leaching of pollutants. Major manifest variable associated with these factors is the unorganized solid waste dumping practiced in all the cities. Bacterial contamination of hand pump samples in Allahabad is attributed to surface water-groundwater interaction. The factor with high loading of Ca and F is indicative of geological conditions of the region. Wells in Yamuna river sub-watershed exhibit less freshwater recharge, which is attributed to surface water pollution and sediment deposition in the river. Thus, the methodology for hydrogeological analysis is useful to identify critical water quality issues and possible sources of pollution in river basins.

  12. Detection of Cervical Cancer Analyzing Blood Samples with Raman Spectroscopy and Multivariate Analysis

    NASA Astrophysics Data System (ADS)

    González-Solís, J. L.; Rodríguez-López, J.; Martínez-Espinosa, J. C.; Frausto-Reyes, C.; Jave-Suárez, L. F.; Aguilar-Lemarroy, A. C.; Vargas-Rodríguez, H.; Martínez-Cano, E.

    2010-05-01

    The use of Raman spectroscopy to analyze blood biochemistry and hence distinguish between normal and abnormal blood was investigated. The blood samples were obtained from 20 patients who were clinically diagnosed with cervical cancer and 10 healthy volunteer. The imprint was put under the Olympus microscope and several points were chosen for Raman measurement. All spectra were collected at a Jobin-Yvon LabRAM HR800 Raman Spectrometer with NIR 830 nm laser. It is shown that the serum samples from patients with cervical cancer and from the control group can be discriminated when the multivariate statistical methods of Principal Component Analysis (PCA) and Linear Discriminated Analysis (LDA) is applied to their Raman spectra. The ratios of some band intensities were analyzed and some band ratios were significant and corresponded to proteins, phospholipids, and polysaccharides. The preliminary results suggest that Raman spectroscopy could be a new technique for the detection using just blood samples.

  13. Denoising and Multivariate Analysis of Time-Of-Flight SIMS Images

    SciTech Connect

    Wickes, Bronwyn; Kim, Y.; Castner, David G.

    2003-08-30

    Time-of-flight SIMS (ToF-SIMS) imaging offers a modality for simultaneously visualizing the spatial distribution of different surface species. However, the utility of ToF-SIMS datasets may be limited by their large size, degraded mass resolution and low ion counts per pixel. Through denoising and multivariate image analysis, regions of similar chemistries may be differentiated more readily in ToF-SIMS image data. Three established denoising algorithms down-binning, boxcar and wavelet filtering were applied to ToF-SIMS images of different surface geometries and chemistries. The effect of these filters on the performance of principal component analysis (PCA) was evaluated in terms of the capture of important chemical image features in the principal component score images, the quality of the principal component

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

    PubMed

    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).

  15. Reflection and transmission mid-infrared spectroscopy for rapid determination of coal properties by multivariate analysis.

    PubMed

    Bona, M T; Andrés, J M

    2008-01-15

    In the present paper, the influence of different acquisition techniques (transmission, diffuse reflectance infrared Fourier transform and attenuated total reflectance) in the determination of nine coal properties related to combustion power plants has been studied. Raw coal samples of different origins were pooled for developing a correlation between the resultant spectra and the corresponding coal properties by multivariate analysis techniques. Thus, the existent collinearity in mid-infrared coal spectra led to the application of partial least squares regression (PLS), studying simultaneously the influence of different spectroscopic units as well as several spectral data mathematical pre-treatments. On the other hand, a principal component analysis (PCA) revealed a relationship between principal components and coal composition in both transmission and reflection techniques. Although the best accuracy and precision results were obtained for coal properties related to organic matter, the system was also able to differentiate coal samples attending to the presence of a specific mineral matter, kaolinite.

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

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

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

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

    PubMed Central

    Ultsch, Alfred; Lötsch, Jörn

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Sayemuzzaman, M.; Ye, M.

    2015-12-01

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

  19. Factors associated with a more rapid recovery after anterior cruciate ligament reconstruction using multivariate analysis.

    PubMed

    Scherer, Job E; Moen, Maarten H; Weir, Adam; Schmikli, Sandor L; Tamminga, Rob; van der Hoeven, Henk

    2016-01-01

    In the past, several studies investigated factors that are prognostic or associated with outcome after anterior cruciate ligament (ACL) reconstruction. A recent review showed that only limited evidence is available for most studied factors, and that insufficient analysis methods were used commonly. Therefore, the aim of this study was to add more weight to the existing evidence, about factors that are associated with a more rapid outcome after ACL reconstruction. The second aim was to use multivariate analysis to study the possible factors independently. A cohort study was conducted with a follow-up of six months. Before surgery, patient variables were scored. Surgical variables were scored during arthroscopic ACL reconstructions with a single-bundle technique and hamstring autograft. The Lysholm score and subscales of the Knee Injury Osteoarthritis Outcome Score (KOOS) were assessed six months post surgery. A multiple analysis of variance (ANOVA) model was used to identify prognostic factors for outcome. In total, 118 patients were included. Patients, aged ≤30years, with a subjective knee score ≥ six, with normal flexion range of motion (ROM) of the knee, with flexion and extension strength deficit of ≤20%, and those with no previous knee surgery in the same knee at baseline scored significantly higher on outcome after multivariate analysis. No significant effect of surgical factors could be found. Younger age, higher subjective knee score, normal knee flexion, normal knee flexion and extension strength, and no previous knee surgery in the patients' history at baseline are associated with a more rapid recovery after ACL reconstruction. Level III, prognostic study. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. ADVANCING THE UNDERSTANDING OF BEHAVIORS ASSOCIATED WITH BACILLE CALMETTE GUÉRIN INFECTION USING MULTIVARIATE ANALYSIS

    PubMed Central

    Rodriguez-Zas, Sandra L.; Nixon, Scott E.; Lawson, Marcus A.; Mccusker, Robert H.; Southey, Bruce R.; O’Connor, Jason C.; Dantzer, Robert; Kelley, Keith W.

    2014-01-01

    Behavioral indicators in the murine Bacille Calmette Guérin (BCG) model of inflammation have been studied individually; however, the variability of the behaviors across BCG levels and the mouse-to-mouse variation within BCG-treatment group are only partially understood. The objectives of this study were: 1) to gain a comprehensive understanding of sickness and depression-like behaviors in a BCG model of inflammation using multivariate approaches, and 2) to explore behavioral differences between BCG-treatment groups and among mice within group. Adult mice were challenged with either 0mg (saline), 5mg or 10mg of BCG (BCG-treatment groups: BCG0, BCG5, or BCG10, respectively) at Day 0 of the experiment. Sickness indicators included body weight changes between Day 0 and Day 2 and between Day 2 and Day 5, and horizontal locomotor activity and vertical activity (rearing) measured at Day 6. Depression-like indicators included duration of immobility in the forced swim test and in the tail suspension test at Day 6 and sucrose consumption in the sucrose preference test at Day 7. The simultaneous consideration of complementary sickness and depression-like indicators enabled a more precise characterization of behavioral changes associated with BCG-treatment and of mouse-to-mouse variation, relative to the analysis of indicators individually. Univariate and multivariate analyses confirmed differences between BCG-treatment groups in weight change early on the trial. Significant differences between BCG-treatment groups in depression-like behaviors were still measurable after Day 5. The potential for multivariate models to account for the correlation between behavioral indicators and to augment the analytical precision relative to univariate models was demonstrated both for sickness and for depression-like indicators. Unsupervised learning approaches revealed the complementary information provided by the sickness and depression-like indicators considered. Supervised learning

  1. A Distance Measure for Automatic Document Classification by Sequential Analysis.

    ERIC Educational Resources Information Center

    Kar, Gautam; White, Lee J.

    1978-01-01

    Investigates the feasibility of using a distance measure for automatic sequential document classification. This property of the distance measure is used to design a sequential classification algorithm which classifies key words and analyzes them separately in order to assign primary and secondary classes to a document. (VT)

  2. Improvement of Automatic Abstracts by the Use of Structural Analysis

    ERIC Educational Resources Information Center

    Mathis, Betty A.; And Others

    1973-01-01

    Results of an attempt to extend the capabilities of a previously-existing automatic abstracting system by adding a modification procedure designed to make system-produced abstracts more acceptable to readers are reported. A rationale for this modification phase is presented, along with several modification rules and methods for improving…

  3. Sentence Similarity Analysis with Applications in Automatic Short Answer Grading

    ERIC Educational Resources Information Center

    Mohler, Michael A. G.

    2012-01-01

    In this dissertation, I explore unsupervised techniques for the task of automatic short answer grading. I compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating…

  4. A Distance Measure for Automatic Document Classification by Sequential Analysis.

    ERIC Educational Resources Information Center

    Kar, Gautam; White, Lee J.

    1978-01-01

    Investigates the feasibility of using a distance measure for automatic sequential document classification. This property of the distance measure is used to design a sequential classification algorithm which classifies key words and analyzes them separately in order to assign primary and secondary classes to a document. (VT)

  5. Sentence Similarity Analysis with Applications in Automatic Short Answer Grading

    ERIC Educational Resources Information Center

    Mohler, Michael A. G.

    2012-01-01

    In this dissertation, I explore unsupervised techniques for the task of automatic short answer grading. I compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating…

  6. EXPERIMENTAL ANALYSIS OF MULTIVARIATE FEMALE CHOICE IN GRAY TREEFROGS (Hyla versicolor)

    PubMed Central

    Gerhardt, H. Carl; Brooks, Robert

    2009-01-01

    Even simple biological signals vary in several measurable dimensions. Understanding their evolution requires, therefore, a multivariate understanding of selection, including how different properties interact to determine the effectiveness of the signal. We combined experimental manipulation with multivariate selection analysis to assess female mate choice on the simple trilled calls of male gray treefrogs. We independently and randomly varied five behaviorally relevant acoustic properties in 154 synthetic calls. We compared response times of each of 154 females to one of these calls with its response to a standard call that had mean values of the five properties. We found directional and quadratic selection on two properties indicative of the amount of signaling, pulse number and call rate. Canonical rotation of the fitness surface showed that these properties, along with pulse rate, contributed heavily to a major axis of stabilizing selection, a result consistent with univariate studies showing diminishing effects of increasing pulse number well beyond the mean. Spectral properties contributed to a second major axis of stabilizing selection. The single major axis of disruptive selection suggested that a combination of two temporal and two spectral properties with values differing from the mean should be especially attractive. PMID:19500145

  7. Quantitative modeling of bioconcentration factors of carbonyl herbicides using multivariate image analysis.

    PubMed

    Freitas, Mirlaine R; Barigye, Stephen J; Daré, Joyce K; Freitas, Matheus P

    2016-06-01

    The bioconcentration factor (BCF) is an important parameter used to estimate the propensity of chemicals to accumulate in aquatic organisms from the ambient environment. While simple regressions for estimating the BCF of chemical compounds from water solubility or the n-octanol/water partition coefficient have been proposed in the literature, these models do not always yield good correlations and more descriptive variables are required for better modeling of BCF data for a given series of organic pollutants, such as some herbicides. Thus, the logBCF values for a set of carbonyl herbicides comprising amide, urea, carbamate and thiocarbamate groups were quantitatively modeled using multivariate image analysis (MIA) descriptors, derived from colored image representations for chemical structures. The logBCF model was calibrated and vigorously validated (r(2) = 0.79, q(2) = 0.70 and rtest(2) = 0.81), providing a comprehensive three-parameter linear equation after variable selection (logBCF = 5.682 - 0.00233 × X9774 - 0.00070 × X813 - 0.00273 × X5144); the variables represent pixel coordinates in the multivariate image. Finally, chemical interpretation of the obtained models in terms of the structural characteristics responsible for the enhanced or reduced logBCF values was performed, providing key leads in the prospective development of more eco-friendly synthetic herbicides.

  8. Predicting malignant neck lymphadenopathy using color duplex sonography based on multivariate analysis.

    PubMed

    Chammas, Maria C; Macedo, Túlio A A; Lo, Victor W; Gomes, Andrea C; Juliano, Adriana; Cerri, Giovanni G

    2016-11-12

    To select the best predictors of cervical lymph node malignancy based on gray-scale and power Doppler sonography using multivariate analysis. We evaluated sonographically a total of 97 lymph nodes in the neck that were subjected to fine-needle aspiration biopsy. The gray-scale and power Doppler sonography parameters that we analyzed using multivariate logistic regression included size, shape, echogenicity, echotexture, margins, hilum, presence of microcalcifications or necrosis, vascularization, and resistance index (RI). The three variables with a diagnostic accuracy exceeding 80% were an altered vascularization, heterogeneous echotexture, and abnormal hilum. Malignant nodes exhibited higher RI and larger sizes than benign nodes, and the best cutoff values to distinguish malignant from benign lymph nodes were an RI of 0.77 and a short axis ≥ 0.9 cm. Altered vascularization, a short axis ≥ 0.9 cm, and abnormal hilum were the best predictors of malignancy. The best sonographic predictors of lymph node malignancy are, in descending order, an altered vascularization, a short axis ≥ 0.9 cm, an abnormal hilum, and a heterogeneous echotexture. © 2016 Wiley Periodicals, Inc. J Clin Ultrasound 44:587-594, 2016. © 2016 Wiley Periodicals, Inc.

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

    PubMed

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

    2010-01-01

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

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

    PubMed

    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.

  11. Increased power of microarray analysis by use of an algorithm based on a multivariate procedure.

    PubMed

    Krohn, K; Eszlinger, M; Paschke, R; Roeder, I; Schuster, E

    2005-09-01

    The power of microarray analyses to detect differential gene expression strongly depends on the statistical and bioinformatical approaches used for data analysis. Moreover, the simultaneous testing of tens of thousands of genes for differential expression raises the 'multiple testing problem', increasing the probability of obtaining false positive test results. To achieve more reliable results, it is, therefore, necessary to apply adjustment procedures to restrict the family-wise type I error rate (FWE) or the false discovery rate. However, for the biologist the statistical power of such procedures often remains abstract, unless validated by an alternative experimental approach. In the present study, we discuss a multiplicity adjustment procedure applied to classical univariate as well as to recently proposed multivariate gene-expression scores. All procedures strictly control the FWE. We demonstrate that the use of multivariate scores leads to a more efficient identification of differentially expressed genes than the widely used MAS5 approach provided by the Affymetrix software tools (Affymetrix Microarray Suite 5 or GeneChip Operating Software). The practical importance of this finding is successfully validated using real time quantitative PCR and data from spike-in experiments. The R-code of the statistical routines can be obtained from the corresponding author. Schuster@imise.uni-leipzig.de

  12. The size and shape of shells used by hermit crabs: A multivariate analysis of Clibanarius erythropus

    NASA Astrophysics Data System (ADS)

    Caruso, Tancredi; Chemello, Renato

    2009-05-01

    Shell attributes such as weight and shape affect the reproduction, growth, predator avoidance and behaviour of several hermit crab species. Although the importance of these attributes has been extensively investigated, it is still difficult to assess the relative role of size and shape. Multivariate techniques allow concise and efficient quantitative analysis of these multidimensional properties, and this paper aims to understand their role in determining patterns of hermit crab shell use. To this end, a multivariate approach based on a combination of size-unconstrained (shape) PCA and RDA ordination was used to model the biometrics of southern Mediterranean Clibanarius erythropus populations and their shells. Patterns of shell utilization and morphological gradients demonstrate that size is more important than shape, probably due to the limited availability of empty shells in the environment. The shape (e.g. the degree of shell elongation) and weight of inhabited shells vary considerably in both female and male crabs. However, these variations are clearly accounted for by crab biometrics in males only. On the basis of statistical evidence and findings from past studies, it is hypothesized that larger males of adequate size and strength have access to the larger, heavier and relatively more available shells of the globose Osilinus turbinatus, which cannot be used by average-sized males or by females investing energy in egg production. This greater availability allows larger males to select more suitable shapes.

  13. Multivariate Analysis of Magnetic Resonance Imaging Signals of the Human Brain.

    PubMed

    Miyawaki, Yoichi

    2016-01-01

    Magnetic resonance imaging (MRI) of the human brain plays an important role in the field of medical imaging as well as basic neuroscience. It measures proton spin relaxation, the time constant of which depends on tissue type, and allows us to visualize anatomical structures in the brain. It can also measure functional signals that depend on the local ratio of oxyhemoglobin to deoxyhemoglobin in the blood, which is believed to reflect the degree of neural activity in the corresponding area. MRI thus provides anatomical and functional information about the human brain with high spatial resolution. Conventionally, MRI signals are measured and analyzed for each individual voxel. However, these signals are essentially multivariate because they are measured from multiple voxels simultaneously, and the pattern of activity might carry more useful information than each individual voxel does. This paper reviews recent trends in multivariate analysis of MRI signals in the human brain, and discusses applications of this technique in the fields of medical imaging and neuroscience.

  14. Multivariate analysis of prognostic factors for salvage nasopharyngectomy via the maxillary swing approach.

    PubMed

    Chan, Jimmy Yu Wai; To, Victor Shing Howe; Chow, Velda Ling Yu; Wong, Stanley Thian Sze; Wei, William Ignace

    2014-07-01

    The purpose of this study was to investigate the prognostic factors for salvage nasopharyngectomy. A retrospective review was conducted on maxillary swing nasopharyngectomy performed between 1998 and 2010. Univariate and multivariate analyses identified prognostic factors affecting actuarial local tumor control and overall survival. The median follow-up duration was 52 months. Among the 268 patients, 79.1% had clear resection margins. The 5-year actuarial local tumor control and overall survival was 74% and 62.1%, respectively. On multivariate analysis, tumor size, resection margin status, and gross tumor in the sphenoid sinus were independent prognostic factors for local tumor control. For overall survival, resection margin status, synchronous cervical nodal recurrence, and cavernous sinus invasion had a negative influence on overall survival after surgery. Extent of nasopharyngectomy should be tailored to the individual tumor to achieve clear resection margins. Cavernous sinus invasion is associated with poor survival outcome, and detailed counseling and meticulous surgical planning is crucial in such circumstances. Copyright © 2014 Wiley Periodicals, Inc.

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

    PubMed

    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.

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

    PubMed

    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.

  17. A Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis

    PubMed Central

    Collins, Tony J.; Ylanko, Jarkko; Geng, Fei

    2015-01-01

    Abstract A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose–response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds. PMID:26422066

  18. Multivariate analysis of risk factors for QT prolongation following subarachnoid hemorrhage

    PubMed Central

    Fukui, Shinji; Katoh, Hiroshi; Tsuzuki, Nobusuke; Ishihara, Shoichiro; Otani, Naoki; Ooigawa, Hidetoshi; Toyooka, Terushige; Ohnuki, Akira; Miyazawa, Takahito; Nawashiro, Hiroshi; Shima, Katsuji

    2003-01-01

    Background Subarachnoid hemorrhage (SAH) often causes a prolongation of the corrected QT (QTc) interval during the acute phase. The aim of the present study was to examine independent risk factors for QTc prolongation in patients with SAH by means of multivariate analysis. Method We studied 100 patients who were admitted within 24 hours after onset of SAH. Standard 12-lead electrocardiography (ECG) was performed immediately after admission. QT intervals were measured from the ECG and were corrected for heart rate using the Bazett formula. We measured serum levels of sodium, potassium, calcium, adrenaline (epinephrine), noradrenaline (norepinephrine), dopamine, antidiuretic hormone, and glucose. Results The average QTc interval was 466 ± 46 ms. Patients were categorized into two groups based on the QTc interval, with a cutoff line of 470 ms. Univariate analyses showed significant relations between categories of QTc interval, and sex and serum concentrations of potassium, calcium, or glucose. Multivariate analyses showed that female sex and hypokalemia were independent risk factors for severe QTc prolongation. Hypokalemia (<3.5 mmol/l) was associated with a relative risk of 4.53 for severe QTc prolongation as compared with normokalemia, while the relative risk associated with female sex was 4.45 as compared with male sex. There was a significant inverse correlation between serum potassium levels and QTc intervals among female patients. Conclusion These findings suggest that female sex and hypokalemia are independent risk factors for severe QTc prolongation in patients with SAH. PMID:12793884

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

    SciTech Connect

    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

  20. Applications of multivariate modeling to neuroimaging group analysis: A comprehensive alternative to univariate general linear model

    PubMed Central

    Chen, Gang; Adleman, Nancy E.; Saad, Ziad S.; Leibenluft, Ellen; Cox, RobertW.

    2014-01-01

    All neuroimaging packages can handle group analysis with t-tests or general linear modeling (GLM). However, they are quite hamstrung when there are multiple within-subject factors or when quantitative covariates are involved in the presence of a within-subject factor. In addition, sphericity is typically assumed for the variance–covariance structure when there are more than two levels in a within-subject factor. To overcome such limitations in the traditional AN(C)OVA and GLM, we adopt a multivariate modeling (MVM) approach to analyzing neuroimaging data at the group level with the following advantages: a) there is no limit on the number of factors as long as sample sizes are deemed appropriate; b) quantitative covariates can be analyzed together with within- subject factors; c) when a within-subject factor is involved, three testing methodologies are provided: traditional univariate testing (UVT)with sphericity assumption (UVT-UC) and with correction when the assumption is violated (UVT-SC), and within-subject multivariate testing (MVT-WS); d) to correct for sphericity violation at the voxel level, we propose a hybrid testing (HT) approach that achieves equal or higher power via combining traditional sphericity correction methods (Greenhouse–Geisser and Huynh–Feldt) with MVT-WS. PMID:24954281