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Sample records for age multivariate analysis

  1. Discrimination of cultivation ages and cultivars of ginseng leaves using Fourier transform infrared spectroscopy combined with multivariate analysis.

    PubMed

    Kwon, Yong-Kook; Ahn, Myung Suk; Park, Jong Suk; Liu, Jang Ryol; In, Dong Su; Min, Byung Whan; Kim, Suk Weon

    2014-01-01

    To determine whether Fourier transform (FT)-IR spectral analysis combined with multivariate analysis of whole-cell extracts from ginseng leaves can be applied as a high-throughput discrimination system of cultivation ages and cultivars, a total of total 480 leaf samples belonging to 12 categories corresponding to four different cultivars (Yunpung, Kumpung, Chunpung, and an open-pollinated variety) and three different cultivation ages (1 yr, 2 yr, and 3 yr) were subjected to FT-IR. The spectral data were analyzed by principal component analysis and partial least squares-discriminant analysis. A dendrogram based on hierarchical clustering analysis of the FT-IR spectral data on ginseng leaves showed that leaf samples were initially segregated into three groups in a cultivation age-dependent manner. Then, within the same cultivation age group, leaf samples were clustered into four subgroups in a cultivar-dependent manner. The overall prediction accuracy for discrimination of cultivars and cultivation ages was 94.8% in a cross-validation test. These results clearly show that the FT-IR spectra combined with multivariate analysis from ginseng leaves can be applied as an alternative tool for discriminating of ginseng cultivars and cultivation ages. Therefore, we suggest that this result could be used as a rapid and reliable F1 hybrid seed-screening tool for accelerating the conventional breeding of ginseng.

  2. Multivariate Quantitative Chemical Analysis

    NASA Technical Reports Server (NTRS)

    Kinchen, David G.; Capezza, Mary

    1995-01-01

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

  3. Multivariate methods for the analysis of complex and big data in forensic sciences. Application to age estimation in living persons.

    PubMed

    Lefèvre, Thomas; Chariot, Patrick; Chauvin, Pierre

    2016-09-01

    Researchers handle increasingly higher dimensional datasets, with many variables to explore. Such datasets pose several problems, since they are difficult to handle and present unexpected features. As dimensionality increases, classical statistical analysis becomes inoperative. Variables can present redundancy, and the reduction of dataset dimensionality to its lowest possible value is often needed. Principal components analysis (PCA) has proven useful to reduce dimensionality but present several shortcomings. As others, forensic sciences will face the issues specific related to an evergrowing quantity of data to be integrated. Age estimation in living persons, an unsolved problem so far, could benefit from the integration of various sources of data, e.g., clinical, dental and radiological data. We present here novel multivariate techniques (nonlinear dimensionality reduction techniques, NLDR), applied to a theoretical example. Results were compared to those of PCA. NLDR techniques were then applied to clinical, dental and radiological data (13 variables) used for age estimation. The correlation dimension of these data was estimated. NLDR techniques outperformed PCA results. They showed that two living persons sharing similar characteristics may present rather different estimated ages. Moreover, data presented a very high informational redundancy, i.e., a correlation dimension of 2. NLDR techniques should be used with or preferred to PCA techniques to analyze complex and big data. Data routinely used for age estimation may not be considered suitable for this purpose. How integrating other data or approaches could improve age estimation in living persons is still uncertain.

  4. Age at Marriage and Marital Satisfaction: A Multivariate Analysis with Implications for Marital Stability

    ERIC Educational Resources Information Center

    Lee, Gary R.

    1977-01-01

    Relationships between age at marriage, marital role performance, and marital satisfaction were investigated. Results show the existence of small positive associations between age at marriage and marital satisfaction, and performance. (Author)

  5. Multivariate analysis in thoracic research.

    PubMed

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

    2015-03-01

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

  6. Multivariate analysis in thoracic research

    PubMed Central

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

    2015-01-01

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

  7. Multivariate image analysis in biomedicine.

    PubMed

    Nattkemper, Tim W

    2004-10-01

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

  8. Method of multivariate spectral analysis

    DOEpatents

    Keenan, Michael R.; Kotula, Paul G.

    2004-01-06

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

  9. A Multivariate Analysis of Age-Related Differences in Default Mode and Task Positive Networks Across Multiple Cognitive Domains

    PubMed Central

    Grady, Cheryl L.; Protzner, Andrea B.; Kovacevic, Natasa; Strother, Stephen C.; Afshin-Pour, Babak; Wojtowicz, Magda; Anderson, John A.E.; Churchill, Nathan; McIntosh, Anthony R.

    2011-01-01

    We explored the effects of aging on two large scale brain networks, the default mode network (DMN) and the task-positive network (TPN). During fMRI scanning, young and older participants carried out four visual tasks: detection, perceptual matching, attentional cueing, and working memory. Accuracy of performance was roughly matched at 80% across tasks and groups. Modulations of activity across conditions were assessed, as well as functional connectivity of both networks. Younger adults showed a broader engagement of the DMN, and older adults a more extensive engagement of the TPN. Functional connectivity in the DMN was reduced in older adults, whereas the main pattern of TPN connectivity was equivalent in the two groups. Age-specific connectivity also was seen in TPN regions. Increased activity in TPN areas predicted worse accuracy on the tasks, but greater expression of a connectivity pattern associated with a right dorsolateral prefrontal TPN region, seen only in older adults, predicted better performance. These results provide further evidence for age-related differences in the DMN, and new evidence of age differences in the TPN. Increased use of the TPN may reflect greater demand on cognitive control processes in older individuals that may be partially offset by alterations in prefrontal functional connectivity. PMID:19789183

  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.

  11. Time since discharge of 9mm cartridges by headspace analysis, part 2: Ageing study and estimation of the time since discharge using multivariate regression.

    PubMed

    Gallidabino, M; Romolo, F S; Weyermann, C

    2017-03-01

    Estimating the time since discharge of spent cartridges can be a valuable tool in the forensic investigation of firearm-related crimes. To reach this aim, it was previously proposed that the decrease of volatile organic compounds released during discharge is monitored over time using non-destructive headspace extraction techniques. While promising results were obtained for large-calibre cartridges (e.g., shotgun shells), handgun calibres yielded unsatisfying results. In addition to the natural complexity of the specimen itself, these can also be attributed to some selective choices in the methods development. Thus, the present series of papers aimed to systematically evaluate the potential of headspace analysis to estimate the time since discharge of cartridges through the use of more comprehensive analytical and interpretative techniques. Following the comprehensive optimisation and validation of an exhaustive headspace sorptive extraction (HSSE) method in the first part of this work, the present paper addresses the application of chemometric tools in order to systematically evaluate the potential of applying headspace analysis to estimate the time since discharge of 9mm Geco cartridges. Several multivariate regression and pre-treatment methods were tested and compared to univariate models based on non-linear regression. Random forests (RF) and partial least squares (PLS) proceeded by pairwise log-ratios normalisation (PLR) showed the best results, and allowed to estimate time since discharge up to 48h of ageing and to differentiate recently fired from older cartridges (e.g., less than 5h compared to more than 1-2 days). The proposed multivariate approaches showed significant improvement compared to univariate models. The effects of storage conditions were also tested and results demonstrated that temperature, humidity and cartridge position should be taken into account when estimating the time since discharge.

  12. 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.

  13. Detrended fluctuation analysis of multivariate time series

    NASA Astrophysics Data System (ADS)

    Xiong, Hui; Shang, P.

    2017-01-01

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

  14. 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.

  15. Schmidt decomposition and multivariate statistical analysis

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

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

  16. 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

  17. Multivariate analysis: greater insights into complex systems

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  18. 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.

  19. 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.

  20. Multivariate analysis of longitudinal rates of change.

    PubMed

    Bryan, Matthew; Heagerty, Patrick J

    2016-12-10

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

  1. 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.

  2. Classification of adulterated honeys by multivariate analysis.

    PubMed

    Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad

    2017-06-01

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

  3. Multivariate Analysis of Genotype–Phenotype Association

    PubMed Central

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

    2016-01-01

    With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated—in terms of effect size—with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype–phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype–phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype–phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype–phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3—the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the

  4. 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

  5. Tailored multivariate analysis for modulated enhanced diffraction

    DOE PAGES

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

    2015-10-21

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

  6. Tailored multivariate analysis for modulated enhanced diffraction

    SciTech Connect

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

    2015-10-21

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

  7. 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.

  8. 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.

  9. 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.

  10. The flyby anomaly: a multivariate analysis approach

    NASA Astrophysics Data System (ADS)

    Acedo, L.

    2017-02-01

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

  11. 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.

  12. Multivariate analysis methods for spectroscopic blood analysis

    NASA Astrophysics Data System (ADS)

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

    2012-01-01

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

  13. 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.

  14. 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).

  15. Gravitational-wave detection using multivariate analysis

    NASA Astrophysics Data System (ADS)

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

    2013-09-01

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

  16. 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…

  17. Cross-Modal Multivariate Pattern Analysis

    PubMed Central

    Meyer, Kaspar; Kaplan, Jonas T.

    2011-01-01

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

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

    ERIC Educational Resources Information Center

    Williams, John D.

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

  19. Exploratory Tobit Factor Analysis for Multivariate Censored Data.

    ERIC Educational Resources Information Center

    Kamakura, Wagner A.; Wedel, Michel

    2001-01-01

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

  20. Multi-Variable Analysis and Design Techniques.

    DTIC Science & Technology

    1981-09-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2004-06-01

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

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

    PubMed

    Nohara Atoh, M

    1981-05-01

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

  3. 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.

  4. Classification Techniques for Multivariate Data Analysis.

    DTIC Science & Technology

    1980-03-28

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

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

    PubMed

    Liu, Xiaotong; Shen, Han-Wei

    2016-01-01

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

  6. Nonlinear independent component analysis and multivariate time series analysis

    NASA Astrophysics Data System (ADS)

    Storck, Jan; Deco, Gustavo

    1997-02-01

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

  7. Multisample Analysis of Multivariate Ordinal Categorical Variables.

    ERIC Educational Resources Information Center

    Poon, Wai-Yin; Tang, Fung-Chu

    2002-01-01

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

  8. MICROSCOPE: A Software System for Multivariate Analysis.

    DTIC Science & Technology

    1984-06-01

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

  9. Multivariate Analysis of Solar Spectral Irradiance Measurements

    NASA Technical Reports Server (NTRS)

    Pilewskie, P.; Rabbette, M.

    2001-01-01

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

  10. 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.

  11. Virulence of Bacillus cereus: a multivariate analysis.

    PubMed

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

    2007-05-10

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

  12. 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…

  13. 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…

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

    NASA Technical Reports Server (NTRS)

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

    1999-01-01

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

  15. 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.

  16. Temporal MDS Plots for Analysis of Multivariate Data.

    PubMed

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

    2016-01-01

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

  17. Voxelwise multivariate analysis of multimodality magnetic resonance imaging.

    PubMed

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

    2014-03-01

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

  18. Multivariate meta-analysis using individual participant data

    PubMed Central

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

    2016-01-01

    When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. PMID:26099484

  19. Multivariate meta-analysis using individual participant data.

    PubMed

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

    2015-06-01

    When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models.

  20. Symbolic observability coefficients for univariate and multivariate analysis

    NASA Astrophysics Data System (ADS)

    Letellier, Christophe; Aguirre, Luis A.

    2009-06-01

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

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

    PubMed

    Arvanitoyannis, Ioannis S; Vlachos, Antonios

    2009-06-01

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

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

    EPA Pesticide Factsheets

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

  3. 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

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

    SciTech Connect

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

    2010-09-01

    this multivariate analysis will enable superior differentiation capabilities. In addition, noise and system artifacts challenge the analysis of GC-MS data collected on lower cost equipment, ubiquitous in commercial laboratories. This research has the potential to affect many areas of analytical chemistry including materials analysis, medical testing, and environmental surveillance. It could also provide a method to measure adsorption parameters for chemical interactions on various surfaces by measuring desorption as a function of temperature for mixtures. We have presented results of a novel method for examining offgas products of a common PDMS material. Our method involves utilizing a stepped TD/GC-MS data acquisition scheme that may be almost totally automated, coupled with multivariate analysis schemes. This method of data generation and analysis can be applied to a number of materials aging and thermal degradation studies.

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

    PubMed

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

    2011-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Tabarangao, Joel T.

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

  7. Multivariate Granger causality analysis of fMRI data.

    PubMed

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

    2009-04-01

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

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

    PubMed

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

    2008-08-01

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

  9. 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.

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

    NASA Astrophysics Data System (ADS)

    Groth, Andreas; Ghil, Michael

    2010-05-01

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

  11. Analytical and multivariate study of roman age architectural terracotta from northeast of Spain.

    PubMed

    Giménez, Rosario García; Villa, Raquel Vigil de la; Rosa, Paloma Recio de la; Domínguez, María Dolores Petit; Rucandio, María Isabel

    2005-02-28

    Roman culture employed architectural terracotta made from baked clay as original material to manufacture ceramic pieces. It was often used as a basis for construction of functional and/or decorative elements in roofs, such as plane and curve tiles as well as antefixes with their corresponding "imbrexes". Some of them are conserved nowadays. They were collected in Roman quarries discovered in old cities and villages sited in the Hispania Citerior (northeast of Spain in Roman age). A study of the origin and manufacturing process (moulding, baking, touching up and painting) of these terracotta pieces has been made on the basis of the data obtained from a physicochemical characterization of samples. The used techniques were mainly flame absorption and emission spectrometry for the elemental analysis (major and minor elements), dilatometry for the study of thermal behaviour, scanning electron microscopy (SEM) for observation of thin layers and X-ray diffraction spectrometry (XRD) for mineralogical composition. In addition, a supervised pattern recognition programme was applied to the results for a selected group of 85 samples and five variables (chromium, copper, lead, nickel and zinc contents). Dilatometry and SEM results showed baking temperatures of these materials below 900 degrees C and the existence of zones with very different porosity in the same ceramic piece. Results obtained from multielemental analysis and multivariate statistical study by linear discriminant analysis lead us to the following conclusions: (i) the high content of lead found in a large number of antefixes demonstrates the use of lead oxide as an additive in the lime grout treatment, (ii) different contents of Cu, Zn, Cr, and Ni were indicative of the use of varied clay types in the manufacture process (even in the same production centre) as well as of the existence of a pigmentation process, although this last affirmation is not corroborated by the presence of remains of evident painting in

  12. Prognosis in equine colic patients using multivariable analysis.

    PubMed Central

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

    1989-01-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1984-01-01

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

  14. 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.

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

    PubMed

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

    2017-02-14

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

  16. 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.

  17. Multivariate Granger Causality Analysis of Obesity Related Variables.

    PubMed

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

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

  18. 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.

  19. Sedimentary chemofacies characterization by means of multivariate analysis

    NASA Astrophysics Data System (ADS)

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

    2010-07-01

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

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

    NASA Astrophysics Data System (ADS)

    Jayarajah, Christine N.; Thompson, Michael

    2005-03-01

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

  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. 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

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

    PubMed

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

    2016-06-01

    OBJECTIVE Intrathecal baclofen infusion systems to manage severe spasticity and dystonia are associated with higher infection rates in children than in adults. Factors unique to this population, such as poor nutrition and physical limitations for pump placement, have been hypothesized as the reasons for this disparity. The authors assessed potential risk factors for infection in a multivariate analysis. METHODS Patients who underwent implantation of a programmable pump and intrathecal catheter for baclofen infusion at a single center between January 1, 2000, and March 1, 2012, were identified in this retrospective cohort study. The primary end point was infection. Potential risk factors investigated included preoperative (i.e., demographics, body mass index [BMI], gastrostomy tube, tracheostomy, previous spinal fusion), intraoperative (i.e., surgeon, antibiotics, pump size, catheter location), and postoperative (i.e., wound dehiscence, CSF leak, and number of revisions) factors. Univariate analysis was performed, and a multivariate logistic regression model was created to identify independent risk factors for infection. RESULTS A total of 254 patients were evaluated. The overall infection rate was 9.8%. Univariate analysis identified young age, shorter height, lower weight, dehiscence, CSF leak, and number of revisions within 6 months of pump placement as significantly associated with infection. Multivariate analysis identified young age, dehiscence, and number of revisions as independent risk factors for infection. CONCLUSIONS Young age, wound dehiscence, and number of revisions were independent risk factors for infection in this pediatric cohort. A low BMI and the presence of either a gastrostomy or tracheostomy were not associated with infection and may not be contraindications for this procedure.

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

    PubMed

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

    1998-02-01

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

  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. A multivariate analysis of childhood abdominal pain in Trinidad.

    PubMed

    Anatol, T I; Holder, Y

    1995-04-01

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

  7. 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.

  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

    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.

  11. 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.

  12. Multivariate image analysis for process monitoring and control

    NASA Astrophysics Data System (ADS)

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

    2001-02-01

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

  13. Multivariate analysis of dim elves from ISUAL observations

    NASA Astrophysics Data System (ADS)

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

    2015-08-01

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

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

    NASA Astrophysics Data System (ADS)

    Coble, Jamie; Orton, Christopher; Schwantes, Jon

    2017-04-01

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

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

    PubMed

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

    2012-06-01

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

  16. Gravitational Wave Detection of Compact Binaries Through Multivariate Analysis

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

  17. A multivariate analysis of arctic climate in GCMs

    SciTech Connect

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

    1994-08-01

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

  18. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2007-02-01

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

  20. 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

  1. 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.

  2. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-06-01

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

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

    DOE PAGES

    Coble, Jamie; Orton, Christopher; Schwantes, Jon

    2017-01-17

    The Multi-Isotope Process (MIP) Monitor provides an efficient means to monitor the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of key stages in the reprocessing stream in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor; PWR and BWR, respectively), initial enrichment, burn up, and cooling time. Simulated gammamore » spectra were used in this paper to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type for the three PWR and three BWR reactor designs studied. Locally weighted PLS models were fitted on-the-fly to estimate the remaining fuel characteristics. For the simulated gamma spectra considered, burn up was predicted with 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment with approximately 2% RMSPE. Finally, this approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.« less

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

    PubMed

    Kotula, Paul G; Keenan, Michael R

    2006-12-01

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

  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 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

  9. 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.

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

    PubMed

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

    2014-11-01

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

  11. Prognostic factors in nodular lymphomas: a multivariate analysis based on the Princess Margaret Hospital experience

    SciTech Connect

    Gospodarowicz, M.K.; Bush, R.S.; Brown, T.C.; Chua, T.

    1984-04-01

    A total of 1,394 patients with non-Hodgkin's lymphoma were treated at the Princess Margaret Hospital between January 1, 1967 and December 31, 1978. Overall actuarial survival of 525 patients with nodular lymphomas was 40% at 12 years; survival of patients with localized (Stage I and III) nodular lymphomas treated with radical radiation therapy was 58%. Significant prognostic factors defined by multivariate analysis included patient's age, stage, histology, tumor bulk, and presence of B symptoms. By combining prognostic factors, distinct prognostic groups have been identified within the overall population. Patients with Stage I and II disease, small or medium bulk, less than 70 years of age achieved 92% 12 year actuarial survival and a 73% relapse-free rate in 12 years of follow-up. These patients represent groups highly curable with irradiation.

  12. 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…

  13. 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.

  14. 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.

  15. 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…

  16. 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…

  17. 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.

  18. 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.

  19. Characterization of Chinese liquor aroma components during aging process and liquor age discrimination using gas chromatography combined with multivariable statistics

    PubMed Central

    Xu, M. L.; Yu, Y.; Ramaswamy, H. S.; Zhu, S. M.

    2017-01-01

    Chinese liquor aroma components were characterized during the aging process using gas chromatography (GC). Principal component and cluster analysis (PCA, CA) were used to discriminate the Chinese liquor age which has a great economic value. Of a total of 21 major aroma components identified and quantified, 13 components which included several acids, alcohols, esters, aldehydes and furans decreased significantly in the first year of aging, maintained the same levels (p > 0.05) for next three years and decreased again (p < 0.05) in the fifth year. On the contrary, a significant increase was observed in propionic acid, furfural and phenylethanol. Ethyl lactate was found to be the most stable aroma component during aging process. Results of PCA and CA demonstrated that young liquor (fresh) and aged liquors were well separated from each other, which is in consistent with the evolution of aroma components along with the aging process. These findings provide a quantitative basis for discriminating the Chinese liquor age and a scientific basis for further research on elucidating the liquor aging process, and a possible tool to guard against counterfeit and defective products. PMID:28059090

  20. Characterization of Chinese liquor aroma components during aging process and liquor age discrimination using gas chromatography combined with multivariable statistics

    NASA Astrophysics Data System (ADS)

    Xu, M. L.; Yu, Y.; Ramaswamy, H. S.; Zhu, S. M.

    2017-01-01

    Chinese liquor aroma components were characterized during the aging process using gas chromatography (GC). Principal component and cluster analysis (PCA, CA) were used to discriminate the Chinese liquor age which has a great economic value. Of a total of 21 major aroma components identified and quantified, 13 components which included several acids, alcohols, esters, aldehydes and furans decreased significantly in the first year of aging, maintained the same levels (p > 0.05) for next three years and decreased again (p < 0.05) in the fifth year. On the contrary, a significant increase was observed in propionic acid, furfural and phenylethanol. Ethyl lactate was found to be the most stable aroma component during aging process. Results of PCA and CA demonstrated that young liquor (fresh) and aged liquors were well separated from each other, which is in consistent with the evolution of aroma components along with the aging process. These findings provide a quantitative basis for discriminating the Chinese liquor age and a scientific basis for further research on elucidating the liquor aging process, and a possible tool to guard against counterfeit and defective products.

  1. Untangling the chemistry of port wine aging with the use of GC-FID, multivariate statistics, and network reconstruction.

    PubMed

    Jacobson, Dan; Monforte, Ana Rita; Silva Ferreira, António César

    2013-03-13

    Chromatography separates the different components of complex mixtures and generates a fingerprint representing the chemical composition of the sample. The resulting data structure depends on the characteristics of the detector used, univariate for devices such as a flame ionization detector (FID) or multivariate for mass spectroscopy (MS). This study addresses the potential use of a univariate signal for a nontargeted approach to (i) classify samples according to a given process or perturbation, (ii) evaluate the feasibility of developing a screening procedure to select candidates related to the process, and (iii) provide insight into the chemical mechanisms that are affected by the perturbation. To achieve this, it was necessary to use and develop methods for data preprocessing and visualization tools to assist an analytical chemist to view and interpret complex multidimensional data sets. Dichloromethane Port wine extracts were collected using GC-FID; the chromatograms were then aligned with correlation optimized warping (COW) and subsequently analyzed with multivariate statistics (MVA) by principal component analysis (PCA) and partial least-squares regression (PLS-R). Furthermore, wavelets were used for peak calling and alignment refinement, and the resulting matrix was used to perform kinetic network reconstruction via correlation networks and maximum spanning trees. Network-target correlation projections were used to screen for potential chromatographic regions/peaks related to aging mechanisms. Results from PLS between aligned chromatograms and target molecules showed high X to Y correlations of 0.91, 092, and 0.89 with 5-hydroxymethylfurfural (HMF) (Maillard), acetaldehyde (oxidation), and 4,5-dimethyl-(5H)-3-hydroxy-2-furanone, respectively. The context of the correlation (and therefore likely kinetic) relationships among compounds detected by GC-FID and the relationships between target compounds within different regions of the network can be clearly seen.

  2. 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.

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

    PubMed

    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.

  4. 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.

  5. [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.

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

    PubMed

    Creagh, U; Reilly, T

    1995-03-01

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

  7. 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.

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

    NASA Technical Reports Server (NTRS)

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

    1985-01-01

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

  9. 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

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

    PubMed

    Lazzeri, L; Colucci, R; Cammi, A; Dragoni, F; Moretti, S

    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.

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

    PubMed

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

    2013-05-01

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

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

    ERIC Educational Resources Information Center

    Gaotlhobogwe, Michael; Laugharne, Janet; Durance, Isabelle

    2011-01-01

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

  13. 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…

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

    PubMed

    Makambi, Kepher H; Seung, Hyunuk

    2015-01-01

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

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

    PubMed Central

    Stern, Yaakov

    2010-01-01

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

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

    PubMed

    Jackson, Daniel; Riley, Richard D

    2014-02-20

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

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

    PubMed Central

    Olswold, Curtis; Andrade, Mariza de

    2003-01-01

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

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

    PubMed

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

    2014-09-01

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

  20. 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

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

    PubMed

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

    2017-01-01

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

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

    PubMed

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

    2011-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Kovács, József; Bodnár, Nikolett; Török, Ákos

    2016-01-01

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

  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 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.

  6. Multivariate Genetic Analysis of Learning and Early Reading Development

    ERIC Educational Resources Information Center

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

    2013-01-01

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

  7. 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.

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

    PubMed

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

    2008-06-01

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

  9. 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.

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

    PubMed

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

    2012-03-01

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

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

    PubMed Central

    Urrechaga, Eloísa; Aguirre, Urko; Izquierdo, Silvia

    2013-01-01

    Introduction. Iron deficiency anemia and thalassemia are the most common causes of microcytic anemia. Powerful statistical computer programming enables sensitive discriminant analyses to aid in the diagnosis. We aimed at investigating the performance of the multiple discriminant analysis (MDA) to the differential diagnosis of microcytic anemia. Methods. The training group was composed of 200 β-thalassemia carriers, 65 α-thalassemia carriers, 170 iron deficiency anemia (IDA), and 45 mixed cases of thalassemia and acute phase response or iron deficiency. A set of potential predictor parameters that could detect differences among groups were selected: Red Blood Cells (RBC), hemoglobin (Hb), mean cell volume (MCV), mean cell hemoglobin (MCH), and RBC distribution width (RDW). The functions obtained with MDA analysis were applied to a set of 628 consecutive patients with microcytic anemia. Results. For classifying patients into two groups (genetic anemia and acquired anemia), only one function was needed; 87.9% β-thalassemia carriers, and 83.3% α-thalassemia carriers, and 72.1% in the mixed group were correctly classified. Conclusion. Linear discriminant functions based on hemogram data can aid in differentiating between IDA and thalassemia, so samples can be efficiently selected for further analysis to confirm the presence of genetic anemia. PMID:24093062

  12. 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

  13. Parental concern about vaccine safety in Canadian children partially immunized at age 2: a multivariable model including system level factors.

    PubMed

    MacDonald, Shannon E; Schopflocher, Donald P; Vaudry, Wendy

    2014-01-01

    Children who begin but do not fully complete the recommended series of childhood vaccines by 2 y of age are a much larger group than those who receive no vaccines. While parents who refuse all vaccines typically express concern about vaccine safety, it is critical to determine what influences parents of 'partially' immunized children. This case-control study examined whether parental concern about vaccine safety was responsible for partial immunization, and whether other personal or system-level factors played an important role. A random sample of parents of partially and completely immunized 2 y old children were selected from a Canadian regional immunization registry and completed a postal survey assessing various personal and system-level factors. Unadjusted odds ratios (OR) and adjusted ORs (aOR) were calculated with logistic regression. While vaccine safety concern was associated with partial immunization (OR 7.338, 95% CI 4.138-13.012), other variables were more strongly associated and reduced the strength of the relationship between concern and partial immunization in multivariable analysis (aOR 2.829, 95% CI 1.151-6.957). Other important factors included perceived disease susceptibility and severity (aOR 4.629, 95% CI 2.017-10.625), residential mobility (aOR 3.908, 95% CI 2.075-7.358), daycare use (aOR 0.310, 95% CI 0.144-0.671), number of needles administered at each visit (aOR 7.734, 95% CI 2.598-23.025) and access to a regular physician (aOR 0.219, 95% CI 0.057-0.846). While concern about vaccine safety may be addressed through educational strategies, this study suggests that additional program and policy-level strategies may positively impact immunization uptake.

  14. Chronological Age, Cognitions, and Practices in European American Mothers: A Multivariate Study of Parenting

    ERIC Educational Resources Information Center

    Bornstein, Marc H.; Putnick, Diane L.

    2007-01-01

    The authors studied multiple parenting cognitions and practices in European American mothers (N=262) who ranged from 15 to 47 years of age. All were 1st-time parents of 20-month-old children. Some age effects were 0; others were linear or nonlinear. Nonlinear age effects determined by spline regression showed significant associations to a "knot"…

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

    PubMed

    Segal, M N

    1992-01-01

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

  16. 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.

  17. Quantitative multivariate analysis of dynamic multicellular morphogenic trajectories.

    PubMed

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

    2015-07-01

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

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

    PubMed

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

    2015-11-06

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

  19. 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.

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

    DTIC Science & Technology

    1991-09-01

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

  1. 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

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

    PubMed

    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.

  3. Chronic myelogenous leukemia: a multivariate analysis of the associations of patient characteristics and therapy with survival.

    PubMed

    Kantarjian, H M; Smith, T L; McCredie, K B; Keating, M J; Walters, R S; Talpaz, M; Hester, J P; Bligham, G; Gehan, E; Freireich, E J

    1985-12-01

    The prognostic importance of patient pretreatment clinical and laboratory features was investigated in a group of 303 patients with Philadelphia chromosome-positive benign-phase chronic myelogenous leukemia. Intensive chemotherapy was given to 97 patients, and 78 underwent an early elective splenectomy. The overall median survival time, dated from hospital admission, was 39 months. Patient characteristics associated with shortened survival were age 60 years or older, black race, the presence of hepatomegaly, splenomegaly, symptoms, weight loss, and poor performance status. Adverse blood and bone marrow parameters were anemia, thrombocytosis or thrombocytopenia, a high proportion of peripheral blasts plus promyelocytes or of basophils, a high proportion of marrow blasts or basophils, decreased marrow megakaryocytes, and cytogenetic abnormalities in addition to the Philadelphia chromosome. Several of these factors were interrelated. A multivariate regression analysis demonstrated that the combination blood basophilia, race, additional cytogenetic abnormalities, age and marrow basophilia had the strongest predictive relationship to survival time. This resulted in a model segregating patients into low-, intermediate-, and high-risk groups, with median survivals of 53, 39, and 25 months, respectively. Another model was derived that did not include the marrow features and identified splenomegaly and platelet counts as adding to the prognosis prediction by blood basophilia, race, and age. Evaluation of the effect of therapy, after adjusting for differences in prognostic characteristics, showed that intensive chemotherapy was associated with survival prolongation among patients at intermediate and high risk of death. We conclude that a combination of pretreatment factors identifies different risk subcategories in patients with chronic myelogenous leukemia and is helpful in assessing overall prognosis and treatment effect.

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

    NASA Technical Reports Server (NTRS)

    Landgrebe, D. A.

    1974-01-01

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

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

    PubMed

    Casarrubea, Maurizio; Sorbera, Filippina; Crescimanno, Giuseppe

    2008-03-18

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

  6. 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.

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

    PubMed Central

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

    2006-01-01

    • Background and Aims One of the problems analysing cause–effect relationships of growth and environmental factors is that a single factor could be correlated with other ones directly influencing growth. One attempt to understand tropical trees' growth cause–effect relationships is integrating research about anatomical, physiological and environmental factors that influence growth in order to develop mathematical models. The relevance is to understand the nature of the process of growth and to model this as a function of the environment. • Methods The relationships of Aphananthe monoica, Pleuranthodendron lindenii and Psychotria costivenia radial growth and phenology with environmental factors (local climate, vertical strata microclimate and physical and chemical soil variables) were evaluated from April 2000 to September 2001. The association among these groups of variables was determined by generalized canonical correlation analysis (GCCA), which considers the probable associations of three or more data groups and the selection of the most important variables for each data group. • Key Results The GCCA allowed determination of a general model of relationships among tree phenology and radial growth with climate, microclimate and soil factors. A strong influence of climate in phenology and radial growth existed. Leaf initiation and cambial activity periods were associated with maximum temperature and day length, and vascular tissue differentiation with soil moisture and rainfall. The analyses of individual species detected different relationships for the three species. • Conclusions The analyses of the individual species suggest that each one takes advantage in a different way of the environment in which they are growing, allowing them to coexist. PMID:16822807

  8. 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

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

    PubMed

    Neupane, Binod; Beyene, Joseph

    2015-01-01

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

  10. 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.

  11. 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.

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

    DTIC Science & Technology

    2012-01-01

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

  13. Expectant management of vestibular schwannoma: a retrospective multivariate analysis of tumor growth and outcome.

    PubMed

    Hughes, Mark; Skilbeck, Christopher; Saeed, Shakeel; Bradford, Robert

    2011-09-01

    We conducted a retrospective observational study to assess the consequences of conservative management of vestibular schwannoma (VS). Data were collected from tertiary neuro-otological referral units in United Kingdom. The study included 59 patients who were managed conservatively with radiological diagnosis of VS. The main outcome measures were growth rate and rate of failure of conservative management. Multivariate analysis sought correlation between tumor growth and (i) demographic features, (ii) tumor characteristics. The mean tumor growth was 0.66 mm/y. 11 patients (19%) required intervention. Mean time to intervention was 37 months with two notable late "failures" occurring at 75 and 84 months. Tumors extending into the cerebellopontine angle (CPA) grew significantly faster than intracanalicular tumors (p = 0.0045). No association was found between growth rate and age, sex, tumor laterality, facial nerve function, and grade of hearing loss. Conservative management is acceptable for a subset of patients. Tumors extending into the CPA at diagnosis grow significantly faster than intracanalicular tumors. No growth within 5 years of surveillance does not guarantee a continued indolent growth pattern; surveillance must therefore continue.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  15. 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…

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

    ERIC Educational Resources Information Center

    Murtagh, F.

    1984-01-01

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

  17. 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…

  18. 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…

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

    ERIC Educational Resources Information Center

    Kirby, Peggy C.; Abernathy, Mari W.

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

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

    ERIC Educational Resources Information Center

    Jackson, Dan; Rollins, Katie; Coughlin, Patrick

    2014-01-01

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

  1. 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.

  2. 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…

  3. 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

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

    PubMed

    Ma, Yan; Mazumdar, Madhu

    2011-10-30

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-01-01

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

  6. Evaluation of beer deterioration by gas chromatography-mass spectrometry/multivariate analysis: a rapid tool for assessing beer composition.

    PubMed

    Rodrigues, João A; Barros, António S; Carvalho, Beatriz; Brandão, Tiago; Gil, Ana M; Ferreira, António C Silva

    2011-02-18

    Beer stability is a major concern for the brewing industry, as beer characteristics may be subject to significant changes during storage. This paper describes a novel non-targeted methodology for monitoring the chemical changes occurring in a lager beer exposed to accelerated aging (induced by thermal treatment: 18 days at 45 °C), using gas chromatography-mass spectrometry in tandem with multivariate analysis (GC-MS/MVA). Optimization of the chromatographic run was performed, achieving a threefold reduction of the chromatographic time. Although losing optimum resolution, rapid GC runs showed similar chromatographic profiles and semi-quantitative ability to characterize volatile compounds. To evaluate the variations on the global volatile signature (chromatographic profile and m/z pattern of fragmentation in each scan) of beer during thermal deterioration, a non-supervised multivariate analysis method, Principal Component Analysis (PCA), was applied to the GC-MS data. This methodology allowed not only the rapid identification of the degree of deterioration affecting beer, but also the identification of specific compounds of relevance to the thermal deterioration process of beer, both well established markers such as 5-hydroxymethylfufural (5-HMF), furfural and diethyl succinate, as well as other compounds, to our knowledge, newly correlated to beer aging.

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

    PubMed

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

    2015-11-01

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

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

    PubMed

    Wang, Liqun; Mizaikoff, Boris

    2008-07-01

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

  9. Use of step scan FT-IR and multivariate curve resolution to understand aging of propellant binder as a function of depth into the polymer material.

    SciTech Connect

    Rivera, Dion Arledge; Alam, Mary Kathleen

    2003-01-01

    A sample of polymeric propellant binder was aged from 0 to 60 days at 95 C and analyzed using FT-IR step scan photoacoustic spectroscopy. This technique has the ability of to obtain spectra of the polymer as a function of depth into the polymer material. Multivariate curve resolution was applied to the spectra data obtained to extract the contributions of the aged and un-aged spectral components from the spectra. It was found that multivariate curve resolution could efficiently separate highly overlapped spectra and yielded insights into the aging process.

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

    PubMed

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

    2014-08-01

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

  11. 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.

  12. Application of Maxent Multivariate Analysis to Define Climate-Change Effects on Species Distributions and Changes

    DTIC Science & Technology

    2014-09-01

    program called Maxent was used to per- form range-extent analyses for two animal species of interest to Army land managers: the Red-Cockaded Woodpecker...2007 Intergovernmental Panel on Climate Change report (IPCC 2007a) indicate that global surface temperature is likely to rise between 1.1 and 6.4 °C...analysis is the basis of modeling software package called Maxent (Phillips 2006), which applies a multivariate technique called maximum entropy analysis

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

    PubMed

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

    2012-11-01

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

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

    PubMed

    Guo, Hanqi; Xiao, He; Yuan, Xiaoru

    2012-09-01

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

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

    PubMed

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

    2016-01-01

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

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

    PubMed Central

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

    2017-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Long, C. L.

    1991-02-01

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

  18. 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

  19. 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.

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

    PubMed

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

    2005-06-15

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

  1. Chemical imaging of articular cartilage sections with Raman mapping, employing uni- and multi-variate methods for data analysis.

    PubMed

    Bonifacio, Alois; Beleites, Claudia; Vittur, Franco; Marsich, Eleonora; Semeraro, Sabrina; Paoletti, Sergio; Sergo, Valter

    2010-12-01

    Raman mapping in combination with uni- and multi-variate methods of data analysis is applied to articular cartilage samples. Main differences in biochemical composition and collagen fibers orientation between superficial, middle and deep zone of the tissue are readily observed in the samples. Collagen, non-collagenous proteins, proteoglycans and nucleic acids can be distinguished on the basis of their different spectral characteristics, and their relative abundance can be mapped in the label-free tissue samples, at so high a resolution as to permit the analysis at the level of single cells. Differences between territorial and inter-territorial matrix, as well as inhomogeneities in the inter-territorial matrix, are properly identified. Multivariate methods of data analysis prove to be complementary to the univariate approach. In particular, our partial least squares regression model gives a semiquantitative mapping of the biochemical constituents in agreement with average composition found in the literature. The combination of hierarchical and fuzzy cluster analysis succeeds in detecting variations between different regions of the extra-cellular matrix. Because of its characteristics as an imaging technique, Raman mapping could be a promising tool for studying biochemical changes in cartilage occurring during aging or osteoarthritis.

  2. 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

  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. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2013-01-01

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

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

    PubMed

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

    2012-09-01

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

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

    PubMed

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

    2010-12-08

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

  8. 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.

  9. 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.

  10. [Association between hip fractures and risk factors for osteoporosis. Multivariate analysis].

    PubMed

    Masoni, Ana; Morosano, Mario; Tomat, María Florencia; Pezzotto, Stella M; Sánchez, Ariel

    2007-01-01

    In this observational, case-control study, 376 inpatients were evaluated in order to determine the association of risk factors (RF) and hip fracture; 151 patients had osteoporotic hip fracture (cases); the remaining were controls. Data were obtained from medical charts, and through a standardized questionnaire about RF. Mean age of the sample (+/- SD) was 80.6 +/- 8.1 years, without statistically significant difference between cases and controls; the female:male ratio was 3:1 in both groups. Fractured women were older than men (82.5 +/- 8.1 vs. 79.7 +/- 7.2 years, respectively; p < 0.01). Physical activity, intake of alcohol and tobacco, and sun exposure were low in all patients. Falls among cases happened predominantly at home (p < 0.001). Among female cases, time spent in household duties was a RF (p = 0.007), which was absent in males. In multivariate analysis, the following RF were significantly more frequent: Cognitive impairment (p = 0.001), and previous falls (p < 0.0001); whereas the following protective factors were significantly different from controls: Calcium intake during youth (p < 0.0001), current calcium intake (p < 0.0001), and mechanical aid for walking (p < 0.0001). Evaluation of RF and protective factors may contribute to diminish the probability of hip fracture, through a modification of personal habits, and measures to prevent falls among elderly adults. Present information can help to develop local and national population-based strategies to diminish the burden of hip fractures for the health system.

  11. Differences in Adolescent Physical Fitness: A Multivariate Approach and Meta-analysis.

    PubMed

    Schutte, Nienke M; Nederend, Ineke; Hudziak, James J; de Geus, Eco J C; Bartels, Meike

    2016-03-01

    Physical fitness can be defined as a set of components that determine exercise ability and influence performance in sports. This study investigates the genetic and environmental influences on individual differences in explosive leg strength (vertical jump), handgrip strength, balance, and flexibility (sit-and-reach) in 227 healthy monozygotic and dizygotic twin pairs and 38 of their singleton siblings (mean age 17.2 ± 1.2). Heritability estimates were 49% (95% CI 35-60%) for vertical jump, 59% (95% CI 46-69%) for handgrip strength, 38% (95% CI 22-52%) for balance, and 77% (95% CI 69-83%) for flexibility. In addition, a meta-analysis was performed on all twin studies in children, adolescents and young adults reporting heritability estimates for these phenotypes. Fifteen studies, including results from our own study, were meta-analyzed by computing the weighted average heritability. This showed that genetic factors explained most of the variance in vertical jump (62%; 95% CI 47-77%, N = 874), handgrip strength (63%; 95% CI 47-73%, N = 4516) and flexibility (50%; 95% CI 38-61%, N = 1130) in children and young adults. For balance this was 35% (95% CI 19-51%, N = 978). Finally, multivariate modeling showed that the phenotypic correlations between the phenotypes in current study (0.07 < r < 0.27) were mostly driven by genetic factors. It is concluded that genetic factors contribute significantly to the variance in muscle strength, flexibility and balance; factors that may play a key role in the individual differences in adolescent exercise ability and sports performance.

  12. 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

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

    PubMed

    Gigi, S; Tangirala, A K

    2010-08-01

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

  17. 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.

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

    PubMed

    Martin, David; Boyle, Fergal

    2015-09-01

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

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

    PubMed

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

    2013-12-03

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

  20. A Multivariate Twin Study of Hippocampal Volume, Self-Esteem and Well-Being in Middle Aged Men

    PubMed Central

    Kubarych, Thomas S.; Prom-Wormley, Elizabeth C.; Franz, Carol E.; Panizzon, Matthew S.; Dale, Anders M.; Fischl, Bruce; Eyler, Lisa T.; Fennema-Notestine, Christine; Grant, Michael D.; Hauger, Richard L.; Hellhammer, Dirk H.; Jak, Amy J.; Jernigan, Terry L.; Lupien, Sonia J.; Lyons, Michael J.; Mendoza, Sally P.; Neale, Michael C.; Seidman, Larry J.; Tsuang, Ming T.; Kremen, William S.

    2012-01-01

    Self-esteem and well-being are important for successful aging, and some evidence suggests that self-esteem and well-being are associated with hippocampal volume, cognition, and stress responsivity. Whereas most of this evidence is based on studies of older adults, we investigated self-esteem, well-being and hippocampal volume in 474 male middle-age twins. Self-esteem was significantly positively correlated with hippocampal volume (.09, p=.03 for left hippocampus, .10, p=.04 for right). Correlations for well-being were not significant (ps ≫.05). There were strong phenotypic correlations between self-esteem and well-being (.72, p<.001) and between left and right hippocampal volume (.72, p<.001). In multivariate genetic analyses, a 2-factor AE model with well-being and self-esteem on one factor and left and right hippocampal volumes on the other factor fit the data better than Cholesky, independent pathway or common pathway models. The correlation between the two genetic factors was .12 (p=.03); the correlation between the environmental factors was .09 (p>05). Our results indicate that largely different genetic and environmental factors underlie self-esteem and well-being on the one hand and hippocampal volume on the other. PMID:22471516

  1. 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.

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

    DOEpatents

    Keenan, Michael R.

    2008-07-15

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

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

    PubMed

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

    2012-09-01

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

  4. 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.

  5. 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.

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

    PubMed

    Li, Wei; Ding, Cheng; Yin, Shaojun

    2015-01-01

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

  8. Heparin-induced thrombocytopenia (HIT II) in liver transplant recipients: a retrospective multivariate analysis of prognostic factors.

    PubMed

    Hüser, Norbert; Aßfalg, Volker; Reim, Daniel; Novotny, Alexander; Thorban, Stefan; Cheng, Zhangjun; Kornberg, Arno; Friess, Helmut; Büchler, Peter; Matevossian, Edouard

    2012-07-01

    We investigated the prevalence of HIT II in liver transplant recipients and analysed associated factors. In recipients with clinically suspected HIT II in the 4Ts pretest clinical scoring system HIPA-assay was performed. Next, 37 clinical variables were analysed retrospectively for their association with HIT II. Factors significantly correlated to our findings in univariate analysis were included in a multivariate model and binary logistic regression analysis. Among 46 recipients 21 patients were suspicious in the 4Ts pretest and 14 of them (30.4%) were diagnosed HIT-antibody positive. Patient's age (P = 0.001), postoperative dialysis (P = 0.028), and postoperative hospital stay (P = 0.035) were significantly associated with development of HIT-antibodies in univariate analysis. Postoperative dialysis and postoperative hospital stay turned out as epiphenomena of patient's age, the only independent predictor (P = 0.021). Using multiple χ(2) -testing, a cut-off could be calculated, assigning patients younger than 59 years to a low risk group and patients of 59 years and older to a high risk group. High incidence of peri-operative HIT II seroconversion in liver transplant recipients is not associated with factors known to induce thrombocyte activation, like blood products or cell-saver. Only patients' age was identified as independent predictor.

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

    PubMed Central

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

    2011-01-01

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

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

    PubMed

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

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Azami, Hamed; Escudero, Javier

    2017-01-01

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

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

    PubMed

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

    2017-02-01

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

  13. 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

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

    PubMed

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

    2017-03-01

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

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

    PubMed

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

    2012-01-01

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

  16. 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.

  17. 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.

  18. 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.

  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. Discrimination of base differences in oligonucleotides using mid-infrared spectroscopy and multivariate analysis.

    PubMed

    Kelly, Jemma G; Martin-Hirsch, Pierre L; Martin, Francis L

    2009-07-01

    Attenuated total reflection Fourier transform-infrared (ATR-FTIR) spectroscopy was employed to interrogate a panel of simple oligonucleotides designed to contain various base differences; combined with subsequent multivariate analysis, we set out to determine whether the specificity of this approach would point to a novel means for mutation detection. Oligonucleotides were designed that were 15 bases in length and contained various combinations of purines (adenine, guanine) or pyrimidines (cytosine, thymine). These were applied to 1 cm x 1 cm low-E reflective glass slides, and triplicate samples were interrogated using ATR-FTIR spectroscopy. Per oligonucleotide sample, 10 independent spectral acquisitions were obtained. Prior to multivariate analysis, infrared spectra were baseline-corrected and vector normalized over the 1750-760 cm(-1) region specific to the chemical bonds of organic molecules. Spectral categories were then analyzed using principal component analysis (PCA) followed by linear discriminant analysis (LDA). Scores plots revealed that PCA-LDA clearly segregated different oligonucleotide sequences, even in the presence of a single base difference. Loadings plots confirmed the chemical entities associated with distinguishing base differences. These results suggest that mid-IR spectroscopy might have future roles in interrogating polymorphic forms of a DNA template.

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

    PubMed

    Lifshits, A M

    1979-01-01

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

  2. 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 ...

  3. ToF-SIMS imaging of PE/PP polymer using multivariate analysis

    NASA Astrophysics Data System (ADS)

    Miyasaka, Toyomitsu; Ikemoto, Takashi; Kohno, Teiichiro

    2008-12-01

    The distribution of polyethylene (PE) and polypropylene (PP) in PE/PP blended-polymer film was determined by applying principal components analysis (PCA) and multivariate curve resolution (MCR) to time-of-flight secondary ion mass spectroscopy (ToF-SIMS) imaging, together with preprocessing by pixel binning, normalization, and autoscaling to increase image contrast by reducing topographic and charge-distribution effects. The PE/PP distribution was confirmed by MVA conducted on the image data over static limit. The MCR score with normalized-autoscaling was found to give the PE/PP distribution distinctly.

  4. Flow Injection Mass Spectroscopic Fingerprinting and Multivariate Analysis for Differentiation of Three Panax Species

    PubMed Central

    Chen, Pei; Harnly, James M.; Harrington, Peter de B.

    2013-01-01

    This study describes the use of spectral fingerprints acquired by flow injection(FI)-MS and multivariate analysis to differentiate three Panax species: P. ginseng, P. quinquefolius, and P. notoginseng. Data were acquired using both high resolution and unit resolution MS, and were processed using principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), partial least squares-discriminant analysis (PLS-DA), and a fuzzy rule-building expert system (FuRES). Both high and unit resolution MS allowed discrimination among the three Panax species. PLS-DA and FuRES provided classification with 100% accuracy while SIMCA provided classification accuracies of 77 and 88% by high- and low-resolution MS, respectively. The method does not quantify any of the sample components. With FI-MS, the analysis time was less than 2 min. PMID:21391484

  5. Characterisation of DNA methylation status using spectroscopy (mid-IR versus Raman) with multivariate analysis.

    PubMed

    Kelly, Jemma G; Najand, Ghazal M; Martin, Francis L

    2011-05-01

    Methylation status plays important roles in the regulation of gene expression and significantly influences the dynamics, bending and flexibility of DNA. The aim of this study was to determine whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) or Raman spectroscopy with subsequent multivariate analysis could determine methylation patterning in oligonucleotides variously containing 5-methylcytosine, cytosine and guanine bases. Applied to Low-E reflective glass slides, 10 independent spectral acquisitions were acquired per oligonucleotide sample. Resultant spectra were baseline-corrected and vector normalised over the 1750 cm(-1) -760 cm(-1) (for ATR-FTIR spectroscopy) or the 1750 cm(-1) -600 cm(-1) (for Raman spectroscopy) regions. Data were then analysed using principal component analysis (PCA) coupled with linear discriminant analysis (LDA). Exploiting this approach, biomolecular signatures enabling sensitive and specific discrimination of methylation patterning were derived. For DNA sequence and methylation analysis, this approach has the potential to be an important tool, especially when material is scarce.

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

    NASA Astrophysics Data System (ADS)

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

    2017-03-01

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

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

    PubMed Central

    Duarte, José Maurício Barbanti

    2016-01-01

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

  8. Analysis of non-stationary turbulent flows using Multivariate EMD and Matching Pursuits

    NASA Astrophysics Data System (ADS)

    Mohan, Arvind; Agostini, Lionel; Gaitonde, Datta; Visbal, Miguel

    2016-11-01

    Time-series analysis of highly transient non-stationary turbulent flow is challenging. Traditional Fourier based techniques are generally difficult to apply because of the highly aperiodic nature of the data. Another significant obstacle is assimilating multivariate data, such as multiple variables at a location or from different sources in a flow-field. Such an analysis has the potential to identify sensitive events common among these sources. In this work, we explore two techniques to address these challenges - Multivariate Empirical Mode Decomposition and Matching Pursuits, on deep dynamic stall of a plunging airfoil in a mixed laminar-transitional-turbulent regime. Although primarily used for neuroscience applications, we use them in fluid mechanics and highlight their significant potential to overcome limitations of more traditional techniques. Application of these methods highlight different stages in the development of stall. A first stage shows development of 2-D boundary layer oscillations at frequencies similar to those associated with trailing edge vortices. Subsequently, new instabilities arise due to imminent separation. The separation bubble itself is characterized by relatively higher frequency content, and further analysis indicates its 3-D collapse.

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

    NASA Astrophysics Data System (ADS)

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

    2016-11-01

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

  10. Multivariate analysis of maize disease resistances suggests a pleiotropic genetic basis and implicates a GST gene

    PubMed Central

    Wisser, Randall J.; Kolkman, Judith M.; Patzoldt, Megan E.; Holland, James B.; Yu, Jianming; Krakowsky, Matthew; Nelson, Rebecca J.; Balint-Kurti, Peter J.

    2011-01-01

    Plants are attacked by pathogens representing diverse taxonomic groups, such that genes providing multiple disease resistance (MDR) are expected to be under positive selection pressure. To address the hypothesis that naturally occurring allelic variation conditions MDR, we extended the framework of structured association mapping to allow for the analysis of correlated complex traits and the identification of pleiotropic genes. The multivariate analytical approach used here is directly applicable to any species and set of traits exhibiting correlation. From our analysis of a diverse panel of maize inbred lines, we discovered high positive genetic correlations between resistances to three globally threatening fungal diseases. The maize panel studied exhibits rapidly decaying linkage disequilibrium that generally occurs within 1 or 2 kb, which is less than the average length of a maize gene. The positive correlations therefore suggested that functional allelic variation at specific genes for MDR exists in maize. Using a multivariate test statistic, a glutathione S-transferase (GST) gene was found to be associated with modest levels of resistance to all three diseases. Resequencing analysis pinpointed the association to a histidine (basic amino acid) for aspartic acid (acidic amino acid) substitution in the encoded protein domain that defines GST substrate specificity and biochemical activity. The known functions of GSTs suggested that variability in detoxification pathways underlie natural variation in maize MDR. PMID:21490302

  11. Visualization of multivariate physiological data for cardiorespiratory fitness assessment through ECG (R-peak) analysis.

    PubMed

    Munoz, J E; Bermudez I Badia, S; Rubio, E; Cameirao, M S

    2015-01-01

    The recent rise and popularization of wearable and ubiquitous fitness sensors has increased our ability to generate large amounts of multivariate data for cardiorespiratory fitness (CRF) assessment. Consequently, there is a need to find new methods to visualize and interpret CRF data without overwhelming users. Current visualizations of CRF data are mainly tabular or in the form of stacked univariate plots. Moreover, normative data differs significantly between gender, age and activity, making data interpretation yet more challenging. Here we present a CRF assessment tool based on radar plots that provides a way to represent multivariate cardiorespiratory data from electrocardiographic (ECG) signals within its normative context. To that end, 5 parameters are extracted from raw ECG data using R-peak information: mean HR, SDNN, RMSSD, HRVI and the maximal oxygen uptake, VO2max. Our tool processes ECG data and produces a visualization of the data in a way that it is easy to compare between the performance of the user and normative data. This type of representation can assist both health professionals and non-expert users in the interpretation of CRF data.

  12. Novel Multivariate Analysis for Soil Carbon Measurements Using Laser-Induced Breakdown Spectroscopy

    SciTech Connect

    Martin, Madhavi Z; Labbe, Nicole; Andre, Nicolas O; Wullschleger, Stan D; Harris, Ronny D; Ebinger, Michael H

    2010-01-01

    Laser-induced breakdown spectroscopy (LIBS), a rapid and potentially field-deployable technology for estimating total carbon in soil, represents a novel approach to address important issues in soil science and carbon management. Our study has shown that models relating LIBS signal intensity at 247.85 nm to percent total carbon determined by dry combustion vary as a function of elemental and textural characteristics of the soil, and, to a lesser extent, wavelength and excitation energy of the laser. To better quantify these sources of variation, two wavelengths and three excitation energies were used to analyze soils from various locations. The emission line of carbon at 247.85 nm was pronounced at an excitation wavelength of 532 nm and energy of 45 mJ, but it was largely obscured by the 248.9 nm Fe line at 1064 nm and excitation energies of 90 and 135 mJ. Univariate analysis revealed linear, but soil-specific correlations between signal intensity at 247.85 nm and total carbon concentration. A single calibration model correlating LIBS spectra to carbon concentration in all samples was obtained using a multivariate approach. Several emission lines in addition to the strong carbon line contributed significantly to the multivariate model. These results show that multivariate analysis can be used to construct a robust calibration model for LIBS spectra and therein provide a reliable estimate of total soil carbon. Such results must be confirmed for a broader range of soils, yet crop and soil scientists, carbon managers, and instrument developers should find these results encouraging.

  13. Multivariate analysis for the optimization of polysaccharide-based nanoparticles prepared by self-assembly.

    PubMed

    Pistone, Sara; Qoragllu, Dafina; Smistad, Gro; Hiorth, Marianne

    2016-10-01

    Polysaccharide-based nanoparticles are promising carriers for drug delivery applications. The particle size influences the biodistribution of the nanoparticles; hence size distributions and polydispersity index (PDI) are critical characteristics. However, the preparation of stable particles with a low PDI is a challenging task and is usually based on empirical trials. In this study, we report the use of multivariate evaluation to optimize the formulation factors for the preparation of alginate-zinc nanoparticles by ionotropic gelation. The PDI was selected as the response variable. Particle size, size distributions, zeta potential and pH of the samples were also recorded. Two full factorial (mixed-level) designs were analyzed by partial least squares regression (PLS). In the first design, the influence of the polysaccharide and the crosslinker concentrations were studied. The results revealed that size distributions with a low PDI were obtained by using a low polysaccharide concentrations (0.03-0.05%) and a zinc concentration of 0.03% (w/w). However, a high polysaccharide concentration can be advantageous for drug delivery systems. Therefore, in the second design, a high alginate concentration was used (0.09%) and a reduction in the PDI was obtained by simultaneously increasing the ionic strength of the solvent and the zinc concentration. The multivariate analysis also revealed the interaction between the factors in terms of their effects on the PDI; hence, compared to traditional univariate analyses, the multivariate analysis allowed us to obtain a more complete understanding of the effects of the factors scrutinized. In addition, the results are considered useful in order to avoid extensive empirical tests for future formulation studies.

  14. Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin

    NASA Astrophysics Data System (ADS)

    zhang, L.

    2011-12-01

    Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be

  15. 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.

  16. Multivariate analysis of chromatographic retention data and lipophilicity of phenylacetamide derivatives.

    PubMed

    Vastag, Gyöngyi; Apostolov, Suzana; Perišić-Janjić, Nada; Matijević, Borko

    2013-03-12

    One of the most important physicochemical parameters of a molecule that determines its bioactivity is its lipophilicity. Cluster analysis (CA), principal component analysis (PCA), and sum of ranking differences (SRD) were used to compare the lipophilic parameters of twenty phenylacetamide derivatives, obtained experimentally as chromatographic retention data in the presence of different solvents and calculated by different mathematical methods. All the applied methods of multivariate analysis gave approximately similar grouping of the studied lipophilic parameters. In the attempt to group the investigated compounds in respect of their lipophilicity, the obtained results appeared to be dependent on the applied chemometric method. The CA and PCA, grouped the compounds on the basis of the nature of the substituents R1 and R2, indicating that they determine to a great extent the lipophilicity of the investigated molecules. Unlike them, the SRD method could not be used to group the studied compounds on the basis of their lipophilic character.

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

    PubMed

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

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Conceição, A. L. C.; Antoniassi, M.; Cunha, D. M.; Ribeiro-Silva, A.; Poletti, M. E.

    2011-10-01

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

  19. [Multivariate Adaptive Regression Splines (MARS), an alternative for the analysis of time series].

    PubMed

    Vanegas, Jairo; Vásquez, Fabián

    2016-12-19

    Multivariate Adaptive Regression Splines (MARS) is a non-parametric modelling method that extends the linear model, incorporating nonlinearities and interactions between variables. It is a flexible tool that automates the construction of predictive models: selecting relevant variables, transforming the predictor variables, processing missing values and preventing overshooting using a self-test. It is also able to predict, taking into account structural factors that might influence the outcome variable, thereby generating hypothetical models. The end result could identify relevant cut-off points in data series. It is rarely used in health, so it is proposed as a tool for the evaluation of relevant public health indicators. For demonstrative purposes, data series regarding the mortality of children under 5 years of age in Costa Rica were used, comprising the period 1978-2008.

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

    PubMed

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

    2017-03-22

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

  1. Cerebral Cortical Folding Analysis with Multivariate Modeling and Testing: Studies on Gender Differences and Neonatal Development

    PubMed Central

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

    2010-01-01

    This paper presents a novel statistical framework for human cortical folding pattern analysis that relies on a rich multivariate descriptor of folding patterns in a region of interest (ROI). The ROI-based approach avoids problems faced by spatial-normalization-based approaches stemming from the deficiency of homologous features between typical human cerebral cortices. Unlike typical ROI-based methods that summarize folding by a single number, the proposed descriptor unifies multiple characteristics of surface geometry in a high-dimensional space (hundreds/thousands of dimensions). In this way, the proposed framework couples the reliability of ROI-based analysis with the richness of the novel cortical folding pattern descriptor. This paper presents new mathematical insights into the relationship of cortical complexity with intra-cranial volume (ICV). It shows that conventional complexity descriptors implicitly handle ICV Differences in Different ways, thereby lending Different meanings to “complexity”. The paper proposes a new application of a non-parametric permutation-based approach for rigorous statistical hypothesis testing with multivariate cortical descriptors. The paper presents two cross-sectional studies applying the proposed framework to study folding Differences between genders and in neonates with complex congenital heart disease. Both studies lead to novel interesting results. PMID:20630489

  2. Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling.

    PubMed

    Korsgaard, Inge Riis; Lund, Mogens Sandø; Sorensen, Daniel; Gianola, Daniel; Madsen, Per; Jensen, Just

    2003-01-01

    A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories) or binary traits, where the grouping is determined via thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed.

  3. 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.

  4. Multivariate Analysis of Factors Associated With Axial Symptoms in Unilateral Expansive Open-Door Cervical Laminoplasty With Miniplate Fixation.

    PubMed

    Chen, Hua; Liu, Hao; Deng, Yuxiao; Gong, Quan; Li, Tao; Song, Yueming

    2016-01-01

    Retrospective case-control study.Unilateral expansive open-door cervical laminoplasty with miniplate fixation is an efficient and increasing popular surgery for multilevel cervical spondylotic myelopathy. Axial symptoms are the most frequent complaints after cervical laminoplasty. But the mechanisms have not been fully clarified yet.The objective of this study is to compare the clinical and radiologic data between patients with or without axial symptoms and to investigate the factors associated with axial symptoms by multivariate analysis in cervical laminoplasty with miniplate fixation.A total of 129 patients who underwent cervical laminoplasty with miniplate fixation were comprised from August 2009 to March 2014. Patients were grouped according to whether they suffered from postoperative axial symptoms (PA) or not (NA). The clinical data including gender, age, duration of symptoms, diagnosis type, medical comorbidity, operative level, blood loss, operative time, pre- and post-Japanese Orthopedic Association (JOA) score, JOA recovery rates, and other complications were recorded. The radiologic data including cervical canal diameter, C2-7 Cobb angle, cervical range of motion (ROM), cross-sectional area, open angle, hinge union, and facet joint destroyed would be measured according to X-ray plain and CT scan images. The univariate analysis and multivariate logistic regression analysis were performed.There were 39 patients in PA group and 90 patients in NA group. Both groups gained significant JOA improvement postoperatively (P < 0.05). The preoperative neck pain (P = 0.048), negative change of cervical ROM (P = 0.018), and facet joints destroyed (P = 0.022) were significant different between the 2 groups. There were no significant differences for other clinical and radiography parameters between the groups (P > 0.05). The multivariate analysis showed that the negative change of cervical ROM (OR = 1.062, P = 0.047) and facet joints

  5. Multivariate Analyses and Classification of Inertial Sensor Data to Identify Aging Effects on the Timed-Up-and-Go Test

    PubMed Central

    Vervoort, Danique; Vuillerme, Nicolas; Kosse, Nienke; Hortobágyi, Tibor; Lamoth, Claudine J. C.

    2016-01-01

    Many tests can crudely quantify age-related mobility decrease but instrumented versions of mobility tests could increase their specificity and sensitivity. The Timed-up-and-Go (TUG) test includes several elements that people use in daily life. The test has different transition phases: rise from a chair, walk, 180° turn, walk back, turn, and sit-down on a chair. For this reason the TUG is an often used test to evaluate in a standardized way possible decline in balance and walking ability due to age and or pathology. Using inertial sensors, qualitative information about the performance of the sub-phases can provide more specific information about a decline in balance and walking ability. The first aim of our study was to identify variables extracted from the instrumented timed-up-and-go (iTUG) that most effectively distinguished performance differences across age (age 18–75). Second, we determined the discriminative ability of those identified variables to classify a younger (age 18–45) and older age group (age 46–75). From healthy adults (n = 59), trunk accelerations and angular velocities were recorded during iTUG performance. iTUG phases were detected with wavelet-analysis. Using a Partial Least Square (PLS) model, from the 72-iTUG variables calculated across phases, those that explained most of the covariance between variables and age were extracted. Subsequently, a PLS-discriminant analysis (DA) assessed classification power of the identified iTUG variables to discriminate the age groups. 27 variables, related to turning, walking and the stand-to-sit movement explained 71% of the variation in age. The PLS-DA with these 27 variables showed a sensitivity and specificity of 90% and 85%. Based on this model, the iTUG can accurately distinguish young and older adults. Such data can serve as a reference for pathological aging with respect to a widely used mobility test. Mobility tests like the TUG supplemented with smart technology could be used in clinical

  6. Predictive diagnostic value for the clinical features accompanying intellectual disability in children with pathogenic copy number variations: a multivariate analysis

    PubMed Central

    2014-01-01

    Background Array comparative genomic hybridization (a-CGH) has become the first-tier investigation in patients with unexplained developmental delay/intellectual disability (DD/ID). Although the costs are progressively decreasing, a-CGH is still an expensive and labour-intensive technique: for this reason a definition of the categories of patients that can benefit the most of the analysis is needed. Aim of the study was to retrospectively analyze the clinical features of children with DD/ID attending the outpatient clinic of the Mother & Child Department of the University Hospital of Modena subjected to a-CGH, to verify by uni- and multivariate analysis the independent predictors of pathogenic CNVs. Methods 116 patients were included in the study. Data relative to the CNVs and to the patients’ clinical features were analyzed for genotype/phenotype correlations. Results and conclusions 27 patients (23.3%) presented pathogenic CNVs (21 deletions, 3 duplications and 3 cases with both duplications and deletions). Univariate analysis showed a significant association of the pathogenic CNVs with the early onset of symptoms (before 1 yr of age) and the presence of malformations and dysmorphisms. Logistic regression analysis showed a significant independent predictive value for diagnosing a pathogenic CNV for malformations (P = 0.002) and dysmorphisms (P = 0.023), suggesting that those features should address a-CGH analysis as a high-priority test for diagnosis. PMID:24775911

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

    PubMed

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

    2015-01-01

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

  8. Surgical workflow analysis with Gaussian mixture multivariate autoregressive (GMMAR) models: a simulation study.

    PubMed

    Loukas, Constantinos; Georgiou, Evangelos

    2013-01-01

    There is currently great interest in analyzing the workflow of minimally invasive operations performed in a physical or simulation setting, with the aim of extracting important information that can be used for skills improvement, optimization of intraoperative processes, and comparison of different interventional strategies. The first step in achieving this goal is to segment the operation into its key interventional phases, which is currently approached by modeling a multivariate signal that describes the temporal usage of a predefined set of tools. Although this technique has shown promising results, it is challenged by the manual extraction of the tool usage sequence and the inability to simultaneously evaluate the surgeon's skills. In this paper we describe an alternative methodology for surgical phase segmentation and performance analysis based on Gaussian mixture multivariate autoregressive (GMMAR) models of the hand kinematics. Unlike previous work in this area, our technique employs signals from orientation sensors, attached to the endoscopic instruments of a virtual reality simulator, without considering which tools are employed at each time-step of the operation. First, based on pre-segmented hand motion signals, a training set of regression coefficients is created for each surgical phase using multivariate autoregressive (MAR) models. Then, a signal from a new operation is processed with GMMAR, wherein each phase is modeled by a Gaussian component of regression coefficients. These coefficients are compared to those of the training set. The operation is segmented according to the prior probabilities of the surgical phases estimated via GMMAR. The method also allows for the study of motor behavior and hand motion synchronization demonstrated in each phase, a quality that can be incorporated into modern laparoscopic simulators for skills assessment.

  9. 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

  10. 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.

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

    PubMed

    Reiter, David A; Irrechukwu, Onyi; Lin, Ping-Chang; Moghadam, Somaieh; Von Thaer, Sarah; Pleshko, Nancy; Spencer, Richard G

    2012-03-01

    Noninvasive monitoring of tissue quality would be of substantial use in the development of cartilage tissue engineering strategies. Conventional MR parameters provide noninvasive measures of biophysical tissue properties and are sensitive to changes in matrix development, but do not clearly distinguish between groups with different levels of matrix development. Furthermore, MR outcomes are nonspecific, with particular changes in matrix components resulting in changes in multiple MR parameters. To address these limitations, we present two new approaches for the evaluation of tissue engineered constructs using MR, and apply them to immature and mature engineered cartilage after 1 and 5 weeks of development, respectively. First, we applied multiexponential T(2) analysis for the quantification of matrix macromolecule-associated water compartments. Second, we applied multivariate support vector machine analysis using multiple MR parameters to improve detection of degree of matrix development. Monoexponential T(2) values decreased with maturation, but without further specificity. Much more specific information was provided by multiexponential analysis. The T(2) distribution in both immature and mature constructs was qualitatively comparable to that of native cartilage. The analysis showed that proteoglycan-bound water increased significantly during maturation, from a fraction of 0.05 ± 0.01 to 0.07 ± 0.01. Classification of samples based on individual MR parameters, T(1), T(2), k(m) or apparent diffusion coefficient, showed that the best classifiers were T(1) and k(m), with classification accuracies of 85% and 84%, respectively. Support vector machine analysis improved the accuracy to 98% using the combination (k(m), apparent diffusion coefficient). These approaches were validated using biochemical and Fourier transform infrared imaging spectroscopic analyses, which showed increased proteoglycan and collagen with maturation. In summary, multiexponential T(2) and

  12. 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.

  13. Denoising and Multivariate Analysis of Time-Of-Flight SIMS Images

    SciTech Connect

    Wickes, Bronwyn; Kim, Y.; Castner, David G.

    2003-08-30

    Time-of-flight SIMS (ToF-SIMS) imaging offers a modality for simultaneously visualizing the spatial distribution of different surface species. However, the utility of ToF-SIMS datasets may be limited by their large size, degraded mass resolution and low ion counts per pixel. Through denoising and multivariate image analysis, regions of similar chemistries may be differentiated more readily in ToF-SIMS image data. Three established denoising algorithms down-binning, boxcar and wavelet filtering were applied to ToF-SIMS images of different surface geometries and chemistries. The effect of these filters on the performance of principal component analysis (PCA) was evaluated in terms of the capture of important chemical image features in the principal component score images, the quality of the principal component

  14. 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.

  15. Reflection and transmission mid-infrared spectroscopy for rapid determination of coal properties by multivariate analysis.

    PubMed

    Bona, M T; Andrés, J M

    2008-01-15

    In the present paper, the influence of different acquisition techniques (transmission, diffuse reflectance infrared Fourier transform and attenuated total reflectance) in the determination of nine coal properties related to combustion power plants has been studied. Raw coal samples of different origins were pooled for developing a correlation between the resultant spectra and the corresponding coal properties by multivariate analysis techniques. Thus, the existent collinearity in mid-infrared coal spectra led to the application of partial least squares regression (PLS), studying simultaneously the influence of different spectroscopic units as well as several spectral data mathematical pre-treatments. On the other hand, a principal component analysis (PCA) revealed a relationship between principal components and coal composition in both transmission and reflection techniques. Although the best accuracy and precision results were obtained for coal properties related to organic matter, the system was also able to differentiate coal samples attending to the presence of a specific mineral matter, kaolinite.

  16. 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.

  17. Simultaneous analysis of riboflavin and aromatic amino acids in beer using fluorescence and multivariate calibration methods.

    PubMed

    Sikorska, Ewa; Gliszczyńska-Swigło, Anna; Insińska-Rak, Małgorzata; Khmelinskii, Igor; De Keukeleire, Denis; Sikorski, Marek

    2008-04-21

    The study demonstrates an application of the front-face fluorescence spectroscopy combined with multivariate regression methods to the analysis of fluorescent beer components. Partial least-squares regressions (PLS1, PLS2, and N-way PLS) were utilized to develop calibration models between synchronous fluorescence spectra and excitation-emission matrices of beers, on one hand, and analytical concentrations of riboflavin and aromatic amino acids, on the other hand. The best results were obtained in the analysis of excitation-emission matrices using the N-way PLS2 method. The respective correlation coefficients, and the values of the root mean-square error of cross-validation (RMSECV), expressed as percentages of the respective mean analytic concentrations, were: 0.963 and 14% for riboflavin, 0.974 and 4% for tryptophan, 0.980 and 4% for tyrosine, and 0.982 and 19% for phenylalanine.

  18. [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.

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

    PubMed

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

    2014-06-01

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

  20. ADVANCING THE UNDERSTANDING OF BEHAVIORS ASSOCIATED WITH BACILLE CALMETTE GUÉRIN INFECTION USING MULTIVARIATE ANALYSIS

    PubMed Central

    Rodriguez-Zas, Sandra L.; Nixon, Scott E.; Lawson, Marcus A.; Mccusker, Robert H.; Southey, Bruce R.; O’Connor, Jason C.; Dantzer, Robert; Kelley, Keith W.

    2014-01-01

    Behavioral indicators in the murine Bacille Calmette Guérin (BCG) model of inflammation have been studied individually; however, the variability of the behaviors across BCG levels and the mouse-to-mouse variation within BCG-treatment group are only partially understood. The objectives of this study were: 1) to gain a comprehensive understanding of sickness and depression-like behaviors in a BCG model of inflammation using multivariate approaches, and 2) to explore behavioral differences between BCG-treatment groups and among mice within group. Adult mice were challenged with either 0mg (saline), 5mg or 10mg of BCG (BCG-treatment groups: BCG0, BCG5, or BCG10, respectively) at Day 0 of the experiment. Sickness indicators included body weight changes between Day 0 and Day 2 and between Day 2 and Day 5, and horizontal locomotor activity and vertical activity (rearing) measured at Day 6. Depression-like indicators included duration of immobility in the forced swim test and in the tail suspension test at Day 6 and sucrose consumption in the sucrose preference test at Day 7. The simultaneous consideration of complementary sickness and depression-like indicators enabled a more precise characterization of behavioral changes associated with BCG-treatment and of mouse-to-mouse variation, relative to the analysis of indicators individually. Univariate and multivariate analyses confirmed differences between BCG-treatment groups in weight change early on the trial. Significant differences between BCG-treatment groups in depression-like behaviors were still measurable after Day 5. The potential for multivariate models to account for the correlation between behavioral indicators and to augment the analytical precision relative to univariate models was demonstrated both for sickness and for depression-like indicators. Unsupervised learning approaches revealed the complementary information provided by the sickness and depression-like indicators considered. Supervised learning

  1. 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

  2. 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

  3. 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.

  4. 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

  5. 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.

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

    PubMed Central

    Chen, Gang; Adleman, Nancy E.; Saad, Ziad S.; Leibenluft, Ellen; Cox, RobertW.

    2014-01-01

    All neuroimaging packages can handle group analysis with t-tests or general linear modeling (GLM). However, they are quite hamstrung when there are multiple within-subject factors or when quantitative covariates are involved in the presence of a within-subject factor. In addition, sphericity is typically assumed for the variance–covariance structure when there are more than two levels in a within-subject factor. To overcome such limitations in the traditional AN(C)OVA and GLM, we adopt a multivariate modeling (MVM) approach to analyzing neuroimaging data at the group level with the following advantages: a) there is no limit on the number of factors as long as sample sizes are deemed appropriate; b) quantitative covariates can be analyzed together with within- subject factors; c) when a within-subject factor is involved, three testing methodologies are provided: traditional univariate testing (UVT)with sphericity assumption (UVT-UC) and with correction when the assumption is violated (UVT-SC), and within-subject multivariate testing (MVT-WS); d) to correct for sphericity violation at the voxel level, we propose a hybrid testing (HT) approach that achieves equal or higher power via combining traditional sphericity correction methods (Greenhouse–Geisser and Huynh–Feldt) with MVT-WS. PMID:24954281

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

    SciTech Connect

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

    1999-04-27

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

  8. 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.

  9. Multivariate General Linear Models (MGLM) on Riemannian Manifolds with Applications to Statistical Analysis of Diffusion Weighted Images

    PubMed Central

    Kim, Hyunwoo J.; Adluru, Nagesh; Collins, Maxwell D.; Chung, Moo K.; Bendlin, Barbara B.; Johnson, Sterling C.; Davidson, Richard J.; Singh, Vikas

    2014-01-01

    Linear regression is a parametric model which is ubiquitous in scientific analysis. The classical setup where the observations and responses, i.e., (xi, yi) pairs, are Euclidean is well studied. The setting where yi is manifold valued is a topic of much interest, motivated by applications in shape analysis, topic modeling, and medical imaging. Recent work gives strategies for max-margin classifiers, principal components analysis, and dictionary learning on certain types of manifolds. For parametric regression specifically, results within the last year provide mechanisms to regress one real-valued parameter, xi ∈ R, against a manifold-valued variable, yi ∈ . We seek to substantially extend the operating range of such methods by deriving schemes for multivariate multiple linear regression —a manifold-valued dependent variable against multiple independent variables, i.e., f : Rn → . Our variational algorithm efficiently solves for multiple geodesic bases on the manifold concurrently via gradient updates. This allows us to answer questions such as: what is the relationship of the measurement at voxel y to disease when conditioned on age and gender. We show applications to statistical analysis of diffusion weighted images, which give rise to regression tasks on the manifold GL(n)/O(n) for diffusion tensor images (DTI) and the Hilbert unit sphere for orientation distribution functions (ODF) from high angular resolution acquisition. The companion open-source code is available on nitrc.org/projects/riem_mglm. PMID:25580070

  10. Multivariate mixed linear model analysis of longitudinal data: an information-rich statistical technique for analyzing plant disease resistance.

    PubMed

    Veturi, Yogasudha; Kump, Kristen; Walsh, Ellie; Ott, Oliver; Poland, Jesse; Kolkman, Judith M; Balint-Kurti, Peter J; Holland, James B; Wisser, Randall J

    2012-11-01

    ABSTRACT The mixed linear model (MLM) is an advanced statistical technique applicable to many fields of science. The multivariate MLM can be used to model longitudinal data, such as repeated ratings of disease resistance taken across time. In this study, using an example data set from a multi-environment trial of northern leaf blight disease on 290 maize lines with diverse levels of resistance, multivariate MLM analysis was performed and its utility was examined. In the population and environments tested, genotypic effects were highly correlated across disease ratings and followed an autoregressive pattern of correlation decay. Because longitudinal data are often converted to the univariate measure of area under the disease progress curve (AUDPC), comparisons between univariate MLM analysis of AUDPC and multivariate MLM analysis of longitudinal data were made. Univariate analysis had the advantage of simplicity and reduced computational demand, whereas multivariate analysis enabled a comprehensive perspective on disease development, providing the opportunity for unique insights into disease resistance. To aid in the application of multivariate MLM analysis of longitudinal data on disease resistance, annotated program syntax for model fitting is provided for the software ASReml.

  11. 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

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

  12. 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

  13. 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.

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

    PubMed Central

    Agostini, M.; Bedin, C.; Enzo, M.V.; Molin, L.; Traldi, P.; D'Angelo, E.; Maschietto, E.; Serraglia, R.; Ragazzi, E.; Prevedello, L.; Foletto, M.; Nitti, D.

    2013-01-01

    Background: Obesity is currently epidemic in many countries worldwide and is strongly related to diabetes and cardiovascular disease. Mass spectrometry, in particular matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) is currently used for detecting different pattern of expressed protein. This study investigated the differences in low molecular weight (LMW) peptide profiles between obese and normal-weight subjects in combination with multivariate statistical analysis. Materials: Serum samples of 60 obese patients and 10 healthy subjects were treated by cut-off membrane (30000 Da) to remove the most abundant proteins. The filtrates containing the LMW protein/peptides were analyzed by MALDI-TOF mass spectrometry. Dataset was elaborated to align and normalize the spectra. We performed cluster analysis and principal component analysis to detect some ionic species that could characterize and classify the subject groups. Results: We observed a down-expression of ionic species at m/z 655.94 and an over-expression of species at m/z 1518.78, 1536.77, 1537.78 and 1537.81 in obese patients. Furthermore we found some ionic species that can distinguish obese patients with diabetes from those with normal glucose level. Conclusion: Serum peptide profile of LMW associate with multivariate statistical approach was revealed as a promising tool to discriminate and characterize obese patients and it was able to stratify them in relation to comorbidity that usually are associated with this disease. Further research involving a larger sample will be required to validate these findings. PMID:23396294

  15. Detection of cervical lesions by multivariate analysis of diffuse reflectance spectra: a clinical study.

    PubMed

    Prabitha, Vasumathi Gopala; Suchetha, Sambasivan; Jayanthi, Jayaraj Lalitha; Baiju, Kamalasanan Vijayakumary; Rema, Prabhakaran; Anuraj, Koyippurath; Mathews, Anita; Sebastian, Paul; Subhash, Narayanan

    2016-01-01

    Diffuse reflectance (DR) spectroscopy is a non-invasive, real-time, and cost-effective tool for early detection of malignant changes in squamous epithelial tissues. The present study aims to evaluate the diagnostic power of diffuse reflectance spectroscopy for non-invasive discrimination of cervical lesions in vivo. A clinical trial was carried out on 48 sites in 34 patients by recording DR spectra using a point-monitoring device with white light illumination. The acquired data were analyzed and classified using multivariate statistical analysis based on principal component analysis (PCA) and linear discriminant analysis (LDA). Diagnostic accuracies were validated using random number generators. The receiver operating characteristic (ROC) curves were plotted for evaluating the discriminating power of the proposed statistical technique. An algorithm was developed and used to classify non-diseased (normal) from diseased sites (abnormal) with a sensitivity of 72 % and specificity of 87 %. While low-grade squamous intraepithelial lesion (LSIL) could be discriminated from normal with a sensitivity of 56 % and specificity of 80 %, and high-grade squamous intraepithelial lesion (HSIL) from normal with a sensitivity of 89 % and specificity of 97 %, LSIL could be discriminated from HSIL with 100 % sensitivity and specificity. The areas under the ROC curves were 0.993 (95 % confidence interval (CI) 0.0 to 1) and 1 (95 % CI 1) for the discrimination of HSIL from normal and HSIL from LSIL, respectively. The results of the study show that DR spectroscopy could be used along with multivariate analytical techniques as a non-invasive technique to monitor cervical disease status in real time.

  16. Spatial assessment of air quality patterns in Malaysia using multivariate analysis

    NASA Astrophysics Data System (ADS)

    Dominick, Doreena; Juahir, Hafizan; Latif, Mohd Talib; Zain, Sharifuddin M.; Aris, Ahmad Zaharin

    2012-12-01

    This study aims to investigate possible sources of air pollutants and the spatial patterns within the eight selected Malaysian air monitoring stations based on a two-year database (2008-2009). The multivariate analysis was applied on the dataset. It incorporated Hierarchical Agglomerative Cluster Analysis (HACA) to access the spatial patterns, Principal Component Analysis (PCA) to determine the major sources of the air pollution and Multiple Linear Regression (MLR) to assess the percentage contribution of each air pollutant. The HACA results grouped the eight monitoring stations into three different clusters, based on the characteristics of the air pollutants and meteorological parameters. The PCA analysis showed that the major sources of air pollution were emissions from motor vehicles, aircraft, industries and areas of high population density. The MLR analysis demonstrated that the main pollutant contributing to variability in the Air Pollutant Index (API) at all stations was particulate matter with a diameter of less than 10 μm (PM10). Further MLR analysis showed that the main air pollutant influencing the high concentration of PM10 was carbon monoxide (CO). This was due to combustion processes, particularly originating from motor vehicles. Meteorological factors such as ambient temperature, wind speed and humidity were also noted to influence the concentration of PM10.

  17. Association vs. causality in transfusion medicine: understanding multivariable analysis in prediction vs. etiologic research.

    PubMed

    Zalpuri, Saurabh; Middelburg, Rutger A; van de Watering, Leo; Vamvakas, Eleftherios; Zwaginga, Jaap Jan; van der Bom, Johanna G

    2013-04-01

    In the current medical literature, etiologic and prediction research aims are frequently confused. Investigators tend to use principles from prediction research for their etiologic research questions, which results in misleading interpretation of risk factor findings at hand. We used a questionnaire-based survey to quantify the proportion of International Society of Blood Transfusion (ISBT) 2012, Cancun, visitors who felt confident with a causal interpretation of a stepwise logistic regression model. We designed and distributed a short online questionnaire survey addressing questions about a constructed abstract entitled "Association of transfusion and clinical outcomes in a large cohort" among the participants of ISBT 2012, Cancun. In addition to asking questions about the demographics (age, sex, country of employment, and highest education level) of the participants, we designed 7 statements representing possible interpretations of the findings presented in the abstract and asked the participants to mark Agree, Disagree, or Do Not Know for each statement. Based on the responses to these statements, we quantified the proportion of participants who inferred causality from stepwise multivariable models built to examine a question of association (or prediction).Thirty percent to 40% of the respondents agreed that a stepwise model was a valid method to adjust for confounding, and 60% of them agreed to a causal interpretation of a model built for prediction purposes. These findings suggest that a large proportion of ISBT visitors confuse etiology with prediction in the published transfusion medicine research. Using the results as a platform, we aim to delineate the distinction between etiologic and prediction research, issues of confounding accompanying these research aims and how a multivariable model deals with confounding.

  18. 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.

  19. Bioleaching kinetics and multivariate analysis of spent petroleum catalyst dissolution using two acidophiles.

    PubMed

    Pradhan, Debabrata; Mishra, Debaraj; Kim, Dong J; Ahn, Jong G; Chaudhury, G Roy; Lee, Seoung W

    2010-03-15

    Bioleaching studies were conducted to evaluate the recovery of metal values from waste petroleum catalyst using two different acidophilic microorganisms, Acidithiobacillus ferrooxidans and Acidithiobacillus thiooxidans. Various leaching parameters such as contact time, pH, oxidant concentration, pulp densities, particle size, and temperature were studied in detail. Activation energy was evaluated from Arrhenius equation and values for Ni, V and Mo were calculated in case of both the acidophiles. In both cases, the dissolution kinetics of Mo was lower than those of V and Ni. The lower dissolution kinetics may have been due to the formation of a sulfur product layer, refractoriness of MoS(2) or both. Multivariate statistical data were presented to interpret the leaching data in the present case. The significance of the leaching parameters was derived through principle component analysis and multi linear regression analyses for both iron and sulfur oxidizing bacteria.

  20. 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.

  1. 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.

  2. FEA and Multivariate Statistical Data Analysis of Polypropylene Tube Forming Process

    NASA Astrophysics Data System (ADS)

    Gavrilidou, Galini; Jain, Mukesh

    2005-08-01

    In present, the automotive and aircraft industries, give a lot of attention to plastic materials due to the advantages of light weight, low cost, and extra strength. Although the metal gas forming is a well-known process, the plastic tube forming is a quite new area of research. In this paper, the FEA of the tube forming process of polypropylene tube with different material properties and process parameters and the following multivariate statistical data analysis is presented. The FEA is performed with implementation of axisymmetrical model in Abaqus Explicit. The quality of the final product is introduced as a tube wall thickness distribution along the length of the tube. The product quality dependence on the material properties and process parameters will be illustrated in this research. Also a technique of the optimal tube wall thickness distribution is proposed. This technique allows to adjust the manipulated variables trajectories, such as temperature, strain rate, internal pressure and axial displacement.

  3. 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.

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

    PubMed

    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.

  5. 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.

  6. 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.

  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 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.

  9. A comparative study of multivariable robustness analysis methods as applied to integrated flight and propulsion control

    NASA Technical Reports Server (NTRS)

    Schierman, John D.; Lovell, T. A.; Schmidt, David K.

    1993-01-01

    Three multivariable robustness analysis methods are compared and contrasted. The focus of the analysis is on system stability and performance robustness to uncertainty in the coupling dynamics between two interacting subsystems. Of particular interest is interacting airframe and engine subsystems, and an example airframe/engine vehicle configuration is utilized in the demonstration of these approaches. The singular value (SV) and structured singular value (SSV) analysis methods are compared to a method especially well suited for analysis of robustness to uncertainties in subsystem interactions. This approach is referred to here as the interacting subsystem (IS) analysis method. This method has been used previously to analyze airframe/engine systems, emphasizing the study of stability robustness. However, performance robustness is also investigated here, and a new measure of allowable uncertainty for acceptable performance robustness is introduced. The IS methodology does not require plant uncertainty models to measure the robustness of the system, and is shown to yield valuable information regarding the effects of subsystem interactions. In contrast, the SV and SSV methods allow for the evaluation of the robustness of the system to particular models of uncertainty, and do not directly indicate how the airframe (engine) subsystem interacts with the engine (airframe) subsystem.

  10. 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).

  11. 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

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

    NASA Astrophysics Data System (ADS)

    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.

  13. 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.

  14. Abnormal Brain Areas Common to the Focal Epilepsies: Multivariate Pattern Analysis of fMRI.

    PubMed

    Pedersen, Mangor; Curwood, Evan K; Vaughan, David N; Omidvarnia, Amir H; Jackson, Graeme D

    2016-04-01

    Individuals with focal epilepsy have heterogeneous sites of seizure origin. However, there may be brain regions that are common to most cases of intractable focal epilepsy. In this study, we aim to identify these using multivariate analysis of task-free functional MRI. Fourteen subjects with extratemporal focal epilepsy and 14 healthy controls were included in the study. Task-free functional MRI data were used to calculate voxel-wise regional connectivity with regional homogeneity (ReHo) and weighted degree centrality (DCw), in addition to regional activity using fraction of amplitude of low-frequency fluctuations (fALFF). Multivariate pattern analysis was applied to each of these metrics to discriminate brain areas that differed between focal epilepsy subjects and healthy controls. ReHo and DCw classified focal epilepsy subjects from healthy controls with high accuracy (89.3% and 75%, respectively). However, fALFF did not significantly classify patients from controls. Increased regional network activity in epilepsy subjects was seen in the ipsilateral piriform cortex, insula, and thalamus, in addition to the dorsal anterior cingulate cortex and lateral frontal cortices. Decreased regional connectivity was observed in the ventromedial prefrontal cortex, as well as lateral temporal cortices. Patients with extratemporal focal epilepsy have common areas of abnormality (ReHo and DCw measures), including the ipsilateral piriform cortex, temporal neocortex, and ventromedial prefrontal cortex. ReHo shows additional increase in the "salience network" that includes anterior insula and anterior cingulate cortex. DCw showed additional effects in the ipsilateral thalamus and striatum. These brain areas may represent key regional network properties underlying focal epilepsy.

  15. 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.

  16. Risk factors for intraoperative massive transfusion in pediatric liver transplantation: a multivariate analysis

    PubMed Central

    Jin, Seok-Joon; Kim, Sun-Key; Choi, Seong-Soo; Kang, Keum Nae; Rhyu, Chang Joon; Hwang, Shin; Lee, Sung-Gyu; Namgoong, Jung-Man; Kim, Young-Kug

    2017-01-01

    Background: Pediatric liver transplantation (LT) is strongly associated with increased intraoperative blood transfusion requirement and postoperative morbidity and mortality. In the present study, we aimed to assess the risk factors associated with massive transfusion in pediatric LT, and examined the effect of massive transfusion on the postoperative outcomes. Methods: We enrolled pediatric patients who underwent LT between December 1994 and June 2015. Massive transfusion was defined as the administration of red blood cells ≥100% of the total blood volume during LT. The cases of pediatric LT were assigned to the massive transfusion or no-massive transfusion (administration of red blood cells <100% of the total blood volume during LT) group. Univariate and multivariate logistic regression analyses were performed to evaluate the risk factors associated with massive transfusion in pediatric LT. Kaplan-Meier survival analysis, with the log rank test, was used to compare graft and patient survival within 6 months after pediatric LT between the 2 groups. Results: The total number of LT was 112 (45.0%) and 137 (55.0%) in the no-massive transfusion and massive transfusion groups, respectively. Multivariate logistic regression analysis indicated that high white blood cell (WBC) count, low platelet count, and cadaveric donors were significant predictive factors of massive transfusion during pediatric LT. The graft failure rate within 6 months in the massive transfusion group tended to be higher than that in the no-massive transfusion group (6.6% vs. 1.8%, P = 0.068). However, the patient mortality rate within 6 months did not differ significantly between the massive transfusion and no-massive transfusion groups (7.3% vs. 7.1%, P = 0.964). Conclusion: Massive transfusion during pediatric LT is significantly associated with a high WBC count, low platelet count, and cadaveric donor. This finding can provide a better understanding of perioperative blood transfusion management

  17. Technique based on LED multispectral imaging and multivariate analysis for monitoring the conservation state of the Dead Sea Scrolls.

    PubMed

    Marengo, Emilio; Manfredi, Marcello; Zerbinati, Orfeo; Robotti, Elisa; Mazzucco, Eleonora; Gosetti, Fabio; Bearman, Greg; France, Fenella; Shor, Pnina

    2011-09-01

    The aim of this project is the development of a noninvasive technique based on LED multispectral imaging (MSI) for monitoring the conservation state of the Dead Sea Scrolls (DSS) collection. It is well-known that changes in the parchment reflectance drive the transition of the scrolls from legible to illegible. Capitalizing on this fact, we will use spectral imaging to detect changes in the reflectance before they become visible to the human eye. The technique uses multivariate analysis and statistical process control theory. The present study was carried out on a "sample" parchment of calfskin. The monitoring of the surface of a commercial modern parchment aged consecutively for 2 h and 6 h at 80 °C and 50% relative humidity (ASTM) was performed at the Imaging Lab of the Library of Congress (Washington, DC, U.S.A.). MSI is here carried out in the vis-NIR range limited to 1 μm, with a number of bands of 13 and bandwidths that range from about 10 nm in UV to 40 nm in IR. Results showed that we could detect and locate changing pixels, on the basis of reflectance changes, after only a few "hours" of aging.

  18. Multivariate Statistical Analysis as a Supplementary Tool for Interpretation of Variations in Salivary Cortisol Level in Women with Major Depressive Disorder

    PubMed Central

    Dziurkowska, Ewelina; Wesolowski, Marek

    2015-01-01

    Multivariate statistical analysis is widely used in medical studies as a profitable tool facilitating diagnosis of some diseases, for instance, cancer, allergy, pneumonia, or Alzheimer's and psychiatric diseases. Taking this in consideration, the aim of this study was to use two multivariate techniques, hierarchical cluster analysis (HCA) and principal component analysis (PCA), to disclose the relationship between the drugs used in the therapy of major depressive disorder and the salivary cortisol level and the period of hospitalization. The cortisol contents in saliva of depressed women were quantified by HPLC with UV detection day-to-day during the whole period of hospitalization. A data set with 16 variables (e.g., the patients' age, multiplicity and period of hospitalization, initial and final cortisol level, highest and lowest hormone level, mean contents, and medians) characterizing 97 subjects was used for HCA and PCA calculations. Multivariate statistical analysis reveals that various groups of antidepressants affect at the varying degree the salivary cortisol level. The SSRIs, SNRIs, and the polypragmasy reduce most effectively the hormone secretion. Thus, both unsupervised pattern recognition methods, HCA and PCA, can be used as complementary tools for interpretation of the results obtained by laboratory diagnostic methods. PMID:26380376

  19. Multivariate Statistical Analysis as a Supplementary Tool for Interpretation of Variations in Salivary Cortisol Level in Women with Major Depressive Disorder.

    PubMed

    Dziurkowska, Ewelina; Wesolowski, Marek

    2015-01-01

    Multivariate statistical analysis is widely used in medical studies as a profitable tool facilitating diagnosis of some diseases, for instance, cancer, allergy, pneumonia, or Alzheimer's and psychiatric diseases. Taking this in consideration, the aim of this study was to use two multivariate techniques, hierarchical cluster analysis (HCA) and principal component analysis (PCA), to disclose the relationship between the drugs used in the therapy of major depressive disorder and the salivary cortisol level and the period of hospitalization. The cortisol contents in saliva of depressed women were quantified by HPLC with UV detection day-to-day during the whole period of hospitalization. A data set with 16 variables (e.g., the patients' age, multiplicity and period of hospitalization, initial and final cortisol level, highest and lowest hormone level, mean contents, and medians) characterizing 97 subjects was used for HCA and PCA calculations. Multivariate statistical analysis reveals that various groups of antidepressants affect at the varying degree the salivary cortisol level. The SSRIs, SNRIs, and the polypragmasy reduce most effectively the hormone secretion. Thus, both unsupervised pattern recognition methods, HCA and PCA, can be used as complementary tools for interpretation of the results obtained by laboratory diagnostic methods.

  20. Determination of volatile organic compounds pollution sources in malaysian drinking water using multivariate analysis.

    PubMed

    Soh, Shiau-Chian; Abdullah, Md Pauzi

    2007-01-01

    A field investigation was conducted at all water treatment plants throughout 11 states and Federal Territory in Peninsular Malaysia. The sampling points in this study include treatment plant operation, service reservoir outlet and auxiliary outlet point at the water pipelines. Analysis was performed by solid phase micro-extraction technique with a 100 microm polydimethylsiloxane fibre using gas chromatography with mass spectrometry detection to analyse 54 volatile organic compounds (VOCs) of different chemical families in drinking water. The concentration of VOCs ranged from undetectable to 230.2 microg/l. Among all of the VOCs species, chloroform has the highest concentration and was detected in all drinking water samples. Average concentrations of total trihalomethanes (THMs) were almost similar among all states which were in the range of 28.4--33.0 microg/l. Apart from THMs, other abundant compounds detected were cis and trans-1,2-dichloroethylene, trichloroethylene, 1,2-dibromoethane, benzene, toluene, ethylbenzene, chlorobenzene, 1,4-dichlorobenzene and 1,2-dichloro - benzene. Principal component analysis (PCA) with the aid of varimax rotation, and parallel factor analysis (PARAFAC) method were used to statistically verify the correlation between VOCs and the source of pollution. The multivariate analysis pointed out that the maintenance of auxiliary pipelines in the distribution systems is vital as it can become significant point source pollution to Malaysian drinking water.

  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 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.

  3. Quantitative multivariate analytical strategy for paleoenvironmental analysis of mixed benthic foraminiferal assemblages

    SciTech Connect

    Lagoe, M.B. )

    1991-03-01

    Fossil assemblages of benthic foraminifera commonly contain taxa that were not associated together during life. A variety of processes act to modify living assemblages during the transition to fossil assemblages-transport of tests by traction and gravity currents, taphonomic filtering, and rapid shifting of environments in response to sea level fluctuations, to name a few. Unraveling the nature of faunal mixing can provide insights into depositional processes and paleoenvironmental history of particular lithofacies. A quantitative multivariate analytical strategy is presented to address these problems, using the late Cenozoic Yakataga Formation, Gulf of Alaska as a specific example. A variety of lithofacies are present within the Yakataga Formation including normal marine mudstones, sandstones, coquinas and conglomerates and glaciomarine diamictites. Comparison of fossil assemblages with modern foraminiferal distributions indicates significant faunal mixing in most lithofacies, particularly the diamictites. Quantitative analysis includes cluster analysis to define broad patterns in faunal similarity, R-mode factor analysis to define species interrelationships, and Q-mode polytopic vector analysis to 'unmix' the assemblages into their component biofacies. Two broad patterns of faunal mixing are identified: (1) comprehensive mixing of all possible biofacies within a particular bathymetric range and (2) mixing of very shallow (innermost neritic) with deeper (upper bathyal) assemblages, bypassing environments from outer neritic areas. Diamictites are shown to form in a variety of water depths from inner neritic to upper bathyal.

  4. Uncertainty analysis for multivariate state estimation in safety-critical and mission-critical maintenance applications

    SciTech Connect

    Zavaljevski, N.; Gross, K. C.

    2000-04-03

    The Multivariate State Estimation Technique (MSET) has been developed at Argonne National Laboratory (ANL) and applied for real time surveillance applications for the purposes of signal validation, sensor operability validation, equipment health monitoring, incipient component fault annunciation, and process anomaly identification. Although MSET was originally developed for applications in the commercial nuclear industry, it has recently been spun off for applications in fields such as aerospace, manufacturing, transportation, robotics, and ship propulsion. Notwithstanding these types of successful applications of MSET in industry, it is necessary for safety-critical and mission-critical applications of MSET to have reliability analysis methods, including a propagation-of-uncertainty tool, which is needed to support safety evaluations in a variety of industries, and technical-specification-change requests in the case of the nuclear industry. For these and related applications, a general purpose uncertainty analysis tool for MSET has been developed that uses Monte Carlo simulation with Latin Hypercube Sampling. For any new application of MSET, the uncertainty analysis tool developed here may be used to investigate quantitative propagation-of-uncertainty behavior for all sensors under surveillance. In addition to supporting safety analysis of surveillance systems that are based on MSET, the tool developed here can be employed in parametric studies to support system designers in evaluating the relative value of adding new sensors to an engineering system during early design stages or for equipment or facility upgrades.

  5. Raman spectroscopy of uranium compounds and the use of multivariate analysis for visualization and classification.

    PubMed

    Ho, Doris Mer Lin; Jones, Andrew E; Goulermas, John Y; Turner, Philip; Varga, Zsolt; Fongaro, Lorenzo; Fanghänel, Thomas; Mayer, Klaus

    2015-06-01

    Raman spectroscopy was used on 95 samples comprising mainly of uranium ore concentrates as well as some UF4 and UO2 samples, in order to classify uranium compounds for nuclear forensic purposes, for the first time. This technique was selected as it is non-destructive and rapid. The spectra obtained from 9 different classes of chemical compounds were subjected to multivariate data analysis such as principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA) and Fisher Discriminant Analysis (FDA). These classes were ammonium diuranate (ADU), sodium diuranate (SDU), ammonium uranyl carbonate (AUC), uranyl hydroxide (UH), UO2, UO3, UO4, U3O8 and UF4. Unsupervised PCA of full spectra shows fairly good distinction among the classes with some overlaps observed with ADU and UH. These overlaps are also reflected in the poorer specificities determined by PLS-DA. Higher values of sensitivities and specificities of remaining compounds were obtained. Supervised FDA based on reduced dataset of only 40 variables shows similar results to that of PCA but with closer clustering of ADU, UH, SDU, AUC. As a rapid and non-destructive technique, Raman spectroscopy is useful and complements existing techniques in multi-faceted nuclear forensics.

  6. [Near infrared spectroscopy and multivariate statistical process analysis for real-time monitoring of production process].

    PubMed

    Wang, Yi; Ma, Xiang; Wen, Ya-Dong; Zou, Quan; Wang, Jun; Tu, Jia-Run; Cai, Wen-Sheng; Shao, Xue-Guang

    2013-05-01

    Near infrared diffusive reflectance spectroscopy has been applied in on-site or on-line analysis due to its characteristics of fastness, non-destruction and the feasibility for real complex sample analysis. The present work reported a real-time monitoring method for industrial production by using near infrared spectroscopic technique and multivariate statistical process analysis. In the method, the real-time near infrared spectra of the materials are collected on the production line, and then the evaluation of the production process can be achieved by a statistic Hotelling T2 calculated with the established model. In this work, principal component analysis (PCA) is adopted for building the model, and the statistic is calculated by projecting the real-time spectra onto the PCA model. With an application of the method in a practical production, it was demonstrated that a real-time evaluation of the variations in the production can be realized by investigating the changes in the statistic, and the comparison of the products in different batches can be achieved by further statistics of the statistic. Therefore, the proposed method may provide a practical way for quality insurance of production processes.

  7. 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.

  8. 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.

  9. 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

  10. 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

  11. 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

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. Univariate and multivariate analysis of tannin-impregnated wood species using vibrational spectroscopy.

    PubMed

    Schnabel, Thomas; Musso, Maurizio; Tondi, Gianluca

    2014-01-01

    Vibrational spectroscopy is one of the most powerful tools in polymer science. Three main techniques--Fourier transform infrared spectroscopy (FT-IR), FT-Raman spectroscopy, and FT near-infrared (NIR) spectroscopy--can also be applied to wood science. Here, these three techniques were used to investigate the chemical modification occurring in wood after impregnation with tannin-hexamine preservatives. These spectroscopic techniques have the capacity to detect the externally added tannin. FT-IR has very strong sensitivity to the aromatic peak at around 1610 cm(-1) in the tannin-treated samples, whereas FT-Raman reflects the peak at around 1600 cm(-1) for the externally added tannin. This high efficacy in distinguishing chemical features was demonstrated in univariate analysis and confirmed via cluster analysis. Conversely, the results of the NIR measurements show noticeable sensitivity for small differences. For this technique, multivariate analysis is required and with this chemometric tool, it is also possible to predict the concentration of tannin on the surface.

  17. 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.

  18. 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.

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

    DOE PAGES

    Belianinov, Alex; Panchapakesan, G.; Lin, Wenzhi; ...

    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

  20. 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.

  1. 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.

  2. 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.

  3. The impact of education and media on contraceptive use in Bangladesh: a multivariate analysis.

    PubMed

    Goni, Abdul; Rahman, Mahfuzar

    2012-12-01

    In this paper, an attempt is made to show the impact of education and media on contraceptive use and also to identify the factors that associated with the current use of contraception and continuing of contraception. To reach our goal, Bangladesh Demographic and Health Survey (BDHS 2007) data were used. Findings of this study reveal that education, age of the respondents, religion, media exposure, area of residence and if they belong to any of the non-governmental organizations (Grameen Bank, Bangladesh Rural Advancement Committee, ASA, Mother's club, etc.) have significant contribution to current use of contraception and continuing of contraception. Media, particularly TV, and education play the leading role regarding this issue, whereas the others have an indirect relationship. Multivariate analyses showed that contraceptive use were higher among educated women and those women who watch TV at least once a week as compared with their respective counterpart. The results indicate urgent need to give emphasis on education, ensuring electronic media exposure, head-to-head communication programme, institutional-based family planning education and necessary information to learn about the impact of overpopulation for the people all over the country.

  4. 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.

  5. Narrow band quantitative and multivariate electroencephalogram analysis of peri-adolescent period

    PubMed Central

    2012-01-01

    Background The peri-adolescent period is a crucial developmental moment of transition from childhood to emergent adulthood. The present report analyses the differences in Power Spectrum (PS) of the Electroencephalogram (EEG) between late childhood (24 children between 8 and 13 years old) and young adulthood (24 young adults between 18 and 23 years old). Results The narrow band analysis of the Electroencephalogram was computed in the frequency range of 0–20 Hz. The analysis of mean and variance suggested that six frequency ranges presented a different rate of maturation at these ages, namely: low delta, delta-theta, low alpha, high alpha, low beta and high beta. For most of these bands the maturation seems to occur later in anterior sites than posterior sites. Correlational analysis showed a lower pattern of correlation between different frequencies in children than in young adults, suggesting a certain asynchrony in the maturation of different rhythms. The topographical analysis revealed similar topographies of the different rhythms in children and young adults. Principal Component Analysis (PCA) demonstrated the same internal structure for the Electroencephalogram of both age groups. Principal Component Analysis allowed to separate four subcomponents in the alpha range. All these subcomponents peaked at a lower frequency in children than in young adults. Conclusions The present approaches complement and solve some of the incertitudes when the classical brain broad rhythm analysis is applied. Children have a higher absolute power than young adults for frequency ranges between 0-20 Hz, the correlation of Power Spectrum (PS) with age and the variance age comparison showed that there are six ranges of frequencies that can distinguish the level of EEG maturation in children and adults. The establishment of maturational order of different frequencies and its possible maturational interdependence would require a complete series including all the different ages. PMID

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

    PubMed

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

    2016-12-01

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

  7. A cross-species socio-emotional behaviour development revealed by a multivariate analysis.

    PubMed

    Koshiba, Mamiko; Senoo, Aya; Mimura, Koki; Shirakawa, Yuka; Karino, Genta; Obara, Saya; Ozawa, Shinpei; Sekihara, Hitomi; Fukushima, Yuta; Ueda, Toyotoshi; Kishino, Hirohisa; Tanaka, Toshihisa; Ishibashi, Hidetoshi; Yamanouchi, Hideo; Yui, Kunio; Nakamura, Shun

    2013-01-01

    Recent progress in affective neuroscience and social neurobiology has been propelled by neuro-imaging technology and epigenetic approach in neurobiology of animal behaviour. However, quantitative measurements of socio-emotional development remains lacking, though sensory-motor development has been extensively studied in terms of digitised imaging analysis. Here, we developed a method for socio-emotional behaviour measurement that is based on the video recordings under well-defined social context using animal models with variously social sensory interaction during development. The behaviour features digitized from the video recordings were visualised in a multivariate statistic space using principal component analysis. The clustering of the behaviour parameters suggested the existence of species- and stage-specific as well as cross-species behaviour modules. These modules were used to characterise the behaviour of children with or without autism spectrum disorders (ASDs). We found that socio-emotional behaviour is highly dependent on social context and the cross-species behaviour modules may predict neurobiological basis of ASDs.

  8. 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.

  9. Rice Seed Cultivar Identification Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis

    PubMed Central

    Kong, Wenwen; Zhang, Chu; Liu, Fei; Nie, Pengcheng; He, Yong

    2013-01-01

    A near-infrared (NIR) hyperspectral imaging system was developed in this study. NIR hyperspectral imaging combined with multivariate data analysis was applied to identify rice seed cultivars. Spectral data was exacted from hyperspectral images. Along with Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA), K-Nearest Neighbor Algorithm (KNN) and Support Vector Machine (SVM), a novel machine learning algorithm called Random Forest (RF) was applied in this study. Spectra from 1,039 nm to 1,612 nm were used as full spectra to build classification models. PLS-DA and KNN models obtained over 80% classification accuracy, and SIMCA, SVM and RF models obtained 100% classification accuracy in both the calibration and prediction set. Twelve optimal wavelengths were selected by weighted regression coefficients of the PLS-DA model. Based on optimal wavelengths, PLS-DA, KNN, SVM and RF models were built. All optimal wavelengths-based models (except PLS-DA) produced classification rates over 80%. The performances of full spectra-based models were better than optimal wavelengths-based models. The overall results indicated that hyperspectral imaging could be used for rice seed cultivar identification, and RF is an effective classification technique. PMID:23857260

  10. Influence of water pressure on the final quality of arabica espresso coffee. Application of multivariate analysis.

    PubMed

    Andueza, S; Maeztu, L; Dean, B; de Peña, M P; Bello, J; Cid, C

    2002-12-04

    Water pressure is one of the most important factors which influence the final quality of espresso coffee (EC). However, few studies dealing with this issue have been found. The aim of this work was to study the effect of water pressure on the final quality of Arabica ECs as well as to classify ECs prepared at different pressures (7, 9, and 11 atm) according to their physicochemical and sensory characteristics, key odorants, by means of multivariate analysis. Statistically, principal component 1 (PC1) separated ECs prepared at 7 and 9 atm from ECs prepared at 11 atm and included the main foam and taste characteristics as well as some key odorants and flavor compounds. ECs prepared at 7 and 9 atm were separated by principal component 2 (PC2). Coffees prepared at 9 atm showed consistency of foam and a high percentage of key odorants related to freshness and fruity, malty, and buttery flavors. A simple discriminate function was obtained by discriminate analysis, allowing the classification of ECs prepared at three pressures into their respective groups with a success rate of 100%.

  11. Investigating the Moisture Content of Polyamide 6 by Raman-Microscopy and Multivariate Data Analysis

    NASA Astrophysics Data System (ADS)

    Lechner, Tobias; Noack, Kristina; Thöne, Manuel; Amend, Philipp; Schmidt, Michael; Will, Stefan

    Thermal malleability of thermoplastics results in a high product diversity in various industry sectors. However, industrial applications require a constant and high component quality. Hence, material processing such as laser welding has to consider that, e.g., the moisture content of thermoplastics influences the mechanical properties such as the tensile strength. Moreover, water evaporates during laser welding and can form pores and defects. Thus, there is a large need for non-invasive material inspection before processing. To that end, we developed a methodology based on Raman-microscopy and multivariate data analysis (MVD) to determine the moisture content of polyamide (MCP). Further, the impact of the MCP on the mechanical properties was verified. For samples with a defined variation of the MCP, xyz-Raman-scans were carried out and analysed using MVD. For reference purposes, the samples were weighted and tensile tests were performed. An evaluation by means of partial least squares regression analysis (PLSR) resulted in a prediction of the MCP with a correlation coefficient >98%. Consequently, Raman-microscopy shows large potential for developing new techniques for inspection and quality control of plastics before processing. Dedicated to Professor Alfred Leipertz on the occasion of his 70th birthday.

  12. 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

  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. 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.

  15. Student construction of differential length elements in multivariable coordinate systems: A symbolic forms analysis

    NASA Astrophysics Data System (ADS)

    Thompson, John; Schermerhorn, Benjamin

    2017-01-01

    Analysis of properties of physical quantities represented by vector fields often involves symmetries and spatial relationships best expressed in non-Cartesian coordinate systems. Many important quantities are determined by integrals that can involve multivariable vector differential quantities. Four pairs of students in junior-level Electricity and Magnetism (E&M) were interviewed to investigate their understanding of the structure of non-Cartesian coordinate systems and the associated differential elements. Pairs were asked to construct differential length elements for an unconventional spherical coordinate system. In order to explore how student conceptual understanding interacts with their understanding of the specific structures of these expressions, a symbolic forms framework was used. Analysis of student reasoning revealed both known and novel forms as well as the general progression of students--use and combination of symbol templates during the construction process. Each group invoked and combined symbolic forms in a similar sequence. Difficulties with the construction of expressions seem to be related almost exclusively to the conceptual schema (e.g., neglecting the role of projection) rather than with symbol templates. Supported in part by NSF Grant PHY-1405726.

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

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

    PubMed

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

    2017-03-17

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

  18. 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.

  19. 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.

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

    PubMed

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

    2013-01-01

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

  1. Bayesian meta-analysis for longitudinal data models using multivariate mixture priors.

    PubMed

    Lopes, Hedibert Freitas; Müller, Peter; Rosner, Gary L

    2003-03-01

    We propose a class of longitudinal data models with random effects that generalizes currently used models in two important ways. First, the random-effects model is a flexible mixture of multivariate normals, accommodating population heterogeneity, outliers, and nonlinearity in the regression on subject-specific covariates. Second, the model includes a hierarchical extension to allow for meta-analysis over related studies. The random-effects distributions are decomposed into one part that is common across all related studies (common measure), and one part that is specific to each study and that captures the variability intrinsic between patients within the same study. Both the common measure and the study-specific measures are parameterized as mixture-of-normals models. We carry out inference using reversible jump posterior simulation to allow a random number of terms in the mixtures. The sampler takes advantage of the small number of entertained models. The motivating application is the analysis of two studies carried out by the Cancer and Leukemia Group B (CALGB). In both studies, we record for each patient white blood cell counts (WBC) over time to characterize the toxic effects of treatment. The WBCs are modeled through a nonlinear hierarchical model that gathers the information from both studies.

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. Multivariate image analysis-thin layer chromatography (MIA-TLC) for simultaneous determination of co-eluting components.

    PubMed

    Hemmateenejad, Bahram; Mobaraki, Nabiollah; Shakerizadeh-Shirazi, Fatemeh; Miri, Ramin

    2010-07-01

    This paper addresses the solution of peak overlapping, as a fundamental problem in TLC, by multivariate analysis of the images recorded by a digital camera. We report the results of our study on the application of multivariate image analysis (MIA) for simultaneous determination of several species on thin layer chromatography (TLC) sheet for the first time. An imaging system, composed of a dark cabinet, a digital camera and a multivariate image analysis program, was prepared for recording the images of TLC plates after development of a multi-component solution. The written program was able to produce 2- and 3-dimensional chromatograms of the solutions, which were subsequently used as inputs of partial least squares, as an efficient multivariate calibration method. The ability of the proposed MIA-TLC method for simultaneous determination of the co-eluting components was validated by analysis of ternary synthetic mixtures of indicators of highly overlapped chromatograms (i.e., methyl yellow, bromocresol green and creseol red) and a real mixture of nifedipine and its photo-degradation product. By application of different strategies like principal component analysis and variable selection, models were obtained that could estimate the concentration of indicators in the external prediction set with relative errors of lower than 10% and in most cases lower than 5%.

  8. Comparative forensic soil analysis of New Jersey state parks using a combination of simple techniques with multivariate statistics.

    PubMed

    Bonetti, Jennifer; Quarino, Lawrence

    2014-05-01

    This study has shown that the combination of simple techniques with the use of multivariate statistics offers the potential for the comparative analysis of soil samples. Five samples were obtained from each of twelve state parks across New Jersey in both the summer and fall seasons. Each sample was examined using particle-size distribution, pH analysis in both water and 1 M CaCl2 , and a loss on ignition technique. Data from each of the techniques were combined, and principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for multivariate data transformation. Samples from different locations could be visually differentiated from one another using these multivariate plots. Hold-one-out cross-validation analysis showed error rates as low as 3.33%. Ten blind study samples were analyzed resulting in no misclassifications using Mahalanobis distance calculations and visual examinations of multivariate plots. Seasonal variation was minimal between corresponding samples, suggesting potential success in forensic applications.

  9. Multivariate normative comparisons using an aggregated database.

    PubMed

    Agelink van Rentergem, Joost A; Murre, Jaap M J; Huizenga, Hilde M

    2017-01-01

    In multivariate normative comparisons, a patient's profile of test scores is compared to those in a normative sample. Recently, it has been shown that these multivariate normative comparisons enhance the sensitivity of neuropsychological assessment. However, multivariate normative comparisons require multivariate normative data, which are often unavailable. In this paper, we show how a multivariate normative database can be constructed by combining healthy control group data from published neuropsychological studies. We show that three issues should be addressed to construct a multivariate normative database. First, the database may have a multilevel structure, with participants nested within studies. Second, not all tests are administered in every study, so many data may be missing. Third, a patient should be compared to controls of similar age, gender and educational background rather than to the entire normative sample. To address these issues, we propose a multilevel approach for multivariate normative comparisons that accounts for missing data and includes covariates for age, gender and educational background. Simulations show that this approach controls the number of false positives and has high sensitivity to detect genuine deviations from the norm. An empirical example is provided. Implications for other domains than neuropsychology are also discussed. To facilitate broader adoption of these methods, we provide code implementing the entire analysis in the open source software package R.

  10. Multivariate normative comparisons using an aggregated database

    PubMed Central

    Murre, Jaap M. J.; Huizenga, Hilde M.

    2017-01-01

    In multivariate normative comparisons, a patient’s profile of test scores is compared to those in a normative sample. Recently, it has been shown that these multivariate normative comparisons enhance the sensitivity of neuropsychological assessment. However, multivariate normative comparisons require multivariate normative data, which are often unavailable. In this paper, we show how a multivariate normative database can be constructed by combining healthy control group data from published neuropsychological studies. We show that three issues should be addressed to construct a multivariate normative database. First, the database may have a multilevel structure, with participants nested within studies. Second, not all tests are administered in every study, so many data may be missing. Third, a patient should be compared to controls of similar age, gender and educational background rather than to the entire normative sample. To address these issues, we propose a multilevel approach for multivariate normative comparisons that accounts for missing data and includes covariates for age, gender and educational background. Simulations show that this approach controls the number of false positives and has high sensitivity to detect genuine deviations from the norm. An empirical example is provided. Implications for other domains than neuropsychology are also discussed. To facilitate broader adoption of these methods, we provide code implementing the entire analysis in the open source software package R. PMID:28267796

  11. 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

  12. A simple ergonomic measure reduces fluoroscopy time during ERCP: A multivariate analysis

    PubMed Central

    Jowhari, Fahd; Hopman, Wilma M.; Hookey, Lawrence

    2017-01-01

    Background and study aims Endoscopic retrograde cholangiopancreatgraphy (ERCP) carries a radiation risk to patients undergoing the procedure and the team performing it. Fluoroscopy time (FT) has been shown to have a linear relationship with radiation exposure during ERCP. Recent modifications to our ERCP suite design were felt to impact fluoroscopy time and ergonomics. This multivariate analysis was therefore undertaken to investigate these effects, and to identify and validate various clinical, procedural and ergonomic factors influencing the total fluoroscopy time during ERCP. This would better assist clinicians with predicting prolonged fluoroscopic durations and to undertake relevant precautions accordingly. Patients and methods A retrospective analysis of 299 ERCPs performed by 4 endoscopists over an 18-month period, at a single tertiary care center was conducted. All inpatients/outpatients (121 males, 178 females) undergoing ERCP for any clinical indication from January 2012 to June 2013 in the chosen ERCP suite were included in the study. Various predetermined clinical, procedural and ergonomic factors were obtained via chart review. Univariate analyses identified factors to be included in the multivariate regression model with FT as the dependent variable. Results Bringing the endoscopy and fluoroscopy screens next to each other was associated with a significantly lesser FT than when the screens were separated further (–1.4 min, P = 0.026). Other significant factors associated with a prolonged FT included having a prior ERCP (+ 1.4 min, P = 0.031), and more difficult procedures (+ 4.2 min for each level of difficulty, P < 0.001). ERCPs performed by high-volume endoscopists used lesser FT vs. low-volume endoscopists (–1.82, P = 0.015). Conclusions Our study has identified and validated various factors that affect the total fluoroscopy time during ERCP. This is the first study to show that decreasing the distance between

  13. A simple ergonomic measure reduces fluoroscopy time during ERCP: A multivariate analysis.

    PubMed

    Jowhari, Fahd; Hopman, Wilma M; Hookey, Lawrence

    2017-03-01

    Background and study aims Endoscopic retrograde cholangiopancreatgraphy (ERCP) carries a radiation risk to patients undergoing the procedure and the team performing it. Fluoroscopy time (FT) has been shown to have a linear relationship with radiation exposure during ERCP. Recent modifications to our ERCP suite design were felt to impact fluoroscopy time and ergonomics. This multivariate analysis was therefore undertaken to investigate these effects, and to identify and validate various clinical, procedural and ergonomic factors influencing the total fluoroscopy time during ERCP. This would better assist clinicians with predicting prolonged fluoroscopic durations and to undertake relevant precautions accordingly. Patients and methods A retrospective analysis of 299 ERCPs performed by 4 endoscopists over an 18-month period, at a single tertiary care center was conducted. All inpatients/outpatients (121 males, 178 females) undergoing ERCP for any clinical indication from January 2012 to June 2013 in the chosen ERCP suite were included in the study. Various predetermined clinical, procedural and ergonomic factors were obtained via chart review. Univariate analyses identified factors to be included in the multivariate regression model with FT as the dependent variable. Results Bringing the endoscopy and fluoroscopy screens next to each other was associated with a significantly lesser FT than when the screens were separated further (-1.4 min, P = 0.026). Other significant factors associated with a prolonged FT included having a prior ERCP (+ 1.4 min, P = 0.031), and more difficult procedures (+ 4.2 min for each level of difficulty, P < 0.001). ERCPs performed by high-volume endoscopists used lesser FT vs. low-volume endoscopists (-1.82, P = 0.015). Conclusions Our study has identified and validated various factors that affect the total fluoroscopy time during ERCP. This is the first study to show that decreasing the distance between the

  14. Discerning some Tylenol brands using attenuated total reflection Fourier transform infrared data and multivariate analysis techniques.

    PubMed

    Msimanga, Huggins Z; Ollis, Robert J

    2010-06-01

    Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to classify acetaminophen-containing medicines using their attenuated total reflection Fourier transform infrared (ATR-FT-IR) spectra. Four formulations of Tylenol (Arthritis Pain Relief, Extra Strength Pain Relief, 8 Hour Pain Relief, and Extra Strength Pain Relief Rapid Release) along with 98% pure acetaminophen were selected for this study because of the similarity of their spectral features, with correlation coefficients ranging from 0.9857 to 0.9988. Before acquiring spectra for the predictor matrix, the effects on spectral precision with respect to sample particle size (determined by sieve size opening), force gauge of the ATR accessory, sample reloading, and between-tablet variation were examined. Spectra were baseline corrected and normalized to unity before multivariate analysis. Analysis of variance (ANOVA) was used to study spectral precision. The large particles (35 mesh) showed large variance between spectra, while fine particles (120 mesh) indicated good spectral precision based on the F-test. Force gauge setting did not significantly affect precision. Sample reloading using the fine particle size and a constant force gauge setting of 50 units also did not compromise precision. Based on these observations, data acquisition for the predictor matrix was carried out with the fine particles (sieve size opening of 120 mesh) at a constant force gauge setting of 50 units. After removing outliers, PCA successfully classified the five samples in the first and second components, accounting for 45.0% and 24.5% of the variances, respectively. The four-component PLS-DA model (R(2)=0.925 and Q(2)=0.906) gave good test spectra predictions with an overall average of 0.961 +/- 7.1% RSD versus the expected 1.0 prediction for the 20 test spectra used.

  15. 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

  16. Multivariate analysis of the geochemistry and mineralogy of soils along two continental-scale transects in North America.

    PubMed

    Drew, Lawrence J; Grunsky, Eric C; Sutphin, David M; Woodruff, Laurel G

    2010-12-01

    Soils collected in 2004 along two North American continental-scale transects were subjected to geochemical and mineralogical analyses. In previous interpretations of these analyses, data were expressed in weight percent and parts per million, and thus were subject to the effect of the constant-sum phenomenon. In a new approach to the data, this effect was removed by using centered log-ratio transformations to 'open' the mineralogical and geochemical arrays. Multivariate analyses, including principal component and linear discriminant analyses, of the centered log-ratio data reveal the effects of soil-forming processes, including soil parent material, weathering, and soil age, at the continental-scale of the data arrays that were not readily apparent in the more conventionally presented data. Linear discriminant analysis of the data arrays indicates that the majority of the soil samples collected along the transects can be more successfully classified with Level 1 ecological regional-scale classification by the soil geochemistry than soil mineralogy. A primary objective of this study is to discover and describe, in a parsimonious way, geochemical processes that are both independent and inter-dependent and manifested through compositional data including estimates of the elements and corresponding mineralogy.

  17. Multivariate analysis of the geochemistry and mineralogy of soils along two continental-scale transects in North America

    USGS Publications Warehouse

    Drew, L.J.; Grunsky, E.C.; Sutphin, D.M.; Woodruff, L.G.

    2010-01-01

    Soils collected in 2004 along two North American continental-scale transects were subjected to geochemical and mineralogical analyses. In previous interpretations of these analyses, data were expressed in weight percent and parts per million, and thus were subject to the effect of the constant-sum phenomenon. In a new approach to the data, this effect was removed by using centered log-ratio transformations to 'open' the mineralogical and geochemical arrays. Multivariate analyses, including principal component and linear discriminant analyses, of the centered log-ratio data reveal the effects of soil-forming processes, including soil parent material, weathering, and soil age, at the continental-scale of the data arrays that were not readily apparent in the more conventionally presented data. Linear discriminant analysis of the data arrays indicates that the majority of the soil samples collected along the transects can be more successfully classified with Level 1 ecological regional-scale classification by the soil geochemistry than soil mineralogy. A primary objective of this study is to discover and describe, in a parsimonious way, geochemical processes that are both independent and inter-dependent and manifested through compositional data including estimates of the elements and corresponding mineralogy. ?? 2010.

  18. Dose–response effects for depression and Schizophrenia management on hospital utilization in Illinois Medicaid: a multivariate regression analysis

    PubMed Central

    2014-01-01

    Background The prevalence of schizophrenia and depression in the United States is far higher among Medicaid recipients than in the general population. Individuals suffering from mental illness, including schizophrenia and depression, also have higher rates of emergency department utilization, which is costly and may not generate the positive health outcomes desired. Disease management programs strive to help individuals suffering from chronic illnesses better manage their condition(s) and seek health care in the appropriate settings. The objective of this manuscript is to estimate a dose–response impact on hospital inpatient and emergency room utilizations for any reason by Medicaid recipients with depression or schizophrenia who received disease management contacts. Methods Multivariate regression analysis of panel data taken from administrative claims was conducted to test the hypothesis that increased contacts lower the likelihood of all-cause inpatient admissions and emergency room visits. Subjects included 6,274 members of Illinois’ non-institutionalized Medicaid-only aged, blind or disabled population diagnosed with depression or schizophrenia. The statistical measure is the odds ratio. The odds ratio association is between the monthly utilization indicators and the number of contacts (doses) a member had for each particular disease management intervention. Results Higher numbers of intervention contacts for Medicaid recipients diagnosed with depression or schizophrenia were associated with statistically significant reductions in all-cause inpatient admissions and emergency room utilizations. Conclusions There is a high correlation between depression and schizophrenia disease management contacts and lowered all-cause hospital inpatient and emergency room utilizations. PMID:24989717

  19. 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…

  20. Multivariate Analysis of Repeated Measures with a Design on the Measures and a Design on the Subjects--An Example.

    ERIC Educational Resources Information Center

    McLean, Leslie D.; Keeton, Anne

    An exact multivariate analysis for troublesome repeated measures designs has been described by Bock and programmed by Finn. The method is applied to digit span from an actual experiment involving first-grade pupils in an inner-city school and a suburban school in Canada. The repeated measures are first transformed by an orthogonal matrix derived…

  1. Nonparametric One-Way Multivariate Analysis of Variance: A Computational Approach Based on the Pillai-Bartlett Trace.

    ERIC Educational Resources Information Center

    Zwick, Rebecca

    1985-01-01

    Describes how the test statistic for nonparametric one-way multivariate analysis of variance can be obtained by submitting the data to a packaged computer program. Monte Carlo evidence indicates that the nonparametric approach is advantageous under certain violations of the assumptions of multinormality and homogeneity of covariance matrices.…

  2. Batch-to-batch quality consistency evaluation of botanical drug products using multivariate statistical analysis of the chromatographic fingerprint.

    PubMed

    Xiong, Haoshu; Yu, Lawrence X; Qu, Haibin

    2013-06-01

    Botanical drug products have batch-to-batch quality variability due to botanical raw materials and the current manufacturing process. The rational evaluation and control of product quality consistency are essential to ensure the efficacy and safety. Chromatographic fingerprinting is an important and widely used tool to characterize the chemical composition of botanical drug products. Multivariate statistical analysis has showed its efficacy and applicability in the quality evaluation of many kinds of industrial products. In this paper, the combined use of multivariate statistical analysis and chromatographic fingerprinting is presented here to evaluate batch-to-batch quality consistency of botanical drug products. A typical botanical drug product in China, Shenmai injection, was selected as the example to demonstrate the feasibility of this approach. The high-performance liquid chromatographic fingerprint data of historical batches were collected from a traditional Chinese medicine manufacturing factory. Characteristic peaks were weighted by their variability among production batches. A principal component analysis model was established after outliers were modified or removed. Multivariate (Hotelling T(2) and DModX) control charts were finally successfully applied to evaluate the quality consistency. The results suggest useful applications for a combination of multivariate statistical analysis with chromatographic fingerprinting in batch-to-batch quality consistency evaluation for the manufacture of botanical drug products.

  3. Bias and Precision of Measures of Association for a Fixed-Effect Multivariate Analysis of Variance Model

    ERIC Educational Resources Information Center

    Kim, Soyoung; Olejnik, Stephen

    2005-01-01

    The sampling distributions of five popular measures of association with and without two bias adjusting methods were examined for the single factor fixed-effects multivariate analysis of variance model. The number of groups, sample sizes, number of outcomes, and the strength of association were manipulated. The results indicate that all five…

  4. 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.

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

    PubMed

    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.

  6. 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.

  7. 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

  8. 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

  9. Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets

    PubMed Central

    Rahmatallah, Yasir; Emmert-Streib, Frank; Glazko, Galina

    2014-01-01

    Motivation: To date, gene set analysis approaches primarily focus on identifying differentially expressed gene sets (pathways). Methods for identifying differentially coexpressed pathways also exist but are mostly based on aggregated pairwise correlations or other pairwise measures of coexpression. Instead, we propose Gene Sets Net Correlations Analysis (GSNCA), a multivariate differential coexpression test that accounts for the complete correlation structure between genes. Results: In GSNCA, weight factors are assigned to genes in proportion to the genes’ cross-correlations (intergene correlations). The problem of finding the weight vectors is formulated as an eigenvector problem with a unique solution. GSNCA tests the null hypothesis that for a gene set there is no difference in the weight vectors of the genes between two conditions. In simulation studies and the analyses of experimental data, we demonstrate that GSNCA captures changes in the structure of genes’ cross-correlations rather than differences in the averaged pairwise correlations. Thus, GSNCA infers differences in coexpression networks, however, bypassing method-dependent steps of network inference. As an additional result from GSNCA, we define hub genes as genes with the largest weights and show that these genes correspond frequently to major and specific pathway regulators, as well as to genes that are most affected by the biological difference between two conditions. In summary, GSNCA is a new approach for the analysis of differentially coexpressed pathways that also evaluates the importance of the genes in the pathways, thus providing unique information that may result in the generation of novel biological hypotheses. Availability and implementation: Implementation of the GSNCA test in R is available upon request from the authors. Contact: YRahmatallah@uams.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24292935

  10. NIR hyperspectral imaging and multivariate image analysis to characterize spent mushroom substrate: a preliminary study.

    PubMed

    Wei, Maogui; Geladi, Paul; Xiong, Shaojun

    2017-03-01

    Commercial mushroom growth on substrate material produces a heterogeneous waste that can be used for bioenergy purposes. Hyperspectral imaging in the near-infrared (NHI) was used to experimentally study a number of spent mushroom substrate (SMS) packed samples under different conditions (wet vs. dry, open vs. plastic covering, and round or cuboid) and to explore the possibilities of direct characterization of the fresh substrate within a plastic bag. Principal components analysis (PCA) was used to remove the background of images, explore the important studied factors, and identify SMS and mycelia (Myc) based on the pixel clusters within the score plot. Overview PCA modeling indicated high moisture content caused the most significant effects on spectra followed by the uneven distribution of Myc and the plastic cover. There were well-separated pixel clusters for SMS and Myc under different conditions: dry, wet, or wet and plastic covering. The loading peaks of the related component and the second derivative of the mean spectra of pixel clusters of SMS and Myc indicated that there are chemical differences between SMS and Myc. Partial least squares discriminant analysis (PLS-DA) models were calculated and classification of SMS and Myc was successful, whether the materials were dry or wet. Peak shifts because of high moisture content and unexpected peaks from the plastic covering were found. Although the best results were obtained for dried cylinders, it was shown that almost equally good results could be obtained for the wet material and for the wet material covered by plastic. Furthermore, PLS-DA prediction showed that a side face hyperspectral image could represent the information for the entire SMS cylinder when Myc was removed. Thus, the combination of NHI and multivariate image analysis has great potential to develop calibration models to directly predict the contents of water, carbohydrates, lignin, and protein in wet and plastic-covered SMS cylinders.

  11. Solution identification and quantitative analysis of fiber-capacitive drop analyzer based on multivariate statistical methods

    NASA Astrophysics Data System (ADS)

    Chen, Zhe; Qiu, Zurong; Huo, Xinming; Fan, Yuming; Li, Xinghua

    2017-03-01

    A fiber-capacitive drop analyzer is an instrument which monitors a growing droplet to produce a capacitive opto-tensiotrace (COT). Each COT is an integration of fiber light intensity signals and capacitance signals and can reflect the unique physicochemical property of a liquid. In this study, we propose a solution analytical and concentration quantitative method based on multivariate statistical methods. Eight characteristic values are extracted from each COT. A series of COT characteristic values of training solutions at different concentrations compose a data library of this kind of solution. A two-stage linear discriminant analysis is applied to analyze different solution libraries and establish discriminant functions. Test solutions can be discriminated by these functions. After determining the variety of test solutions, Spearman correlation test and principal components analysis are used to filter and reduce dimensions of eight characteristic values, producing a new representative parameter. A cubic spline interpolation function is built between the parameters and concentrations, based on which we can calculate the concentration of the test solution. Methanol, ethanol, n-propanol, and saline solutions are taken as experimental subjects in this paper. For each solution, nine or ten different concentrations are chosen to be the standard library, and the other two concentrations compose the test group. By using the methods mentioned above, all eight test solutions are correctly identified and the average relative error of quantitative analysis is 1.11%. The method proposed is feasible which enlarges the applicable scope of recognizing liquids based on the COT and improves the concentration quantitative precision, as well.

  12. Differentiation of foodborne bacteria using NIR hyperspectral imaging and multivariate data analysis.

    PubMed

    Kammies, Terri-Lee; Manley, Marena; Gouws, Pieter A; Williams, Paul J

    2016-11-01

    The potential for near-infrared (NIR) hyperspectral imaging and multivariate data analysis to be used as a rapid non-destructive tool for detection and differentiation of bacteria was investigated. NIR hyperspectral images were collected of Bacillus cereus, Escherichia coli, Salmonella enteritidis, Staphylococcus aureus and Staphylococcus epidermidis grown on agar for 20 h at 37 °C. Principal component analysis (PCA) was applied to mean-centred data. Standard normal variate (SNV) correction and the Savitzky-Golay technique was applied (2nd derivative, 3rd-order polynomial; 25 point smoothing) to wavelengths in the range of 1103 to 2471 nm. Chemical differences between colonies which appeared similar in colour on growth media (B. cereus, E. coli and S. enteritidis.) were evident in the PCA score plots. It was possible to distinguish B. cereus from E. coli and S. enteritidis along PC1 (59 % sum of squares (SS)) and between E. coli and S. enteritidis in the direction of PC2 (6.85 % SS). S. epidermidis was separated from B. cereus and S. aureus along PC1 (37.5 % SS) and was attributed to variation in amino acid and carbohydrate content. Two clusters were evident in the PC1 vs. PC2 PCA score plot for the images of S. aureus and S. epidermidis, thus permitting distinction between species. Differentiation between genera (similarly coloured on growth media), Gram-positive and Gram-negative bacteria and pathogenic and non-pathogenic species was possible using NIR hyperspectral imaging. Partial least squares discriminant analysis (PLS-DA) models were used to confirm the PCA data. The best predictions were made for B. cereus and Staphylococcus species, where results ranged from 82.0 to 99.96 % correctly predicted pixels.

  13. 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.

  14. 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.

  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. 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.

  17. 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

  18. 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.

  19. A comparison of several similarity indices used in the classification of protein sequences: a multivariate analysis.

    PubMed Central

    Landès, C; Hénaut, A; Risler, J L

    1992-01-01

    The present work describes an attempt to identify reliable criteria which could be used as distance indices between protein sequences. Seven different criteria have been tested: i and ii) the scores of the alignments as given by the BESTFIT and the FASTA programs; iii) the ratio parameter, i.e. the BESTFIT score divided by the length of the aligned peptides; iv and v) the statistical significance (Z-scores) of the scores calculated by BESTFIT and FASTA, as obtained by comparison with shuffled sequences; vi) the Z-scores provided by the program RELATE which performs a segment-by-segment comparison of 2 sequences, and vii) an original distance index calculated by the program DOCMA from all the pairwise dotplots between the sequences. These 7 criteria have been tested against the aminoacid sequences of 39 globins and those of the 20 aminoacyl-tRNA synthetases from E. coli. The distances between the sequences were analyzed by the multivariate analysis techniques. The results show that the distances calculated from the scores of the pairwise alignments are not adequately sensitive. The Z-score from RELATE is not selective enough and too demanding in computer time. Three criteria gave a classification consistent with the known similarities between the sequences in the sets, namely the Z-scores from BESTFIT and FASTA and the multiple dotplot comparison distance index from DOCMA. PMID:1641329

  20. 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.

  1. 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

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

    NASA Astrophysics Data System (ADS)

    Ryan, Elaine A.; Farquharson, Michael J.

    2007-11-01

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

  3. Molecular docking and multivariate analysis of xanthones as antimicrobial and antiviral agents.

    PubMed

    Bernal, Freddy A; Coy-Barrera, Ericsson

    2015-07-21

    Xanthones are secondary metabolites which have drawn considerable interest over the last decades due to their antimicrobial properties, among others. A great number of this kind of compounds has been therefore reported, but there is a limited amount of studies on screening for biological activity. Thus, as part of our research on antimicrobial agents of natural origin, a set of 272 xanthones were submitted to molecular docking (MD) calculations with a group of seven fungal and two viral enzymes. The results indicated that prenylated xanthones are important hits for inhibition of the analyzed enzymes. The MD scores were also analyzed by multivariate statistics. Important structural details were found to be crucial for the inhibition of the tested enzymes by the xanthones. In addition, the classification of active xanthones can be achieved by statistical analysis on molecular docking scores by an affinity-antifungal activity relationship approach. The obtained results therefore are a suitable starting point for the development of antifungal and antiviral agents based on xanthones.

  4. Bayesian analysis of a multivariate null intercept errors-in-variables regression model.

    PubMed

    Aoki, Reiko; Bolfarine, Heleno; Achcar, Jorge A; Dorival, Leão P Júnior

    2003-11-01

    Longitudinal data are of great interest in analysis of clinical trials. In many practical situations the covariate can not be measured precisely and a natural alternative model is the errors-in-variables regression models. In this paper we study a null intercept errors-in-variables regression model with a structure of dependency between the response variables within the same group. We apply the model to real data presented in Hadgu and Koch (Hadgu, A., Koch, G. (1999). Application of generalized estimating equations to a dental randomized clinical trial. J. Biopharmaceutical Statistics 9(1):161-178). In that study volunteers with preexisting dental plaque were randomized to two experimental mouth rinses (A and B) or a control mouth rinse with double blinding. The dental plaque index was measured for each subject in the beginning of the study and at two follow-up times, which leads to the presence of an interclass correlation. We propose the use of a Bayesian approach to model a multivariate null intercept errors-in-variables regression model to the longitudinal data. The proposed Bayesian approach accommodates the correlated measurements and incorporates the restriction that the slopes must lie in the (0, 1) interval. A Gibbs sampler is used to perform the computations.

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

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

  6. 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

  7. Multivariate Visual Explanation for High Dimensional Datasets

    PubMed Central

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

    2010-01-01

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

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

    PubMed

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

    2016-01-13

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

  9. 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.

  10. 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.

  11. 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.

  12. Multivariate Analysis Strategies for Processing ToF-SIMS Images of Biomaterials

    PubMed Central

    Tyler, Bonnie J.; Rayal, Gaurav; Castner, David G.

    2007-01-01

    Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a hyperspectral imaging technique. Each pixel in a two-dimensional ToF-SIMS image (or each voxel in a three-dimensional ToF-SIMS image) contains a full mass spectrum. Thus, multivariate analysis methods are being increasingly used to process biomaterial ToF-SIMS images so the maximum amount of information can be extracted from the images. This study examines the use of principal component analysis (PCA) and maximum autocorrelation factors (MAF) on four different ToF-SIMS images. These images were selected because they represent significant challenges for biomedical ToF-SIMS image processing (topographical features, low count rates, surface contaminants, etc.). With PCA four different types of scaling methods (auto, root mean, filter, and shift variance scaling) were used. The effect of two preprocessing methods (normalization and mean centering) was also examined for both PCA and MAF. The more computational intense MAF provided the best results for all the images investigated in this study, doing the best job of reducing the number of variables required to describe the image, enhancing image contrast and recovering key spectral features. MAF was particularly good at identifying subtle features that were often lost in PCA and impossible to visualize in single peak images. However, the combination of PCA with either root mean or shift variance scaling provided similar results to MAF. Thus, these combinations offer promising alternatives to MAF for working with large data sets encountered in three-dimensional imaging. Also, the new method of filter scaling is promising for processing low count rate images with salt and pepper noise. Normalization proved an important tool for deconvoluting chemical effects from topographic and/or matrix effects. Mean centering aided in reducing the dimensionality of the data, but in one case resulted in a loss of information. PMID:17335898

  13. 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.

  14. Molecular imaging by confocal Raman mapping: enabling technologies for speed, multivariate analysis, and convenience

    NASA Astrophysics Data System (ADS)

    Adar, Fran; Lee, Eunah; Whitley, Andrew

    2009-05-01

    In spite of the fact that the original Raman microscope was designed in the early 1970's for Raman imaging, wide-spread practical use of the technology did not appear until the last 5 years. The instruments are smaller, faster, easier-to-use, promoting reports of a variety of interesting applications in fields as diverse as nanomaterials, pharmaceuticals, composites, semiconductors, bio-clinical studies, polymers, ceramics and glasses. While the information content in Raman analysis is quite high, the time to acquire an image has been a deterrent to its application. Recent innovations including Swift and DUO Scan have addressed and are addressing these issues. SWIFT (Scanning with Incredibly Fast Times) is a rapid CCD read-out technique that is based on the synchronization between the XY motion of the motorized or piezo stage and the CCD readout. DUO scanning uses a set of scanning mirrors above the microscope objective to raster rapidly the laser beam across a sample area. This can be used to create a "giant pixel" in the map without compromising the NA of the light collection, or to create a map with step sizes as small as 10nm. Swift, in combination with DUO scan, as been used to produce full spectral maps of pharmaceutical tablets in times as short as 10 minutes, something that was previously believed to be near impossible. Off-line analysis of such a map using multivariate techniques produces Raman images indicating the quality of component mixing, and also the presence of minor, difficult-to-detect components (such as Mgstearate in pharmaceutical tablets).

  15. An alternative pseudolikelihood method for multivariate random-effects meta-analysis

    PubMed Central

    Chen, Yong; Hong, Chuan; Riley, Richard D

    2015-01-01

    Recently, multivariate random-effects meta-analysis models have received a great deal of attention, despite its greater complexity compared to univariate meta-analyses. One of its advantages is its ability to account for the within-study and between-study correlations. However, the standard inference procedures, such as the maximum likelihood or maximum restricted likelihood inference, require the within-study correlations, which are usually unavailable. In addition, the standard inference procedures suffer from the problem of singular estimated covariance matrix. In this paper, we propose a pseudolikelihood method to overcome the aforementioned problems. The pseudolikelihood method does not require within-study correlations and is not prone to singular covariance matrix problem. In addition, it can properly estimate the covariance between pooled estimates for different outcomes, which enables valid inference on functions of pooled estimates, and can be applied to meta-analysis where some studies have outcomes missing completely at random. Simulation studies show that the pseudolikelihood method provides unbiased estimates for functions of pooled estimates, well-estimated standard errors, and confidence intervals with good coverage probability. Furthermore, the pseudolikelihood method is found to maintain high relative efficiency compared to that of the standard inferences with known within-study correlations. We illustrate the proposed method through three meta-analyses for comparison of prostate cancer treatment, for the association between paraoxonase 1 activities and coronary heart disease, and for the association between homocysteine level and coronary heart disease. © 2014 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. PMID:25363629

  16. 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.

  17. Survival analysis of aging aircraft

    NASA Astrophysics Data System (ADS)

    Benavides, Samuel

    This study pushes systems engineering of aging aircraft beyond the boundaries of empirical and deterministic modeling by making a sharp break with the traditional laboratory-derived corrosion prediction algorithms that have shrouded real-world failures of aircraft structure. At the heart of this problem is the aeronautical industry's inability to be forthcoming in an accurate model that predicts corrosion failures in aircraft in spite of advances in corrosion algorithms or improvements in simulation and modeling. The struggle to develop accurate corrosion probabilistic models stems from a multitude of real-world interacting variables that synergistically influence corrosion in convoluted and complex ways. This dissertation, in essence, offers a statistical framework for the analysis of structural airframe corrosion failure by utilizing real-world data while considering the effects of interacting corrosion variables. This study injects realism into corrosion failures of aging aircraft systems by accomplishing four major goals related to the conceptual and methodological framework of corrosion modeling. First, this work connects corrosion modeling from the traditional, laboratory derived algorithms to corrosion failures in actual operating aircraft. This work augments physics-based modeling by examining the many confounding and interacting variables, such as environmental, geographical and operational, that impact failure of airframe structure. Examined through the lens of censored failure data from aircraft flying in a maritime environment, this study enhances the understanding between the triad of the theoretical, laboratory and real-world corrosion. Secondly, this study explores the importation and successful application of an advanced biomedical statistical tool---survival analysis---to model censored corrosion failure data. This well-grounded statistical methodology is inverted from a methodology that analyzes survival to one that examines failures. Third, this

  18. A Computer-Based Content Analysis of Interview Texts: Numeric Description and Multivariate Analysis.

    ERIC Educational Resources Information Center

    Bierschenk, B.

    1977-01-01

    A method is described by which cognitive structures in verbal data can be identified and categorized through numerical analysis and quantitative description. Transcriptions of interviews (in this case, the verbal statements of 40 researchers) are manually coded and subjected to analysis following the AaO (Agent action Object) paradigm. The texts…

  19. 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

  20. 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

  1. 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.

  2. Fourier transform infrared spectroscopy (FTIR) and multivariate analysis for identification of different vegetable oils used in biodiesel production.

    PubMed

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

    2013-03-28

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

  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. Multivariate sib-pair linkage analysis of longitudinal phenotypes by three step-wise analysis approaches

    PubMed Central

    Guo, Zheng; Li, Xia; Rao, Shaoqi; Moser, Kathy L; Zhang, Tianwen; Gong, Binsheng; Shen, Gongqing; Li, Lin; Cannata, Ruth; Zirzow, Erich; Topol, Eric J; Wang, Qing

    2003-01-01

    Background Current statistical methods for sib-pair linkage analysis of complex diseases include linear models, generalized linear models, and novel data mining techniques. The purpose of this study was to further investigate the utility and properties of a novel pattern recognition technique (step-wise discriminant analysis) using the chromosome 10 linkage data from the Framingham Heart Study and by comparing it with step-wise logistic regression and linear regression. Results The three step-wise approaches were compared in terms of statistical significance and gene localization. Step-wise discriminant linkage analysis approach performed best; next was step-wise logistic regression; and step-wise linear regression was the least efficient because it ignored the categorical nature of disease phenotypes. Nevertheless, all three methods successfully identified the previously reported chromosomal region linked to human hypertension, marker GATA64A09. We also explored the possibility of using the discriminant analysis to detect gene × gene and gene × environment interactions. There was evidence to suggest the existence of gene × environment interactions between markers GATA64A09 or GATA115E01 and hypertension treatment and gene × gene interactions between markers GATA64A09 and GATA115E01. Finally, we answered the theoretical question "Is a trichotomous phenotype more efficient than a binary?" Unlike logistic regression, discriminant sib-pair linkage analysis might have more power to detect linkage to a binary phenotype than a trichotomous one. Conclusion We confirmed our previous speculation that step-wise discriminant analysis is useful for genetic mapping of complex diseases. This analysis also supported the possibility of the pattern recognition technique for investigating gene × gene or gene × environment interactions. PMID:14975136

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

    PubMed Central

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

    2016-01-01

    Dissolved oxygen (DO) is an important factor in the fermentation process of Corynebacterium glutamicum, which is a widely used aerobic microbe in bio-industry. Herein, we described RNA-seq for C. glutamicum under different DO levels (50%, 30% and 0%) in 5 L bioreactors. Multivariate data analysis (MVDA) models were used to analyze the RNA-seq and metabolism data to investigate the global effect of DO on the transcriptional distinction of the substance and energy metabolism of C. glutamicum. The results showed that there were 39 and 236 differentially expressed genes (DEGs) under the 50% and 0% DO conditions, respectively, compared to the 30% DO condition. Key genes and pathways affected by DO were analyzed, and the result of the MVDA and RNA-seq revealed that different DO levels in the fermenter had large effects on the substance and energy metabolism and cellular redox balance of C. glutamicum. At low DO, the glycolysis pathway was up-regulated, and TCA was shunted by the up-regulation of the glyoxylate pathway and over-production of amino acids, including valine, cysteine and arginine. Due to the lack of electron-acceptor oxygen, 7 genes related to the electron transfer chain were changed, causing changes in the intracellular ATP content at 0% and 30% DO. The metabolic flux was changed to rebalance the cellular redox. This study applied deep sequencing to identify a wealth of genes and pathways that changed under different DO conditions and provided an overall comprehensive view of the metabolism of C. glutamicum. The results provide potential ways to improve the oxygen tolerance of C. glutamicum and to modify the metabolic flux for amino acid production and heterologous protein expression. PMID:27907077

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

    PubMed

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

    2015-01-01

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

  8. 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.

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

    PubMed Central

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

    2010-01-01

    Background Adoption of new and underutilized vaccines by national immunization programs is an essential step towards reducing child mortality. Policy decisions to adopt new vaccines in high mortality countries often lag behind decisions in high-income countries. Using the case of Haemophilus influenzae type b (Hib) vaccine, this paper endeavors to explain these delays through the analysis of country-level economic, epidemiological, programmatic and policy-related factors, as well as the role of the Global Alliance for Vaccines and Immunisation (GAVI Alliance). Methods and Findings Data for 147 countries from 1990 to 2007 were analyzed in accelerated failure time models to identify factors that are associated with the time to decision to adopt Hib vaccine. In multivariable models that control for Gross National Income, region, and burden of Hib disease, the receipt of GAVI support speeded the time to decision by a factor of 0.37 (95% CI 0.18–0.76), or 63%. The presence of two or more neighboring country adopters accelerated decisions to adopt by a factor of 0.50 (95% CI 0.33–0.75). For each 1% increase in vaccine price, decisions to adopt are delayed by a factor of 1.02 (95% CI 1.00–1.04). Global recommendations and local studies were not associated with time to decision. Conclusions This study substantiates previous findings related to vaccine price and presents new evidence to suggest that GAVI eligibility is associated with accelerated decisions to adopt Hib vaccine. The influence of neighboring country decisions was also highly significant, suggesting that approaches to support the adoption of new vaccines should consider supply- and demand-side factors. Please see later in the article for the Editors' Summary PMID:20305714

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

    PubMed

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

    2016-06-20

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

  11. 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.

  12. Multivariate analysis of GPS position time series of JPL second reprocessing campaign

    NASA Astrophysics Data System (ADS)

    Amiri-Simkooei, A. R.; Mohammadloo, T. H.; Argus, D. F.

    2017-01-01

    The second reprocessing of all GPS data gathered by the Analysis Centers of IGS was conducted in late 2013 using the latest models and methodologies. Improved models of antenna phase center variations and solar radiation pressure in JPL's reanalysis are expected to significantly reduce errors. In an earlier work, JPL estimates of position time series, termed first reprocessing campaign, were examined in terms of their spatial and temporal correlation, power spectra, and draconitic signal. Similar analyses are applied to GPS time series at 89 and 66 sites of the second reanalysis with the time span of 7 and 21 years, respectively, to study possible improvements. Our results indicate that the spatial correlations are reduced on average by a factor of 1.25. While the white and flicker noise amplitudes for all components are reduced by 29-56 %, the random walk amplitude is enlarged. The white, flicker, and random walk noise amount to rate errors of, respectively, 0.01, 0.12, and 0.09 mm/yr in the horizontal and 0.04, 0.41 and 0.3 mm/yr in the vertical. Signals reported previously, such as those with periods of 13.63, 14.76, 5.5, and 351.4 / n for n=1,2,ldots,8 days, are identified in multivariate spectra of both data sets. The oscillation of the draconitic signal is reduced by factors of 1.87, 1.87, and 1.68 in the east, north and up components, respectively. Two other signals with Chandlerian period and a period of 380 days can also be detected.

  13. 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

  14. Multivariate proteomic analysis of the cerebrospinal fluid of patients with peripheral neuropathic pain and healthy controls – a hypothesis-generating pilot study

    PubMed Central

    Bäckryd, Emmanuel; Ghafouri, Bijar; Carlsson, Anders K; Olausson, Patrik; Gerdle, Björn

    2015-01-01

    Pain medicine lacks objective biomarkers to guide diagnosis and treatment. Combining two-dimensional gel proteomics with multivariate data analysis by projection, we exploratively analyzed the cerebrospinal fluid of eleven patients with severe peripheral neuropathic pain due to trauma and/or surgery refractory to conventional treatment and eleven healthy controls. Using orthogonal partial least squares discriminant analysis, we identified a panel of 36 proteins highly discriminating between the two groups. Due to a possible confounding effect of age, a new model with age as outcome variable was computed for patients (n=11), and four out of 36 protein spots were excluded due to a probable influence of age. Of the 32 remaining proteins, the following seven had the highest discriminatory power between the two groups: an isoform of angiotensinogen (upregulated in patients), two isoforms of alpha-1-antitrypsin (downregulated in patients), three isoforms of haptoglobin (upregulated in patients), and one isoform of pigment epithelium-derived factor (downregulated in patients). It has recently been hypothesized that the renin–angiotensin system may play a role in the pathophysiology of neuropathic pain, and a clinical trial of an angiotensin II receptor antagonist was recently published. It is noteworthy that when searching for neuropathic pain biomarkers with a purely explorative methodology, it was indeed a renin–angiotensin system protein that had the highest discriminatory power between patients and controls in the present study. The results from this hypothesis-generating pilot study have to be confirmed in larger, hypothesis-driven studies with age-matched controls, but the present study illustrates the fruitfulness of combining proteomics with multivariate data analysis in hypothesis-generating pain biomarker studies in humans. PMID:26170714

  15. 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.

  16. 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

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

    PubMed

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

    2017-01-01

    Rapidly evolving technical and regulatory landscapes of the pharmaceutical product development necessitates risk management with application of multivariate analysis using Process Analytical Technology (PAT) and Quality by Design (QbD). Poorly soluble, high dose drug, Satranidazole was optimally nanoprecipitated (SAT-NP) employing principles of Formulation by Design (FbD). The potential risk factors influencing the critical quality attributes (CQA) of SAT-NP were identified using Ishikawa diagram. Plackett-Burman screening design was adopted to screen the eight critical formulation and process parameters influencing the mean particle size, zeta potential and dissolution efficiency at 30min in pH7.4 dissolution medium. Pareto charts (individual and cumulative) revealed three most critical factors influencing CQA of SAT-NP viz. aqueous stabilizer (Polyvinyl alcohol), release modifier (Eudragit® S 100) and volume of aqueous phase. The levels of these three critical formulation attributes were optimized by FbD within established design space to minimize mean particle size, poly dispersity index, and maximize encapsulation efficiency of SAT-NP. Lenth's and Bayesian analysis along with mathematical modeling of results allowed identification and quantification of critical formulation attributes significantly active on the selected CQAs. The optimized SAT-NP exhibited mean particle size; 216nm, polydispersity index; 0.250, zeta potential; -3.75mV and encapsulation efficiency; 78.3%. The product was lyophilized using mannitol to form readily redispersible powder. X-ray diffraction analysis confirmed the conversion of crystalline SAT to amorphous form. In vitro release of SAT-NP in gradually pH changing media showed <20% release in pH1.2 and pH6.8 in 5h, while, complete release (>95%) in pH7.4 in next 3h, indicative of burst release after a lag time. This investigation demonstrated effective application of risk management and QbD tools in developing site-specific release

  18. Use of Complementary and Alternative Medicine (CAM) for Treatment among African-Americans: A Multivariate Analysis

    PubMed Central

    Barner, Jamie C.; Bohman, Thomas M.; Brown, Carolyn M.; Richards, Kristin M.

    2009-01-01

    Background Complementary and alternative medicine (CAM) use is substantial among African-Americans; however, research on characteristics of African-Americans who use of CAM to treat specific conditions is scarce. Objective To determine what predisposing, enabling, need, and disease state factors are related to CAM use for treatment among a nationally representative sample of African-Americans. Methods A cross-sectional study design was employed using the 2002 National Health Interview Survey (NHIS). A nationwide representative sample of adult (≥ 18 years) African-Americans who used CAM in the past 12 months (n= 16,113,651 weighted; n=2,952 unweighted) were included. The Andersen Healthcare Utilization Model served the framework with CAM use for treatment as the main outcome measure. Independent variables included: predisposing (e.g., age, gender, education), enabling (e.g., income, employment, access to care); need (e.g., health status, physician visits, prescription medication use); and disease state (i.e., most prevalent conditions among African-Americans) factors. Multivariate logistic regression was used to address the study objective. Results Approximately one in five (20.2%) CAM past 12 month users used CAM to treat a specific condition. Ten of the 15 CAM modalities were used primarily for treatment by African-Americans. CAM for treatment was significantly (p<0.05) associated with the following factors: graduate education, smaller family size, higher income, region (northeast, midwest, west more likely than south), depression/anxiety, more physician visits, less likely to engage in preventive care, more frequent exercise behavior, more activities of daily living (ADL) limitations, and neck pain. Conclusions Twenty percent of African-Americans who used CAM in the past year were treating a specific condition. Alternative medical systems, manipulative and body-based therapies, as well as folk medicine, prayer, biofeedback, and energy/Reiki were used most often

  19. Multivariate threshold model analysis of clinical mastitis in multiparous norwegian dairy cattle.

    PubMed

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

    2004-09-01

    A Bayesian multivariate threshold model was fitted to clinical mastitis (CM) records from 372,227 daughters of 2411 Norwegian Dairy Cattle (NRF) sires. All cases of veterinary-treated CM occurring from 30 d before first calving to culling or 300 d after third calving were included. Lactations were divided into 4 intervals: -30 to 0 d, 1 to 30 d, 31 to 120 d, and 121 to 300 d after calving. Within each interval, absence or presence of CM was scored as "0" or "1" based on the CM episodes. A 12-variate (3 lactations x 4 intervals) threshold model was used, assuming that CM was a different trait in each interval. Residuals were assumed correlated within lactation but independent between lactations. The model for liability to CM had interval-specific effects of month-year of calving, age at calving (first lactation), or calving interval (second and third lactations), herd-5-yr-period, sire of the cow, plus a residual. Posterior mean of heritability of liability to CM was 0.09 and 0.05 in the first and last intervals, respectively, and between 0.06 and 0.07 for other intervals. Posterior means of genetic correlations of liability to CM between intervals ranged from 0.24 (between intervals 1 and 12) to 0.73 (between intervals 1 and 2), suggesting interval-specific genetic control of resistance to mastitis. Residual correlations ranged from 0.08 to 0.17 for adjacent intervals, and between -0.01 and 0.03 for nonadjacent intervals. Trends of mean sire posterior means by birth year of daughters were used to assess genetic change. The 12 traits showed similar trends, with little or no genetic change from 1976 to 1986, and genetic improvement in resistance to mastitis thereafter. Annual genetic change was larger for intervals in first lactation when compared with second or third lactation. Within lactation, genetic change was larger for intervals early in lactation, and more so in the first lactation. This reflects that selection against mastitis in NRF has emphasized mainly CM

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

    PubMed Central

    2013-01-01

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

  1. Spermiogram and sperm head morphometry assessed by multivariate cluster analysis results during adolescence (12-18 years) and the effect of varicocele

    PubMed Central

    Vásquez, Fernando; Soler, Carles; Camps, Patricia; Valverde, Anthony; García-Molina, Almudena

    2016-01-01

    This work evaluates sperm head morphometric characteristics in adolescents from 12 to 18 years of age, and the effect of varicocele. Volunteers between 150 and 224 months of age (mean 191, n = 87), who had reached oigarche by 12 years old, were recruited in the area of Barranquilla, Colombia. Morphometric analysis of sperm heads was performed with principal component (PC) and discriminant analysis. Combining seminal fluid and sperm parameters provided five PCs: two related to sperm morphometry, one to sperm motility, and two to seminal fluid components. Discriminant analysis on the morphometric results of varicocele and nonvaricocele groups did not provide a useful classification matrix. Of the semen-related PCs, the most explanatory (40%) was related to sperm motility. Two PCs, including sperm head elongation and size, were sufficient to evaluate sperm morphometric characteristics. Most of the morphometric variables were correlated with age, with an increase in size and decrease in the elongation of the sperm head. For head size, the entire sperm population could be divided into two morphometric subpopulations, SP1 and SP2, which did not change during adolescence. In general, for varicocele individuals, SP1 had larger and more elongated sperm heads than SP2, which had smaller and more elongated heads than in nonvaricocele men. In summary, sperm head morphometry assessed by CASA-Morph and multivariate cluster analysis provides a better comprehension of the ejaculate structure and possibly sperm function. Morphometric analysis provides much more information than data obtained from conventional semen analysis. PMID:27751986

  2. Spermiogram and sperm head morphometry assessed by multivariate cluster analysis results during adolescence (12-18 years) and the effect of varicocele.

    PubMed

    Vásquez, Fernando; Soler, Carles; Camps, Patricia; Valverde, Anthony; García-Molina, Almudena

    2016-01-01

    This work evaluates sperm head morphometric characteristics in adolescents from 12 to 18 years of age, and the effect of varicocele. Volunteers between 150 and 224 months of age (mean 191, n = 87), who had reached oigarche by 12 years old, were recruited in the area of Barranquilla, Colombia. Morphometric analysis of sperm heads was performed with principal component (PC) and discriminant analysis. Combining seminal fluid and sperm parameters provided five PCs: two related to sperm morphometry, one to sperm motility, and two to seminal fluid components. Discriminant analysis on the morphometric results of varicocele and nonvaricocele groups did not provide a useful classification matrix. Of the semen-related PCs, the most explanatory (40%) was related to sperm motility. Two PCs, including sperm head elongation and size, were sufficient to evaluate sperm morphometric characteristics. Most of the morphometric variables were correlated with age, with an increase in size and decrease in the elongation of the sperm head. For head size, the entire sperm population could be divided into two morphometric subpopulations, SP1 and SP2, which did not change during adolescence. In general, for varicocele individuals, SP1 had larger and more elongated sperm heads than SP2, which had smaller and more elongated heads than in nonvaricocele men. In summary, sperm head morphometry assessed by CASA-Morph and multivariate cluster analysis provides a better comprehension of the ejaculate structure and possibly sperm function. Morphometric analysis provides much more information than data obtained from conventional semen analysis.

  3. A Monte Carlo Power Analysis of Traditional Repeated Measures and Hierarchical Multivariate Linear Models in Longitudinal Data Analysis.

    PubMed

    Fang, Hua; Brooks, Gordon P; Rizzo, Maria L; Espy, Kimberly A; Barcikowski, Robert S

    2008-01-01

    The power properties of traditional repeated measures and hierarchical linear models have not been clearly determined in the balanced design for longitudinal studies in the current literature. A Monte Carlo power analysis of traditional repeated measures and hierarchical multivariate linear models are presented under three variance-covariance structures. Results suggest that traditional repeated measures have higher power than hierarchical linear models for main effects, but lower power for interaction effects. Significant power differences are also exhibited when power is compared across different covariance structures. Results also supplement more comprehensive empirical indexes for estimating model precision via bootstrap estimates and the approximate power for both main effects and interaction tests under standard model assumptions.

  4. Noninvasive, quantitative analysis of drug mixtures in containers using spatially offset Raman spectroscopy (SORS) and multivariate statistical analysis.

    PubMed

    Olds, William J; Sundarajoo, Shankaran; Selby, Mark; Cletus, Biju; Fredericks, Peter M; Izake, Emad L

    2012-05-01

    In this paper, spatially offset Raman spectroscopy (SORS) is demonstrated for noninvasively investigating the composition of drug mixtures inside an opaque plastic container. The mixtures consisted of three components including a target drug (acetaminophen or phenylephrine hydrochloride) and two diluents (glucose and caffeine). The target drug concentrations ranged from 5% to 100%. After conducting SORS analysis to ascertain the Raman spectra of the concealed mixtures, principal component analysis (PCA) was performed on the SORS spectra to reveal trends within the data. Partial least squares (PLS) regression was used to construct models that predicted the concentration of each target drug, in the presence of the other two diluents. The PLS models were able to predict the concentration of acetaminophen in the validation samples with a root-mean-square error of prediction (RMSEP) of 3.8% and the concentration of phenylephrine hydrochloride with an RMSEP of 4.6%. This work demonstrates the potential of SORS, used in conjunction with multivariate statistical techniques, to perform noninvasive, quantitative analysis on mixtures inside opaque containers. This has applications for pharmaceutical analysis, such as monitoring the degradation of pharmaceutical products on the shelf, in forensic investigations of counterfeit drugs, and for the analysis of illicit drug mixtures which may contain multiple components.

  5. Risk Factors of Ototoxicity After Cisplatin-Based Chemo-Irradiation in Patients With Locally Advanced Head-and-Neck Cancer: A Multivariate Analysis

    SciTech Connect

    Zuur, Charlotte L. . E-mail: cl.zuur@vumc.nl; Simis, Yvonne J.; Lansdaal, Pauline E.; Hart, Augustinus A.; Rasch, Coen R.; Schornagel, Jan H.; Dreschler, Wouter A.; Balm, Alfons J.

    2007-08-01

    Purpose: Cisplatin chemo-irradiation is increasingly used in locally advanced squamous cell carcinoma of the head and neck. The objective of this study is to determine risk factors of ototoxicity due to intra-arterial high-dose cisplatin chemoradiation. Methods and Materials: A prospective analysis of hearing thresholds at low and (ultra) high frequencies obtained before, during, and after treatment in 146 patients. Treatment consisted of intra-arterial infusion of high-dose cisplatin (150 mg/m{sup 2}, four courses) with sodium thiosulfate rescue and concurrent radiation therapy (70 Gy). Patient and chemoradiation variables were studied in a multivariate analysis. Results: After treatment, 23% of the ears were under consideration for hearing aids because of therapy. Twenty-two percent of the patients developed an increase in air-bone gap >10 dB during or after therapy. In the multivariate explanatory analysis, cumulative dose of cisplatin and radiation therapy, and young age displayed a causal relationship with increased sensorineural hearing loss during and after therapy (p < 0.001). In the multivariate prediction analysis, pretreatment hearing level of the concerning ear was identified as an independent predictive factor for hearing capability after therapy (p < 0.0001). Conclusions: Both cisplatin and radiation therapy were proven to induce sensorineural hearing loss, in this study with short-term follow-up. Of all patient and treatment variables studied, the patients pretreatment hearing level appeared to be the main predictive factor for hearing capability after high-dose intra-arterial cisplatin chemoradiation.

  6. Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model

    PubMed Central

    Snell, Kym I.E.; Hua, Harry; Debray, Thomas P.A.; Ensor, Joie; Look, Maxime P.; Moons, Karel G.M.; Riley, Richard D.

    2016-01-01

    Objectives Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. Study Design and Setting We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of “good” performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. Results In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of “good” performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of “good” performance. Conclusion Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. PMID:26142114

  7. 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

  8. A Multivariate Analysis of Emotional and Behavioral Adjustment and Preschool Educational Outcomes

    ERIC Educational Resources Information Center

    Fantuzzo, John; Bulotsky, Rebecca; McDermott, Paul; Mosca, Samuel; Lutz, Megan Noone

    2003-01-01

    The study examined the multivariate relationship between dimensions of preschool emotional and behavioral adjustment assessed at the beginning of the year by the Adjustment Scales for Preschool Intervention (ASPI) and multiple learning and social competencies at the end of the year with an urban Head Start sample. This study also examined the…

  9. 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…

  10. 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.

  11. Amatoxin poisoning treatment decision-making: pharmaco-therapeutic clinical strategy assessment using multidimensional multivariate statistic analysis.

    PubMed

    Poucheret, Patrick; Fons, Françoise; Doré, Jean Christophe; Michelot, Didier; Rapior, Sylvie

    2010-06-15

    Ninety percent of fatal higher fungus poisoning is due to amatoxin-containing mushroom species. In addition to absence of antidote, no chemotherapeutic consensus was reported. The aim of the present study is to perform a retrospective multidimensional multivariate statistic analysis of 2110 amatoxin poisoning clinical cases, in order to optimize therapeutic decision-making. Our results allowed to classify drugs as a function of their influence on one major parameter: patient survival. Active principles were classified as first intention, second intention, adjuvant or controversial pharmaco-therapeutic clinical intervention. We conclude that (1) retrospective multidimensional multivariate statistic analysis of complex clinical dataset might help future therapeutic decision-making and (2) drugs such as silybin, N-acetylcystein and putatively ceftazidime are clearly associated, in amatoxin poisoning context, with higher level of patient survival.

  12. Mini-DIAL system measurements coupled with multivariate data analysis to identify TIC and TIM simulants: preliminary absorption database analysis.

    NASA Astrophysics Data System (ADS)

    Gaudio, P.; Malizia, A.; Gelfusa, M.; Martinelli, E.; Di Natale, C.; Poggi, L. A.; Bellecci, C.

    2017-01-01

    Nowadays Toxic Industrial Components (TICs) and Toxic Industrial Materials (TIMs) are one of the most dangerous and diffuse vehicle of contamination in urban and industrial areas. The academic world together with the industrial and military one are working on innovative solutions to monitor the diffusion in atmosphere of such pollutants. In this phase the most common commercial sensors are based on “point detection” technology but it is clear that such instruments cannot satisfy the needs of the smart cities. The new challenge is developing stand-off systems to continuously monitor the atmosphere. Quantum Electronics and Plasma Physics (QEP) research group has a long experience in laser system development and has built two demonstrators based on DIAL (Differential Absorption of Light) technology could be able to identify chemical agents in atmosphere. In this work the authors will present one of those DIAL system, the miniaturized one, together with the preliminary results of an experimental campaign conducted on TICs and TIMs simulants in cell with aim of use the absorption database for the further atmospheric an analysis using the same DIAL system. The experimental results are analysed with standard multivariate data analysis technique as Principal Component Analysis (PCA) to develop a classification model aimed at identifying organic chemical compound in atmosphere. The preliminary results of absorption coefficients of some chemical compound are shown together pre PCA analysis.

  13. 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%.

  14. A multivariate analysis of factors determining tumor progression in childhood low-grade glioma: a population-based cohort study (CCLG CNS9702)

    PubMed Central

    Stokland, Tore; Liu, Jo-Fen; Ironside, James W.; Ellison, David W.; Taylor, Roger; Robinson, Kathryn J.; Picton, Susan V.; Walker, David A.

    2010-01-01

    The purpose of this study was to identify risk factors for the progression of low-grade glioma in children from a large population-based cohort. Patient and tumor details of a national cohort of children with low-grade glioma, recruited into an international multidisciplinary clinical strategy, were subjected to univariate and multivariate analyses of progression-free survival and overall survival. From the cohort of 798 patients, 639 patients were eligible, with a median age 6.71 years (0.26–16.75 years); 49% were males; 15.9% had neurofibromatosis type 1, 63.7% pilocytic astrocytoma, 5.9% fibrillary astrocytoma, 4.2% mixed neuronal-glial tumors, and 3.6% others; 21.1% were diagnosed clinically. Anatomically implicated were 31.6% cerebellum, 24.6% chiasma/hypothalamus, 16.0% cerebral hemispheres, 9.9% brain stem, 6.1% other supratentorial midline structures, 5.9% optic nerve only, 4.5% spinal cord, and 1.4% others. The 5-year overall survival and progression-free survival in the whole cohort were 94.6% and 69.4%, respectively. There was a significant association between age and site (P < .001) and extent of tumor resection and site (P < .001). Multivariate analysis identified young age, fibrillary astrocytoma, and extent of surgical resection as significant independent risk factors for progression. Hypothalamic/chiasmatic tumors demonstrated the most sustained tendency to progress. In conclusion, the influence of age and anatomical site upon the risk of tumor progression suggests that these factors strongly influence tumor behavior for the majority of pilocytic tumors. Age <1 year and 1–5 years, fibrillary histology, completeness of resection, and chiasmatic location are candidates for stratification in future studies. PMID:20861086

  15. 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.

  16. The application of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the stratum corneum.

    PubMed

    Goh, Choon Fu; Craig, Duncan Q M; Hadgraft, Jonathan; Lane, Majella E

    2017-02-01

    Drug permeation through the intercellular lipids, which pack around and between corneocytes, may be enhanced by increasing the thermodynamic activity of the active in a formulation. However, this may also result in unwanted drug crystallisation on and in the skin. In this work, we explore the combination of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the skin. Ex vivo permeation studies of saturated solutions of diclofenac sodium (DF Na) in two vehicles, propylene glycol (PG) and dimethyl sulphoxide (DMSO), were carried out in porcine ear skin. Tape stripping and ATR-FTIR spectroscopy were conducted simultaneously to collect spectral data as a function of skin depth. Multivariate data analysis was applied to visualise and categorise the spectral data in the region of interest (1700-1500cm(-1)) containing the carboxylate (COO(-)) asymmetric stretching vibrations of DF Na. Spectral data showed the redshifts of the COO(-) asymmetric stretching vibrations for DF Na in the solution compared with solid drug. Similar shifts were evident following application of saturated solutions of DF Na to porcine skin samples. Multivariate data analysis categorised the spectral data based on the spectral differences and drug crystallisation was found to be confined to the upper layers of the skin. This proof-of-concept study highlights the utility of ATR-FTIR spectroscopy in combination with multivariate data analysis as a simple and rapid approach in the investigation of drug deposition in the skin. The approach described here will be extended to the study of other actives for topical application to the skin.

  17. Genetic analysis of mechanisms of aging.

    PubMed

    Rose, M R; Archer, M A

    1996-06-01

    A wide range of genetic models with postponed aging are now available, from selected mice and Drosophilia to mutant Caenorhabditis elegans and Saccharomyces cerevisiae. These systems allow efficient testing of alternative mechanistic hypotheses for aging. Genetic analysis is forging stronger connections between particular alleles and susceptibility to particular 'diseases of aging'; for example, two different genes for Alzheimer disease have been identified.

  18. 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

    2011-10-13

    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.

  19. 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.

  20. A multivariate analysis of genetic constraints to life history evolution in a wild population of red deer.

    PubMed

    Walling, Craig A; Morrissey, Michael B; Foerster, Katharina; Clutton-Brock, Tim H; Pemberton, Josephine M; Kruuk, Loeske E B

    2014-12-01

    Evolutionary theory predicts that genetic constraints should be widespread, but empirical support for their existence is surprisingly rare. Commonly applied univariate and bivariate approaches to detecting genetic constraints can underestimate their prevalence, with important aspects potentially tractable only within a multivariate framework. However, multivariate genetic analyses of data from natural populations are challenging because of modest sample sizes, incomplete pedigrees, and missing data. Here we present results from a study of a comprehensive set of life history traits (juvenile survival, age at first breeding, annual fecundity, and longevity) for both males and females in a wild, pedigreed, population of red deer (Cervus elaphus). We use factor analytic modeling of the genetic variance-covariance matrix ( G: ) to reduce the dimensionality of the problem and take a multivariate approach to estimating genetic constraints. We consider a range of metrics designed to assess the effect of G: on the deflection of a predicted response to selection away from the direction of fastest adaptation and on the evolvability of the traits. We found limited support for genetic constraint through genetic covariances between traits, both within sex and between sexes. We discuss these results with respect to other recent findings and to the problems of estimating these parameters for natural populations.

  1. A Multivariate Analysis of Genetic Constraints to Life History Evolution in a Wild Population of Red Deer

    PubMed Central

    Walling, Craig A.; Morrissey, Michael B.; Foerster, Katharina; Clutton-Brock, Tim H.; Pemberton, Josephine M.; Kruuk, Loeske E. B.

    2014-01-01

    Evolutionary theory predicts that genetic constraints should be widespread, but empirical support for their existence is surprisingly rare. Commonly applied univariate and bivariate approaches to detecting genetic constraints can underestimate their prevalence, with important aspects potentially tractable only within a multivariate framework. However, multivariate genetic analyses of data from natural populations are challenging because of modest sample sizes, incomplete pedigrees, and missing data. Here we present results from a study of a comprehensive set of life history traits (juvenile survival, age at first breeding, annual fecundity, and longevity) for both males and females in a wild, pedigreed, population of red deer (Cervus elaphus). We use factor analytic modeling of the genetic variance–covariance matrix (G) to reduce the dimensionality of the problem and take a multivariate approach to estimating genetic constraints. We consider a range of metrics designed to assess the effect of G on the deflection of a predicted response to selection away from the direction of fastest adaptation and on the evolvability of the traits. We found limited support for genetic constraint through genetic covariances between traits, both within sex and between sexes. We discuss these results with respect to other recent findings and to the problems of estimating these parameters for natural populations. PMID:25278555

  2. 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

  3. Multivariate and univariate analysis of energy balance data from lactating dairy cows.

    PubMed

    Moraes, L E; Kebreab, E; Strathe, A B; Dijkstra, J; France, J; Casper, D P; Fadel, J G

    2015-06-01

    The objectives of the study were to develop a multivariate framework for analyzing energy balance data from lactating cows and investigate potential changes in maintenance requirements and partial efficiencies of energy utilization by lactating cows over the years. The proposed model accounted for the fact that metabolizable energy intake, milk energy output, and tissue energy balance are random variables that interact mutually. The model was specified through structural equations implemented in a Bayesian framework. The structural equations, along with a model traditionally used to estimate energetic parameters, were fitted to a large database of indirect calorimetry records from lactating cows. Maintenance requirements and partial efficiencies for both models were similar to values reported in the literature. In particular, the estimated parameters (with 95% credible interval in parentheses) for the proposed model were: net energy requirement for maintenance equal to 0.36 (0.34, 0.38) MJ/kg of metabolic body weight·day; the efficiency of utilizing dietary energy for milk production and tissue gain were 0.63 (0.61, 0.64) and 0.70 (0.68, 0.72), respectively; the efficiency of utilizing body stores for milk production was 0.89 (0.87, 0.91). Furthermore, additional analyses were conducted for which energetic parameters were allowed to depend on the decade in which studies were conducted. These models investigated potential changes in maintenance requirements and partial efficiencies over the years. Canonical correlation analysis was used to investigate the association between changes in energetic parameters with additional dietary and animal characteristics available in the database. For both models, net energy requirement for maintenance and the efficiency of utilizing dietary energy for milk production and tissue gain increased in the more recent decades, whereas the efficiency of utilizing body stores for milk production remained unchanged. The increase in

  4. Chemical imaging and spectroscopy using tunable filters: Instrumentation, methodology, and multivariate analysis

    NASA Astrophysics Data System (ADS)

    Turner, John Frederick, II

    based assays. The instrument has been incorporated into a commercial microtiter plate reagent dispenser and can image the fluorescence emission from microtiter plates at rates up to 10 frames/second. The instrument design and its evaluation using model fluorophores is described in detail. The final emphasis of my research has been to explore and develop rapid multivariate analyses that complement the high throughput acquisition methods employed in our laboratory. A new technique called cosine correlation analysis (CCA) is introduced which rapidly generates image contrast based on spectral shape. The theory and implementation of CCA are described using model data and Raman image data from thermoplastic olefin and silicon semiconductor materials.

  5. 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

  6. Stability of binary and ternary model oil-field particle suspensions: a multivariate analysis approach.

    PubMed

    Dudásová, Dorota; Rune Flåten, Geir; Sjöblom, Johan; Øye, Gisle

    2009-09-15

    The transmission profiles of one- to three-component particle suspension mixtures were analyzed by multivariate methods such as principal component analysis (PCA) and partial least-squares regression (PLS). The particles mimic the solids present in oil-field-produced water. Kaolin and silica represent solids of reservoir origin, whereas FeS is the product of bacterial metabolic activities, and Fe(3)O(4) corrosion product (e.g., from pipelines). All particles were coated with crude oil surface active components to imitate particles in real systems. The effects of different variables (concentration, temperature, and coating) on the suspension stability were studied with Turbiscan LAb(Expert). The transmission profiles over 75 min represent the overall water quality, while the transmission during the first 15.5 min gives information for suspension behavior during a representative time period for the hold time in the separator. The behavior of the mixed particle suspensions was compared to that of the single particle suspensions and models describing the systems were built. The findings are summarized as follows: silica seems to dominate the mixture properties in the binary suspensions toward enhanced separation. For 75 min, temperature and concentration are the most significant, while for 15.5 min, concentration is the only significant variable. Models for prediction of transmission spectra from run parameters as well as particle type from transmission profiles (inverse calibration) give a reasonable description of the relationships. In ternary particle mixtures, silica is not dominant and for 75 min, the significant variables for mixture (temperature and coating) are more similar to single kaolin and FeS/Fe(3)O(4). On the other hand, for 15.5 min, the coating is the most significant and this is similar to one for silica (at 15.5 min). The model for prediction of transmission spectra from run parameters gives good estimates of the transmission profiles. Although the