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Sample records for addition multivariate analysis

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

    NASA Astrophysics Data System (ADS)

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

    2015-02-01

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

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

    PubMed

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

    2015-02-25

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

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

    PubMed

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

    2015-02-25

    A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety. PMID:25300041

  4. Application of multivariate analysis to the effects of additives on chemical and sensory quality of stored coffee brew.

    PubMed

    Pérez-Martínez, Mónica; Sopelana, Patricia; de Peña, M Paz; Cid, Concepción

    2008-12-24

    The aim of this work was to obtain a black coffee brew to be consumed hot by extension of its shelf life, by addition of additives. Four pH-regulator agents (sodium and potassium carbonates and bicarbonates), one pH regulator and antioxidant (sodium citrate), three antioxidants [sodium ascorbate, ethylenediaminetetracetic acid (EDTA), and sodium sulfite], and lactoserum were tested by sensory analysis. Sodium carbonate and bicarbonate were selected for a study of the physicochemical (soluble and volatile compounds related to the sensory properties) and sensorial quality of coffee brew stored for 90 days at 4 degrees C. Although both additives extended the shelf life of the coffee brew up to 60 days, sodium carbonate was the chosen additive because it was the most useful in limiting the pH decrease and perception of sourness, which are some of the main factors involved in the rejection of stored coffee brews, and it better maintained the aroma and taste/flavor. Moreover, the application of multivariate analysis facilitated first the description of the global changes of the coffee brews with or without additives throughout the storage using principal component analysis and second the obtainment of a simple equation only with pH and caffeic acid parameters to discriminate the three types of coffee brews and simplify the analytical process, by means of the stepwise discriminant analysis.

  5. Multivariate Analysis in Metabolomics

    PubMed Central

    Worley, Bradley; Powers, Robert

    2015-01-01

    Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions. PMID:26078916

  6. 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.

  7. A screening method based on UV-Visible spectroscopy and multivariate analysis to assess addition of filler juices and water to pomegranate juices.

    PubMed

    Boggia, Raffaella; Casolino, Maria Chiara; Hysenaj, Vilma; Oliveri, Paolo; Zunin, Paola

    2013-10-15

    Consumer demand for pomegranate juice has considerably grown, during the last years, for its potential health benefits. Since it is an expensive functional food, cheaper fruit juices addition (i.e., grape and apple juices) or its simple dilution, or polyphenols subtraction are deceptively used. At present, time-consuming analyses are used to control the quality of this product. Furthermore these analyses are expensive and require well-trained analysts. Thus, the purpose of this study was to propose a high-speed and easy-to-use shortcut. Based on UV-VIS spectroscopy and chemometrics, a screening method is proposed to quickly screening some common fillers of pomegranate juice that could decrease the antiradical scavenging capacity of pure products. The analytical method was applied to laboratory prepared juices, to commercial juices and to representative experimental mixtures at different levels of water and filler juices. The outcomes were evaluated by means of multivariate exploratory analysis. The results indicate that the proposed strategy can be a useful screening tool to assess addition of filler juices and water to pomegranate juices. PMID:23692760

  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 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

  10. 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. PMID:27537692

  11. 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

  12. 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.

  13. Multivariate data analysis for outcome studies.

    PubMed

    Spector, P E

    1981-02-01

    The use of multivariate statistical techniques for analyzing the complex data often gathered in outcome studies is discussed. The multivariate analysis of variance (MANOVA) is suggested for multiple group studies common to outcome studies. This technique can be utilized for a large number of specific research designs whenever multiple outcome measures are collected. MANOVA offers two specific advantages over more familiar univariate approaches: it presents better control over Type 1 error rates while preserving statistical power, and it allows more thorough analysis of complex data. PMID:7223728

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. Acute proliferative retrolental fibroplasia: multivariate risk analysis.

    PubMed Central

    Flynn, J T

    1983-01-01

    This study has presented a two-way analysis of a data set consisting of demographic, diagnostic, and therapeutic variables against the risk of occurrence of APRLF and its location in the retina in a population of 639 infants in birthweights ranging from 600 to 1500 gm. Univariate and multivariate risk analysis techniques were employed to analyze the data. As established from previous studies, birthweight was a powerful predictor of the outcome variable. Oxygen therapy as defined and quantified in this study was not. Duration of ventilatory assistance did seem associated. The population was not uniform. Infants below 1000 gm birthweight had such a high incidence of APRLF that no other exogenous risk factors seemed of significance. Above 1000 gm birthweight, certain factors, particularly duration of ventilation, seemed of predictive strength and significance. Images FIGURE 5 A FIGURE 5 B FIGURE 4 A FIGURE 4 B PMID:6689564

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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).

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

    NASA Astrophysics Data System (ADS)

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

    2015-01-01

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

  7. 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…

  8. Second-order multivariate models for the processing of standard-addition synchronous fluorescence-pH data. Application to the analysis of salicylic acid and its major metabolite in human urine.

    PubMed

    Pagani, Ariana P; Ibañez, Gabriela A

    2014-05-01

    In the present work, we describe the determination of salicylic acid and its major metabolite, salicyluric acid, in spiked human urine samples, using synchronous fluorescence spectra measured in a flow-injection system with a double pH gradient. Because the fluorescent urine background constitutes a potentially interfering signal, it becomes necessary to achieve the second-order advantage. Moreover, due to significant changes in the signal of the analytes in the presence of the urine matrix, mainly for salicyluric acid, standard addition was required in order to obtain appropriate quantifications. Several second-order multivariate calibration models were evaluated for this purpose: PARAFAC and MCR-ALS in two different modes, and PLS/RBL.

  9. Multivariate analysis of environmental data for two hydrographic basins

    SciTech Connect

    Andrade, J.M.; Prada, D.; Muniategui, S.; Gonzalez, E.; Alonso, E. )

    1992-02-01

    A multivariate study (PCA Analysis and Cluster analysis) of two Spanish hydrographic basins (The Mandeo and Mero basins) was made to achieve reliable conclusions about their actual physico-chemical environmental situation. Two police-samples' are defined, their effects explained, and are introduced in Cluster analysis as a way to examine sample quality. The multivariate analysis shows different qualities in the two hydrographic basins.

  10. Multivariate Analysis of Ipsative Data: Problems and Solutions.

    ERIC Educational Resources Information Center

    McLean, James E.; Chissom, Brad S.

    The term "ipsative" refers to measurement based on intra-individual comparisons. The research literature in the social sciences contains many cautions about using ipsative data in multivariate analysis. The purpose of this paper is to identify the problems associated with the multivariate and regression analyses of ipsative data and to provide…

  11. Regional dissociated heterochrony in multivariate analysis.

    PubMed

    Mitteroecker, P; Gunz, P; Weber, G W; Bookstein, F L

    2004-12-01

    Heterochrony, the classic framework to study ontogeny and phylogeny, in essence relies on a univariate concept of shape. Though principal component plots of multivariate shape data seem to resemble classical bivariate allometric plots, the language of heterochrony cannot be translated directly into general multivariate methodology. We simulate idealized multivariate ontogenetic trajectories and demonstrate their behavior in principal component plots in shape space and in size-shape space. The concept of "dissociation", which is conventionally regarded as a change in the relationship between shape change and size change, appears to be algebraically the same as regional dissociation - the variation of apparent heterochrony by region. Only if the trajectories of two related species lie along exactly the same path in shape space can the classic terminology of heterochrony apply so that pure dissociation of size change against shape change can be detected. We demonstrate a geometric morphometric approach to these issues using adult and subadult crania of 48 Pan paniscus and 47 P. troglodytes. On each specimen we digitized 47 landmarks and 144 semilandmarks on ridge curves and the external neurocranial surface. The relation between these two species' growth trajectories is too complex for a simple summary in terms of global heterochrony.

  12. Multivariate linkage analysis of specific language impairment (SLI).

    PubMed

    Monaco, Anthony P

    2007-09-01

    Specific language impairment (SLI) is defined as an inability to develop appropriate language skills without explanatory medical conditions, low intelligence or lack of opportunity. Previously, a genome scan of 98 families affected by SLI was completed by the SLI Consortium, resulting in the identification of two quantitative trait loci (QTL) on chromosomes 16q (SLI1) and 19q (SLI2). This was followed by a replication of both regions in an additional 86 families. Both these studies applied linkage methods to one phenotypic trait at a time. However, investigations have suggested that simultaneous analysis of several traits may offer more power. The current study therefore applied a multivariate variance-components approach to the SLI Consortium dataset using additional phenotypic data. A multivariate genome scan was completed and supported the importance of the SLI1 and SLI2 loci, whilst highlighting a possible novel QTL on chromosome 10. Further investigation implied that the effect of SLI1 on non-word repetition was equally as strong on reading and spelling phenotypes. In contrast, SLI2 appeared to have influences on a selection of expressive and receptive language phenotypes in addition to non-word repetition, but did not show linkage to literacy phenotypes.

  13. Multivariate statistical analysis of environmental monitoring data

    SciTech Connect

    Ross, D.L.

    1997-11-01

    EPA requires statistical procedures to determine whether soil or ground water adjacent to or below waste units is contaminated. These statistical procedures are often based on comparisons between two sets of data: one representing background conditions, and one representing site conditions. Since statistical requirements were originally promulgated in the 1980s, EPA has made several improvements and modifications. There are, however, problems which remain. One problem is that the regulations do not require a minimum probability that contaminated sites will be correctly identified. Another problems is that the effect of testing several correlated constituents on the probable outcome of the statistical tests has not been quantified. Results from computer simulations to determine power functions for realistic monitoring situations are presented here. Power functions for two different statistical procedures: the Student`s t-test, and the multivariate Hotelling`s T{sup 2} test, are compared. The comparisons indicate that the multivariate test is often more powerful when the tests are applied with significance levels to control the probability of falsely identifying clean sites as contaminated. This program could also be used to verify that statistical procedures achieve some minimum power standard at a regulated waste unit.

  14. 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. PMID:16024067

  15. 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.

  16. 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

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

    PubMed Central

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

    2009-01-01

    Data analysis is an essential tenet of analytical chemistry, extending the possible information obtained from the measurement of chemical phenomena. Chemometric methods have grown considerably in recent years, but their wide use is hindered because some still consider them too complicated. The purpose of this review is to describe a multivariate chemometric method, principal component regression, in a simple manner from the point of view of an analytical chemist, to demonstrate the need for proper quality-control (QC) measures in multivariate analysis and to advocate the use of residuals as a proper QC method. PMID:20160977

  18. 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.

  19. 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…

  20. 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…

  1. 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.

  2. Comparing Predictors in Multivariate Regression Models: An Extension of Dominance Analysis

    ERIC Educational Resources Information Center

    Azen, Razia; Budescu, David V.

    2006-01-01

    Dominance analysis (DA) is a method used to compare the relative importance of predictors in multiple regression. DA determines the dominance of one predictor over another by comparing their additional R[squared] contributions across all subset models. In this article DA is extended to multivariate models by identifying a minimal set of criteria…

  3. Data-based transformations in multivariate analysis. Final report

    SciTech Connect

    Dunn, J.E.

    1980-04-15

    Univariate transformations are considered initially, because of the common practice of transforming separately the marginal distribution of each variable of a multivariate observation. Familiar examples include those based on a priori assumptions about the underlying sampling distribution, as well as several general classes of empirical transformations recommended in a recent text by Mosteller and Tukey. Multi-normal criteria are considered as a basis for obtaining and evaluating multivariate transformations, including the likelihood criterion and various transformations to uniform statistics. The extension of power and shifted-power transformations to multivariate analysis is reviewed in detail, including recently published work involving q-sample problems. Finally, applications of projective transformations are proposed in order to remove the effects of extraneous sources of variation, e.g., specimens of different ages, from different nutritional backgrounds, etc. It is shown that the actual values of these ancillary variables will not be required if the analysis is performed in a subspace which is orthogonal to the gradients attributable to these variables. Models are proposed for principal components analysis, canonical correlation, linear classification functions, and discriminant function analysis in the general MANOVA context.

  4. 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.

  5. Multivariate pattern analysis of fMRI: The early beginnings

    PubMed Central

    Haxby, James. V.

    2012-01-01

    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. PMID:22425670

  6. Multivariate statistical analysis of atom probe tomography data.

    PubMed

    Parish, Chad M; Miller, Michael K

    2010-10-01

    The application of spectrum imaging multivariate statistical analysis methods, specifically principal component analysis (PCA), to atom probe tomography (APT) data has been investigated. The mathematical method of analysis is described and the results for two example datasets are analyzed and presented. The first dataset is from the analysis of a PM 2000 Fe-Cr-Al-Ti steel containing two different ultrafine precipitate populations. PCA properly describes the matrix and precipitate phases in a simple and intuitive manner. A second APT example is from the analysis of an irradiated reactor pressure vessel steel. Fine, nm-scale Cu-enriched precipitates having a core-shell structure were identified and qualitatively described by PCA. Advantages, disadvantages, and future prospects for implementing these data analysis methodologies for APT datasets, particularly with regard to quantitative analysis, are also discussed. PMID:20650566

  7. 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

  8. 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.

  9. 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.

  10. Simplifying multivariate survival analysis using global score test methodology

    NASA Astrophysics Data System (ADS)

    Zain, Zakiyah; Aziz, Nazrina; Ahmad, Yuhaniz

    2015-12-01

    In clinical trials, the main purpose is often to compare efficacy between experimental and control treatments. Treatment comparisons often involve multiple endpoints, and this situation further complicates the analysis of survival data. In the case of tumor patients, endpoints concerning survival times include: times from tumor removal until the first, the second and the third tumor recurrences, and time to death. For each patient, these endpoints are correlated, and the estimation of the correlation between two score statistics is fundamental in derivation of overall treatment advantage. In this paper, the bivariate survival analysis method using the global score test methodology is extended to multivariate 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. Decoding neural representational spaces using multivariate pattern analysis.

    PubMed

    Haxby, James V; Connolly, Andrew C; Guntupalli, J Swaroop

    2014-01-01

    A major challenge for systems neuroscience is to break the neural code. Computational algorithms for encoding information into neural activity and extracting information from measured activity afford understanding of how percepts, memories, thought, and knowledge are represented in patterns of brain activity. The past decade and a half has seen significant advances in the development of methods for decoding human neural activity, such as multivariate pattern classification, representational similarity analysis, hyperalignment, and stimulus-model-based encoding and decoding. This article reviews these advances and integrates neural decoding methods into a common framework organized around the concept of high-dimensional representational spaces.

  13. Multivariate analysis of human immunodeficiency virus type 1 neutralization data.

    PubMed Central

    Nyambi, P N; Nkengasong, J; Lewi, P; Andries, K; Janssens, W; Fransen, K; Heyndrickx, L; Piot, P; van der Groen, G

    1996-01-01

    We report on the use of spectral map analysis of the inter- and intraclade neutralization data of 14 sera of human immunodeficiency virus type 1 (HIV-1)-infected individuals and 16 primary isolates, representing genetic clades A to H in group M and group O. This multivariate analysis has been used previously to study the interaction between drugs and receptors and between viruses and antiviral compounds. The analysis reveals the existence of neutralization clusters, not correlated with the known genetic clades. The structural factors that have been identified may correlate with the most important neutralization epitopes. Three key primary HIV-1 isolates, which allow discrimination of sera that are likely or unlikely to neutralize primary isolates from most of the genetic clades, were identified. Our method of analysis will facilitate the evaluation as well as the design of suitable HIV-1 vaccines, which induce high-titer interclade cross-neutralizing antibodies. PMID:8709250

  14. 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

  15. 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.

  16. Hydrogeochemical characteristics of groundwater in Latvia using multivariate statistical analysis

    NASA Astrophysics Data System (ADS)

    Retike, Inga; Kalvans, Andis; Bikse, Janis; Popovs, Konrads; Babre, Alise

    2015-04-01

    product between the two previously named clusters. Groundwater in cluster 2, 6 and 7 is considered to be a result of carbonate weathering with some addition of sea salts or gypsum dissolution. As a conclusion, the highest or lowest concentrations of some trace elements in groundwater was found out to be strongly associated with certain clusters. For example, Cluster 9 represents gypsum dissolution and has the highest concentrations of F, Sr, Rb, Li and the lowest levels of Ba. It can be also concluded that multivariate statistical analysis of major components can be used as an exploratory and predictive tool to identify groundwater objects with high possibility of elevated or reduced concentrations of harmful or essential trace elements. The research is supported by the European Union through the ESF Mobilitas grant No MJD309 and the European Regional Development Fund project Nr.2013/0054/2DP/2.1.1.1.0/13/APIA/VIAA/007 and NRP project EVIDENnT project "Groundwater and climate scenarios" subproject "Groundwater Research".

  17. 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.

  18. Latent fingerprints analysis using tape-lift, Raman microscopy, and multivariate data analysis methods.

    PubMed

    Widjaja, Effendi

    2009-04-01

    This paper describes the use of combined techniques, i.e. Raman spectral mapping, tape-lift, and multivariate data analysis, to extract chemical information of latent fingerprint and/or trace amounts of materials deposited in fingerprints. The tape-lift method was employed to lift trace particles, extrinsic materials, or sebum deposited on the finger of an individual after recent handling of such materials. The analysis of the tape-lifted materials was performed by Raman spectral mapping at a specific area. The collected mixture Raman spectra containing signals from lifting media and lifted materials was then deconvoluted using a powerful multivariate technique, namely band-target entropy minimization (BTEM). Three cases, i.e. a sebum-rich fingerprint after touching the forehead, a drug model comprising ibuprofen, L-arginine, and sodium bicarbonate, and an additive model comprising sucrose and aspartame were investigated. BTEM could recover all pure component spectra of both lifting media and tape-lifted materials. As such, all these test substances can be correctly identified using their unique pure Raman spectral signatures. In addition, the spatial distributions of all these identified components could also be determined. These combined three techniques hold promise as a new tool in forensic applications.

  19. Monitoring Quality of Biotherapeutic Products Using Multivariate Data Analysis.

    PubMed

    Rathore, Anurag S; Pathak, Mili; Jain, Renu; Jadaun, Gaurav Pratap Singh

    2016-07-01

    Monitoring the quality of pharmaceutical products is a global challenge, heightened by the implications of letting subquality drugs come to the market on public safety. Regulatory agencies do their due diligence at the time of approval as per their prescribed regulations. However, product quality needs to be monitored post-approval as well to ensure patient safety throughout the product life cycle. This is particularly complicated for biotechnology-based therapeutics where seemingly minor changes in process and/or raw material attributes have been shown to have a significant effect on clinical safety and efficacy of the product. This article provides a perspective on the topic of monitoring the quality of biotech therapeutics. In the backdrop of challenges faced by the regulatory agencies, the potential use of multivariate data analysis as a tool for effective monitoring has been proposed. Case studies using data from several insulin biosimilars have been used to illustrate the key concepts. PMID:27044370

  20. 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.

  1. 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

  2. Optimal Multicomponent Analysis Using the Generalized Standard Addition Method.

    ERIC Educational Resources Information Center

    Raymond, Margaret; And Others

    1983-01-01

    Describes an experiment on the simultaneous determination of chromium and magnesium by spectophotometry modified to include the Generalized Standard Addition Method computer program, a multivariate calibration method that provides optimal multicomponent analysis in the presence of interference and matrix effects. Provides instructions for…

  3. 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.

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

    PubMed

    Zhang, Sheng; Hu, Sien; Sinha, Rajita; Potenza, Marc N; Malison, Robert T; Li, Chiang-Shan R

    2016-01-01

    Cocaine dependence is associated with deficits in cognitive control. Previous studies demonstrated that chronic cocaine use affects the activity and functional connectivity of the thalamus, a subcortical structure critical for cognitive functioning. However, the thalamus contains nuclei heterogeneous in functions, and it is not known how thalamic subregions contribute to cognitive dysfunctions in cocaine dependence. To address this issue, we used multivariate pattern analysis (MVPA) to examine how functional connectivity of the thalamus distinguishes 100 cocaine-dependent participants (CD) from 100 demographically matched healthy control individuals (HC). We characterized six task-related networks with independent component analysis of fMRI data of a stop signal task and employed MVPA to distinguish CD from HC on the basis of voxel-wise thalamic connectivity to the six independent components. In an unbiased model of distinct training and testing data, the analysis correctly classified 72% of subjects with leave-one-out cross-validation (p < 0.001), superior to comparison brain regions with similar voxel counts (p < 0.004, two-sample t test). Thalamic voxels that form the basis of classification aggregate in distinct subclusters, suggesting that connectivities of thalamic subnuclei distinguish CD from HC. Further, linear regressions provided suggestive evidence for a correlation of the thalamic connectivities with clinical variables and performance measures on the stop signal task. Together, these findings support thalamic circuit dysfunction in cognitive control as an important neural marker of cocaine dependence. PMID:27556009

  5. 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.

  6. 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.

  7. Characterization and Classification of Lanthanides by Multivariate-Analysis Methods

    NASA Astrophysics Data System (ADS)

    Horovitz, Ossi; Sârbu, Costel

    2005-03-01

    A chemometric study was conducted on a data set consisting of 18 characteristics, mainly physical properties of the 14 lanthanides and lanthanum, including Sc and Y. Classical methods of multivariate analysis, namely, principal component analysis (PCA) and cluster analysis (CA) were applied. The results obtained by using the Statistica software package are presented and discussed concerning the correlations between the properties and those between the elements themselves. The discussion and findings are based on the tables of correlation, the eigenvectors and eigenvalues of PCA, the 2D- and 3D-representations of the loadings of variables and scores of the elements corresponding to the first principal components, including also the dendrograms obtained by using CA. Loadings scatterplots are used as a display tool for examining the relationships between properties, looking for trends, grouping, or outliers. In the same way, the scatterplots of scores emphasized the difference between La and the lanthanides on the one side and Sc and Y on the other and support setting Lu as their homologue, rather than La. On the basis of these findings, a ”periodic system“ of the lanthanides is suggested that agrees well with chemical intuition.

  8. 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.

  9. 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…

  10. 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.

  11. 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.

  12. Multivariate analysis of identity of imported technical PCN formulation.

    PubMed

    Falandysz, J; Chudzyński, K; Takekuma, M; Yamamoto, T; Noma, Y; Hanari, N; Yamashita, N

    2008-10-01

    Chloronaphthalenes form a class of compounds consisting of 8 CN homologue groups and altogether of 75 congeners, which used have been most extensively in 1930--1950. An investigation have been performed on the possible origin of unidentified by name technical chloronaphthalene formulation unlawfully imported recently from the United Kingdom to Japan. Principal component analysis (PCA) and Cluster Analysis of chloronaphthalene congener isomer-specific and homologue classes' compositional HRGC/HRMS data of imported CN formulation and of certain brands of technical CN formulation called Halowax (Halowax 1000, 1001 and 1031) enabled to identify that unnamed product is not Halowax 1001. A less accurate multivariate examination based on CN homologue classes patter did indicate on large similarity between unlawfully imported technical CN formulation and Halowax 1001 (manufactured by the Koppers Ind. Co., USA), while a more accurate based on CN congeners pattern differentiated them as to of various origin mixtures. Based on chlorine content of imported CN formulation (50-52%) and its no similarity to Halowax 1001 it seems reasonable to conclude that unnamed CN mixture examined could be a sample of stockpiled Seekay wax R93.

  13. Comparison of multivariate calibration methods for quantitative spectral analysis

    SciTech Connect

    Thomas, E.V.; Haaland, D.M. )

    1990-05-15

    The quantitative prediction abilities of four multivariate calibration methods for spectral analyses are compared by using extensive Monte Carlo simulations. The calibration methods compared include inverse least-squares (ILS), classical least-squares (CLS), partial least-squares (PLS), and principal component regression (PCR) methods. ILS is a frequency-limited method while the latter three are capable of full-spectrum calibration. The simulations were performed assuming Beer's law holds and that spectral measurement errors and concentration errors associated with the reference method are normally distributed. Eight different factors that could affect the relative performance of the calibration methods were varied in a two-level, eight-factor experimental design in order to evaluate their effect on the prediction abilities of the four methods. It is found that each of the three full-spectrum methods has its range of superior performance. The frequency-limited ILS method was never the best method, although in the presence of relatively large concentration errors it sometimes yields comparable analysis precision to the full-spectrum methods for the major spectral component. The importance of each factor in the absolute and relative performances of the four methods is compared.

  14. 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.

  15. 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.

  16. 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

  17. A multivariate analysis of beta diversity across organisms and environments.

    PubMed

    Soininen, Janne; Lennon, Jack J; Hillebrand, Helmut

    2007-11-01

    We examined variability in hierarchical beta diversity across ecosystems, geographical gradients, and organism groups using multivariate spatial mixed modeling analysis of two independent data sets. The larger data set comprised reported ratios of regional species richness (RSR) to local species richness (LSR) and the second data set consisted of RSR:LSR ratios derived from nested species-area relationships. There was a negative, albeit relatively weak, relationship between beta diversity and latitude. We found only relatively subtle differences in beta diversity among the realms, yet beta diversity was lower in marine systems than in terrestrial or freshwater realms. Beta diversity varied significantly among organisms' major characteristics such as body mass, trophic position, and dispersal type in the larger data set. Organisms that disperse via seeds had highest beta diversity, and passively dispersed organisms showed the lowest beta diversity. Furthermore, autotrophs had lower beta diversity than organisms higher up the food web; omnivores and carnivores had consistently higher beta diversity. This is evidence that beta diversity is simultaneously controlled by extrinsic factors related to geography and environment, and by intrinsic factors related to organism characteristics.

  18. 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

  19. 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.

  20. 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

  1. 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.

  2. 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),…

  3. 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…

  4. 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

  5. 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.

  6. 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…

  7. 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.

  8. 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…

  9. 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.

  10. 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.

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

    PubMed

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

    2015-01-01

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

  12. Variations in GP nursing home patient workload: results of a multivariate analysis.

    PubMed

    O'Neill, C; Groom, L; Avery, A J; Boot, D; Thornhill, K

    2000-11-01

    The number of old people living in UK nursing homes has increased substantially over the past 15 y. There is evidence that such patients generate larger workloads for primary carers than do those of similar age and sex living in their own homes. Clearly, any extra workload involved in providing primary care services to nursing home patients, needs to be reflected in the resources afforded general practitioners (GPs) who are tasked with its provision. By the same token variations in workloads between patients need to be examined and explained for any insights these might provide on funding issues. To examine and explain variations in GP workload associated with nursing home patients and determine the implications of these for GP funding, a 12 month case control study of all nursing home residents over 65 y old registered with nine general practices was undertaken. A multivariate regression analysis was used to examine variations in GP workload associated with 270 nursing home patients. Multivariate regression models explaining the variation in workload cost per month in terms of the GP practice delivering care and patients age and sex had little explanatory power (R(2)=0.07). A fuller method including the patient's Barthel score and initial diagnosis as additional explanatory variables added little to the explanatory power of the model (R(2)=0.12). The ability of the multivariate models used here to explain the variation in GP workload was poor. GPs may require an allowance to compensate for differences in workload associated with nursing home patients but adjusting these payments for differences in age, sex, initial diagnosis or the other variables included in this analysis would not appear to be supported. PMID:11114754

  13. [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. PMID:25775806

  14. Evolvability of individual traits in a multivariate context: partitioning the additive genetic variance into common and specific components.

    PubMed

    McGuigan, Katrina; Blows, Mark W

    2010-07-01

    Genetic covariation among multiple traits will bias the direction of evolution. Although a trait's phenotypic context is crucial for understanding evolutionary constraints, the evolutionary potential of one (focal) trait, rather than the whole phenotype, is often of interest. The extent to which a focal trait can evolve independently depends on how much of the genetic variance in that trait is unique. Here, we present a hypothesis-testing framework for estimating the genetic variance in a focal trait that is independent of variance in other traits. We illustrate our analytical approach using two Drosophila bunnanda trait sets: a contact pheromone system comprised of cuticular hydrocarbons (CHCs), and wing shape, characterized by relative warps of vein position coordinates. Only 9% of the additive genetic variation in CHCs was trait specific, suggesting individual traits are unlikely to evolve independently. In contrast, most (72%) of the additive genetic variance in wing shape was trait specific, suggesting relative warp representations of wing shape could evolve independently. The identification of genetic variance in focal traits that is independent of other traits provides a way of studying the evolvability of individual traits within the broader context of the multivariate phenotype.

  15. Sources of geographic mobility among professional workers: A multivariate analysis.

    PubMed

    Ladinsky, J

    1967-03-01

    Using the 1960 Census of Population one-in-a-thousand sample, this study investigates determinants of geographic mobility among professional, technical, and kindred workers. Multiple regression analysis reveals that age accounts for most of the explained variance in mobility, followed by income, education, regional location, sex, family size, and marital status. Additional variables-class of worker, race, nativity, professional type, size of place and industry-add no significant increments to explained variance.More specifically, low income and high education stimulate mobility and increases in family size and age slacken it. Young married professionals move the most and farthest, males somewhat more than females. Mobility is greatest in the West, least in the Northeast. Age reduces or reverses contrasts between single and married, large and small families, high and low incomes, little and much education, and residents of East and West.Factor analysis suggests that migration is part of two orderly processes-occupational career mobility and family life cycle. The bearing of these findings on the relationship between geographic mobility and social integration for the middleclass in the United States is considered. PMID:21279781

  16. 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

  17. 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. PMID:25761965

  18. 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.

  19. Multivariate analysis of somatosensory evoked potential parameters in normal adults.

    PubMed

    Strenge, H; Gundel, A

    1983-01-01

    Cervical and cortical somatosensory evoked potentials (SEP) to median nerve stimulation were recorded in 65 normal subjects. Absolute peak latencies and amplitudes of cervical components N9, P10, N11, N13, P17, and cortical components P16, N20, P25, and N35 were measured. By means of partial correlations the interdependency of SEP-features could be verified in addition to the well-known dependence on arm length and age. In certain respects our results replicate other studies finding significant correlations between age and latency of early SEP-components as well as inverse relations between age and cervical amplitudes. Further analysis disclosed high inter-correlations between the latencies and between the amplitudes of the cervical and cortical components also revealing a certain exceptional position of the positive wave P17. In contrast to an inverse relation of amplitude and latency of the cervical components there were positive correlations between the respective features in the cortical evoked response. The findings are discussed with regard to the current knowledge about the origins of the SEP-components. PMID:6667105

  20. 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.

  1. 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. PMID:26857005

  2. Bioelectronic tongue and multivariate analysis: a next step in BOD measurements.

    PubMed

    Raud, Merlin; Kikas, Timo

    2013-05-01

    Seven biosensors based on different semi-specific and universal microorganisms were constructed for biochemical oxygen demand (BOD) measurements in various synthetic industrial wastewaters. All biosensors were calibrated using OECD synthetic wastewater and the resulting calibration curves were used in the calculations of the sensor-BOD values for all biosensors. In addition, the output signals of all biosensors were analyzed as a bioelectronic tongue and comprehensive multivariate data analysis was applied to extract qualitative and quantitative information from the samples. In the case of individual biosensor measurements, most accurate result was gained when semi-specific biosensor was applied to analyze sample specific to that biosensor. Universal biosensors or biosensors semi-specific to other samples underestimated the BOD7 of the sample 10-25%. PLS regression method was used for the multivariate calibration of the biosensor array. The calculated sensor-BOD values differed from BOD7 less than 5.6% in all types of samples. By applying PCA and using three first principal components, giving 99.66% of variation, it was possible to differentiate samples by their compositions.

  3. 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.

  4. 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. PMID:18576207

  5. Multivariate analysis of noise in genetic regulatory networks.

    PubMed

    Tomioka, Ryota; Kimura, Hidenori; J Kobayashi, Tetsuya; Aihara, Kazuyuki

    2004-08-21

    Stochasticity is an intrinsic property of genetic regulatory networks due to the low copy numbers of the major molecular species, such as, DNA, mRNA, and regulatory proteins. Therefore, investigation of the mechanisms that reduce the stochastic noise is essential in understanding the reproducible behaviors of real organisms and is also a key to design synthetic genetic regulatory networks that can reliably work. We use an analytical and systematic method, the linear noise approximation of the chemical master equation along with the decoupling of a stoichiometric matrix. In the analysis of fluctuations of multiple molecular species, the covariance is an important measure of noise. However, usually the representation of a covariance matrix in the natural coordinate system, i.e. the copy numbers of the molecular species, is intractably complicated because reactions change copy numbers of more than one molecular species simultaneously. Decoupling of a stoichiometric matrix, which is a transformation of variables, significantly simplifies the representation of a covariance matrix and elucidates the mechanisms behind the observed fluctuations in the copy numbers. We apply our method to three types of fundamental genetic regulatory networks, that is, a single-gene autoregulatory network, a two-gene autoregulatory network, and a mutually repressive network. We have found that there are multiple noise components differently originating. Each noise component produces fluctuation in the characteristic direction. The resulting fluctuations in the copy numbers of the molecular species are the sum of these fluctuations. In the examples, the limitation of the negative feedback in noise reduction and the trade-off of fluctuations in multiple molecular species are clearly explained. The analytical representations show the full parameter dependence. Additionally, the validity of our method is tested by stochastic simulations. PMID:15246787

  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 data analysis in empirical research. A look on the bright side.

    PubMed

    Garbriel, R M; Glavin, G B

    1978-01-01

    The interpretive benefits of employing multivariate analysis methods on experimental data with more than one dependent variable are described heuristically and illustrated on a set of data from a simply designed experiment in physiological psychology. Multivariate analysis of variance (MANOVA) is performed on the 9 dependent variables contained in the sample data and on the four composites derived from a principal components analysis (PCA) of the variability of the nine. A linear discriminant analysis (LDA) is conducted following both MANOVA results, and 5 methods of determining the "important" dependent variables in the experimental-control group difference are presented and discussed in terms of the data at hand. PMID:683726

  9. 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…

  10. 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…

  11. Application of Model-Selection Criteria to Some Problems in Multivariate Analysis.

    ERIC Educational Resources Information Center

    Sclove, Stanley L.

    1987-01-01

    A review of model-selection criteria is presented, suggesting their similarities. Some problems treated by hypothesis tests may be more expeditiously treated by the application of model-selection criteria. Multivariate analysis, cluster analysis, and factor analysis are considered. (Author/GDC)

  12. 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

  13. [A multivariate nonlinear model for quantitative analysis in laser-induced breakdown spectroscopy].

    PubMed

    Chen, Xing-Long; Fu, Hong-Bo; Wang, Jing-Ge; Ni, Zhi-Bo; He, Wen-Gan; Xu, Jun; Rao Rui-zhong; Dong, Rui-Zhong

    2014-11-01

    Most quantitative models used in laser-induced breakdown spectroscopy (LIBS) are based on the hypothesis that laser-induced plasma approaches the state of local thermal equilibrium (LTE). However, the local equilibrium is possible only at a specific time segment during the evolution. As the populations of each energy level does not follow Boltzmann distribution in non-LTE condition, those quantitative models using single spectral line would be inaccurate. A multivariate nonlinear model, in which the LTE is not required, was proposed in this article to reduce the signal fluctuation and improve the accuracy of quantitative analysis. This multivariate nonlinear model was compared with the internal calibration model which is based on the LTE condition. The content of Mn in steel samples was determined by using the two models, respectively. A minor error and a minor relative standard deviation (RSD) were observed in multivariate nonlinear model. This result demonstrates that multivariate nonlinear model can improve measurement accuracy and repeatability.

  14. 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

  15. 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.

  16. Multivariate curve resolution: a powerful tool for the analysis of conformational transitions in nucleic acids

    PubMed Central

    Jaumot, Joaquim; Escaja, Núria; Gargallo, Raimundo; González, Carlos; Pedroso, Enrique; Tauler, Romà

    2002-01-01

    A successful application is reported of the multivariate curve resolution alternating least-squares method (MCR-ALS) for the analysis of nucleic acid melting and salt-induced transitions. Under conditions where several structures co-exist in a conformational equilibrium, MCR-ALS analysis of the UV and circular dichroism (CD) spectra at different temperatures, ionic strength and oligonucleotide concentration allows for the resolution of concentration profiles and pure spectra of the different species. The methodology is illustrated by the case of the cyclic oligonucleotide d. The melting transition of this molecule at different oligonucleotide concentrations was studied at 0, 2 and 10 mM MgCl2 by UV and CD spectroscopy. In addition, salt titration experiments were carried out at 21.0 and 54.0°C. The MCR-ALS analysis indicates that three different conformations of this molecule co-exist in solution. In agreement with previous NMR studies, these conformations were assigned to a monomeric dumbbell-like structure, a dimeric four-stranded conformation and a disordered (random coil) structure. The MCR-ALS methodology allows for a detailed analysis of how this equilibrium is affected by temperature, salt and oligonucleotide concentration. PMID:12202780

  17. Multivariate Curve Resolution Applied to Hyperspectral Imaging Analysis of Chocolate Samples.

    PubMed

    Zhang, Xin; de Juan, Anna; Tauler, Romà

    2015-08-01

    This paper shows the application of Raman and infrared hyperspectral imaging combined with multivariate curve resolution (MCR) to the analysis of the constituents of commercial chocolate samples. The combination of different spectral data pretreatment methods allowed decreasing the high fluorescent Raman signal contribution of whey in the investigated chocolate samples. Using equality constraints during MCR analysis, estimations of the pure spectra of the chocolate sample constituents were improved, as well as their relative contributions and their spatial distribution on the analyzed samples. In addition, unknown constituents could be also resolved. White chocolate constituents resolved from Raman hyperspectral image indicate that, at macro scale, sucrose, lactose, fat, and whey constituents were intermixed in particles. Infrared hyperspectral imaging did not suffer from fluorescence and could be applied for white and milk chocolate. As a conclusion of this study, micro-hyperspectral imaging coupled to the MCR method is confirmed to be an appropriate tool for the direct analysis of the constituents of chocolate samples, and by extension, it is proposed for the analysis of other mixture constituents in commercial food samples.

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

    NASA Astrophysics Data System (ADS)

    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.

  19. 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…

  20. 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.

  1. Multivariate analysis of adaptive capacity for upper thermal limits in Drosophila simulans.

    PubMed

    van Heerwaarden, B; Sgrò, C M

    2013-04-01

    Thermal tolerance is an important factor influencing the distribution of ectotherms, but our understanding of the ability of species to evolve different thermal limits is limited. Based on univariate measures of adaptive capacity, it has recently been suggested that species may have limited evolutionary potential to extend their upper thermal limits under ramping temperature conditions that better reflect heat stress in nature. To test these findings more broadly, we used a paternal half-sibling breeding design to estimate the multivariate evolutionary potential for upper thermal limits in Drosophila simulans. We assessed heat tolerance using static (basal and hardened) and ramping assays. Our analyses revealed significant evolutionary potential for all three measures of heat tolerance. Additive genetic variances were significantly different from zero for all three traits. Our G matrix analysis revealed that all three traits would contribute to a response to selection for increased heat tolerance. Significant additive genetic covariances and additive genetic correlations between static basal and hardened heat-knockdown time, marginally nonsignificant between static basal and ramping heat-knockdown time, indicate that direct and correlated responses to selection for increased upper thermal limits are possible. Thus, combinations of all three traits will contribute to the evolution of upper thermal limits in response to selection imposed by a warming climate. Reliance on univariate estimates of evolutionary potential may not provide accurate insight into the ability of organisms to evolve upper thermal limits in nature.

  2. Mixture design and multivariate analysis in mixture research.

    PubMed Central

    Eide, I; Johnsen, H G

    1998-01-01

    Mixture design has been used to identify possible interactions between mutagens in a mixture. In this paper the use of mixture design in multidimensional isobolographic studies is introduced. Mutagenicity of individual nitro-polycyclic aromatic hydrocarbons (PAH) was evaluated is an organic extract of diesel exhaust particles (DEPs). The particles were extracted with dichloromethane (DCM). After replacing DCM with dimethyl sulfoxide, the extract was spiked with three individual nitro-PAH: 1-nitropyrene, 2-nitrofluorene, and 1,8-dinitropyrene. The nitro-PAH were added separately and in various combinations to the extract to determine the effects of each variable and to identify possible interactions between the individual nitro-PAH and between the nitro-PAH and the extract. The composition of the mixtures was determined by mixture design (linear axial normal) with four variables (the DEP extract and the three nitro-PAH, giving 8 different mixtures plus a triplicate centerpoint, i.e., a total of 11. The design supports a model with linear and interaction (product) terms. Two different approaches were used: traditional mixture design within a well-defined range on the linear part of the dose-response curves and an isobolographic mixture design with equipotent doses of each variable. The mixtures were tested for mutagenicity in the Ames assay using the TA98 strain of Salmonella typhimurium. The data were analyzed with projections to latent structures (PLS). The three individual nitro-PAH and the DEP extract acted additively in the Ames test. The use of mixture design either within a well-defined range of the linear part on the dose-response curve or with equipotent doses saves experiments and reduces the possibility of false interaction terms in situations with dose additivity or response additivity. Images Figure 1 Figure 2 PMID:9860895

  3. Mixture design and multivariate analysis in mixture research.

    PubMed

    Eide, I; Johnsen, H G

    1998-12-01

    Mixture design has been used to identify possible interactions between mutagens in a mixture. In this paper the use of mixture design in multidimensional isobolographic studies is introduced. Mutagenicity of individual nitro-polycyclic aromatic hydrocarbons (PAH) was evaluated is an organic extract of diesel exhaust particles (DEPs). The particles were extracted with dichloromethane (DCM). After replacing DCM with dimethyl sulfoxide, the extract was spiked with three individual nitro-PAH: 1-nitropyrene, 2-nitrofluorene, and 1,8-dinitropyrene. The nitro-PAH were added separately and in various combinations to the extract to determine the effects of each variable and to identify possible interactions between the individual nitro-PAH and between the nitro-PAH and the extract. The composition of the mixtures was determined by mixture design (linear axial normal) with four variables (the DEP extract and the three nitro-PAH, giving 8 different mixtures plus a triplicate centerpoint, i.e., a total of 11. The design supports a model with linear and interaction (product) terms. Two different approaches were used: traditional mixture design within a well-defined range on the linear part of the dose-response curves and an isobolographic mixture design with equipotent doses of each variable. The mixtures were tested for mutagenicity in the Ames assay using the TA98 strain of Salmonella typhimurium. The data were analyzed with projections to latent structures (PLS). The three individual nitro-PAH and the DEP extract acted additively in the Ames test. The use of mixture design either within a well-defined range of the linear part on the dose-response curve or with equipotent doses saves experiments and reduces the possibility of false interaction terms in situations with dose additivity or response additivity. PMID:9860895

  4. 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.

  5. 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. PMID:11816450

  6. 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

  7. Multivariate analysis and chemometric characterisation of textile wastewater streams.

    PubMed

    Kavsek, Darja; Jeric, Tina; Le Marechal, Alenka Majcen; Vajnhandl, Simona; Bednárová, Adriána; Voncina, Darinka Brodnjak

    2013-01-01

    The aim of this work was to design a quick and reliable method for the evaluation and classification of wastewater streams into treatable and non-treatable effluents for reuse/recycling. Different chemometric methods were used for this purpose handling the enormous amount of data, and additionally to find any hidden information, which would increase our knowledge and improve the classification. The data obtained from the processes description, together with the analytical results of measured parameters' characterising the wastewater of a particular process, enabled us to build a fast-decision model for separating different textile wastewater outlets. Altogether 49 wastewater samples from the textile finishing company were analysed, and 19 different physical chemical measurements were performed for each of them. The resulting classification model was aimed at an automated decision about the choice of treatment technologies or a prediction about the reusability of wastewaters within any textile finishing or other company having similar characteristics of wastewater streams.

  8. Multivariate analysis and chemometric characterisation of textile wastewater streams.

    PubMed

    Kavsek, Darja; Jeric, Tina; Le Marechal, Alenka Majcen; Vajnhandl, Simona; Bednárová, Adriána; Voncina, Darinka Brodnjak

    2013-01-01

    The aim of this work was to design a quick and reliable method for the evaluation and classification of wastewater streams into treatable and non-treatable effluents for reuse/recycling. Different chemometric methods were used for this purpose handling the enormous amount of data, and additionally to find any hidden information, which would increase our knowledge and improve the classification. The data obtained from the processes description, together with the analytical results of measured parameters' characterising the wastewater of a particular process, enabled us to build a fast-decision model for separating different textile wastewater outlets. Altogether 49 wastewater samples from the textile finishing company were analysed, and 19 different physical chemical measurements were performed for each of them. The resulting classification model was aimed at an automated decision about the choice of treatment technologies or a prediction about the reusability of wastewaters within any textile finishing or other company having similar characteristics of wastewater streams. PMID:23878942

  9. Assessing statistical significance in multivariable genome wide association analysis

    PubMed Central

    Buzdugan, Laura; Kalisch, Markus; Navarro, Arcadi; Schunk, Daniel; Fehr, Ernst; Bühlmann, Peter

    2016-01-01

    Motivation: Although Genome Wide Association Studies (GWAS) genotype a very large number of single nucleotide polymorphisms (SNPs), the data are often analyzed one SNP at a time. The low predictive power of single SNPs, coupled with the high significance threshold needed to correct for multiple testing, greatly decreases the power of GWAS. Results: We propose a procedure in which all the SNPs are analyzed in a multiple generalized linear model, and we show its use for extremely high-dimensional datasets. Our method yields P-values for assessing significance of single SNPs or groups of SNPs while controlling for all other SNPs and the family wise error rate (FWER). Thus, our method tests whether or not a SNP carries any additional information about the phenotype beyond that available by all the other SNPs. This rules out spurious correlations between phenotypes and SNPs that can arise from marginal methods because the ‘spuriously correlated’ SNP merely happens to be correlated with the ‘truly causal’ SNP. In addition, the method offers a data driven approach to identifying and refining groups of SNPs that jointly contain informative signals about the phenotype. We demonstrate the value of our method by applying it to the seven diseases analyzed by the Wellcome Trust Case Control Consortium (WTCCC). We show, in particular, that our method is also capable of finding significant SNPs that were not identified in the original WTCCC study, but were replicated in other independent studies. Availability and implementation: Reproducibility of our research is supported by the open-source Bioconductor package hierGWAS. Contact: peter.buehlmann@stat.math.ethz.ch Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153677

  10. 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.

  11. 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.

  12. 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.

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

    PubMed

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

    2014-01-01

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

  14. Multivariate analysis of coconut residues by near infrared spectroscopy.

    PubMed

    Rambo, M K D; Alves, A R; Garcia, W T; Ferreira, M M C

    2015-06-01

    Near infrared (NIR) spectroscopy was used to determine the content of Klason lignin, acid-soluble lignin, total lignin, extractives, ash, acid-insoluble residue, glucose, xylose, rhamnose, galactose, arabinose, mannose and total sugars in coconut residues. The samples were analyzed at several processing stages: wet unground (WU), dried unground (DU) and dried and sieved (DS). Partial least squares models were built, and the models for the analytes exhibited R(2)>0.80, with the exceptions of rhamnose, arabinose, galactose, mannose and ash from all fractions, and the lignin content from the WU fraction, which were predicted poorly (R(2)<0.70). There were some significant differences between the models for the main lignocellulosic components at the various stages of biomass. These results proved that NIR spectroscopy is useful for analysis at biorefineries, and it can be used as a faster and more economical alternative to the standard methods.

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

    PubMed

    Sakhanenko, Nikita A; Galas, David J

    2015-11-01

    Information theory is valuable in multiple-variable analysis for being model-free and nonparametric, and for the modest sensitivity to undersampling. We previously introduced a general approach to finding multiple dependencies that provides accurate measures of levels of dependency for subsets of variables in a data set, which is significantly nonzero only if the subset of variables is collectively dependent. This is useful, however, only if we can avoid a combinatorial explosion of calculations for increasing numbers of variables.  The proposed dependence measure for a subset of variables, τ, differential interaction information, Δ(τ), has the property that for subsets of τ some of the factors of Δ(τ) are significantly nonzero, when the full dependence includes more variables. We use this property to suppress the combinatorial explosion by following the "shadows" of multivariable dependency on smaller subsets. Rather than calculating the marginal entropies of all subsets at each degree level, we need to consider only calculations for subsets of variables with appropriate "shadows." The number of calculations for n variables at a degree level of d grows therefore, at a much smaller rate than the binomial coefficient (n, d), but depends on the parameters of the "shadows" calculation. This approach, avoiding a combinatorial explosion, enables the use of our multivariable measures on very large data sets. We demonstrate this method on simulated data sets, and characterize the effects of noise and sample numbers. In addition, we analyze a data set of a few thousand mutant yeast strains interacting with a few thousand chemical compounds. PMID:26335709

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

    PubMed

    Sakhanenko, Nikita A; Galas, David J

    2015-11-01

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

  17. Biological Data Analysis as an Information Theory Problem: Multivariable Dependence Measures and the Shadows Algorithm

    PubMed Central

    Sakhanenko, Nikita A.

    2015-01-01

    Abstract Information theory is valuable in multiple-variable analysis for being model-free and nonparametric, and for the modest sensitivity to undersampling. We previously introduced a general approach to finding multiple dependencies that provides accurate measures of levels of dependency for subsets of variables in a data set, which is significantly nonzero only if the subset of variables is collectively dependent. This is useful, however, only if we can avoid a combinatorial explosion of calculations for increasing numbers of variables.  The proposed dependence measure for a subset of variables, τ, differential interaction information, Δ(τ), has the property that for subsets of τ some of the factors of Δ(τ) are significantly nonzero, when the full dependence includes more variables. We use this property to suppress the combinatorial explosion by following the “shadows” of multivariable dependency on smaller subsets. Rather than calculating the marginal entropies of all subsets at each degree level, we need to consider only calculations for subsets of variables with appropriate “shadows.” The number of calculations for n variables at a degree level of d grows therefore, at a much smaller rate than the binomial coefficient (n, d), but depends on the parameters of the “shadows” calculation. This approach, avoiding a combinatorial explosion, enables the use of our multivariable measures on very large data sets. We demonstrate this method on simulated data sets, and characterize the effects of noise and sample numbers. In addition, we analyze a data set of a few thousand mutant yeast strains interacting with a few thousand chemical compounds. PMID:26335709

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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…

  3. 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…

  4. 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…

  5. 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…

  6. White Male Suicide in the United States: A Multivariate Individual-Level Analysis.

    ERIC Educational Resources Information Center

    Kposowa, Augustine J.; And Others

    1995-01-01

    Multivariate hazards regression analysis of data from the 1979-85 National Longitudinal Mortality Study provided mixed results concerning the social integration hypothesis of suicide. Among white males, divorced or separated men and those living alone had significantly higher risks of suicide mortality, but single and widowed men did not have…

  7. Analysis of Feedback Mechanisms with Unknown Delay Using Sparse Multivariate Autoregressive Method

    PubMed Central

    Ip, Edward H.; Zhang, Qiang; Sowinski, Tomasz; Simpson, Sean L.

    2015-01-01

    This paper discusses the study of two interacting processes in which a feedback mechanism exists between the processes. The study was motivated by problems such as the circadian oscillation of gene expression where two interacting protein transcriptions form both negative and positive feedback loops with long delays to equilibrium. Traditionally, data of this type could be examined using autoregressive analysis. However, in circadian oscillation the order of an autoregressive model cannot be determined a priori. We propose a sparse multivariate autoregressive method that incorporates mixed linear effects into regression analysis, and uses a forward-backward greedy search algorithm to select non-zero entries in the regression coefficients, the number of which is constrained not to exceed a pre-specified number. A small simulation study provides preliminary evidence of the validity of the method. Besides the circadian oscillation example, an additional example of blood pressure variations using data from an intervention study is used to illustrate the method and the interpretation of the results obtained from the sparse matrix method. These applications demonstrate how sparse representation can be used for handling high dimensional variables that feature dynamic, reciprocal relationships. PMID:26252637

  8. A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses.

    PubMed

    Buttigieg, Pier Luigi; Ramette, Alban

    2014-12-01

    The application of multivariate statistical analyses has become a consistent feature in microbial ecology. However, many microbial ecologists are still in the process of developing a deep understanding of these methods and appreciating their limitations. As a consequence, staying abreast of progress and debate in this arena poses an additional challenge to many microbial ecologists. To address these issues, we present the GUide to STatistical Analysis in Microbial Ecology (GUSTA ME): a dynamic, web-based resource providing accessible descriptions of numerous multivariate techniques relevant to microbial ecologists. A combination of interactive elements allows users to discover and navigate between methods relevant to their needs and examine how they have been used by others in the field. We have designed GUSTA ME to become a community-led and -curated service, which we hope will provide a common reference and forum to discuss and disseminate analytical techniques relevant to the microbial ecology community.

  9. A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses.

    PubMed

    Buttigieg, Pier Luigi; Ramette, Alban

    2014-12-01

    The application of multivariate statistical analyses has become a consistent feature in microbial ecology. However, many microbial ecologists are still in the process of developing a deep understanding of these methods and appreciating their limitations. As a consequence, staying abreast of progress and debate in this arena poses an additional challenge to many microbial ecologists. To address these issues, we present the GUide to STatistical Analysis in Microbial Ecology (GUSTA ME): a dynamic, web-based resource providing accessible descriptions of numerous multivariate techniques relevant to microbial ecologists. A combination of interactive elements allows users to discover and navigate between methods relevant to their needs and examine how they have been used by others in the field. We have designed GUSTA ME to become a community-led and -curated service, which we hope will provide a common reference and forum to discuss and disseminate analytical techniques relevant to the microbial ecology community. PMID:25314312

  10. Selection Indices and Multivariate Analysis Show Similar Results in the Evaluation of Growth and Carcass Traits in Beef Cattle

    PubMed Central

    Brito Lopes, Fernando; da Silva, Marcelo Corrêa; Magnabosco, Cláudio Ulhôa; Goncalves Narciso, Marcelo; Sainz, Roberto Daniel

    2016-01-01

    This research evaluated a multivariate approach as an alternative tool for the purpose of selection regarding expected progeny differences (EPDs). Data were fitted using a multi-trait model and consisted of growth traits (birth weight and weights at 120, 210, 365 and 450 days of age) and carcass traits (longissimus muscle area (LMA), back-fat thickness (BF), and rump fat thickness (RF)), registered over 21 years in extensive breeding systems of Polled Nellore cattle in Brazil. Multivariate analyses were performed using standardized (zero mean and unit variance) EPDs. The k mean method revealed that the best fit of data occurred using three clusters (k = 3) (P < 0.001). Estimates of genetic correlation among growth and carcass traits and the estimates of heritability were moderate to high, suggesting that a correlated response approach is suitable for practical decision making. Estimates of correlation between selection indices and the multivariate index (LD1) were moderate to high, ranging from 0.48 to 0.97. This reveals that both types of indices give similar results and that the multivariate approach is reliable for the purpose of selection. The alternative tool seems very handy when economic weights are not available or in cases where more rapid identification of the best animals is desired. Interestingly, multivariate analysis allowed forecasting information based on the relationships among breeding values (EPDs). Also, it enabled fine discrimination, rapid data summarization after genetic evaluation, and permitted accounting for maternal ability and the genetic direct potential of the animals. In addition, we recommend the use of longissimus muscle area and subcutaneous fat thickness as selection criteria, to allow estimation of breeding values before the first mating season in order to accelerate the response to individual selection. PMID:26789008

  11. Rank estimation and the multivariate analysis of in vivo fast-scan cyclic voltammetric data

    PubMed Central

    Keithley, Richard B.; Carelli, Regina M.; Wightman, R. Mark

    2010-01-01

    Principal component regression has been used in the past to separate current contributions from different neuromodulators measured with in vivo fast-scan cyclic voltammetry. Traditionally, a percent cumulative variance approach has been used to determine the rank of the training set voltammetric matrix during model development, however this approach suffers from several disadvantages including the use of arbitrary percentages and the requirement of extreme precision of training sets. Here we propose that Malinowski’s F-test, a method based on a statistical analysis of the variance contained within the training set, can be used to improve factor selection for the analysis of in vivo fast-scan cyclic voltammetric data. These two methods of rank estimation were compared at all steps in the calibration protocol including the number of principal components retained, overall noise levels, model validation as determined using a residual analysis procedure, and predicted concentration information. By analyzing 119 training sets from two different laboratories amassed over several years, we were able to gain insight into the heterogeneity of in vivo fast-scan cyclic voltammetric data and study how differences in factor selection propagate throughout the entire principal component regression analysis procedure. Visualizing cyclic voltammetric representations of the data contained in the retained and discarded principal components showed that using Malinowski’s F-test for rank estimation of in vivo training sets allowed for noise to be more accurately removed. Malinowski’s F-test also improved the robustness of our criterion for judging multivariate model validity, even though signal-to-noise ratios of the data varied. In addition, pH change was the majority noise carrier of in vivo training sets while dopamine prediction was more sensitive to noise. PMID:20527815

  12. 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.

  13. Multivariate, non-linear trend analysis of heterogeneous water quality monitoring data

    NASA Astrophysics Data System (ADS)

    Lischeid, Gunnar; Kalettka, Thomas; Steidl, Jörg; Merz, Christoph; Lehr, Christian

    2014-05-01

    in the graph is proportional to the dissimilarity of the two respective water samples with respect to all 13 solutes. In our study, the non-linear 2D projection of the SOM-SM reflected 75% of the variance of the 13D data set. For further analyses the same graph was used again and again, where different colouring revealed different information. Thus the user rapidly became acquainted with the large, high dimensional data set. At a first glance outliers easily could be identified as well as clusters of samples with similar solute concentration. Different groups of samples were analysed for the degree of overlap. Multivariate trend analysis was performed that did not only account for increasing or decreasing concentration of single solutes but for systemic shifts of characteristic solute concentration patterns as well. Partly converging trends were found, that is, sampling sites becoming more similar to each other. In addition, long-term decreasing variance was found at some sites. For checking for significant differences between different time periods confidence intervals were included in the graph. We conclude that the SOM-SM proved to be a powerful and extremely helpful tool for analysis of this large, heterogeneous water quality data set.

  14. Analysis of heterogeneous dengue transmission in Guangdong in 2014 with multivariate time series model

    PubMed Central

    Cheng, Qing; Lu, Xin; Wu, Joseph T.; Liu, Zhong; Huang, Jincai

    2016-01-01

    Guangdong experienced the largest dengue epidemic in recent history. In 2014, the number of dengue cases was the highest in the previous 10 years and comprised more than 90% of all cases. In order to analyze heterogeneous transmission of dengue, a multivariate time series model decomposing dengue risk additively into endemic, autoregressive and spatiotemporal components was used to model dengue transmission. Moreover, random effects were introduced in the model to deal with heterogeneous dengue transmission and incidence levels and power law approach was embedded into the model to account for spatial interaction. There was little spatial variation in the autoregressive component. In contrast, for the endemic component, there was a pronounced heterogeneity between the Pearl River Delta area and the remaining districts. For the spatiotemporal component, there was considerable heterogeneity across districts with highest values in some western and eastern department. The results showed that the patterns driving dengue transmission were found by using clustering analysis. And endemic component contribution seems to be important in the Pearl River Delta area, where the incidence is high (95 per 100,000), while areas with relatively low incidence (4 per 100,000) are highly dependent on spatiotemporal spread and local autoregression. PMID:27666657

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

    USGS Publications Warehouse

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

    2001-01-01

    Six treatments of eastern Kansas tallgrass prairie - native prairie, hayed, mowed, grazed, burned and untreated - were studied to examine the biophysical effects of land management practices on grasslands. On each treatment, measurements of plant biomass, leaf area index, plant cover, leaf moisture and soil moisture were collected. In addition, measurements were taken of the Normalized Difference Vegetation Index (NDVI), which is derived from spectral reflectance measurements. Measurements were taken in mid-June, mid-July and late summer of 1990 and 1991. Multivariate analysis of variance was used to determine whether there were differences in the set of variables among treatments and years. Follow-up tests included univariate t-tests to determine which variables were contributing to any significant difference. Results showed a significant difference (p < 0.0005) among treatments in the composite of parameters during each of the months sampled. In most treatment types, there was a significant difference between years within each month. The univariate tests showed, however, that only some variables, primarily soil moisture, were contributing to this difference. We conclude that biomass and % plant cover show the best potential to serve as long-term indicators of grassland condition as they generally were sensitive to effects of different land management practices but not to yearly change in weather conditions. NDVI was insensitive to precipitation differences between years in July for most treatments, but was not in the native prairie. Choice of sampling time is important for these parameters to serve effectively as indicators.

  16. Multivariate sensitivity analysis of saturated flow through simulated highly heterogeneous groundwater aquifers

    SciTech Connect

    Winter, C.L. . E-mail: lwinter@ucar.edu; Guadagnini, A.; Nychka, D.; Tartakovsky, D.M.

    2006-09-01

    A multivariate Analysis of Variance (ANOVA) is used to measure the relative sensitivity of groundwater flow to two factors that indicate different dimensions of aquifer heterogeneity. An aquifer is modeled as the union of disjoint volumes, or blocks, composed of different materials with different hydraulic conductivities. The factors are correlation between the hydraulic conductivities of the different materials and the contrast between mean conductivities in the different materials. The precise values of aquifer properties are usually uncertain because they are only sparsely sampled, yet are highly heterogeneous. Hence, the spatial distribution of blocks and the distribution of materials in blocks are uncertain and are modeled as stochastic processes. The ANOVA is performed on a large sample of Monte Carlo simulations of a simple model flow system composed of two materials distributed within three disjoint blocks. Our key finding is that simulated flow is much more sensitive to the contrast between mean conductivities of the blocks than it is to the intensity of correlation, although both factors are statistically significant. The methodology of the experiment - ANOVA performed on Monte Carlo simulations of a multi-material flow system - constitutes the basis of additional studies of more complicated interactions between factors that define flow and transport in aquifers with uncertain properties.

  17. 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.

  18. Experimental estimation of postmortem interval using multivariate analysis of proton NMR metabolomic data.

    PubMed

    Hirakawa, Keiko; Koike, Kaoru; Uekusa, Kyoko; Nihira, Makoto; Yuta, Kohtaro; Ohno, Youkichi

    2009-04-01

    Nuclear magnetic resonance (NMR) spectroscopy has recently been applied to metabolic studies. In particular, metabolic profiles of tissues or of the whole body can easily be acquired through multivariate analysis of NMR spectra. The present study investigates metabolic changes after death in rat femoral muscles using pattern recognition of proton NMR spectra. Rats were killed by suffocation, cocaine overdose and induced respiratory failure, and then low molecular weight metabolites extracted using perchlorate from excised tissues were measured using proton NMR. All spectral data were processed and assessed by multivariate analysis to obtain metabolic profiles of the tissues. The results of principal component analysis (PCA) score plots soon after death showed that the metabolic profiles of the tissues differed according to the mode of death. The principal component (PC) scores of the data varied hourly and correlated with postmortem interval. The present results showed that NMR-based metabolic profiling could provide useful information with which to estimate postmortem intervals and causes of death.

  19. Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data.

    PubMed

    Kim, Jieun; Zhu, Wei; Chang, Linda; Bentler, Peter M; Ernst, Thomas

    2007-02-01

    The ultimate goal of brain connectivity studies is to propose, test, modify, and compare certain directional brain pathways. Path analysis or structural equation modeling (SEM) is an ideal statistical method for such studies. In this work, we propose a two-stage unified SEM plus GLM (General Linear Model) approach for the analysis of multisubject, multivariate functional magnetic resonance imaging (fMRI) time series data with subject-level covariates. In Stage 1, we analyze the fMRI multivariate time series for each subject individually via a unified SEM model by combining longitudinal pathways represented by a multivariate autoregressive (MAR) model, and contemporaneous pathways represented by a conventional SEM. In Stage 2, the resulting subject-level path coefficients are merged with subject-level covariates such as gender, age, IQ, etc., to examine the impact of these covariates on effective connectivity via a GLM. Our approach is exemplified via the analysis of an fMRI visual attention experiment. Furthermore, the significant path network from the unified SEM analysis is compared to that from a conventional SEM analysis without incorporating the longitudinal information as well as that from a Dynamic Causal Modeling (DCM) approach.

  20. Compositional differences among Chinese soy sauce types studied by (13)C NMR spectroscopy coupled with multivariate statistical analysis.

    PubMed

    Kamal, Ghulam Mustafa; Wang, Xiaohua; Bin Yuan; Wang, Jie; Sun, Peng; Zhang, Xu; Liu, Maili

    2016-09-01

    Soy sauce a well known seasoning all over the world, especially in Asia, is available in global market in a wide range of types based on its purpose and the processing methods. Its composition varies with respect to the fermentation processes and addition of additives, preservatives and flavor enhancers. A comprehensive (1)H NMR based study regarding the metabonomic variations of soy sauce to differentiate among different types of soy sauce available on the global market has been limited due to the complexity of the mixture. In present study, (13)C NMR spectroscopy coupled with multivariate statistical data analysis like principle component analysis (PCA), and orthogonal partial least square-discriminant analysis (OPLS-DA) was applied to investigate metabonomic variations among different types of soy sauce, namely super light, super dark, red cooking and mushroom soy sauce. The main additives in soy sauce like glutamate, sucrose and glucose were easily distinguished and quantified using (13)C NMR spectroscopy which were otherwise difficult to be assigned and quantified due to serious signal overlaps in (1)H NMR spectra. The significantly higher concentration of sucrose in dark, red cooking and mushroom flavored soy sauce can directly be linked to the addition of caramel in soy sauce. Similarly, significantly higher level of glutamate in super light as compared to super dark and mushroom flavored soy sauce may come from the addition of monosodium glutamate. The study highlights the potentiality of (13)C NMR based metabonomics coupled with multivariate statistical data analysis in differentiating between the types of soy sauce on the basis of level of additives, raw materials and fermentation procedures. PMID:27343582

  1. 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

  2. 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. PMID:26528117

  3. 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.

  4. TOF-SIMS analysis of polystyrene/polybutadiene blend using chemical derivatization and multivariate analysis

    NASA Astrophysics Data System (ADS)

    Kono, Teiichiro; Iwase, Eijiro; Kanamori, Yukiko

    2008-12-01

    Chemical imaging with high spatial resolution is one of the features of TOF-SIMS. However, degradation of the sample due to primary ion bombardment becomes problematic when the analysis area is small. Although polystyrene (PS) and polybutadiene (PB) separately show relatively distinct spectra, observation of their phase separation in PS/PB blends is difficult when the analysis area is small because degradation of both polymers and especially PS leads to disappearance of their characteristic peaks, resulting in low chemical image contrast. We therefore investigated the application of various forms of multivariate analysis (MVA) to the TOF-SIMS image data to improve the chemical image contrast. PCA, MCR, and the other forms of MVA provided improvement in contrast, but the images were still obscure and observation of phase separation remained difficult. Chemical derivatization using osmium tetroxide was also investigated, and found to give clear images of phase separation in the PS/PB blend. In quantitative determinations with MVA and chemical derivatization, PLS demonstrated the best predictive capability and chemical derivatization resulted in large deviations from both the bulk chemical composition and the determinations with MVA, particularly in regions of low PB content.

  5. Applications of multivariate modeling to neuroimaging group analysis: a comprehensive alternative to univariate general linear model.

    PubMed

    Chen, Gang; Adleman, Nancy E; Saad, Ziad S; Leibenluft, Ellen; Cox, Robert W

    2014-10-01

    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. To validate the MVM methodology, we performed simulations to assess the controllability for false positives and power achievement. A real FMRI dataset was analyzed to demonstrate the capability of the MVM approach. The methodology has been implemented into an open source program 3dMVM in AFNI, and all the statistical tests can be performed through symbolic coding with variable names instead of the tedious process of dummy coding. Our data indicates that the severity of sphericity violation varies substantially across brain regions. The differences among various modeling methodologies were addressed through direct comparisons between the MVM approach and some of the GLM implementations in

  6. 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.

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

    PubMed

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

    2006-05-17

    In this work, the effect of temperature on charcoal structure and chemical composition is investigated for four tree species. Wood charcoal carbonized at various temperatures is analyzed by mid infrared spectroscopy coupled with multivariate analysis and by thermogravimetric analysis to characterize the chemical composition during the carbonization process. The multivariate models of charcoal were able to distinguish between species and wood thermal treatments, revealing that the characteristics of the wood charcoal depend not only on the wood species, but also on the carbonization temperature. This work demonstrates the potential of mid infrared spectroscopy in the whiskey industry, from the identification and classification of the wood species for the mellowing process to the chemical characterization of the barrels after the toasting and charring process. PMID:19127715

  8. Multivariate analysis of factors predicting prostate dose in intensity-modulated radiotherapy

    SciTech Connect

    Tomita, Tsuneyuki; Nakamura, Mitsuhiro; Hirose, Yoshinori; Kitsuda, Kenji; Notogawa, Takuya; Miki, Katsuhito; Nakamura, Kiyonao; Ishigaki, Takashi

    2014-01-01

    We conducted a multivariate analysis to determine relationships between prostate radiation dose and the state of surrounding organs, including organ volumes and the internal angle of the levator ani muscle (LAM), based on cone-beam computed tomography (CBCT) images after bone matching. We analyzed 270 CBCT data sets from 30 consecutive patients receiving intensity-modulated radiation therapy for prostate cancer. With patients in the supine position on a couch with the HipFix system, data for center of mass (COM) displacement of the prostate and the state of individual organs were acquired and compared between planning CT and CBCT scans. Dose distributions were then recalculated based on CBCT images. The relative effects of factors on the variance in COM, dose covering 95% of the prostate volume (D{sub 95%}), and percentage of prostate volume covered by the 100% isodose line (V{sub 100%}) were evaluated by a backward stepwise multiple regression analysis. COM displacement in the anterior-posterior direction (COM{sub AP}) correlated significantly with the rectum volume (δVr) and the internal LAM angle (δθ; R = 0.63). Weak correlations were seen for COM in the left-right (R = 0.18) and superior-inferior directions (R = 0.31). Strong correlations between COM{sub AP} and prostate D{sub 95%} and V{sub 100%} were observed (R ≥ 0.69). Additionally, the change ratios in δVr and δθ remained as predictors of prostate D{sub 95%} and V{sub 100%}. This study shows statistically that maintaining the same rectum volume and LAM state for both the planning CT simulation and treatment is important to ensure the correct prostate dose in the supine position with bone matching.

  9. Does motor imagery share neural networks with executed movement: a multivariate fMRI analysis

    PubMed Central

    Sharma, Nikhil; Baron, Jean-Claude

    2013-01-01

    Introduction: Motor imagery (MI) is the mental rehearsal of a motor first person action-representation. There is interest in using MI to access the motor network after stroke. Conventional fMRI modeling has shown that MI and executed movement (EM) activate similar cortical areas but it remains unknown whether they share cortical networks. Proving this is central to using MI to access the motor network and as a form of motor training. Here we use multivariate analysis (tensor independent component analysis-TICA) to map the array of neural networks involved during MI and EM. Methods: Fifteen right-handed healthy volunteers (mean-age 28.4 years) were recruited and screened for their ability to carry out MI (Chaotic MI Assessment). fMRI consisted of an auditory-paced (1 Hz) right hand finger-thumb opposition sequence (2,3,4,5; 2…) with two separate runs acquired (MI & rest and EM & rest: block design). No distinction was made between MI and EM until the final stage of processing. This allowed TICA to identify independent-components (IC) that are common or distinct to both tasks with no prior assumptions. Results: TICA defined 52 ICs. Non-significant ICs and those representing artifact were excluded. Components in which the subject scores were significantly different to zero (for either EM or MI) were included. Seven IC remained. There were IC's shared between EM and MI involving the contralateral BA4, PMd, parietal areas and SMA. IC's exclusive to EM involved the contralateral BA4, S1 and ipsilateral cerebellum whereas the IC related exclusively to MI involved ipsilateral BA4 and PMd. Conclusion: In addition to networks specific to each task indicating a degree of independence, we formally demonstrate here for the first time that MI and EM share cortical networks. This significantly strengthens the rationale for using MI to access the motor networks, but the results also highlight important differences. PMID:24062666

  10. Atomic-scale phase composition through multivariate statistical analysis of atom probe tomography data.

    PubMed

    Keenan, Michael R; Smentkowski, Vincent S; Ulfig, Robert M; Oltman, Edward; Larson, David J; Kelly, Thomas F

    2011-06-01

    We demonstrate for the first time that multivariate statistical analysis techniques can be applied to atom probe tomography data to estimate the chemical composition of a sample at the full spatial resolution of the atom probe in three dimensions. Whereas the raw atom probe data provide the specific identity of an atom at a precise location, the multivariate results can be interpreted in terms of the probabilities that an atom representing a particular chemical phase is situated there. When aggregated to the size scale of a single atom (∼0.2 nm), atom probe spectral-image datasets are huge and extremely sparse. In fact, the average spectrum will have somewhat less than one total count per spectrum due to imperfect detection efficiency. These conditions, under which the variance in the data is completely dominated by counting noise, test the limits of multivariate analysis, and an extensive discussion of how to extract the chemical information is presented. Efficient numerical approaches to performing principal component analysis (PCA) on these datasets, which may number hundreds of millions of individual spectra, are put forward, and it is shown that PCA can be computed in a few seconds on a typical laptop computer.

  11. 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

  12. [Multivariate analysis of heavy metal element concentrations in atmospheric deposition in Harbin City, northeast China].

    PubMed

    Tang, Jie; Han, Wei-Zheng; Li, Na; Li, Zhao-Yang; Bian, Jian-Min; Li, Hai-Yi

    2011-11-01

    In order to understand the characteristics of atmospheric heavy metal deposition in Harbin City, 46 deposition samples were collected which were taken using bulk deposition samplers during the period of 2008-2009 (about 365 days). The samples were analyzed for heavy metal concentration by atomic fluorescence spectrometry (AFS) and inductively coupled plasma-atomic spectrometry (ICP-AES). The deposition flux was calculated. Sources analysis was made by the method of principal component analysis (PCA), Pearsons and enrichment factor (EF). The following points can be gained through multivariate analysis. Mn and Co are mostly from natural sources while the others may be brought by coal dust, vehicle emissions and metal smelting.

  13. 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.

  14. 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

  15. 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

  16. Multivariate Analysis of Dopaminergic Gene Variants as Risk Factors of Heroin Dependence

    PubMed Central

    Vereczkei, Andrea; Demetrovics, Zsolt; Szekely, Anna; Sarkozy, Peter; Antal, Peter; Szilagyi, Agnes; Sasvari-Szekely, Maria; Barta, Csaba

    2013-01-01

    Background Heroin dependence is a debilitating psychiatric disorder with complex inheritance. Since the dopaminergic system has a key role in rewarding mechanism of the brain, which is directly or indirectly targeted by most drugs of abuse, we focus on the effects and interactions among dopaminergic gene variants. Objective To study the potential association between allelic variants of dopamine D2 receptor (DRD2), ANKK1 (ankyrin repeat and kinase domain containing 1), dopamine D4 receptor (DRD4), catechol-O-methyl transferase (COMT) and dopamine transporter (SLC6A3) genes and heroin dependence in Hungarian patients. Methods 303 heroin dependent subjects and 555 healthy controls were genotyped for 7 single nucleotide polymorphisms (SNPs) rs4680 of the COMT gene; rs1079597 and rs1800498 of the DRD2 gene; rs1800497 of the ANKK1 gene; rs1800955, rs936462 and rs747302 of the DRD4 gene. Four variable number of tandem repeats (VNTRs) were also genotyped: 120 bp duplication and 48 bp VNTR in exon 3 of DRD4 and 40 bp VNTR and intron 8 VNTR of SLC6A3. We also perform a multivariate analysis of associations using Bayesian networks in Bayesian multilevel analysis (BN-BMLA). Findings and conclusions In single marker analysis the TaqIA (rs1800497) and TaqIB (rs1079597) variants were associated with heroin dependence. Moreover, –521 C/T SNP (rs1800955) of the DRD4 gene showed nominal association with a possible protective effect of the C allele. After applying the Bonferroni correction TaqIB was still significant suggesting that the minor (A) allele of the TaqIB SNP is a risk component in the genetic background of heroin dependence. The findings of the additional multiple marker analysis are consistent with the results of the single marker analysis, but this method was able to reveal an indirect effect of a promoter polymorphism (rs936462) of the DRD4 gene and this effect is mediated through the –521 C/T (rs1800955) polymorphism in the promoter. PMID:23840506

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

    PubMed

    Ogawa, Y; Daigo, M; Amasaki, H

    1989-01-01

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

  18. 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. PMID:17943497

  19. 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.

  20. 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

  1. 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

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

    PubMed

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

    2013-12-01

    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

  3. Inheritance of Nitrogen Use Efficiency in Inbred Progenies of Tropical Maize Based on Multivariate Diallel Analysis

    PubMed Central

    Guedes, Fernando Lisboa; Diniz, Rafael Parreira; Balestre, Marcio; Ribeiro, Camila Bastos; Camargos, Renato Barbosa; Souza, João Cândido

    2014-01-01

    The objective of our study was to characterize and determine the patterns of genetic control in relation to tolerance and efficiency of nitrogen use by means of a complete diallel cross involving contrasting inbred progenies of tropical maize based on a univariate approach within the perspective of a multivariate mixed model. Eleven progenies, previously classified regarding the tolerance and responsiveness to nitrogen, were crossed in a complete diallel cross. Fifty-five hybrids were obtained. The hybrids and the progenies were evaluated at two different nitrogen levels, in two locations. The grain yield was measured as well as its yield components. The heritability values between the higher and lower nitrogen input environment did not differ among themselves. It was observed that the general combining ability values were similar for both approaches univariate and multivariate, when it was analyzed within each location and nitrogen level. The estimate of variance of the specific combining ability was higher than general combining ability estimate and the ratio between them was 0.54. The univariate and multivariate approaches are equivalent in experiments with good precision and high heritability. The nonadditive genetic effects exhibit greater quantities than the additive genetic effects for the genetic control of nitrogen use efficiency. PMID:25587575

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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.

  9. Testing multivariate analysis in paleoenvironmental reconstructions using pollen records from Lagoa Salgada, NE Rio de Janeiro State, Brazil.

    PubMed

    Toledo, Mauro B de; Barth, Ortrud M; Silva, Cleverson G; Barros, Marcia A

    2009-12-01

    Despite the indisputable significance of identification of modern analogs for Paleoecology research, relatively few studies attempted to integrate modern and fossil samples on paleoenvironmental reconstructions. In Palynology, this general pattern is not different from other fields of Paleoecology. This study demonstrates the practical application of modern pollen deposition data on paleoenvironmental reconstructions based on fossil pollen by using multivariate analysis. The main goal of this study was to use Detrended Correspondence Analysis (DCA) to compare pollen samples from two sediment cores collected at Lagoa Salgada, a coastal lagoon located at northeastern Rio de Janeiro State. Furthermore, modern surface samples were also statistically compared with samples from both cores, providing new paleoecological insights. DCA demonstrated that samples from both cores are more similar than previously expected, and that a strong pattern, related to a paleoenvironmental event, is present within the fossil data, clearly identifying in the scatter plot samples that represent pre- and post-environmental change. Additionally, it became apparent that modern vegetation and environmental conditions were established in this region 2500 years before present (BP). Multivariate Analysis allowed a more reliable integration of modern and fossil pollen data, proving to be a powerful tool in Paleoecology studies that should be employed more often on paleoclimate and paleoenvironmental reconstructions.

  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. 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. PMID:26653752

  12. Multivariate whole genome average interval mapping: QTL analysis for multiple traits and/or environments.

    PubMed

    Verbyla, Arūnas P; Cullis, Brian R

    2012-09-01

    A major aim in some plant-based studies is the determination of quantitative trait loci (QTL) for multiple traits or across multiple environments. Understanding these QTL by trait or QTL by environment interactions can be of great value to the plant breeder. A whole genome approach for the analysis of QTL is presented for such multivariate applications. The approach is an extension of whole genome average interval mapping in which all intervals on a linkage map are included in the analysis simultaneously. A random effects working model is proposed for the multivariate (trait or environment) QTL effects for each interval, with a variance-covariance matrix linking the variates in a particular interval. The significance of the variance-covariance matrix for the QTL effects is tested and if significant, an outlier detection technique is used to select a putative QTL. This QTL by variate interaction is transferred to the fixed effects. The process is repeated until the variance-covariance matrix for QTL random effects is not significant; at this point all putative QTL have been selected. Unlinked markers can also be included in the analysis. A simulation study was conducted to examine the performance of the approach and demonstrated the multivariate approach results in increased power for detecting QTL in comparison to univariate methods. The approach is illustrated for data arising from experiments involving two doubled haploid populations. The first involves analysis of two wheat traits, α-amylase activity and height, while the second is concerned with a multi-environment trial for extensibility of flour dough. The method provides an approach for multi-trait and multi-environment QTL analysis in the presence of non-genetic sources of variation. PMID:22692445

  13. 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. PMID:27364290

  14. 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.

  15. 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.

  16. 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.

  17. 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. PMID:19184561

  18. 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. PMID:22935861

  19. 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.

  20. 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 ...

  1. 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.

  2. 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.

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

    PubMed

    Chen, Ying; Chen, Kani; Ying, Zhiliang

    2010-01-01

    The marginal proportional hazards model is an important tool in the analysis of multivariate failure time data in the presence of censoring. We propose a method of estimation via the linear combinations of martingale residuals. The estimation and inference procedures are easy to implement numerically. The estimation is generally more accurate than the existing pseudo-likelihood approach: the size of efficiency gain can be considerable in some cases, and the maximum relative efficiency in theory is infinite. Consistency and asymptotic normality are established. Empirical evidence in support of the theoretical claims is shown in simulation studies. PMID:24307815

  4. KINETIC ANALYSIS OF HIGH-NITROGEN ENERGETIC MATERIALS USING MULTIVARIATE NONLINEAR REGRESSION

    SciTech Connect

    Campbell, M. S.; Rabie, R. L.; Diaz-Acosta, I.; Pulay, P.

    2001-01-01

    New high-nitrogen energetic materials were synthesized by Hiskey and Naud. J. Opfermann reported a new tool for finding the probable model of the complex reactions using multivariate non-linear regression analysis of DSC and TGA data from several measurements run at different heating rates. This study is to take the kinetic parameters from the different steps and discover which reaction step is responsible for the runaway reaction by comparing predicted results from the Frank-Kamenetsckii equation with the critical temperature found experimentally using the modified Henkin test.

  5. Application of multivariate statistical methods to the analysis of ancient Turkish potsherds

    SciTech Connect

    Martin, R.C.

    1986-01-01

    Three hundred ancient Turkish potsherds were analyzed by instrumental neutron activation analysis, and the resulting data analyzed by several techniques of multivariate statistical analysis, some only recently developed. The programs AGCLUS, MASLOC, and SIMCA were sequentially employed to characterize and group the samples by type of pottery and site of excavation. Comparison of the statistical analyses by each method provided archaeological insight into the site/type relationships of the samples and ultimately evidence relevant to the commercial relations between the ancient communities and specialization of pottery production over time. The techniques used for statistical analysis were found to be of significant potential utility in the future analysis of other archaeometric data sets. 25 refs., 33 figs.

  6. SEBAL-based Daily Actual Evapotranspiration Forecasting using Wavelets Decomposition Analysis and Multivariate Relevance Vector Machines

    NASA Astrophysics Data System (ADS)

    Torres, A. F.

    2011-12-01

    Agricultural lands are sources of food and energy for population around the globe. These lands are vulnerable to the impacts of climate change including variations in rainfall regimes, weather patterns, and decreased availability of water for irrigation. In addition, it is not unusual that irrigated agriculture is forced to divert less water in order to make it available for other uses, e.g. human consumption and others. As part of implementation of better policies for water control and management, irrigation companies and water user associations have been implemented water conveyance and distribution monitoring systems along with soil moisture sensors networks in the last decades. These systems allow them to manage and distribute water among the users based on their requirements and water availability while collecting information about actual soil moisture conditions in representative crop fields. In spite of this, requested water deliveries by farmers/water users is based typically on total water share, traditions and past experience on irrigation, which in most cases do not correspond to the actual crop evapotranspiration, already affected by climate change. Therefore it is necessary to provide actual information about the crop water requirements to water users/managers, so they can better quantify the required vs. available water for the irrigation events along the irrigation season. To estimate the actual evapotranspiration in a spatial extent the Sensitivity Analysis of the Surface Energy Balance Algorithm for Land (SEBAL) algorithm has demonstrated its effectiveness using satellite or airborne data. Nonetheless the estimation is restricted to the day when the geospatial information was obtained. Without information of precise future daily water crop demand there is a continuous challenge for the implementation of better water distribution and management policies in the irrigation system. The purpose of this study is to investigate the plausibility of using

  7. 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.

  8. 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.

  9. Improved MR-based characterization of engineered cartilage using multiexponential T2 relaxation and multivariate analysis

    PubMed Central

    Reiter, D.A.; Irrechukwu, O.; Lin, P.-C.; Moghadam, S.; Von, Thaer S.; Pleshko, N.; Spencer, R.G.

    2012-01-01

    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 cleanly distinguish between groups with different levels of matrix development. Furthermore, MR outcomes are nonspecific, with specific changes in matrix components resulting in changes in multiple MR parameters. To address these limitations, we present two new approaches for evaluation of tissue engineered constructs using MR, and apply them to immature and mature engineered cartilage after 1 week and 5 weeks of development, respectively. First, we applied multiexponential T2 analysis for quantification of matrix macromolecule-associated water compartments. Second, we applied multivariate support vector machine (SVM) analysis using multiple MR parameters to improve detection of degree of matrix development. Classification of samples based on individual MR parameters, T1, T2, km, or ADC, showed that the best classifiers were T1 and km, with classification accuracies of 85% and 84%, respectively. SVM analysis improved accuracy to 98% using the combination (km, ADC). These approaches were validated using biochemical and Fourier transform infrared imaging spectroscopy analyses, which showed increased proteoglycan and collagen with maturation. Monoexponential T2 values decreased with maturation, but without further specificity. Much more specific information was provided by multiexponential analysis. The T2 distribution in both immature and mature constructs was comparable to that of native cartilage. The analysis also showed that proteoglycan-bound water increased significantly during maturation, from a fraction of 0.05±0.01 to 0.07±0.01. In summary, multivariate SVM and multiexponential T2 analysis provide improved sensitivity to changes in matrix development and specificity to matrix

  10. 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. PMID:24594186

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. Additional EIPC Study Analysis. Final Report

    SciTech Connect

    Hadley, Stanton W; Gotham, Douglas J.; Luciani, Ralph L.

    2014-12-01

    Between 2010 and 2012 the Eastern Interconnection Planning Collaborative (EIPC) conducted a major long-term resource and transmission study of the Eastern Interconnection (EI). With guidance from a Stakeholder Steering Committee (SSC) that included representatives from the Eastern Interconnection States Planning Council (EISPC) among others, the project was conducted in two phases. Phase 1 involved a long-term capacity expansion analysis that involved creation of eight major futures plus 72 sensitivities. Three scenarios were selected for more extensive transmission- focused evaluation in Phase 2. Five power flow analyses, nine production cost model runs (including six sensitivities), and three capital cost estimations were developed during this second phase. The results from Phase 1 and 2 provided a wealth of data that could be examined further to address energy-related questions. A list of 14 topics was developed for further analysis. This paper brings together the earlier interim reports of the first 13 topics plus one additional topic into a single final report.

  16. Discrimination of chemical warfare simulants via multiplex coherent anti-Stokes Raman scattering and multivariate statistical analysis

    NASA Astrophysics Data System (ADS)

    Brady, John J.; Farrell, Mikella E.; Pellegrino, Paul M.

    2014-02-01

    Multiplex coherent anti-Stokes Raman scattering (MCARS) is used to detect several chemical warfare simulants, such as dimethyl methylphosphonate and 2-chloroethyl ethyl sulfide, with high specificity. The spectral bandwidth of the femtosecond laser pulse used in these studies is sufficient to coherently and simultaneously drive all the vibrational modes in the molecule of interest. Evidence shows that MCARS is capable of overcoming common sensitivity limitations of spontaneous Raman scattering, thus allowing for the detection of the target material in milliseconds with standard, uncooled universal serial bus spectrometers as opposed to seconds with cooled, intensified CCD-based spectrometers. In addition, the obtained MCARS spectrum of the investigated sample provides multiple unique signatures. These signatures are used in an off-line multivariate statistical analysis allowing for the material's discrimination with high fidelity.

  17. 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

  18. 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.

  19. Multivariate statistical analysis of stream-sediment geochemistry in the Grazer Paläozoikum, Austria

    USGS Publications Warehouse

    Weber, L.; Davis, J.C.

    1990-01-01

    The Austrian reconnaissance study of stream-sediment composition — more than 30000 clay-fraction samples collected over an area of 40000 km2 — is summarized in an atlas of regional maps that show the distributions of 35 elements. These maps, rich in information, reveal complicated patterns of element abundance that are difficult to compare on more than a small number of maps at one time. In such a study, multivariate procedures such as simultaneous R-Q mode components analysis may be helpful. They can compress a large number of variables into a much smaller number of independent linear combinations. These composite variables may be mapped and relationships sought between them and geological properties. As an example, R-Q mode components analysis is applied here to the Grazer Paläozoikum, a tectonic unit northeast of the city of Graz, which is composed of diverse lithologies and contains many mineral deposits.

  20. 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.

  1. MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information

    PubMed Central

    Lee, S. H.; van der Werf, J. H. J.

    2016-01-01

    Summary: 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. Availability and implementation: MTG2 is available in https://sites.google.com/site/honglee0707/mtg2. Contact: hong.lee@une.edu.au Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26755623

  2. 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. PMID:19552076

  3. 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

  4. [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.

  5. 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.

  6. 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

  7. 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

  8. 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. PMID:26971171

  9. A Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis.

    PubMed

    Collins, Tony J; Ylanko, Jarkko; Geng, Fei; Andrews, David W

    2015-11-01

    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.

  10. 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.

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

    PubMed

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

    2015-01-01

    This case study uses several univariate and multivariate statistical techniques to evaluate and interpret a water quality data set obtained from the Klang River basin located within the state of Selangor and the Federal Territory of Kuala Lumpur, Malaysia. The river drains an area of 1,288 km(2), from the steep mountain rainforests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, into the Straits of Malacca. Water quality was monitored at 20 stations, nine of which are situated along the main river and 11 along six tributaries. Data was collected from 1997 to 2007 for seven parameters used to evaluate the status of the water quality, namely dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, ammoniacal nitrogen, pH, and temperature. The data were first investigated using descriptive statistical tools, followed by two practical multivariate analyses that reduced the data dimensions for better interpretation. The analyses employed were factor analysis and principal component analysis, which explain 60 and 81.6% of the total variation in the data, respectively. We found that the resulting latent variables from the factor analysis are interpretable and beneficial for describing the water quality in the Klang River. This study presents the usefulness of several statistical methods in evaluating and interpreting water quality data for the purpose of monitoring the effectiveness of water resource management. The results should provide more straightforward data interpretation as well as valuable insight for managers to conceive optimum action plans for controlling pollution in river water. PMID:25433545

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

    NASA Astrophysics Data System (ADS)

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

    2010-10-01

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

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

    PubMed

    Thavamani, Palanisami; Megharaj, Mallavarapu; Naidu, Ravi

    2012-06-01

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

  14. Multivariate Meta-Analysis of Preference-Based Quality of Life Values in Coronary Heart Disease

    PubMed Central

    Stevanović, Jelena; Pechlivanoglou, Petros; Kampinga, Marthe A.; Krabbe, Paul F. M.; Postma, Maarten J.

    2016-01-01

    Background There are numerous health-related quality of life (HRQol) measurements used in coronary heart disease (CHD) in the literature. However, only values assessed with preference-based instruments can be directly applied in a cost-utility analysis (CUA). Objective To summarize and synthesize instrument-specific preference-based values in CHD and the underlying disease-subgroups, stable angina and post-acute coronary syndrome (post-ACS), for developed countries, while accounting for study-level characteristics, and within- and between-study correlation. Methods A systematic review was conducted to identify studies reporting preference-based values in CHD. A multivariate meta-analysis was applied to synthesize the HRQoL values. Meta-regression analyses examined the effect of study level covariates age, publication year, prevalence of diabetes and gender. Results A total of 40 studies providing preference-based values were detected. Synthesized estimates of HRQoL in post-ACS ranged from 0.64 (Quality of Well-Being) to 0.92 (EuroQol European”tariff”), while in stable angina they ranged from 0.64 (Short form 6D) to 0.89 (Standard Gamble). Similar findings were observed in estimates applying to general CHD. No significant improvement in model fit was found after adjusting for study-level covariates. Large between-study heterogeneity was observed in all the models investigated. Conclusions The main finding of our study is the presence of large heterogeneity both within and between instrument-specific HRQoL values. Current economic models in CHD ignore this between-study heterogeneity. Multivariate meta-analysis can quantify this heterogeneity and offers the means for uncertainty around HRQoL values to be translated to uncertainty in CUAs. PMID:27011260

  15. Use of Multivariate Analysis Techniques to Form a Comparison of Mars Odyssey Gamma Ray Elemental Data to Neutron Data

    NASA Astrophysics Data System (ADS)

    Abbazia, Paul

    2009-03-01

    The Lunar Reconnaissance Orbiter's (LRO) primary mission is exploration. Additional science falls to a secondary focus. LRO does not possess a gamma ray spectrometer, but it has the collimated neutron detector LEND (Lunar Exploration Neutron Detector). It is of interest to determine as much as possible about the moon's elemental composition using LEND. To do so, data from a similar instrument on Mars Odyssey, HEND (High Energy Neutron Detector), was compared to data from Mars Odyssey's gamma ray spectrometer (GRS). Elemental maps were previously derived from the GRS data, and a relation to HEND would allow for LEND to fulfill this role on LRO. Toward this purpose, different multivariate analysis techniques were used to compare GRS and HEND data, including Principal Components Analysis (PCA), K-means clustering, and Pearson product-moment correlation. Results indicate that two elements well known to effect neutron counts, hydrogen and iron, can be identified by these techniques. Further analysis may find additional relations, which would have benefits to the fields of geochemistry and neutron spectroscopy.

  16. A multivariate analysis of the effects of multiple extrusion cycles on high density polyethylene bottle resin

    SciTech Connect

    Zahavich, A.

    1995-10-01

    The recycling of post consumer (PCR) high density polyethylene (HDPE) blow molding resins has increased dramatically over the past 5 years. The focus of research for this product has been on specific performance and processing properties such as tensile or melt strength. Little work has been done on studying the entire range of properties as a whole, particularly in the area of multiple extrusions. This paper describes a designed experiment study where multivariate statistical techniques were used to compare 2 HDPE and 2 HDPE PCR materials, in terms of changes in a number of properties with exposure to multiple extrusions. Virgin homopolymer and copolymer resins and PCR, mixed color bottle and natural, were passed through 4 extrusion cycles. Viscosity, swell, melt strength, crystallinity, polydispersity and ESCR properties were studied using principal component analysis.

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

    PubMed Central

    Anderson, John R.

    2011-01-01

    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 involves using fMRI activity to track what students are doing as they solve a sequence of algebra problems. The methodology achieves considerable accuracy at determining both what problem-solving step the students are taking and whether they are performing that step correctly. The second “model discovery” application involves using statistical model evaluation to determine how many substates are involved in performing a step of algebraic problem solving. This research indicates that different steps involve different numbers of substates and these substates are associated with different fluency in algebra problem solving. PMID:21820455

  18. Note: Multivariate system spectroscopic model using Lorentz oscillators and partial least squares regression analysis.

    PubMed

    Gad, R S; Parab, J S; Naik, G M

    2010-11-01

    Multivariate system spectroscopic model plays important role in understanding chemometrics of ensemble under study. Here in this manuscript we discuss various approaches of modeling of spectroscopic system and demonstrate how Lorentz oscillator can be used to model any general spectroscopic system. Chemometric studies require customized templates design for the corresponding variants participating in ensemble, which generates the characteristic matrix of the ensemble under study. The typical biological system that resembles human blood tissue consisting of five major constituents i.e., alanine, urea, lactate, glucose, ascorbate; has been tested on the model. The model was validated using three approaches, namely, root mean square error (RMSE) analysis in the range of ±5% confidence interval, clerk gird error plot, and RMSE versus percent noise level study. Also the model was tested across various template sizes (consisting of samples ranging from 10 up to 1000) to ascertain the validity of partial least squares regression. The model has potential in understanding the chemometrics of proteomics pathways.

  19. Note: Multivariate system spectroscopic model using Lorentz oscillators and partial least squares regression analysis

    NASA Astrophysics Data System (ADS)

    Gad, R. S.; Parab, J. S.; Naik, G. M.

    2010-11-01

    Multivariate system spectroscopic model plays important role in understanding chemometrics of ensemble under study. Here in this manuscript we discuss various approaches of modeling of spectroscopic system and demonstrate how Lorentz oscillator can be used to model any general spectroscopic system. Chemometric studies require customized templates design for the corresponding variants participating in ensemble, which generates the characteristic matrix of the ensemble under study. The typical biological system that resembles human blood tissue consisting of five major constituents i.e., alanine, urea, lactate, glucose, ascorbate; has been tested on the model. The model was validated using three approaches, namely, root mean square error (RMSE) analysis in the range of ±5% confidence interval, clerk gird error plot, and RMSE versus percent noise level study. Also the model was tested across various template sizes (consisting of samples ranging from 10 up to 1000) to ascertain the validity of partial least squares regression. The model has potential in understanding the chemometrics of proteomics pathways.

  20. Enhancing multivariate singular spectrum analysis for phase synchronization: The role of observability

    NASA Astrophysics Data System (ADS)

    Portes, Leonardo L.; Aguirre, Luis A.

    2016-09-01

    Multivariate singular spectrum analysis (M-SSA) was recently adapted to study systems of coupled oscillators. It does not require an a priori definition for phase nor detailed knowledge of the individual oscillators, but it uses all the variables of each system. This aspect could be restrictive for practical applications, since usually just a few (sometimes only one) variables are measured. Based on dynamical systems and observability theories, we first show how to apply the M-SSA with only one variable and show the conditions to achieve good performance. Next, we provide numerical evidence that this single-variable approach enhances the explanatory power compared to the original M-SSA when computed with all the system variables. This could have important practical implications, as pointed out using benchmark oscillators.

  1. A multivariate variational objective analysis-assimilation method. Part 1: Development of the basic model

    NASA Technical Reports Server (NTRS)

    Achtemeier, Gary L.; Ochs, Harry T., III

    1988-01-01

    The variational method of undetermined multipliers is used to derive a multivariate model for objective analysis. The model is intended for the assimilation of 3-D fields of rawinsonde height, temperature and wind, and mean level temperature observed by satellite into a dynamically consistent data set. Relative measurement errors are taken into account. The dynamic equations are the two nonlinear horizontal momentum equations, the hydrostatic equation, and an integrated continuity equation. The model Euler-Lagrange equations are eleven linear and/or nonlinear partial differential and/or algebraic equations. A cyclical solution sequence is described. Other model features include a nonlinear terrain-following vertical coordinate that eliminates truncation error in the pressure gradient terms of the horizontal momentum equations and easily accommodates satellite observed mean layer temperatures in the middle and upper troposphere. A projection of the pressure gradient onto equivalent pressure surfaces removes most of the adverse impacts of the lower coordinate surface on the variational adjustment.

  2. Tracking problem solving by multivariate pattern analysis and Hidden Markov Model algorithms.

    PubMed

    Anderson, John R

    2012-03-01

    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 involves using fMRI activity to track what students are doing as they solve a sequence of algebra problems. The methodology achieves considerable accuracy at determining both what problem-solving step the students are taking and whether they are performing that step correctly. The second "model discovery" application involves using statistical model evaluation to determine how many substates are involved in performing a step of algebraic problem solving. This research indicates that different steps involve different numbers of substates and these substates are associated with different fluency in algebra problem solving.

  3. Multivariate analysis to discriminate yeast strains with technological applications in table olive processing.

    PubMed

    Rodríguez-Gómez, Francisco; Romero-Gil, Veronica; Bautista-Gallego, Joaquín; Garrido-Fernández, Antonio; Arroyo-López, Francisco Noé

    2012-04-01

    This survey uses a multivariate classification analysis to discriminate yeast strains with interesting biochemical activities for the processing of table olives among a collection of 32 isolates belonging to 16 different yeast species. Lipase, esterase and β-glucosidase activities (desirable characteristics) were quantitatively evaluated in both extracellular and cellular fractions for all isolates in different types of culture media. The study of the quantitative data by cluster and principal component analyses led to the identification of several Wickerhamomyces anomalus, Candida boidinii and Candida diddensiae isolates with promising characteristics (the best global activity levels), clearly differentiated from the rest of the yeasts. The results obtained in this work open up new alternatives to this methodology for the study, classification and selection of the most suitable yeasts to be used as starters, alone or in combination with lactic acid bacteria, during table olive processing.

  4. Chemical and meteorological characteristics of atmospheric particulates in southeastern Virginia utilizing multi-variant analysis

    SciTech Connect

    Brooks, L.; Salop, J.

    1983-01-01

    Nontraditional sources of total suspended particulate (TSP) loading include reentrainment of road dust and fugitive dust from construction operations. A study of the field particle characterization as it relates to the existing meteorology used multi-variant computer analysis techniques in the Tidewater region of southeastern Virginia. The region was meteorologically uniform, with pressure and wind direction the two parameters for predicting TSP mass loading. A relationship between mass loading and chemical species appears feasible. The results suggest that major point sources for TSP appear to contribute insignificantly to any one particulate receptor, and that reentrained dust may be the major contribution to levels of particulates. Further study needs to investigate the sources of the high percentage of sulfates in all samples. 3 references, 1 figure, 1 table.

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

    NASA Astrophysics Data System (ADS)

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

    2007-01-01

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

  6. A multivariate model for the meta-analysis of study level survival data at multiple times.

    PubMed

    Jackson, Dan; Rollins, Katie; Coughlin, Patrick

    2014-09-01

    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 we compare the results to those obtained from standard methodologies. Our method uses exact binomial within-study distributions and enforces the constraints that both the study specific and the overall mortality rates must not decrease over time. We directly model the probabilities of mortality at each time point, which are the quantities of primary clinical interest. We also present I(2) statistics that quantify the impact of the between-study heterogeneity, which is very considerable in our data set.

  7. A FORTRAN program for multivariate survival analysis on the personal computer.

    PubMed

    Mulder, P G

    1988-01-01

    In this paper a FORTRAN program is presented for multivariate survival or life table regression analysis in a competing risks' situation. The relevant failure rate (for example, a particular disease or mortality rate) is modelled as a log-linear function of a vector of (possibly time-dependent) explanatory variables. The explanatory variables may also include the variable time itself, which is useful for parameterizing piecewise exponential time-to-failure distributions in a Gompertz-like or Weibull-like way as a more efficient alternative to Cox's proportional hazards model. Maximum likelihood estimates of the coefficients of the log-linear relationship are obtained from the iterative Newton-Raphson method. The program runs on a personal computer under DOS; running time is quite acceptable, even for large samples.

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  9. Combining multivariate statistics and analysis of variance to redesign a water quality monitoring network.

    PubMed

    Guigues, Nathalie; Desenfant, Michèle; Hance, Emmanuel

    2013-09-01

    The objective of this paper was to demonstrate how multivariate statistics combined with the analysis of variance could support decision-making during the process of redesigning a water quality monitoring network with highly heterogeneous datasets in terms of time and space. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were selected to optimise the selection of water quality parameters to be monitored as well as the number and location of monitoring stations. Sampling frequency was specifically investigated through the analysis of variance. The data used were obtained between 2007 and 2010 at the Long-term Environmental Research Monitoring and Testing System (OPE) located in the north-eastern part of France in relation with a geological disposal of radioactive waste project. PCA results showed that no substantial reduction among the parameters was possible as strong correlation only exists between electrical conductivity, calcium or bicarbonates. HCA results were geospatially represented for each field campaign and compared to one another in terms of similarities and differences allowing us to group the monitoring stations into 12 categories. This approach enabled us to take into account not only the spatial variability of water quality but also its temporal variability. Finally, the analysis of variances showed that three very different behaviours occurred: parameters with high temporal variability and low spatial variability (e.g. suspended matter), parameters with high spatial variability and average temporal variability (e.g. calcium) and finally parameters with both high temporal and spatial variability (e.g. nitrate).

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

    PubMed

    Jørgensen, Anna Cecilia; Miroshnyk, Inna; Karjalainen, Milja; Jouppila, Kirsi; Siiriä, Simo; Antikainen, Osmo; Rantanen, Jukka

    2006-04-01

    A thorough understanding of solid state properties is of growing importance. It is often necessary to apply multiple techniques offering complementary information to fully understand the solid state behavior of a given compound and the relations between various polymorphic forms. The vast amount of information generated can be overwhelming and the need for more effective data analysis tools is well recognized. The aim of this study was to investigate the use of multivariate data analysis, in particular principal component analysis (PCA), for fast analysis of solid state information. The data sets analyzed covered dehydration phenomena of a set of hydrates followed by variable temperature X-ray powder diffractometry and Raman spectroscopy and the crystallization of amorphous lactose monitored by Raman spectroscopy. Identification of different transitional states upon the dehydration enabled the molecular level interpretation of the structural changes related to the loss of water, as well as interpretation of the phenomena related to the crystallization. The critical temperatures or critical time points were identified easily using the principal component analysis. The variables (diffraction angles or wavenumbers) that changed could be identified by the careful interpretation of the loadings plots. The PCA approach provides an effective tool for fast screening of solid state information.

  11. Multivariate statistical analysis of radioactive variables in two phosphate ores from Sudan.

    PubMed

    Adam, Abdel Majid A; Eltayeb, Mohamed Ahmed H

    2012-05-01

    Multivariate statistical techniques are efficient ways to display complex relationships among many objects. An attempt was made to study the radioactive data in two types of Sudanese phosphate deposits; Kurun and Uro phosphate, using several multivariate statistical methods. Pearson correlation coefficient revealed that a U-238 distribution in Kurun phosphate is controlled by the variation of K-40 concentration, whereas in Uro phosphate it is controlled by the variation of U-235 and U-234 concentration. Histograms and normal Q-Q plots clearly show that the radioactive variables did not follow a normal distribution. This non-normality feature observed may be attributed to complicating influence of geological factors. The principal components analysis (PCA) gives a model of five components for representing the acquired data from Kurun phosphate, where 89.5% of the total variance is explained. A model of four components was sufficient to represent the acquired data from Uro phosphate, where 87.5% of the total data variance is explained. The hierarchical cluster analysis (HCA) indicates that U-238 behaves in the same manner in the two types of phosphates; it associated with a group of four radionuclides; U-234, Po-210, Ra-226, Th-230, which the most abundant radionuclides, and all belong to the uranium-238 decay series. Two parameters have been adapted for the direct differentiate between the two phosphates. Firstly, U-238 in Uro phosphate have shown higher degree of mobility (CV% = 82.6) than that in Kurun phosphate (CV% = 64.7), and secondly, the activity ratio of Th-230/Th-232 in Uro phosphate is nine times than that in Kurun phosphate.

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

    NASA Astrophysics Data System (ADS)

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

    2010-08-01

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

  13. Multivariate Analysis of Risk Factors of Cerebral Infarction in 439 Patients Undergoing Thoracic Endovascular Aneurysm Repair

    PubMed Central

    Kanaoka, Yuji; Ohki, Takao; Maeda, Koji; Baba, Takeshi; Fujita, Tetsuji

    2016-01-01

    Abstract The aim of the study is to identify the potential risk factors of cerebral infarction associated with thoracic endovascular aneurysm repair (TEVAR). TEVAR was developed as a less invasive surgical alternative to conventional open repair for thoracic aortic aneurysm treatment. However, outcomes following TEVAR of aortic and distal arch aneurysms remain suboptimal. Cerebral infarction is a major concern during the perioperative period. We included 439 patients who underwent TEVAR of aortic aneurysms at a high-volume teaching hospital between July 2006 and June 2013. Univariate and multivariate logistic regression analyses were performed to identify perioperative cerebral infarction risk factors. Four patients (0.9%) died within 30 days of TEVAR; 17 (3.9%) developed cerebral infarction. In univariate analysis, history of ischemic heart disease and cerebral infarction and concomitant cerebrovascular disease were significantly associated with cerebral infarction. “Shaggy aorta” presence, left subclavian artery coverage, carotid artery debranching, and pull-through wire use were identified as independent risk factors of cerebral infarction. In multivariate analysis, history of ischemic heart disease (odds ratio [OR] 6.49, P = 0.046) and cerebral infarction (OR 43.74, P = 0.031), “shaggy aorta” (OR 30.32, P < 0.001), pull-through wire use during surgery (OR 7.196, P = 0.014), and intraoperative blood loss ≥800 mL (OR 24.31, P = 0.017) were found to be independent risk factors of cerebral infarction. This study identified patient- and procedure-related risk factors of cerebral infarction following TEVAR. These results indicate that patient outcomes could be improved through the identification and management of procedure-related risk factors. PMID:27082585

  14. Groundwater source contamination mechanisms: physicochemical profile clustering, risk factor analysis and multivariate modelling.

    PubMed

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

    2014-04-01

    An integrated domestic well sampling and "susceptibility assessment" programme was undertaken in the Republic of Ireland from April 2008 to November 2010. Overall, 211 domestic wells were sampled, assessed and collated with local climate data. Based upon groundwater physicochemical profile, three clusters have been identified and characterised by source type (borehole or hand-dug well) and local geological setting. Statistical analysis indicates that cluster membership is significantly associated with the prevalence of bacteria (p=0.001), with mean Escherichia coli presence within clusters ranging from 15.4% (Cluster-1) to 47.6% (Cluster-3). Bivariate risk factor analysis shows that on-site septic tank presence was the only risk factor significantly associated (p<0.05) with bacterial presence within all clusters. Point agriculture adjacency was significantly associated with both borehole-related clusters. Well design criteria were associated with hand-dug wells and boreholes in areas characterised by high permeability subsoils, while local geological setting was significant for hand-dug wells and boreholes in areas dominated by low/moderate permeability subsoils. Multivariate susceptibility models were developed for all clusters, with predictive accuracies of 84% (Cluster-1) to 91% (Cluster-2) achieved. Septic tank setback was a common variable within all multivariate models, while agricultural sources were also significant, albeit to a lesser degree. Furthermore, well liner clearance was a significant factor in all models, indicating that direct surface ingress is a significant well contamination mechanism. Identification and elucidation of cluster-specific contamination mechanisms may be used to develop improved overall risk management and wellhead protection strategies, while also informing future remediation and maintenance efforts.

  15. Multivariate analysis of the volumetric capnograph for PaCO2 estimation

    PubMed Central

    Belenkiy, Slava M; Baker, William L; Batchinsky, Andriy I; Mittal, Sumit; Watkins, Taylor; Salinas, Jose; Cancio, Leopoldo C

    2015-01-01

    Purpose: End-tidal CO2 (eTCO2) can be used to estimate the arterial CO2 (PaCO2) under steady-state conditions, but that relationship deteriorates during hemodynamic or respiratory instability. We developed a multivariate method to improve our ability to estimate the PaCO2, by using additional information contained in the volumetric capnograph (Vcap) waveform. We tested this approach using data from a porcine model of chest trauma/hemorrhage. Methods: This experiment consisted of 3 stages: pre-injury, injury/resuscitation, and post-injury. In stage I, anesthetized pigs (n=26) underwent ventilator maneuvers (tidal volume and respiratory rate) to induce hypo-or hyper-ventilation. In stage II, pigs underwent either (A) unilateral pulmonary contusion, hemorrhage, and resuscitation (n=13); or (B) bilateral pulmonary contusion (n=13) followed by 30 min of monitoring. In stage III, the ventilator maneuvers were repeated. The following Vcap features were measured: eTCO2, phase 2 slope (p2m), phase 3 slope (p3m), and inter-breath interval. The data were fit to 2 models: (1) multivariate linear regression and (2) a machine-learning model (M5P). Results: 1750 10-breath sets were analyzed. Univariate models employing eTCO2 alone were adequate during stages I and III. During stage II, mean error for the linear model was -8.44 mmHg (R2=0.14, P<0.001) and for M5P it was -5.98 mmHg (R2=0.13, P<0.01). By adding Vcap features, all models exhibited improvement. In stage II, the mean error of the linear model improved to -4.64 mmHg (R2=0.11, P<0.01), and that of the M5P model improved to -1.62 mmHg (R2=0.25, P<0.01). Conclusions: By incorporating Vcap waveform features, multivariate methods modestly improved PaCO2 estimation, especially during periods of hemodynamic and respiratory instability. Further work would be needed to produce a clinically useful CO2 monitoring system under these challenging conditions. PMID:26550531

  16. Multivariate data analysis as a PAT tool for early bioprocess development data.

    PubMed

    Mercier, Sarah M; Diepenbroek, Bas; Dalm, Marcella C F; Wijffels, Rene H; Streefland, Mathieu

    2013-09-10

    Early development datasets are typically unstructured, incomplete and truncated, yet they are readily available and contain relevant process information which is not extracted using classical data analysis techniques. In this paper, we illustrate the power of multivariate data analysis (MVDA) as a Process Analytical Technology tool to analyze early development data of a PER.C6® cell cultivation process. MVDA increased our understanding of the process studied. Principal component analysis enabled a thorough exploration of the dataset, identifying causes for batch deviations and revealing sensitivity of the process to scale. These findings were previously undetected using traditional univariate analysis. The lack of structure and gaps in the early development datasets made it impossible to fit them to more advanced partial least square regression models. This paper clearly shows that MVDA should be routinely used to analyze early development data to reveal relevant information for later development and scale-up. The value of these early development runs can be greatly enhanced if the experiments are well-structured and accompanied with full process analytics. This up-front investment will result in shorter and more efficient process development paths, resulting in lower overall development costs for new biopharmaceutical products.

  17. Multivariate curve resolution for hyperspectral image analysis :applications to microarray technology.

    SciTech Connect

    Van Benthem, Mark Hilary; Sinclair, Michael B.; Haaland, David Michael; Martinez, M. Juanita (University of New Mexico, Albuquerque, NM); Timlin, Jerilyn Ann; Werner-Washburne, Margaret C. (University of New Mexico, Albuquerque, NM); Aragon, Anthony D. (University of New Mexico, Albuquerque, NM)

    2003-01-01

    Multivariate curve resolution (MCR) using constrained alternating least squares algorithms represents a powerful analysis capability for a quantitative analysis of hyperspectral image data. We will demonstrate the application of MCR using data from a new hyperspectral fluorescence imaging microarray scanner for monitoring gene expression in cells from thousands of genes on the array. The new scanner collects the entire fluorescence spectrum from each pixel of the scanned microarray. Application of MCR with nonnegativity and equality constraints reveals several sources of undesired fluorescence that emit in the same wavelength range as the reporter fluorphores. MCR analysis of the hyperspectral images confirms that one of the sources of fluorescence is due to contaminant fluorescence under the printed DNA spots that is spot localized. Thus, traditional background subtraction methods used with data collected from the current commercial microarray scanners will lead to errors in determining the relative expression of low-expressed genes. With the new scanner and MCR analysis, we generate relative concentration maps of the background, impurity, and fluorescent labels over the entire image. Since the concentration maps of the fluorescent labels are relatively unaffected by the presence of background and impurity emissions, the accuracy and useful dynamic range of the gene expression data are both greatly improved over those obtained by commercial microarray scanners.

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

    PubMed

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

    2015-03-27

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

  19. 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

  20. Multivariate analysis of cell culture bioprocess data--lactate consumption as process indicator.

    PubMed

    Le, Huong; Kabbur, Santosh; Pollastrini, Luciano; Sun, Ziran; Mills, Keri; Johnson, Kevin; Karypis, George; Hu, Wei-Shou

    2012-12-31

    Multivariate analysis of cell culture bioprocess data has the potential of unveiling hidden process characteristics and providing new insights into factors affecting process performance. This study investigated the time-series data of 134 process parameters acquired throughout the inoculum train and the production bioreactors of 243 runs at the Genentech's Vacaville manufacturing facility. Two multivariate methods, kernel-based support vector regression (SVR) and partial least square regression (PLSR), were used to predict the final antibody concentration and the final lactate concentration. Both product titer and the final lactate level were shown to be predicted accurately when data from the early stages of the production scale were employed. Using only process data from the inoculum train, the prediction accuracy of the final process outcome was lower; the results nevertheless suggested that the history of the culture may exert significant influence on the final process outcome. The parameters contributing most significantly to the prediction accuracy were related to lactate metabolism and cell viability in both the production scale and the inoculum train. Lactate consumption, which occurred rather independently of the residual glucose and lactate concentrations, was shown to be a prominent factor in determining the final outcome of production-scale cultures. The results suggest possible opportunities to intervene in metabolism, steering it towards the type with a strong propensity towards high productivity. Such intervention could occur in the inoculum stage or in the early stage of the production-scale reactors. Overall, this study presents pattern recognition as an important process analytical technology (PAT). Furthermore, the high correlation between lactate consumption and high productivity can provide a guide to apply quality by design (QbD) principles to enhance process robustness. PMID:22974585

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

    SciTech Connect

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

    2012-08-01

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

  2. Multivariate statistical data analysis methods for detecting baroclinic wave interactions in the thermally driven rotating annulus

    NASA Astrophysics Data System (ADS)

    von Larcher, Thomas; Harlander, Uwe; Alexandrov, Kiril; Wang, Yongtai

    2010-05-01

    Experiments on baroclinic wave instabilities in a rotating cylindrical gap have been long performed, e.g., to unhide regular waves of different zonal wave number, to better understand the transition to the quasi-chaotic regime, and to reveal the underlying dynamical processes of complex wave flows. We present the application of appropriate multivariate data analysis methods on time series data sets acquired by the use of non-intrusive measurement techniques of a quite different nature. While the high accurate Laser-Doppler-Velocimetry (LDV ) is used for measurements of the radial velocity component at equidistant azimuthal positions, a high sensitive thermographic camera measures the surface temperature field. The measurements are performed at particular parameter points, where our former studies show that kinds of complex wave patterns occur [1, 2]. Obviously, the temperature data set has much more information content as the velocity data set due to the particular measurement techniques. Both sets of time series data are analyzed by using multivariate statistical techniques. While the LDV data sets are studied by applying the Multi-Channel Singular Spectrum Analysis (M - SSA), the temperature data sets are analyzed by applying the Empirical Orthogonal Functions (EOF ). Our goal is (a) to verify the results yielded with the analysis of the velocity data and (b) to compare the data analysis methods. Therefor, the temperature data are processed in a way to become comparable to the LDV data, i.e. reducing the size of the data set in such a manner that the temperature measurements would imaginary be performed at equidistant azimuthal positions only. This approach initially results in a great loss of information. But applying the M - SSA to the reduced temperature data sets enable us to compare the methods. [1] Th. von Larcher and C. Egbers, Experiments on transitions of baroclinic waves in a differentially heated rotating annulus, Nonlinear Processes in Geophysics

  3. Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

    DOE PAGES

    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 signaturemore » 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.« less

  4. Multivariate statistical analysis of radiological data of building materials used in Tiruvannamalai, Tamilnadu, India.

    PubMed

    Ravisankar, R; Vanasundari, K; Suganya, M; Raghu, Y; Rajalakshmi, A; Chandrasekaran, A; Sivakumar, S; Chandramohan, J; Vijayagopal, P; Venkatraman, B

    2014-02-01

    Using γ spectrometry, the concentration of the naturally occurring radionuclides (226)Ra, (232)Th and (40)K has been measured in soil, sand, cement, clay and bricks, which are used as building materials in Tiruvannamalai, Tamilnadu, India. The radium equivalent activity (Raeq), the criterion formula (CF), indoor gamma absorbed dose rate (DR), annual effective dose (HR), activity utilization index (AUI), alpha index (Iα), gamma index (Iγ), external radiation hazard index (Hex), internal radiation hazard index (Hin), representative level index (RLI), excess lifetime cancer risk (ELCR) and annual gonadal dose equivalent (AGDE) associated with the natural radionuclides are calculated to assess the radiation hazard of the natural radioactivity in the building materials. From the analysis, it is found that these materials used for the construction of dwellings are safe for the inhabitants. The radiological data were processed using multivariate statistical methods to determine the similarities and correlation among the various samples. The frequency distributions for all radionuclides were analyzed. The data set consisted of 15 measured variables. The Pearson correlation coefficient reveals that the (226)Ra distribution in building materials is controlled by the variation of the (40)K concentration. Principal component analysis (PCA) yields a two-component representation of the acquired data from the building materials in Tiruvannamalai, wherein 94.9% of the total variance is explained. The resulting dendrogram of hierarchical cluster analysis (HCA) classified the 30 building materials into four major groups using 15 variables.

  5. Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

    SciTech Connect

    Belianinov, Alex Ganesh, Panchapakesan; Lin, Wenzhi; Jesse, Stephen; Pan, Minghu; Kalinin, Sergei V.; Sales, Brian C.; Sefat, Athena S.

    2014-12-01

    Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe{sub 0.55}Se{sub 0.45} (T{sub c} = 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 FeTe{sub 1−x}Se{sub x} 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.

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

    PubMed

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

    2016-01-15

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

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

    PubMed

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

    2013-10-21

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

  8. Multivariate statistical analysis of heavy metal concentration in soils of Yelagiri Hills, Tamilnadu, India - Spectroscopical approach

    NASA Astrophysics Data System (ADS)

    Chandrasekaran, A.; Ravisankar, R.; Harikrishnan, N.; Satapathy, K. K.; Prasad, M. V. R.; Kanagasabapathy, K. V.

    2015-02-01

    Anthropogenic activities increase the accumulation of heavy metals in the soil environment. Soil pollution significantly reduces environmental quality and affects the human health. In the present study soil samples were collected at different locations of Yelagiri Hills, Tamilnadu, India for heavy metal analysis. The samples were analyzed for twelve selected heavy metals (Mg, Al, K, Ca, Ti, Fe, V, Cr, Mn, Co, Ni and Zn) using energy dispersive X-ray fluorescence (EDXRF) spectroscopy. Heavy metals concentration in soil were investigated using enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (CF) and pollution load index (PLI) to determine metal accumulation, distribution and its pollution status. Heavy metal toxicity risk was assessed using soil quality guidelines (SQGs) given by target and intervention values of Dutch soil standards. The concentration of Ni, Co, Zn, Cr, Mn, Fe, Ti, K, Al, Mg were mainly controlled by natural sources. Multivariate statistical methods such as correlation matrix, principal component analysis and cluster analysis were applied for the identification of heavy metal sources (anthropogenic/natural origin). Geo-statistical methods such as kirging identified hot spots of metal contamination in road areas influenced mainly by presence of natural rocks.

  9. Characterization of hydrocarbon contaminated areas by multivariate statistical analysis: Case studies.

    PubMed

    Saenz, G; Pingitore, N E

    1991-01-01

    Analysis of soil gases is a relatively rapid and inexpensive method to delineate and measure hydrocarbon contamination in the subsurface caused by diesel or gasoline. Techniques originally developed for petroleum exploration have been adapted to tracking hydrocarbons which have leaked or spilled at or below the earth's surface.Discriminant analysis (a multivariate statistical technique) is used to classify soil gas samples of C1 to C7 hydrocarbons as biogenic (natural soil gases) or thermogenic (contaminant hydrocarbons). Map plots of C1 to C7 total interstitial hydrocarbons, C2 to C7 interstitial hydrocarbons, and C1/ΣC n rations are used to further delineate and document the extent and migration of contamination.Three case studies of the technique are presented: each involves leakage of hydrocarbons from underground storage tanks. Soil gas analysis clearly defines the spread of contamination and can serve as the basis for the correct placement of monitoring wells. The method proved to be accurate, rapid, and cost-effective; it therefore has potential for widespread application to the identification of soil and groundwater contaminated by hydrocarbons.

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

    PubMed

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

    2013-10-21

    Identification of American cochineal species (Dactylopius genus) can provide important information for the study of historical works of art, entomology, cosmetics, pharmaceuticals and foods. In this study, validated species of Dactylopius, including the domesticated cochineal D. coccus, were analysed by high-performance liquid chromatography with a diode array detector (HPLC-DAD) and submitted to multivariate data analysis, in order to discriminate the species and hence construct a reference library for a wide range of applications. Principal components analysis (PCA) and partial least squares discriminant analysis (PLSDA) models successfully provided accurate species classifications. This library was then applied to the identification of 72 historical insect specimens of unidentified species, mostly dating from the 19th century, and belonging to the Economic Botany Collection, Royal Botanic Gardens, Kew, England. With this approach it was possible to identify anomalies in how insects were labelled historically, as several of them were revealed not to be cochineal. Nevertheless, more than 85% of the collection was determined to be species of Dactylopius and the majority of the specimens were identified as D. coccus. These results have shown that HPLC-DAD, in combination with suitable chemometric methods, is a powerful approach for discriminating related cochineal species. PMID:23961534

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

    PubMed

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

    2016-01-15

    Multivariate analysis was used to identify the variables affecting the performance of pilot-scale activated sludge (AS) reactors treating old leachate from a landfill and from domestic wastewater. Raw leachate was pre-treated using air stripping to partially remove the total ammoniacal nitrogen (TAN). The control AS reactor (AS-0%) was loaded only with domestic wastewater, whereas the other reactor was loaded with mixtures containing leachate at volumetric ratios of 2 and 5%. The best removal efficiencies were obtained for a ratio of 2%, as follows: 70 ± 4% for total suspended solids (TSS), 70 ± 3% for soluble chemical oxygen demand (SCOD), 70 ± 4% for dissolved organic carbon (DOC), and 51 ± 9% for the leachate slowly biodegradable organic matter (SBOM). Fourier transform infrared (FTIR) spectroscopic analysis confirmed that most of the SBOM was removed by partial biodegradation rather than dilution or adsorption of organics in the sludge. Nitrification was approximately 80% in the AS-0% and AS-2% reactors. No significant accumulation of heavy metals was observed for any of the tested volumetric ratios. Principal component analysis (PCA) and partial least squares (PLS) indicated that the data dimension could be reduced and that TAN, SCOD, DOC and nitrification efficiency were the main variables that affected the performance of the AS reactors. PMID:26551262

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

    NASA Astrophysics Data System (ADS)

    Myoung, Se Hun; Kim, Jin-Koo

    2016-03-01

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

  13. [Multivariate analysis of planktonic Ostracoda communities during autumn and winter in the Beibu Gulf].

    PubMed

    Zhao, Hanqu; Jia, Xiaoping; Li, Chunhou; Du, Feiyan; Li, Zhandong; Wang, Xuefeng

    2006-12-01

    Base on the zooplankton samples collected in two cruises for studying the change tendency of ecological environment in the Beibu Gulf, planktonic Ostracoda communities in autumn and winter in the Beibu Gulf were investigated by multivariate analysis. A total of 11 planktonic Ostracoda species were identified and grouped into three ecotypes, i.e., Huposaline and warm-water group, Eurythermal and eurysaline group, as well as Hyperthermal and hysaline group. The average abundance of all species was low, and Euconchoecia aculeate was the most abundant one. Based on the cluster analysis and multidimensional scaling analysis, planktonic Ostracoda in the Beibu Gulf was a community of stable structure, while in autumn and winter, the community also could be divided into two different sub-communities. To understand the relationship between the community structure and the environmental factors, correlation coefficients for the abundance and the environmental factors were calculated. It indicated that water temperature and salinity had tiny impact on planktonic Ostracoda communities, but there was some rule in its change rhythms. Sub-water temperature and salinity had strong impact on community II in winter.

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

    PubMed

    Firdous, Shahana; Begum, Shaheen; Yasmin, Azra

    2016-09-01

    Soil providing a wide array of ecosystem services is subjected to quality deterioration due to natural and anthropogenic factors. Most of the soils in Pakistan have poor status of available plant nutrients and cannot support optimum levels of crop productivity. The present study statistically analyzed ten soil quality parameters in five subwatersheds (Bari Imam, Chattar, Rumli, Shahdra, and Shahpur) of the Rawal Lake. Analysis of variance (ANOVA), cluster analysis (CA), and principal component analysis (PCA) were performed to evaluate correlation in soil quality parameters on spatiotemporal and vertical scales. Soil organic matter, electrical conductivity, nitrates, and sulfates were found to be lower than that required for good quality soil. Soil pH showed significant difference (p < 0.05) in mean values at different sampling sites and sampling months indicating that it is affected and determined by land uses and seasons. Pearson correlation revealed a strong positive correlation (r = 0.437) between nitrates and organic matter. Application of principal component analysis resulted in three major factors contributing 76 % of the total variance. For factor 1, temperature, sand, silt, clay, and nitrates had the highest factor loading values (>0.75) and indicated that these were the most influential parameters of first factor or component. Cluster analysis separated five sampling sites into three statistically significant clusters: I (Shahdra-Bari Imam), II (Chattar), and III (Shahpur-Rumli). Among the five sites, Shahdra was found to have good quality soil followed by Bari Imam. The present study illustrated the usefulness of multivariate statistical approaches for the analysis and interpretation of complex datasets to understand variations in soil quality for effective watershed management. PMID:27553947

  15. Multivariate analysis of spectral data with frequency shifts: application to temperature dependent infrared spectra of peptides and proteins.

    PubMed

    Kubelka, Jan

    2013-10-15

    Changes in the amide I' IR band with temperature are widely used for elucidation of peptide and protein conformational transitions and folding equilibria. Since amide I' exhibits inherent temperature dependent frequency shifts, standard mixture analysis methods are not applicable. To reliably extract the true thermodynamic states, frequency shifts of the component spectra must be explicitly taken into account. For this purpose, new methods termed shifted multivariate spectra analysis (SMSA) and parametric SMSA (pSMSA) are developed and tested on sets of synthetic data as well as real experimental amide I' spectra for thermal unfolding of an α-helical peptide. SMSA uses no specific functional form for the transition (soft modeling), while the parametric variant (pSMSA) assumes a thermodynamic model (hard modeling). The implementation is optimized specifically for amide I' IR in that it takes advantage of known, linear dependence of the frequencies as well as intensities on temperature. The synthetic data tests demonstrate the robustness of the methods; the initial test parameters are recovered with a high degree of reliability, although the nonparameteric SMSA is subject to the rotational ambiguity. Application to the peptide experimental amide I' data illustrates additional complications encountered with the analysis of real systems, such as correction for the side-chain spectra and interference of spectral shape changes. Nevertheless, the results are in excellent agreement with the independent control using circular dichroism (CD) data. The general applicability and limitations of the methods are discussed along with potential extensions.

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

    PubMed

    Tan Boon Ann

    1987-06-01

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

  17. Acid Rain Analysis by Standard Addition Titration.

    ERIC Educational Resources Information Center

    Ophardt, Charles E.

    1985-01-01

    The standard addition titration is a precise and rapid method for the determination of the acidity in rain or snow samples. The method requires use of a standard buret, a pH meter, and Gran's plot to determine the equivalence point. Experimental procedures used and typical results obtained are presented. (JN)

  18. Application of multivariate, fuzzy set and neural network analysis in quantitative cytological examinations.

    PubMed

    Molnar, B; Szentirmay, Z; Bodo, M; Sugar, J; Feher, J

    1993-05-01

    Multivariate statistical methods have been used in several studies to increase the diagnostic reliability of TV image analyser systems. In recent years some algorithms for decision support (fuzzy logic) and for pattern recognition (neural nets), both non-linear, were developed. This paper reports on preliminary results obtained with these methods in quantitative cytology and compares them to the traditional classifiers. A total of 21 normal, 15 dysplastic and 23 malignant, gastric imprint smears were Feulgen stained and analysed on a Leitz Miamed DNA cytophotometer system. Mean DNA content, the 2c deviation index (2cDI), 5c exceeding rate (5cER), G1, S, G2 phase fraction ratios, cell nucleus area and form factor were determined. Diagnostic accuracy of the discriminant analysis was 96% for the malignant cases, 87% for dysplasias and 81% for normal cases. Cluster analysis gave no significant result. Our diagnostic system utilizing fuzzy logic has made the diagnostic borders adjustable and reliable. The back-propagation neural net correctly classified the normal and malignant cases (100%) and all but one of the dysplasias (98%). The non-linear mathematical methods improved the reliability of the diagnostic system. These new algorithms gave results comparable to traditional classifiers. The application of these methods to clinical samples is encouraging.

  19. Detection of Leukemia with Blood Samples Using Raman Spectroscopy and Multivariate Analysis

    NASA Astrophysics Data System (ADS)

    Martínez-Espinosa, J. C.; González-Solís, J. L.; Frausto-Reyes, C.; Miranda-Beltrán, M. L.; Soria-Fregoso, C.; Medina-Valtierra, J.

    2009-06-01

    The use of Raman spectroscopy to analyze blood biochemistry and hence distinguish between normal and abnormal blood was investigated. Blood samples were obtained from 6 patients who were clinically diagnosed with leukemia and 6 healthy volunteers. The imprint was put under the microscope and several points were chosen for Raman measurement. All the spectra were collected by a confocal Raman micro-spectroscopy (Renishaw) with a NIR 830 nm laser. It is shown that the serum samples from patients with leukemia and from the control group can be discriminated when the multivariate statistical methods of principal component analysis (PCA) and linear discriminated analysis (LDA) are 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 to study the degree of damage to the bone marrow using just blood samples instead of biopsies, treatment very painful for patients.

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

    PubMed Central

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

    2015-01-01

    A novel multi-scale approach for quantifying both inter- and intra-component dependence of a complex system is introduced. This is achieved using empirical mode decomposition (EMD), which, unlike conventional scale-estimation methods, obtains a set of scales reflecting the underlying oscillations at the intrinsic scale level. This enables the data-driven operation of several standard data-association measures (intrinsic correlation, intrinsic sample entropy (SE), intrinsic phase synchrony) and, at the same time, preserves the physical meaning of the analysis. The utility of multi-variate extensions of EMD is highlighted, both in terms of robust scale alignment between system components, a pre-requisite for inter-component measures, and in the estimation of feature relevance. We also illuminate that the properties of EMD scales can be used to decouple amplitude and phase information, a necessary step in order to accurately quantify signal dynamics through correlation and SE analysis which are otherwise not possible. Finally, the proposed multi-scale framework is applied to detect directionality, and higher order features such as coupling and regularity, in both synthetic and biological systems. PMID:25568621

  1. Non-linear multivariate curve resolution analysis of voltammetric pH titrations.

    PubMed

    Díaz Cruz, José Manuel; Sanchís, Josep; Chekmeneva, Elena; Ariño, Cristina; Esteban, Miquel

    2010-07-01

    A new chemometric approach is put forward, dealing with the non-linear behaviour observed in the multivariate curve resolution (MCR) analysis of certain overlapping voltammetric signals obtained in titrations of metal complexes where pH is progressively changed. In such cases, non-reversible reduction signals move along the potential axis as a consequence of the involvement of H(+)-ions in the electrochemical process and cause a dramatic loss of linearity, which hinders accurate MCR analysis. The method proposed is based on the least-squares fitting of peak potential vs. pH datasets to parametric linear and sigmoid functions through the decomposition of the data matrix into both a concentration profile matrix and a unit signal matrix, in a similar way as in the alternating least-squares algorithm of MCR (ALS). Such calculations are carried out through several home-made Matlab programs which are freely available as Supplementary Material of the present work. The fitted parameters, along with the evolution of resolved concentrations and potential shifts with pH, provide valuable information on the complexation/reduction processes. The method is tested first on the relatively simple Cd(II)-NTA system and then applied to the study of the binding of Cd(II)-ions by glutathione (gamma-Glu-Cys-Gly, GSH) and the phytochelatin PC(2) ((gamma-Glu-Cys)(2)-Gly).

  2. The importance of gel properties for mucoadhesion measurements: a multivariate data analysis approach.

    PubMed

    Hägerström, Helene; Bergström, Christel A S; Edsman, Katarina

    2004-02-01

    In this study we used tensile strength measurements and a recently developed interpretation procedure to evaluate the mucoadhesive properties of a large set of gel preparations with diverse rheological properties. Multivariate data analysis in the form of principal component analysis (PCA) and partial least square projection to latent structures (PLS) was applied to extract useful information from the rather large quantities of data obtained. PCA showed that the selected series of gels was heterogeneous. Some groupings could be detected but none of the gels was identified as an outlier. By using PLS we investigated the relations between the rheological properties of a gel and the parameters defining the cohesiveness, as measured with the texture analyser used for the mucoadhesion measurements. The rheological properties proved to be important for the results of both the mucoadhesion and the cohesiveness measurements. Furthermore, by using PLS two different measurement configurations were evaluated and it was concluded that the combination of a relatively small volume of gel and two pieces of mucosa seems to be more appropriate than a large volume of gel in combination with one piece of mucosa. PMID:15005874

  3. Detection of neuroinflammation through the retina by means of Raman spectroscopy and multivariate analysis

    NASA Astrophysics Data System (ADS)

    Marro, Monica; Taubes, Alice; Villoslada, Pablo; Petrov, Dmitri

    2012-06-01

    Retinal nervous tissue sustains a substantial damage during the autoimmune inflammatory processes characteristic for Multiple Sclerosis (MS). The damage can be characterized non-surgically by Raman Spectroscopy, a non-invasive optical imaging technology. We used non-resonant near-infrared Raman spectrosocopy to create a spectral library of eight pivotal biomolecules known to be involved in neuroinflammation: Nicotinamide Adenine Dinucliotide (NADH), Flavin Adenine Nucleotide (FAD), Lactate, Cytochrome C, Glutamate, N-Acetyl- Aspartate (NAA), Phosphotidylcholine, with Advanced Glycolization End Products (AGEs) analyzed as a reference. Principal Component Analysis (PCA) of 50 spectra taken of murine retinal tissue culture undergoing an inflammatory response and healthy controls was used in order to characterize the molecular makeup of the inflammation. The loading plots revealed a heavy influence of peaks related to Glutamate, NADH, and Phosphotidylcholine to inflammation-related spectral changes. Partial Least Squares - Discriminant analysis (PLS-DA) was performed to create a multivariate classifier for the spectral diagnosis of neuroinflammed tissue and yielded a diagnostic sensitivity of 100% and specificity of 100%. We demonstrate then the effectiveness of combining Raman spectroscopy with PCA and PLS-DA statistical techniques to detect and monitor neuroinflamation in retina. With this technique Glutamate, NAA and NADH are detected in retina tissue as signs for neuroinflammation.

  4. 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.

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

    NASA Astrophysics Data System (ADS)

    Jensen, Kasper L.; Spiild, Henrik; Toftum, Jørn

    2012-01-01

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

  6. Application of projection methods of multivariate data analysis in eddy current testing of materials

    NASA Astrophysics Data System (ADS)

    Polyakov, V. V.; Egorov, A. V.; Pirogov, A. A.; Kolubaev, E. A.

    2015-10-01

    The paper considers the applicability of projection methods of multivariate data analysis to discriminate between the factors that simultaneously affect the results of multi-frequency eddy current testing of nonmagnetic metals and alloys. Measurements were carried out for copper, magnesium, aluminum alloy and bronze specimens with different electrical conductivity equal to 57, 22, 16 and 7.5 S/m, respectively. The measured probe impedance changes were used to plot hodographs within the frequency range from 100 Hz to 6.4 kHz. The gap width between an attachable parametric probe and the specimen surface was specified using dielectric spacers within the range from 0 to 1 mm. The principal component analysis applied to experimental hodographs allowed us to safely discriminate between the influence of such factors as electrical conductivity of the material and gap width. The proposed approach to discriminating between individual factors that strongly affect eddy current measurement results is an enhancement in eddy current testing of materials.

  7. Assessment of changes of vector borne diseases with wetland characteristics using multivariate analysis.

    PubMed

    Sheela, A M; Sarun, S; Justus, J; Vineetha, P; Sheeja, R V

    2015-04-01

    Vector borne diseases are a threat to human health. Little attention has been paid to the prevention of these diseases. We attempted to identify the significant wetland characteristics associated with the spread of chikungunya, dengue fever and malaria in Kerala, a tropical region of South West India using multivariate analyses (hierarchical cluster analysis, factor analysis and multiple regression). High/medium turbid coastal lagoons and inland water-logged wetlands with aquatic vegetation have significant effect on the incidence of chikungunya while dengue influenced by high turbid coastal beaches and malaria by medium turbid coastal beaches. The high turbidity in water is due to the urban waste discharge namely sewage, sullage and garbage from the densely populated cities and towns. The large extent of wetland is low land area favours the occurrence of vector borne diseases. Hence the provision of pollution control measures at source including soil erosion control measures is vital. The identification of vulnerable zones favouring the vector borne diseases will help the authorities to control pollution especially from urban areas and prevent these vector borne diseases. Future research should cover land use cover changes, climatic factors, seasonal variations in weather and pollution factors favouring the occurrence of vector borne diseases. PMID:25412801

  8. Multivariate statistical analysis of a high rate biofilm process treating kraft mill bleach plant effluent.

    PubMed

    Goode, C; LeRoy, J; Allen, D G

    2007-01-01

    This study reports on a multivariate analysis of the moving bed biofilm reactor (MBBR) wastewater treatment system at a Canadian pulp mill. The modelling approach involved a data overview by principal component analysis (PCA) followed by partial least squares (PLS) modelling with the objective of explaining and predicting changes in the BOD output of the reactor. Over two years of data with 87 process measurements were used to build the models. Variables were collected from the MBBR control scheme as well as upstream in the bleach plant and in digestion. To account for process dynamics, a variable lagging approach was used for variables with significant temporal correlations. It was found that wood type pulped at the mill was a significant variable governing reactor performance. Other important variables included flow parameters, faults in the temperature or pH control of the reactor, and some potential indirect indicators of biomass activity (residual nitrogen and pH out). The most predictive model was found to have an RMSEP value of 606 kgBOD/d, representing a 14.5% average error. This was a good fit, given the measurement error of the BOD test. Overall, the statistical approach was effective in describing and predicting MBBR treatment performance. PMID:17486834

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

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

  10. Assessment of changes of vector borne diseases with wetland characteristics using multivariate analysis.

    PubMed

    Sheela, A M; Sarun, S; Justus, J; Vineetha, P; Sheeja, R V

    2015-04-01

    Vector borne diseases are a threat to human health. Little attention has been paid to the prevention of these diseases. We attempted to identify the significant wetland characteristics associated with the spread of chikungunya, dengue fever and malaria in Kerala, a tropical region of South West India using multivariate analyses (hierarchical cluster analysis, factor analysis and multiple regression). High/medium turbid coastal lagoons and inland water-logged wetlands with aquatic vegetation have significant effect on the incidence of chikungunya while dengue influenced by high turbid coastal beaches and malaria by medium turbid coastal beaches. The high turbidity in water is due to the urban waste discharge namely sewage, sullage and garbage from the densely populated cities and towns. The large extent of wetland is low land area favours the occurrence of vector borne diseases. Hence the provision of pollution control measures at source including soil erosion control measures is vital. The identification of vulnerable zones favouring the vector borne diseases will help the authorities to control pollution especially from urban areas and prevent these vector borne diseases. Future research should cover land use cover changes, climatic factors, seasonal variations in weather and pollution factors favouring the occurrence of vector borne diseases.

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

    PubMed

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

    2013-11-27

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

  12. Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula

    NASA Astrophysics Data System (ADS)

    Sarhadi, Ali; Burn, Donald H.; Concepción Ausín, María.; Wiper, Michael P.

    2016-03-01

    A time-varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time-varying dependence structure between mixed continuous and discrete multiattributes of multidimensional hydrometeorological phenomena. Joint Bayesian inference is carried out to fit the marginals and copula in an illustrative example using an adaptive, Gibbs Markov Chain Monte Carlo (MCMC) sampler. Posterior mean estimates and credible intervals are provided for the model parameters and the Deviance Information Criterion (DIC) is used to select the model that best captures different forms of nonstationarity over time. This study also introduces a fully Bayesian, time-varying joint return period for multivariate time-dependent risk analysis in nonstationary environments. The results demonstrate that the nature and the risk of extreme-climate multidimensional processes are changed over time under the impact of climate change, and accordingly the long-term decision making strategies should be updated based on the anomalies of the nonstationary environment.

  13. Fingerprinting of morphine using chromatographic purity profiling and multivariate data analysis.

    PubMed

    Acevska, Jelena; Stefkov, Gjoshe; Cvetkovikj, Ivana; Petkovska, Rumenka; Kulevanova, Svetlana; Cho, JungHwan; Dimitrovska, Aneta

    2015-05-10

    Chromatographic purity profiling (CPP) is the common name of a group of analytical and chemometric applications for detection, identification and quantitative determination of related substances and other impurities in active pharmaceutical ingredients (APIs) and finished dosage forms (FDFs). CPP is used for fingerprinting and discriminating between samples, thus representing a core activity in modern drug analysis. The worldwide demand for morphine and its congeners is tremendous and depends entirely on the supply of natural opiates. The aim of this research was to develop a methodology that enables identification of a source of morphine, thus revealing falsification of the substance. The characteristic and reproducible features of impurity profiles for 28 samples of morphine (6 morphine sulfate, 9 morphine hydrochloride and 13 morphine base) were captured by a new LC/MS method for impurity profiling of morphine. The impurity profile encompasses the related substances specified in relevant Ph.Eur. monographs, as well as the other morphinane like impurities, including the naturally occurring co-extracted alkaloids. Different pattern recognition techniques (unsupervised and supervised) were used to reveal the differentiation features of the morphine fingerprints for classification and authentication purposes. The results described in this research open the possibility of using the chromatographic purity profile combined with multivariate data analysis for fingerprinting of morphine samples.

  14. Multivariate analysis of trace metals in textile effluents in relation to soil and groundwater.

    PubMed

    Manzoor, S; Shah, Munir H; Shaheen, N; Khalique, A; Jaffar, M

    2006-09-01

    This paper deals with the multivariate analysis of metal data in effluents, soil and groundwater to find the distribution and source identification of the selected metals in the three media. Samples were collected from three textile industries located in Hattar Industrial Estate, Pakistan. Metals were estimated by flame atomic absorption spectrophotometry. The results showed elevated levels of Cr, Pb, Ni, Co, Fe, Ca, Na, K and Zn in these media, following the order: soil>effluent>water. Principle component analysis (PCA) of the data showed that the textile effluents are contaminating the soil wherein Cr and Pb were dominant toxic metals having concentrations of 5.96 mg/kg and 4.46 mg/kg, respectively. Other toxic metals such as Co, Cd, Zn, Ni, Mn and Fe, were found to have common origin in the textile effluents. The correlation study along with linear regression and PCA, supported the fact that various elevated metal concentrations emerged from the textile industrial effluents ultimately leading to contamination of the soil and groundwater in their proximity. The estimated metal levels in the water/soil system are compared with the safe limits laid down by WHO.

  15. Assessing heavy metal sources in sugarcane Brazilian soils: an approach using multivariate analysis.

    PubMed

    da Silva, Fernando Bruno Vieira; do Nascimento, Clístenes Williams Araújo; Araújo, Paula Renata Muniz; da Silva, Luiz Henrique Vieira; da Silva, Roberto Felipe

    2016-08-01

    Brazil is the world's largest sugarcane producer and soils in the northeastern part of the country have been cultivated with the crop for over 450 years. However, so far, there has been no study on the status of heavy metal accumulation in these long-history cultivated soils. To fill the gap, we collect soil samples from 60 sugarcane fields in order to determine the contents of Cd, Cr, Cu, Ni, Pb, and Zn. We used multivariate analysis to distinguish between natural and anthropogenic sources of these metals in soils. Analytical determinations were performed in ICP-OES after microwave acid solution digestion. Mean concentrations of Cd, Cr, Cu, Ni, Pb, and Zn were 1.9, 18.8, 6.4, 4.9, 11.2, and 16.2 mg kg(-1), respectively. The principal component one was associated with lithogenic origin and comprised the metals Cr, Cu, Ni, and Zn. Cluster analysis confirmed that 68 % of the evaluated sites have soil heavy metal concentrations close to the natural background. The Cd concentration (principal component two) was clearly associated with anthropogenic sources with P fertilization being the most likely source of Cd to soils. On the other hand, the third component (Pb concentration) indicates a mixed origin for this metal (natural and anthropogenic); hence, Pb concentrations are probably related not only to the soil parent material but also to industrial emissions and urbanization in the vicinity of the agricultural areas. PMID:27395358

  16. Multivariate analysis of Raman spectra for in vitro non-invasive studies of living cells

    NASA Astrophysics Data System (ADS)

    Notingher, Ioan; Jell, Gavin; Notingher, Petronela L.; Bisson, Isabelle; Tsigkou, Olga; Polak, Julia M.; Stevens, Molly M.; Hench, Larry L.

    2005-06-01

    Understanding the biochemical and biophysical properties of live cells is fundamental for unravelling the secrets of many diseases and developing new therapies. Raman micro-spectroscopy is a powerful non-invasive technique that allows in vitro studies of individual living cells or groups of cells without the use of any labels or contrast enhancing chemicals. We describe the use of various multivariate statistical methods, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Classical Least Square (CLS) fitting, to extract biochemical information related to various cellular events. Such methods are required because of the high complexity of the Raman spectra obtained from living cells. PCA and LDA are used to discriminate between healthy and tumor cells. A leave-one-out cross-validation method indicated high prediction accuracy (95%) in identification of tumorogenic bone cells. The CLS fitting method using commercially available biopolymers makes it possible to monitor biochemical changes during the differentiation of embryonic stem cells and foetal bone cells. The results suggest that in both cases differentiated cells are characterised by lower concentrations of RNA compared to undifferentiated cells. These studies suggest that Raman micro-spectroscopy could become an invaluable tool for in vitro cellular biochemistry studies.

  17. Study of archaeological coins of different dynasties using libs coupled with multivariate analysis

    NASA Astrophysics Data System (ADS)

    Awasthi, Shikha; Kumar, Rohit; Rai, G. K.; Rai, A. K.

    2016-04-01

    Laser Induced Breakdown Spectroscopy (LIBS) is an atomic emission spectroscopic technique having unique capability of an in-situ monitoring tool for detection and quantification of elements present in different artifacts. Archaeological coins collected form G.R. Sharma Memorial Museum; University of Allahabad, India has been analyzed using LIBS technique. These coins were obtained from excavation of Kausambi, Uttar Pradesh, India. LIBS system assembled in the laboratory (laser Nd:YAG 532 nm, 4 ns pulse width FWHM with Ocean Optics LIBS 2000+ spectrometer) is employed for spectral acquisition. The spectral lines of Ag, Cu, Ca, Sn, Si, Fe and Mg are identified in the LIBS spectra of different coins. LIBS along with Multivariate Analysis play an effective role for classification and contribution of spectral lines in different coins. The discrimination between five coins with Archaeological interest has been carried out using Principal Component Analysis (PCA). The results show the potential relevancy of the methodology used in the elemental identification and classification of artifacts with high accuracy and robustness.

  18. Multivariable harmonic balance analysis of the neuronal oscillator for leech swimming.

    PubMed

    Chen, Zhiyong; Zheng, Min; Friesen, W Otto; Iwasaki, Tetsuya

    2008-12-01

    Biological systems, and particularly neuronal circuits, embody a very high level of complexity. Mathematical modeling is therefore essential for understanding how large sets of neurons with complex multiple interconnections work as a functional system. With the increase in computing power, it is now possible to numerically integrate a model with many variables to simulate behavior. However, such analysis can be time-consuming and may not reveal the mechanisms underlying the observed phenomena. An alternative, complementary approach is mathematical analysis, which can demonstrate direct and explicit relationships between a property of interest and system parameters. This paper introduces a mathematical tool for analyzing neuronal oscillator circuits based on multivariable harmonic balance (MHB). The tool is applied to a model of the central pattern generator (CPG) for leech swimming, which comprises a chain of weakly coupled segmental oscillators. The results demonstrate the effectiveness of the MHB method and provide analytical explanations for some CPG properties. In particular, the intersegmental phase lag is estimated to be the sum of a nominal value and a perturbation, where the former depends on the structure and span of the neuronal connections and the latter is roughly proportional to the period gradient, communication delay, and the reciprocal of the intersegmental coupling strength. PMID:18663565

  19. Guidance for performing multivariate data analysis of bioprocessing data: pitfalls and recommendations.

    PubMed

    Rathore, Anurag S; Mittal, Shachi; Pathak, Mili; Arora, Arushi

    2014-01-01

    Biotech unit operations are often characterized by a large number of inputs (operational parameters) and outputs (performance parameters) along with complex correlations among them. A typical biotech process starts with the vial of the cell bank, ends with the final product, and has anywhere from 15 to 30 such unit operations in series. Besides the above-mentioned operational parameters, raw material attributes can also impact process performance and product quality as well as interact among each other. Multivariate data analysis (MVDA) offers an effective approach to gather process understanding from such complex datasets. Review of literature suggests that the use of MVDA is rapidly increasing, fuelled by the gradual acceptance of quality by design (QbD) and process analytical technology (PAT) among the regulators and the biotech industry. Implementation of QbD and PAT requires enhanced process and product understanding. In this article, we first discuss the most critical issues that a practitioner needs to be aware of while performing MVDA of bioprocessing data. Next, we present a step by step procedure for performing such analysis. Industrial case studies are used to elucidate the various underlying concepts. With the increasing usage of MVDA, we hope that this article would be a useful resource for present and future practitioners of MVDA. PMID:24778085

  20. Multivariate analysis in the pharmaceutical industry: enabling process understanding and improvement in the PAT and QbD era.

    PubMed

    Ferreira, Ana P; Tobyn, Mike

    2015-01-01

    In the pharmaceutical industry, chemometrics is rapidly establishing itself as a tool that can be used at every step of product development and beyond: from early development to commercialization. This set of multivariate analysis methods allows the extraction of information contained in large, complex data sets thus contributing to increase product and process understanding which is at the core of the Food and Drug Administration's Process Analytical Tools (PAT) Guidance for Industry and the International Conference on Harmonisation's Pharmaceutical Development guideline (Q8). This review is aimed at providing pharmaceutical industry professionals an introduction to multivariate analysis and how it is being adopted and implemented by companies in the transition from "quality-by-testing" to "quality-by-design". It starts with an introduction to multivariate analysis and the two methods most commonly used: principal component analysis and partial least squares regression, their advantages, common pitfalls and requirements for their effective use. That is followed with an overview of the diverse areas of application of multivariate analysis in the pharmaceutical industry: from the development of real-time analytical methods to definition of the design space and control strategy, from formulation optimization during development to the application of quality-by-design principles to improve manufacture of existing commercial products.

  1. Electrophoretic analysis of Allium alien addition lines.

    PubMed

    Peffley, E B; Corgan, J N; Horak, K E; Tanksley, S D

    1985-12-01

    Meiotic pairing in an interspecific triploid of Allium cepa and A. fistulosum, 'Delta Giant', exhibits preferential pairing between the two A. cepa genomes, leaving the A. fistulosum genome as univalents. Multivalent pairing involving A. fistulosum chromosomes occurs at a low level, allowing for recombination between the genomes. Ten trisomies were recovered from the backcross of 'Delta Giant' x A. cepa cv., 'Temprana', representing a minimum of four of the eight possible alien addition lines. The alien addition lines possessed different A. fistulosum enzyme markers. Those markers, Adh-1, Idh-1 and Pgm-1 reside on different A. fistulosum chromosomes, whereas Pgi-1 and Idh-1 may be linked. Diploid, trisomic and hyperploid progeny were recovered that exhibited putative pink root resistance. The use of interspecific plants as a means to introgress A. fistulosum genes into A. cepa appears to be successful at both the trisomic and the diploid levels. If introgression can be accomplished using an interspecific triploid such as 'Delta Giant' to generate fertile alien addition lines and subsequent fertile diploids, or if introgression can be accomplished directly at the diploid level, this will have accomplished gene flow that has not been possible at the interspecific diploid level.

  2. Quantitative Raman spectroscopy of highly fluorescent samples using pseudosecond derivatives and multivariate analysis.

    PubMed

    O'Grady, A; Dennis, A C; Denvir, D; McGarvey, J J; Bell, S E

    2001-05-01

    Intense luminescence backgrounds cause significant problems in quantitative Raman spectroscopy, particularly in multivariate analysis where background suppression is essential. Taking second derivatives reduces the background, but differentiation increases the apparent noise that arises on spectra recorded with CCD detectors due to random, but fixed, variations in the pixel-to-pixel response. We have recently reported a very general method for correcting CCD fixed-pattern response in which spectra are taken at two or more slightly shifted spectrometer positions and are then subtracted to give a derivative-like shifted, subtracted Raman (SSR) spectrum. Here we show that differentiating SSR data (which has inherently higher S/N than the undifferenced data) yields spectra that are similar to those that are obtained from the normal two-step differentiation process and can be characterized as pseudo-second-derivative, PSD, spectra. The backgrounds are suppressed in the PSD spectra, which means they can be used directly in multivariate data analysis, but they have significantly higher S/N ratios than do simple second derivatives. To demonstrate the improvement brought about by using PSD spectra, we have analyzed known samples, consisting of simple binary mixtures of methanol and ethanol doped with laser dye. When the background levels of all samples included in the models were < or =10x greater than the intensity of the strongest Raman bands, partial least-squares calibration of the PSD data gave a standard error of prediction of 3.2%. Calibration using second derivatives gave a prediction error which was approximately twice as large, at 6.5%; however, when data with background levels . approximately 100x larger than the strongest Raman bands were included, the noise on the second-derivative spectra was so large as to prevent a meaningful calibration. Conversely, the PSD treatment of these samples gave a very satisfactory calibration with a standard error of prediction

  3. Redox State of Iron in Lunar Glasses using X-ray Absorption Spectroscopy and Multivariate Analysis

    NASA Astrophysics Data System (ADS)

    Dyar, M. D.; McCanta, M. C.; Lanzirotti, A.; Sutton, S. R.; Carey, C. J.; Mahadevan, S.; Rutherford, M. J.

    2014-12-01

    The oxidation state of igneous materials on a planet is a critically-important variable in understanding magma evolution on bodies in our solar system. However, direct and indirect methods for quantifying redox states are challenging, especially across the broad spectrum of silicate glass compositions found on airless bodies. On the Moon, early Mössbauer studies of bulk samples suggested the presence of significant Fe3+ (>10%) in lunar glasses (green, orange, brown); lunar analog glasses synthesized at fO2 <10-11 have similar Fe3+. All these Mössbauer spectra are challenging to interpret due to the presence of multiple coordination environments in the glasses. X-ray absorption spectroscopy (XAS) allows pico- and nano-scale interrogation of primitive planetary materials using the pre-edge, main edge, and EXAFS regions of absorption edge spectra. Current uses of XAS require availability of standards with compositions similar to those of unknowns and complex procedures for curve-fitting of pre-edge features that produce results with poorly constrained accuracy. A new approach to accurate and quantitative redox measurements with XAS is to couple use of spectra from synthetic glass standards covering a broad compositional range with multivariate analysis (MVA) techniques. Mössbauer and XAS spectra from a suite of 33 synthetic glass standards covering a wide range of compositions and fO2(Dyar et al., this meeting) were used to develop a MVA model that utilizes valuable predictive information not only in the major spectral peaks/features, but in all channels of the XAS region. Algorithms for multivariate analysis t were used to "learn" the characteristics of a data set as a function of varying spectral characteristics. These models were applied to the study of lunar glasses, which provide a challenging test case for these newly-developed techniques due to their very low fO2. Application of the new XAS calibration model to Apollo 15 green (15426, 15427 and 15425

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

    NASA Technical Reports Server (NTRS)

    Ulbrich, Norbert M.

    2013-01-01

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

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

    NASA Technical Reports Server (NTRS)

    Ulbrich, Norbert Manfred

    2013-01-01

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

  6. Reagent-free bacterial identification using multivariate analysis of transmission spectra

    NASA Astrophysics Data System (ADS)

    Smith, Jennifer M.; Huffman, Debra E.; Acosta, Dayanis; Serebrennikova, Yulia; García-Rubio, Luis; Leparc, German F.

    2012-10-01

    The identification of bacterial pathogens from culture is critical to the proper administration of antibiotics and patient treatment. Many of the tests currently used in the clinical microbiology laboratory for bacterial identification today can be highly sensitive and specific; however, they have the additional burdens of complexity, cost, and the need for specialized reagents. We present an innovative, reagent-free method for the identification of pathogens from culture. A clinical study has been initiated to evaluate the sensitivity and specificity of this approach. Multiwavelength transmission spectra were generated from a set of clinical isolates including Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus. Spectra of an initial training set of these target organisms were used to create identification models representing the spectral variability of each species using multivariate statistical techniques. Next, the spectra of the blinded isolates of targeted species were identified using the model achieving >94% sensitivity and >98% specificity, with 100% accuracy for P. aeruginosa and S. aureus. The results from this on-going clinical study indicate this approach is a powerful and exciting technique for identification of pathogens. The menu of models is being expanded to include other bacterial genera and species of clinical significance.

  7. Reagent-free bacterial identification using multivariate analysis of transmission spectra.

    PubMed

    Smith, Jennifer M; Huffman, Debra E; Acosta, Dayanis; Serebrennikova, Yulia; García-Rubio, Luis; Leparc, German F

    2012-10-01

    The identification of bacterial pathogens from culture is critical to the proper administration of antibiotics and patient treatment. Many of the tests currently used in the clinical microbiology laboratory for bacterial identification today can be highly sensitive and specific; however, they have the additional burdens of complexity, cost, and the need for specialized reagents. We present an innovative, reagent-free method for the identification of pathogens from culture. A clinical study has been initiated to evaluate the sensitivity and specificity of this approach. Multiwavelength transmission spectra were generated from a set of clinical isolates including Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus. Spectra of an initial training set of these target organisms were used to create identification models representing the spectral variability of each species using multivariate statistical techniques. Next, the spectra of the blinded isolates of targeted species were identified using the model achieving >94% sensitivity and >98% specificity, with 100% accuracy for P. aeruginosa and S. aureus. The results from this on-going clinical study indicate this approach is a powerful and exciting technique for identification of pathogens. The menu of models is being expanded to include other bacterial genera and species of clinical significance.

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    1992-07-01

    logistic regression statistical techniques as tools for discriminant analysis. A randomization-simulation technique can also be used to make distribution-independent comparisons and to verify that any observed differences are not due to insufficient samples or too many independent variables (Lipschutz and Samuels, 1991). These methods allow us to test for the existence of distinct compositional subpopulations in what is supposedly a single meteorite population. At the time of writing this abstract our database consists of 55 H4-6 chondrites (Lingner et al, 1987 and this work). Nine of these meteorites are members of the proposed "cluster 1" co-orbital meteoroid stream. For these 9 samples, linear discriminant analysis based on the concentrations of 10 labile trace elements reveals a difference between the "cluster 1" subpopulation of H chondrite falls and all other H chondrite falls at the <0.03 significance level. Logistic regression reveals a difference at the <0.0001 significance level. Normalization of data to Allende standard meteorite reference standard to eliminate bias conceivably due to different analysts yields results comparable to results from the non-normalized data. Additional evidence for the absence of interanalyst bias is provided by data of samples from Victoria Land, Antarctica: random populations analyzed by the present authors (Wolf and Lipschutz, 1992) are statistically indistinguishable from populations analyzed previously (Dennison and Lipschutz, 1987). A logistic regression validation run also supports the lack of interanalyst bias. Results from linear discriminant analysis, and logistic regression randomization-simulations will be presented in Copenhagen. These results on a limited population, which may be expanded by meeting time demonstrate that the "cluster 1" subpopulation of H chondrite falls are distinguishable from all other H chondrite falls on the basis of their labile trace elements, a result that is consistent with the idea that these

  10. Precipitation estimation in mountainous terrain using multivariate geostatistics. Part I: structural analysis

    USGS Publications Warehouse

    Hevesi, Joseph A.; Istok, Jonathan D.; Flint, Alan L.

    1992-01-01

    Values of average annual precipitation (AAP) are desired for hydrologic studies within a watershed containing Yucca Mountain, Nevada, a potential site for a high-level nuclear-waste repository. Reliable values of AAP are not yet available for most areas within this watershed because of a sparsity of precipitation measurements and the need to obtain measurements over a sufficient length of time. To estimate AAP over the entire watershed, historical precipitation data and station elevations were obtained from a network of 62 stations in southern Nevada and southeastern California. Multivariate geostatistics (cokriging) was selected as an estimation method because of a significant (p = 0.05) correlation of r = .75 between the natural log of AAP and station elevation. A sample direct variogram for the transformed variable, TAAP = ln [(AAP) 1000], was fitted with an isotropic, spherical model defined by a small nugget value of 5000, a range of 190 000 ft, and a sill value equal to the sample variance of 163 151. Elevations for 1531 additional locations were obtained from topographic maps to improve the accuracy of cokriged estimates. A sample direct variogram for elevation was fitted with an isotropic model consisting of a nugget value of 5500 and three nested transition structures: a Gaussian structure with a range of 61 000 ft, a spherical structure with a range of 70 000 ft, and a quasi-stationary, linear structure. The use of an isotropic, stationary model for elevation was considered valid within a sliding-neighborhood radius of 120 000 ft. The problem of fitting a positive-definite, nonlinear model of coregionalization to an inconsistent sample cross variogram for TAAP and elevation was solved by a modified use of the Cauchy-Schwarz inequality. A selected cross-variogram model consisted of two nested structures: a Gaussian structure with a range of 61 000 ft and a spherical structure with a range of 190 000 ft. Cross validation was used for model selection and for

  11. Additives

    NASA Technical Reports Server (NTRS)

    Smalheer, C. V.

    1973-01-01

    The chemistry of lubricant additives is discussed to show what the additives are chemically and what functions they perform in the lubrication of various kinds of equipment. Current theories regarding the mode of action of lubricant additives are presented. The additive groups discussed include the following: (1) detergents and dispersants, (2) corrosion inhibitors, (3) antioxidants, (4) viscosity index improvers, (5) pour point depressants, and (6) antifouling agents.

  12. Exploring the Structure of Library and Information Science Web Space Based on Multivariate Analysis of Social Tags

    ERIC Educational Resources Information Center

    Joo, Soohyung; Kipp, Margaret E. I.

    2015-01-01

    Introduction: This study examines the structure of Web space in the field of library and information science using multivariate analysis of social tags from the Website, Delicious.com. A few studies have examined mathematical modelling of tags, mainly examining tagging in terms of tripartite graphs, pattern tracing and descriptive statistics. This…

  13. Age and Anomia in Middle and Later Life: A Multivariate Analysis of a National Sample of White Men.

    ERIC Educational Resources Information Center

    Pope, Hallowell; Ferguson, Miller Dwayne

    1982-01-01

    Tested whether the aged status in America results in distrust and despair with the social order (anomia). A multivariate regression analysis utilizing data on 354 men aged 40 and older showed no relationship between age or other indicators of life chances or anomia, net of education and/or verbal intelligence. (Author/RC)

  14. Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data

    PubMed Central

    Xiong, Lie; Kuan, Pei-Fen; Tian, Jianan; Keles, Sunduz; Wang, Sijian

    2015-01-01

    In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies. PMID:26609213

  15. Multivariate permutation entropy and its application for complexity analysis of chaotic systems

    NASA Astrophysics Data System (ADS)

    He, Shaobo; Sun, Kehui; Wang, Huihai

    2016-11-01

    To measure the complexity of multivariate systems, the multivariate permutation entropy (MvPE) algorithm is proposed. It is employed to measure complexity of multivariate system in the phase space. As an application, MvPE is applied to analyze the complexity of chaotic systems, including hyperchaotic Hénon map, fractional-order simplified Lorenz system and financial chaotic system. Results show that MvPE algorithm is effective for analyzing the complexity of the multivariate systems. It also shows that fractional-order system does not become more complex with derivative order varying. Compared with PE, MvPE has better robustness for noise and sampling interval, and the results are not affected by different normalization methods.

  16. Estimation of the synthetic routes of seized methamphetamines using GC-MS and multivariate analysis.

    PubMed

    Choe, Sanggil; Lee, Jaesin; Choi, Hyeyoung; Park, Yujin; Lee, Heesang; Jo, Jiyeong; Park, Yonghoon; Kim, Eunmi; Pyo, Jaesung; Lee, Hun Joo; Kim, Suncheun

    2016-02-01

    One hundred and twenty six seized methamphetamine (MA) samples were analyzed using GC-MS. All the peaks that appeared in the chromatograms were investigated and 61 impurities including n-octacosane (internal standard) were identified. Among them, 37 impurities were already known or newly identified by comparing with commercial library entries and 18 impurities were detected for the first time. To estimate the synthetic routes of MA samples, route specific impurities had to be selected for each method. Two naphthalenes, 1,3-dimethyl-2-phenylnaphthalene and 1-benzyl-3-methylnaphthalene were selected as Nagai route specific impurities and three diasteromers, UK-19.62(58_165_178) I, UK-19.95(58_165_178) II, UK-20.49(58_165_178) III were also selected not only for their high frequency detection only in Nagai samples but also for the high principal component analysis (PCA) correlation values. For the Emde route, N,N-dimethyl-3,4-diphenylhexane-2,5-diamine and N-methyl-1-{4-[2-(methylamino)propyl]phenyl}-1-phenylpropan-2-amine were selected as route specific impurities, and N,N-di(β-phenylisopropyl)amine I (DPIA I), N,N-di(β-phenylisopropyl)amine II (DPIA II), N,N-di(β-phenylisopropyl)methylamine I (DPIMA I) and N,N-di(β-phenylisopropyl)methylamine II (DPIMA II) were selected for the Leuckart route. With these route specific impurities, synthetic routes could be identified for 78 of the 126 samples. The 61 impurities were registered in AMDIS target component library and the GC-MS data were deconvoluted. After AMDIS deconvolution, a matrix file was composed and then multivariate analyses were performed to estimate the synthetic route for unknown samples. The unsupervised methods, hierarchical clustering analysis (HCA) and PCA clustered the samples according to the closeness between samples. Two classification functions were obtained from discriminant analysis (DA) and the synthetic routes of the unknown samples were predicted using these two functions.

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

    NASA Technical Reports Server (NTRS)

    Djorgovski, S. George

    1994-01-01

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

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

    PubMed

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

    2012-01-01

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

  19. The use of modern and traditional methods of fertility control in Bangladesh: a multivariate analysis.

    PubMed

    Ullah, M S; Chakraborty, N

    1994-10-01

    An attempt has been made to study the use pattern of traditional and modern methods of fertility control among currently married women of reproductive ages utilizing the 1989 BFS data. Bivariate analysis has been employed to study the differentials in the use pattern by some selected demographic and socio-economic characteristics. Also, multivariate logistic regression analysis has been used to identify independent contributions of each selected covariate. It has been observed, however, that there is universality of knowledge about contraceptive methods. Of the total 31 percent, about 23 percent were using modern methods and the rest, 8 percent, traditional methods. Analysis using a logistic regression model showed that visits of family planning workers have very strong and positive influence on the current use of modern contraceptives as compared to traditional methods. Duration of effective marriage also emerged as a strong determinant of modern versus traditional methods use but it influenced modern methods use negatively. Also, administrative division is an important variable. Residents of Rajshahi division were significantly more (relative odds of 2.5) likely to be using modern methods than residents of Chittagong division. The likelihood of women having electricity in their household of being a current user of modern contraceptives is almost 2 times higher compared to women without electricity in their households. Education and occupation of husbands also exerts effect on current use of modern contraceptives. The odds of current use of modern methods among women whose husbands have secondary and higher level of education is one-and-a-half times higher than that of women with husbands having no formal education. However, wives of landowners were less (relative odds of 0.72) likely to use these methods as compared to traditional methods than wives of labourers or farmers. The probability of current use of modern contraceptives was higher (relative odds of 1

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

    NASA Technical Reports Server (NTRS)

    Djorgovski, S. G.

    1994-01-01

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

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

    PubMed Central

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

    2013-01-01

    SUMMARY The behavioural response study (BRS) is an experimental design used by field biologists to determine the function and/or behavioural effects of conspecific, heterospecific or anthropogenic stimuli. When carrying out these studies in marine mammals it is difficult to make basic observations and achieve sufficient samples sizes because of the high cost and logistical difficulties. Rarely are other factors such as social context or the physical environment considered in the analysis because of these difficulties. This paper presents results of a BRS carried out in humpback whales to test the response of groups to one recording of conspecific social sounds and an artificially generated tone stimulus. Experiments were carried out in September/October 2004 and 2008 during the humpback whale southward migration along the east coast of Australia. In total, 13 ‘tone’ experiments, 15 ‘social sound’ experiments (using one recording of social sounds) and three silent controls were carried out over two field seasons. The results (using a mixed model statistical analysis) suggested that humpback whales responded differently to the two stimuli, measured by changes in course travelled and dive behaviour. Although the response to ‘tones’ was consistent, in that groups moved offshore and surfaced more often (suggesting an aversion to the stimulus), the response to ‘social sounds’ was highly variable and dependent upon the composition of the social group. The change in course and dive behaviour in response to ‘tones’ was found to be related to proximity to the source, the received signal level and signal-to-noise ratio (SNR). This study demonstrates that the behavioural responses of marine mammals to acoustic stimuli are complex. In order to tease out such multifaceted interactions, the number of replicates and factors measured must be sufficient for multivariate analysis. PMID:23155085

  2. Differences in chewing sounds of dry-crisp snacks by multivariate data analysis

    NASA Astrophysics Data System (ADS)

    De Belie, N.; Sivertsvik, M.; De Baerdemaeker, J.

    2003-09-01

    Chewing sounds of different types of dry-crisp snacks (two types of potato chips, prawn crackers, cornflakes and low calorie snacks from extruded starch) were analysed to assess differences in sound emission patterns. The emitted sounds were recorded by a microphone placed over the ear canal. The first bite and the first subsequent chew were selected from the time signal and a fast Fourier transformation provided the power spectra. Different multivariate analysis techniques were used for classification of the snack groups. This included principal component analysis (PCA) and unfold partial least-squares (PLS) algorithms, as well as multi-way techniques such as three-way PLS, three-way PCA (Tucker3), and parallel factor analysis (PARAFAC) on the first bite and subsequent chew. The models were evaluated by calculating the classification errors and the root mean square error of prediction (RMSEP) for independent validation sets. It appeared that the logarithm of the power spectra obtained from the chewing sounds could be used successfully to distinguish the different snack groups. When different chewers were used, recalibration of the models was necessary. Multi-way models distinguished better between chewing sounds of different snack groups than PCA on bite or chew separately and than unfold PLS. From all three-way models applied, N-PLS with three components showed the best classification capabilities, resulting in classification errors of 14-18%. The major amount of incorrect classifications was due to one type of potato chips that had a very irregular shape, resulting in a wide variation of the emitted sounds.

  3. Multivariate statistical analysis of heavy metal signature in the Dongjiang River Basin in southeastern China

    NASA Astrophysics Data System (ADS)

    Ding, Z.

    2011-12-01

    Concentrations of heavy metals (Zn, Cr, Pb, As, Cu, Ni, Hg, Cd) have been measured in water and bottom sediment for the main stream of the Dongjiang River Basin, where is the word's most populous (40,000,000 people) and highly economic development region over decades. While the enrichment of heavy metals in the sediment indicates a strong historical pollution, the heavy metal concentrations in water reflect a concurrent anthropogenic influence. Multiple 87 samples were taken from the tributaries of the river network to investigate the characteristics of heavy metal pollutants within the catchment. Different multivariate statistical techniques are combined to analyse the spatial pattern and the origin of, and the land-use effects on heavy metal pollutants. First, a clear regional similarity of the pollutants is exhibited by cluster analysis (CA), and no longitudinal accumulation along the river can be observed. Then, principal component analysis (PCA) is applied to group the different heavy metals according to their variability at different sites. The first principal component (PC) containing Cr, Mn, Ni and Cu shows the feature of point sources, whereas the second PC loaded with Zn and Cd is probably derived from non-point sources. It also implied the particular favorite conveyances and processes from sources to sediments for Hg within the third PC. In the last step, redundant analysis (RDA) is used to correlate environmental variables (e.g. land use types, physiochemical properties including PH, DOC, DO, TSS) to heavy metal pollutants. The correlation of different heavy metals with different physiochemical properties reveals the anthropogenic impacts on the water quality. Land use types exhibits the highest relevance to heavy metal pollution, i.e., intensive industrial areas shows aggravated pollution, whereas areas with mainly forest and agriculture are rarely polluted by heavy metals.

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

    PubMed Central

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

    2014-01-01

    We investigate the mode of action and classification of antibiotic agents (ceftazidime, patulin, and epigallocatechin gallate; EGCG) on Pseudomonas aeruginosa (P. aeruginosa) biofilm using Raman spectroscopy with multivariate analysis, including support vector machine (SVM) and principal component analysis (PCA). This method allows for quantitative, label-free, non-invasive and rapid monitoring of biochemical changes in complex biofilm matrices with high sensitivity and specificity. In this study, the biofilms were grown and treated with various agents in the microfluidic device, and then transferred onto gold-coated substrates for Raman measurement. Here, we show changes in biochemical properties, and this technology can be used to distinguish between changes induced in P. aeruginosa biofilms using three antibiotic agents. The Raman band intensities associated with DNA and proteins were decreased, compared to control biofilms, when the biofilms were treated with antibiotics. Unlike with exposure to ceftazidime and patulin, the Raman spectrum of biofilms exposed to EGCG showed a shift in the spectral position of the CH deformation stretch band from 1313 cm−1 to 1333 cm−1, and there was no difference in the band intensity at 1530 cm−1 (C = C stretching, carotenoids). The PCA-SVM analysis results show that antibiotic-treated biofilms can be detected with high sensitivity of 93.33%, a specificity of 100% and an accuracy of 98.33%. This method also discriminated the three antibiotic agents based on the cellular biochemical and structural changes induced by antibiotics with high sensitivity and specificity of 100%. This study suggests that Raman spectroscopy with PCA-SVM is potentially useful for the rapid identification and classification of clinically-relevant antibiotics of bacteria biofilm. Furthermore, this method could be a powerful approach for the development and screening of new antibiotics. PMID:25401035

  5. Dittrichia graveolens (L.) Greuter Essential Oil: Chemical Composition, Multivariate Analysis, and Antimicrobial Activity.

    PubMed

    Mitic, Violeta; Stankov Jovanovic, Vesna; Ilic, Marija; Jovanovic, Olga; Djordjevic, Aleksandra; Stojanovic, Gordana

    2016-01-01

    The chemical composition and in vitro antimicrobial activities of Dittrichia graveolens (L.) Greuter essential oil was studied. Moreover, using agglomerative hierarchical cluster (AHC) and principal component analyses (PCA), the interrelationships of the D. graveolens essential-oil profiles characterized so far (including the sample from this study) were investigated. To evaluate the chemical composition of the essential oil, GC-FID and GC/MS analyses were performed. Altogether, 54 compounds were identified, accounting for 92.9% of the total oil composition. The D. graveolens oil belongs to the monoterpenoid chemotype, with monoterpenoids comprising 87.4% of the totally identified compounds. The major components were borneol (43.6%) and bornyl acetate (38.3%). Multivariate analysis showed that the compounds borneol and bornyl acetate exerted the greatest influence on the spatial differences in the composition of the reported oils. The antimicrobial activity against five bacterial and one fungal strain was determined using a disk-diffusion assay. The studied essential oil was active only against Gram-positive bacteria.

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

    PubMed

    Beiras, Ricardo; Durán, Iria

    2014-12-01

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

  7. Comparative multivariate analysis of biometric traits of West African Dwarf and Red Sokoto goats.

    PubMed

    Yakubu, Abdulmojeed; Salako, Adebowale E; Imumorin, Ikhide G

    2011-03-01

    The population structure of 302 randomly selected West African Dwarf (WAD) and Red Sokoto (RS) goats was examined using multivariate morphometric analyses. This was to make the case for conservation, rational management and genetic improvement of these two most important Nigerian goat breeds. Fifteen morphometric measurements were made on each individual animal. RS goats were superior (P<0.05) to the WAD for the body size and skeletal proportions investigated. The phenotypic variability between the two breeds was revealed by their mutual responses in the principal components. While four principal components were extracted for WAD goats, three components were obtained for their RS counterparts with variation in the loading traits of each component for each breed. The Mahalanobis distance of 72.28 indicated a high degree of spatial racial separation in morphology between the genotypes. The Ward's option of the cluster analysis consolidated the morphometric distinctness of the two breeds. Application of selective breeding to genetic improvement would benefit from the detected phenotypic differentiation. Other implications for management and conservation of the goats are highlighted. PMID:21080228

  8. The Multi-Isotope Process Monitor: Multivariate Analysis of Gamma Spectra

    SciTech Connect

    Orton, Christopher R.; Rutherford, Crystal E.; Fraga, Carlos G.; Schwantes, Jon M.

    2011-10-30

    The International Atomic Energy Agency (IAEA) has established international safeguards standards for fissionable material at spent fuel reprocessing plants to ensure that significant quantities of nuclear material are not diverted from these facilities. Currently, methods to verify material control and accountancy (MC&A) at these facilities require time-consuming and resource-intensive destructive assay (DA). The time delay between sampling and subsequent DA provides a potential opportunity to divert the material out of the appropriate chemical stream. Leveraging new on-line nondestructive assay (NDA) techniques in conjunction with the traditional and highly precise DA methods may provide a more timely, cost-effective and resource efficient means for MC&A verification at such facilities. Pacific Northwest National Laboratory (PNNL) is developing on-line NDA process monitoring technologies, including the Multi-Isotope Process (MIP) Monitor. The MIP Monitor uses gamma spectroscopy and pattern recognition software to identify off-normal conditions in process streams. Recent efforts have been made to explore the basic limits of using multivariate analysis techniques on gamma-ray spectra. This paper will provide an overview of the methods and report our on-going efforts to develop and demonstrate the technology.

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

    PubMed Central

    Steinhausen, Charlene; Zehl, Lyuba; Haas-Rioth, Michaela; Morcinek, Kerstin; Walkowiak, Wolfgang; Huggenberger, Stefan

    2016-01-01

    The general assumption that brain size differences are an adequate proxy for subtler differences in brain organization turned neurobiologists toward the question why some groups of mammals such as primates, elephants, and whales have such remarkably large brains. In this meta-analysis, an extensive sample of eutherian mammals (115 species distributed in 14 orders) provided data about several different biological traits and measures of brain size such as absolute brain mass (AB), relative brain mass (RB; quotient from AB and body mass), and encephalization quotient (EQ). These data were analyzed by established multivariate statistics without taking specific phylogenetic information into account. Species with high AB tend to (1) feed on protein-rich nutrition, (2) have a long lifespan, (3) delayed sexual maturity, and (4) long and rare pregnancies with small litter sizes. Animals with high RB usually have (1) a short life span, (2) reach sexual maturity early, and (3) have short and frequent gestations. Moreover, males of species with high RB also have few potential sexual partners. In contrast, animals with high EQs have (1) a high number of potential sexual partners, (2) delayed sexual maturity, and (3) rare gestations with small litter sizes. Based on these correlations, we conclude that Eutheria with either high AB or high EQ occupy positions at the top of the network of food chains (high trophic levels). Eutheria of low trophic levels can develop a high RB only if they have small body masses. PMID:27746724

  10. Multivariate analysis of the heterogeneous geochemical processes controlling arsenic enrichment in a shallow groundwater system.

    PubMed

    Huang, Shuangbing; Liu, Changrong; Wang, Yanxin; Zhan, Hongbin

    2014-01-01

    The effects of various geochemical processes on arsenic enrichment in a high-arsenic aquifer at Jianghan Plain in Central China were investigated using multivariate models developed from combined adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR). The results indicated that the optimum variable group for the AFNIS model consisted of bicarbonate, ammonium, phosphorus, iron, manganese, fluorescence index, pH, and siderite saturation. These data suggest that reductive dissolution of iron/manganese oxides, phosphate-competitive adsorption, pH-dependent desorption, and siderite precipitation could integrally affect arsenic concentration. Analysis of the MLR models indicated that reductive dissolution of iron(III) was primarily responsible for arsenic mobilization in groundwaters with low arsenic concentration. By contrast, for groundwaters with high arsenic concentration (i.e., > 170 μg/L), reductive dissolution of iron oxides approached a dynamic equilibrium. The desorption effects from phosphate-competitive adsorption and the increase in pH exhibited arsenic enrichment superior to that caused by iron(III) reductive dissolution as the groundwater chemistry evolved. The inhibition effect of siderite precipitation on arsenic mobilization was expected to exist in groundwater that was highly saturated with siderite. The results suggest an evolutionary dominance of specific geochemical process over other factors controlling arsenic concentration, which presented a heterogeneous distribution in aquifers. Supplemental materials are available for this article. Go to the publisher's online edition of the Journal of Environmental Science and Health, Part A, to view the supplemental file. PMID:24345245

  11. Early differentiation of cardioembolic from atherothrombotic cerebral infarction: a multivariate analysis.

    PubMed

    Arboix, A; Oliveres, M; Massons, J; Pujades, R; Garcia-Eroles, L

    1999-11-01

    The aim of this study was to determine factors predictive of cerebral infarction subtype from clinical data collected within 48 h of neurologic deficit. All cardioembolic (n = 231) and atherothrombotic infarctions (n = 369) included in prospective stroke registry of the Sagrat Cor-Alianza Hospital of Barcelona were analysed. Demographic characteristics, anamnestic findings, cerebrovascular risk factors and clinical data of patients with embolic stroke and patients with thrombotic infarction were compared. Predictors of stroke subtype were assessed by means of a logistic regression model based on 16 clinical variables. After multivariate analysis, atrial dysrhythmia and sudden onset to maximal deficit were significant predictors of embolic stroke, whereas hypertension, chronic obstructive pulmonary disease, diabetes, hypercholesterolemia and/or hypertriglyceridemia and age were independent predictive factors of atherothrombotic stroke. Setting a cut-off point of 0.50 for predicting mechanism of stroke on admission resulted in a sensitivity of 76%, specificity of 87% and total correct classification of 83%. Clinical features alone that are observed at stroke onset can help to distinguish cardioembolic from atherothrombotic infarctions.

  12. Revealing the metabonomic variation of rosemary extracts using 1H NMR spectroscopy and multivariate data analysis.

    PubMed

    Xiao, Chaoni; Dai, Hui; Liu, Hongbing; Wang, Yulan; Tang, Huiru

    2008-11-12

    The molecular compositions of rosemary ( Rosmarinus officinalis L.) extracts and their dependence on extraction solvents, seasons, and drying processes were systematically characterized using NMR spectroscopy and multivariate data analysis. The results showed that the rosemary metabonome was dominated by 33 metabolites including sugars, amino acids, organic acids, polyphenolic acids, and diterpenes, among which quinate, cis-4-glucosyloxycinnamic acid, and 3,4,5-trimethoxyphenylmethanol were found in rosemary for the first time. Compared with water extracts, the 50% aqueous methanol extracts contained higher levels of sucrose, succinate, fumarate, malonate, shikimate, and phenolic acids, but lower levels of fructose, glucose, citrate, and quinate. Chloroform/methanol was an excellent solvent for selective extraction of diterpenes. From February to August, the levels of rosmarinate and quinate increased, whereas the sucrose level decreased. The sun-dried samples contained higher concentrations of rosmarinate, sucrose, and some amino acids but lower concentrations of glucose, fructose, malate, succinate, lactate, and quinate than freeze-dried ones. These findings will fill the gap in the understanding of rosemary composition and its variations.

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

    PubMed

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

    2015-11-15

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

  14. Adult Onset Vitiligo: Multivariate Analysis Suggests the Need for a Thyroid Screening

    PubMed Central

    Lazzeri, L.; Cammi, A.; Dragoni, F.

    2016-01-01

    Background. There are limited epidemiological studies evaluating the effect of age at onset on disease features in vitiligo. Objectives. To identify factors associated with adult onset vitiligo in comparison with childhood onset vitiligo. Patients and Methods. We retrospectively collected medical records of 191 patients. Such records included clinical examination, personal and familial medical history, laboratory evaluations, concomitant vitiligo treatment and drug assumption. Results. 123 patients with a disease onset after the age of 40 (adult onset vitiligo) were compared with 68 patients who developed vitiligo before the age of 12 (childhood onset vitiligo). Multivariate analysis revealed that personal history of thyroid diseases (P = 0.04; OR 0.4), stress at onset (P = 0.002; OR = 0.34), personal history of autoimmune thyroid disease (ATD) (P = 0.003; OR = 0.23), and thyroid nodules (P = 0.001; OR 0.90) were independently associated with adult onset vitiligo, whereas family history of dermatological diseases (P = 0.003; OR = 2.87) and Koebner phenomenon (P < 0.001; OR = 4.73) with childhood onset vitiligo. Moreover, in the adult onset group, concomitant thyroid disease preceded vitiligo in a statistically significant number of patients (P = 0.014). Conclusions. Childhood onset and adult onset vitiligo have different clinical features. In particular, ATD and thyroid nodules were significantly associated with adult onset vitiligo, suggesting that a thyroid screening should be recommended in this group of patients. PMID:27747240

  15. Advanced qualification of pharmaceutical excipient suppliers by multiple analytics and multivariate analysis combined.

    PubMed

    Hertrampf, A; Müller, H; Menezes, J C; Herdling, T

    2015-11-10

    Pharmaceutical excipients have different functions within a drug formulation, consequently they can influence the manufacturability and/or performance of medicinal products. Therefore, critical to quality attributes should be kept constant. Sometimes it may be necessary to qualify a second supplier, but its product will not be completely equal to the first supplier product. To minimize risks of not detecting small non-similarities between suppliers and to detect lot-to-lot variability for each supplier, multivariate data analysis (MVA) can be used as a more powerful alternative to classical quality control that uses one-parameter-at-a-time monitoring. Such approach is capable of supporting the requirements of a new guideline by the European Parliament and Council (2015/C-95/02) demanding appropriate quality control strategies for excipients based on their criticality and supplier risks in ensuring quality, safety and function. This study compares calcium hydrogen phosphate from two suppliers. It can be assumed that both suppliers use different manufacturing processes. Therefore, possible chemical and physical differences were investigated by using Raman spectroscopy, laser diffraction and X-ray powder diffraction. Afterwards MVA was used to extract relevant information from each analytical technique. Both CaHPO4 could be discriminated by their supplier. The gained knowledge allowed to specify an enhanced strategy for second supplier qualification.

  16. Determinants of hepatic function in liver cirrhosis in the rat. Multivariate analysis.

    PubMed Central

    Reichen, J; Egger, B; Ohara, N; Zeltner, T B; Zysset, T; Zimmermann, A

    1988-01-01

    We investigated the determinants of hepatic clearance functions in a rat model of liver cirrhosis induced by phenobarbital/CCl4. Aminopyrine N-demethylation (ABT), galactose elimination (GBT), and serum bile acids (SBA) were determined in vivo. The livers were then characterized hemodynamically: intrahepatic shunting (IHS) was determined by microspheres and sinusoidal capillarization by measuring the extravascular albumin space (EVA) by a multiple indicator dilution technique. The intrinsic clearance was determined by assaying the activity of the rate-limiting enzymes in vitro. Hepatocellular volume (HCV) was measured by morphometry. ABT and SBA, but not GBT, differentiated cirrhotic from normal liver. IHS ranged from normal to 10%; all cirrhotic livers showed evidence of sinusoidal capillarization (reduced EVA). The cirrhotic livers showed a bimodal distribution of HCV, HCV being decreased in 50% of the cirrhotic livers. Multivariate analysis showed EVA and portal flow to be the main determinants of microsomal (ABT) and cytosolic (GBT) clearance function; SBA, by contrast, were determined solely by IHS. We conclude that sinusoidal capillarization is the main determinant of hepatic clearance, while serum bile acids reflect intrahepatic shunting. These findings emphasize the importance of alterations of hepatic nutritional flow to explain reduced clearance function in cirrhosis of the liver. PMID:3198765

  17. Effective connectivity of neural pathways underlying disgust by multivariate Granger causality analysis

    NASA Astrophysics Data System (ADS)

    Yan, Hao; Wang, Yonghui; Tian, Jie; Liu, Yijun

    2011-03-01

    The disgust system arises phylogenetically in response to dangers to the internal milieu from pathogens and their toxic products. Functional imaging studies have demonstrated that a much wider range of neural structures was involved in triggering disgust reactions. However, less is known regarding how and what neural pathways these neural structures interact. To address this issue, we adopted an effective connectivity based analysis, namely the multivariate Granger causality approach, to explore the causal interactions within these brain networks. Results presented that disgust can induce a wide range of brain activities, such as the insula, the anterior cingulate cortex, the parahippocampus lobe, the dorsal lateral prefrontal cortex, the superior occipital gyrus, and the supplementary motor cortex. These brain areas constitute as a whole, with much denser connectivity following disgust stimuli, in comparison with that of the neutral condition. Moreover, the anterior insula, showing multiple casual interactions with limbic and subcortical areas, was implicated as a central hub in organizing multiple information processing in the disgust system.

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

    PubMed

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

    2008-12-01

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

  19. 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

  20. Multivariate analysis of the impacts of the turbine fuel JP-4 in a microcosm toxicity test with implications for the evaluation of ecosystem dynamics and risk assessment.

    PubMed

    Landis, W G; Matthews, R A; Markiewicz, A J; Matthews, G B

    1993-12-01

    Turbine fuels are often the only aviation fuel available in most of the world. Turbine fuels consist of numerous constituents with varying water solubilities, volatilities and toxicities. This study investigates the toxicity of the water soluble fraction (WSF) of JP-4 using the Standard Aquatic Microcosm (SAM). Multivariate analysis of the complex data, including the relatively new method of nonmetric clustering, was used and compared to more traditional analyses. Particular emphasis is placed on ecosystem dynamics in multivariate space.The WSF is prepared by vigorously mixing the fuel and the SAM microcosm media in a separatory funnel. The water phase, which contains the water-soluble fraction of JP-4 is then collected. The SAM experiment was conducted using concentrations of 0.0, 1.5 and 15% WSF. The WSF is added on day 7 of the experiments by removing 450 ml from each microcosm including the controls, then adding the appropriate amount of toxicant solution and finally bringing the final volume to 3 L with microcosm media. Analysis of the WSF was performed by purge and trap gas chromatography. The organic constituents of the WSF were not recoverable from the water column within several days of the addition of the toxicant. However, the impact of the WSF on the microcosm was apparent. In the highest initial concentration treatment group an algal bloom ensued, generated by the apparent toxicity of the WSF of JP-4 to the daphnids. As the daphnid populations recovered the algal populations decreased to control values. Multivariate methods clearly demonstrated this initial impact along with an additional oscillation seperating the four treatment groups in the latter segment of the experiment. Apparent recovery may be an artifact of the projections used to describe the multivariate data. The variables that were most important in distinguishing the four groups shifted during the course of the 63 day experiment. Even this simple microcosm exhibited a variety of dynamics

  1. Quality evaluation and prediction of Citrullus lanatus by 1H NMR-based metabolomics and multivariate analysis.

    PubMed

    Tarachiwin, Lucksanaporn; Masako, Osawa; Fukusaki, Eiichiro

    2008-07-23

    (1)H NMR spectrometry in combination with multivariate analysis was considered to provide greater information on quality assessment over an ordinary sensory testing method due to its high reliability and high accuracy. The sensory quality evaluation of watermelon (Citrullus lanatus (Thunb.) Matsum. & Nakai) was carried out by means of (1)H NMR-based metabolomics. Multivariate analyses by partial least-squares projections to latent structures-discrimination analysis (PLS-DA) and PLS-regression offered extensive information for quality differentiation and quality evaluation, respectively. The impact of watermelon and rootstock cultivars on the sensory qualities of watermelon was determined on the basis of (1)H NMR metabolic fingerprinting and profiling. The significant metabolites contributing to the discrimination were also identified. A multivariate calibration model was successfully constructed by PLS-regression with extremely high reliability and accuracy. Thus, (1)H NMR-based metabolomics with multivariate analysis was considered to be one of the most suitable complementary techniques that could be applied to assess and predict the sensory quality of watermelons and other horticultural plants.

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

    PubMed

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

    2013-01-01

    Acquisition of three-dimensional (3D) spectral data is nowadays common using many different microanalytical techniques. In order to proceed to the 3D reconstruction, data processing is necessary not only to deal with noisy acquisitions but also to segment the data in term of chemical composition. In this article, we demonstrate the value of multivariate statistical analysis (MSA) methods for this purpose, allowing fast and reliable results. Using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) coupled with a focused ion beam (FIB), a stack of spectrum images have been acquired on a sample produced by laser welding of a nickel-titanium wire and a stainless steel wire presenting a complex microstructure. These data have been analyzed using principal component analysis (PCA) and factor rotations. PCA allows to significantly improve the overall quality of the data, but produces abstract components. Here it is shown that rotated components can be used without prior knowledge of the sample to help the interpretation of the data, obtaining quickly qualitative mappings representative of elements or compounds found in the material. Such abundance maps can then be used to plot scatter diagrams and interactively identify the different domains in presence by defining clusters of voxels having similar compositions. Identified voxels are advantageously overlaid on secondary electron (SE) images with higher resolution in order to refine the segmentation. The 3D reconstruction can then be performed using available commercial softwares on the basis of the provided segmentation. To asses the quality of the segmentation, the results have been compared to an EDX quantification performed on the same data. PMID:24035679

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

    PubMed Central

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

    2014-01-01

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

  4. Multi-variant analysis of otoacoustic emissions and estimation of hearing thresholds: transient evoked otoacoustic emissions.

    PubMed

    Vinck, B M; Van Cauwenberge, P B; Corthals, P; De Vel, E

    1998-01-01

    Evaluation of cochlear hearing loss by means of transiently evoked otoacoustic emissions is already established in clinical practice. However, accurate prediction of pure-tone thresholds is still questioned and is still regarded as troublesome. Both click- and tone-burst-evoked otoacoustic emissions at several intensity levels were measured and analysed in 157 ears from normally hearing and 432 ears from patients with different degrees of pure sensory hearing loss using the ILO88/92 equipment. Results of otoacoustic emissions (OAE), elicited by clicks and tone-bursts at centre frequencies from 1 to 5 kHz, were analysed using two different statistical methods. Both multivariate discriminant analysis and forward multiple regression analysis were used to determine which OAE variables were most discriminating and best at predicting hearing thresholds. We found that a limited set of variables obtained from both tone-burst and click measurements can accurately predict and categorize hearing loss levels up to a limit of 60 dB HL. We found correct classification scores of pure-tone thresholds between 500 and 4000 Hz up to 100 per cent when using combined click and tone-burst otoacoustic measurements. Prediction of pure-tone thresholds was correct with a maximum estimation error of 10 dB for audiometric octave frequencies between 500 and 4000 Hz. Measurements of multiple tone-bursts OAEs have a significant clinical advantage over the use of clicks alone for clinical applications, and a good classification and prediction of pure-tone thresholds with otoacoustic emissions is possible.

  5. Multivariate analysis of sexual size dimorphism in local turkeys (Meleagris gallopavo) in Nigeria.

    PubMed

    Ajayi, Oyeyemi O; Yakubu, Abdulmojeed; Jayeola, Oluwaseun O; Imumorin, Ikhide G; Takeet, Michael I; Ozoje, Michael O; Ikeobi, Christian O N; Peters, Sunday O

    2012-06-01

    Sexual size dimorphism is a key evolutionary feature that can lead to important biological insights. To improve methods of sexing live birds in the field, we assessed sexual size dimorphism in Nigerian local turkeys (Meleagris gallopavo) using multivariate techniques. Measurements were taken on 125 twenty-week-old birds reared under the intensive management system. The body parameters measured were body weight, body length, breast girth, thigh length, shank length, keel length, wing length and wing span. Univariate analysis revealed that toms (males) had significantly (P < 0.05) higher mean values than hens (females) in all the measured traits. Positive phenotypic correlations between body weight and body measurements ranged from 0.445 to 0.821 in toms and 0.053-0.660 in hens, respectively. Three principal components (PC1, PC2 and PC3) were extracted in toms, each accounting for 63.70%, 19.42% and 5.72% of the total variance, respectively. However, four principal components (PC1, PC2, PC3 and PC4) were extracted in hens, which explained 54.03%, 15.29%, 11.68% and 6.95%, respectively of the generalised variance. A stepwise discriminant function analysis of the eight morphological traits indicated that body weight, body length, tail length and wing span were the most discriminating variables in separating the sexes. The single discriminant function obtained was able to correctly classify 100% of the birds into their source population. The results obtained from the present study could aid future management decisions, ecological studies and conservation of local turkeys in a developing economy.

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

    PubMed

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

    2013-05-01

    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.

  7. Risk factors predicting recurrence in patients operated on for intracranial meningioma. A multivariate analysis.

    PubMed

    Ayerbe, J; Lobato, R D; de la Cruz, J; Alday, R; Rivas, J J; Gómez, P A; Cabrera, A

    1999-01-01

    The authors undertook a follow-up study of 286 patients who underwent surgical treatment for intracranial meningioma between 1973 and 1994, in order to analyse clinical, radiological, topographic, histopathological and therapeutic factors significantly influencing tumour recurrence. All patients were followed by using either computed tomography (CT) or magnetic resonance from 3 months to 17 years since first surgery (mean follow-up: 4.1 years). Forty-four (15.4%) recurrences were detected during this time period. Overall recurrence rates were 14%, 37% and 61% at 5, 10 and 15 years, respectively. Factors significantly associated with tumour relapse in bivariate analysis were: tumour location at petroclival and parasagittal (middle third) regions, incomplete surgical resection (assessed by Simpson's classification), atypical and malignant histological types (WHO classification), presence of nucleolar prominence, presence of more than 2 mitosis per 10 high-power fields, and heterogeneous tumour contrast enhancement on the CT scan. The multivariate analysis using the Cox's proportional hazards model identified the following risk factors for recurrence: incomplete surgical resection (Relative risk: 2.2; 95% Confidence interval: 1.33-3.64), non conventional histological type (RR: 2.13; 95%CI: 1-4.53), heterogeneous contrast enhancement on the CT scan (RR: 2.25; 95%CI: 1.1-4.72) and presence of more than 2 mitosis per 10 high-power fields (RR: 2.28; 95%CI: 0.99-5.27). Patients without any of these features showed low recurrence rates (4% and 18% at 5 and 10 years), and thus, they need less clinical and radiological controls through the follow-up than patients with some of these risk factors. PMID:10526073

  8. Investigation of intervertebral disc degeneration using multivariate FTIR spectroscopic imaging† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c5fd00160a Click here for additional data file.

    PubMed Central

    Peeters, Mirte; Detiger, Suzanne E. L.; Helder, Marco N.; Smit, Theo H.; Le Maitre, Christine L.; Sammon, Chris

    2016-01-01

    Traditionally tissue samples are analysed using protein or enzyme specific stains on serial sections to build up a picture of the distribution of components contained within them. In this study we investigated the potential of multivariate curve resolution-alternating least squares (MCR-ALS) to deconvolute 2nd derivative spectra of Fourier transform infrared (FTIR) microscopic images measured in transflectance mode of goat and human paraffin embedded intervertebral disc (IVD) tissue sections, to see if this methodology can provide analogous information to that provided by immunohistochemical stains and bioassays but from a single section. MCR-ALS analysis of non-degenerate and enzymatically in vivo degenerated goat IVDs reveals five matrix components displaying distribution maps matching histological stains for collagen, elastin and proteoglycan (PG), as well as immunohistochemical stains for collagen type I and II. Interestingly, two components exhibiting characteristic spectral and distribution profiles of proteoglycans were found, and relative component/tissue maps of these components (labelled PG1 and PG2) showed distinct distributions in non-degenerate versus mildly degenerate goat samples. MCR-ALS analysis of human IVD sections resulted in comparable spectral profiles to those observed in the goat samples, highlighting the inter species transferability of the presented methodology. Multivariate FTIR image analysis of a set of 43 goat IVD sections allowed the extraction of semi-quantitative information from component/tissue gradients taken across the IVD width of collagen type I, collagen type II, PG1 and PG2. Regional component/tissue parameters were calculated and significant correlations were found between histological grades of degeneration and PG parameters (PG1: p = 0.0003, PG2: p < 0.0001); glycosaminoglycan (GAG) content and PGs (PG1: p = 0.0055, PG2: p = 0.0001); and MRI T2* measurements and PGs (PG1: p = 0.0021, PG2: p < 0.0001). Additionally

  9. Analysis and assessment on heavy metal sources in the coastal soils developed from alluvial deposits using multivariate statistical methods.

    PubMed

    Li, Jinling; He, Ming; Han, Wei; Gu, Yifan

    2009-05-30

    An investigation on heavy metal sources, i.e., Cu, Zn, Ni, Pb, Cr, and Cd in the coastal soils of Shanghai, China, was conducted using multivariate statistical methods (principal component analysis, clustering analysis, and correlation analysis). All the results of the multivariate analysis showed that: (i) Cu, Ni, Pb, and Cd had anthropogenic sources (e.g., overuse of chemical fertilizers and pesticides, industrial and municipal discharges, animal wastes, sewage irrigation, etc.); (ii) Zn and Cr were associated with parent materials and therefore had natural sources (e.g., the weathering process of parent materials and subsequent pedo-genesis due to the alluvial deposits). The effect of heavy metals in the soils was greatly affected by soil formation, atmospheric deposition, and human activities. These findings provided essential information on the possible sources of heavy metals, which would contribute to the monitoring and assessment process of agricultural soils in worldwide regions. PMID:18976857

  10. Analysis and assessment on heavy metal sources in the coastal soils developed from alluvial deposits using multivariate statistical methods.

    PubMed

    Li, Jinling; He, Ming; Han, Wei; Gu, Yifan

    2009-05-30

    An investigation on heavy metal sources, i.e., Cu, Zn, Ni, Pb, Cr, and Cd in the coastal soils of Shanghai, China, was conducted using multivariate statistical methods (principal component analysis, clustering analysis, and correlation analysis). All the results of the multivariate analysis showed that: (i) Cu, Ni, Pb, and Cd had anthropogenic sources (e.g., overuse of chemical fertilizers and pesticides, industrial and municipal discharges, animal wastes, sewage irrigation, etc.); (ii) Zn and Cr were associated with parent materials and therefore had natural sources (e.g., the weathering process of parent materials and subsequent pedo-genesis due to the alluvial deposits). The effect of heavy metals in the soils was greatly affected by soil formation, atmospheric deposition, and human activities. These findings provided essential information on the possible sources of heavy metals, which would contribute to the monitoring and assessment process of agricultural soils in worldwide regions.

  11. Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis.

    PubMed

    Wang, Longfei; Lee, Sungyoung; Gim, Jungsoo; Qiao, Dandi; Cho, Michael; Elston, Robert C; Silverman, Edwin K; Won, Sungho

    2016-09-01

    Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows. PMID:27312886

  12. Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis.

    PubMed

    Wang, Longfei; Lee, Sungyoung; Gim, Jungsoo; Qiao, Dandi; Cho, Michael; Elston, Robert C; Silverman, Edwin K; Won, Sungho

    2016-09-01

    Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows.

  13. Multivariate Analysis and Modeling of Sediment Pollution Using Neural Network Models and Geostatistics

    NASA Astrophysics Data System (ADS)

    Golay, Jean; Kanevski, Mikhaïl

    2013-04-01

    The present research deals with the exploration and modeling of a complex dataset of 200 measurement points of sediment pollution by heavy metals in Lake Geneva. The fundamental idea was to use multivariate Artificial Neural Networks (ANN) along with geostatistical models and tools in order to improve the accuracy and the interpretability of data modeling. The results obtained with ANN were compared to those of traditional geostatistical algorithms like ordinary (co)kriging and (co)kriging with an external drift. Exploratory data analysis highlighted a great variety of relationships (i.e. linear, non-linear, independence) between the 11 variables of the dataset (i.e. Cadmium, Mercury, Zinc, Copper, Titanium, Chromium, Vanadium and Nickel as well as the spatial coordinates of the measurement points and their depth). Then, exploratory spatial data analysis (i.e. anisotropic variography, local spatial correlations and moving window statistics) was carried out. It was shown that the different phenomena to be modeled were characterized by high spatial anisotropies, complex spatial correlation structures and heteroscedasticity. A feature selection procedure based on General Regression Neural Networks (GRNN) was also applied to create subsets of variables enabling to improve the predictions during the modeling phase. The basic modeling was conducted using a Multilayer Perceptron (MLP) which is a workhorse of ANN. MLP models are robust and highly flexible tools which can incorporate in a nonlinear manner different kind of high-dimensional information. In the present research, the input layer was made of either two (spatial coordinates) or three neurons (when depth as auxiliary information could possibly capture an underlying trend) and the output layer was composed of one (univariate MLP) to eight neurons corresponding to the heavy metals of the dataset (multivariate MLP). MLP models with three input neurons can be referred to as Artificial Neural Networks with EXternal

  14. Multivariate Analysis of Traumatic Brain Injury: Development of an Assessment Score

    PubMed Central

    Buonora, John E.; Yarnell, Angela M.; Lazarus, Rachel C.; Mousseau, Michael; Latour, Lawrence L.; Rizoli, Sandro B.; Baker, Andrew J.; Rhind, Shawn G.; Diaz-Arrastia, Ramon; Mueller, Gregory P.

    2015-01-01

    Important challenges for the diagnosis and monitoring of mild traumatic brain injury (mTBI) include the development of plasma biomarkers for assessing neurologic injury, monitoring pathogenesis, and predicting vulnerability for the development of untoward neurologic outcomes. While several biomarker proteins have shown promise in this regard, used individually, these candidates lack adequate sensitivity and/or specificity for making a definitive diagnosis or identifying those at risk of subsequent pathology. The objective for this study was to evaluate a panel of six recognized and novel biomarker candidates for the assessment of TBI in adult patients. The biomarkers studied were selected on the basis of their relative brain-specificities and potentials to reflect distinct features of TBI mechanisms including (1) neuronal damage assessed by neuron-specific enolase (NSE) and brain derived neurotrophic factor (BDNF); (2) oxidative stress assessed by peroxiredoxin 6 (PRDX6); (3) glial damage and gliosis assessed by glial fibrillary acidic protein and S100 calcium binding protein beta (S100b); (4) immune activation assessed by monocyte chemoattractant protein 1/chemokine (C–C motif) ligand 2 (MCP1/CCL2); and (5) disruption of the intercellular adhesion apparatus assessed by intercellular adhesion protein-5 (ICAM-5). The combined fold-changes in plasma levels of PRDX6, S100b, MCP1, NSE, and BDNF resulted in the formulation of a TBI assessment score that identified mTBI with a receiver operating characteristic (ROC) area under the curve of 0.97, when compared to healthy controls. This research demonstrates that a profile of biomarker responses can be used to formulate a diagnostic score that is sensitive for the detection of mTBI. Ideally, this multivariate assessment strategy will be refined with additional biomarkers that can effectively assess the spectrum of TBI and identify those at particular risk for developing neuropathologies as consequence of a mTBI event

  15. Study of groundwater arsenic pollution in Lanyang Plain using multivariate statistical analysis

    NASA Astrophysics Data System (ADS)

    chan, S.

    2013-12-01

    The study area, Lanyang Plain in the eastern Taiwan, has highly developed agriculture and aquaculture, which consume over 70% of the water supplies. Groundwater is frequently considered as an alternative water source. However, the serious arsenic pollution of groundwater in Lanyan Plain should be well studied to ensure the safety of groundwater usage. In this study, 39 groundwater samples were collected. The results of hydrochemistry demonstrate two major trends in Piper diagram. The major trend with most of groundwater samples is determined with water type between Ca+Mg-HCO3 and Na+K-HCO3. This can be explained with cation exchange reaction. The minor trend is obviously corresponding to seawater intrusion, which has water type of Na+K-Cl, because the localities of these samples are all in the coastal area. The multivariate statistical analysis on hydrochemical data was conducted for further exploration on the mechanism of arsenic contamination. Two major factors can be extracted with factor analysis. The major factor includes Ca, Mg and Sr while the minor factor includes Na, K and As. This reconfirms that cation exchange reaction mainly control the groundwater hydrochemistry in the study area. It is worth to note that arsenic is positively related to Na and K. The result of cluster analysis shows that groundwater samples with high arsenic concentration can be grouped into that with high Na, K and HCO3. This supports that cation exchange would enhance the release of arsenic and exclude the effect of seawater intrusion. In other words, the water-rock reaction time is key to obtain higher arsenic content. In general, the major source of arsenic in sediments include exchangeable, reducible and oxidizable phases, which are adsorbed ions, Fe-Mn oxides and organic matters/pyrite, respectively. However, the results of factor analysis do not show apparent correlation between arsenic and Fe/Mn. This may exclude Fe-Mn oxides as a major source of arsenic. The other sources

  16. Fourier Transform Infrared Spectroscopy (FTIR) and Multivariate Analysis for Identification of Different Vegetable Oils Used in Biodiesel Production

    PubMed Central

    Mueller, Daniela; Ferrão, Marco Flôres; Marder, Luciano; da Costa, Adilson Ben; de Cássia de Souza Schneider, Rosana

    2013-01-01

    The main objective of this study was to use infrared spectroscopy to identify vegetable oils used as raw material for biodiesel production and apply multivariate analysis to the data. Six different vegetable oil sources—canola, cotton, corn, palm, sunflower and soybeans—were used to produce biodiesel batches. The spectra were acquired by Fourier transform infrared spectroscopy using a universal attenuated total reflectance sensor (FTIR-UATR). For the multivariate analysis principal component analysis (PCA), hierarchical cluster analysis (HCA), interval principal component analysis (iPCA) and soft independent modeling of class analogy (SIMCA) were used. The results indicate that is possible to develop a methodology to identify vegetable oils used as raw material in the production of biodiesel by FTIR-UATR applying multivariate analysis. It was also observed that the iPCA found the best spectral range for separation of biodiesel batches using FTIR-UATR data, and with this result, the SIMCA method classified 100% of the soybean biodiesel samples. PMID:23539030

  17. The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

    PubMed

    Hurtado Rúa, Sandra M; Mazumdar, Madhu; Strawderman, Robert L

    2015-12-30

    Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta-analysis example from the periodontal field and a medium meta-analysis from the study of stroke. Copyright © 2015 John Wiley & Sons, Ltd.

  18. The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

    PubMed

    Hurtado Rúa, Sandra M; Mazumdar, Madhu; Strawderman, Robert L

    2015-12-30

    Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta-analysis example from the periodontal field and a medium meta-analysis from the study of stroke. Copyright © 2015 John Wiley & Sons, Ltd. PMID:26303671

  19. Use of Raman microscopy and multivariate data analysis to observe the biomimetic growth of carbonated hydroxyapatite on bioactive glass.

    PubMed

    Seah, Regina K H; Garland, Marc; Loo, Joachim S C; Widjaja, Effendi

    2009-02-15

    In the present contribution, the biomimetic growth of carbonated hydroxyapatite (HA) on bioactive glass were investigated by Raman microscopy. Bioactive glass samples were immersed in simulated body fluid (SBF) buffered solution at pH 7.40 up to 17 days at 37 degrees C. Raman microscopy mapping was performed on the bioglass samples immersed in SBF solution for different periods of time. The collected data was then analyzed using the band-target entropy minimization technique to extract the observable pure component Raman spectral information. In this study, the pure component Raman spectra of the precursor amorphous calcium phosphate, transient octacalcium phosphate, and matured HA were all recovered. In addition, pure component Raman spectra of calcite, silica glass, and some organic impurities were also recovered. The resolved pure component spectra were fit to the normalized measured Raman data to provide the spatial distribution of these species on the sample surfaces. The current results show that Raman microscopy and multivariate data analysis provide a sensitive and accurate tool to characterize the surface morphology, as well as to give more specific information on the chemical species present and the phase transformation of phosphate species during the formation of HA on bioactive glass. PMID:19170517

  20. Laser-induced breakdown spectroscopy-based investigation and classification of pharmaceutical tablets using multivariate chemometric analysis.

    PubMed

    Myakalwar, Ashwin Kumar; Sreedhar, S; Barman, Ishan; Dingari, Narahara Chari; Venugopal Rao, S; Prem Kiran, P; Tewari, Surya P; Manoj Kumar, G

    2011-12-15

    We report the effectiveness of laser-induced breakdown spectroscopy (LIBS) in probing the content of pharmaceutical tablets and also investigate its feasibility for routine classification. This method is particularly beneficial in applications where its exquisite chemical specificity and suitability for remote and on site characterization significantly improves the speed and accuracy of quality control and assurance process. Our experiments reveal that in addition to the presence of carbon, hydrogen, nitrogen and oxygen, which can be primarily attributed to the active pharmaceutical ingredients, specific inorganic atoms were also present in all the tablets. Initial attempts at classification by a ratiometric approach using oxygen (∼777 nm) to nitrogen (742.36 nm, 744.23 nm and 746.83 nm) compositional values yielded an optimal value at 746.83 nm with the least relative standard deviation but nevertheless failed to provide an acceptable classification. To overcome this bottleneck in the detection process, two chemometric algorithms, i.e. principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA), were implemented to exploit the multivariate nature of the LIBS data demonstrating that LIBS has the potential to differentiate and discriminate among pharmaceutical tablets. We report excellent prospective classification accuracy using supervised classification via the SIMCA algorithm, demonstrating its potential for future applications in process analytical technology, especially for fast on-line process control monitoring applications in the pharmaceutical industry.

  1. Carbon dioxide emissions, GDP, energy use, and population growth: a multivariate and causality analysis for Ghana, 1971-2013.

    PubMed

    Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

    2016-07-01

    In this study, the relationship between carbon dioxide emissions, GDP, energy use, and population growth in Ghana was investigated from 1971 to 2013 by comparing the vector error correction model (VECM) and the autoregressive distributed lag (ARDL). Prior to testing for Granger causality based on VECM, the study tested for unit roots, Johansen's multivariate co-integration and performed a variance decomposition analysis using Cholesky's technique. Evidence from the variance decomposition shows that 21 % of future shocks in carbon dioxide emissions are due to fluctuations in energy use, 8 % of future shocks are due to fluctuations in GDP, and 6 % of future shocks are due to fluctuations in population. There was evidence of bidirectional causality running from energy use to GDP and a unidirectional causality running from carbon dioxide emissions to energy use, carbon dioxide emissions to GDP, carbon dioxide emissions to population, and population to energy use. Evidence from the long-run elasticities shows that a 1 % increase in population in Ghana will increase carbon dioxide emissions by 1.72 %. There was evidence of short-run equilibrium relationship running from energy use to carbon dioxide emissions and GDP to carbon dioxide emissions. As a policy implication, the addition of renewable energy and clean energy technologies into Ghana's energy mix can help mitigate climate change and its impact in the future.

  2. Carbon dioxide emissions, GDP, energy use, and population growth: a multivariate and causality analysis for Ghana, 1971-2013.

    PubMed

    Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

    2016-07-01

    In this study, the relationship between carbon dioxide emissions, GDP, energy use, and population growth in Ghana was investigated from 1971 to 2013 by comparing the vector error correction model (VECM) and the autoregressive distributed lag (ARDL). Prior to testing for Granger causality based on VECM, the study tested for unit roots, Johansen's multivariate co-integration and performed a variance decomposition analysis using Cholesky's technique. Evidence from the variance decomposition shows that 21 % of future shocks in carbon dioxide emissions are due to fluctuations in energy use, 8 % of future shocks are due to fluctuations in GDP, and 6 % of future shocks are due to fluctuations in population. There was evidence of bidirectional causality running from energy use to GDP and a unidirectional causality running from carbon dioxide emissions to energy use, carbon dioxide emissions to GDP, carbon dioxide emissions to population, and population to energy use. Evidence from the long-run elasticities shows that a 1 % increase in population in Ghana will increase carbon dioxide emissions by 1.72 %. There was evidence of short-run equilibrium relationship running from energy use to carbon dioxide emissions and GDP to carbon dioxide emissions. As a policy implication, the addition of renewable energy and clean energy technologies into Ghana's energy mix can help mitigate climate change and its impact in the future. PMID:27030236

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

    NASA Astrophysics Data System (ADS)

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

    2009-12-01

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

  4. Multivariate analysis of groundwater quality and modeling impact of ground heat pump system

    NASA Astrophysics Data System (ADS)

    Thuyet, D. Q.; Saito, H.; Muto, H.; Saito, T.; Hamamoto, S.; Komatsu, T.

    2013-12-01

    The ground source heat pump system (GSHP) has recently become a popular building heating or cooling method, especially in North America, Western Europe, and Asia, due to advantages in reducing energy consumption and greenhouse gas emission. Because of the stability of the ground temperature, GSHP can effectively exchange the excess or demand heat of the building to the ground during the building air conditioning in the different seasons. The extensive use of GSHP can potentially disturb subsurface soil temperature and thus the groundwater quality. Therefore the assessment of subsurface thermal and environmental impacts from the GSHP operations is necessary to ensure sustainable use of GSHP system as well as the safe use of groundwater resources. This study aims to monitor groundwater quality during GSHP operation and to develop a numerical model to assess changes in subsurface soil temperature and in groundwater quality as affected by GSHP operation. A GSHP system was installed in Fuchu city, Tokyo, and consists of two closed double U-tubes (50-m length) buried vertically in the ground with a distance of 7.3 m from each U-tube located outside a building. An anti-freezing solution was circulated inside the U-tube for exchanging the heat between the building and the ground. The temperature at every 5-m depth and the groundwater quality including concentrations of 16 trace elements, pH, EC, Eh and DO in the shallow aquifer (32-m depth) and the deep aquifer (44-m depth) were monitored monthly since 2012, in an observation well installed 3 m from the center of the two U-tubes.Temporal variations of each element were evaluated using multivariate analysis and geostatistics. A three-dimensional heat exchange model was developed in COMSOL Multiphysics4.3b to simulate the heat exchange processes in subsurface soils. Results showed the difference in groundwater quality between the shallow and deep aquifers to be significant for some element concentrations and DO, but

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

    USGS Publications Warehouse

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

    2008-01-01

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

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

    PubMed Central

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

    2016-01-01

    Background Urological mobile medical (mHealth) apps are gaining popularity with both clinicians and patients. mHealth is a rapidly evolving and heterogeneous field, with some urology apps being downloaded over 10,000 times and others not at all. The factors that contribute to medical app downloads have yet to be identified, including the hypothetical influence of expert involvement in app development. Objective The objective of our study was to identify predictors of the number of urology app downloads. Methods We reviewed urology apps available in the Google Play Store and collected publicly available data. Multivariate ordinal logistic regression evaluated the effect of publicly available app variables on the number of apps being downloaded. Results Of 129 urology apps eligible for study, only 2 (1.6%) had >10,000 downloads, with half having ≤100 downloads and 4 (3.1%) having none at all. Apps developed with expert urologist involvement (P=.003), optional in-app purchases (P=.01), higher user rating (P<.001), and more user reviews (P<.001) were more likely to be installed. App cost was inversely related to the number of downloads (P<.001). Only data from the Google Play Store and the developers’ websites, but not other platforms, were publicly available for analysis, and the level and nature of expert involvement was not documented. Conclusions The explicit participation of urologists in app development is likely to enhance its chances to have a higher number of downloads. This finding should help in the design of better apps and further promote urologist involvement in mHealth. Official certification processes are required to ensure app quality and user safety. PMID:27421338

  7. Multivariate pattern analysis of obsessive-compulsive disorder using structural neuroanatomy.

    PubMed

    Hu, Xinyu; Liu, Qi; Li, Bin; Tang, Wanjie; Sun, Huaiqiang; Li, Fei; Yang, Yanchun; Gong, Qiyong; Huang, Xiaoqi

    2016-02-01

    Magnetic resonance imaging (MRI) studies have revealed brain structural abnormalities in obsessive-compulsive disorder (OCD) patients, involving both gray matter (GM) and white matter (WM). However, the results of previous publications were based on average differences between groups, which limited their usages in clinical practice. Therefore, the aim of this study was to examine whether the application of multivariate pattern analysis (MVPA) to high-dimensional structural images would allow accurate discrimination between OCD patients and healthy control subjects (HCS). High-resolution T1-weighted images were acquired from 33 OCD patients and 33 demographically matched HCS in a 3.0 T scanner. Differences in GM and WM volume between OCD and HCS were examined using two types of well-established MVPA techniques: support vector machine (SVM) and Gaussian process classifier (GPC). We also drew a receiver operating characteristic (ROC) curve to evaluate the performance of each classifier. The classification accuracies for both classifiers using GM and WM anatomy were all above 75%. The highest classification accuracy (81.82%, P<0.001) was achieved with the SVM classifier using WM information. Regional brain anomalies with high discriminative power were based on three distributed networks including the fronto-striatal circuit, the temporo-parieto-occipital junction and the cerebellum. Our study illustrated that both GM and WM anatomical features may be useful in differentiating OCD patients from HCS. WM volume using the SVM approach showed the highest accuracy in our population for revealing group differences, which suggested its potential diagnostic role in detecting highly enriched OCD patients at the level of the individual. PMID:26708318

  8. The Relationship Between Relative Value Units and Outcomes: A Multivariate Analysis of Plastic Surgery Procedures

    PubMed Central

    Nguyen, Khang T.; Gart, Michael S.; Smetona, John T.; Aggarwal, Apas; Bilimoria, Karl Y.; Kim, John Y. S.

    2012-01-01

    Introduction: Relative value units (RVUs) were developed as a quantifier of requisite training, knowledge, and technical expertise for performing various procedures. In select procedures, increasing RVUs have been shown to substitute well for increasing surgical complexity and have been linked to greater risk of complications. The relationship of RVU to outcomes has yet to be examined in the plastic surgery population. Methods: This study analyzed nearly 15,000 patients from a standardized, multicenter database to better define the link between RVUs and outcomes in this surgical population. The American College of Surgeons’ National Surgical Quality Improvement Program was retrospectively reviewed from 2006 to 2010. Results: A total of 14,936 patients undergoing primary procedures of plastic surgery were identified. Independent risk factors for complications were analyzed using multivariable logistic regression. A unit increase in RVUs was associated with a 1.7% increase in the odds of overall complications and 1.0% increase in the odds of surgical site complications but did not predict mortality or reoperation. A unit increase in RVUs was also associated with a prolongation of operative time by 0.41 minutes, but RVUs only accounted for 15.6% of variability in operative times. Conclusions: In the plastic surgery population, increasing RVUs correlates with increased risks of overall complications and surgical site complications. While increasing RVUs may independently prolong operative times, they only accounted for 15.6% of observed variance, indicating that other factors are clearly involved. These findings must be weighed against the benefits of performing more complex surgeries, including time and cost savings, and considered in each patient's risk-benefit analysis. PMID:23308307

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

    NASA Technical Reports Server (NTRS)

    Lehtomaki, N. A.

    1981-01-01

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

  10. A multivariate nonlinear mixed effects model for longitudinal image analysis: Application to amyloid imaging.

    PubMed

    Bilgel, Murat; Prince, Jerry L; Wong, Dean F; Resnick, Susan M; Jedynak, Bruno M

    2016-07-01

    It is important to characterize the temporal trajectories of disease-related biomarkers in order to monitor progression and identify potential points of intervention. These are especially important for neurodegenerative diseases, as therapeutic intervention is most likely to be effective in the preclinical disease stages prior to significant neuronal damage. Neuroimaging allows for the measurement of structural, functional, and metabolic integrity of the brain at the level of voxels, whose volumes are on the order of mm(3). These voxelwise measurements provide a rich collection of disease indicators. Longitudinal neuroimaging studies enable the analysis of changes in these voxelwise measures. However, commonly used longitudinal analysis approaches, such as linear mixed effects models, do not account for the fact that individuals enter a study at various disease stages and progress at different rates, and generally consider each voxelwise measure independently. We propose a multivariate nonlinear mixed effects model for estimating the trajectories of voxelwise neuroimaging biomarkers from longitudinal data that accounts for such differences across individuals. The method involves the prediction of a progression score for each visit based on a collective analysis of voxelwise biomarker data within an expectation-maximization framework that efficiently handles large amounts of measurements and variable number of visits per individual, and accounts for spatial correlations among voxels. This score allows individuals with similar progressions to be aligned and analyzed together, which enables the construction of a trajectory of brain changes as a function of an underlying progression or disease stage. We apply our method to studying cortical β-amyloid deposition, a hallmark of preclinical Alzheimer's disease, as measured using positron emission tomography. Results on 104 individuals with a total of 300 visits suggest that precuneus is the earliest cortical region to

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

    NASA Astrophysics Data System (ADS)

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

    2010-05-01

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

  12. Classifying Schizophrenia Using Multimodal Multivariate Pattern Recognition Analysis: Evaluating the Impact of Individual Clinical Profiles on the Neurodiagnostic Performance.

    PubMed

    Cabral, Carlos; Kambeitz-Ilankovic, Lana; Kambeitz, Joseph; Calhoun, Vince D; Dwyer, Dominic B; von Saldern, Sebastian; Urquijo, Maria F; Falkai, Peter; Koutsouleris, Nikolaos

    2016-07-01

    Previous studies have shown that structural brain changes are among the best-studied candidate markers for schizophrenia (SZ) along with functional connectivity (FC) alterations of resting-state (RS) patterns. This study aimed to investigate effects of clinical and sociodemographic variables on the classification by applying multivariate pattern analysis (MVPA) to both gray matter (GM) volume and FC measures in patients with SZ and healthy controls (HC). RS and structural magnetic resonance imaging data (sMRI) from 74 HC and 71 SZ patients were obtained from a Mind Research Network COBRE dataset available via COINS (http://coins.mrn.org/dx). We used a MVPA framework using support-vector machines embedded in a repeated, nested cross-validation to generate a multi-modal diagnostic system and evaluate its generalizability. The dependence of neurodiagnostic performance on clinical and sociodemographic variables was evaluated. The RS classifier showed a slightly higher accuracy (70.5%) compared to the structural classifier (69.7%). The combination of sMRI and RS outperformed single MRI modalities classification by reaching 75% accuracy. The RS based moderator analysis revealed that the neurodiagnostic performance was driven by older SZ patients with an earlier illness onset and more pronounced negative symptoms. In contrast, there was no linear relationship between the clinical variables and neuroanatomically derived group membership measures. This study achieved higher accuracy distinguishing HC from SZ patients by fusing 2 imaging modalities. In addition the results of RS based moderator analysis showed that age of patients, as well as their age at the illness onset were the most important clinical features. PMID:27460614

  13. Classifying Schizophrenia Using Multimodal Multivariate Pattern Recognition Analysis: Evaluating the Impact of Individual Clinical Profiles on the Neurodiagnostic Performance.

    PubMed

    Cabral, Carlos; Kambeitz-Ilankovic, Lana; Kambeitz, Joseph; Calhoun, Vince D; Dwyer, Dominic B; von Saldern, Sebastian; Urquijo, Maria F; Falkai, Peter; Koutsouleris, Nikolaos

    2016-07-01

    Previous studies have shown that structural brain changes are among the best-studied candidate markers for schizophrenia (SZ) along with functional connectivity (FC) alterations of resting-state (RS) patterns. This study aimed to investigate effects of clinical and sociodemographic variables on the classification by applying multivariate pattern analysis (MVPA) to both gray matter (GM) volume and FC measures in patients with SZ and healthy controls (HC). RS and structural magnetic resonance imaging data (sMRI) from 74 HC and 71 SZ patients were obtained from a Mind Research Network COBRE dataset available via COINS (http://coins.mrn.org/dx). We used a MVPA framework using support-vector machines embedded in a repeated, nested cross-validation to generate a multi-modal diagnostic system and evaluate its generalizability. The dependence of neurodiagnostic performance on clinical and sociodemographic variables was evaluated. The RS classifier showed a slightly higher accuracy (70.5%) compared to the structural classifier (69.7%). The combination of sMRI and RS outperformed single MRI modalities classification by reaching 75% accuracy. The RS based moderator analysis revealed that the neurodiagnostic performance was driven by older SZ patients with an earlier illness onset and more pronounced negative symptoms. In contrast, there was no linear relationship between the clinical variables and neuroanatomically derived group membership measures. This study achieved higher accuracy distinguishing HC from SZ patients by fusing 2 imaging modalities. In addition the results of RS based moderator analysis showed that age of patients, as well as their age at the illness onset were the most important clinical features.

  14. Characterization of Used Nuclear Fuel with Multivariate Analysis for Process Monitoring

    SciTech Connect

    Dayman, Kenneth J.; Coble, Jamie B.; Orton, Christopher R.; Schwantes, Jon M.

    2014-01-01

    The Multi-Isotope Process (MIP) Monitor combines gamma spectroscopy and multivariate analysis to detect anomalies in various process streams in a nuclear fuel reprocessing system. Measured spectra are compared to models of nominal behavior at each measurement location to detect unexpected changes in system behavior. In order to improve the accuracy and specificity of process monitoring, fuel characterization may be used to more accurately train subsequent models in a full analysis scheme. This paper presents initial development of a reactor-type classifier that is used to select a reactor-specific partial least squares model to predict fuel burnup. Nuclide activities for prototypic used fuel samples were generated in ORIGEN-ARP and used to investigate techniques to characterize used nuclear fuel in terms of reactor type (pressurized or boiling water reactor) and burnup. A variety of reactor type classification algorithms, including k-nearest neighbors, linear and quadratic discriminant analyses, and support vector machines, were evaluated to differentiate used fuel from pressurized and boiling water reactors. Then, reactor type-specific partial least squares models were developed to predict the burnup of the fuel. Using these reactor type-specific models instead of a model trained for all light water reactors improved the accuracy of burnup predictions. The developed classification and prediction models were combined and applied to a large dataset that included eight fuel assembly designs, two of which were not used in training the models, and spanned the range of the initial 235U enrichment, cooling time, and burnup values expected of future commercial used fuel for reprocessing. Error rates were consistent across the range of considered enrichment, cooling time, and burnup values. Average absolute relative errors in burnup predictions for validation data both within and outside the training space were 0.0574% and 0.0597%, respectively. The errors seen in this

  15. Significant drivers of the virtual water trade evaluated with a multivariate regression analysis

    NASA Astrophysics Data System (ADS)

    Tamea, Stefania; Laio, Francesco; Ridolfi, Luca

    2014-05-01

    International trade of food is vital for the food security of many countries, which rely on trade to compensate for an agricultural production insufficient to feed the population. At the same time, food trade has implications on the distribution and use of water resources, because through the international trade of food commodities, countries virtually displace the water used for food production, known as "virtual water". Trade thus implies a network of virtual water fluxes from exporting to importing countries, which has been estimated to displace more than 2 billions of m3 of water per year, or about the 2% of the annual global precipitation above land. It is thus important to adequately identify the dynamics and the controlling factors of the virtual water trade in that it supports and enables the world food security. Using the FAOSTAT database of international trade and the virtual water content available from the Water Footprint Network, we reconstructed 25 years (1986-2010) of virtual water fluxes. We then analyzed the dependence of exchanged fluxes on a set of major relevant factors, that includes: population, gross domestic product, arable land, virtual water embedded in agricultural production and dietary consumption, and geographical distance between countries. Significant drivers have been identified by means of a multivariate regression analysis, applied separately to the export and import fluxes of each country; temporal trends are outlined and the relative importance of drivers is assessed by a commonality analysis. Results indicate that population, gross domestic product and geographical distance are the major drivers of virtual water fluxes, with a minor (but non-negligible) contribution given by the agricultural production of exporting countries. Such drivers have become relevant for an increasing number of countries throughout the years, with an increasing variance explained by the distance between countries and a decreasing role of the gross

  16. Multivariate interactive digital analysis system /MIDAS/ - A new fast multispectral recognition system

    NASA Technical Reports Server (NTRS)

    Kriegler, F.; Marshall, R.; Lampert, S.; Gordon, M.; Cornell, C.; Kistler, R.

    1973-01-01

    The MIDAS system is a prototype, multiple-pipeline digital processor mechanizing the multivariate-Gaussian, maximum-likelihood decision algorithm operating at 200,000 pixels/second. It incorporates displays and film printer equipment under control of a general purpose midi-computer and possesses sufficient flexibility that operational versions of the equipment may be subsequently specified as subsets of the system.

  17. Multivariate Genetic Analysis of Specific Cognitive Abilities in the Colorado Adoption Project at Age 7.

    ERIC Educational Resources Information Center

    Cardon, Lon R.; And Others

    1992-01-01

    A multivariate hierarchical model of specific cognitive abilities was fitted to data from 196 adopted and 213 nonadopted children from the Colorado Adoption Project and 120 of their siblings to assess genetic influence on specific mental abilities. Genetic effects occur in middle childhood that differentially influence mental ability scores.…

  18. A Multivariate Genetic Analysis of Specific Phobia, Separation Anxiety and Social Phobia in Early Childhood

    ERIC Educational Resources Information Center

    Eley, Thalia C.; Rijsdijk, Fruhling V.; Perrin, Sean; O'Connor, Thomas G.; Bolton, Derek

    2008-01-01

    Background: Comorbidity amongst anxiety disorders is very common in children as in adults and leads to considerable distress and impairment, yet is poorly understood. Multivariate genetic analyses can shed light on the origins of this comorbidity by revealing whether genetic or environmental risks for one disorder also influence another. We…

  19. Discrimination between Bacillus and Alicyclobacillus isolates in apple juice by Fourier transform infrared spectroscopy and multivariate analysis.

    PubMed

    Al-Holy, Murad A; Lin, Mengshi; Alhaj, Omar A; Abu-Goush, Mahmoud H

    2015-02-01

    Alicyclobacillus is a causative agent of spoilage in pasteurized and heat-treated apple juice products. Differentiating between this genus and the closely related Bacillus is crucially important. In this study, Fourier transform infrared spectroscopy (FT-IR) was used to identify and discriminate between 4 Alicyclobacillus strains and 4 Bacillus isolates inoculated individually into apple juice. Loading plots over the range of 1350 and 1700 cm(-1) reflected the most distinctive biochemical features of Bacillus and Alicyclobacillus. Multivariate statistical methods (for example, principal component analysis and soft independent modeling of class analogy) were used to analyze the spectral data. Distinctive separation of spectral samples was observed. This study demonstrates that FT-IR spectroscopy in combination with multivariate analysis could serve as a rapid and effective tool for fruit juice industry to differentiate between Bacillus and Alicyclobacillus and to distinguish between species belonging to these 2 genera.

  20. Analysis of antique bronze coins by Laser Induced Breakdown Spectroscopy and multivariate analysis

    NASA Astrophysics Data System (ADS)

    Bachler, M. Orlić; Bišćan, M.; Kregar, Z.; Jelovica Badovinac, I.; Dobrinić, J.; Milošević, S.

    2016-09-01

    This work presents a feasibility study of applying the Principal Component Analysis (PCA) to data obtained by Laser-Induced Breakdown Spectroscopy (LIBS) with the aim of determining correlation between different samples. The samples were antique bronze coins coated in silver (follis) dated in the Roman Empire period and were made during different rulers in different mints. While raw LIBS data revealed that in the period from the year 286 to 383 CE content of silver was constantly decreasing, the PCA showed that the samples can be somewhat grouped together based on their place of origin, which could be a useful hint when analysing unknown samples. It was also found that PCA can help in discriminating spectra corresponding to ablation from the surface and from the bulk. Furthermore, Partial Least Squares method (PLS) was used to obtain, based on a set of samples with known composition, an estimation of relative copper concentration in studied ancient coins. This analysis showed that copper concentration in surface layers ranged from 83% to 90%.

  1. Classification of worldwide drainage basins through the multivariate analysis of variables controlling their hydrosedimentary response

    NASA Astrophysics Data System (ADS)

    Raux, Julie; Copard, Yoann; Laignel, Benoît; Fournier, Matthieu; Masseï, Nicolas

    2011-04-01

    Quality and amount of waters and sediments conveyed within large drainage basins are crucial for human societies and biodiversity concerns. This work aims to determine the factors controlling the hydrosedimentary response (water discharge and sediment load) of 24 worldwide large drainage basins. In this respect, eleven geomorphologic and climatic variables routinely used in the literature were considered and others as fractal dimension, elongation and mean channel slope are novel for such an issue. In addition, two variables, land cover and lithology indexes, somewhat different from the literature in terms of calculation principles, were also included. All these variables were then subjected to multivariate statistical analyses (CA and PCA) and confronted in a matrix correlation. On the whole, our results display that water discharge is controlled by runoff, precipitation, basin area, elongation and fractal dimension while sediment load is governed by runoff, precipitation and maximum elevation. Mean channel slope and land-use have a minor role while other parameters (hypsometry, lithology, length, slope, mean elevation and temperature) do not play a significant role in the hydrosedimentary response. Such statistical analyses also bring out a classification of these drainage basins, comprising five to six main clusters which are ranged according to the main variables ruling their hydrosedimentary response. Two clusters are essentially governed by geomorphometric parameters (area, elongation, fractal dimension, mean elevation and hypsometry) while one cluster is rather controlled by transfer processes (runoff) and by active tectonic (maximum elevation). Hydrosedimentary response of arctic and continental rivers is controlled by low temperature while two drainage basins show any trend. A comparison of our results with other previous works dealing with this same issue points to some significant disagreements essentially based on the number of drainage basins

  2. Multivariate Statistical Analysis of Volatile Trace Elements in H Chondrites: Implications for Parent Body Structure

    NASA Astrophysics Data System (ADS)

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

    1993-07-01

    The perception among meteoriticists is that contents of the volatile trace elements systematically decrease with shock and particularly petrologic type. This perception affects views that investigators have of the early history and structure of the H chondrite parent body. Measurement of a variety of volatile trace elements in a statistically significant number of samples accompanied by chemometric data analysis techniques developed for interpretation of trace- element data [1] should maximize the amount of genetic information available from the volatile trace elements and offer clues to the early thermal history of the H chondrite parent body. Volatile trace-element data exist for 58 H chondrite falls: the complete dataset includes Co, Rb, Ag, Se, Cs, Te, Zn, Cd, Bi, Tl, and In (listed in increasing order of volatility) [2,3]. This dataset includes 13 H4, 32 H5, and 13 H6 chondrites, which cover the full range of shock facies from a through f. To examine the effect that shock has on volatile trace-element concentrations in H4-6 chondrites, we have compared data for the least-shocked samples (shock facies a-b) with the most shocked samples (shock facies c-f) using both univariate (Student's t-test) and multivariate techniques (linear discriminant analysis). The results demonstrate no reason to doubt the null hypothesis of no difference in volatile trace-element composition between shocked and unshocked H4-6 chondrites at any reasonable significance level. This situation contrasts sharply with the strong difference found between shocked and unshocked L chondrites [4]. The role of shock in establishing volatile trace- element contents in H and L chondrites clearly differs. Univariate comparisons between H4, H5, and H6 chondrites demonstrate that only Cs varies significantly with petrologic type (prob. > F 0.0006) with concentration decreasing monotonically with increasing petrographic type. Box- and-whisker plots of volatile trace-element contents reveal a general

  3. Multivariate data analysis to characterize gas chromatography columns for dioxin analysis.

    PubMed

    Do, Lan; Geladi, Paul; Haglund, Peter

    2014-06-20

    Principal component analysis (PCA) was applied for evaluating the selectivity of 22 GC columns for which complete retention data were available for the 136 tetra- to octa-chlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs). Because the hepta- and octa-homologues are easy to separate the PCA was focused on the 128 tetra- to hexa-CDD/Fs. The analysis showed that 21 of the 22 GC columns could be subdivided into four groups with different selectivity. Group I consists of columns with non-polar thermally stable phases (Restek 5Sil MS and Dioxin 2, SGE BPX-DXN, Supelco Equity-5, and Agilent DB-1, DB-5, DB-5ms, VF-5ms, VF-Xms and DB-XLB). Group II includes ionic liquid columns (Supelco SLB-IL61, SLB-IL111 and SLB-IL76) with very high polarity. Group III includes columns with high-percentage phenyl and cyanopropyl phases (Agilent DB-17 and DB-225, Quadrex CPS-1, Supelco SP-2331, and Agilent CP-Sil 88), and Group IV columns with shape selectivity (Dionex SB-Smectic and Restek LC-50, Supelco βDEXcst, Agilent VF-Xms and DB-XLB). Thus, two columns appeared in both Group I and IV (Agilent VF-Xms and DB-XLB). The selectivity of the other column, Agilent DB-210, differs from those of these four groups. Partial least squares (PLS) regression was used to correlate the retention times of the tetra- to hexa-CDD/Fs on the 22 stationary phases with a set of physicochemical and structural descriptors to identify parameters that significantly influence the solute-stationary phase interactions. The most influential physicochemical parameters for the interaction were associated with molecular size (as reflects in the total energy, electron energy, core-core repulsion and standard entropy), solubility (aqueous solubility and n-octanol/water partition coefficient), charge distribution (molecular polarizability and dipolar moment), and reactivity (relative Gibbs free energy); and the most influential structural descriptors were related to these parameters, in particular, size and

  4. Multivariate analysis of the effects of manganese on the reproductive physiology and behavior of the male house mouse

    SciTech Connect

    Gray, L.E. Jr.; Laskey, J.W.

    1980-07-01

    Chronic exposure to Mn/sub 3/O/sub 4/ in the diet at 1050 ppM Mn retarded the sexual development and lowered reactive locomotor activity levels in male mice. A multivariate analysis of variance indicated that testis, seminal vesicle, and preputial gland weights were significantly smaller as a result of Mn administration. These results support earlier observations of altered locomotor activity levels and reproductive development in male rats in the absence of other signs of toxicity.

  5. Cross-scale predictive modeling of CHO cell culture growth and metabolites using Raman spectroscopy and multivariate analysis.

    PubMed

    Berry, Brandon; Moretto, Justin; Matthews, Thomas; Smelko, John; Wiltberger, Kelly

    2015-01-01

    Multi-component, multi-scale Raman spectroscopy modeling results from a monoclonal antibody producing CHO cell culture process including data from two development scales (3 L, 200 L) and a clinical manufacturing scale environment (2,000 L) are presented. Multivariate analysis principles are a critical component to partial least squares (PLS) modeling but can quickly turn into an overly iterative process, thus a simplified protocol is proposed for addressing necessary steps including spectral preprocessing, spectral region selection, and outlier removal to create models exclusively from cell culture process data without the inclusion of spectral data from chemically defined nutrient solutions or targeted component spiking studies. An array of single-scale and combination-scale modeling iterations were generated to evaluate technology capabilities and model scalability. Analysis of prediction errors across models suggests that glucose, lactate, and osmolality are well modeled. Model strength was confirmed via predictive validation and by examining performance similarity across single-scale and combination-scale models. Additionally, accurate predictive models were attained in most cases for viable cell density and total cell density; however, these components exhibited some scale-dependencies that hindered model quality in cross-scale predictions where only development data was used in calibration. Glutamate and ammonium models were also able to achieve accurate predictions in most cases. However, there are differences in the absolute concentration ranges of these components across the datasets of individual bioreactor scales. Thus, glutamate and ammonium PLS models were forced to extrapolate in cases where models were derived from small scale data only but used in cross-scale applications predicting against manufacturing scale batches. PMID:25504860

  6. Integrated biomarker response in catfish Hypostomus ancistroides by multivariate analysis in the Pirapó River, southern Brazil.

    PubMed

    Ghisi, Nédia C; Oliveira, Elton C; Mendonça Mota, Thais F; Vanzetto, Guilherme V; Roque, Aliciane A; Godinho, Jayson P; Bettim, Franciele Lima; Silva de Assis, Helena Cristina da; Prioli, Alberto J

    2016-10-01

    Aquatic pollutants produce multiple consequences in organisms, populations, communities and ecosystems, affecting the function of organs, reproductive state, population size, species survival and even biodiversity. In order to monitor the health of aquatic organisms, biomarkers have been used as effective tools in environmental risk assessment. The aim of this study is to evaluate, through a multivariate and integrative analysis, the response of the native species Hypostomus ancistroides over a pollution gradient in the main water supply body of northwestern Paraná state (Brazil). The condition factor, micronucleus test and erythrocyte nuclear abnormalities (ENA), comet assay, measurement of the cerebral and muscular enzyme acetylcholinesterase (AChE), and histopathological analysis of liver and gill were evaluated in fishes from three sites of the Pirapó River during the dry and rainy seasons. The multivariate general result showed that the interaction between the seasons and the sites was significant: there are variations in the rates of alterations in the biological parameters, depending on the time of year researched at each site. In general, the best results were observed for the site nearest the spring, and alterations in the parameters at the intermediate and downstream sites. In sum, the results of this study showed the necessity of a multivariate analysis, evaluating several biological parameters, to obtain an integrated response to the effects of the environmental pollutants on the organisms. PMID:27421103

  7. Integrated biomarker response in catfish Hypostomus ancistroides by multivariate analysis in the Pirapó River, southern Brazil.

    PubMed

    Ghisi, Nédia C; Oliveira, Elton C; Mendonça Mota, Thais F; Vanzetto, Guilherme V; Roque, Aliciane A; Godinho, Jayson P; Bettim, Franciele Lima; Silva de Assis, Helena Cristina da; Prioli, Alberto J

    2016-10-01

    Aquatic pollutants produce multiple consequences in organisms, populations, communities and ecosystems, affecting the function of organs, reproductive state, population size, species survival and even biodiversity. In order to monitor the health of aquatic organisms, biomarkers have been used as effective tools in environmental risk assessment. The aim of this study is to evaluate, through a multivariate and integrative analysis, the response of the native species Hypostomus ancistroides over a pollution gradient in the main water supply body of northwestern Paraná state (Brazil). The condition factor, micronucleus test and erythrocyte nuclear abnormalities (ENA), comet assay, measurement of the cerebral and muscular enzyme acetylcholinesterase (AChE), and histopathological analysis of liver and gill were evaluated in fishes from three sites of the Pirapó River during the dry and rainy seasons. The multivariate general result showed that the interaction between the seasons and the sites was significant: there are variations in the rates of alterations in the biological parameters, depending on the time of year researched at each site. In general, the best results were observed for the site nearest the spring, and alterations in the parameters at the intermediate and downstream sites. In sum, the results of this study showed the necessity of a multivariate analysis, evaluating several biological parameters, to obtain an integrated response to the effects of the environmental pollutants on the organisms.

  8. Multivariate analysis of morphological characteristics of two species of passion flower with ornamental potential and of hybrids between them.

    PubMed

    Santos, E A; Souza, M M; Viana, A P; Almeida, A A F; Freitas, J C O; Lawinscky, P R

    2011-01-01

    We estimated genetic parameters through multivariate analysis of two species of Passiflora and their hybrids, considered of ornamental potential, based on the morphological characteristics: flower diameter, corona diameter, corona filament size, flower peduncle length, petal length and width, sepal length and width, internode length, stem diameter, leaf length, leaf width (mm), and leaf area (cm(2)). Five specimens of Passiflora sublanceolata [ex P. palmeri var. sublanceolata], five of P. foetida var. foetida and 20 F(1) hybrids between the two were evaluated. A randomized block design with four replications was used. The data were submitted to variance analysis and multivariate procedures, principal components analysis and unweighted pair group method with arithmetic mean grouping. We found significant differences between genotypes for all these morphological parameters. The hybrid plants had the highest variability, making them the most indicated for future improvement programs. The various multivariate techniques gave similar results, allowing separation of the plants into three distinct groups, these being the two paternal species and the hybrids. The hybrids were closer to the male genitors, revealing a paternal effect on the inheritance of vegetative and floral characters. Based on estimates of genetic parameters, the floral characteristics are the most indicated for the selection of plants for ornamental purposes, since these characteristics displayed greatest variability, a variation index of more than one, and high genotypic determination coefficients. PMID:22009858

  9. Spatial segregation between populations of ponto-cerebellar neurons: statistical analysis of multivariate spatial interactions.

    PubMed

    Bjaalie, J G; Diggle, P J; Nikundiwe, A; Karagülle, T; Brodal, P

    1991-12-01

    This study applies terms and methods for describing spatial interactions between multivariate spatial point patterns, which are, to our knowledge, new in neurobiology. We consider two categories of points, type 1 and 2, distributed within a certain reference volume (such as a nucleus of the brainstem or a cortical area). The points may, for example, represent different categories of labelled cells or axonal fields of termination. We say that there is spatial neutrality between points of type 1 and 2 if the types are signed by random labelling. If a mechanism drives the two point categories together, we say that the point patterns are positively associated. Conversely, if a mechanism drives type 1 and 2 points apart, we say that they are segregated. By comparing two cumulative distribution functions of distances between points, we can distinguish neutrality, positive association, and segregation. One function, H12(t), is the cumulative distribution function of the distance t between a pair of randomly selected points of type 1 and 2. The other, H00(t), is the corresponding function for a pair of points randomly selected without reference to type. Plots of the estimated difference between these two functions give an indication of positive association, neutrality, or segregation. A statistical test, based on simulations of random (neutral) distributions, can be used to see whether deviations from neutrality are significant. We apply the analysis described above to a major pathway of the brain, namely the ponto-cerebellar projection. Different types of cells in the pontine nuclei are retrogradely labelled with the fluorescent tracers Rhodamine-B-isothiocyanate, Fluoro-Gold, and Fast Blue. The tracers are injected in adjacent or more distant folia of the cerebellar paraflocculus. The location of the somata of labelled cells are recorded and the total distribution reconstructed in three dimensions and displayed on a dynamic graphics workstation. We ask whether different

  10. Applications of Linear Systems Theory to Spectroscopic Instrumentation and Multivariate Analysis

    NASA Astrophysics Data System (ADS)

    Erickson, Chris L.

    This research employs linear systems theory to design novel spectroscopic instruments, explain their operation, and provide insight into methods of data analysis. The first study examines the relationship between digital filtering, a technique based on linear systems theory, and multivariate regression, a statistical method. The study focuses on quantitative property estimation for one -sided, repetitive, linear, shift-invariant systems, and compares matched filtering, Kalman innovation filtering, classical least-squares regression, and principal components regression. Kalman innovation filters, which are derived by making signals independent of interferences via orthogonalization, are similar to the respective columns of the pseudo-inverse of the pure signal matrix in classical least-squares regression, and to the regression vectors of principal components least -squares regression, which are derived via calibration. Inverse regression methods, such as principal components regression, are advantageous in that if the experiment is carefully designed, interferences need not be explicitly defined and properties that depend on multiple components can be estimated. In the second study, an absorption spectrophotometer based on a novel stationary interferometer is described. A major advantage of the interferometer is that it requires few optical components: minimally a slit, a collimator, a planar mirror, a magnification lens, and a photodiode array detector. The interferometer images a linear spatial interferogram on a photodiode array. Fourier transformation of the detected interferogram yields the desired spectrum. Equations describing interferometer operation are derived using electromagnetic wave theory and linear systems theory. Systems theory is also used to model and correct systematic errors. The interferometer's baseline noise, resolution, dynamic range and precision are assessed and compared to those of a modern grating-based photodiode-array spectrograph

  11. Clinical Risk Characteristics of Upper Gastrointestinal Hemorrhage Severity: A Multivariable Risk Analysis

    PubMed Central

    Chaikitamnuaychok, Rangson; Patumanond, Jayanton

    2012-01-01

    Background Upper gastrointestinal hemorrhage (UGIH) is one of the common clinical manifestations encountered in most emergency departments. Patient characteristics indicating UGIH severity in developing countries may be different from those in developed countries. The present study was designed to explore clinical prognostic indicators for UGIH severity. Methods A retrospective cohort study was conducted in a university affiliated tertiary hospital in Kamphaeng Phet, Thailand. Medical folders of patients with UGIH were reviewed. Patients were grouped into 3 severity levels, based on criteria proposed by The American College of Surgeon. Pre-defined prognostic indicators were compared. The prognostic indicators for UGIH severity were analyzed by a multivariable continuation ratio ordinal logistic regression and presented with odds ratios. Results From 1,043 eligible medical folders, 984 (94.3%) complete folders were used in analysis. There were 241, 631 and 112 patients in the mild, moderate and severe UGIH groups. Six independent indicators of severe UGIH were, hemoglobin < 100 g/dL (OR = 13.82, 95% CI = 9.40 to 20.33, P < 0.001), systolic blood pressure < 100 mmHg (OR = 11.01, 95% CI = 7.41 to 16.36, P < 0.001), presence of hepatic failure (OR = 5.50, 95% CI = 1.14 to26.64, P = 0.037), presence of cirrhosis (OR = 2.03, 95% CI = 1.32 to 3.11, P = 0.001), blood urea nitrogen ≥ 35 mmol/L (OR = 1.73, 95% CI = 1.25 to 2.40, P = 0.001), and pulse rate ≥ 100 per minute (OR = 1.72, 95% CI = 1.21 to 2.45, P = 0.003). Conclusions Pulse rate ≥ 100 per minute, systolic blood pressure < 100 mmHg, hemoglobin < 10 g/dL, blood urea nitrogen ≥ 35 mmol/L, presence of cirrhosis and presence of hepatic failure are prognostic indicators for an increase in UGIH severity levels. They are potentially useful in UGIH risk stratification.

  12. Hydrochemical analysis of groundwater using multivariate statistical methods - The Volta region, Ghana

    USGS Publications Warehouse

    Banoeng-Yakubo, B.; Yidana, S.M.; Nti, E.

    2009-01-01

    Q and R-mode multivariate statistical analyses were applied to groundwater chemical data from boreholes and wells in the northern section of the Volta region Ghana. The objective was to determine the processes that affect the hydrochemistry and the variation of these processes in space among the three main geological terrains: the Buem formation, Voltaian System and the Togo series that underlie the area. The analyses revealed three zones in the groundwater flow system: recharge, intermediate and discharge regions. All three zones are clearly different with respect to all the major chemical parameters, with concentrations increasing from the perceived recharge areas through the intermediate regions to the discharge areas. R-mode HCA and factor analysis (using varimax rotation and Kaiser Criterion) were then applied to determine the significant sources of variation in the hydrochemistry. This study finds that groundwater hydrochemistry in the area is controlled by the weathering of silicate and carbonate minerals, as well as the chemistry of infiltrating precipitation. This study finds that the ??D and ??18O data from the area fall along the Global Meteoric Water Line (GMWL). An equation of regression derived for the relationship between ??D and ??18O bears very close semblance to the equation which describes the GMWL. On the basis of this, groundwater in the study area is probably meteoric and fresh. The apparently low salinities and sodicities of the groundwater seem to support this interpretation. The suitability of groundwater for domestic and irrigation purposes is related to its source, which determines its constitution. A plot of the sodium adsorption ratio (SAR) and salinity (EC) data on a semilog axis, suggests that groundwater serves good irrigation quality in the area. Sixty percent (60%), 20% and 20% of the 67 data points used in this study fall within the medium salinity - low sodicity (C2-S1), low salinity -low sodicity (C1-S1) and high salinity - low

  13. [Comparative analysis of trace elements in five marine-derived shell TCM using multivariate statistical analysis].

    PubMed

    Zhang, Shuai; Chen, Zhen; Fu, Yu-qiang; Gong, Hui-li; Guan, Hua-shi; Liu, Hong-bing

    2015-11-01

    A comparable study were carried out by determination of trace elements on five marine-derived shell traditional Chinese medicine (TCM) (Ostreae Concha, Haliotidis Concha, Margaritifera Concha, Meretricis Concha, and Arcae Concha), which were recorded in the Chinese Pharmacopoeia (2010 version). Seven trace elements in 51 batches of this type of shell TCM were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS), combined with principal component analysis (PCA) methods. The content of element Se, which exhibited significant differences among different drugs, could be used as a key element to distinguish this type of drugs. Meanwhile, the contents of elements Co, Cu, Mo, and Ba in Haliotidis Concha, Co and As in Margaritifera Concha, Mo and As in Meretricis Concha, Mo, As, and Ba in Arcae Concha, and Zn in Meretricis Concha were relatively stable. In the PCA plot, Arcae Concha and Meretricis Concha could be efficiently distinguished from Ostreae Concha together with Haliotidis Concha, and Margaritifera Concha. The results also showed a correlation with their medicinal function. In conclusion, trace elements in marine-derived shell TCM could not be neglected for their quality control.

  14. [Comparative analysis of trace elements in five marine-derived shell TCM using multivariate statistical analysis].

    PubMed

    Zhang, Shuai; Chen, Zhen; Fu, Yu-qiang; Gong, Hui-li; Guan, Hua-shi; Liu, Hong-bing

    2015-11-01

    A comparable study were carried out by determination of trace elements on five marine-derived shell traditional Chinese medicine (TCM) (Ostreae Concha, Haliotidis Concha, Margaritifera Concha, Meretricis Concha, and Arcae Concha), which were recorded in the Chinese Pharmacopoeia (2010 version). Seven trace elements in 51 batches of this type of shell TCM were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS), combined with principal component analysis (PCA) methods. The content of element Se, which exhibited significant differences among different drugs, could be used as a key element to distinguish this type of drugs. Meanwhile, the contents of elements Co, Cu, Mo, and Ba in Haliotidis Concha, Co and As in Margaritifera Concha, Mo and As in Meretricis Concha, Mo, As, and Ba in Arcae Concha, and Zn in Meretricis Concha were relatively stable. In the PCA plot, Arcae Concha and Meretricis Concha could be efficiently distinguished from Ostreae Concha together with Haliotidis Concha, and Margaritifera Concha. The results also showed a correlation with their medicinal function. In conclusion, trace elements in marine-derived shell TCM could not be neglected for their quality control. PMID:27071261

  15. metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

    PubMed Central

    Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J.; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T.; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

    2016-01-01

    Motivation: A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. Results: We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Availability and implementation: Code is available at https://github.com/aalto-ics-kepaco Contacts: anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153689

  16. Application of infrared microspectroscopy and multivariate analysis for monitoring the effect of adjunct cultures during Swiss cheese ripening.

    PubMed

    Chen, G; Kocaoglu-Vurma, N A; Harper, W J; Rodriguez-Saona, L E

    2009-08-01

    Improved cheese flavor has been attributed to the addition of adjunct cultures, which provide certain key enzymes for proteolysis and affect the dynamics of starter and nonstarter cultures. Infrared microspectroscopy provides unique fingerprint-like spectra for cheese samples and allows for rapid monitoring of cheese composition during ripening. The objective was to use infrared microspectroscopy and multivariate analysis to evaluate the effect of adjunct cultures on Swiss cheeses during ripening. Swiss cheeses, manufactured using a commercial starter culture combination and 1 of 3 adjunct Lactobacillus spp., were evaluated at d 1, 6, 30, 60, and 90 of ripening. Cheese samples (approximately 20 g) were powdered with liquid nitrogen and homogenized using water and organic solvents, and the water-soluble components were separated. A 3-microL aliquot of the extract was applied onto a reflective microscope slide, vacuum-dried, and analyzed by infrared microspectroscopy. The infrared spectra (900 to 1,800 cm(-1)) produced specific absorption profiles that allowed for discrimination among different cheese samples. Cheeses manufactured with adjunct cultures showed more uniform and consistent spectral profiles, leading to the formation of tight clusters by pattern-recognition analysis (soft independent modeling of class analogy) as compared with cheeses with no adjuncts, which exhibited more spectral variability among replicated samples. In addition, the soft independent modeling of class analogy discriminating power indicated that cheeses were differentiated predominantly based on the band at 1,122 cm(-1), which was associated with S-O vibrations. The greatest changes in the chemical profile of each cheese occurred between d 6 and 30 of warm-room ripening. The band at 1,412 cm(-1), which was associated with acidic AA, had the greatest contribution to differentiation, indicating substantial changes in levels of proteolysis during warm-room ripening in addition to propionic

  17. Operational modal analysis approach based on multivariable transmissibility with different transferring outputs

    NASA Astrophysics Data System (ADS)

    Gómez Araújo, Iván; Laier, Jose Elias

    2015-09-01

    In recent years, transmissibility functions have been used as alternatives to identify the modal parameters of structures under operating conditions. The scalar power spectrum density transmissibility (PSDT), which relates only two responses, was proposed to extract modal parameters by combining different PSDTs with different transferring outputs. In this sense, this paper proposes extending the scalar PSDT concept to multivariable PSDT by relating multiple responses instead of only two. This extension implies the definition of a transmissibility matrix, relating the cross-spectral density matrix among the responses at coordinates Z and U with the cross-spectral density matrix among the responses at coordinates Z and K. The coordinates in Z are known as the transferring outputs. By defining the same coordinates K and U, but with different transferring outputs Z, we prove that the multivariable PSDT converges to the same matrix when it approaches the system poles. This property is used to define only one matrix with different multivariable PSDTs with same coordinates K and U, but with different transferring outputs. The resulting matrix is singular at the system poles, meaning that by applying the inverse of the matrix, the modal parameters can be identified. Here, a numeric example of a beam model subjected to excitations and data from an operational vibration bridge test shows that the proposed method is capable of identifying modal parameters. Furthermore, the results demonstrate the possibility of estimating the same modal parameters by changing only the coordinates K and U, providing greater reliability during modal parameter identification.

  18. Role of clinical, radiological, and neurophysiological changes in predicting the outcome of tuberculous meningitis: a multivariable analysis

    PubMed Central

    Misra, U; Kalita, J; Roy, A; Mandal, S; Srivastava, M

    2000-01-01

    OBJECTIVES—The role of EEG and evoked potentials has not been evaluated in predicting the prognosis of tuberculous (TB) meningitis. The present study was aimed at evaluating the prognostic significance of clinical, radiological, and neurophysiological variables using multi-variable analysis.
METHODS—Patients with TB meningitis diagnosed on the basis of clinical, radiological, and CSF criteria have been prospectively evaluated. All the patients were subjected to a detailed neurological evaluation. The outcome was defined 6 months after starting treatment on the basis of the Barthel index (BI) score into poor (BI <12) and good recovery (BI⩾12). Death was included in the poor recovery group for statistical analysis. Thirteen clinical (age, sex, seizure, focal weakness, stage of meningitis, Glasgow coma scale score, methyl prednisolone therapy), CT (infarction, hydrocephalus, tuberculoma) and neurophysiological (EEG, motor and somatosensory evoked potentials) variables were evaluated employing single variable logistic regression followed by multivariable logistic regression analysis. The best set of predictors were obtained by stepdown logistic regression analysis.
RESULTS—Fifty four patients were included in the present study. Their age ranged between 5 and 62 years, 11 were children younger than 12 years and 14 were female. Nine patients were in stage I meningitis, 12 in stage II, and 33 in stage III. On single variable logistic regression analysis the significant predictors of 6 months outcome of TB meningitis included focal weakness, Glasgow coma scale (GCS), motor evoked potential (MEP) and somatosensory evoked potential (SEP). On multivariable analysis the best set of predictors comprised focal weakness, GCS, and SEP.
CONCLUSIONS—In patients with TB meningitis focal weakness, GCS, and SEP are the best predictors of 6 month outcome.

 PMID:10675210

  19. Placebo group improvement in trials of pharmacotherapies for alcohol use disorders: A multivariate meta-analysis examining change over time

    PubMed Central

    Del Re, AC; Maisel, Natalya; Blodgett, Janet; Wilbourne, Paula; Finney, John

    2014-01-01

    Objective Placebo group improvement in pharmacotherapy trials has been increasing over time across several pharmacological treatment areas. However, it is unknown to what degree increasing improvement has occurred in pharmacotherapy trials for alcohol use disorders or what factors may account for placebo group improvement. This meta-analysis of 47 alcohol pharmacotherapy trials evaluated (1) the magnitude of placebo group improvement, (2) the extent to which placebo group improvement has been increasing over time, and (3) several potential moderators that might account for variation in placebo group improvement. Method Random-effects univariate and multivariate analyses were conducted that examined the magnitude of placebo group improvement in the 47 studies and several potential moderators of improvement: (a) publication year, (b) country in which the study was conducted, (c) outcome data source/type, (d) number of placebo administrations, (e) overall severity of study participants, and (f) additional psychosocial treatment. Results Substantial placebo group improvement was found overall and improvement was larger in more recent studies. Greater improvement was found on moderately subjective outcomes, with more frequent administrations of the placebo, and in studies with greater participant severity of illness. However, even after controlling for these moderators, placebo group improvement remained significant, as did placebo group improvement over time. Conclusion Similar to previous pharmacotherapy placebo research, substantial pre- to post-test placebo group improvement has occurred in alcohol pharmacotherapy trials, an effect that has been increasing over time. However, several plausible moderator variables were not able to explain why placebo group improvement has been increasing over time. PMID:23857312

  20. Inferring Instantaneous, Multivariate and Nonlinear Sensitivities for the Analysis of Feedback Processes in a Dynamical System: Lorenz Model Case Study

    NASA Technical Reports Server (NTRS)

    Aires, Filipe; Rossow, William B.; Hansen, James E. (Technical Monitor)

    2001-01-01

    A new approach is presented for the analysis of feedback processes in a nonlinear dynamical system by observing its variations. The new methodology consists of statistical estimates of the sensitivities between all pairs of variables in the system based on a neural network modeling of the dynamical system. The model can then be used to estimate the instantaneous, multivariate and nonlinear sensitivities, which are shown to be essential for the analysis of the feedbacks processes involved in the dynamical system. The method is described and tested on synthetic data from the low-order Lorenz circulation model where the correct sensitivities can be evaluated analytically.

  1. Additive interaction in survival analysis: use of the additive hazards model.

    PubMed

    Rod, Naja Hulvej; Lange, Theis; Andersen, Ingelise; Marott, Jacob Louis; Diderichsen, Finn

    2012-09-01

    It is a widely held belief in public health and clinical decision-making that interventions or preventive strategies should be aimed at patients or population subgroups where most cases could potentially be prevented. To identify such subgroups, deviation from additivity of absolute effects is the relevant measure of interest. Multiplicative survival models, such as the Cox proportional hazards model, are often used to estimate the association between exposure and risk of disease in prospective studies. In Cox models, deviations from additivity have usually been assessed by surrogate measures of additive interaction derived from multiplicative models-an approach that is both counter-intuitive and sometimes invalid. This paper presents a straightforward and intuitive way of assessing deviation from additivity of effects in survival analysis by use of the additive hazards model. The model directly estimates the absolute size of the deviation from additivity and provides confidence intervals. In addition, the model can accommodate both continuous and categorical exposures and models both exposures and potential confounders on the same underlying scale. To illustrate the approach, we present an empirical example of interaction between education and smoking on risk of lung cancer. We argue that deviations from additivity of effects are important for public health interventions and clinical decision-making, and such estimations should be encouraged in prospective studies on health. A detailed implementation guide of the additive hazards model is provided in the appendix.

  2. Multivariate analysis of reflectance spectra from propolis: geographical variation in Romanian samples.

    PubMed

    Moţ, Augustin Cătălin; Soponar, Florin; Sârbu, Costel

    2010-05-15

    The present study described reflectance spectroscopy as a suitable analytical tool to discriminate the floral origin of 39 Romanian propolis samples. Relevant differences between the UV-vis reflectance spectra of the investigated propolis samples within the 220-850nm spectral range were found. The results obtained applying cluster analysis, principal component analysis and linear discriminant analysis to the digitized data of zero order, zero order normalized and first order derivative spectra support the reliability of this technique. In addition, the application of the linear discriminant analysis to the score matrices corresponding to the first principal components appeared to be an illuminating solution. Generally, the samples have been assigned to two large groups in a good agreement with their vegetal sampling location, samples originating from predominant forest area and samples originating from meadows. Within the first group, two subgroups were identified according to the dominant type of the forest, deciduous or resinous, while within the last group three subgroups were found according to the extend and variety of the meadow.

  3. Multivariate Statistical Analysis: a tool for groundwater quality assessment in the hidrogeologic region of the Ring of Cenotes, Yucatan, Mexico.

    NASA Astrophysics Data System (ADS)

    Ye, M.; Pacheco Castro, R. B.; Pacheco Avila, J.; Cabrera Sansores, A.

    2014-12-01

    The karstic aquifer of Yucatan is a vulnerable and complex system. The first fifteen meters of this aquifer have been polluted, due to this the protection of this resource is important because is the only source of potable water of the entire State. Through the assessment of groundwater quality we can gain some knowledge about the main processes governing water chemistry as well as spatial patterns which are important to establish protection zones. In this work multivariate statistical techniques are used to assess the groundwater quality of the supply wells (30 to 40 meters deep) in the hidrogeologic region of the Ring of Cenotes, located in Yucatan, Mexico. Cluster analysis and principal component analysis are applied in groundwater chemistry data of the study area. Results of principal component analysis show that the main sources of variation in the data are due sea water intrusion and the interaction of the water with the carbonate rocks of the system and some pollution processes. The cluster analysis shows that the data can be divided in four clusters. The spatial distribution of the clusters seems to be random, but is consistent with sea water intrusion and pollution with nitrates. The overall results show that multivariate statistical analysis can be successfully applied in the groundwater quality assessment of this karstic aquifer.

  4. A Bayesian approach to joint analysis of multivariate longitudinal data and parametric accelerated failure time

    PubMed Central

    Luo, Sheng

    2013-01-01

    Impairment caused by Parkinson’s disease (PD) is multidimensional (e.g., sensoria, functions, and cognition) and progressive. Its multidimensional nature precludes a single outcome to measure disease progression. Clinical trials of PD use multiple categorical and continuous longitudinal outcomes to assess the treatment effects on overall improvement. A terminal event such as death or dropout can stop the follow-up process. Moreover, the time to the terminal event may be dependent on the multivariate longitudinal measurements. In this article, we consider a joint random-effects model for the correlated outcomes. A multilevel item response theory model is used for the multivariate longitudinal outcomes and a parametric accelerated failure time model is used for the failure time because of the violation of proportional hazard assumption. These two models are linked via random effects. The Bayesian inference via MCMC is implemented in ‘ BUGS’ language. Our proposed method is evaluated by a simulation study and is applied to DATATOP study, a motivating clinical trial to determine if deprenyl slows the progression of PD. PMID:24009073

  5. A Bayesian approach to joint analysis of multivariate longitudinal data and parametric accelerated failure time.

    PubMed

    Luo, Sheng

    2014-02-20

    Impairment caused by Parkinson's disease (PD) is multidimensional (e.g., sensoria, functions, and cognition) and progressive. Its multidimensional nature precludes a single outcome to measure disease progression. Clinical trials of PD use multiple categorical and continuous longitudinal outcomes to assess the treatment effects on overall improvement. A terminal event such as death or dropout can stop the follow-up process. Moreover, the time to the terminal event may be dependent on the multivariate longitudinal measurements. In this article, we consider a joint random-effects model for the correlated outcomes. A multilevel item response theory model is used for the multivariate longitudinal outcomes and a parametric accelerated failure time model is used for the failure time because of the violation of proportional hazard assumption. These two models are linked via random effects. The Bayesian inference via MCMC is implemented in 'BUGS' language. Our proposed method is evaluated by a simulation study and is applied to DATATOP study, a motivating clinical trial to determine if deprenyl slows the progression of PD. PMID:24009073

  6. Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.

    PubMed

    Davatzikos, Christos

    2016-10-01

    The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. PMID:27514582

  7. Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.

    PubMed

    Davatzikos, Christos

    2016-10-01

    The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges.

  8. Interpreting support vector machine models for multivariate group wise analysis in neuroimaging.

    PubMed

    Gaonkar, Bilwaj; T Shinohara, Russell; Davatzikos, Christos

    2015-08-01

    Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier's decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification.

  9. Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

    PubMed Central

    Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

    2015-01-01

    Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

  10. Multivariate analysis of the sexual dimorphism of the hip bone in a modern human population and in early hominids.

    PubMed

    Arsuaga, J L; Carretero, J M

    1994-02-01

    A large sample of hip bones of known sex coming from one modern population is studied morphologically and by multivariate analysis to investigate sexual dimorphism patterns. A principal component analysis of raw data shows that a large amount of the hip bone sexual dimorphism is accounted for by size differences, but that sex-linked shape variation is also very conspicuous and cannot be considered an allometric consequence of differences in body size between the sexes. The PCA of transformed ("shape") variables indicates that the female hip bones are different in those traits associated with a relatively larger pelvic inlet (longer pubic bones, a greater degree of curvature of the iliopectineal line, and more posterior position of the auricular surface), as well as a broader sciatic notch. The analysis of nonmetric traits also shows marked sexual dimorphism in the position of the sacroiliac joint in the iliac bone, in the shape of the sciatic notch, in pubic morphology, and in the presence of the pre-auricular sulcus in females. When the australopithecine AL 288-1 and Sts 14 hip bones are included in the multivariate analysis, they appear as "ultra-females." In particular these early hominids exhibit extraordinarily long pubic bones and iliopectineal lines, which cannot be explained by allometry. PMID:8147439

  11. Discrimination of Wild Paris Based on Near Infrared Spectroscopy and High Performance Liquid Chromatography Combined with Multivariate Analysis

    PubMed Central

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

    2014-01-01

    Different geographical origins and species of Paris obtained from southwestern China were discriminated by near infrared (NIR) spectroscopy and high performance liquid chromatography (HPLC) combined with multivariate analysis. The NIR parameter settings were scanning (64 times), resolution (4 cm−1), scanning range (10000 cm−1∼4000 cm−1) and parallel collection (3 times). NIR spectrum was optimized by TQ 8.6 software, and the ranges 7455∼6852 cm−1 and 5973∼4007 cm−1 were selected according to the spectrum standard deviation. The contents of polyphyllin I, polyphyllin II, polyphyllin VI, and polyphyllin VII and total steroid saponins were detected by HPLC. The contents of chemical components data matrix and spectrum data matrix were integrated and analyzed by partial least squares discriminant analysis (PLS-DA). From the PLS-DA model of NIR spectrum, Paris samples were separated into three groups according to the different geographical origins. The R2X and Q2Y described accumulative contribution rates were 99.50% and 94.03% of the total variance, respectively. The PLS-DA model according to 12 species of Paris described 99.62% of the variation in X and predicted 95.23% in Y. The results of the contents of chemical components described differences among collections quantitatively. A multivariate statistical model of PLS-DA showed geographical origins of Paris had a much greater influence on Paris compared with species. NIR and HPLC combined with multivariate analysis could discriminate different geographical origins and different species. The quality of Paris showed regional dependence. PMID:24558477

  12. The source identification of ambient aerosols in Beijing, China by multivariate analysis coupled with {sup 14}C tracer

    SciTech Connect

    Xiaoyan Tang; Min Shao; Yuanhang Zhang

    1996-12-31

    Ambient aerosol is one of most important pollutants in China. This paper showed the results of aerosol sources of Beijing area revealed by combination of multivariate analysis models and 14C tracer measured on Accelerator Mass Spectrometry (AMS). The results indicated that the mass concentration of particulate (<100 (M)) didn`t increase rapidly, compared with economic development in Beijing city. The multivariate analysis showed that the predominant source was soil dust which contributed more than 50% to atmospheric particles. However, it would be a risk to conclude that the aerosol pollution from anthropogenic sources was less important in Beijing city based on above phenomenon. Due to lack of reliable tracers, it was very hard to distinguish coal burning from soil source. Thus, it was suspected that the soil source above might be the mixture of soil dust and coal burning. The 14C measurement showed that carbonaceous species of aerosol had quite different emission sources. For carbonaceous aerosols in Beijing, the contribution from fossil fuel to ambient particles was nearly 2/3, as the man-made activities ( coal-burning, etc.) increased, the fossil part would contribute more to atmospheric carbonaceous particles. For example, in downtown Beijing at space-heating seasons, the fossil fuel even contributed more than 95% to carbonaceous particles, which would be potential harmful to population. By using multivariate analysis together with 14C data, two important sources of aerosols in Beijing (soil and coal) combustion were more reliably distinguished, which was critical important for the assessment of aerosol problem in China.

  13. Forensic analysis of Salvia divinorum using multivariate statistical procedures. Part I: discrimination from related Salvia species.

    PubMed

    Willard, Melissa A Bodnar; McGuffin, Victoria L; Smith, Ruth Waddell

    2012-01-01

    Salvia divinorum is a hallucinogenic herb that is internationally regulated. In this study, salvinorin A, the active compound in S. divinorum, was extracted from S. divinorum plant leaves using a 5-min extraction with dichloromethane. Four additional Salvia species (Salvia officinalis, Salvia guaranitica, Salvia splendens, and Salvia nemorosa) were extracted using this procedure, and all extracts were analyzed by gas chromatography-mass spectrometry. Differentiation of S. divinorum from other Salvia species was successful based on visual assessment of the resulting chromatograms. To provide a more objective comparison, the total ion chromatograms (TICs) were subjected to principal components analysis (PCA). Prior to PCA, the TICs were subjected to a series of data pretreatment procedures to minimize non-chemical sources of variance in the data set. Successful discrimination of S. divinorum from the other four Salvia species was possible based on visual assessment of the PCA scores plot. To provide a numerical assessment of the discrimination, a series of statistical procedures such as Euclidean distance measurement, hierarchical cluster analysis, Student's t tests, Wilcoxon rank-sum tests, and Pearson product moment correlation were also applied to the PCA scores. The statistical procedures were then compared to determine the advantages and disadvantages for forensic applications.

  14. Catchments Classification: Multivariate Statistical Analysis for Physiographic Similarity in the Niger Basin

    NASA Astrophysics Data System (ADS)

    Chaibou Begou, Jamilatou; Jomaa, Seifeddine; Benabdallah, Sihem; Bazie, Pibgnina; Afouda, Abel; Rode, Michael

    2016-04-01

    The objective of this study was to determine physiographic similarity, as indicator of hydrologic similarity between catchments located in the Bani basin, and to derive the dominant factors controlling each group singularity. We utilized a dataset of 28 catchments described by 16 physical and climatic properties distributed across a wide region with strong environmental gradients. Catchments attributes were first standardized before they underwent an integrated exploratory data analysis composed by Principal Component Analysis (PCA) followed by Hierarchical Clustering. Results showed a clear distribution into 3 major clusters. Two of them were well separated and partitioned into northerly flat and semi-arid catchments, and southerly hilly and humid catchments. This nomenclature came from the interpretation of the main factors, topography, precipitation and latitude, which seem to control the most important variability inside these clusters. Moreover, the group of northerly catchments was designated to be dominated by agricultural land use and ferric luvisols soil type, two additional drivers of similarity. The third cluster was located in the center of the study basin, inside which, none of the descriptors seems to exert a strong control on the similarity. The outcome of this study can help understanding catchment functioning and provide a support for a regionalization of hydrological information.

  15. Place as a predictor of health insurance coverage: A multivariate analysis of counties in the United States.

    PubMed

    Stone, Lisa Cacari; Boursaw, Blake; Bettez, Sonia P; Larzelere Marley, Tennille; Waitzkin, Howard

    2015-07-01

    This study assessed the importance of county characteristics in explaining county-level variations in health insurance coverage. Using public databases from 2008 to 2012, we studied 3112 counties in the United States. Rates of uninsurance ranged widely from 3% to 53%. Multivariate analysis suggested that poverty, unemployment, Republican voting, and percentages of Hispanic and American Indian/Alaskan Native residents in a county were significant predictors of uninsurance rates. The associations between uninsurance rates and both race/ethnicity and poverty varied significantly between metropolitan and non-metropolitan counties. Collaborative actions by the federal, tribal, state, and county governments are needed to promote coverage and access to care. PMID:26086690

  16. Multivariate statistical analysis of Raman spectra to distinguish normal, tumor, lymph nodes and mastitis in mouse mammary tissues

    NASA Astrophysics Data System (ADS)

    Dai, H.; Thakur, J. S.; Serhatkulu, G. K.; Pandya, A. K.; Auner, G. W.; Naik, R.; Freeman, D. C.; Naik, V. M.; Cao, A.; Klein, M. D.; Rabah, R.

    2006-03-01

    Raman spectra ( > 680) of normal mammary gland, malignant mammary gland tumors, and lymph node tissues from mice injected with 4T1 tumor cells have been recorded using 785 nm excitation laser. The state of the tissues was confirmed by standard pathological tests. The multivariate statistical analysis methods (principle component analysis and discriminant functional analysis) have been used to categorize the Raman spectra. The statistical algorithms based on the Raman spectral peak heights, clearly separated tissues into six distinct classes, including mastitis, which is clearly separated from normal and tumor. This study suggests that the Raman spectroscopy can possibly perform a real-time analysis of the human mammary tissues for the detection of cancer.

  17. Determination of the main solid-state form of albendazole in bulk drug, employing Raman spectroscopy coupled to multivariate analysis.

    PubMed

    Calvo, Natalia L; Arias, Juan M; Altabef, Aída Ben; Maggio, Rubén M; Kaufman, Teodoro S

    2016-09-10

    Albendazole (ALB) is a broad-spectrum anthelmintic, which exhibits two solid-state forms (Forms I and II). The Form I is the metastable crystal at room temperature, while Form II is the stable one. Because the drug has poor aqueous solubility and Form II is less soluble than Form I, it is desirable to have a method to assess the solid-state form of the drug employed for manufacturing purposes. Therefore, a Partial Least Squares (PLS) model was developed for the determination of Form I of ALB in its mixtures with Form II. For model development, both solid-state forms of ALB were prepared and characterized by microscopic (optical and with normal and polarized light), thermal (DSC) and spectroscopic (ATR-FTIR, Raman) techniques. Mixtures of solids in different ratios were prepared by weighing and mechanical mixing of the components. Their Raman spectra were acquired, and subjected to peak smoothing, normalization, standard normal variate correction and de-trending, before performing the PLS calculations. The optimal spectral region (1396-1280cm(-1)) and number of latent variables (LV=3) were obtained employing a moving window of variable size strategy. The method was internally validated by means of the leave one out procedure, providing satisfactory statistics (r(2)=0.9729 and RMSD=5.6%) and figures of merit (LOD=9.4% and MDDC=1.4). Furthermore, the method's performance was also evaluated by analysis of two validation sets. Validation set I was used for assessment of linearity and range and Validation set II, to demonstrate accuracy and precision (Recovery=101.4% and RSD=2.8%). Additionally, a third set of spiked commercial samples was evaluated, exhibiting excellent recoveries (94.2±6.4%). The results suggest that the combination of Raman spectroscopy with multivariate analysis could be applied to the assessment of the main crystal form and its quantitation in samples of ALB bulk drug, in the routine quality control laboratory. PMID:27429368

  18. Applying multivariate analysis as decision tool for evaluating sediment-specific remediation strategies.

    PubMed

    Pedersen, Kristine B; Lejon, Tore; Jensen, Pernille E; Ottosen, Lisbeth M

    2016-05-01

    Multivariate methodology was employed for finding optimum remediation conditions for electrodialytic remediation of harbour sediment from an Arctic location in Norway. The parts of the experimental domain in which both sediment- and technology-specific remediation objectives were met were identified. Objectives targeted were removal of the sediment-specific pollutants Cu and Pb, while minimising the effect on the sediment matrix by limiting the removal of naturally occurring metals while maintaining low energy consumption. Two different cell designs for electrochemical remediation were tested and final concentrations of Cu and Pb were below background levels in large parts of the experimental domain when operating at low current densities (<0.12 mA/cm(2)). However, energy consumption, remediation times and the effect on naturally occurring metals were different for the 2- and 3-compartment cells. PMID:26928331

  19. MIDAS, prototype Multivariate Interactive Digital Analysis System, Phase 1. Volume 2: Diagnostic system

    NASA Technical Reports Server (NTRS)

    Kriegler, F. J.; Christenson, D.; Gordon, M.; Kistler, R.; Lampert, S.; Marshall, R.; Mclaughlin, R.

    1974-01-01

    The MIDAS System is a third-generation, fast, multispectral recognition system able to keep pace with the large quantity and high rates of data acquisition from present and projected sensors. A principal objective of the MIDAS Program is to provide a system well interfaced with the human operator and thus to obtain large overall reductions in turn-around time and significant gains in throughout. The hardware and software generated in Phase I of the over-all program are described. The system contains a mini-computer to control the various high-speed processing elements in the data path and a classifier which implements an all-digital prototype multivariate-Gaussian maximum likelihood decision algorithm operating 2 x 105 pixels/sec. Sufficient hardware was developed to perform signature extraction from computer-compatible tapes, compute classifier coefficients, control the classifier operation, and diagnose operation. Diagnostic programs used to test MIDAS' operations are presented.

  20. Quantitative vibrational imaging by hyperspectral stimulated Raman scattering microscopy and multivariate curve resolution analysis.

    PubMed

    Zhang, Delong; Wang, Ping; Slipchenko, Mikhail N; Ben-Amotz, Dor; Weiner, Andrew M; Cheng, Ji-Xin

    2013-01-01

    Spectroscopic imaging has been an increasingly critical approach for unveiling specific molecules in biological environments. Toward this goal, we demonstrate hyperspectral stimulated Raman loss (SRL) imaging by intrapulse spectral scanning through a femtosecond pulse shaper. The hyperspectral stack of SRL images is further analyzed by a multivariate curve resolution (MCR) method to reconstruct quantitative concentration images for each individual component and retrieve the corresponding vibrational Raman spectra. Using these methods, we demonstrate quantitative mapping of dimethyl sulfoxide concentration in aqueous solutions and in fat tissue. Moreover, MCR is performed on SRL images of breast cancer cells to generate maps of principal chemical components along with their respective vibrational spectra. These results show the great capability and potential of hyperspectral SRL microscopy for quantitative imaging of complicated biomolecule mixtures through resolving overlapped Raman bands.

  1. Conformational study of arbutin by quantum chemical calculations and multivariate analysis

    NASA Astrophysics Data System (ADS)

    Araujo-Andrade, Cuauhtémoc; Lopes, Susy; Fausto, Rui; Gómez-Zavaglia, Andrea

    2010-06-01

    A conformational study of the molecule of arbutin (4-hydroxyphenyl-β- D-glucopyranoside) has been undertaken. The molecule is composed by a glucopyranoside moiety bound to a phenol ring. It has eight conformationally relevant dihedral angles, five of them related with the orientation of the hydroxyl groups and the remaining three taking part in the skeleton of the molecule. A systematic search on the conformational space of arbutin was performed using molecular orbital methods, followed by the identification of structural similarities between the different conformers, using multivariate analyses. This approach allowed the grouping of conformers according to their structural affinity and the establishment of correlations between their structures and several properties. Intramolecular interactions involving OH groups were also investigated and correlations between spectroscopic, structural and thermodynamic properties established. The developed strategy might be useful to investigate the structure and structure/properties correlations in other conformationally flexible molecules.

  2. Analysis of fatty acid composition of sea cucumber Apostichopus japonicus using multivariate statistics

    NASA Astrophysics Data System (ADS)

    Xu, Qinzeng; Gao, Fei; Xu, Qiang; Yang, Hongsheng

    2014-11-01

    Fatty acids (FAs) provide energy and also can be used to trace trophic relationships among organisms. Sea cucumber Apostichopus japonicus goes into a state of aestivation during warm summer months. We examined fatty acid profiles in aestivated and non-aestivated A. japonicus using multivariate analyses (PERMANOVA, MDS, ANOSIM, and SIMPER). The results indicate that the fatty acid profiles of aestivated and non-aestivated sea cucumbers differed significantly. The FAs that were produced by bacteria and brown kelp contributed the most to the differences in the fatty acid composition of aestivated and nonaestivated sea cucumbers. Aestivated sea cucumbers may synthesize FAs from heterotrophic bacteria during early aestivation, and long chain FAs such as eicosapentaenoic (EPA) and docosahexaenoic acid (DHA) that produced from intestinal degradation, are digested during deep aestivation. Specific changes in the fatty acid composition of A. japonicus during aestivation needs more detailed study in the future.

  3. MIDAS, prototype Multivariate Interactive Digital Analysis System, phase 1. Volume 1: System description

    NASA Technical Reports Server (NTRS)

    Kriegler, F. J.

    1974-01-01

    The MIDAS System is described as a third-generation fast multispectral recognition system able to keep pace with the large quantity and high rates of data acquisition from present and projected sensors. A principal objective of the MIDAS program is to provide a system well interfaced with the human operator and thus to obtain large overall reductions in turnaround time and significant gains in throughput. The hardware and software are described. The system contains a mini-computer to control the various high-speed processing elements in the data path, and a classifier which implements an all-digital prototype multivariate-Gaussian maximum likelihood decision algorithm operating at 200,000 pixels/sec. Sufficient hardware was developed to perform signature extraction from computer-compatible tapes, compute classifier coefficients, control the classifier operation, and diagnose operation.

  4. MIDAS, prototype Multivariate Interactive Digital Analysis System, phase 1. Volume 3: Wiring diagrams

    NASA Technical Reports Server (NTRS)

    Kriegler, F. J.; Christenson, D.; Gordon, M.; Kistler, R.; Lampert, S.; Marshall, R.; Mclaughlin, R.

    1974-01-01

    The Midas System is a third-generation, fast, multispectral recognition system able to keep pace with the large quantity and high rates of data acquisition from present and projected sensors. A principal objective of the MIDAS Program is to provide a system well interfaced with the human operator and thus to obtain large overall reductions in turn-around time and significant gains in throughput. The hardware and software generated in Phase I of the overall program are described. The system contains a mini-computer to control the various high-speed processing elements in the data path and a classifier which implements an all-digital prototype multivariate-Gaussian maximum likelihood decision algorithm operating at 2 x 100,000 pixels/sec. Sufficient hardware was developed to perform signature extraction from computer-compatible tapes, compute classifier coefficients, control the classifier operation, and diagnose operation. The MIDAS construction and wiring diagrams are given.

  5. Multivariate statistical analysis of stream sediments for mineral resources from the Craig NTMS Quadrangle, Colorado

    SciTech Connect

    Beyth, M.; McInteer, C.; Broxton, D.E.; Bolivar, S.L.; Luke, M.E.

    1980-06-01

    Multivariate statistical analyses were carried out on Hydrogeochemical and Stream Sediment Reconnaissance data from the Craig quadrangle, Colorado, to support the National Uranium Resource Evaluation and to evaluate strategic or other important commercial mineral resources. A few areas for favorable uranium mineralization are suggested for parts of the Wyoming Basin, Park Range, and Gore Range. Six potential source rocks for uranium are postulated based on factor score mapping. Vanadium in stream sediments is suggested as a pathfinder for carnotite-type mineralization. A probable northwest trend of lead-zinc-copper mineralization associated with Tertiary intrusions is suggested. A few locations are mapped where copper is associated with cobalt. Concentrations of placer sands containing rare earth elements, probably of commercial value, are indicated for parts of the Sand Wash Basin.

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

    PubMed

    Wallace, Jack; Champagne, Pascale; Hall, Geof

    2016-06-01

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

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

    PubMed

    Wallace, Jack; Champagne, Pascale; Hall, Geof

    2016-06-01

    The wastewater stabilization ponds (WSPs) at a wastewater treatment facility in eastern Ontario, Canada, have experienced excessive algae growth and high pH levels in the summer months. A full range of parameters were sampled from the system and the chemical dynamics in the three WSPs were assessed through multivariate statistical analysis. The study presents a novel approach for exploratory analysis of a comprehensive water chemistry dataset, incorporating principal components analysis (PCA) and principal components (PC) and partial least squares (PLS) regressions. The analyses showed strong correlations between chl-a and sunlight, temperature, organic matter, and nutrients, and weak and negative correlations between chl-a and pH and chl-a and DO. PCA reduced the data from 19 to 8 variables, with a good fit to the original data matrix (similarity measure of 0.73). Multivariate regressions to model system pH in terms of these key parameters were performed on the reduced variable set and the PCs generated, for which strong fits (R(2) > 0.79 with all data) were observed. The methodologies presented in this study are applicable to a wide range of natural and engineered systems where a large number of water chemistry parameters are monitored resulting in the generation of large data sets. PMID:27038585

  8. Prognostic factors in anal squamous carcinoma: a multivariate analysis of clinical, pathological and flow cytometric parameters in 235 cases.

    PubMed

    Shepherd, N A; Scholefield, J H; Love, S B; England, J; Northover, J M

    1990-06-01

    Clinical, pathological and flow cytometric parameters have been analysed by univariate and multivariate analysis to define those parameters of important prognostic influence in 235 cases of surgically treated squamous carcinoma of the anus and perianal skin. Patients had been treated by anorectal excision (166 patients) or by local excision (69). Analyses were carried out on five data sets--the two surgical subgroups, two groups distinguished by site of tumour and on all 235 patients. Univariate analysis showed many parameters to be of prognostic influence, although histological typing of tumours into the more common histological subtypes was of no prognostic value. Parameters of independent prognostic significance in multivariate analysis were those indicating depth of spread, inguinal lymph node involvement and DNA-ploidy. In this study the subdivision of the rarer types of anal canal tumour, such as mucoepidermoid carcinoma, microcystic squamous carcinoma and small cell anaplastic carcinoma, was relevant confirming that these tumours have a poor prognosis. It is now felt that surgery should not be employed as primary treatment in most cases of anal cancer and the results of this study have to be interpreted with caution when applied to patients treated with radiotherapy with or without chemotherapy. Nevertheless, our findings suggest that the most useful prognostic information can be gleaned from accurate clinical staging and an assessment of DNA-ploidy status. PMID:2376397

  9. Application of principal component analysis-multivariate adaptive regression splines for the simultaneous spectrofluorimetric determination of dialkyltins in micellar media

    NASA Astrophysics Data System (ADS)

    Ghasemi, Jahan B.; Zolfonoun, Ehsan

    2013-11-01

    A new multicomponent analysis method, based on principal component analysis-multivariate adaptive regression splines (PC-MARS) is proposed for the determination of dialkyltin compounds. In Tween-20 micellar media, dimethyl and dibutyltin react with morin to give fluorescent complexes with the maximum emission peaks at 527 and 520 nm, respectively. The spectrofluorimetric matrix data, before building the MARS models, were subjected to principal component analysis and decomposed to PC scores as starting points for the MARS algorithm. The algorithm classifies the calibration data into several groups, in each a regression line or hyperplane is fitted. Performances of the proposed methods were tested in term of root mean square errors of prediction (RMSEP), using synthetic solutions. The results show the strong potential of PC-MARS, as a multivariate calibration method, to be applied to spectral data for multicomponent determinations. The effect of different experimental parameters on the performance of the method were studied and discussed. The prediction capability of the proposed method compared with GC-MS method for determination of dimethyltin and/or dibutyltin.

  10. NIR and Py-mbms coupled with multivariate data analysis as a high-throughput biomass characterization technique: a review

    PubMed Central

    Xiao, Li; Wei, Hui; Himmel, Michael E.; Jameel, Hasan; Kelley, Stephen S.

    2014-01-01

    Optimizing the use of lignocellulosic biomass as the feedstock for renewable energy production is currently being developed globally. Biomass is a complex mixture of cellulose, hemicelluloses, lignins, extractives, and proteins; as well as inorganic salts. Cell wall compositional analysis for biomass characterization is laborious and time consuming. In order to characterize biomass fast and efficiently, several high through-put technologies have been successfully developed. Among them, near infrared spectroscopy (NIR) and pyrolysis-molecular beam mass spectrometry (Py-mbms) are complementary tools and capable of evaluating a large number of raw or modified biomass in a short period of time. NIR shows vibrations associated with specific chemical structures whereas Py-mbms depicts the full range of fragments from the decomposition of biomass. Both NIR vibrations and Py-mbms peaks are assigned to possible chemical functional groups and molecular structures. They provide complementary information of chemical insight of biomaterials. However, it is challenging to interpret the informative results because of the large amount of overlapping bands or decomposition fragments contained in the spectra. In order to improve the efficiency of data analysis, multivariate analysis tools have been adapted to define the significant correlations among data variables, so that the large number of bands/peaks could be replaced by a small number of reconstructed variables representing original variation. Reconstructed data variables are used for sample comparison (principal component analysis) and for building regression models (partial least square regression) between biomass chemical structures and properties of interests. In this review, the important biomass chemical structures measured by NIR and Py-mbms are summarized. The advantages and disadvantages of conventional data analysis methods and multivariate data analysis methods are introduced, compared and evaluated. This review

  11. Quantitative analysis of essential oils in perfume using multivariate curve resolution combined with comprehensive two-dimensional gas chromatography.

    PubMed

    de Godoy, Luiz Antonio Fonseca; Hantao, Leandro Wang; Pedroso, Marcio Pozzobon; Poppi, Ronei Jesus; Augusto, Fabio

    2011-08-01

    The use of multivariate curve resolution (MCR) to build multivariate quantitative models using data obtained from comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) is presented and evaluated. The MCR algorithm presents some important features, such as second order advantage and the recovery of the instrumental response for each pure component after optimization by an alternating least squares (ALS) procedure. A model to quantify the essential oil of rosemary was built using a calibration set containing only known concentrations of the essential oil and cereal alcohol as solvent. A calibration curve correlating the concentration of the essential oil of rosemary and the instrumental response obtained from the MCR-ALS algorithm was obtained, and this calibration model was applied to predict the concentration of the oil in complex samples (mixtures of the essential oil, pineapple essence and commercial perfume). The values of the root mean square error of prediction (RMSEP) and of the root mean square error of the percentage deviation (RMSPD) obtained were 0.4% (v/v) and 7.2%, respectively. Additionally, a second model was built and used to evaluate the accuracy of the method. A model to quantify the essential oil of lemon grass was built and its concentration was predicted in the validation set and real perfume samples. The RMSEP and RMSPD obtained were 0.5% (v/v) and 6.9%, respectively, and the concentration of the essential oil of lemon grass in perfume agreed to the value informed by the manufacturer. The result indicates that the MCR algorithm is adequate to resolve the target chromatogram from the complex sample and to build multivariate models of GC×GC-FID data.

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

    PubMed Central

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

    2015-01-01

    Worldwide growth and performance-enhancing substances are used in cattle husbandry to increase productivity. In certain countries however e.g., in the EU, these practices are forbidden to prevent the consumers from potential health risks of substance residues in food. To maximize economic profit, ‘black sheep‘ among farmers might circumvent the detection methods used in routine controls, which highlights the need for an innovative and reliable detection method. Transcriptomics is a promising new approach in the discovery of veterinary medicine biomarkers and also a missing puzzle piece, as up to date, metabolomics and proteomics are paramount. Due to increased stability and easy sampling, circulating extracellular small RNAs (smexRNAs) in bovine plasma were small RNA-sequenced and their potential to serve as biomarker candidates was evaluated using multivariate data analysis tools. After running the data evaluation pipeline, the proportion of miRNAs (microRNAs) and piRNAs (PIWI-interacting small non-coding RNAs) on the total sequenced reads was calculated. Additionally, top 10 signatures were compared which revealed that the readcount data sets were highly affected by the most abundant miRNA and piRNA profiles. To evaluate the discriminative power of multivariate data analyses to identify animals after veterinary drug application on the basis of smexRNAs, OPLS-DA was performed. In summary, the quality of miRNA models using all mapped reads for both treatment groups (animals treated with steroid hormones or the β-agonist clenbuterol) is predominant to those generated with combined data sets or piRNAs alone. Using multivariate projection methodologies like OPLS-DA have proven the best potential to generate discriminative miRNA models, supported by small RNA-Seq data. Based on the presented comparative OPLS-DA, miRNAs are the favorable smexRNA biomarker candidates in the research field of veterinary drug abuse. PMID:27077039

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

    PubMed

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

    2015-09-01

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

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

    PubMed

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

    2016-01-15

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

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

  16. Near infrared spectroscopy, cluster and multivariate analysis hyphenated to thin layer chromatography for the analysis of amino acids.

    PubMed

    Heigl, N; Huck, C W; Rainer, M; Najam-Ul-Haq, M; Bonn, G K

    2006-07-01

    A method based on near-infrared spectroscopy (NIRS) was developed for the rapid and non-destructive determination and quantification of solid and dissolved amino acids. The statistical results obtained after optimisation of measurement conditions were evaluated on the basis of statistical parameters, Q-value (quality of calibrations), R(2), standard error of estimation (SEE), standard error of prediction (SEP), BIAS applying cluster and different multivariate analytical procedures. Experimental optimisation comprised the selection of the highest suitable optical thin-layer (0.5, 1.0, 1.5, 2.0, 2.5, 3.0 mm), sample temperature (10-30 degrees C), measurement option (light fibre, 0.5 mm optical thin-layer; boiling point tube; different types of cuvettes) and sample concentration in the range between 100 and 500 ppm. Applying the optimised conditions and a 115-QS Suprasil cuvette (V = 400 microl), the established qualitative model enabled to distinguish between different dissolved amino acids with a Q-value of 0.9555. Solid amino acids were investigated in the transflectance mode, allowing to differentiate them with a Q-value of 0.9155. For the qualitative and quantitative analysis of amino acids in complex matrices NIRS was established as a detection system directly onto the plate after prior separation on cellulose based thin-layer chromatography (TLC) sheets employing n-butanol, acetic acid and distilled water at a ratio of 8:4:2 (v/v/v) as an optimised mobile phase. Due to the prior separation step, the established calibration curve was found to be more stable than the one calculated from the dissolved amino acids. The found lower limit of detection was 0.01 mg/ml. Finally, this optimised TLC-NIRS method was successfully applied for the qualitative and quantitative analysis of L-lysine in apple juice. NIRS is shown not only to offer a fast, non-destructive detection tool but also to provide an easy-to-use alternative to more complicated detection methods such as

  17. Near infrared spectroscopy and multivariate analysis to evaluate wheat flour doughs leavening and bread properties.

    PubMed

    Li Vigni, Mario; Cocchi, Marina

    2013-02-18

    A mixture design of experiment approach was followed to explore formulation effects on the technological properties of wheat flours optimized for industrial bread-making purposes. Ten different flour mixtures were investigated by means of near infrared spectroscopy (NIRS) to obtain information on flour performance in a critical phase such as dough leavening. For each mixture, a laboratory-scale bread making experiment was carried out according to a standardized recipe and the leavening phase of each dough sample was monitored by means of NIRS at different times. Parallel factor analysis (PARAFAC) was used to highlight the existence of differences among the mixtures on the basis of NIR spectrum variability with respect to the leavening time. Additionally, the relationship among the 3-way NIR dataset and some parameters measured on the baked bread loaves (dimensions, volume, weight) was investigated by means of the n-way extension of partial least squares regression (nPLS), in order to evaluate product properties from its leavening step and mixture formulation. The results give better insight on the relationships among wheat flour formulation and its performance in the leavening phase and as far as some properties of the final product are concerned, thus offering a way to monitor the leavening phase and give information on its influence on the final product properties.

  18. Multivariate analysis of 200-m front crawl swimming performance in young male swimmers.

    PubMed

    Nasirzade, Alireza; Sadeghi, Heydar; Sobhkhiz, Azade; Mohammadian, Kamran; Nikouei, Ameneh; Baghaiyan, Mahdi; Fattahi, Ali

    2015-01-01

    The aim of the present study was to evaluate the biomechanical (stroke rate, stroke length, and stroke index), anthropometrical (body height, body mass, body mass index, arm span, shoulders width, thigh, leg and upper arm lengths), and muscle architectural (muscle thickness, pennation angle, and fascicle length) parameters as predictors of 200-m front crawl swimming performance in young male swimmers. Twenty-two county level male swimmers (mean ±SD: age: 14.52 ± 0.77 years; body height: 173 ± 5 m; body mass: 60.5 ± 5.7 kg) performed a 200-m front crawl swimming test in a 25-m pool. Stepwise regression analysis revealed that biomechanical parameters (87%) characterized best 200-m front crawl swimming performance, followed by anthropometrical (82%) and muscle architectural (72%) parameters. Also, stroke length (R2 = 0.623), body height (R2 = 0.541), fascicle length of Triceps Brachii (R2 = 0.392) were the best single predictors that together explained 92% of the variability of the 200-m front crawl swimming performance in these swimmers. As a conclusion, with respect to higher performance prediction power of biomechanical parameters, technique should represent the core of the training program at these ages. In addition, these findings could be used for male young swimmers selection and talent identification. PMID:26686911

  19. Water O–H Stretching Raman Signature for Strong Acid Monitoring via Multivariate Analysis

    SciTech Connect

    Casella, Amanda J.; Levitskaia, Tatiana G.; Peterson, James M.; Bryan, Samuel A.

    2013-04-16

    Spectroscopic techniques have been applied extensively for quantification and analysis of solution compositions. In addition to static measurements, these techniques have been implemented in flow systems providing real-time solution information. A distinct need exists for information regarding acid concentration as it affects extraction efficiency and selectivity of many separation processes. Despite of the seeming simplicity of the problem, no practical solution has been offered yet particularly for the large-scale schemes involving toxic streams such as highly radioactive nuclear wastes. Classic potentiometric technique is not amiable for on-line measurements in nuclear fuel reprocessing due to requirements of frequent calibration/maintenance and poor long-term stability in the aggressive chemical and radiation environments. In this work, the potential of using Raman spectroscopic measurements for on-line monitoring of strong acid concentration in the solutions relevant to the dissolved used fuel was investigated. The Raman water signature was monitored and recorded for nitric and hydrochloric acid solution systems of systematically varied chemical composition, ionic strength, and temperature. The generated Raman spectroscopic database was used to develop predictive chemometric models for the quantification of the acid concentration (H+), neodymium concentration (Nd3+), nitrate concentration (NO3-), density, and ionic strength. This approach was validated using a flow solvent extraction system.

  20. Element-tracing of mineral matters in Dendrobium officinale using ICP-MS and multivariate analysis.

    PubMed

    Zhu, Nannan; Han, Shen; Yang, Chunning; Qu, Jixu; Sun, Zhirong; Liu, Wenjie; Zhang, Xiaomin

    2016-01-01

    Rare studies have been performed to trace the mineral elements in Dendrobium officinale. In this study, we aim to trace the mineral elements in D. officinale collected from ten geographical locations in China. ICP-MS system was used for simultaneous determination of mineral elements. Principal component analysis was performed using the obtained data in the quantification of mineral contents. Cluster analysis was performed using the Ward's method. Several of essential microelments were detected in D. officinale, including ferrum (Fe), manganese (Mn), zinc (Zn), chromium (Cr), nickel (Ni) and vanadium (V). Among these elements, three elements (i.e. Fe, Mn and Zn) were highly and simultaneously detected in the D. officinale collected from the ten locations. The level of Ni was positively associated with that of Zn (r = 0.986, P < 0.01). The level of titanium (Ti) was positively associated with that of V (r = 0.669, P < 0.05), and negatively associated with Cr (r = -0.710, P < 0.05). In addition, the level of Mn was positively associated with that of barium (r = 0.749, P < 0.05). Further, the level of Fe was positively associated with that of Ni (r = 0.664, P < 0.05), Zn (r = 0.742, P < 0.05), and rare earth elements (r = 0.847, P < 0.01), respectively. Three eigenvalues explained about 86.60 % of the total variance, which contributed significantly to the explanation of cumulative variance. Cluster analysis indicated the cultivars were categorized into 3 clusters. Ni, Zn, Fe, Cr, Ti and rare earth elements were designated as the characteristic elements. Cultivars collected from Yulin, Menghai, and Shaoguan ranked the top 3 in the comprehensive scores, indicating the content of the mineral elements was comparatively higher in these locations. PMID:27429889

  1. Determination of boiling point of petrochemicals by gas chromatography-mass spectrometry and multivariate regression analysis of structural activity relationship.

    PubMed

    Fakayode, Sayo O; Mitchell, Breanna S; Pollard, David A

    2014-08-01

    Accurate understanding of analyte boiling points (BP) is of critical importance in gas chromatographic (GC) separation and crude oil refinery operation in petrochemical industries. This study reported the first combined use of GC separation and partial-least-square (PLS1) multivariate regression analysis of petrochemical structural activity relationship (SAR) for accurate BP determination of two commercially available (D3710 and MA VHP) calibration gas mix samples. The results of the BP determination using PLS1 multivariate regression were further compared with the results of traditional simulated distillation method of BP determination. The developed PLS1 regression was able to correctly predict analytes BP in D3710 and MA VHP calibration gas mix samples, with a root-mean-square-%-relative-error (RMS%RE) of 6.4%, and 10.8% respectively. In contrast, the overall RMS%RE of 32.9% and 40.4%, respectively obtained for BP determination in D3710 and MA VHP using a traditional simulated distillation method were approximately four times larger than the corresponding RMS%RE of BP prediction using MRA, demonstrating the better predictive ability of MRA. The reported method is rapid, robust, and promising, and can be potentially used routinely for fast analysis, pattern recognition, and analyte BP determination in petrochemical industries.

  2. Evaluation of Extraction Protocols for Simultaneous Polar and Non-Polar Yeast Metabolite Analysis Using Multivariate Projection Methods

    PubMed Central

    Tambellini, Nicolas P.; Zaremberg, Vanina; Turner, Raymond J.; Weljie, Aalim M.

    2013-01-01

    Metabolomic and lipidomic approaches aim to measure metabolites or lipids in the cell. Metabolite extraction is a key step in obtaining useful and reliable data for successful metabolite studies. Significant efforts have been made to identify the optimal extraction protocol for various platforms and biological systems, for both polar and non-polar metabolites. Here we report an approach utilizing chemoinformatics for systematic comparison of protocols to extract both from a single sample of the model yeast organism Saccharomyces cerevisiae. Three chloroform/methanol/water partitioning based extraction protocols found in literature were evaluated for their effectiveness at reproducibly extracting both polar and non-polar metabolites. Fatty acid methyl esters and methoxyamine/trimethylsilyl derivatized aqueous compounds were analyzed by gas chromatography mass spectrometry to evaluate non-polar or polar metabolite analysis. The comparative breadth and amount of recovered metabolites was evaluated using multivariate projection methods. This approach identified an optimal protocol consisting of 64 identified polar metabolites from 105 ion hits and 12 fatty acids recovered, and will potentially attenuate the error and variation associated with combining metabolite profiles from different samples for untargeted analysis with both polar and non-polar analytes. It also confirmed the value of using multivariate projection methods to compare established extraction protocols. PMID:24958140

  3. Determination of boiling point of petrochemicals by gas chromatography-mass spectrometry and multivariate regression analysis of structural activity relationship.

    PubMed

    Fakayode, Sayo O; Mitchell, Breanna S; Pollard, David A

    2014-08-01

    Accurate understanding of analyte boiling points (BP) is of critical importance in gas chromatographic (GC) separation and crude oil refinery operation in petrochemical industries. This study reported the first combined use of GC separation and partial-least-square (PLS1) multivariate regression analysis of petrochemical structural activity relationship (SAR) for accurate BP determination of two commercially available (D3710 and MA VHP) calibration gas mix samples. The results of the BP determination using PLS1 multivariate regression were further compared with the results of traditional simulated distillation method of BP determination. The developed PLS1 regression was able to correctly predict analytes BP in D3710 and MA VHP calibration gas mix samples, with a root-mean-square-%-relative-error (RMS%RE) of 6.4%, and 10.8% respectively. In contrast, the overall RMS%RE of 32.9% and 40.4%, respectively obtained for BP determination in D3710 and MA VHP using a traditional simulated distillation method were approximately four times larger than the corresponding RMS%RE of BP prediction using MRA, demonstrating the better predictive ability of MRA. The reported method is rapid, robust, and promising, and can be potentially used routinely for fast analysis, pattern recognition, and analyte BP determination in petrochemical industries. PMID:24881546

  4. Electrocardiographic diagnosis of posterior myocardial infarction revisited: a new approach using a multivariate discriminant analysis and thallium-201 myocardial scintigraphy

    SciTech Connect

    Nestico, P.F.; Hakki, A.H.; Iskandrian, A.S.; Anderson, G.J.

    1986-01-01

    This study examined the feasibility of using a multivariate discriminant analysis to design a useful electrocardiographic (ECG) model to diagnose posterior myocardial infarction (MI). Thallium-20) scintigraphy was used as a reference standard to identify posterior scar (fixed perfusion defects). The model was derived from 111 patients of whom 37 had fixed posterior defects and 74 had normal images, and its validity was subsequently tested in a separate group of 180 patients. In the initial group of patients, the fixed perfusion defects involved the posterior left ventricular wall alone in 15 patients, and the posterior and inferior walls in 22 patients. Stepwise multivariate discriminant analysis of 26 ECG variables produced a model of two variables (Q-wave duration in a VF and T-wave amplitude in V1) which provided a sensitivity of 78%, a specificity of 89%, and a predictive accuracy of 86% for the diagnosis of posterior MI. This model, when tested in the second group of 180 patients, yielded an overall prediction accuracy of 82% (sensitivity 65%, specificity 85%). Thus, the combination of Q-wave in a VF and upright T wave in V1 is the best ECG predictor of posterior MI. These two variables reflect the frequent association of posterior MI with inferior MI, and the reciprocal repolarization changes in the right precordial leads.

  5. Fermentanomics: Relating quality attributes of a monoclonal antibody to cell culture process variables and raw materials using multivariate data analysis.

    PubMed

    Rathore, Anurag S; Kumar Singh, Sumit; Pathak, Mili; Read, Erik K; Brorson, Kurt A; Agarabi, Cyrus D; Khan, Mansoor

    2015-01-01

    Fermentanomics is an emerging field of research and involves understanding the underlying controlled process variables and their effect on process yield and product quality. Although major advancements have occurred in process analytics over the past two decades, accurate real-time measurement of significant quality attributes for a biotech product during production culture is still not feasible. Researchers have used an amalgam of process models and analytical measurements for monitoring and process control during production. This article focuses on using multivariate data analysis as a tool for monitoring the internal bioreactor dynamics, the metabolic state of the cell, and interactions among them during culture. Quality attributes of the monoclonal antibody product that were monitored include glycosylation profile of the final product along with process attributes, such as viable cell density and level of antibody expression. These were related to process variables, raw materials components of the chemically defined hybridoma media, concentration of metabolites formed during the course of the culture, aeration-related parameters, and supplemented raw materials such as glucose, methionine, threonine, tryptophan, and tyrosine. This article demonstrates the utility of multivariate data analysis for correlating the product quality attributes (especially glycosylation) to process variables and raw materials (especially amino acid supplements in cell culture media). The proposed approach can be applied for process optimization to increase product expression, improve consistency of product quality, and target the desired quality attribute profile.

  6. Multivariate Data EXplorer (MDX)

    SciTech Connect

    Steed, Chad Allen

    2012-08-01

    The MDX toolkit facilitates exploratory data analysis and visualization of multivariate datasets. MDX provides and interactive graphical user interface to load, explore, and modify multivariate datasets stored in tabular forms. MDX uses an extended version of the parallel coordinates plot and scatterplots to represent the data. The user can perform rapid visual queries using mouse gestures in the visualization panels to select rows or columns of interest. The visualization panel provides coordinated multiple views whereby selections made in one plot are propagated to the other plots. Users can also export selected data or reconfigure the visualization panel to explore relationships between columns and rows in the data.

  7. Multi-variate analysis of burns patients in the Singapore General Hospital Burns Centre (2003-2005).

    PubMed

    Chong, S J; Song, C; Tan, T W; Kusumawijaja, G; Chew, K Y

    2009-03-01

    The Burns Centre at the Singapore General Hospital (SGH) serves as a tertiary referral centre for burns management for Singapore's 4 million residents as well as the Southeast Asia region. Our study is a multivariate analysis of all burns patients admitted between 2003 and 2005. A total of 482 patients were admitted during this period with an average annual admission of 161. This represents a low incidence of 0.04 per 1000 admissions for the Singapore population. 13.3% of the study population were children, which is lower than previous studies. The mean age at admission was 35 years old and the male:female ratio was 1.9:1. We found a significant difference in age between the local and foreign patients, with the latter being younger. Our study demonstrated a 7.3% increase in cases of occupational burns. The bulk of our patients (57.3%) were directly admitted from SGH's Accident and Emergency Department. The patient characteristics of the various referral sources were found to be very different. GP referrals had significantly lower TBSA while overseas patients had significantly higher TBSA and longer length of stay. The mean and median time to admission was 3.05 days (+/-6.26) and 0 (0-60) day, respectively and the mean and median time to surgery was 7.33 days (+/-8.18) and 5 (0-22) days, respectively. The most common cause of burns was due to scalding. The mean extent of burn (TBSA) was 13.5% (+/-18.0), with significant correlation with the social background. Length of stay was dependent on the need for surgery. The overall mortality rate in this study population was 4.5%, with inhalation injury the main aetiological factor. In addition, the mean duration of the first surgery that patients undergo was significantly longer than that of the second one. This information will be useful for estimating operation times in the future. Finally, Acinetobacter baumannii was the most common bacteria in wound cultures. There is a need for periodic reviews of wound cultures in

  8. A multivariate analysis using physiology and behavior to characterize robustness in two isogenic lines of rainbow trout exposed to a confinement stress.

    PubMed

    Sadoul, Bastien; Leguen, Isabelle; Colson, Violaine; Friggens, Nicolas C; Prunet, Patrick

    2015-03-01

    Robustness is a complex trait difficult to characterize and phenotype. In the present study, two features of robustness in rainbow trout were investigated: sensitivity and resilience to an acute stressor. For that purpose, oxygen consumption, cortisol release, group dispersion and group activity of two isogenic lines of juvenile rainbow trout were followed before and after an environmental challenge. The effect of a 4h confinement protocol (~140kg/m(3)), which is generally considered as a highly stressful challenge, was investigated. Temporal patterns produced by this experiment were analyzed using multivariate statistics on curve characteristics to describe physiological and behavioral adaptive systems for each isogenic line. The two isogenic lines were found to be highly divergent in their corticosteroid reactivity. However, no correlation between physiological and behavioral sensitivity or resilience was observed. Furthermore, the multivariate analysis results indicated two separate and independent fish group coping strategies, i.e. by favoring either behavioral or physiological responses. In addition, considerable intra-line variabilities were observed, suggesting the importance of micro-environment effects on perturbation sensitivities. In this context, cortisol release rate variability was found to be related to the pre-stress social environment, with a strong correlation between pre-stress aggressiveness and cortisol release rate amplitude. Overall, this approach allowed us to extract important characteristics from dynamic data in physiology and behavior to describe components of robustness in two isogenic lines of rainbow trout.

  9. Physical vs. photolithographic patterning of plasma polymers: an investigation by ToF-SSIMS and multivariate analysis

    PubMed Central

    Mishra, Gautam; Easton, Christopher D.; McArthur, Sally L.

    2009-01-01

    Physical and photolithographic techniques are commonly used to create chemical patterns for a range of technologies including cell culture studies, bioarrays and other biomedical applications. In this paper, we describe the fabrication of chemical micropatterns from commonly used plasma polymers. Atomic force microcopy (AFM) imaging, Time-of-Flight Static Secondary Ion Mass Spectrometry (ToF-SSIMS) imaging and multivariate analysis have been employed to visualize the chemical boundaries created by these patterning techniques and assess the spatial and chemical resolution of the patterns. ToF-SSIMS analysis demonstrated that well defined chemical and spatial boundaries were obtained from photolithographic patterning, while the resolution of physical patterning via a transmission electron microscopy (TEM) grid varied depending on the properties of the plasma system including the substrate material. In general, physical masking allowed diffusion of the plasma species below the mask and bleeding of the surface chemistries. Multivariate analysis techniques including Principal Component Analysis (PCA) and Region of Interest (ROI) assessment were used to investigate the ToF-SSIMS images of a range of different plasma polymer patterns. In the most challenging case, where two strongly reacting polymers, allylamine and acrylic acid were deposited, PCA confirmed the fabrication of micropatterns with defined spatial resolution. ROI analysis allowed for the identification of an interface between the two plasma polymers for patterns fabricated using the photolithographic technique which has been previously overlooked. This study clearly demonstrated the versatility of photolithographic patterning for the production of multichemistry plasma polymer arrays and highlighted the need for complimentary characterization and analytical techniques during the fabrication plasma polymer micropatterns. PMID:19950941

  10. Cortical Source Multivariate EEG Synchronization Analysis on Amnestic Mild Cognitive Impairment in Type 2 Diabetes

    PubMed Central

    Bian, Zhijie; Li, Qiuli; Wang, Lei; Li, Xiaoli

    2014-01-01

    Is synchronization altered in amnestic mild cognitive impairment (aMCI) and normal cognitive functions subjects in type 2 diabetes mellitus (T2DM)? Resting eye-closed EEG data were recorded in 8 aMCI subjects and 11 age-matched controls in T2DM. Three multivariate synchronization algorithms (S-estimator (S), synchronization index (SI), and global synchronization index (GSI)) were used to measure the synchronization in five ROIs of sLORETA sources for seven bands. Results showed that aMCI group had lower synchronization values than control groups in parietal delta and beta2 bands, temporal delta and beta2 bands, and occipital theta and beta2 bands significantly. Temporal (r = 0.629; P = 0.004) and occipital (r = 0.648; P = 0.003) theta S values were significantly positive correlated with Boston Name Testing. In sum, each of methods reflected that the cortical source synchronization was significantly different between aMCI and control group, and these difference correlated with cognitive functions. PMID:25254248

  11. Estimating mean crystallite size of magnetite using multivariate calibration and powder x-ray diffraction analysis.

    PubMed

    Lemes, Maykon A; Godinho, Mariana S; Rabelo, Denilson; Martins, Felipe T; Mesquita, Alexandre; Neto, Francisco N De Souza; Araujo, Olacir A; Oliveira, Anselmo E De

    2014-01-01

    Powder X-ray diffraction patterns for 29 samples of magnetite, acquired using a conventional diffractometer, were used to build PLS calibration-based methods and variable selection to estimate mean crystallite size of magnetite directly from powder X-ray diffraction patterns. The best IPLS model corresponds to the Bragg reflections at 35.4° (h k l = 3 1 1), 43.0° (h k l = 4 0 0), 53.6° (h k l = 4 2 2), and 57.0° (h k l = 5 1 1) in 2θ. The best model was a GA-PLS which produced a model with RMSEP of 0.9 nm, and a correlation coefficient of 0.9976 between mean crystallite sizes calculated using Williamson-Hall approach and the ones predicted by GA-PLS method. These results indicate that magnetite mean crystallite sizes can be predicted directly from Powder X-Ray Diffraction and multivariate calibration using PLS variable selection approach.

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

    USGS Publications Warehouse

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

    2010-01-01

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

  13. Simultaneous determination of nifuroxazide and drotaverine hydrochloride in pharmaceutical preparations by bivariate and multivariate spectral analysis.

    PubMed

    Metwally, Fadia H

    2008-02-01

    The quantitative predictive abilities of the new and simple bivariate spectrophotometric method are compared with the results obtained by the use of multivariate calibration methods [the classical least squares (CLS), principle component regression (PCR) and partial least squares (PLS)], using the information contained in the absorption spectra of the appropriate solutions. Mixtures of the two drugs Nifuroxazide (NIF) and Drotaverine hydrochloride (DRO) were resolved by application of the bivariate method. The different chemometric approaches were applied also with previous optimization of the calibration matrix, as they are useful in simultaneous inclusion of many spectral wavelengths. The results found by application of the bivariate, CLS, PCR and PLS methods for the simultaneous determinations of mixtures of both components containing 2-12microgml(-1) of NIF and 2-8microgml(-1) of DRO are reported. Both approaches were satisfactorily applied to the simultaneous determination of NIF and DRO in pure form and in pharmaceutical formulation. The results were in accordance with those given by the EVA Pharma reference spectrophotometric method. PMID:17631041

  14. Multivariate curve resolution for the analysis of remotely sensed thermal infrared hyperspectral images.

    SciTech Connect

    Haaland, David Michael; Stork, Christopher Lyle; Keenan, Michael Robert

    2004-07-01

    While hyperspectral imaging systems are increasingly used in remote sensing and offer enhanced scene characterization relative to univariate and multispectral technologies, it has proven difficult in practice to extract all of the useful information from these systems due to overwhelming data volume, confounding atmospheric effects, and the limited a priori knowledge regarding the scene. The need exists for the ability to perform rapid and comprehensive data exploitation of remotely sensed hyperspectral imagery. To address this need, this paper describes the application of a fast and rigorous multivariate curve resolution (MCR) algorithm to remotely sensed thermal infrared hyperspectral images. Employing minimal a priori knowledge, notably non-negativity constraints on the extracted endmember profiles and a constant abundance constraint for the atmospheric upwelling component, it is demonstrated that MCR can successfully compensate thermal infrared hyperspectral images for atmospheric upwelling and, thereby, transmittance effects. We take a semi-synthetic approach to obtaining image data containing gas plumes by adding emission gas signals onto real hyperspectral images. MCR can accurately estimate the relative spectral absorption coefficients and thermal contrast distribution of an ammonia gas plume component added near the minimum detectable quantity.

  15. 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

  16. Simultaneous determination of nifuroxazide and drotaverine hydrochloride in pharmaceutical preparations by bivariate and multivariate spectral analysis.

    PubMed

    Metwally, Fadia H

    2008-02-01

    The quantitative predictive abilities of the new and simple bivariate spectrophotometric method are compared with the results obtained by the use of multivariate calibration methods [the classical least squares (CLS), principle component regression (PCR) and partial least squares (PLS)], using the information contained in the absorption spectra of the appropriate solutions. Mixtures of the two drugs Nifuroxazide (NIF) and Drotaverine hydrochloride (DRO) were resolved by application of the bivariate method. The different chemometric approaches were applied also with previous optimization of the calibration matrix, as they are useful in simultaneous inclusion of many spectral wavelengths. The results found by application of the bivariate, CLS, PCR and PLS methods for the simultaneous determinations of mixtures of both components containing 2-12microgml(-1) of NIF and 2-8microgml(-1) of DRO are reported. Both approaches were satisfactorily applied to the simultaneous determination of NIF and DRO in pure form and in pharmaceutical formulation. The results were in accordance with those given by the EVA Pharma reference spectrophotometric method.

  17. Explaining public support for space exploration funding in America: A multivariate analysis

    NASA Astrophysics Data System (ADS)

    Nadeau, François

    2013-05-01

    Recent studies have identified the need to understand what shapes public attitudes toward space policy. I address this gap in the literature by developing a multivariate regression model explaining why many Americans support government spending on space exploration. Using pooled data from the 2006 and 2008 General Social Surveys, the study reveals that spending preferences on space exploration are largely apolitical and associated instead with knowledge and opinions about science. In particular, the odds of wanting to increase funding for space exploration are significantly higher for white, male Babyboomers with a higher socio-economic status, a fondness for organized science, and a post-secondary science education. As such, I argue that public support for NASA's spending epitomizes what Launius termed "Apollo Nostalgia" in American culture. That is, Americans benefitting most from the old social order of the 1960s developed a greater fondness for science that makes them more likely to lament the glory days of space exploration. The article concludes with suggestions for how to elaborate on these findings in future studies.

  18. The top down design flow of a-Si:H photodiodes with multivariate methods of analysis

    NASA Astrophysics Data System (ADS)

    Merfort, Christian; Bablich, Andreas; Schwaneberg, Oliver; Watty, Krystian; Böhm, Markus

    2011-11-01

    A fast and reliable detection of potentially dangerous substances has become very important in ensuring civilian security. Currently, modern security systems have proven to be more effective on the basis that objects should be properly characterized and identified. For instance, chemical tests are used to identify samples of whitish powder that is suspected to be dangerous or illegal. Although these chemical tests are conducted very quickly, they are relatively expensive. However, well established methods of optical characterization offer a suitable alternative. The demand for low-cost and disposable devices have escalated the development of intelligent photodiodes, especially of tunable a-Si:H multispectral photodiodes1. Our aim of reengineering is to develop the best match for the spectral response adjustment. Unfortunately, it is not sufficient to optimize the spectral response only. The top down design flow begins with the calculation of the photocurrent for different combinations of light sources, spectral responses and whitish powder samples to build up a multivariate data set. The optimum combination is found at the point of intersection in the factor values in a 2-D scattergram. It is therefore, required that the use optimized photodiodes would simplify and accelerate the identification of potentially dangerous substances.

  19. Improved Quantitative Analysis of Ion Mobility Spectrometry by Chemometric Multivariate Calibration

    SciTech Connect

    Fraga, Carlos G.; Kerr, Dayle; Atkinson, David A.

    2009-09-01

    Traditional peak-area calibration and the multivariate calibration methods of principle component regression (PCR) and partial least squares (PLS), including unfolded PLS (U-PLS) and multi-way PLS (N-PLS), were evaluated for the quantification of 2,4,6-trinitrotoluene (TNT) and cyclo-1,3,5-trimethylene-2,4,6-trinitramine (RDX) in Composition B samples analyzed by temperature step desorption ion mobility spectrometry (TSD-IMS). The true TNT and RDX concentrations of eight Composition B samples were determined by high performance liquid chromatography with UV absorbance detection. Most of the Composition B samples were found to have distinct TNT and RDX concentrations. Applying PCR and PLS on the exact same IMS spectra used for the peak-area study improved quantitative accuracy and precision approximately 3 to 5 fold and 2 to 4 fold, respectively. This in turn improved the probability of correctly identifying Composition B samples based upon the estimated RDX and TNT concentrations from 11% with peak area to 44% and 89% with PLS. This improvement increases the potential of obtaining forensic information from IMS analyzers by providing some ability to differentiate or match Composition B samples based on their TNT and RDX concentrations.

  20. The Tasmanian SIDS Case-Control Study: univariable and multivariable risk factor analysis.

    PubMed

    Ponsonby, A L; Dwyer, T; Kasl, S V; Cochrane, J A

    1995-07-01

    A population-based retrospective case-control study has been conducted in Tasmania since October 1988. Study measurements pertained to the scene of death of last sleep, as well as a verbal questionnaire on relevant exposures. From 1 October 1988 to 1 October 1991, 62 cases of sudden infant death syndrome (SIDS) occurred. Case response rate for retrospective interviews was 94% (58/62). The initial control response rate was 84% (101/121). After stratification for maternal age and birthweight, there was no increase in risk associated with the usual side position (odds ratio [OR] 1.05 [0.27, 5.02]), compared with the supine position (OR 1.00, reference). The prone position was associated with increased risk [OR 5.70 (1.67, 25.58)], relative to the supine position. In the final multivariable model, predictors of SIDS in this study were usual prone position (P < 0.001), maternal smoking (P = 0.008), a family history of asthma (P = 0.045) and bedroom heating during last sleep (P = 0.039). Protective factors were maternal age over 25 years (P = 0.013) and more than one child health clinic attendance (P = 0.003). The results provide further support for current health education activities which aim to inform parents of modifiable risk factors for SIDS, including the prone sleeping position, thermal stress and infant exposure to tobacco smoke.

  1. Simultaneous determination of Nifuroxazide and Drotaverine hydrochloride in pharmaceutical preparations by bivariate and multivariate spectral analysis

    NASA Astrophysics Data System (ADS)

    Metwally, Fadia H.

    2008-02-01

    The quantitative predictive abilities of the new and simple bivariate spectrophotometric method are compared with the results obtained by the use of multivariate calibration methods [the classical least squares (CLS), principle component regression (PCR) and partial least squares (PLS)], using the information contained in the absorption spectra of the appropriate solutions. Mixtures of the two drugs Nifuroxazide (NIF) and Drotaverine hydrochloride (DRO) were resolved by application of the bivariate method. The different chemometric approaches were applied also with previous optimization of the calibration matrix, as they are useful in simultaneous inclusion of many spectral wavelengths. The results found by application of the bivariate, CLS, PCR and PLS methods for the simultaneous determinations of mixtures of both components containing 2-12 μg ml -1 of NIF and 2-8 μg ml -1 of DRO are reported. Both approaches were satisfactorily applied to the simultaneous determination of NIF and DRO in pure form and in pharmaceutical formulation. The results were in accordance with those given by the EVA Pharma reference spectrophotometric method.

  2. Multivariate analysis of clinical, demographic, and laboratory data for classification of disorders of calcium homeostasis.

    PubMed

    O'Neill, Stacey S; Gordon, Christopher J; Guo, Ruixin; Zhu, Hongtu; McCudden, Christopher R

    2011-01-01

    Parathyroid hormone (PTH) nomograms combine total calcium and intact PTH (iPTH) measurements to classify disorders of calcium homeostasis. Our objective was to determine if using a combination of laboratory, demographic, and clinical parameters improves the accuracy of classification of these disorders. Chart data were collected for 236 patients with physician-ordered iPTH and total calcium tests. Classification was done using 3 approaches: (1) PTH nomogram plotting total calcium and iPTH results against known cases; (2) review of all available chart data ("gold standard"); and (3) multivariate model (classification and regression tree [CART] or logistic regression) using 24 variables. The CART model was developed using the gold standard patient classification and validated using leave-one-out cross-validation. The CART model was significantly (P = .002) more accurate (80.6%) than the PTH nomogram (59.7%) and logistic regression (66.2%) at classifying calcium homeostasis disorders. The CART model used 6 of 24 variables (iPTH, calcium, creatinine, renal transplantation, percentage of females, and urea nitrogen) and had a misclassification error rate of 0.194 (27/139). Classification of disorders of calcium homeostasis based on the PTH nomogram can be improved by using the CART model developed in this study.

  3. Multivariate statistical analysis of diffusion imaging parameters using partial least squares: Application to white matter variations in Alzheimer's disease.

    PubMed

    Konukoglu, Ender; Coutu, Jean-Philippe; Salat, David H; Fischl, Bruce

    2016-07-01

    Diffusion magnetic resonance imaging (dMRI) is a unique technology that allows the noninvasive quantification of microstructural tissue properties of the human brain in healthy subjects as well as the probing of disease-induced variations. Population studies of dMRI data have been essential in identifying pathological structural changes in various conditions, such as Alzheimer's and Huntington's diseases (Salat et al., 2010; Rosas et al., 2006). The most common form of dMRI involves fitting a tensor to the underlying imaging data (known as diffusion tensor imaging, or DTI), then deriving parametric maps, each quantifying a different aspect of the underlying microstructure, e.g. fractional anisotropy and mean diffusivity. To date, the statistical methods utilized in most DTI population studies either analyzed only one such map or analyzed several of them, each in isolation. However, it is most likely that variations in the microstructure due to pathology or normal variability would affect several parameters simultaneously, with differing variations modulating the various parameters to differing degrees. Therefore, joint analysis of the available diffusion maps can be more powerful in characterizing histopathology and distinguishing between conditions than the widely used univariate analysis. In this article, we propose a multivariate approach for statistical analysis of diffusion parameters that uses partial least squares correlation (PLSC) analysis and permutation testing as building blocks in a voxel-wise fashion. Stemming from the common formulation, we present three different multivariate procedures for group analysis, regressing-out nuisance parameters and comparing effects of different conditions. We used the proposed procedures to study the effects of non-demented aging, Alzheimer's disease and mild cognitive impairment on the white matter. Here, we present results demonstrating that the proposed PLSC-based approach can differentiate between effects of

  4. Multivariate normality

    NASA Technical Reports Server (NTRS)

    Crutcher, H. L.; Falls, L. W.

    1976-01-01

    Sets of experimentally determined or routinely observed data provide information about the past, present and, hopefully, future sets of similarly produced data. An infinite set of statistical models exists which may be used to describe the data sets. The normal distribution is one model. If it serves at all, it serves well. If a data set, or a transformation of the set, representative of a larger population can be described by the normal distribution, then valid statistical inferences can be drawn. There are several tests which may be applied to a data set to determine whether the univariate normal model adequately describes the set. The chi-square test based on Pearson's work in the late nineteenth and early twentieth centuries is often used. Like all tests, it has some weaknesses which are discussed in elementary texts. Extension of the chi-square test to the multivariate normal model is provided. Tables and graphs permit easier application of the test in the higher dimensions. Several examples, using recorded data, illustrate the procedures. Tests of maximum absolute differences, mean sum of squares of residuals, runs and changes of sign are included in these tests. Dimensions one through five with selected sample sizes 11 to 101 are used to illustrate the statistical tests developed.

  5. Preliminary study on classification of rice and detection of paraffin in the adulterated samples by Raman spectroscopy combined with multivariate analysis.

    PubMed

    Feng, Xinwei; Zhang, Qinghua; Cong, Peisheng; Zhu, Zhongliang

    2013-10-15

    Rice has played an important role in staple food supply of over approximately one-half of the world population. In this study, Raman spectroscopy and several multivariate data analysis methods were applied for discrimination of rice samples from different districts of China. A total of 42 samples were examined. It is shown that the representative Raman spectra in each group are different according to geographical origin after baseline correction to enhance spectral features. Moreover, adulteration of rice is a serious problem for consumers. In addition to the obvious effect on producer profits, adulteration can also cause severe health and safety problems. Paraffin was added to give the rice a desirable translucent appearance and increase its marketability. Detection of paraffin in the adulterated rice samples was preliminarily investigated as well. The results showed that Raman spectroscopy data with chemometric techniques can be applied to rapid detecting rice adulteration with paraffin.

  6. Metabolite profiling of Curcuma species grown in different regions using 1H NMR spectroscopy and multivariate analysis.

    PubMed

    Jung, Youngae; Lee, Jueun; Kim, Ho Kyoung; Moon, Byeong Cheol; Ji, Yunui; Ryu, Do Hyun; Hwang, Geum-Sook

    2012-12-01

    Curcuma is used to treat skin diseases and colic inflammatory disorders, and in insect repellants and antimicrobial and antidiabetic medications. Two Curcuma species (C. aromatica and C. longa) grown in Jeju-do and Jin-do were used in this study. Methanolic extracts were analyzed by (1)H NMR spectroscopy, and metabolite profiling coupled with multivariate analysis was applied to characterize the differences between species or origin. PCA analysis showed significantly greater differences between species than origins, and the metabolites responsible for the differences were identified. The concentrations of sugars (glucose, fructose, and sucrose) and essential oils (eucalyptol, curdione, and germacrone) were significantly different between the two species. However, the samples from Jeju-do and Jin-do were different mainly in their concentrations of organic acids (fumarate, succinate, acetate, and formate) and sugars. This study demonstrates that NMR-based metabolomics is an efficient method for fingerprinting and determining differences between Curcuma species or those grown in different regions.

  7. Computed Tomography Inspection and Analysis for Additive Manufacturing Components

    NASA Technical Reports Server (NTRS)

    Beshears, Ronald D.

    2016-01-01

    Computed tomography (CT) inspection was performed on test articles additively manufactured from metallic materials. Metallic AM and machined wrought alloy test articles with programmed flaws were inspected using a 2MeV linear accelerator based CT system. Performance of CT inspection on identically configured wrought and AM components and programmed flaws was assessed using standard image analysis techniques to determine the impact of additive manufacturing on inspectability of objects with complex geometries.

  8. Geostatistics and multivariate analysis as a tool to characterize volcaniclastic deposits: Application to Nevado de Toluca volcano, Mexico

    NASA Astrophysics Data System (ADS)

    Bellotti, F.; Capra, L.; Sarocchi, D.; D'Antonio, M.

    2010-03-01

    Grain size analysis of volcaniclastic deposits is mainly used to study flow transport and depositional processes, in most cases by comparing some statistical parameters and how they change with distance from the source. In this work the geospatial and multivariate analyses are presented as a strong adaptable geostatistical tool applied to volcaniclastic deposits in order to provide an effective and relatively simple methodology for texture description, deposit discrimination and interpretation of depositional processes. We choose the case of Nevado de Toluca volcano (Mexico) due to existing knowledge of its geological evolution, stratigraphic succession and spatial distribution of volcaniclastic units. Grain size analyses and frequency distribution curves have been carried out to characterize and compare the 28-ka block-and-ash flow deposit associated to a dome destruction episode, and the El Morral debris avalanche deposit originated from the collapse of the south-eastern sector of the volcano. The geostatistical interpolation of sedimentological data allows to realize bidimensional maps draped over the volcano topography, showing the granulometric distribution, sorting and fine material concentration into the whole deposit with respect to topographic changes. In this way, it is possible to analyze a continuous surface of the grain size distribution of volcaniclastic deposits and better understand flow transport processes. The application of multivariate statistic analysis (discriminant function) indicates that this methodology could be useful in discriminating deposits with different origin or different depositional lithofacies within the same deposit. The proposed methodology could be an interesting approach to sustain more classical analysis of volcaniclastic deposits, especially where a clear field classification appears problematic because of a homogeneous texture of the deposits or their scarce and discontinuous outcrops. Our study is an example of the

  9. Effects of Flavor and Texture on the Sensory Perception of Gouda-Type Cheese Varieties during Ripening Using Multivariate Analysis.

    PubMed

    Shiota, Makoto; Iwasawa, Ai; Suzuki-Iwashima, Ai; Iida, Fumiko

    2015-12-01

    The impact of flavor composition, texture, and other factors on desirability of different commercial sources of Gouda-type cheese using multivariate analyses on the basis of sensory and instrumental analyses were investigated. Volatile aroma compounds were measured using headspace solid-phase microextraction gas chromatography/mass spectrometry (GC/MS) and steam distillation extraction (SDE)-GC/MS, and fatty acid composition, low-molecular-weight compounds, including amino acids, and organic acids, as well pH, texture, and color were measured to determine their relationship with sensory perception. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed to discriminate between 2 different ripening periods in 7 sample sets, revealing that ethanol, ethyl acetate, hexanoic acid, and octanoic acid increased with increasing sensory attribute scores for sweetness, fruity, and sulfurous. A partial least squares (PLS) regression model was constructed to predict the desirability of cheese using these parameters. We showed that texture and buttery flavors are important factors affecting the desirability of Gouda-type cheeses for Japanese consumers using these multivariate analyses. PMID:26551333

  10. Multivariate class modeling techniques applied to multielement analysis for the verification of the geographical origin of chili pepper.

    PubMed

    Naccarato, Attilio; Furia, Emilia; Sindona, Giovanni; Tagarelli, Antonio

    2016-09-01

    Four class-modeling techniques (soft independent modeling of class analogy (SIMCA), unequal dispersed classes (UNEQ), potential functions (PF), and multivariate range modeling (MRM)) were applied to multielement distribution to build chemometric models able to authenticate chili pepper samples grown in Calabria respect to those grown outside of Calabria. The multivariate techniques were applied by considering both all the variables (32 elements, Al, As, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Fe, Ga, La, Li, Mg, Mn, Na, Nd, Ni, Pb, Pr, Rb, Sc, Se, Sr, Tl, Tm, V, Y, Yb, Zn) and variables selected by means of stepwise linear discriminant analysis (S-LDA). In the first case, satisfactory and comparable results in terms of CV efficiency are obtained with the use of SIMCA and MRM (82.3 and 83.2% respectively), whereas MRM performs better than SIMCA in terms of forced model efficiency (96.5%). The selection of variables by S-LDA permitted to build models characterized, in general, by a higher efficiency. MRM provided again the best results for CV efficiency (87.7% with an effective balance of sensitivity and specificity) as well as forced model efficiency (96.5%).

  11. Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

    PubMed

    Linn, Kristin A; Gaonkar, Bilwaj; Satterthwaite, Theodore D; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T

    2016-05-15

    Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases.

  12. Use of multivariate analysis to improve the accuracy of radionuclide angiography with stress in detecting coronary artery disease in men

    SciTech Connect

    Greenberg, P.S.; Bible, M.; Ellestad, M.H.; Berge, R.; Johnson, K.; Hayes, M.

    1983-01-01

    A multivariate analysis (MVA) system was derived retrospectively from a population of 76 males with coronary artery disease and 18 control subjects. Posterior probabilities were then derived from such a system prospectively in a new male population of 11 subjects with normal coronary arteries and hemodynamics and 63 patients with coronary artery disease. The sensitivity was 84% compared to that for change in ejection fraction (delta EF) greater than or equal to 5 criterion of 71% (p less than 0.01), the specificity was 91% compared to 73% for the delta EF greater than or equal to 5 criterion (p greater than 0.05), and the correct classification rate was 85% compared to 72% for the delta EF greater than or equal to 5 criterion (p less than 0.01). The significant variables were: change in EF with exercise, percent maximal heart rate, change in end-diastolic volume (delta EDV) with exercise, change in R wave, and exercise duration. Application of the multivariate approach to radionuclide imaging with stress, including both exercise and nuclear parameters, significantly improved the diagnostic accuracy of the test and allowed for a probability statement concerning the likelihood of disease.

  13. Multivariate class modeling techniques applied to multielement analysis for the verification of the geographical origin of chili pepper.

    PubMed

    Naccarato, Attilio; Furia, Emilia; Sindona, Giovanni; Tagarelli, Antonio

    2016-09-01

    Four class-modeling techniques (soft independent modeling of class analogy (SIMCA), unequal dispersed classes (UNEQ), potential functions (PF), and multivariate range modeling (MRM)) were applied to multielement distribution to build chemometric models able to authenticate chili pepper samples grown in Calabria respect to those grown outside of Calabria. The multivariate techniques were applied by considering both all the variables (32 elements, Al, As, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Fe, Ga, La, Li, Mg, Mn, Na, Nd, Ni, Pb, Pr, Rb, Sc, Se, Sr, Tl, Tm, V, Y, Yb, Zn) and variables selected by means of stepwise linear discriminant analysis (S-LDA). In the first case, satisfactory and comparable results in terms of CV efficiency are obtained with the use of SIMCA and MRM (82.3 and 83.2% respectively), whereas MRM performs better than SIMCA in terms of forced model efficiency (96.5%). The selection of variables by S-LDA permitted to build models characterized, in general, by a higher efficiency. MRM provided again the best results for CV efficiency (87.7% with an effective balance of sensitivity and specificity) as well as forced model efficiency (96.5%). PMID:27041319

  14. Multivariate analysis of oestrogen receptor alpha, pS2, metallothionein and CD24 expression in invasive breast cancers

    PubMed Central

    Surowiak, P; Materna, V; Györffy, B; Matkowski, R; Wojnar, A; Maciejczyk, A; Paluchowski, P; Dzięgiel, P; Pudełko, M; Kornafel, J; Dietel, M; Kristiansen, G; Zabel, M; Lage, H

    2006-01-01

    Determination of oestrogen receptor alpha (ER) represents at present the most important predictive factor in breast cancers. Data of ours and of other authors suggest that promising predictive/prognostic factors may also include pS2, metallothionein (MT) and CD24. Present study aimed at determining prognostic and predictive value of immunohistochemical determination of ER, pS2, MT, and CD24 expression in sections originating from 104 patients with breast cancer. An univariate and multivariate analysis was performed. Both univariate and multivariate analyses demonstrated that cytoplasmic-membranous expression of CD24 (CD24c-m) represents a strong unfavourable prognostic factor in the entire group and in most of the subgroups of patients. In several subgroups of the patients also a prognostic value was demonstrated of elevated expression of pS2 and of membranous expression of CD24. Our studies demonstrated that all patients with good prognostic factors (higher ER and pS2 expressions, lower MT expression, CD24c-m negativity) survived total period of observation (103 months). The study documented that cytoplasmic-membranous expression of CD24 represented an extremely strong unfavourable prognostic factor in breast cancer. Examination of the entire panel of the studied proteins permitted to select a group of patients of an exceptionally good prognosis. PMID:16892043

  15. Electrochemical degradation of malachite green: Multivariate optimization, pathway identification and toxicity analysis.

    PubMed

    Sasidharan Pillai, Indu M; Gupta, Ashok K

    2016-11-01

    Application of a newly developed electrode material, PbO2 coated on mild steel plate (MS-PbO2), for the degradation of malachite green (MG) by photocatalytic oxidation (PCO), electrochemical oxidation (ECO) and photoelectrochemical oxidation (PEC) was explored. PEC performed marginally better at lower current density. However, the performances of PEC and ECO were equally good at higher current densities. One variable at a time optimization was carried out to identify the major parameters influencing ECO. Multivariate optimization was carried out with NaCl concentration, current density and pH as the variables and chemical oxygen demand (COD) removal efficiency and current efficiency (CE) as the responses. Increasing the current density aided the COD removal efficiency, but decreased the CE. Low NaCl concentration and acidic pH were beneficial for both. The optimum condition for maximizing the COD removal efficiency and CE of MG (50 mg L(-1)) was obtained as NaCl concentration of 1.56 g L(-1), a current density of 1.91 mA cm(-2) and pH 5. The maximum predicted and experimental COD removal efficiencies were 89.41% and 90.8%, and CEs were 21.52% and 21.1%, respectively. Degradation intermediates were identified and a possible pathway of degradation was proposed. Disc inhibition study showed that the degraded samples are non-toxic. The efficacy of the method was tested for treating wastewater collected from dyebath having a COD of about 2000 mg L(-1). COD removal efficiency of greater than 90% was achieved within 12 h at a current density of 7.2 mA cm(-2).

  16. Electrochemical degradation of malachite green: Multivariate optimization, pathway identification and toxicity analysis.

    PubMed

    Sasidharan Pillai, Indu M; Gupta, Ashok K

    2016-11-01

    Application of a newly developed electrode material, PbO2 coated on mild steel plate (MS-PbO2), for the degradation of malachite green (MG) by photocatalytic oxidation (PCO), electrochemical oxidation (ECO) and photoelectrochemical oxidation (PEC) was explored. PEC performed marginally better at lower current density. However, the performances of PEC and ECO were equally good at higher current densities. One variable at a time optimization was carried out to identify the major parameters influencing ECO. Multivariate optimization was carried out with NaCl concentration, current density and pH as the variables and chemical oxygen demand (COD) removal efficiency and current efficiency (CE) as the responses. Increasing the current density aided the COD removal efficiency, but decreased the CE. Low NaCl concentration and acidic pH were beneficial for both. The optimum condition for maximizing the COD removal efficiency and CE of MG (50 mg L(-1)) was obtained as NaCl concentration of 1.56 g L(-1), a current density of 1.91 mA cm(-2) and pH 5. The maximum predicted and experimental COD removal efficiencies were 89.41% and 90.8%, and CEs were 21.52% and 21.1%, respectively. Degradation intermediates were identified and a possible pathway of degradation was proposed. Disc inhibition study showed that the degraded samples are non-toxic. The efficacy of the method was tested for treating wastewater collected from dyebath having a COD of about 2000 mg L(-1). COD removal efficiency of greater than 90% was achieved within 12 h at a current density of 7.2 mA cm(-2). PMID:27419534

  17. Sexual initiation and emotional/behavioral problems in Taiwanese adolescents: a multivariate response profile analysis.

    PubMed

    Chan, Chia-Hua; Ting, Te-Tien; Chen, Yen-Tyng; Chen, Chuan-Yu; Chen, Wei J

    2015-04-01

    This study aimed to investigate the relations of adolescent sexual experiences (particularly early initiation) to a spectrum of emotional/behavioral problems and to probe possible gender difference in such relationships. The 10th (N = 8,842) and 12th (N = 10,083) grade students, aged 16-19 years, participating in national surveys in 2005 and 2006 in Taiwan were included for this study. A self-administered web-based questionnaire was designed to collect information on sociodemographic characteristics, sexual experience, substance use, and the Youth Self-Report Form. For the sexually experienced adolescents, their sexual initiation was classified as early initiation (<16 years) or non-early initiation (16-19 years). Gender-specific multivariate response profile regression was used to examine the relationship between sexual experience and the behavioral syndromes. Externalizing problems, including Rule-breaking Behavior and Aggressive Behavior, were strongly associated with sexual initiation in adolescence; the magnitude of the association increased for earlier sexual initiation, especially for females. As to internalizing problems, the connection was rather heterogeneous. The scores on some syndromes, such as Somatic Complaints and Anxious/Depressed, were higher only for females with early or non-early sexual initiation whereas the score on Withdrawn, along with Social Problems that is neither internalizing nor externalizing, was lower for the sexually experienced adolescents than for the sexually inexperienced ones. We concluded that earlier sexual initiation was associated with a wider range of behavioral problems in adolescents for both genders, yet the increased risk with emotional problems was predominately found in females.

  18. Sexual initiation and emotional/behavioral problems in Taiwanese adolescents: a multivariate response profile analysis.

    PubMed

    Chan, Chia-Hua; Ting, Te-Tien; Chen, Yen-Tyng; Chen, Chuan-Yu; Chen, Wei J

    2015-04-01

    This study aimed to investigate the relations of adolescent sexual experiences (particularly early initiation) to a spectrum of emotional/behavioral problems and to probe possible gender difference in such relationships. The 10th (N = 8,842) and 12th (N = 10,083) grade students, aged 16-19 years, participating in national surveys in 2005 and 2006 in Taiwan were included for this study. A self-administered web-based questionnaire was designed to collect information on sociodemographic characteristics, sexual experience, substance use, and the Youth Self-Report Form. For the sexually experienced adolescents, their sexual initiation was classified as early initiation (<16 years) or non-early initiation (16-19 years). Gender-specific multivariate response profile regression was used to examine the relationship between sexual experience and the behavioral syndromes. Externalizing problems, including Rule-breaking Behavior and Aggressive Behavior, were strongly associated with sexual initiation in adolescence; the magnitude of the association increased for earlier sexual initiation, especially for females. As to internalizing problems, the connection was rather heterogeneous. The scores on some syndromes, such as Somatic Complaints and Anxious/Depressed, were higher only for females with early or non-early sexual initiation whereas the score on Withdrawn, along with Social Problems that is neither internalizing nor externalizing, was lower for the sexually experienced adolescents than for the sexually inexperienced ones. We concluded that earlier sexual initiation was associated with a wider range of behavioral problems in adolescents for both genders, yet the increased risk with emotional problems was predominately found in females. PMID:24590627

  19. MyriMatch: highly accurate tandem mass spectral peptide identification by multivariate hypergeometric analysis

    PubMed Central

    Tabb, David L.; Fernando, Christopher G.; Chambers, Matthew C.

    2008-01-01

    Shotgun proteomics experiments are dependent upon database search engines to identify peptides from tandem mass spectra. Many of these algorithms score potential identifications by evaluating the number of fragment ions matched between each peptide sequence and an observed spectrum. These systems, however, generally do not distinguish between matching an intense peak and matching a minor peak. We have developed a statistical model to score peptide matches that is based upon the multivariate hypergeometric distribution. This scorer, part of the “MyriMatch” database search engine, places greater emphasis on matching intense peaks. The probability that the best match for each spectrum has occurred by random chance can be employed to separate correct matches from random ones. We evaluated this software on data sets from three different laboratories employing three different ion trap instruments. Employing a novel system for testing discrimination, we demonstrate that stratifying peaks into multiple intensity classes improves the discrimination of scoring. We compare MyriMatch results to those of Sequest and X!Tandem, revealing that it is capable of higher discrimination than either of these algorithms. When minimal peak filtering is employed, performance plummets for a scoring model that does not stratify matched peaks by intensity. On the other hand, we find that MyriMatch discrimination improves as more peaks are retained in each spectrum. MyriMatch also scales well to tandem mass spectra from high-resolution mass analyzers. These findings may indicate limitations for existing database search scorers that count matched peaks without differentiating them by intensity. This software and source code is available under Mozilla Public License at this URL: http://www.mc.vanderbilt.edu/msrc/bioinformatics/. PMID:17269722

  20. Use of multivariate analysis to suggest a new molecular classification of colorectal cancer

    PubMed Central

    Domingo, Enric; Ramamoorthy, Rajarajan; Oukrif, Dahmane; Rosmarin, Daniel; Presz, Michal; Wang, Haitao; Pulker, Hannah; Lockstone, Helen; Hveem, Tarjei; Cranston, Treena; Danielsen, Havard; Novelli, Marco; Davidson, Brian; Xu, Zheng-Zhou; Molloy, Peter; Johnstone, Elaine; Holmes, Christopher; Midgley, Rachel; Kerr, David; Sieber, Oliver; Tomlinson, Ian

    2013-01-01

    Abstract Molecular classification of colorectal cancer (CRC) is currently based on microsatellite instability (MSI), KRAS or BRAF mutation and, occasionally, chromosomal instability (CIN). Whilst useful, these categories may not fully represent the underlying molecular subgroups. We screened 906 stage II/III CRCs from the VICTOR clinical trial for somatic mutations. Multivariate analyses (logistic regression, clustering, Bayesian networks) identified the primary molecular associations. Positive associations occurred between: CIN and TP53 mutation; MSI and BRAF mutation; and KRAS and PIK3CA mutations. Negative associations occurred between: MSI and CIN; MSI and NRAS mutation; and KRAS mutation, and each of NRAS, TP53 and BRAF mutations. Some complex relationships were elucidated: KRAS and TP53 mutations had both a direct negative association and a weaker, confounding, positive association via TP53–CIN–MSI–BRAF–KRAS. Our results suggested a new molecular classification of CRCs: (1) MSI+ and/or BRAF-mutant; (2) CIN+ and/or TP53– mutant, with wild-type KRAS and PIK3CA; (3) KRAS- and/or PIK3CA-mutant, CIN+, TP53-wild-type; (4) KRAS– and/or PIK3CA-mutant, CIN–, TP53-wild-type; (5) NRAS-mutant; (6) no mutations; (7) others. As expected, group 1 cancers were mostly proximal and poorly differentiated, usually occurring in women. Unexpectedly, two different types of CIN+ CRC were found: group 2 cancers were usually distal and occurred in men, whereas group 3 showed neither of these associations but were of higher stage. CIN+ cancers have conventionally been associated with all three of these variables, because they have been tested en masse. Our classification also showed potentially improved prognostic capabilities, with group 3, and possibly group 1, independently predicting disease-free survival. Copyright © 2012 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. PMID:23165447

  1. Additivity in the Analysis and Design of HIV Protease Inhibitors

    PubMed Central

    Jorissen, Robert N.; Kiran Kumar Reddy, G. S.; Ali, Akbar; Altman, Michael D.; Chellappan, Sripriya; Anjum, Saima G.; Tidor, Bruce; Schiffer, Celia A.; Rana, Tariq M.; Gilson, Michael K.

    2009-01-01

    We explore the applicability of an additive treatment of substituent effects to the analysis and design of HIV protease inhibitors. Affinity data for a set of inhibitors with a common chemical framework were analyzed to provide estimates of the free energy contribution of each chemical substituent. These estimates were then used to design new inhibitors, whose high affinities were confirmed by synthesis and experimental testing. Derivations of additive models by least-squares and ridge-regression methods were found to yield statistically similar results. The additivity approach was also compared with standard molecular descriptor-based QSAR; the latter was not found to provide superior predictions. Crystallographic studies of HIV protease-inhibitor complexes help explain the perhaps surprisingly high degree of substituent additivity in this system, and allow some of the additivity coefficients to be rationalized on a structural basis. PMID:19193159

  2. Chemical fingerprinting of petroleum biomarkers in biota samples using retention-time locking chromatography and multivariate analysis.

    PubMed

    Bartolomé, Luis; Deusto, Miren; Etxebarria, Nestor; Navarro, Patricia; Usobiaga, Aresatz; Zuloaga, Olatz

    2007-07-20

    This work was conducted to study a new separation and evaluation approach for the chemical fingerprinting of petroleum biomarkers in biota samples. The final aim of this work was to study the correlation between the observed effects in the shore habitats (mussels and limpets) and one pollution source: the oil spill of the Prestige tanker. The method combined a clean-up step of the biota extracts (mussels and limpets), the retention-time locking of the gas chromatographic set up, and the multivariate data analysis of the chromatograms. For clean-up, solid-phase extraction and gel permeation chromatography were compared, and 5g Florisil cartridges assured the lack of interfering compounds in the last extracts. In order to assure reproducible retention times and to avoid the realignment of the chromatograms, the retention-time locking feature of our gas chromatography-mass spectrometry (GC-MS) set up was used. Finally, in the case of multivariate analysis, the GC-MS chromatograms were treated, essentially by derivatization and by normalization, and all the chromatograms at m/z 191 (terpenes), m/z 217-218 (steranes and diasteranes) and m/z 231 (triaromatic steranes) were treated by means of principal component analysis. Furthermore, slightly different four oil samples from the Prestige oil spill were analyzed following the Nordtest method, and the GC-MS chromatograms were considered as the reference chemical fingerprints of the sources. In this sense, the correlation between the studied samples, including sediments and biota samples, and the source candidate was completed by means of a supervised pattern recognition method. As a result, the method proposed in this work was useful to identify the Prestige oil spill as the source of many of the analyzed samples.

  3. The association between tranexamic acid and convulsive seizures after cardiac surgery: a multivariate analysis in 11 529 patients.

    PubMed

    Sharma, V; Katznelson, R; Jerath, A; Garrido-Olivares, L; Carroll, J; Rao, V; Wasowicz, M; Djaiani, G

    2014-02-01

    Because of a lack of contemporary data regarding seizures after cardiac surgery, we undertook a retrospective analysis of prospectively collected data from 11 529 patients in whom cardiopulmonary bypass was used from January 2004 to December 2010. A convulsive seizure was defined as a transient episode of disturbed brain function characterised by abnormal involuntary motor movements. Multivariate regression analysis was performed to identify independent predictors of postoperative seizures. A total of 100 (0.9%) patients developed postoperative convulsive seizures. Generalised and focal seizures were identified in 68 and 32 patients, respectively. The median (IQR [range]) time after surgery when the seizure occurred was 7 (6-12 [1-216]) h and 8 (6-11 [4-18]) h, respectively. Epileptiform findings on electroencephalography were seen in 19 patients. Independent predictors of postoperative seizures included age, female sex, redo cardiac surgery, calcification of ascending aorta, congestive heart failure, deep hypothermic circulatory arrest, duration of aortic cross-clamp and tranexamic acid. When tested in a multivariate regression analysis, tranexamic acid was a strong independent predictor of seizures (OR 14.3, 95% CI 5.5-36.7; p < 0.001). Patients with convulsive seizures had 2.5 times higher in-hospital mortality rates and twice the length of hospital stay compared with patients without convulsive seizures. Mean (IQR [range]) length of stay in the intensive care unit was 115 (49-228 [32-481]) h in patients with convulsive seizures compared with 26 (22-69 [14-1080]) h in patients without seizures (p < 0.001). Convulsive seizures are a serious postoperative complication after cardiac surgery. As tranexamic acid is the only modifiable factor, its administration, particularly in doses exceeding 80 mg.kg(-1), should be weighed against the risk of postoperative seizures. PMID:24588023

  4. Statistical multivariate analysis of airborne geophysical data on the SE border of the Central Lapland Greenstone complex

    SciTech Connect

    Lanne, E.

    1986-11-01

    Statistical multivariate methods for the integrated processing of airborne geophysical data were tested. The data consisted of magnetic, electromagnetic and gamma radiation measurements, to which cluster analysis, principal components analysis and discriminant analysis were applied. Also, auxiliary variables were derived from the original ones and their value was tested. Although the frequency distributions of the data do not favour statistical analysis, the practical results are acceptable. Principal component analyses show geological and technical aspects that are difficult to obtain from the original observations. In cluster analyses, the sources of measured fields control the grouping of variables. Discriminant analysis was applied to the automatic identification of rocks by geophysical data. The rocks investigated are metasediments and metavolcanics, some magnetic and others conductive. When all available geophysical data were included, correct identifications were made in more than 60% of cases. In particular, gamma ray observations were found to improve the discrimination of non-magnetic and non-conductive rocks. The geophysical similarity of rocks studied by cluster analysis depends on electrical and magnetic properties as well as on their origin; the content of radioactive elements in turn is related to the origin.

  5. Pleiotropy and genotype by diet interaction: A multivariate genetic analysis of HDL-C subfractions

    SciTech Connect

    Mahaney, M.C.; Blangero, J.; Comuzzie, A.G.

    1994-09-01

    Reduced high density lipoprotein cholesterol (HDL-C) is a risk factor for cardiovascular disease in humans. Both major genes and major genotype by diet interaction have been reported for HDL-C, but the genetics of the HDL-C subfractions are less well known. In a baboon model for human atherosclerosis, we investigated the pleiotropic effects of genes on normal quantitative variation in three HDL-C subfractions (HDL{sub 1}-C, HDL{sub 2}-C, and HDL{sub 3}-C) in two dietary environments -- a basal diet and a 7 week high cholesterol, saturated fat (HCSF) diet. We analyzed data on serum HDL-C subfraction levels, quantified by gradient gel eletrophoresis, for 942 baboons (Papo hamadryas, sensu lato) from 17 pedigrees. We used multivariate maximum likelihood methods to simultaneously estimate phenotypic means, standard deviations, and heritabilities (h{sup 2}); effects of sex, age-by-sex, age{sup 2}-by-sex, percent subspecies admixture, and infant feeding modality; plus estimated significant h{sup 2} values for all three subfractions on both diets. When tested within dietary environments, we obtained significant genetic correlations between all three subfractions [i.e., P({rho}{sub G} = 0) < 0.001] and evidence of complete pleiotropy [i.e., P({vert_bar}{rho}{sub G}{vert_bar} = 1.0) > 0.1] between HDL{sub 1}-C and HDL{sub 3}-C ({rho}{sub G} = 0.81) on the basal diet. On the HCSF diet, only the genetic correlation between HDL{sub 1}-C and HDL{sub 3}-C ({rho}{sub g} = 0.61) was significant (p > 0.1). Complete pleiotropy was observed for each of the three subfractions between both diets. Given these results, we reject genotype by diet interaction for HDL{sub 1}-C, HDL{sub 2}-C or HDL{sub 3}-C; i.e., the same genes influence variation in each subfraction to the same degree on either diet. However, the apparent disruption of pleiotropy between HDL{sub 2}-C and the other two subfractions needs to be investigated further.

  6. Multivariate