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Sample records for automatic multivariable analysis

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

    PubMed Central

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

    2016-01-01

    We have developed an automatic and objective method for detecting human oral squamous cell carcinoma (OSCC) tissues with Raman microspectroscopy. We measure 196 independent Raman spectra from 196 different points of one oral tissue sample and globally analyze these spectra using a Multivariate Curve Resolution (MCR) analysis. Discrimination of OSCC tissues is automatically and objectively made by spectral matching comparison of the MCR decomposed Raman spectra and the standard Raman spectrum of keratin, a well-established molecular marker of OSCC. We use a total of 24 tissue samples, 10 OSCC and 10 normal tissues from the same 10 patients, 3 OSCC and 1 normal tissues from different patients. Following the newly developed protocol presented here, we have been able to detect OSCC tissues with 77 to 92% sensitivity (depending on how to define positivity) and 100% specificity. The present approach lends itself to a reliable clinical diagnosis of OSCC substantiated by the “molecular fingerprint” of keratin. PMID:26806007

  2. Multivariate Analysis in Metabolomics

    PubMed Central

    Worley, Bradley; Powers, Robert

    2015-01-01

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

  3. 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).

  4. Multivariate data analysis of proteome data.

    PubMed

    Engkilde, Kåre; Jacobsen, Susanne; Søndergaard, Ib

    2007-01-01

    We present the background for multivariate data analysis on proteomics data with a hands-on section on how to transfer data between different software packages. The techniques can also be used for other biological and biochemical problems in which structures have to be found in a large amount of data. Digitalization of the 2D gels, analysis using image processing software, transfer of data, multivariate data analysis, interpretation of the results, and finally we return to biology. PMID:17093312

  5. Accuracy analysis of automatic distortion correction

    NASA Astrophysics Data System (ADS)

    Kolecki, Jakub; Rzonca, Antoni

    2015-06-01

    The paper addresses the problem of the automatic distortion removal from images acquired with non-metric SLR camera equipped with prime lenses. From the photogrammetric point of view the following question arises: is the accuracy of distortion control data provided by the manufacturer for a certain lens model (not item) sufficient in order to achieve demanded accuracy? In order to obtain the reliable answer to the aforementioned problem the two kinds of tests were carried out for three lens models. Firstly the multi-variant camera calibration was conducted using the software providing full accuracy analysis. Secondly the accuracy analysis using check points took place. The check points were measured in the images resampled based on estimated distortion model or in distortion-free images simply acquired in the automatic distortion removal mode. The extensive conclusions regarding application of each calibration approach in practice are given. Finally the rules of applying automatic distortion removal in photogrammetric measurements are suggested.

  6. Multivariate Longitudinal Analysis with Bivariate Correlation Test.

    PubMed

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

    2016-01-01

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

  7. Multivariate Longitudinal Analysis with Bivariate Correlation Test

    PubMed Central

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

    2016-01-01

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

  8. 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.

  9. Biological Sequence Analysis with Multivariate String Kernels.

    PubMed

    Kuksa, Pavel P

    2013-03-01

    String kernel-based machine learning methods have yielded great success in practical tasks of structured/sequential data analysis. They often exhibit state-of-the-art performance on many practical tasks of sequence analysis such as biological sequence classification, remote homology detection, or protein superfamily and fold prediction. However, typical string kernel methods rely on analysis of discrete one-dimensional (1D) string data (e.g., DNA or amino acid sequences). In this work we address the multi-class biological sequence classification problems using multivariate representations in the form of sequences of features vectors (as in biological sequence profiles, or sequences of individual amino acid physico-chemical descriptors) and a class of multivariate string kernels that exploit these representations. On a number of protein sequence classification tasks proposed multivariate representations and kernels show significant 15-20\\% improvements compared to existing state-of-the-art sequence classification methods. PMID:23509193

  10. Multivariate Analysis of Ladle Vibration

    NASA Astrophysics Data System (ADS)

    Yenus, Jaefer; Brooks, Geoffrey; Dunn, Michelle

    2016-05-01

    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

  11. 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

  12. Multivariate multiscale entropy for brain consciousness analysis.

    PubMed

    Ahmed, Mosabber Uddin; Li, Ling; Cao, Jianting; Mandic, Danilo P

    2011-01-01

    The recently introduced multiscale entropy (MSE) method accounts for long range correlations over multiple time scales and can therefore reveal the complexity of biological signals. The existing MSE algorithm deals with scalar time series whereas multivariate time series are common in experimental and biological systems. To that cause, in this paper the MSE method is extended to the multivariate case. This allows us to gain a greater insight into the complexity of the underlying signal generating system, producing multifaceted and more robust estimates than standard single channel MSE. Simulations on both synthetic data and brain consciousness analysis support the approach. PMID:22254434

  13. 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.

  14. 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.

  15. 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.

  16. Damage detection using multivariate recurrence quantification analysis

    NASA Astrophysics Data System (ADS)

    Nichols, J. M.; Trickey, S. T.; Seaver, M.

    2006-02-01

    Recurrence-quantification analysis (RQA) has emerged as a useful tool for detecting subtle non-stationarities and/or changes in time-series data. Here, we extend the RQA analysis methods to multivariate observations and present a method by which the "length scale" parameter ɛ (the only parameter required for RQA) may be selected. We then apply the technique to the difficult engineering problem of damage detection. The structure considered is a finite element model of a rectangular steel plate where damage is represented as a cut in the plate, starting at one edge and extending from 0% to 25% of the plate width in 5% increments. Time series, recorded at nine separate locations on the structure, are used to reconstruct the phase space of the system's dynamics and subsequently generate the multivariate recurrence (and cross-recurrence) plots. Multivariate RQA is then used to detect damage-induced changes to the structural dynamics. These results are then compared with shifts in the plate's natural frequencies. Two of the RQA-based features are found to be more sensitive to damage than are the plate's frequencies.

  17. Multivariate streamflow forecasting using independent component analysis

    NASA Astrophysics Data System (ADS)

    Westra, Seth; Sharma, Ashish; Brown, Casey; Lall, Upmanu

    2008-02-01

    Seasonal forecasting of streamflow provides many benefits to society, by improving our ability to plan and adapt to changing water supplies. A common approach to developing these forecasts is to use statistical methods that link a set of predictors representing climate state as it relates to historical streamflow, and then using this model to project streamflow one or more seasons in advance based on current or a projected climate state. We present an approach for forecasting multivariate time series using independent component analysis (ICA) to transform the multivariate data to a set of univariate time series that are mutually independent, thereby allowing for the much broader class of univariate models to provide seasonal forecasts for each transformed series. Uncertainty is incorporated by bootstrapping the error component of each univariate model so that the probability distribution of the errors is maintained. Although all analyses are performed on univariate time series, the spatial dependence of the streamflow is captured by applying the inverse ICA transform to the predicted univariate series. We demonstrate the technique on a multivariate streamflow data set in Colombia, South America, by comparing the results to a range of other commonly used forecasting methods. The results show that the ICA-based technique is significantly better at representing spatial dependence, while not resulting in any loss of ability in capturing temporal dependence. As such, the ICA-based technique would be expected to yield considerable advantages when used in a probabilistic setting to manage large reservoir systems with multiple inflows or data collection points.

  18. Automatic Error Analysis Using Intervals

    ERIC Educational Resources Information Center

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

    2012-01-01

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

  19. FAMA: Fast Automatic MOOG Analysis

    NASA Astrophysics Data System (ADS)

    Magrini, Laura; Randich, Sofia; Friel, Eileen; Spina, Lorenzo; Jacobson, Heather; Cantat-Gaudin, Tristan; Donati, Paolo; Baglioni, Roberto; Maiorca, Enrico; Bragaglia, Angela; Sordo, Rosanna; Vallenari, Antonella

    2014-02-01

    FAMA (Fast Automatic MOOG Analysis), written in Perl, computes the atmospheric parameters and abundances of a large number of stars using measurements of equivalent widths (EWs) automatically and independently of any subjective approach. Based on the widely-used MOOG code, it simultaneously searches for three equilibria, excitation equilibrium, ionization balance, and the relationship between logn(FeI) and the reduced EWs. FAMA also evaluates the statistical errors on individual element abundances and errors due to the uncertainties in the stellar parameters. Convergence criteria are not fixed "a priori" but instead are based on the quality of the spectra.

  20. Automatic fringe analysis

    NASA Technical Reports Server (NTRS)

    Chiu, Arnold; Ladewski, Ted; Turney, Jerry

    1991-01-01

    To satisfy the requirement for fast, accurate interferometric analytical tools, the Fringe Analysis Workstation (FAW) has been developed to analyze complex fringe image data easily and rapidly. FAW is employed for flow studies in hydrodynamics and aerodynamics experiments, and for target shell characterization in inertial confinement fusion research. Three major components of the FAW system: fringe analysis/image processing, input/output, and visualization/graphical user interface are described.

  1. COSIMA data analysis using multivariate techniques

    NASA Astrophysics Data System (ADS)

    Silén, J.; Cottin, H.; Hilchenbach, M.; Kissel, J.; Lehto, H.; Siljeström, S.; Varmuza, K.

    2015-02-01

    We describe how to use multivariate analysis of complex TOF-SIMS (time-of-flight secondary ion mass spectrometry) spectra by introducing the method of random projections. The technique allows us to do full clustering and classification of the measured mass spectra. In this paper we use the tool for classification purposes. The presentation describes calibration experiments of 19 minerals on Ag and Au substrates using positive mode ion spectra. The discrimination between individual minerals gives a cross-validation Cohen κ for classification of typically about 80%. We intend to use the method as a fast tool to deduce a qualitative similarity of measurements.

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. Toward automatic finite element analysis

    NASA Technical Reports Server (NTRS)

    Kela, Ajay; Perucchio, Renato; Voelcker, Herbert

    1987-01-01

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

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

    PubMed

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

    2015-04-01

    Traditional sample entropy fails to quantify inherent long-range dependencies among real data. Multiscale sample entropy (MSE) can detect intrinsic correlations in data, but it is usually used in univariate data. To generalize this method for multichannel data, we introduced multivariate multiscale entropy into multiscale signals as a reflection of the nonlinear dynamic correlation. But traditional multivariate multiscale entropy has a large quantity of computation and costs a large period of time and space for more channel system, so that it can not reflect the correlation between variables timely and accurately. In this paper, therefore, an improved multivariate multiscale entropy embeds on all variables at the same time, instead of embedding on a single variable as in the traditional methods, to solve the memory overflow while the number of channels rise, and it is more suitable for the actual multivariate signal analysis. The method was tested in simulation data and Bonn epilepsy dataset. The simulation results showed that the proposed method had a good performance to distinguish correlation data. Bonn epilepsy dataset experiment also showed that the method had a better classification accuracy among the five data set, especially with an accuracy of 100% for data collection of Z and S. PMID:26211236

  8. 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.

  9. 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).

  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. A Multivariate Analysis of Galaxy Cluster Properties

    NASA Astrophysics Data System (ADS)

    Ogle, P. M.; Djorgovski, S.

    1993-05-01

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

  12. Multivariate analysis of environmental data for two hydrographic basins

    SciTech Connect

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

    1992-02-01

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

  13. Regional dissociated heterochrony in multivariate analysis.

    PubMed

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

    2004-12-01

    Heterochrony, the classic framework to study ontogeny and phylogeny, in essence relies on a univariate concept of shape. Though principal component plots of multivariate shape data seem to resemble classical bivariate allometric plots, the language of heterochrony cannot be translated directly into general multivariate methodology. We simulate idealized multivariate ontogenetic trajectories and demonstrate their behavior in principal component plots in shape space and in size-shape space. The concept of "dissociation", which is conventionally regarded as a change in the relationship between shape change and size change, appears to be algebraically the same as regional dissociation - the variation of apparent heterochrony by region. Only if the trajectories of two related species lie along exactly the same path in shape space can the classic terminology of heterochrony apply so that pure dissociation of size change against shape change can be detected. We demonstrate a geometric morphometric approach to these issues using adult and subadult crania of 48 Pan paniscus and 47 P. troglodytes. On each specimen we digitized 47 landmarks and 144 semilandmarks on ridge curves and the external neurocranial surface. The relation between these two species' growth trajectories is too complex for a simple summary in terms of global heterochrony. PMID:15646279

  14. Heavy flavor identification using multivariate analysis at H1

    SciTech Connect

    Pandurovic, Mila; Bozovic-Jelisavcic, Ivanka; Mudrinic, Mihajlo

    2010-01-21

    We discuss b quark identification in deep inelastic scattering of electron on proton at H1 by applying multivariate analysis method. Separation between heavy and light flavors can be further used to extract proton quark content.

  15. Search for the top quark using multivariate analysis techniques

    SciTech Connect

    Bhat, P.C.; D0 Collaboration

    1994-08-01

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

  16. The statistical analysis of multivariate serological frequency data.

    PubMed

    Reyment, Richard A

    2005-11-01

    Data occurring in the form of frequencies are common in genetics-for example, in serology. Examples are provided by the AB0 group, the Rhesus group, and also DNA data. The statistical analysis of tables of frequencies is carried out using the available methods of multivariate analysis with usually three principal aims. One of these is to seek meaningful relationships between the components of a data set, the second is to examine relationships between populations from which the data have been obtained, the third is to bring about a reduction in dimensionality. This latter aim is usually realized by means of bivariate scatter diagrams using scores computed from a multivariate analysis. The multivariate statistical analysis of tables of frequencies cannot safely be carried out by standard multivariate procedures because they represent compositions and are therefore embedded in simplex space, a subspace of full space. Appropriate procedures for simplex space are compared and contrasted with simple standard methods of multivariate analysis ("raw" principal component analysis). The study shows that the differences between a log-ratio model and a simple logarithmic transformation of proportions may not be very great, particularly as regards graphical ordinations, but important discrepancies do occur. The divergencies between logarithmically based analyses and raw data are, however, great. Published data on Rhesus alleles observed for Italian populations are used to exemplify the subject. PMID:16024067

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

    NASA Astrophysics Data System (ADS)

    von Hippel, G. M.

    2010-03-01

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

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

    PubMed Central

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

    2009-01-01

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

  19. Multivariate analysis of TLD orientation effects

    SciTech Connect

    Archer, B.R.; Bushong, S.C.; Thornby, J.I.

    1980-07-01

    The effect of orientation on extruded thermoluminescent dosimeters has been investigated. TLD's placed on the surface and within a phantom were exposed separately to five diagnostic beam qualities and to /sup 60/Co ..gamma.. rays. The resulting data were subjected to analysis of variance and examined for significant correlations. The response of dosimeters on the phantom surface varied with orientation and was energy dependent. In the phantom and with /sup 60/Co, no orientation effects were observed.

  20. Are propensity scores really superior to standard multivariable analysis?

    PubMed

    Biondi-Zoccai, Giuseppe; Romagnoli, Enrico; Agostoni, Pierfrancesco; Capodanno, Davide; Castagno, Davide; D'Ascenzo, Fabrizio; Sangiorgi, Giuseppe; Modena, Maria Grazia

    2011-09-01

    Clinicians often face difficult decisions despite the lack of evidence from randomized trials. Thus, clinical evidence is often shaped by non-randomized studies exploiting multivariable approaches to limit the extent of confounding. Since their introduction, propensity scores have been used more and more frequently to estimate relevant clinical effects adjusting for established confounders, especially in small datasets. However, debate persists on their real usefulness in comparison to standard multivariable approaches such as logistic regression and Cox proportional hazard analysis. This holds even truer in light of key quantitative developments such as bootstrap and Bayesian methods. This qualitative review aims to provide a concise and practical guide to choose between propensity scores and standard multivariable analysis, emphasizing strengths and weaknesses of both approaches. PMID:21616172

  1. 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.

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

    NASA Astrophysics Data System (ADS)

    Haaland, David M.

    1992-03-01

    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 mid- or near-infrared spectra of the blood. Progress toward the noninvasive determination of glucose levels in diabetics is an ultimate goal of this research.

  3. 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.

  4. Multivariate Probabilistic Analysis of an Hydrological Model

    NASA Astrophysics Data System (ADS)

    Franceschini, Samuela; Marani, Marco

    2010-05-01

    Model predictions derived based on rainfall measurements and hydrological model results are often limited by the systematic error of measuring instruments, by the intrinsic variability of the natural processes and by the uncertainty of the mathematical representation. We propose a means to identify such sources of uncertainty and to quantify their effects based on point-estimate approaches, as a valid alternative to cumbersome Montecarlo methods. We present uncertainty analyses on the hydrologic response to selected meteorological events, in the mountain streamflow-generating portion of the Brenta basin at Bassano del Grappa, Italy. The Brenta river catchment has a relatively uniform morphology and quite a heterogeneous rainfall-pattern. In the present work, we evaluate two sources of uncertainty: data uncertainty (the uncertainty due to data handling and analysis) and model uncertainty (the uncertainty related to the formulation of the model). We thus evaluate the effects of the measurement error of tipping-bucket rain gauges, the uncertainty in estimating spatially-distributed rainfall through block kriging, and the uncertainty associated with estimated model parameters. To this end, we coupled a deterministic model based on the geomorphological theory of the hydrologic response to probabilistic methods. In particular we compare the results of Monte Carlo Simulations (MCS) to the results obtained, in the same conditions, using Li's Point Estimate Method (LiM). The LiM is a probabilistic technique that approximates the continuous probability distribution function of the considered stochastic variables by means of discrete points and associated weights. This allows to satisfactorily reproduce results with only few evaluations of the model function. The comparison between the LiM and MCS results highlights the pros and cons of using an approximating method. LiM is less computationally demanding than MCS, but has limited applicability especially when the model

  5. 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.

  6. Strength of Relationship in Multivariate Analysis of Variance.

    ERIC Educational Resources Information Center

    Smith, I. Leon

    Methods for the calculation of eta coefficient, or correlation ratio, squared have recently been presented for examining the strength of relationship in univariate analysis of variance. This paper extends them to the multivariate case in which the effects of independent variables may be examined in relation to two or more dependent variables, and…

  7. 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…

  8. Multivariate geometry as an approach to algal community analysis

    USGS Publications Warehouse

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

    1973-01-01

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

  9. Automatic emotional expression analysis from eye area

    NASA Astrophysics Data System (ADS)

    Akkoç, Betül; Arslan, Ahmet

    2015-02-01

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

  10. Multivariate analysis of pathophysiological factors in reflux oesophagitis.

    PubMed Central

    Cadiot, G; Bruhat, A; Rigaud, D; Coste, T; Vuagnat, A; Benyedder, Y; Vallot, T; Le Guludec, D; Mignon, M

    1997-01-01

    BACKGROUND: Reflux oesophagitis is considered a multifactorial disease, but the respective roles of the main factors involved in its pathophysiology have not been clearly established. AIMS: To attempt to assign these roles by means of a multivariate logistic regression analysis of the main parameters associated with reflux oesophagitis. PATIENTS: Eighty seven patients with gastro-oesophageal reflux disease were studied: 41 without oesophagitis and 46 with reflux oesophagitis grade 1 to 3. METHODS: (1) Monovariate comparison of patients' characteristics and of parameters derived from in hospital 24 hour oesophageal pH monitoring, oesophageal manometry, double isotope gastric emptying studies, and basal and pentagastrin stimulated gastric acid and pepsin output determinations, between patients with and without oesophagitis. (2) Multivariate logistic regression analysis including the parameters significant in the monovariate analysis. RESULTS: Among the 16 significant parameters from monovariate analysis, three significant independent parameters were identified by multivariate logistic regression analysis: number of refluxes lasting more than five minutes, reflecting oesophageal acid clearance (p = 0.002); basal lower oesophageal sphincter pressure (p = 0.008); and peak acid output (p = 0.012). These three parameters were not correlated with each other. The multivariate model was highly discriminant (correct classification of 81.3% of the cases (95% confidence intervals 0.723, 0.903). Risk for oesophagitis increased as a function of the tercile threshold values of the three parameters. Odds ratios of the three parameters for oesophagitis risk were similar, regardless of whether they were calculated when the patients were compared as a function of oesophagitis grade or the presence or absence of oesophagitis. CONCLUSIONS: This multivariate approach adds evidence that impaired oesophageal acid clearance and hypotonic lower oesophageal sphincter are the two major

  11. Automatic Syntactic Analysis of Free Text.

    ERIC Educational Resources Information Center

    Schwarz, Christoph

    1990-01-01

    Discusses problems encountered with the syntactic analysis of free text documents in indexing. Postcoordination and precoordination of terms is discussed, an automatic indexing system call COPSY (context operator syntax) that uses natural language processing techniques is described, and future developments are explained. (60 references) (LRW)

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

    SciTech Connect

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

    1985-01-01

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

  13. 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. PMID:26099484

  14. Automatic photointerpretation via texture and morphology analysis

    NASA Technical Reports Server (NTRS)

    Tou, J. T.

    1982-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-01-01

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

  16. Dating violence, social learning theory, and gender: a multivariate analysis.

    PubMed

    Tontodonato, P; Crew, B K

    1992-01-01

    The study of violence between dating partners is a logical extension of interest in marital violence. However, little of this research tests explanations of intimate violence using multivariate techniques, and only recently have such tests occurred within a theoretical framework. Drawing on a recent social learning model of courtship violence (Riggs & O'Leary, 1989), this paper empirically examines constructs hypothesized to be predictive of the use of dating violence and investigates possible gender differences in the underlying causal structure of such violence. Logit analysis indicates that parent-child violence, drug use, and knowledge of use of dating violence by others predict the use of courtship violence by females. Belief that violence between intimates is justifiable, drug use, and parental divorce are related to perpetration of dating aggression by males. Explanations for these results and the importance of a multivariate approach to the problem are discussed. PMID:1504032

  17. Multivariate statistical analysis of atom probe tomography data

    SciTech Connect

    Parish, Chad M; Miller, Michael K

    2010-01-01

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

  18. Multivariate statistical analysis of atom probe tomography data.

    PubMed

    Parish, Chad M; Miller, Michael K

    2010-10-01

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

  19. Comparing G: multivariate analysis of genetic variation in multiple populations.

    PubMed

    Aguirre, J D; Hine, E; McGuigan, K; Blows, M W

    2014-01-01

    The additive genetic variance-covariance matrix (G) summarizes the multivariate genetic relationships among a set of traits. The geometry of G describes the distribution of multivariate genetic variance, and generates genetic constraints that bias the direction of evolution. Determining if and how the multivariate genetic variance evolves has been limited by a number of analytical challenges in comparing G-matrices. Current methods for the comparison of G typically share several drawbacks: metrics that lack a direct relationship to evolutionary theory, the inability to be applied in conjunction with complex experimental designs, difficulties with determining statistical confidence in inferred differences and an inherently pair-wise focus. Here, we present a cohesive and general analytical framework for the comparative analysis of G that addresses these issues, and that incorporates and extends current methods with a strong geometrical basis. We describe the application of random skewers, common subspace analysis, the 4th-order genetic covariance tensor and the decomposition of the multivariate breeders equation, all within a Bayesian framework. We illustrate these methods using data from an artificial selection experiment on eight traits in Drosophila serrata, where a multi-generational pedigree was available to estimate G in each of six populations. One method, the tensor, elegantly captures all of the variation in genetic variance among populations, and allows the identification of the trait combinations that differ most in genetic variance. The tensor approach is likely to be the most generally applicable method to the comparison of G-matrices from any sampling or experimental design. PMID:23486079

  20. Automatic Prosodic Analysis to Identify Mild Dementia

    PubMed Central

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

    2015-01-01

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

  1. Automatic Prosodic Analysis to Identify Mild Dementia.

    PubMed

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

    2015-01-01

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

  2. Simplifying multivariate survival analysis using global score test methodology

    NASA Astrophysics Data System (ADS)

    Zain, Zakiyah; Aziz, Nazrina; Ahmad, Yuhaniz

    2015-12-01

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

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

    USGS Publications Warehouse

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

    1980-01-01

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

  4. Automatic analysis and classification of surface electromyography.

    PubMed

    Abou-Chadi, F E; Nashar, A; Saad, M

    2001-01-01

    In this paper, parametric modeling of surface electromyography (EMG) algorithms that facilitates automatic SEMG feature extraction and artificial neural networks (ANN) are combined for providing an integrated system for the automatic analysis and diagnosis of myopathic disorders. Three paradigms of ANN were investigated: the multilayer backpropagation algorithm, the self-organizing feature map algorithm and a probabilistic neural network model. The performance of the three classifiers was compared with that of the old Fisher linear discriminant (FLD) classifiers. The results have shown that the three ANN models give higher performance. The percentage of correct classification reaches 90%. Poorer diagnostic performance was obtained from the FLD classifier. The system presented here indicates that surface EMG, when properly processed, can be used to provide the physician with a diagnostic assist device. PMID:11556501

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

    PubMed

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

    2014-10-01

    Accurate monitoring of degradation levels in soils is essential in order to understand and achieve complete degradation of petroleum hydrocarbons in contaminated soils. We aimed to develop the use of multivariate methods for the monitoring of biodegradation of diesel in soils and to determine if diesel contaminated soils could be remediated to a chemical composition similar to that of an uncontaminated soil. An incubation experiment was set up with three contrasting soil types. Each soil was exposed to diesel at varying stages of degradation and then analysed for key hydrocarbons throughout 161 days of incubation. Hydrocarbon distributions were analysed by Principal Coordinate Analysis and similar samples grouped by cluster analysis. Variation and differences between samples were determined using permutational multivariate analysis of variance. It was found that all soils followed trajectories approaching the chemical composition of the unpolluted soil. Some contaminated soils were no longer significantly different to that of uncontaminated soil after 161 days of incubation. The use of cluster analysis allows the assignment of a percentage chemical similarity of a diesel contaminated soil to an uncontaminated soil sample. This will aid in the monitoring of hydrocarbon contaminated sites and the establishment of potential endpoints for successful remediation. PMID:25028320

  6. Decoding neural representational spaces using multivariate pattern analysis.

    PubMed

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

    2014-01-01

    A major challenge for systems neuroscience is to break the neural code. Computational algorithms for encoding information into neural activity and extracting information from measured activity afford understanding of how percepts, memories, thought, and knowledge are represented in patterns of brain activity. The past decade and a half has seen significant advances in the development of methods for decoding human neural activity, such as multivariate pattern classification, representational similarity analysis, hyperalignment, and stimulus-model-based encoding and decoding. This article reviews these advances and integrates neural decoding methods into a common framework organized around the concept of high-dimensional representational spaces. PMID:25002277

  7. a Multivariate Statistical Analysis of Visibility at California Regions.

    NASA Astrophysics Data System (ADS)

    Motallebi, Nehzat

    This study summarizes the results of a comprehensive study of visibility in California. California is one of the few states that has promulgated air quality standards for visibility. The study was concerned not only with major metropolitan areas such as Los Angeles, but also with deterioration of visibility in the less urbanized areas of California. The relationships among visibility reduction, atmospheric pollutants, and meteorological conditions were examined by using the multivariate statistical techniques of principal component analysis and multiple linear regression analysis. The primary concern of this work was to find which of the many atmospheric constituents most effectively reduce visibility, and to determine the role of the different meteorological variables on these relationships. Another objective was to identify the major pollutant sources and transport routes which contribute to visibility degradation. In order to establish the relationship between the light scattering coefficient and particulate data, both the size distribution and the elemental composition of particulate aerosols were considered. Meanwhile, including meteorological parameters in the principal component analysis made it possible to investigate meteorological effects on the observed pollution patterns. The associations among wind direction, elemental concentration, and additional meteorological parameters were considered by using a special modification of principal component analysis. This technique can identify all of the main features, and provides reasonable source direction for particular elements. It is appropriate to note that there appeared to be no published accounts of a principal component analysis for a data set similar to that analyzed in this work. Finally, the results of the multivariate statistical analyses, multiple linear regression analysis and principal component analysis, indicate that intermediate size sulfur containing aerosols, sulfur size mode 0.6 (mu)m < D

  8. Automatic processing, analysis, and recognition of images

    NASA Astrophysics Data System (ADS)

    Abrukov, Victor S.; Smirnov, Evgeniy V.; Ivanov, Dmitriy G.

    2004-11-01

    New approaches and computer codes (A&CC) for automatic processing, analysis and recognition of images are offered. The A&CC are based on presentation of object image as a collection of pixels of various colours and consecutive automatic painting of distinguished itself parts of the image. The A&CC have technical objectives centred on such direction as: 1) image processing, 2) image feature extraction, 3) image analysis and some others in any consistency and combination. The A&CC allows to obtain various geometrical and statistical parameters of object image and its parts. Additional possibilities of the A&CC usage deal with a usage of artificial neural networks technologies. We believe that A&CC can be used at creation of the systems of testing and control in a various field of industry and military applications (airborne imaging systems, tracking of moving objects), in medical diagnostics, at creation of new software for CCD, at industrial vision and creation of decision-making system, etc. The opportunities of the A&CC are tested at image analysis of model fires and plumes of the sprayed fluid, ensembles of particles, at a decoding of interferometric images, for digitization of paper diagrams of electrical signals, for recognition of the text, for elimination of a noise of the images, for filtration of the image, for analysis of the astronomical images and air photography, at detection of objects.

  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. Automatic analysis of computation in biochemical reactions.

    PubMed

    Egri-Nagy, Attila; Nehaniv, Chrystopher L; Rhodes, John L; Schilstra, Maria J

    2008-01-01

    We propose a modeling and analysis method for biochemical reactions based on finite state automata. This is a completely different approach compared to traditional modeling of reactions by differential equations. Our method aims to explore the algebraic structure behind chemical reactions using automatically generated coordinate systems. In this paper we briefly summarize the underlying mathematical theory (the algebraic hierarchical decomposition theory of finite state automata) and describe how such automata can be derived from the description of chemical reaction networks. We also outline techniques for the flexible manipulation of existing models. As a real-world example we use the Krebs citric acid cycle. PMID:18606208

  11. Research on automatic human chromosome image analysis

    NASA Astrophysics Data System (ADS)

    Ming, Delie; Tian, Jinwen; Liu, Jian

    2007-11-01

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

  12. 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.

  13. 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.

  14. 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.

  15. Semi-automatic analysis of fire debris

    PubMed

    Touron; Malaquin; Gardebas; Nicolai

    2000-05-01

    Automated analysis of fire residues involves a strategy which deals with the wide variety of received criminalistic samples. Because of unknown concentration of accelerant in a sample and the wide range of flammable products, full attention from the analyst is required. Primary detection with a photoionisator resolves the first problem, determining the right method to use: the less responsive classical head-space determination or absorption on active charcoal tube, a better fitted method more adapted to low concentrations can thus be chosen. The latter method is suitable for automatic thermal desorption (ATD400), to avoid any risk of cross contamination. A PONA column (50 mx0.2 mm i.d.) allows the separation of volatile hydrocarbons from C(1) to C(15) and the update of a database. A specific second column is used for heavy hydrocarbons. Heavy products (C(13) to C(40)) were extracted from residues using a very small amount of pentane, concentrated to 1 ml at 50 degrees C and then placed on an automatic carousel. Comparison of flammables with referenced chromatograms provided expected identification, possibly using mass spectrometry. This analytical strategy belongs to the IRCGN quality program, resulting in analysis of 1500 samples per year by two technicians. PMID:10802196

  16. 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.

  17. Toxicological evaluation of complex mixtures: fingerprinting and multivariate analysis.

    PubMed

    Eide, Ingvar; Neverdal, Gunhild; Thorvaldsen, Bodil; Arneberg, Reidar; Grung, Bjørn; Kvalheim, Olav M

    2004-11-01

    The present paper describes a strategy for toxicological evaluation of complex mixtures based on chemical "fingerprinting" followed by pattern recognition (multivariate data analysis). The purpose is to correlate chemical fingerprints to measured toxicological endpoints, identify all major contributors to toxicity, and predict toxicity of additional mixtures. The strategy is illustrated with organic extracts of exhaust particles which are characterized by full scan gas chromatography-mass spectrometry (GC-MS). The complex GC-MS data are resolved into peaks and spectra for individual compounds using an automated curve resolution procedure. Projections to latent structures (PLS) is used for the regression modeling to correlate the GC-MS data to the measured responses; mutagenicity in the Ames Salmonella assay. The regression model identifies those peaks that co-vary with the observed mutagenicity. These peaks may be identified chemically from their spectra. Furthermore, the regression model can be used to predict mutagenicity from GC-MS chromatograms of additional samples. PMID:21782741

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

    PubMed

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

    2016-07-01

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

  19. Successes, Challenges and Future Outlook of Multivariate Analysis In HEP

    NASA Astrophysics Data System (ADS)

    Voss, Helge

    2015-05-01

    Multivariate techniques using machine learning algorithms have become an integral part in many High Energy Physics data analyses. This article is intended to sketch how this development took place by pointing out a few analyses that pushed forward the exploitation of these powerful analysis techniques. This article does not focus on controversial issues like for example how systematic uncertainties can be dealt with when using such techniques, which have been widely discussed previously by other authors. The main purpose here is to point to the gain in physics reach of the physics experiments due to the adaptation of machine learning techniques and to the challenges the HEP community faces in the light a rapid development in the field of machine learning if we want to make successful use of these powerful selection and reconstruction techniques.

  20. Multispectral light scattering imaging and multivariate analysis of airborne particulates

    NASA Astrophysics Data System (ADS)

    Holler, Stephen; Skelsey, Charles R.; Fuerstenau, Stephen D.

    2005-05-01

    Light scattering patterns from non-spherical particles and aggregates exhibit complex structure that is only revealed when observing in two angular dimensions. However, due to the varied shape and packing of such aerosols, the rich structure in the two-dimensional angular optical scattering (TAOS) pattern varies from particle to particle. We examine two-dimensional light scattering patterns obtained at multiple wavelengths using a single CCD camera with minimal cross talk between channels. The integration of the approach with a single CCD camera assures that data is acquired within the same solid angle and orientation. Since the optical size of the scattering particle is inversely proportional to the illuminating wavelength, the spectrally resolved scattering information provides characteristic information about the airborne particles simultaneously in two different scaling regimes. The simultaneous acquisition of data from airborne particulate matter at two different wavelengths allows for additional degrees of freedom in the analysis and characterization of the aerosols. Whereas our previous multivariate analyses of aerosol particles has relied solely on spatial frequency components, our present approach attempts to incorporate the relative symmetry of the particledetector system while extracting information content from both spectral channels. In addition to single channel data, this current approach also examines relative metrics. Consequently, we have begun to employ multivariate techniques based on novel morphological descriptors in order to classify "unknown" particles within a database of TAOS patterns from known aerosols utilizing both spectral and spatial information acquired. A comparison is made among several different classification metrics, all of which show improved classification capabilities relative to our previous approaches.

  1. Automatic cortical thickness analysis on rodent brain

    NASA Astrophysics Data System (ADS)

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

    2011-03-01

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

  2. 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.

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

    PubMed

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

    2016-01-01

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

  4. 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.

  5. High dimensional data analysis using multivariate generalized spatial quantiles

    PubMed Central

    Mukhopadhyay, Nitai D.; Chatterjee, Snigdhansu

    2015-01-01

    High dimensional data routinely arises in image analysis, genetic experiments, network analysis, and various other research areas. Many such datasets do not correspond to well-studied probability distributions, and in several applications the data-cloud prominently displays non-symmetric and non-convex shape features. We propose using spatial quantiles and their generalizations, in particular, the projection quantile, for describing, analyzing and conducting inference with multivariate data. Minimal assumptions are made about the nature and shape characteristics of the underlying probability distribution, and we do not require the sample size to be as high as the data-dimension. We present theoretical properties of the generalized spatial quantiles, and an algorithm to compute them quickly. Our quantiles may be used to obtain multidimensional confidence or credible regions that are not required to conform to a pre-determined shape. We also propose a new notion of multidimensional order statistics, which may be used to obtain multidimensional outliers. Many of the features revealed using a generalized spatial quantile-based analysis would be missed if the data was shoehorned into a well-known probabilistic configuration. PMID:26617421

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

    NASA Astrophysics Data System (ADS)

    Horovitz, Ossi; Sârbu, Costel

    2005-03-01

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

  7. 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…

  8. 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.

  9. 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.

  10. Multivariate Analysis of Conformational Changes Induced by Macromolecular Interactions

    NASA Astrophysics Data System (ADS)

    Mitra, Indranil; Alexov, Emil

    2009-11-01

    Understanding protein-protein binding and associated conformational changes is critical for both understanding thermodynamics of protein interactions and successful drug discovery. Our study focuses on computational analysis of plausible correlations between induced conformational changes and set of biophysical characteristics of interacting monomers. It was done by comparing 3D structures of unbound and bound monomers to calculate the RMSD which is used as measure of the structural changed induced by the binding. We correlate RMSD with volumetric and interfacial charge of the monomers, the amino acid composition, the energy of binding, and type of amino acids at the interface. as predictors. The data set was analyzed with SVM in R & SPSS which is trained on a combination of a new robust evolutionary conservation signal with the monomeric properties to predict the induced RMSD. The goal of this study is to undergo parametric tests and heirchiacal cluster and discriminant multivariate analysis to find key predictors which will be used to develop algorithm to predict the magnitude of conformational changes provided by the structure of interacting monomers. Results indicate that the most promising predictor is the net charge of the monomers, however, other parameters as the type of amino acids at the interface have significant contribution as well.

  11. Multivariate Sensitivity Analysis of Time-of-Flight Sensor Fusion

    NASA Astrophysics Data System (ADS)

    Schwarz, Sebastian; Sjöström, Mårten; Olsson, Roger

    2014-09-01

    Obtaining three-dimensional scenery data is an essential task in computer vision, with diverse applications in various areas such as manufacturing and quality control, security and surveillance, or user interaction and entertainment. Dedicated Time-of-Flight sensors can provide detailed scenery depth in real-time and overcome short-comings of traditional stereo analysis. Nonetheless, they do not provide texture information and have limited spatial resolution. Therefore such sensors are typically combined with high resolution video sensors. Time-of-Flight Sensor Fusion is a highly active field of research. Over the recent years, there have been multiple proposals addressing important topics such as texture-guided depth upsampling and depth data denoising. In this article we take a step back and look at the underlying principles of ToF sensor fusion. We derive the ToF sensor fusion error model and evaluate its sensitivity to inaccuracies in camera calibration and depth measurements. In accordance with our findings, we propose certain courses of action to ensure high quality fusion results. With this multivariate sensitivity analysis of the ToF sensor fusion model, we provide an important guideline for designing, calibrating and running a sophisticated Time-of-Flight sensor fusion capture systems.

  12. 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.

  13. Automatic variance analysis of multistage care pathways.

    PubMed

    Li, Xiang; Liu, Haifeng; Zhang, Shilei; Mei, Jing; Xie, Guotong; Yu, Yiqin; Li, Jing; Lakshmanan, Geetika T

    2014-01-01

    A care pathway (CP) is a standardized process that consists of multiple care stages, clinical activities and their relations, aimed at ensuring and enhancing the quality of care. However, actual care may deviate from the planned CP, and analysis of these deviations can help clinicians refine the CP and reduce medical errors. In this paper, we propose a CP variance analysis method to automatically identify the deviations between actual patient traces in electronic medical records (EMR) and a multistage CP. As the care stage information is usually unavailable in EMR, we first align every trace with the CP using a hidden Markov model. From the aligned traces, we report three types of deviations for every care stage: additional activities, absent activities and violated constraints, which are identified by using the techniques of temporal logic and binomial tests. The method has been applied to a CP for the management of congestive heart failure and real world EMR, providing meaningful evidence for the further improvement of care quality. PMID:25160280

  14. Prognostic factors of adult metastatic renal carcinoma: a multivariate analysis.

    PubMed

    de Forges, A; Rey, A; Klink, M; Ghosn, M; Kramar, A; Droz, J P

    1988-01-01

    In order to define the prognostic factors for metastatic renal carcinoma, we reviewed 134 patients who were treated from 1971 through 1986. Survival rates were 72, 45, and 25% at 6, 12, and 18 months, respectively. Seventeen variables were tested using the logrank test. Improved survival was correlated with normal performance status, and an absence of fever, weight loss, hepatic metastasis, and lung metastasis (or, if lung metastasis was present, less than 2 cm in diameter and limited to one site), a disease-free interval, sedimentation rate less than 100, and renal surgery. Four variables retained significant value in the multivariate analysis: hepatic metastasis, lung metastasis, disease-free interval, and a variable combining the sedimentation rate and the weight loss (SWRL). Predictive survival rates based on these variables were calculated from the Cox model. Six subgroups of patients were identified. The estimation of survival is clinically of value for future phase II trials of chemotherapy in patients with adult metastatic renal carcinoma. PMID:3187293

  15. 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. PMID:21744100

  16. 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.

  17. Adaptive multiscale entropy analysis of multivariate neural data.

    PubMed

    Hu, Meng; Liang, Hualou

    2012-01-01

    Multiscale entropy (MSE) has been widely used to quantify a system's complexity by taking into account the multiple time scales inherent in physiologic time series. The method, however, is biased toward the coarse scale, i.e., low-frequency components due to the progressive smoothing operations. In addition, the algorithm for extracting the different scales is not well adapted to nonlinear/nonstationary signals. In this letter, we introduce adaptive multiscale entropy (AME) measures in which the scales are adaptively derived directly from the data by virtue of recently developed multivariate empirical mode decomposition. Depending on the consecutive removal of low-frequency or high-frequency components, our AME can be estimated at either coarse-to-fine or fine-to-coarse scales over which the sample entropy is performed. Computer simulations are performed to verify the effectiveness of AME for analysis of the highly nonstationary data. Local field potentials collected from the visual cortex of macaque monkey while performing a generalized flash suppression task are used as an example to demonstrate the usefulness of our AME approach to reveal the underlying dynamics in complex neural data. PMID:21788182

  18. 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.

  19. ADS-Demo Fuel Rod Performance: Multivariate Statistical Analysis

    SciTech Connect

    Calabrese, R.; Vettraino, F.; Luzzi, L.

    2004-07-01

    A forward step in the development of Accelerator Driven System (ADS) for the Pu, MA and LLFP transmutation, is the realisation of a 80 MWt ADS-demo (XADS) whose basic objective is the system feasibility demonstration. The XADS is forecasted to adopt the UO{sub 2}-PuO{sub 2} mixed-oxides fuel already experimented in the sodium cooled fast reactors such as the french SPX-1. The present multivariate statistical analysis performed by using the Transuranus Code, was carried out for the Normal Operation at the so-called Enhanced Nominal Conditions (120% nominal reactor power), aimed at verifying that the fuel system complies with the stated design limits, i.e. centerline fuel temperature, cladding temperature and damage, during all the in-reactor lifetime. A statistical input set similar to SPX and PEC fuel case, was adopted. One most relevant assumption in the present calculations was a 30% AISI-316 cladding thickness corrosion at EOL. Relative influence of main fuel rod parameters on fuel centerline temperature was also evaluated. (authors)

  20. Multivariate multiscale entropy: a tool for complexity analysis of multichannel data.

    PubMed

    Ahmed, Mosabber Uddin; Mandic, Danilo P

    2011-12-01

    This work generalizes the recently introduced univariate multiscale entropy (MSE) analysis to the multivariate case. This is achieved by introducing multivariate sample entropy (MSampEn) in a rigorous way, in order to account for both within- and cross-channel dependencies in multiple data channels, and by evaluating it over multiple temporal scales. The multivariate MSE (MMSE) method is shown to provide an assessment of the underlying dynamical richness of multichannel observations, and more degrees of freedom in the analysis than standard MSE. The benefits of the proposed approach are illustrated by simulations on complexity analysis of multivariate stochastic processes and on real-world multichannel physiological and environmental data. PMID:22304127

  1. 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.

  2. 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

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

    PubMed

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

    2013-09-30

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

  4. 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

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

    PubMed

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

    2014-11-01

    The results of analysis using correlative and multivariate methods, as developed for data analysis in high-energy physics and implemented in the Toolkit for Multivariate Analysis software package, of the relations of the variation of increased radon concentration with climate variables in shallow underground laboratory is presented. Multivariate regression analysis identified a number of multivariate methods which can give a good evaluation of increased radon concentrations based on climate variables. The use of the multivariate regression methods will enable the investigation of the relations of specific climate variable with increased radon concentrations by analysis of regression methods resulting in 'mapped' underlying functional behaviour of radon concentrations depending on a wide spectrum of climate variables. PMID:25080439

  6. 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.

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

    PubMed Central

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

    2008-01-01

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

  8. Selective Exposure and Foreign News: A Multivariate Analysis.

    ERIC Educational Resources Information Center

    Kim, Hyun Kap

    This multivariate study examined attitudinal and demographic variables affecting the degree of foreign news exposure on the basis of the data collected from 102 daily newspaper readers in Carbondale, Illinois. The data were obtained in personal interviews with the respondents. The ultimate goal of the study was to contribute to the investigation…

  9. 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…

  10. A Multivariate Generalizability Analysis of the Multistate Bar Examination

    ERIC Educational Resources Information Center

    Yin, Ping

    2005-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  12. 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

  13. 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…

  14. 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…

  15. 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.

  16. Multivariate analysis of elemental chemistry as a robust biosignature

    NASA Astrophysics Data System (ADS)

    Storrie-Lombardi, M.; Nealson, K.

    2003-04-01

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

  17. Remote weapon station for automatic target recognition system demand analysis

    NASA Astrophysics Data System (ADS)

    Lei, Zhang; Li, Sheng-cai; Shi, Cai

    2015-08-01

    Introduces a remote weapon station basic composition and the main advantage, analysis of target based on image automatic recognition system for remote weapon station of practical significance, the system elaborated the image based automatic target recognition system in the photoelectric stabilized technology, multi-sensor image fusion technology, integrated control target image enhancement, target behavior risk analysis technology, intelligent based on the character of the image automatic target recognition algorithm research, micro sensor technology as the key technology of the development in the field of demand.

  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. Multivariate pattern analysis of FMRI in breast cancer survivors and healthy women.

    PubMed

    Hosseini, S M Hadi; Kesler, Shelli R

    2014-04-01

    Advances in breast cancer (BC) treatments have resulted in significantly improved survival rates. However, BC chemotherapy is often associated with several side effects including cognitive dysfunction. We applied multivariate pattern analysis (MVPA) to functional magnetic resonance imaging (fMRI) to find a brain connectivity pattern that accurately and automatically distinguishes chemotherapy-treated (C+) from non-chemotherapy treated (C-) BC females and healthy female controls (HC). Twenty-seven C+, 29 C-, and 30 HC underwent fMRI during an executive-prefrontal task (Go/Nogo). The pattern of functional connectivity associated with this task discriminated with significant accuracy between C+ and HC groups (72%, p = .006) and between C+ and C- groups (71%, p = .012). However, the accuracy of discrimination between C- and HC was not significant (51%, p = .46). Compared with HC, behavioral performance of the C+ and C- groups during the task was intact. However, the C+ group demonstrated altered functional connectivity in the right frontoparietal and left supplementary motor area networks compared to HC, and in the right middle frontal and left superior frontal gyri networks, compared to C-. Our results provide further evidence that executive function performance may be preserved in some chemotherapy-treated BC survivors through recruitment of additional neural connections. PMID:24135221

  20. Automatic analysis of the corneal ulcer

    NASA Astrophysics Data System (ADS)

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

    1999-06-01

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

  1. Automatic basal slice detection for cardiac analysis

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  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. Greenland Scotland overflow studied by hydro-chemical multivariate analysis

    NASA Astrophysics Data System (ADS)

    Fogelqvist, E.; Blindheim, J.; Tanhua, T.; Østerhus, S.; Buch, E.; Rey, F.

    2003-01-01

    Hydrographic, nutrient and halocarbon tracer data collected in July-August 1994 in the Norwegian Sea, the Faroe Bank Channel (FBC), the Iceland and Irminger Basins and the Iceland Sea are presented. Special attention was given to the overflow waters over the Iceland-Scotland Ridge (ISOW). The Iceland-Scottland overflow water (ISOW) was identified along its pathway in the Iceland Basin, and entrainment of overlying water masses was quantified by multivariate analysis (MVA) using principal component analysis (PCA) and Partial Least Square (PLS) calibration. It was concluded that the deeper portion of the ISOW in the FBC was a mixture of about equal parts of Norwegian Sea Deep Water (NSDW) and Norwegian Sea Arctic Intermediate Water (NSAIW). The mixing development of ISOW during its descent in the Iceland Basin was analysed in three sections across the plume. In the southern section at 61°N, where the ISOW core was observed at 2300 m depth, the fraction of waters originating north of the ridge was assessed to be 54%. MVA assessed the fractional composition of the ISOW to be 21% NSDW, 22% NSAIW, 18% Northeast Atlantic Water (NEAW), 11% Modified East Icelandic Water, 25% Labrador Sea Water (LSW) and 3% North East Atlantic Deep Water. It may be noted that the fraction of NEAW is of the same volume as the NSDW. On its further path around the Reykjanes Ridge, the ISOW mixed mainly with LSW, and at 63°N in the Irminger Basin, it was warmer and fresher ( θ=2.8°C and S=34.92) than at 61°N east of the ridge (θ=2.37° C, S=34.97) . The most intensive mixing occurred immediately west of the FBC, probably due to high velocity of the overflow plume through the channel, where annual velocity means exceeded 1.1 m s -1. This resulted in shear instabilities towards the overlying Atlantic waters and cross-stream velocities exceeding 0.3 m s -1 in the bottom boundary layer. The role of NSAIW as a component of ISOW is increasing. Being largely a product of winter convection in the

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

    PubMed

    Raffa, Jesse D; Dubin, Joel A

    2015-09-01

    Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies. PMID:25761965

  5. Automatism

    PubMed Central

    McCaldon, R. J.

    1964-01-01

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

  6. Automatic ionospheric layers detection: Algorithms analysis

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  7. A regularized multivariate regression approach for eQTL analysis

    PubMed Central

    Zhang, Hexin; Zhang, Yuzheng; Hsu, Li; Wang, Pei

    2013-01-01

    Expression quantitative trait loci (eQTLs) are genomic loci that regulate expression levels of mRNAs or proteins. Understanding these regulatory provides important clues to biological pathways that underlie diseases. In this paper, we propose a new statistical method, GroupRemMap, for identifying eQTLs. We model the relationship between gene expression and single nucleotide variants (SNVs) through multivariate linear regression models, in which gene expression levels are responses and SNV genotypes are predictors. To handle the high-dimensionality as well as to incorporate the intrinsic group structure of SNVs, we introduce a new regularization scheme to (1) control the overall sparsity of the model; (2) encourage the group selection of SNVs from the same gene; and (3) facilitate the detection of trans-hub-eQTLs. We apply the proposed method to the colorectal and breast cancer data sets from The Cancer Genome Atlas (TCGA), and identify several biologically interesting eQTLs. These findings may provide insight into biological processes associated with cancers and generate hypotheses for future studies. PMID:26085849

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

    PubMed

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

    2008-01-01

    A multivariate model was developed incorporating various socio-demographic, social-environmental, and social-psychological factors in an attempt to predict suicidal ideation among Canadian youth. The main research objective sought to determine what socially based factors elevate or reduce suicidal ideation within this population. Using data from the National Longitudinal Study of Children and Youth-Cycle 5 (2003), a cross-sectional sample of 1,032 was used to empirically identify various social determinants of suicidal ideation among youth between the ages of 12 and 15. Results reveal statistically significant correlations between suicide ideation and some lesser examined socially based measures. In particular, ability to communicate feelings, negative attachment to parents/guardians, taunting/bullying or abuse, and presence of deviant peers were significant predictors of suicidal ideation. As expected, depression/anxiety, gender, and age were also correlated with thoughts of suicide. Research findings should help foster a better understanding toward the social elements of suicide and provide insight into how suicide prevention strategies may be improved through an increased emphasis on substance use education, direct targeting of dysfunctional families and deviant peer groups, and exploring more avenues of self-expression for youth. PMID:18576207

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

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

  10. Automatic Analysis of Critical Incident Reports: Requirements and Use Cases.

    PubMed

    Denecke, Kerstin

    2016-01-01

    Increasingly, critical incident reports are used as a means to increase patient safety and quality of care. The entire potential of these sources of experiential knowledge remains often unconsidered since retrieval and analysis is difficult and time-consuming, and the reporting systems often do not provide support for these tasks. The objective of this paper is to identify potential use cases for automatic methods that analyse critical incident reports. In more detail, we will describe how faceted search could offer an intuitive retrieval of critical incident reports and how text mining could support in analysing relations among events. To realise an automated analysis, natural language processing needs to be applied. Therefore, we analyse the language of critical incident reports and derive requirements towards automatic processing methods. We learned that there is a huge potential for an automatic analysis of incident reports, but there are still challenges to be solved. PMID:27139389

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

    NASA Astrophysics Data System (ADS)

    Wan, Boyong

    2007-12-01

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

  12. Project Report: Automatic Sequence Processor Software Analysis

    NASA Technical Reports Server (NTRS)

    Benjamin, Brandon

    2011-01-01

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

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

    NASA Technical Reports Server (NTRS)

    Kela, Ajay; Saxena, Mukul; Perucchio, Renato

    1987-01-01

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

  14. 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…

  15. 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…

  16. Profiling School Shooters: Automatic Text-Based Analysis

    PubMed Central

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

    2015-01-01

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

  17. Profiling School Shooters: Automatic Text-Based Analysis.

    PubMed

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

    2015-01-01

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

  18. 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

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

    ERIC Educational Resources Information Center

    Karabatsos, G.; Walker, S.G.

    2010-01-01

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

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

    PubMed

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

    2012-12-01

    ¹H-High resolution magic angle spinning-nuclear magnetic resonance spectroscopy was employed to gain the metabolic profile of longissimus dorsi and semitendinosus muscles of four different breeds: Chianina, Holstein Friesian, Maremmana and Buffalo. Principal component analysis, partial least squares projection to latent structure - discriminant analysis and orthogonal partial least squares projection to latent structure - discriminant analysis were used to build models capable of discriminating the muscle type according to the breed. Data analysis led to an excellent classification for Buffalo and Chianina, while for Holstein Friesian the separation was lower. In the case of Maremmana the use of intelligent bucketing was necessary due to some resonances shifting allowed improvement of the discrimination ability. Finally, by using the Variable Importance in Projection values the metabolites relevant for the classification were identified. PMID:22819725

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

    ERIC Educational Resources Information Center

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

    2010-01-01

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

  2. Application of software technology to automatic test data analysis

    NASA Technical Reports Server (NTRS)

    Stagner, J. R.

    1991-01-01

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

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

    SciTech Connect

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

    1982-03-01

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

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

    PubMed Central

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

    2013-01-01

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

  5. 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.

  6. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-11-01

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

  8. 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.

  9. 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…

  10. 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

  11. 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.

  12. Multivariate analysis of Buteo nest site selection in Washington

    SciTech Connect

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

    1983-03-01

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

  13. Multivariate Analysis of the Ecoregion Delineation for Aquatic Systems

    NASA Astrophysics Data System (ADS)

    Jenerette, G. Darrel; Lee, Jay; Waller, David W.; Carlson, Robert E.

    2002-01-01

    The ecoregion concept is a popular method of understanding the spatial distribution of the environment', however, it has yet to be adequately demonstrated that the environment is distributed in accordance with these bounded units. In this paper, we generated a testable hypothesis based on the current usage of ecoregions: the ecoregion classification will allow for discrimination between lakes of different water quality. The ecoregion classification should also be more effective better than a comparably scaled classification based on political boundaries, land-use class, or random grouping. To test this hypothesis we used the Environmental Monitoring and Assessment Program (EMAP) lake water chemistry data from the northeast United States. The water chemistry data were reduced to four components using principal component analysis. For comparison to an optimal grouping of these data we used K-means cluster analysis to define the extent at which these lakes could be segregated into distinct classes. Jackknifed discriminant analysis was used to determine the classification rate of ecoregions, the three alternative spatial classification methods, and the clustering algorithm. The classification based on ecoregions was successful for 35% of the lakes included in this study, in comparison to the clustered groups accuracy of 98%. These results suggest that the large scale spatial distribution of ecosystem types is more complicated than that suggested by the present ecoregion boundaries. Further tests of ecoregion delineations are needed and alternative large-scale management strategies should be investigated.

  14. 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

  15. The analysis of multivariate longitudinal data: A review

    PubMed Central

    Verbeke, Geert; Fieuws, Steffen; Molenberghs, Geert; Davidian, Marie

    2012-01-01

    Longitudinal experiments often involve multiple outcomes measured repeatedly within a set of study participants. While many questions can be answered by modeling the various outcomes separately, some questions can only be answered in a joint analysis of all of them. In this paper, we will present a review of the many approaches proposed in the statistical literature. Four main model families will be presented, discussed and compared. Focus will be on presenting advantages and disadvantages of the different models rather than on the mathematical or computational details. PMID:22523185

  16. Scaling analysis of multi-variate intermittent time series

    NASA Astrophysics Data System (ADS)

    Kitt, Robert; Kalda, Jaan

    2005-08-01

    The scaling properties of the time series of asset prices and trading volumes of stock markets are analysed. It is shown that similar to the asset prices, the trading volume data obey multi-scaling length-distribution of low-variability periods. In the case of asset prices, such scaling behaviour can be used for risk forecasts: the probability of observing next day a large price movement is (super-universally) inversely proportional to the length of the ongoing low-variability period. Finally, a method is devised for a multi-factor scaling analysis. We apply the simplest, two-factor model to equity index and trading volume time series.

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

    NASA Astrophysics Data System (ADS)

    Heinrich, Durwood J.

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

  18. 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. PMID:26095427

  19. 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.

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

    PubMed

    Widjaja, Effendi

    2009-04-01

    This paper describes the use of combined techniques, i.e. Raman spectral mapping, tape-lift, and multivariate data analysis, to extract chemical information of latent fingerprint and/or trace amounts of materials deposited in fingerprints. The tape-lift method was employed to lift trace particles, extrinsic materials, or sebum deposited on the finger of an individual after recent handling of such materials. The analysis of the tape-lifted materials was performed by Raman spectral mapping at a specific area. The collected mixture Raman spectra containing signals from lifting media and lifted materials was then deconvoluted using a powerful multivariate technique, namely band-target entropy minimization (BTEM). Three cases, i.e. a sebum-rich fingerprint after touching the forehead, a drug model comprising ibuprofen, L-arginine, and sodium bicarbonate, and an additive model comprising sucrose and aspartame were investigated. BTEM could recover all pure component spectra of both lifting media and tape-lifted materials. As such, all these test substances can be correctly identified using their unique pure Raman spectral signatures. In addition, the spatial distributions of all these identified components could also be determined. These combined three techniques hold promise as a new tool in forensic applications. PMID:19305929

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

    PubMed

    Ghirardo, Andrea; Sørensen, Helle Aagaard; Petersen, Marianne; Jacobsen, Susanne; Søndergaard, Ib

    2005-01-01

    Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry and multivariate data analysis have been used for the determination of wheat quality at different stages of grain development. Wheat varieties with one of two different end-use qualities (i.e. suitable or not suitable for bread-making purposes) were investigated. The samples were collected from grains from 15 until 45 days post-anthesis (dpa). Gluten proteins from wheat grains were extracted and subsequently analysed by mass spectrometry. Discrimination partial least-squares regression and soft independent modelling of class analogy were used to determine the quality of new and unknown wheat samples. With these methods, we were able to predict correctly the end-use qualities at every stage investigated. This new fast technique, based on the rapidity of mass spectrometry combined with the objectivity of multivariate data analysis, offers a method that can replace the traditional rather time-consuming ones such as gel electrophoresis. This study focused on the determination of wheat quality at 15 dpa, when the grain is due for harvest 1 month later. PMID:15655793

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

    PubMed

    Strenge, H; Gundel, A

    1983-01-01

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

  3. Multivariate analysis of regional differentials of nuptiality in Bangladesh.

    PubMed

    Chowdhury, A A; Islam, M A

    1981-01-01

    The importance of socioeconomics differentials in nuptiality has occupied a very important position in recent demographic research. An effort has been made in this paper to find out the nature and extent of the causal relationship between the dependent variable--nuptiality, and its determinants. Our findings suggest that education may play a vital role in raising mean age at marriage. This may be done by extending free and compulsory mass and primary education throughout the country. It has further been observed that urbanization through economic development is a precondition to increase the literacy rate and hence female labor force participation in the country's economy. Thus proper education will increase the female employment rate which in turn will raise the age at marriage. Equal distribution of population and insurance schemes for childless couples may also indirectly put a positive effect on nuptiality. Finally, this paper provides a guideline for using the path analysis technique in determining the factors causing the changes and the effects of these factors on nuptiality in Bangladesh. However, caution should be made in taking into account the causal ordering of the indices. Different ordering may give different results. PMID:12312786

  4. 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.

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

    SciTech Connect

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

    2010-09-01

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

  6. Multivariate analysis of noise in genetic regulatory networks.

    PubMed

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

    2004-08-21

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

  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. Multivariate analysis of a small pleistocene catchment: tracing hydrological change

    NASA Astrophysics Data System (ADS)

    Boettcher, Steven; Merz, Christoph; Dannowski, Ralf

    2013-04-01

    The water budget of catchments in north-east Germany has decreased considerably over the last decades. Especially small catchments are affected due to the small amount of water stored within. Climate projections for the next decades hint to even more negative impacts on the water budgets of these catchments. Therefore, a new concept of water resource management for this region must be developed, including counter measures to extreme events such as low and high flow conditions. In order to manage a hydrological system one needs to know the typical behavior and be able to effectively counteract if needed. Within the network activity INKA-BB (Inovationsnetzwerk Klimaanpassung Brandenburg Berlin) dealing with possible adaptation measures to climate change in the Brandenburg and Berlin region, this study aims at identifying the typical hydraulic behavior of the Fredersdorfer Mühlenfließ catchment located north-east of Berlin as a basis for a sustainable water resource management concept. Established schemes are followed, including the application of numerical geochemical and hydraulic models as well as chemical graphical interpretation approaches. A common problem is the sparse spatial as well as temporal resolution of the data at hand. Here, these schemes are too inflexible and vague with respect to analyzing and parameterization of complex features used for identifying operative hydraulic-geochemical processes including intensive non-linear interactions. Hence, methods must be applied that are able to effectively utilize the limited information available. Ordination methods such as the Principle Component Analysis (PCA) or the non-linear Isometric Feature Mapping (Isomap) can provide such a tool. Ordination methods are used in order to derive a meaningful low-dimensional representation of a high-dimensional input data set. The approach is based on the hypothesis, that the amount of processes which explain the variance of the data is relative low although the

  9. 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

  10. 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.

  11. 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…

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

    ERIC Educational Resources Information Center

    Papa, Frank, Jr.

    2007-01-01

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

  13. 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...

  14. 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…

  15. The Relevance of Oral Language Skills to Early Literacy: A Multivariate Analysis.

    ERIC Educational Resources Information Center

    Speece, Deborah L.; Roth, Froma P.; Cooper, David H.

    1999-01-01

    Examined the relationship between oral language and literacy in a two-year, multivariate design. Through empirical cluster analysis of a sample of 88 kindergarten children, four oral language subtypes were identified based on measures of semantics, syntax, metalinguistics, and oral narration. (Author/VWL)

  16. Functional Path Analysis as a Multivariate Technique in Developing a Theory of Participation in Adult Education.

    ERIC Educational Resources Information Center

    Martin, James L.

    This paper reports on attempts by the author to construct a theoretical framework of adult education participation using a theory development process and the corresponding multivariate statistical techniques. Two problems are identified: the lack of theoretical framework in studying problems, and the limiting of statistical analysis to univariate…

  17. 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…

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

    ERIC Educational Resources Information Center

    Dinov, Ivo D.; Christou, Nicolas

    2011-01-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1976-01-01

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

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

    PubMed Central

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

    2016-01-01

    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. PMID:26848405

  1. Extracting tidal frequencies using multivariate harmonic analysis of sea level height time series

    NASA Astrophysics Data System (ADS)

    Amiri-Simkooei, A. R.; Zaminpardaz, S.; Sharifi, M. A.

    2014-10-01

    This contribution is seen as a first attempt to extract the tidal frequencies using a multivariate spectral analysis method applied to multiple time series of tide-gauge records. The existing methods are either physics-based in which the ephemeris of Moon, Sun and other planets are used, or are observation-based in which univariate analysis methods—Fourier and wavelet for instance—are applied to tidal observations. The existence of many long tide-gauge records around the world allows one to use tidal observations and extract the main tidal constituents for which efficient multivariate methods are to be developed. This contribution applies the multivariate least-squares harmonic estimation (LS-HE) to the tidal time series of the UK tide-gauge stations. The first 413 harmonics of the tidal constituents and their nonlinear components are provided using the multivariate LS-HE. A few observations of the research are highlighted: (1) the multivariate analysis takes information of multiple time series into account in an optimal least- squares sense, and thus the tidal frequencies have higher detection power compared to the univariate analysis. (2) Dominant tidal frequencies range from the long-term signals to the sixth-diurnal species interval. Higher frequencies have negligible effects. (3) The most important tidal constituents (the first 50 frequencies) ordered from their amplitudes range from 212 cm (M2) to 1 cm (OQ2) for the data set considered. There are signals in this list that are not available in the 145 main tidal frequencies of the literature. (4) Tide predictions using different lists of tidal frequencies on five different data sets around the world are compared. The prediction results using the first significant 50 constituents provided promising results on these locations of the world.

  2. Multivariate analysis of Ion Beam Induced Luminescence spectra of irradiated silver ion-exchanged silicate glasses

    NASA Astrophysics Data System (ADS)

    Valotto, Gabrio; Quaranta, Alberto; Cattaruzza, Elti; Gonella, Francesco; Rampazzo, Giancarlo

    A multivariate analysis is used for the identification of the spectral features in Ion Beam Induced Luminescence (IBIL) spectra of soda-lime silicate glasses doped with silver by Ag+-Na+ ion exchange. Both Principal Component Analysis and multivariate analysis were used to characterize time-evolving IBIL spectra of Ag-doped glasses, by means of the identification of the number and of the wavelength positions of the main luminescent features and the study of their evolution during irradiation. This method helps to identify the spectral features of the samples spectra, even when partially overlapped or less intense. This analysis procedure does not require additional input such as the number of peaks.

  3. Multivariate analysis of Ion Beam Induced Luminescence spectra of irradiated silver ion-exchanged silicate glasses.

    PubMed

    Valotto, Gabrio; Quaranta, Alberto; Cattaruzza, Elti; Gonella, Francesco; Rampazzo, Giancarlo

    2012-09-01

    A multivariate analysis is used for the identification of the spectral features in Ion Beam Induced Luminescence (IBIL) spectra of soda-lime silicate glasses doped with silver by Ag(+)-Na(+) ion exchange. Both Principal Component Analysis and multivariate analysis were used to characterize time-evolving IBIL spectra of Ag-doped glasses, by means of the identification of the number and of the wavelength positions of the main luminescent features and the study of their evolution during irradiation. This method helps to identify the spectral features of the samples spectra, even when partially overlapped or less intense. This analysis procedure does not require additional input such as the number of peaks. PMID:22571943

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

    PubMed

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

    2015-12-01

    Routinely, diagnostic and microbiology laboratories perform antibiogram analysis which can present some difficulties leading to misreadings and intra and inter-reader deviations. An Automatic Identification Algorithm (AIA) has been proposed as a solution to overcome some issues associated with the disc diffusion method, which is the main goal of this work. AIA allows automatic scanning of inhibition zones obtained by antibiograms. More than 60 environmental isolates were tested using susceptibility tests which were performed for 12 different antibiotics for a total of 756 readings. Plate images were acquired and classified as standard or oddity. The inhibition zones were measured using the AIA and results were compared with reference method (human reading), using weighted kappa index and statistical analysis to evaluate, respectively, inter-reader agreement and correlation between AIA-based and human-based reading. Agreements were observed in 88% cases and 89% of the tests showed no difference or a <4mm difference between AIA and human analysis, exhibiting a correlation index of 0.85 for all images, 0.90 for standards and 0.80 for oddities with no significant difference between automatic and manual method. AIA resolved some reading problems such as overlapping inhibition zones, imperfect microorganism seeding, non-homogeneity of the circumference, partial action of the antimicrobial, and formation of a second halo of inhibition. Furthermore, AIA proved to overcome some of the limitations observed in other automatic methods. Therefore, AIA may be a practical tool for automated reading of antibiograms in diagnostic and microbiology laboratories. PMID:26513468

  5. Automatic Generation of User Material Subroutines for Biomechanical Growth Analysis

    PubMed Central

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

    2010-01-01

    Background The analysis of the biomechanics of growth and remodeling in soft tissues requires the formulation of specialized pseudoelastic constitutive relations. The nonlinear finite element analysis (FEA) package Abaqus allows the user to implement such specialized material responses through the coding of a user material subroutine called UMAT. However, hand coding UMAT subroutines is a challenge even for simple pseudoelastic materials and requires substantial time to debug and test the code. Method To resolve this issue, we develop an automatic UMAT code generation procedure for pseudoelastic materials using the symbolic mathematics package Mathematica, and extend the UMAT generator to include continuum growth. The performance of the automatically coded UMAT is tested by simulating the stress–stretch response of a material defined by a Fung-Orthotropic strain energy function, subject to uniaxial stretching, equibiaxial stretching, and simple shear in Abaqus. The Mathematica UMAT generator is then extended to include continuum growth, by adding a growth subroutine to the automatically generated UMAT. Results The Mathematica UMAT generator correctly derives the variables required in the UMAT code, quickly providing a ready-to-use UMAT. In turn, the UMAT accurately simulates the pseudoelastic response. In order to test the growth UMAT we simulate the growth-based bending of a bilayered bar with differing fiber directions in a non-growing passive layer. The anisotropic passive layer, being topologically tied to the growing isotropic layer, causes the bending bar to twist laterally. Conclusions The results of simulations demonstrate the validity of the automatically coded UMAT, used in both standardized tests of hyperelastic materials and for biomechanical growth analysis. PMID:20887023

  6. Cernuc: A program for automatic high-resolution radioelemental analysis

    NASA Astrophysics Data System (ADS)

    Roca, V.; Terrasi, F.; Moro, R.; Sorrentino, G.

    1981-04-01

    A computer program capable of qualitative and quantitative radioelemental analysis with high accuracy, a high degree of automatism and great ease in utilization, is presented. It has been produced to be used for Ge(Li) gammay-ray spectroscopy and can be used for X-ray spectroscopy as well. This program provides automatic searching and fitting of peaks, energy and intensity determination, identification and calculation of activities of the radioisotopes present in the sample. The last step is carried out by using a radionuclides library. The problem of a gamma line being assigned to more than one nuclide, is solved by searching the least-squares solution of a set of equations for the activities of the isotopes. Two versions of this program have been written to be run batchwise on a medium sized computer (UNIVAC 1106) and interactively on a small computer (HP 2100A).

  7. An integrated spatial signature analysis and automatic defect classification system

    SciTech Connect

    Gleason, S.S.; Tobin, K.W.; Karnowski, T.P.

    1997-08-01

    An integrated Spatial Signature Analysis (SSA) and automatic defect classification (ADC) system for improved automatic semiconductor wafer manufacturing characterization is presented. Both concepts of SSA and ADC methodologies are reviewed and then the benefits of an integrated system are described, namely, focused ADC and signature-level sampling. Focused ADC involves the use of SSA information on a defect signature to reduce the number of possible classes that an ADC system must consider, thus improving the ADC system performance. Signature-level sampling improved the ADC system throughput and accuracy by intelligently sampling defects within a given spatial signature for subsequent off-line, high-resolution ADC. A complete example of wafermap characterization via an integrated SSA/ADC system is presented where a wafer with 3274 defects is completely characterized by revisiting only 25 defects on an off-line ADC review station. 13 refs., 7 figs.

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

    PubMed Central

    Hall, Genae A.

    1993-01-01

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

  9. Multivariate analysis of intracranial pressure (ICP) signal using principal component analysis.

    PubMed

    Al-Zubi, N; Momani, L; Al-Kharabsheh, A; Al-Nuaimy, W

    2009-01-01

    The diagnosis and treatment of hydrocephalus and other neurological disorders often involve the acquisition and analysis of large amount of intracranial pressure (ICP) signal. Although the analysis and subsequent interpretation of this data is an essential part of the clinical management of the disorders, it is typically done manually by a trained clinician, and the difficulty in interpreting some of the features of this complex time series can sometimes lead to issues of subjectivity and reliability. This paper presents a method for the quantitative analysis of this data using a multivariate approach based on principal component analysis, with the aim of optimising symptom diagnosis, patient characterisation and treatment simulation and personalisation. In this method, 10 features are extracted from the ICP signal and principal components that represent these features are defined and analysed. Results from ICP traces of 40 patients show that the chosen features have relevant information about the ICP signal and can be represented with a few components of the PCA (approximately 91% of the total variance of the data is represented by the first four components of the PCA) and that these components can be helpful in characterising subgroups in the patient population that would otherwise not have been apparent. The introduction of supplementaty (non-ICP) variables has offered insight into additional groupings and relationships which may prove to be a fruitful avenue for exploration. PMID:19964826

  10. 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.

  11. 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. PMID:15937024

  12. 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.

  13. 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

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

    PubMed

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

    2015-01-01

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

  15. Spectral analysis methods for automatic speech recognition applications

    NASA Astrophysics Data System (ADS)

    Parinam, Venkata Neelima Devi

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

  16. Automatic 3-D grayscale volume matching and shape analysis.

    PubMed

    Guétat, Grégoire; Maitre, Matthieu; Joly, Laurène; Lai, Sen-Lin; Lee, Tzumin; Shinagawa, Yoshihisa

    2006-04-01

    Recently, shape matching in three dimensions (3-D) has been gaining importance in a wide variety of fields such as computer graphics, computer vision, medicine, and biology, with applications such as object recognition, medical diagnosis, and quantitative morphological analysis of biological operations. Automatic shape matching techniques developed in the field of computer graphics handle object surfaces, but ignore intensities of inner voxels. In biology and medical imaging, voxel intensities obtained by computed tomography (CT), magnetic resonance imagery (MRI), and confocal microscopes are important to determine point correspondences. Nevertheless, most biomedical volume matching techniques require human interactions, and automatic methods assume matched objects to have very similar shapes so as to avoid combinatorial explosions of point. This article is aimed at decreasing the gap between the two fields. The proposed method automatically finds dense point correspondences between two grayscale volumes; i.e., finds a correspondent in the second volume for every voxel in the first volume, based on the voxel intensities. Mutiresolutional pyramids are introduced to reduce computational load and handle highly plastic objects. We calculate the average shape of a set of similar objects and give a measure of plasticity to compare them. Matching results can also be used to generate intermediate volumes for morphing. We use various data to validate the effectiveness of our method: we calculate the average shape and plasticity of a set of fly brain cells, and we also match a human skull and an orangutan skull. PMID:16617625

  17. 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.

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

    PubMed

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

    2006-05-17

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

  19. 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.

  20. [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. PMID:23227556

  1. Automatic selection of region of interest for radiographic texture analysis

    NASA Astrophysics Data System (ADS)

    Lan, Li; Giger, Maryellen L.; Wilkie, Joel R.; Vokes, Tamara J.; Chen, Weijie; Li, Hui; Lyons, Tracy; Chinander, Michael R.; Pham, Ann

    2007-03-01

    We have been developing radiographic texture analysis (RTA) for assessing osteoporosis and the related risk of fracture. Currently, analyses are performed on heel images obtained from a digital imaging device, the GE/Lunar PIXI, that yields both the bone mineral density (BMD) and digital images (0.2-mm pixels; 12-bit quantization). RTA is performed on the image data in a region-of-interest (ROI) placed just below the talus in order to include the trabecular structure in the analysis. We have found that variations occur from manually selecting this ROI for RTA. To reduce the variations, we present an automatic method involving an optimized Canny edge detection technique and parameterized bone segmentation, to define bone edges for the placement of an ROI within the predominantly calcaneus portion of the radiographic heel image. The technique was developed using 1158 heel images and then tested on an independent set of 176 heel images. Results from a subjective analysis noted that 87.5% of ROI placements were rated as "good". In addition, an objective overlap measure showed that 98.3% of images had successful ROI placements as compared to placement by an experienced observer at an overlap threshold of 0.4. In conclusion, our proposed method for automatic ROI selection on radiographic heel images yields promising results and the method has the potential to reduce intra- and inter-observer variations in selecting ROIs for radiographic texture analysis.

  2. Feature++: Automatic Feature Construction for Clinical Data Analysis.

    PubMed

    Sun, Wen; Hao, Bibo; Yu, Yiqin; Li, Jing; Hu, Gang; Xie, Guotong

    2016-01-01

    With the rapid growth of clinical data and knowledge, feature construction for clinical analysis becomes increasingly important and challenging. Given a clinical dataset with up to hundreds or thousands of columns, the traditional manual feature construction process is usually too labour intensive to generate a full spectrum of features with potential values. As a result, advanced large-scale data analysis technologies, such as feature selection for predictive modelling, cannot be fully utilized for clinical data analysis. In this paper, we propose an automatic feature construction framework for clinical data analysis, namely, Feature++. It leverages available public knowledge to understand the semantics of the clinical data, and is able to integrate external data sources to automatically construct new features based on predefined rules and clinical knowledge. We demonstrate the effectiveness of Feature++ in a typical predictive modelling use case with a public clinical dataset, and the results suggest that the proposed approach is able to fulfil typical feature construction tasks with minimal dataset specific configurations, so that more accurate models can be obtained from various clinical datasets in a more efficient way. PMID:27577443

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

    NASA Technical Reports Server (NTRS)

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

    1988-01-01

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

  4. 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.

  5. Rapid automatic keyword extraction for information retrieval and analysis

    DOEpatents

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

    2012-03-06

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

  6. Automatic Analysis of Radio Meteor Events Using Neural Networks

    NASA Astrophysics Data System (ADS)

    Roman, Victor Ştefan; Buiu, Cătălin

    2015-12-01

    Meteor Scanning Algorithms (MESCAL) is a software application for automatic meteor detection from radio recordings, which uses self-organizing maps and feedforward multi-layered perceptrons. This paper aims to present the theoretical concepts behind this application and the main features of MESCAL, showcasing how radio recordings are handled, prepared for analysis, and used to train the aforementioned neural networks. The neural networks trained using MESCAL allow for valuable detection results, such as high correct detection rates and low false-positive rates, and at the same time offer new possibilities for improving the results.

  7. Automatic Analysis of Radio Meteor Events Using Neural Networks

    NASA Astrophysics Data System (ADS)

    Roman, Victor Ştefan; Buiu, Cătălin

    2015-07-01

    Meteor Scanning Algorithms (MESCAL) is a software application for automatic meteor detection from radio recordings, which uses self-organizing maps and feedforward multi-layered perceptrons. This paper aims to present the theoretical concepts behind this application and the main features of MESCAL, showcasing how radio recordings are handled, prepared for analysis, and used to train the aforementioned neural networks. The neural networks trained using MESCAL allow for valuable detection results, such as high correct detection rates and low false-positive rates, and at the same time offer new possibilities for improving the results.

  8. 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

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

    PubMed

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

    2013-01-01

    Automated structural magnetic resonance imaging (MRI) processing pipelines are gaining popularity for Alzheimer's disease (AD) research. They generate regional volumes, cortical thickness measures and other measures, which can be used as input for multivariate analysis. It is not clear which combination of measures and normalization approach are most useful for AD classification and to predict mild cognitive impairment (MCI) conversion. The current study includes MRI scans from 699 subjects [AD, MCI and controls (CTL)] from the Alzheimer's disease Neuroimaging Initiative (ADNI). The Freesurfer pipeline was used to generate regional volume, cortical thickness, gray matter volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures. 259 variables were used for orthogonal partial least square to latent structures (OPLS) multivariate analysis. Normalisation approaches were explored and the optimal combination of measures determined. Results indicate that cortical thickness measures should not be normalized, while volumes should probably be normalized by intracranial volume (ICV). Combining regional cortical thickness measures (not normalized) with cortical and subcortical volumes (normalized with ICV) using OPLS gave a prediction accuracy of 91.5 % when distinguishing AD versus CTL. This model prospectively predicted future decline from MCI to AD with 75.9 % of converters correctly classified. Normalization strategy did not have a significant effect on the accuracies of multivariate models containing multiple MRI measures for this large dataset. The appropriate choice of input for multivariate analysis in AD and MCI is of great importance. The results support the use of un-normalised cortical thickness measures and volumes normalised by ICV. PMID:22890700

  10. Automatic analysis of attack data from distributed honeypot network

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

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

  11. Analysis of spatial and temporal water pollution patterns in Lake Dianchi using multivariate statistical methods.

    PubMed

    Yang, Yong-Hui; Zhou, Feng; Guo, Huai-Cheng; Sheng, Hu; Liu, Hui; Dao, Xu; He, Cheng-Jie

    2010-11-01

    Various multivariate statistical methods including cluster analysis (CA), discriminant analysis (DA), factor analysis (FA), and principal component analysis (PCA) were used to explain the spatial and temporal patterns of surface water pollution in Lake Dianchi. The dataset, obtained during the period 2003-2007 from the Kunming Environmental Monitoring Center, consisted of 12 variables surveyed monthly at eight sites. The CA grouped the 12 months into two groups, August-September and the remainder, and divided the lake into two regions based on their different physicochemical properties and pollution levels. The DA showed the best results for data reduction and pattern recognition in both temporal and spatial analysis. It calculated four parameters (TEMP, pH, CODMn, and Chl-a) to 85.4% correct assignment in the temporal analysis and three parameters (BOD, NH₄+-N, and TN) to almost 71.7% correct assignment in spatial analysis of the two clusters. The FA/PCA applied to datasets of two special clusters of the lake calculated four factors for each region, capturing 72.5% and 62.5% of the total variance, respectively. Strong loadings included DO, BOD, TN, CODCr, CODMn, NH₄+-N, TP, and EC. In addition, box-whisker plots and GIS further facilitated and supported the multivariate analysis results. PMID:19936953

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

    PubMed

    Ogawa, Y; Daigo, M; Amasaki, H

    1989-01-01

    The present study on measurement of the skull of Mishima cattle, which has been postulated as the only pure representative breed of native Japanese cattle, was performed using craniometrical multivariate analysis. The data of the skull of Mishima cattle was compared with 17 breeds of cattle, i.e. Korean cattle (Hamhung, Pyongyang, Chinju Suwon, and Kwangju), Mongolian cattle, Hainan Tao cattle, northeastern Chinese cattle (Shuangliao, Shenyang, Tongliao, Lüta, and Chilin), Astatic Water Buffalo, Yak, Bos Banteng, American Bison, and Holstein-Friesian. The Mishima cattle was included in the group of Korean breeds, especially it was closed on the group of Pyongyang and Chinju breeds. The distance on the craniometrical multivariate analyzing co-ordinate between Mishima cattle and Hainan Tao breed of Zebu cattle was larger than the distance between Mishima cattle and Korean breeds. While result, as a above the present study was very important for the origin of "Wagyu" (native Japanese cattle). Since the northern route theory of the origin of Mishima cattle has been reported on the type of serum enzymes and hemotypes. It was suggested that the craniometrical multivariate analysis supported to the northern route theory of the origin of Mishima cattle. PMID:2764276

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

    NASA Astrophysics Data System (ADS)

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

    2014-09-01

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

  14. Model selection using multivariate functional data analysis for fast uncertainty quantification in subsurface reservoir forecasting

    NASA Astrophysics Data System (ADS)

    Grujic, O.; Caers, J.

    2014-12-01

    Modern approaches to uncertainty quantification in the subsurface rely on complex procedures of geological modeling combined with numerical simulation of flow & transport. This approach requires long computational times rendering any full Monte Carlo simulation infeasible, in particular solving the flow & transport problem takes hours of computing time in real field problems. This motivated the development of model selection methods aiming to identify a small subset of models that capture important statistics of a larger ensemble of geological model realization. A recent method, based on model selection in metric space, termed distance-kernel method (DKM) allows selecting representative models though kernel k-medoid clustering. The distance defining the metric space is usually based on some approximate flow model. However, the output of an approximate flow model can be multi-variate (reporting heads/pressures, saturation, rates). In addition, the modeler may have information from several other approximate models (e.g. upscaled models) or summary statistical information about geological heterogeneity that could allow for a more accurate selection. In an effort to perform model selection based on multivariate attributes, we rely on functional data analysis which allows for an exploitation of covariances between time-varying multivariate numerical simulation output. Based on mixed functional principal component analysis, we construct a lower dimensional space in which kernel k-medoid clustering is used for model selection. In this work we demonstrate the functional approach on a complex compositional flow problem where the geological uncertainty consists of channels with uncertain spatial distribution of facies, proportions, orientations and geometries. We illustrate that using multivariate attributes and multiple approximate models provides accuracy improvement over using a single attribute.

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

    PubMed

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

    2015-06-01

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

  16. Breast Density Analysis Using an Automatic Density Segmentation Algorithm.

    PubMed

    Oliver, Arnau; Tortajada, Meritxell; Lladó, Xavier; Freixenet, Jordi; Ganau, Sergi; Tortajada, Lidia; Vilagran, Mariona; Sentís, Melcior; Martí, Robert

    2015-10-01

    Breast density is a strong risk factor for breast cancer. In this paper, we present an automated approach for breast density segmentation in mammographic images based on a supervised pixel-based classification and using textural and morphological features. The objective of the paper is not only to show the feasibility of an automatic algorithm for breast density segmentation but also to prove its potential application to the study of breast density evolution in longitudinal studies. The database used here contains three complete screening examinations, acquired 2 years apart, of 130 different patients. The approach was validated by comparing manual expert annotations with automatically obtained estimations. Transversal analysis of the breast density analysis of craniocaudal (CC) and mediolateral oblique (MLO) views of both breasts acquired in the same study showed a correlation coefficient of ρ = 0.96 between the mammographic density percentage for left and right breasts, whereas a comparison of both mammographic views showed a correlation of ρ = 0.95. A longitudinal study of breast density confirmed the trend that dense tissue percentage decreases over time, although we noticed that the decrease in the ratio depends on the initial amount of breast density. PMID:25720749

  17. Spectral saliency via automatic adaptive amplitude spectrum analysis

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  18. 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.

  19. Identification of Unknown Substances by Terahertz Spectroscopy and Multivariate Data Analysis

    NASA Astrophysics Data System (ADS)

    Pohl, Andreas; Deßmann, Nils; Dutzi, Katja; Hübers, Heinz-Wilhelm

    2016-02-01

    The identification of various substances by multivariate data analysis of terahertz transmittance spectra is demonstrated. Transmittance spectra were obtained by the use of a Fourier transform infrared spectrometer. By means of principal component analysis and partial least squares regression, the spectral data were analyzed in order to identify substances and mixtures of several substances. With only three principal components, detection and identification of substances are possible with high accuracy. Using these methods, concentration ratios of substances in mixtures of two substances can be determined with an accuracy of 10 %. It is shown that the method is robust against disturbances in the spectra such as standing waves. This is particularly important for practical applications.

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. 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. PMID:26550157

  5. Automatic Visual Tracking and Social Behaviour Analysis with Multiple Mice

    PubMed Central

    Giancardo, Luca; Sona, Diego; Huang, Huiping; Sannino, Sara; Managò, Francesca; Scheggia, Diego; Papaleo, Francesco; Murino, Vittorio

    2013-01-01

    Social interactions are made of complex behavioural actions that might be found in all mammalians, including humans and rodents. Recently, mouse models are increasingly being used in preclinical research to understand the biological basis of social-related pathologies or abnormalities. However, reliable and flexible automatic systems able to precisely quantify social behavioural interactions of multiple mice are still missing. Here, we present a system built on two components. A module able to accurately track the position of multiple interacting mice from videos, regardless of their fur colour or light settings, and a module that automatically characterise social and non-social behaviours. The behavioural analysis is obtained by deriving a new set of specialised spatio-temporal features from the tracker output. These features are further employed by a learning-by-example classifier, which predicts for each frame and for each mouse in the cage one of the behaviours learnt from the examples given by the experimenters. The system is validated on an extensive set of experimental trials involving multiple mice in an open arena. In a first evaluation we compare the classifier output with the independent evaluation of two human graders, obtaining comparable results. Then, we show the applicability of our technique to multiple mice settings, using up to four interacting mice. The system is also compared with a solution recently proposed in the literature that, similarly to us, addresses the problem with a learning-by-examples approach. Finally, we further validated our automatic system to differentiate between C57B/6J (a commonly used reference inbred strain) and BTBR T+tf/J (a mouse model for autism spectrum disorders). Overall, these data demonstrate the validity and effectiveness of this new machine learning system in the detection of social and non-social behaviours in multiple (>2) interacting mice, and its versatility to deal with different experimental settings and

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

    PubMed

    Verbyla, Arūnas P; Cullis, Brian R

    2012-09-01

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

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

    PubMed

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

    2016-01-01

    The trace elements metabolism has been reported to possess specific roles in the pathogenesis and progress of diabetes mellitus. Due to the continuous increase in the population of patients with Type 2 diabetes (T2D), this study aims to assess the levels and inter-relationships of fast blood glucose (FBG) and serum trace elements in Type 2 diabetic patients. This study was conducted on 40 Egyptian Type 2 diabetic patients and 36 healthy volunteers (Hospital of Tanta University, Tanta, Egypt). The blood serum was digested and then used to determine the levels of 24 trace elements using an inductive coupled plasma mass spectroscopy (ICP-MS). Multivariate statistical analysis depended on correlation coefficient, cluster analysis (CA) and principal component analysis (PCA), were used to analysis the data. The results exhibited significant changes in FBG and eight of trace elements, Zn, Cu, Se, Fe, Mn, Cr, Mg, and As, levels in the blood serum of Type 2 diabetic patients relative to those of healthy controls. The statistical analyses using multivariate statistical techniques were obvious in the reduction of the experimental variables, and grouping the trace elements in patients into three clusters. The application of PCA revealed a distinct difference in associations of trace elements and their clustering patterns in control and patients group in particular for Mg, Fe, Cu, and Zn that appeared to be the most crucial factors which related with Type 2 diabetes. Therefore, on the basis of this study, the contributors of trace elements content in Type 2 diabetic patients can be determine and specify with correlation relationship and multivariate statistical analysis, which confirm that the alteration of some essential trace metals may play a role in the development of diabetes mellitus. PMID:26653752

  8. Monitoring of an industrial process by multivariate control charts based on principal component analysis.

    PubMed

    Marengo, Emilio; Gennaro, Maria Carla; Gianotti, Valentina; Robotti, Elisa

    2003-01-01

    The control and monitoring of an industrial process is performed in this paper by the multivariate control charts. The process analysed consists of the bottling of the entire production of 1999 of the sparkling wine "Asti Spumante". This process is characterised by a great number of variables that can be treated with multivariate techniques. The monitoring of the process performed with classical Shewhart charts is very dangerous because they do not take into account the presence of functional relationships between the variables. The industrial process was firstly analysed by multivariate control charts based on Principal Component Analysis. This approach allowed the identification of problems in the process and of their causes. Successively, the SMART Charts (Simultaneous Scores Monitoring And Residual Tracking) were built in order to study the process in its whole. In spite of the successful identification of the presence of problems in the monitored process, the Smart chart did not allow an easy identification of the special causes of variation which casued the problems themselves. PMID:12911145

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

    PubMed Central

    Baty, Florent; Jaeger, Daniel; Preiswerk, Frank; Schumacher, Martin M; Brutsche, Martin H

    2008-01-01

    Background Multivariate ordination methods are powerful tools for the exploration of complex data structures present in microarray data. These methods have several advantages compared to common gene-by-gene approaches. However, due to their exploratory nature, multivariate ordination methods do not allow direct statistical testing of the stability of genes. Results In this study, we developed a computationally efficient algorithm for: i) the assessment of the significance of gene contributions and ii) the identification of sample outliers in multivariate analysis of microarray data. The approach is based on the use of resampling methods including bootstrapping and jackknifing. A statistical package of R functions was developed. This package includes tools for both inferring the statistical significance of gene contributions and identifying outliers among samples. Conclusion The methodology was successfully applied to three published data sets with varying levels of signal intensities. Its relevance was compared with alternative methods. Overall, it proved to be particularly effective for the evaluation of the stability of microarray data. PMID:18570644

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

    PubMed

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

    2014-01-01

    This paper presents the work on Automatic Speech Recognition of Urdu language, using a comparative analysis for Discrete Wavelets Transform (DWT) based features and Mel Frequency Cepstral Coefficients (MFCC). These features have been extracted for one hundred isolated words of Urdu, each word uttered by ten different speakers. The words have been selected from the most frequently used words of Urdu. A variety of age and dialect has been covered by using a balanced corpus approach. After extraction of features, the classification has been achieved by using Linear Discriminant Analysis. After the classification task, the confusion matrix obtained for the DWT features has been compared with the one obtained for Mel-Frequency Cepstral Coefficients based speech recognition. The framework has been trained and tested for speech data recorded under controlled environments. The experimental results are useful in determination of the optimum features for speech recognition task. PMID:25674450

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

    PubMed Central

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

    2012-01-01

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

  12. 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

  13. 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. PMID:26919315

  14. Automatic analysis for neuron by confocal laser scanning microscope

    NASA Astrophysics Data System (ADS)

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

    2005-12-01

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

  15. The automaticity of emotional Stroop: a meta-analysis.

    PubMed

    Phaf, R Hans; Kan, Kees-Jan

    2007-06-01

    An automatic bias to threat is often invoked to account for colour-naming interference in emotional Stroop. Recent findings by McKenna and Sharma [(2004). Reversing the emotional Stroop effect reveals that it is not what it seems: The role of fast and slow components. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 382-392], however, cast doubt on the fast and non-conscious nature of emotional Stroop. Interference by threat words only occurred with colour naming in the trial subsequent to the threat trial (i.e., a "slow" effect), but not immediately (i.e., a "fast" effect, as would be predicted by the bias hypothesis). In a meta-analysis of 70 published emotional Stroop studies the largest effects occurred when presentation of threat words was blocked, suggesting a strong contribution by slow interference. We did not find evidence; moreover, for interference in suboptimal (less conscious) presentation conditions and the only significant effects were observed in optimal (fully conscious) conditions with high-anxious non-clinical participants and patients. The emotional Stroop effect seems to rely more on a slow disengagement process than on a fast, automatic, bias. PMID:17112461

  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. PMID:27364290

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

    PubMed Central

    Liu, Zitao; Hauskrecht, Milos

    2015-01-01

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

  18. 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 ...

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

    PubMed

    Posthuma, D; de Geus, E J; Neale, M C; Hulshoff Pol, H E; Baaré WEC; Kahn, R S; Boomsma, D

    2000-07-01

    The hunt for genes influencing behavior may be aided by the study of intermediate phenotypes for several reasons. First, intermediate phenotypes may be influenced by only a few genes, which facilitates their detection. Second, many intermediate phenotypes can be measured on a continuous quantitative scale and thus can be assessed in affected and unaffected individuals. Continuous measures increase the statistical power to detect genetic effects (Neale et al., 1994), and allow studies to be designed to collect data from informative subjects such as extreme concordant or discordant pairs. Intermediate phenotypes for discrete traits, such as psychiatric disorders, can be neurotransmitter levels, brain function, or structure. In this paper we conduct a multivariate analysis of data from 111 twin pairs and 34 additional siblings on cerebellar volume, intracranial space, and body height. The analysis is carried out on the raw data and specifies a model for the mean and the covariance structure. Results suggest that cerebellar volume and intracranial space vary with age and sex. Brain volumes tend to decrease slightly with age, and males generally have a larger brain volume than females. The remaining phenotypic variance of cerebellar volume is largely genetic (88%). These genetic factors partly overlap with the genetic factors that explain variance in intracranial space and body height. The applied method is presented as a general approach for the analysis of intermediate phenotypes in which the effects of correlated variables on the observed scores are modeled through multivariate analysis. PMID:11206086

  20. PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data

    PubMed Central

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

    2009-01-01

    Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine-learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability. PMID:19184561

  1. Groundwater quality in Imphal West district, Manipur, India, with multivariate statistical analysis of data.

    PubMed

    Singh, Elangbam J K; Gupta, Abhik; Singh, N R

    2013-04-01

    The aim of this paper was to analyze the groundwater quality of Imphal West district, Manipur, India, and assess its suitability for drinking, domestic, and agricultural use. Eighteen physico-chemical variables were analyzed in groundwater from 30 different hand-operated tube wells in urban, suburban, and rural areas in two seasons. The data were subjected to uni-, bi-, and multivariate statistical analysis, the latter comprising cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA). Arsenic concentrations exceed the Indian standard in 23.3% and the WHO limit in 73.3% of the groundwater sources with only 26.7% in the acceptable range. Several variables like iron, chloride, sodium, sulfate, total dissolved solids, and turbidity are also beyond their desirable limits for drinking water in a number of sites. Sodium concentrations and sodium absorption ratio (SAR) are both high to render the water from the majority of the sources unsuitable for agricultural use. Multivariate statistical techniques, especially varimax rotation of PCA data helped to bring to focus the hidden yet important variables and understand their roles in influencing groundwater quality. Widespread arsenic contamination and high sodium concentration of groundwater pose formidable constraints towards its exploitation for drinking and other domestic and agricultural use in the study area, although urban anthropogenic impacts are not yet pronounced. PMID:22935861

  2. 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.

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

    SciTech Connect

    Martin, R.C.

    1986-01-01

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

  4. Applications of multivariate analysis to precious metal exploration in the western US

    SciTech Connect

    Nelson, C.E.

    1985-01-01

    Precious metal exploration in the western United States relies heavily on geochemical analyses for so-called pathfinder elements. These are elements known to be enriched in or near ore deposits. Carlin-type ore bodies, for example, contain anomalous, but quite variable, amounts of Au, Ag, As, Sb, Hg, Tl, and Ba. Unfortunately, many Carlin-type hydrothermal systems with no associated gold ore are equally anomalous in all members of the pathfinder suite. Large geochemical databases, containing thirty to fifty elements, have been gathered for a variety of epithermal precious metal deposits in the western United States. Multivariate analysis of the data identifies new element groups, some of which are distinctive of ore-related systems. These new geochemical fingerprints are more effective than the well-known epithermal suite at establishing ore potential in untested epithermal targets. Several multivariate techniques have been applied. Discriminant analysis uses raw geochemical data to provide a function that maximizes the separation between known groups, such as ore bodies and barren systems. Factor analysis reduce the raw data to a number of geologically interpretable element groups (factors). Each of the described techniques provides tools which can be quantitatively applied to exploration.

  5. 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. PMID:27288663

  6. 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

  7. Detection of counterfeit Viagra® by Raman microspectroscopy imaging and multivariate analysis.

    PubMed

    Sacré, Pierre-Yves; Deconinck, Eric; Saerens, Lien; De Beer, Thomas; Courselle, Patricia; Vancauwenberghe, Roy; Chiap, Patrice; Crommen, Jacques; De Beer, Jacques O

    2011-09-10

    During the past years, pharmaceutical counterfeiting was mainly a problem of developing countries with weak enforcement and inspection programs. However, Europe and North America are more and more confronted with the counterfeiting problem. During this study, 26 counterfeits and imitations of Viagra® tablets and 8 genuine tablets of Viagra® were analysed by Raman microspectroscopy imaging. After unfolding the data, three maps are combined per sample and a first PCA is realised on these data. Then, the first principal components of each sample are assembled. The exploratory and classification analysis are performed on that matrix. PCA was applied as exploratory analysis tool on different spectral ranges to detect counterfeit medicines based on the full spectra (200-1800 cm⁻¹), the presence of lactose (830-880 cm⁻¹) and the spatial distribution of sildenafil (1200-1290 cm⁻¹) inside the tablet. After the exploratory analysis, three different classification algorithms were applied on the full spectra dataset: linear discriminant analysis, k-nearest neighbour and soft independent modelling of class analogy. PCA analysis of the 830-880 cm⁻¹ spectral region discriminated genuine samples while the multivariate analysis of the spectral region between 1200 cm⁻¹ and 1290 cm⁻¹ returns no satisfactory results. A good discrimination of genuine samples was obtained with multivariate analysis of the full spectra region (200-1800 cm⁻¹). Application of the k-NN and SIMCA algorithm returned 100% correct classification during both internal and external validation. PMID:21715121

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

    SciTech Connect

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

    2009-10-19

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

  9. Automatic Analysis of Cellularity in Glioblastoma and Correlation with ADC Using Trajectory Analysis and Automatic Nuclei Counting

    PubMed Central

    Burth, Sina; Kieslich, Pascal J.; Jungk, Christine; Sahm, Felix; Kickingereder, Philipp; Kiening, Karl; Unterberg, Andreas; Wick, Wolfgang; Schlemmer, Heinz-Peter; Bendszus, Martin; Radbruch, Alexander

    2016-01-01

    Objective Several studies have analyzed a correlation between the apparent diffusion coefficient (ADC) derived from diffusion-weighted MRI and the tumor cellularity of corresponding histopathological specimens in brain tumors with inconclusive findings. Here, we compared a large dataset of ADC and cellularity values of stereotactic biopsies of glioblastoma patients using a new postprocessing approach including trajectory analysis and automatic nuclei counting. Materials and Methods Thirty-seven patients with newly diagnosed glioblastomas were enrolled in this study. ADC maps were acquired preoperatively at 3T and coregistered to the intraoperative MRI that contained the coordinates of the biopsy trajectory. 561 biopsy specimens were obtained; corresponding cellularity was calculated by semi-automatic nuclei counting and correlated to the respective preoperative ADC values along the stereotactic biopsy trajectory which included areas of T1-contrast-enhancement and necrosis. Results There was a weak to moderate inverse correlation between ADC and cellularity in glioblastomas that varied depending on the approach towards statistical analysis: for mean values per patient, Spearman’s ρ = -0.48 (p = 0.002), for all trajectory values in one joint analysis Spearman’s ρ = -0.32 (p < 0.001). The inverse correlation was additionally verified by a linear mixed model. Conclusions Our data confirms a previously reported inverse correlation between ADC and tumor cellularity. However, the correlation in the current article is weaker than the pooled correlation of comparable previous studies. Hence, besides cell density, other factors, such as necrosis and edema might influence ADC values in glioblastomas. PMID:27467557

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

    Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe0.55Se0.45 (Tc = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe1-xSex structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.

  11. A versatile multivariate image analysis pipeline reveals features of Xenopus extract spindles.

    PubMed

    Grenfell, Andrew W; Strzelecka, Magdalena; Crowder, Marina E; Helmke, Kara J; Schlaitz, Anne-Lore; Heald, Rebecca

    2016-04-11

    Imaging datasets are rich in quantitative information. However, few cell biologists possess the tools necessary to analyze them. Here, we present a large dataset ofXenopusextract spindle images together with an analysis pipeline designed to assess spindle morphology across a range of experimental conditions. Our analysis of different spindle types illustrates how kinetochore microtubules amplify spindle microtubule density. Extract mixing experiments reveal that some spindle features titrate, while others undergo switch-like transitions, and multivariate analysis shows the pleiotropic morphological effects of modulating the levels of TPX2, a key spindle assembly factor. We also apply our pipeline to analyze nuclear morphology in human cell culture, showing the general utility of the segmentation approach. Our analyses provide new insight into the diversity of spindle types and suggest areas for future study. The approaches outlined can be applied by other researchers studying spindle morphology and adapted with minimal modification to other experimental systems. PMID:27044897

  12. Multivariate data analysis for depth resolved chemical classification and quantification of sulfur in SNMS

    NASA Astrophysics Data System (ADS)

    Sommer, M.; Goschnick, J.

    2005-09-01

    The quantification of elements in quadrupole based SNMS is hampered by superpositions of atomic and cluster signals. Moreover, the conventional SNMS data evaluation employs only atomic signals to determine elemental concentrations, which not allows any chemical specifications of the determined elements. Improvements in the elemental quantification and additional chemical information can be obtained from kinetic energy analysis and the inclusion of molecular signals into mass spectra evaluation. With the help of multivariate data analysis techniques, the combined information is used for the first time for a quantitative and chemically distinctive determination of sulfur. The kinetic energy analysis, used to solve the interference of sulfur with O 2 at masses 32-34 D, turned out to be highly important for the new type of evaluation.

  13. Differentiation of aged fibers by Raman spectroscopy and multivariate data analysis.

    PubMed

    Bianchi, Federica; Riboni, Nicolò; Trolla, Valentina; Furlan, Giada; Avantaggiato, Giorgio; Iacobellis, Giuliano; Careri, Maria

    2016-07-01

    Raman spectroscopy followed by multivariate data analysis was used to analyze cotton fibers dyed using similar formulations and submitted to different aging conditions. Spectra were collected on a commercial instrument using a near-infrared laser with a 780nm light source. Discriminant analysis allowed to correctly classify the aged fibers 100% of the time. The prediction ability of the calculated model was estimated to be 100% by the "leave-one-out" cross-validation for 3 out of the 4 series under investigation. Finally, reliability of the developed approach for the discrimination of aged vs new fibers was confirmed by the analysis of commercial polyamide and polyester textiles submitted to the same aging process. PMID:27154701

  14. 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.

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

    PubMed

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

    2014-07-15

    This work investigates the capability of Fourier-Transform near infrared (FT-NIR) spectroscopy to monitor and assess process parameters in beer fermentation at different operative conditions. For this purpose, the fermentation of wort with two different yeast strains and at different temperatures was monitored for nine days by FT-NIR. To correlate the collected spectra with °Brix, pH and biomass, different multivariate data methodologies were applied. Principal component analysis (PCA), partial least squares (PLS) and locally weighted regression (LWR) were used to assess the relationship between FT-NIR spectra and the abovementioned process parameters that define the beer fermentation. The accuracy and robustness of the obtained results clearly show the suitability of FT-NIR spectroscopy, combined with multivariate data analysis, to be used as a quality control tool in the beer fermentation process. FT-NIR spectroscopy, when combined with LWR, demonstrates to be a perfectly suitable quantitative method to be implemented in the production of beer. PMID:24594186

  16. What Makes a Pattern? Matching Decoding Methods to Data in Multivariate Pattern Analysis

    PubMed Central

    Kragel, Philip A.; Carter, R. McKell; Huettel, Scott A.

    2012-01-01

    Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data. While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals. We review published studies employing multivariate pattern classification since the technique’s introduction, which reveal an extensive focus on the improved detection power that linear classifiers provide over traditional analysis techniques. We demonstrate using simulations and a searchlight approach, however, that non-linear classifiers are capable of extracting distinct information about interactions within a local region. We conclude that for spatially localized analyses, such as searchlight and region of interest, multiple classification approaches should be compared in order to match fMRI analyses to the properties of local circuits. PMID:23189035

  17. Detecting Neuroimaging Biomarkers for Schizophrenia: A Meta-Analysis of Multivariate Pattern Recognition Studies

    PubMed Central

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

    2015-01-01

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

  18. Automatic quantitative analysis of cardiac MR perfusion images

    NASA Astrophysics Data System (ADS)

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

    2001-07-01

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

  19. Predicting Cytotoxic T-cell Age from Multivariate Analysis of Static and Dynamic Biomarkers*

    PubMed Central

    Rivet, Catherine A.; Hill, Abby S.; Lu, Hang; Kemp, Melissa L.

    2011-01-01

    Adoptive T-cell transfer therapy relies upon in vitro expansion of autologous cytotoxic T cells that are capable of tumor recognition. The success of this cell-based therapy depends on the specificity and responsiveness of the T cell clones before transfer. During ex vivo expansion, CD8+ T cells present signs of replicative senescence and loss of function. The transfer of nonresponsive senescent T cells is a major bottleneck for the success of adoptive T-cell transfer therapy. Quantitative methods for assessing cellular age and responsiveness will facilitate the development of appropriate cell expansion and selection protocols. Although several biomarkers of lymphocyte senescence have been identified, these proteins in isolation are not sufficient to determine the age-dependent responsiveness of T cells. We have developed a multivariate model capable of extracting combinations of markers that are the most informative to predict cellular age. To acquire signaling information with high temporal resolution, we designed a microfluidic chip enabling parallel lysis and fixation of stimulated cell samples on-chip. The acquisition of 25 static biomarkers and 48 dynamic signaling measurements at different days in culture, integrating single-cell and population based information, allowed the multivariate regression model to accurately predict CD8+ T-cell age. From surface marker expression and early phosphorylation events following T-cell receptor stimulation, the model successfully predicts days in culture and number of population doublings with R2 = 0.91 and 0.98, respectively. Furthermore, we found that impairment of early signaling events following T cell receptor stimulation because of long term culture allows prediction of costimulatory molecules CD28 and CD27 expression levels and the number of population divisions in culture from a limited subset of signaling proteins. The multivariate analysis highlights the information content of both averaged biomarker values and

  20. 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. PMID:25809955

  1. Automatic measuring device for octave analysis of noise

    NASA Technical Reports Server (NTRS)

    Memnonov, D. L.; Nikitin, A. M.

    1973-01-01

    An automatic decoder is described that counts noise levels by pulse counters and forms audio signals proportional in duration to the total or to one of the octave noise levels. Automatic ten fold repetition of the measurement cycle is provided at each measurement point before the transition to a new point is made.

  2. Multivariate analysis of groundwater resources in Ganga-Yamuna basin (India).

    PubMed

    Sargaonkar, Aabha P; Gupta, Apurba; Devotta, Sukumar

    2008-07-01

    Groundwater quality data on physico-chemical, bacteriological and heavy metal concentrations in three cities (Faridabad, Allahabad and Varanasi) in Ganga-Yamuna basin was subjected to multivariate analysis (MVA) using SPSS. The factors extracted showed high loading (> 0.3) of various parameters, such as Cl, conductivity, TDS, hardness, Na, Mg, and SO4, indicating contamination due to leaching of pollutants. Major manifest variable associated with these factors is the unorganized solid waste dumping practiced in all the cities. Bacterial contamination of hand pump samples in Allahabad is attributed to surface water-groundwater interaction. The factor with high loading of Ca and F is indicative of geological conditions of the region. Wells in Yamuna river sub-watershed exhibit less freshwater recharge, which is attributed to surface water pollution and sediment deposition in the river. Thus, the methodology for hydrogeological analysis is useful to identify critical water quality issues and possible sources of pollution in river basins. PMID:19552076

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

    PubMed Central

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

    2016-01-01

    Summary: We have developed an algorithm for genetic analysis of complex traits using genome-wide SNPs in a linear mixed model framework. Compared to current standard REML software based on the mixed model equation, our method is substantially faster. The advantage is largest when there is only a single genetic covariance structure. The method is particularly useful for multivariate analysis, including multi-trait models and random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss the advantages and limitations. Availability and implementation: MTG2 is available in https://sites.google.com/site/honglee0707/mtg2. Contact: hong.lee@une.edu.au Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26755623

  4. Web-based tools for modelling and analysis of multivariate data: California ozone pollution activity

    PubMed Central

    Dinov, Ivo D.; Christou, Nicolas

    2014-01-01

    This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting and statistical inference on these data are presented. All components of this case study (data, tools, activity) are freely available online at: http://wiki.stat.ucla.edu/socr/index.php/SOCR_MotionCharts_CAOzoneData. Several types of exploratory (motion charts, box-and-whisker plots, spider charts) and quantitative (inference, regression, analysis of variance (ANOVA)) data analyses tools are demonstrated. Two specific human health related questions (temporal and geographic effects of ozone pollution) are discussed as motivational challenges. PMID:24465054

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

    USGS Publications Warehouse

    Weber, L.; Davis, J.C.

    1990-01-01

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

  6. Eco-Hydrological Feedback Dynamics: Application of Information Theory to multivariate timeseries data analysis.

    NASA Astrophysics Data System (ADS)

    Ruddell, B. L.; Kumar, P.

    2006-12-01

    Information-Theoretic approaches to the stochastic study of hydrology and other environmental sciences have become popular in recent years, owing to a greater availability of environmental data and the need to develop tools that can dissect the complex dynamics of environmental systems. Entropy-based metrics such at Transfer Entropy can go beyond correlation and uncover the cardinality of relationships between measured signals, and are therefore useful for the study of feedback-based complex environmental systems that are rich in data. Because information theoretic approaches to signal analysis are relatively immature, effort is needed to refine the technique in terms of algorithms, controls, and interpretation of results. A basic framework for environmental system analysis and interpretation is presented, and this framework is applied to simultaneous multivariate timeseries data. Eco-hydrological data from the Ameriflux network is examined, and the complex dynamics and feedbacks of the Eco-hydrological system are discussed.

  7. Distinguishing Monosaccharide Stereo- and Structural Isomers with ToF-SIMS and Multivariate Statistical Analysis

    SciTech Connect

    Berman, E F; Kulp, K S; Knize, M G; Wu, L; Nelson, E J; Nelson, D O; Wu, K J

    2006-05-04

    Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) is utilized to examine the mass spectra and fragmentation patterns of seven isomeric monosaccharides. Multivariate statistical analysis techniques, including principal component analysis (PCA), allow discrimination of the extremely similar mass spectra of stereoisomers. Furthermore, PCA identifies those fragment peaks which vary significantly between spectra. Heavy isotope studies confirm that these peaks are indeed sugar fragments, allow identification of the fragments, and provide clues to the fragmentation pathways. Excellent reproducibility is shown by multiple experiments performed over time and on separate samples. This study demonstrates the combined selectivity and discrimination power of ToF-SIMS and PCA, and suggests new applications of the technique including differentiation of subtle chemical changes in biological samples that may provide insights into cellular processes, disease progress, and disease diagnosis.

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

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

    PubMed

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

    2015-11-01

    A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose-response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds. PMID:26422066

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

    PubMed

    Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

    2016-05-01

    The fundamental goal of the Government of Ghana's development agenda as enshrined in the Growth and Poverty Reduction Strategy to grow the economy to a middle income status of US$1000 per capita by the end of 2015 could be met by increasing the labour force, increasing energy supplies and expanding the energy infrastructure in order to achieve the sustainable development targets. In this study, a multivariate co-integration analysis of the Kaya factors namely carbon dioxide, total primary energy consumption, population and GDP was investigated in Ghana using vector error correction model with data spanning from 1980 to 2012. Our research results show an existence of long-run causality running from population, GDP and total primary energy consumption to carbon dioxide emissions. However, there is evidence of short-run causality running from population to carbon dioxide emissions. There was a bi-directional causality running from carbon dioxide emissions to energy consumption and vice versa. In other words, decreasing the primary energy consumption in Ghana will directly reduce carbon dioxide emissions. In addition, a bi-directional causality running from GDP to energy consumption and vice versa exists in the multivariate model. It is plausible that access to energy has a relationship with increasing economic growth and productivity in Ghana. PMID:26857005

  15. EXPERIMENTAL ANALYSIS OF MULTIVARIATE FEMALE CHOICE IN GRAY TREEFROGS (Hyla versicolor)

    PubMed Central

    Gerhardt, H. Carl; Brooks, Robert

    2009-01-01

    Even simple biological signals vary in several measurable dimensions. Understanding their evolution requires, therefore, a multivariate understanding of selection, including how different properties interact to determine the effectiveness of the signal. We combined experimental manipulation with multivariate selection analysis to assess female mate choice on the simple trilled calls of male gray treefrogs. We independently and randomly varied five behaviorally relevant acoustic properties in 154 synthetic calls. We compared response times of each of 154 females to one of these calls with its response to a standard call that had mean values of the five properties. We found directional and quadratic selection on two properties indicative of the amount of signaling, pulse number and call rate. Canonical rotation of the fitness surface showed that these properties, along with pulse rate, contributed heavily to a major axis of stabilizing selection, a result consistent with univariate studies showing diminishing effects of increasing pulse number well beyond the mean. Spectral properties contributed to a second major axis of stabilizing selection. The single major axis of disruptive selection suggested that a combination of two temporal and two spectral properties with values differing from the mean should be especially attractive. PMID:19500145

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

    PubMed

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

    2013-11-01

    Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input-Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e-waste estimation studies. PMID:23899476

  17. Multivariate statistical analysis and partitioning of sedimentary geochemical data sets: General principles and specific MATLAB scripts

    NASA Astrophysics Data System (ADS)

    Pisias, Nicklas G.; Murray, Richard W.; Scudder, Rachel P.

    2013-10-01

    Multivariate statistical treatments of large data sets in sedimentary geochemical and other fields are rapidly becoming more popular as analytical and computational capabilities expand. Because geochemical data sets present a unique set of conditions (e.g., the closed array), application of generic off-the-shelf applications is not straightforward and can yield misleading results. We present here annotated MATLAB scripts (and specific guidelines for their use) for Q-mode factor analysis, a constrained least squares multiple linear regression technique, and a total inversion protocol, that are based on the well-known approaches taken by Dymond (1981), Leinen and Pisias (1984), Kyte et al. (1993), and their predecessors. Although these techniques have been used by investigators for the past decades, their application has been neither consistent nor transparent, as their code has remained in-house or in formats not commonly used by many of today's researchers (e.g., FORTRAN). In addition to providing the annotated scripts and instructions for use, we discuss general principles to be considered when performing multivariate statistical treatments of large geochemical data sets, provide a brief contextual history of each approach, explain their similarities and differences, and include a sample data set for the user to test their own manipulation of the scripts.

  18. Ganalyzer: A Tool for Automatic Galaxy Image Analysis

    NASA Astrophysics Data System (ADS)

    Shamir, Lior

    2011-08-01

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

  19. GANALYZER: A TOOL FOR AUTOMATIC GALAXY IMAGE ANALYSIS

    SciTech Connect

    Shamir, Lior

    2011-08-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  1. 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.

  2. [Factors behind global fertility development after 1950: a multivariate analysis of 128 countries].

    PubMed

    Lutz, W

    1984-01-01

    Time series of selected socioeconomic indicators of 128 countries representing 97.4% of the world population in 1975 are related to gross reproduction rates (GRR) in various models of bivariate and multivariate analysis assuming linear as well as logistic functional relationships. The data base stems mainly from UN publications and special attention is given to China. For the pooling of time series and cross-sectional data, countries are grouped according to geographical and cultural criteria, and variables accounting for these regional effects are included in the equations. Special emphasis is placed on the development of an analytical framework trying to combine aspects of economic and sociological fertility analysis which account for shortterm economic determination as well as for changes in the system of social norms and in the degree to which these norms are forced on individual behavior. Among other findings, there is a pronounced dichotomy between more and less developed countries with respect to most variables but especially so with fertility. There are those countries with GRRs above 2.0 and below. The explanatory values of the models of multivariate analysis are generally very high with the RZs ranging from 0.73-0.97, depending on the weighting used and on the specification of the model. Mutlicollinearity was reduced by transformation and aggregation of variables. Female life expectancy at birth seems to be the single most important variable in explaining differential fertility; however, no direct causal link may be assumed; instead life expectancy can be seen as a very general indicator of health conditions and quality of life which in turn influence fertility development. Female educational status is the 2nd most important variable. Measures of female educational status relative to men also supports the argument that female social status is relevant for fertility. A positive income effect on fertility appears for the gross domestic product per caput in

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

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2016-01-01

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

  5. 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.

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

    NASA Technical Reports Server (NTRS)

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

    1973-01-01

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

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

    PubMed Central

    Anderson, John R.

    2011-01-01

    Multivariate pattern analysis can be combined with hidden Markov model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first “mind reading” application involves using fMRI activity to track what students are doing as they solve a sequence of algebra problems. The methodology achieves considerable accuracy at determining both what problem-solving step the students are taking and whether they are performing that step correctly. The second “model discovery” application involves using statistical model evaluation to determine how many substates are involved in performing a step of algebraic problem solving. This research indicates that different steps involve different numbers of substates and these substates are associated with different fluency in algebra problem solving. PMID:21820455

  8. 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. PMID:19879686

  9. Search for a three lepton and missing transverse energy signature of supersymmetry using a multivariate analysis

    NASA Astrophysics Data System (ADS)

    Lampen, Caleb Parnell

    A search for evidence of supersymmetry with a three lepton and missing transverse energy signature is presented. This signature is a possible final state from the associated production of a chargino and a neutralino, two particles predicted by supersymmetry. The study is performed on 2.06 fb-1 of 7 TeV center of mass energy proton-proton collisions, recorded with the ATLAS experiment at the Large Hadron Collider in 2011. A multivariate analysis is utilized, implementing a boosted decision tree classifier using lepton pT, missing transverse energy, dilepton mass, and the razor variable R as inputs. No significant excess over the Standard Model prediction is observed, and upper limits are placed on the cross section times branching ratio of simplified models across a mass parameter space.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. [Identification of Staphylococcus aureus by determining nuclease activity and using methods of multivariate statistical analysis].

    PubMed

    Generalova, A G; Berzhets, V M; Novikov, D K; Generalov, I I

    1998-01-01

    A new quantitative method for the determination of the DNA-se activity of bacteria, based on the prevention of the clot formation of DNA with rivanol under the action of microbial DNA-ses, has been developed. The sensitivity of this method is 0.005-0.01 units of activity (1-2 ng) of the enzyme in a sample for 1 hour of incubation; the variation coefficient of the method is 8-9%. The use of this method has made it possible to reduce the time of the identification of S.aureus by 2-3 days in comparison with the commonly used methods. As revealed on the model of differentiation between S.aureus and S.epidermidis, the methods of multivariate statistical analysis (dispersion, discriminant, cluster methods) may find wide application for the discrimination of bacteria. They are supposed to be used for the interspecific identification of coagulase-negative staphylococci. PMID:9700871

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

    NASA Astrophysics Data System (ADS)

    Manohar, Arun; Lanza di Scalea, Francesco

    2011-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-08-01

    This study used scientometric methods to conduct an automatic content analysis on the development trends of science education research from the published articles in the four journals of International Journal of Science Education, Journal of Research in Science Teaching, Research in Science Education, and Science Education from 1990 to 2007. The multi-stage clustering technique was employed to investigate with what topics, to what development trends, and from whose contribution that the journal publications constructed as a science education research field. This study found that the research topic of Conceptual Change & Concept Mapping was the most studied topic, although the number of publications has slightly declined in the 2000's. The studies in the themes of Professional Development, Nature of Science and Socio-Scientific Issues, and Conceptual Chang and Analogy were found to be gaining attention over the years. This study also found that, embedded in the most cited references, the supporting disciplines and theories of science education research are constructivist learning, cognitive psychology, pedagogy, and philosophy of science.

  17. Variable frame rate analysis for automatic speech recognition

    NASA Astrophysics Data System (ADS)

    Tan, Zheng-Hua

    2007-09-01

    In this paper we investigate the use of variable frame rate (VFR) analysis in automatic speech recognition (ASR). First, we review VFR technique and analyze its behavior. It is experimentally shown that VFR improves ASR performance for signals with low signal-to-noise ratios since it generates improved acoustic models and substantially reduces insertion and substitution errors although it may increase deletion errors. It is also underlined that the match between the average frame rate and the number of hidden Markov model states is critical in implementing VFR. Secondly, we analyze an effective VFR method that uses a cumulative, weighted cepstral-distance criterion for frame selection and present a revision for it. Lastly, the revised VFR method is combined with spectral- and cepstral-domain enhancement methods including the minimum statistics noise estimation (MSNE) based spectral subtraction and the cepstral mean subtraction, variance normalization and ARMA filtering (MVA) process. Experiments on the Aurora 2 database justify that VFR is highly complementary to the enhancement methods. Enhancement of speech both facilitates the frame selection in VFR and provides de-noised speech for recognition.

  18. Automatic analysis of ciliary beat frequency using optical flow

    NASA Astrophysics Data System (ADS)

    Figl, Michael; Lechner, Manuel; Werther, Tobias; Horak, Fritz; Hummel, Johann; Birkfellner, Wolfgang

    2012-02-01

    Ciliary beat frequency (CBF) can be a useful parameter for diagnosis of several diseases, as e.g. primary ciliary dyskinesia. (PCD). CBF computation is usually done using manual evaluation of high speed video sequences, a tedious, observer dependent, and not very accurate procedure. We used the OpenCV's pyramidal implementation of the Lukas-Kanade algorithm for optical flow computation and applied this to certain objects to follow the movements. The objects were chosen by their contrast applying the corner detection by Shi and Tomasi. Discrimination between background/noise and cilia by a frequency histogram allowed to compute the CBF. Frequency analysis was done using the Fourier transform in matlab. The correct number of Fourier summands was found by the slope in an approximation curve. The method showed to be usable to distinguish between healthy and diseased samples. However there remain difficulties in automatically identifying the cilia, and also in finding enough high contrast cilia in the image. Furthermore the some of the higher contrast cilia are lost (and sometimes found) by the method, an easy way to distinguish the correct sub-path of a point's path have yet to be found in the case where the slope methods doesn't work.

  19. Automatic comic page image understanding based on edge segment analysis

    NASA Astrophysics Data System (ADS)

    Liu, Dong; Wang, Yongtao; Tang, Zhi; Li, Luyuan; Gao, Liangcai

    2013-12-01

    Comic page image understanding aims to analyse the layout of the comic page images by detecting the storyboards and identifying the reading order automatically. It is the key technique to produce the digital comic documents suitable for reading on mobile devices. In this paper, we propose a novel comic page image understanding method based on edge segment analysis. First, we propose an efficient edge point chaining method to extract Canny edge segments (i.e., contiguous chains of Canny edge points) from the input comic page image; second, we propose a top-down scheme to detect line segments within each obtained edge segment; third, we develop a novel method to detect the storyboards by selecting the border lines and further identify the reading order of these storyboards. The proposed method is performed on a data set consisting of 2000 comic page images from ten printed comic series. The experimental results demonstrate that the proposed method achieves satisfactory results on different comics and outperforms the existing methods.

  20. 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.

  1. 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

  2. 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.

  3. Risk Factors for Early Colonoscopic Perforation Include Non-Gastroenterologist Endoscopists: a Multivariable Analysis

    PubMed Central

    Bielawska, Barbara; Day, Andrew G; Lieberman, David A; Hookey, Lawrence C

    2014-01-01

    Background & Aims Bowel perforation is a rare but serious complication of colonoscopy. Its prevalence is increasing with the rapidly growing volume of procedures performed. Although colonoscopies have been performed for decades, the risk factors for perforation are not completely understood. We investigated risk factors for perforation during colonoscopy, assessing variables that included sedation type and endoscopist specialty and level of training. Methods We performed a retrospective multivariate analysis of risk factors for early perforation (occurring at any point during the colonoscopy but recognized during or immediately after the procedure) in adult patients using the Clinical Outcomes Research Initiative National Endoscopic Database. Risk factors were determined from published articles. Additional variables assessed included endoscopist specialty and years of experience, trainee involvement, and sedation with propofol. Results We identified 192 perforation events during 1,144,900 colonoscopies from 85 centers entered into the database from January 2000 through March 2011. On multivariate analysis, increasing age, American Society of Anesthesia class, female sex, hospital setting, any therapy, and polyps >10 mm were significantly associated with increased risk of early perforation. Colonoscopies performed by surgeons and endoscopists of unknown specialty had higher rates of perforation than those performed by gastroenterologists (odds ratio, 2.00; 95% confidence interval, 1.30–3.08). Propofol sedation did not significantly affect risk for perforation. Conclusions In addition to previously established risk factors, non-gastroenterologist specialty was found to affect risk for perforations detected during or immediately after colonoscopy. This finding could result from differences in volume and style of endoscopy training. Further investigation into these observed associations is warranted. PMID:23891916

  4. 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. PMID:24583518

  5. 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. PMID:26537783

  6. Study on Proper Sample Size for Multivariate Frequency Analysis for Rainfall Quantile

    NASA Astrophysics Data System (ADS)

    Joo, K.; Nam, W.; Choi, S.; Heo, J. H.

    2014-12-01

    For a given rainfall event, it can be characterized into some properties such as rainfall depth (amount), duration, and intensity. By considering these factors simultaneously, the actual phenomenon of rainfall event can be explained better than univariate model. Recently, applications of multivariate analysis for hydrological data such as extreme rainfall, drought and flood events are increasing rapidly. Theoretically, sample size on 2-dimension sample space needs n-square sample size if univariate frequency analysis needs n sample size. Main object of this study is to estimate of appropriate sample size of bivariate frequency analysis (especially using copula model) for rainfall data. Hourly recorded data (1961~2010) of Seoul weather station from Korea Meteorological Administration (KMA) is applied for frequency analysis and three copula models (Clayton, Frank, Gumbel) are used. Parameter estimation is performed by using pseudo-likelihood estimation and estimated mean square error (MSE) on various sample size by peaks over threshold (POT) concept. As a result, estimated thresholds of rainfall depth are 65.4 mm for Clayton, 74.2 mm for Frank, and 76.9 mm for Gumbel, respectively

  7. 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. PMID:25863699

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

    SciTech Connect

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

    2003-01-01

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

  9. 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. PMID:26162693

  10. 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.

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

    PubMed

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

    2015-02-25

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

  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. PMID:22974585

  13. 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

  14. Quantitative modeling of bioconcentration factors of carbonyl herbicides using multivariate image analysis.

    PubMed

    Freitas, Mirlaine R; Barigye, Stephen J; Daré, Joyce K; Freitas, Matheus P

    2016-06-01

    The bioconcentration factor (BCF) is an important parameter used to estimate the propensity of chemicals to accumulate in aquatic organisms from the ambient environment. While simple regressions for estimating the BCF of chemical compounds from water solubility or the n-octanol/water partition coefficient have been proposed in the literature, these models do not always yield good correlations and more descriptive variables are required for better modeling of BCF data for a given series of organic pollutants, such as some herbicides. Thus, the logBCF values for a set of carbonyl herbicides comprising amide, urea, carbamate and thiocarbamate groups were quantitatively modeled using multivariate image analysis (MIA) descriptors, derived from colored image representations for chemical structures. The logBCF model was calibrated and vigorously validated (r(2) = 0.79, q(2) = 0.70 and rtest(2) = 0.81), providing a comprehensive three-parameter linear equation after variable selection (logBCF = 5.682 - 0.00233 × X9774 - 0.00070 × X813 - 0.00273 × X5144); the variables represent pixel coordinates in the multivariate image. Finally, chemical interpretation of the obtained models in terms of the structural characteristics responsible for the enhanced or reduced logBCF values was performed, providing key leads in the prospective development of more eco-friendly synthetic herbicides. PMID:26971171

  15. 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

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

    PubMed

    Sakhanenko, Nikita A; Galas, David J

    2015-11-01

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

  17. 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

  18. 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.

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

    PubMed

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

    2014-02-01

    Using γ spectrometry, the concentration of the naturally occurring radionuclides (226)Ra, (232)Th and (40)K has been measured in soil, sand, cement, clay and bricks, which are used as building materials in Tiruvannamalai, Tamilnadu, India. The radium equivalent activity (Raeq), the criterion formula (CF), indoor gamma absorbed dose rate (DR), annual effective dose (HR), activity utilization index (AUI), alpha index (Iα), gamma index (Iγ), external radiation hazard index (Hex), internal radiation hazard index (Hin), representative level index (RLI), excess lifetime cancer risk (ELCR) and annual gonadal dose equivalent (AGDE) associated with the natural radionuclides are calculated to assess the radiation hazard of the natural radioactivity in the building materials. From the analysis, it is found that these materials used for the construction of dwellings are safe for the inhabitants. The radiological data were processed using multivariate statistical methods to determine the similarities and correlation among the various samples. The frequency distributions for all radionuclides were analyzed. The data set consisted of 15 measured variables. The Pearson correlation coefficient reveals that the (226)Ra distribution in building materials is controlled by the variation of the (40)K concentration. Principal component analysis (PCA) yields a two-component representation of the acquired data from the building materials in Tiruvannamalai, wherein 94.9% of the total variance is explained. The resulting dendrogram of hierarchical cluster analysis (HCA) classified the 30 building materials into four major groups using 15 variables. PMID:24412972

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

    DOE PAGESBeta

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

    2014-12-02

    Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe0.55Se0.45 (Tc = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe1 x Sex structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signaturemore » and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.« less

  1. 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.

  2. Tomographic Spectral Imaging with Multivariate Statistical Analysis: Comprehensive 3D Microanalysis.

    PubMed

    Kotula, Paul G; Keenan, Michael R; Michael, Joseph R

    2006-02-01

    A comprehensive three-dimensional (3D) microanalysis procedure using a combined scanning electron microscope (SEM)/focused ion beam (FIB) system equipped with an energy-dispersive X-ray spectrometer (EDS) has been developed. The FIB system was used first to prepare a site-specific region for X-ray microanalysis followed by the acquisition of an electron-beam generated X-ray spectral image. A small section of material was then removed by the FIB, followed by the acquisition of another X-ray spectral image. This serial sectioning procedure was repeated 10-12 times to sample a volume of material. The series of two-spatial-dimension spectral images were then concatenated into a single data set consisting of a series of volume elements or voxels each with an entire X-ray spectrum. This four-dimensional (three real space and one spectral dimension) spectral image was then comprehensively analyzed with Sandia's automated X-ray spectral image analysis software. This technique was applied to a simple Cu-Ag eutectic and a more complicated localized corrosion study where the powerful site-specific comprehensive analysis capability of tomographic spectral imaging (TSI) combined with multivariate statistical analysis is demonstrated. PMID:17481340

  3. 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. PMID:24233510

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

    PubMed

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

    2016-01-15

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

  5. 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.

  6. A Multivariate Generalizability Analysis of Data from a Performance Assessment of Physicians' Clinical Skills

    ERIC Educational Resources Information Center

    Clauser, Brian E.; Harik, Polina; Margolis, Melissa J.

    2006-01-01

    Although multivariate generalizability theory was developed more than 30 years ago, little published research utilizing this framework exists and most of what does exist examines tests built from tables of specifications. In this context, it is assumed that the universe scores from levels of the fixed multivariate facet will be correlated, but the…

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

    PubMed

    Firdous, Shahana; Begum, Shaheen; Yasmin, Azra

    2016-09-01

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

  8. 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

  9. Univariate and multivariate analysis of associated factors of retinopathy in 894 Italian adult diabetics.

    PubMed

    Pisu, E; Vitelli, F; Coggi, G; Franzone, M; Cavallo, M; Chiara, E; Carta, Q; Pagano, G

    1988-12-01

    The prevalence of diabetic retinopathy and the evaluation of its risk factors is poorly known in Italian population. Therefore, we have studied 894 diabetic outpatients (420 males, 474 females, 27.6% IDDs, 38.1% insulin-treated) in order to investigate the effect of clinical and metabolic characteristics on the frequency of diabetic retinopathy, classified into six different classes. In univariate analyses age, duration of disease, systolic and diastolic blood pressure, blood urea nitrogen, 24 hr proteinuria and fasting glycemia significantly correlated (p less than 0.001) with severity of retinopathy. The significance was confirmed in multivariate analysis for duration, age and systolic blood pressure (p less than 0.001). Stratification by type of diabetes showed that undefined onset of diabetes probably reduced in NID patients the power of duration as an associated factor of retinopathy. Worsening of this complication in three clinical classes of therapy (diet, oral and insulin-treatment) is evident too. Finally, our 11 variables in the step-wise multiple-regression analysis explain only 16.8% of diabetic retinopathy in all patients, but 36.6% in selected ID subjects. PMID:3246287

  10. 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.

  11. 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. PMID:22396069

  12. 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.

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

    PubMed

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

    2007-01-01

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

  14. 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.

  15. 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

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

    PubMed

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

    2015-04-01

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

  17. Multivariate Analysis of State Variation in Breastfeeding Rates in the United States

    PubMed Central

    Singh, Gopal K.; Dee, Deborah L.; Belanoff, Candice; Grummer-Strawn, Laurence M.

    2008-01-01

    Objectives. We sought to determine the impact of sociodemographic and behavioral factors and state legislation on breastfeeding initiation (child ever fed breastmilk) and duration. Methods. We used data from a nationally representative study of children aged 6 to 71 months (N = 33 121); we calculated unadjusted and adjusted state estimates for breastfeeding initiation and duration. We used logistic regression models to examine factors associated with never breastfeeding or breastfeeding less than 6 months. We conducted a multilevel analysis of state legislation's role. Results. There were wide state variations in breastfeeding initiation and duration. The western and northwestern states had the highest rates. Covariate adjustment accounted for 25% to 30% of the disparity. Multivariate analysis showed that the adjusted odds of not being breastfed were 2.5- to 5.15-times greater in southern states compared with Oregon (reference). Children in states without breastfeeding legislation had higher odds of not being breastfed. Conclusions. Sociodemographic and maternal factors do not account for most breastfeeding rate variation. The association with breastfeeding legislation should be explored and may reflect cultural norms. PMID:18703441

  18. 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.

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

    PubMed

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

    2016-08-01

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

  20. Discrimination of snipefish Macroramphosus species and boarfish Capros aper morphotypes through multivariate analysis of body shape

    NASA Astrophysics Data System (ADS)

    Lopes, Marta; Murta, Alberto G.; Cabral, Henrique N.

    2006-03-01

    The existence of two species of the genus Macroramphosus Lacepède 1803, has been discussed based on morphometric characters, diet composition and depth distribution. Another species, the boarfish Capros aper (Linnaeus 1758), caugth along the Portuguese coast, shows two different morphotypes, one type with smaller eyes and a deeper body than the other, occurring with intermediate forms. In both snipefish and boarfish no sexual dimorphism was found with respect to shape and length relationships. However, females in both genera were on average bigger than males. A multidimensional scaling analysis was performed using Procrustes distances, in order to check if shape geometry was effective in distinguishing the species of snipefish as well as the morphotypes of boarfish. A multivariate discriminant analysis using morphometric characters of snipefish and boarfish was carried out to validate the visual criteria for a distinction of species and morphotypes, respectively. Morphometric characters revealed a great discriminatory power to distinguish morphotypes. Both snipefish and boarfish are very abundant in Portuguese waters, showing two well-defined morphologies and intermediate forms. This study suggests that there may be two different species in each genus and that further studies on these fish should be carried out to investigate if there is reproductive isolation between the morphotypes of boarfish and to validate the species of snipefish.

  1. 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.

  2. Applying a multivariate statistical analysis model to evaluate the water quality of a watershed.

    PubMed

    Wu, Edward Ming-Yang; Kuo, Shu-Lung

    2012-12-01

    Multivariate statistics have been applied to evaluate the water quality data collected at six monitoring stations in the Feitsui Reservoir watershed of Taipei, Taiwan. The objective is to evaluate the mutual correlations among the various water quality parameters to reveal the primary factors that affect reservoir water quality, and the differences among the various water quality parameters in the watershed. In this study, using water quality samples collected over a period of two and a half years will effectively raise the efficacy and reliability of the factor analysis results. This will be a valuable reference for managing water pollution in the watershed. Additionally, results obtained using the proposed theory and method to analyze and interpret statistical data must be examined to verify their similarity to field data collected on the stream geographical and geological characteristics, the physical and chemical phenomena of stream self-purification, and the stream hydrological phenomena. In this research, the water quality data has been collected over two and a half years so that sufficient sets of water quality data are available to increase the stability, effectiveness, and reliability of the final factor analysis results. These data sets can be valuable references for managing, regulating, and remediating water pollution in a reservoir watershed. PMID:23342938

  3. 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

  4. 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.

  5. Multivariate analysis of buckwheat sourdough fermentations for metabolic screening of starter cultures.

    PubMed

    Capuani, Alessandro; Stetina, Mandy; Gstattenbauer, Anja; Behr, Jürgen; Vogel, Rudi F

    2014-08-18

    This study investigated the metabolic activity of 35 strains of lactic acid bacteria (LAB), which were able to grow in buckwheat sourdoughs and delivers a detailed explanation of LAB metabolism in that environment. To interpret the high-dimensional dataset, descriptive statistics and linear discriminant analysis (LDA) were used. Heterofermentative LAB showed a clear different metabolism than facultative (f.) heterofermentative and homofermentative LAB, which were more similar. Heterofermentative LAB were mainly characterized by high free SH groups and acetic acid production; they were also able to consume arabinose and glucose. Homofermenters were mainly characterized by lower free amino nitrogen content and they did not show a good capacity to consume arabinose and fructose. Except for the heterofermentative Weissella cibaria strain, only homofermentative strains showed high ornithine yields. Some f. heterofermentative strains differed from homofermentative due to the high lactic acid production as well as low glucose and arginine consumption. LAB containing more genes encoding peptidase activities and genes involved in aroma production showed a high consumption of free amino acids. Strain-dependent activities could be clearly distinguished from group dependent ones (homofermentative, f. heterofermentative and heterofermentative), e.g., some Lactobacillus paracasei and Lactobacillus plantarum strains showed the highest carbohydrate consumption. However, some microbial activities were more strain-dependent than group-dependent. Multivariate analysis of raw data delivered a detailed and clear explanation of LAB metabolism in buckwheat sourdough fermentations. PMID:24992519

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

    PubMed

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

    2008-12-01

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

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

    PubMed

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

    2014-01-01

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

  8. 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

  9. Sentence Similarity Analysis with Applications in Automatic Short Answer Grading

    ERIC Educational Resources Information Center

    Mohler, Michael A. G.

    2012-01-01

    In this dissertation, I explore unsupervised techniques for the task of automatic short answer grading. I compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating…

  10. 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

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

    PubMed Central

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

    2010-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  13. 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…

  14. 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…

  15. Analysis of associations with change in a multivariate outcome variable when baseline is subject to measurement error.

    PubMed

    Chambless, Lloyd E; Davis, Vicki

    2003-04-15

    A simple general algorithm is described for correcting for bias caused by measurement error in independent variables in multivariate linear regression. This algorithm, using standard software, is then applied to several approaches to the analysis of change from baseline as a function of baseline value of the outcome measure plus other covariates, any of which might have measurement error. The algorithm may also be used when the independent variables differ by component of the multivariate independent variable. Simulations indicate that under various conditions bias is much reduced, as is mean squared error, and coverage of 95 per cent confidence intervals is good. PMID:12652553

  16. 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.

  17. 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.

  18. 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

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

    PubMed Central

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

    2014-01-01

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

  20. Diagnostic Prediction for Social Anxiety Disorder via Multivariate Pattern Analysis of the Regional Homogeneity

    PubMed Central

    Zhang, Wenjing; Yang, Xun; Lui, Su; Meng, Yajing; Yao, Li; Xiao, Yuan; Deng, Wei; Zhang, Wei; Gong, Qiyong

    2015-01-01

    Although decades of efforts have been spent studying the pathogenesis of social anxiety disorder (SAD), there are still no objective biological markers that could be reliably used to identify individuals with SAD. Studies using multivariate pattern analysis have shown the potential value in clinically diagnosing psychiatric disorders with neuroimaging data. We therefore examined the diagnostic potential of regional homogeneity (ReHo) underlying neural correlates of SAD using support vector machine (SVM), which has never been studied. Forty SAD patients and pairwise matched healthy controls were recruited and scanned by resting-state fMRI. The ReHo was calculated as synchronization of fMRI signals of nearest neighboring 27 voxels. A linear SVM was then adopted and allowed the classification of the two groups with diagnostic accuracy of ReHo that was 76.25% (sensitivity = 70%, and specificity = 82.5%, P ≤ 0.001). Regions showing different discriminating values between diagnostic groups were mainly located in default mode network, dorsal attention network, self-referential network, and sensory networks, while the left medial prefrontal cortex was identified with the highest weight. These results implicate that ReHo has good diagnostic potential in SAD, and thus may provide an initial step towards the possible use of whole brain local connectivity to inform the clinical evaluation. PMID:26180811

  1. Water O-H stretching Raman signature for strong acid monitoring via multivariate analysis.

    PubMed

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

    2013-04-16

    A distinct need exists for real time information on an acid concentration of industrial aqueous streams. Acid strength affects efficiency and selectivity of many separation processes, including nuclear fuel reprocessing. Despite the seeming simplicity of the problem, no practical solution has been offered yet, particularly for the large-scale schemes involving toxic streams such as highly radioactive nuclear wastes. The classic potentiometric technique is not amiable for online measurements due to the requirements of frequent calibration/maintenance and poor long-term stability in aggressive chemical and radiation environments. Therefore, an alternative analytical method is needed. In this work, the potential of using Raman spectroscopic measurements for online monitoring of strong acid concentration in solutions relevant to dissolved used nuclear fuel was investigated. The Raman water signature was monitored for solution systems containing nitric and hydrochloric acids and their sodium salts of systematically varied composition, ionic strength, and temperature. The trivalent neodymium ion simulated the presence of multivalent f metals. The gaussian deconvolution analysis was used to interpret observed effects of the solution nature on the Raman water O-H stretching spectrum. The generated Raman spectroscopic database was used to develop predictive multivariate regression models for the quantification of the acid and other solution components, as well as selected physicochemical properties. This method was validated using independent experiments conducted in a flow solvent extraction system. PMID:23472939

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

    PubMed

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

    2015-11-15

    Infrared spectroscopic techniques and chemometric methods were used to study oxidation of olive, sunflower and rapeseed oils. Accelerated oxidative degradation of oils at 60°C was monitored using peroxide values and FT-MIR ATR and FT-NIR transmittance spectroscopy. Principal component analysis (PCA) facilitated visualization and interpretation of spectral changes occurring during oxidation. Multivariate curve resolution (MCR) method found three spectral components in the NIR and MIR spectral matrix, corresponding to the oxidation products, and saturated and unsaturated structures. Good quantitative relation was found between peroxide value and contribution of oxidation products evaluated using MCR--based on NIR (R(2) = 0.890), MIR (R(2) = 0.707) and combined NIR and MIR (R(2) = 0.747) data. Calibration models for prediction peroxide value established using partial least squares (PLS) regression were characterized for MIR (R(2) = 0.701, RPD = 1.7), NIR (R(2) = 0.970, RPD = 5.3), and combined NIR and MIR data (R(2) = 0.954, RPD = 3.1). PMID:25977045

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

    PubMed

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

    2014-01-01

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

  4. 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. PMID:26342249

  5. 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.

  6. Multivariate analysis of correlation between electrophysiological and hemodynamic responses during cognitive processing.

    PubMed

    Kujala, Jan; Sudre, Gustavo; Vartiainen, Johanna; Liljeström, Mia; Mitchell, Tom; Salmelin, Riitta

    2014-05-15

    Animal and human studies have frequently shown that in primary sensory and motor regions the BOLD signal correlates positively with high-frequency and negatively with low-frequency neuronal activity. However, recent evidence suggests that this relationship may also vary across cortical areas. Detailed knowledge of the possible spectral diversity between electrophysiological and hemodynamic responses across the human cortex would be essential for neural-level interpretation of fMRI data and for informative multimodal combination of electromagnetic and hemodynamic imaging data, especially in cognitive tasks. We applied multivariate partial least squares correlation analysis to MEG-fMRI data recorded in a reading paradigm to determine the correlation patterns between the data types, at once, across the cortex. Our results revealed heterogeneous patterns of high-frequency correlation between MEG and fMRI responses, with marked dissociation between lower and higher order cortical regions. The low-frequency range showed substantial variance, with negative and positive correlations manifesting at different frequencies across cortical regions. These findings demonstrate the complexity of the neurophysiological counterparts of hemodynamic fluctuations in cognitive processing. PMID:24518260

  7. Multivariate analysis of correlation between electrophysiological and hemodynamic responses during cognitive processing

    PubMed Central

    Kujala, Jan; Sudre, Gustavo; Vartiainen, Johanna; Liljeström, Mia; Mitchell, Tom; Salmelin, Riitta

    2014-01-01

    Animal and human studies have frequently shown that in primary sensory and motor regions the BOLD signal correlates positively with high-frequency and negatively with low-frequency neuronal activity. However, recent evidence suggests that this relationship may also vary across cortical areas. Detailed knowledge of the possible spectral diversity between electrophysiological and hemodynamic responses across the human cortex would be essential for neural-level interpretation of fMRI data and for informative multimodal combination of electromagnetic and hemodynamic imaging data, especially in cognitive tasks. We applied multivariate partial least squares correlation analysis to MEG–fMRI data recorded in a reading paradigm to determine the correlation patterns between the data types, at once, across the cortex. Our results revealed heterogeneous patterns of high-frequency correlation between MEG and fMRI responses, with marked dissociation between lower and higher order cortical regions. The low-frequency range showed substantial variance, with negative and positive correlations manifesting at different frequencies across cortical regions. These findings demonstrate the complexity of the neurophysiological counterparts of hemodynamic fluctuations in cognitive processing. PMID:24518260

  8. 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.

  9. 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.

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

    PubMed

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

    2014-10-01

    All neuroimaging packages can handle group analysis with t-tests or general linear modeling (GLM). However, they are quite hamstrung when there are multiple within-subject factors or when quantitative covariates are involved in the presence of a within-subject factor. In addition, sphericity is typically assumed for the variance-covariance structure when there are more than two levels in a within-subject factor. To overcome such limitations in the traditional AN(C)OVA and GLM, we adopt a multivariate modeling (MVM) approach to analyzing neuroimaging data at the group level with the following advantages: a) there is no limit on the number of factors as long as sample sizes are deemed appropriate; b) quantitative covariates can be analyzed together with within-subject factors; c) when a within-subject factor is involved, three testing methodologies are provided: traditional univariate testing (UVT) with sphericity assumption (UVT-UC) and with correction when the assumption is violated (UVT-SC), and within-subject multivariate testing (MVT-WS); d) to correct for sphericity violation at the voxel level, we propose a hybrid testing (HT) approach that achieves equal or higher power via combining traditional sphericity correction methods (Greenhouse-Geisser and Huynh-Feldt) with MVT-WS. To validate the MVM methodology, we performed simulations to assess the controllability for false positives and power achievement. A real FMRI dataset was analyzed to demonstrate the capability of the MVM approach. The methodology has been implemented into an open source program 3dMVM in AFNI, and all the statistical tests can be performed through symbolic coding with variable names instead of the tedious process of dummy coding. Our data indicates that the severity of sphericity violation varies substantially across brain regions. The differences among various modeling methodologies were addressed through direct comparisons between the MVM approach and some of the GLM implementations in

  11. Multivariate analysis of factors affecting presence and/or agenesis of third molar tooth.

    PubMed

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

    2014-01-01

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

  12. 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.

  13. The effect of spatial resolution on decoding accuracy in fMRI multivariate pattern analysis.

    PubMed

    Gardumi, Anna; Ivanov, Dimo; Hausfeld, Lars; Valente, Giancarlo; Formisano, Elia; Uludağ, Kâmil

    2016-05-15

    Multivariate pattern analysis (MVPA) in fMRI has been used to extract information from distributed cortical activation patterns, which may go undetected in conventional univariate analysis. However, little is known about the physical and physiological underpinnings of MVPA in fMRI as well as about the effect of spatial smoothing on its performance. Several studies have addressed these issues, but their investigation was limited to the visual cortex at 3T with conflicting results. Here, we used ultra-high field (7T) fMRI to investigate the effect of spatial resolution and smoothing on decoding of speech content (vowels) and speaker identity from auditory cortical responses. To that end, we acquired high-resolution (1.1mm isotropic) fMRI data and additionally reconstructed them at 2.2 and 3.3mm in-plane spatial resolutions from the original k-space data. Furthermore, the data at each resolution were spatially smoothed with different 3D Gaussian kernel sizes (i.e. no smoothing or 1.1, 2.2, 3.3, 4.4, or 8.8mm kernels). For all spatial resolutions and smoothing kernels, we demonstrate the feasibility of decoding speech content (vowel) and speaker identity at 7T using support vector machine (SVM) MVPA. In addition, we found that high spatial frequencies are informative for vowel decoding and that the relative contribution of high and low spatial frequencies is different across the two decoding tasks. Moderate smoothing (up to 2.2mm) improved the accuracies for both decoding of vowels and speakers, possibly due to reduction of noise (e.g. residual motion artifacts or instrument noise) while still preserving information at high spatial frequency. In summary, our results show that - even with the same stimuli and within the same brain areas - the optimal spatial resolution for MVPA in fMRI depends on the specific decoding task of interest. PMID:26899782

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2013-01-01

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

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

    PubMed

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

    2016-04-19

    Attribution of the origin of an illicit drug relies on identification of compounds indicative of its clandestine production and is a key component of many modern forensic investigations. The results of these studies can yield detailed information on method of manufacture, starting material source, and final product, all critical forensic evidence. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic fentanyl, N-(1-phenylethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods, all previously published fentanyl synthetic routes or hybrid versions thereof, were studied in an effort to identify and classify route-specific signatures. A total of 160 distinct compounds and inorganic species were identified using gas and liquid chromatographies combined with mass spectrometric methods (gas chromatography/mass spectrometry (GC/MS) and liquid chromatography-tandem mass spectrometry-time of-flight (LC-MS/MS-TOF)) in conjunction with inductively coupled plasma mass spectrometry (ICPMS). The complexity of the resultant data matrix urged the use of multivariate statistical analysis. Using partial least-squares-discriminant analysis (PLS-DA), 87 route-specific CAS were classified and a statistical model capable of predicting the method of fentanyl synthesis was validated and tested against CAS profiles from crude fentanyl products deposited and later extracted from two operationally relevant surfaces: stainless steel and vinyl tile. This work provides the most detailed fentanyl CAS investigation to date by using orthogonal mass spectral data to identify CAS of forensic significance for illicit drug detection, profiling, and attribution. PMID:27010913

  17. 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

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

    PubMed

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

    2009-05-30

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

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

    PubMed

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

    2016-09-01

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

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

    PubMed Central

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

    2013-01-01

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

  1. 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

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

    PubMed

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

    2015-12-30

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

  3. 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

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

    NASA Astrophysics Data System (ADS)

    chan, S.

    2013-12-01

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

  5. Automatic Match between Delimitation Line and Real Terrain Based on Least-Cost Path Analysis

    NASA Astrophysics Data System (ADS)

    Feng, C. Q.; Jiang, N.; Zhang, X. N.; Ma, J.

    2013-11-01

    Nowadays, during the international negotiation on separating dispute areas, manual adjusting is lonely applied to the match between delimitation line and real terrain, which not only consumes much time and great labor force, but also cannot ensure high precision. Concerning that, the paper mainly explores automatic match between them and study its general solution based on Least -Cost Path Analysis. First, under the guidelines of delimitation laws, the cost layer is acquired through special disposals of delimitation line and terrain features line. Second, a new delimitation line gets constructed with the help of Least-Cost Path Analysis. Third, the whole automatic match model is built via Module Builder in order to share and reuse it. Finally, the result of automatic match is analyzed from many different aspects, including delimitation laws, two-sided benefits and so on. Consequently, a conclusion is made that the method of automatic match is feasible and effective.

  6. 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

  7. 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

  8. 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. PMID:27222918

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

    PubMed Central

    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. PMID:25844312

  11. [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. PMID:18051223

  12. 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.

  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 pattern analysis of obsessive-compulsive disorder using structural neuroanatomy.

    PubMed

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

    2016-02-01

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

  15. 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. PMID:26481792

  16. Identifying neuropathic pain using (18)F-FDG micro-PET: a multivariate pattern analysis.

    PubMed

    Kim, Chang-Eop; Kim, Yu Kyeong; Chung, Geehoon; Im, Hyung Jun; Lee, Dong Soo; Kim, Jun; Kim, Sang Jeong

    2014-02-01

    Pain is a multidimensional experience emerging from the flow of information between multiple brain regions. A growing body of evidence suggests that pathological pain causes plastic changes of various brain regions. Here, we hypothesized that the induction of neuropathic pain alters distributed patterns of the resting-state brain activity in animal models, and capturing the altered pattern would enable identification of neuropathic pain at the individual level. We acquired micro-positron emission tomography with [(18)F]fluorodeoxyglucose (FDG micro-PET) images in awake rats with spinal nerve ligation (SNL) and without (sham) (SNL group, n=13; sham group, n=10). Multivariate pattern analysis (MVPA) with linear support vector machine (SVM) successfully identified the brain with SNL (92.31% sensitivity, 90.00% specificity, and 91.30% total accuracy). Predictive brain regions with increased metabolism were mainly located in prefrontal-limbic-brainstem areas including the anterior olfactory nucleus (AON), insular cortex (IC), piriform cortex (PC), septal area (SA), basal forebrain/preoptic area (BF/POA), amygdala (AMY), hypothalamus (HT), rostral ventromedial medulla (RVM) and the ventral midbrain (VMB). In contrast, predictive regions with decreased metabolism were observed in widespread cortical areas including secondary somatosensory cortex (S2), occipital cortex (OC), temporal cortex (TC), retrosplenial cortex (RSC), and the cerebellum (CBL). We also applied the univariate approach and obtained reduced prediction performance compared to MVPA. Our results suggest that developing neuroimaging-based diagnostic tools for pathological pain can be achieved by considering patterns of the resting-state brain activity. PMID:24121088

  17. Uncovering the Formation of Ultracompact Dwarf Galaxies by Multivariate Statistical Analysis

    NASA Astrophysics Data System (ADS)

    Chattopadhyay, Tanuka; Sharina, Margarita; Davoust, Emmanuel; De, Tuli; Chattopadhyay, Asis Kumar

    2012-05-01

    We present a statistical analysis of the properties of a large sample of dynamically hot old stellar systems, from globular clusters (GCs) to giant ellipticals, which was performed in order to investigate the origin of ultracompact dwarf galaxies (UCDs). The data were mostly drawn from Forbes et al. We recalculated some of the effective radii, computed mean surface brightnesses and mass-to-light ratios, and estimated ages and metallicities. We completed the sample with GCs of M31. We used a multivariate statistical technique (K-Means clustering), together with a new algorithm (Gap Statistics) for finding the optimum number of homogeneous sub-groups in the sample, using a total of six parameters (absolute magnitude, effective radius, virial mass-to-light ratio, stellar mass-to-light ratio, and metallicity). We found six groups. FK1 and FK5 are composed of high- and low-mass elliptical galaxies, respectively. FK3 and FK6 are composed of high-metallicity and low-metallicity objects, respectively, and both include GCs and UCDs. Two very small groups, FK2 and FK4, are composed of Local Group dwarf spheroidals. Our groups differ in their mean masses and virial mass-to-light ratios. The relations between these two parameters are also different for the various groups. The probability density distributions of metallicity for the four groups of galaxies are similar to those of the GCs and UCDs. The brightest low-metallicity GCs and UCDs tend to follow the mass-metallicity relation like elliptical galaxies. The objects of FK3 are more metal-rich per unit effective luminosity density than high-mass ellipticals.

  18. Multivariate genetic analysis of plant responses to water deficit and high temperature revealed contrasting adaptive strategies.

    PubMed

    Vasseur, François; Bontpart, Thibaut; Dauzat, Myriam; Granier, Christine; Vile, Denis

    2014-12-01

    How genetic factors control plant performance under stressful environmental conditions is a central question in ecology and for crop breeding. A multivariate framework was developed to examine the genetic architecture of performance-related traits in response to interacting environmental stresses. Ecophysiological and life history traits were quantified in the Arabidopsis thaliana Ler × Cvi mapping population exposed to constant soil water deficit and high air temperature. The plasticity of the genetic variance-covariance matrix (G-matrix) was examined using mixed-effects models after regression into principal components. Quantitative trait locus (QTL) analysis was performed on the predictors of genotype effects and genotype by environment interactions (G × E). Three QTLs previously identified for flowering time had antagonistic G × E effects on carbon acquisition and the other traits (phenology, growth, leaf morphology, and transpiration). This resulted in a size-dependent response of water use efficiency (WUE) to high temperature but not soil water deficit, indicating that most of the plasticity of carbon acquisition and WUE to temperature is controlled by the loci that control variation of development, size, growth, and transpiration. A fourth QTL, MSAT2.22, controlled the response of carbon acquisition to specific combinations of watering and temperature irrespective of plant size and development, growth, and transpiration rate, which resulted in size-independent plasticity of WUE. These findings highlight how the strategies to optimize plant performance may differ in response to water deficit and high temperature (or their combination), and how different G × E effects could be targeted to improve plant tolerance to these stresses. PMID:25246443

  19. 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. PMID:25844312

  20. Automatic Crowd Analysis from Very High Resolution Satellite Images

    NASA Astrophysics Data System (ADS)

    Sirmacek, B.; Reinartz, P.

    2011-04-01

    Recently automatic detection of people crowds from images became a very important research field, since it can provide crucial information especially for police departments and crisis management teams. Due to the importance of the topic, many researchers tried to solve this problem using street cameras. However, these cameras cannot be used to monitor very large outdoor public events. In order to bring a solution to the problem, herein we propose a novel approach to detect crowds automatically from remotely sensed images, and especially from very high resolution satellite images. To do so, we use a local feature based probabilistic framework. We extract local features from color components of the input image. In order to eliminate redundant local features coming from other objects in given scene, we apply a feature selection method. For feature selection purposes, we benefit from three different type of information; digital elevation model (DEM) of the region which is automatically generated using stereo satellite images, possible street segment which is obtained by segmentation, and shadow information. After eliminating redundant local features, remaining features are used to detect individual persons. Those local feature coordinates are also assumed as observations of the probability density function (pdf) of the crowds to be estimated. Using an adaptive kernel density estimation method, we estimate the corresponding pdf which gives us information about dense crowd and people locations. We test our algorithm usingWorldview-2 satellite images over Cairo and Munich cities. Besides, we also provide test results on airborne images for comparison of the detection accuracy. Our experimental results indicate the possible usage of the proposed approach in real-life mass events.

  1. Explodet Project:. Methods of Automatic Data Processing and Analysis for the Detection of Hidden Explosive

    NASA Astrophysics Data System (ADS)

    Lecca, Paola

    2003-12-01

    The research of the INFN Gruppo Collegato di Trento in the ambit of EXPLODET project for the humanitarian demining, is devoted to the development of a software procedure for the automatization of data analysis and decision taking about the presence of hidden explosive. Innovative algorithms of likely background calculation, a system based on neural networks for energy calibration and simple statistical methods for the qualitative consistency check of the signals are the main parts of the software performing the automatic data elaboration.

  2. 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.

  3. An analysis of automatic human detection and tracking

    NASA Astrophysics Data System (ADS)

    Demuth, Philipe R.; Cosmo, Daniel L.; Ciarelli, Patrick M.

    2015-12-01

    This paper presents an automatic method to detect and follow people on video streams. This method uses two techniques to determine the initial position of the person at the beginning of the video file: one based on optical flow and the other one based on Histogram of Oriented Gradients (HOG). After defining the initial bounding box, tracking is done using four different trackers: Median Flow tracker, TLD tracker, Mean Shift tracker and a modified version of the Mean Shift tracker using HSV color space. The results of the methods presented in this paper are then compared at the end of the paper.

  4. AVTA: a device for automatic vocal transaction analysis1

    PubMed Central

    Cassotta, Louis; Feldstein, Stanley; Jaffe, Joseph

    1964-01-01

    The Automatic Vocal Transaction Analyzer was designed to recognize the pattern of certain variables in spontaneous vocal transactions. In addition, it records these variables directly in a machine-readable form and preserves their sequential relationships. This permits the immediate extraction of data by a digital computer. The AVTA system reliability has been shown to be equal to or better than that of a trained human operator in uncomplicated interaction. The superiority of the machine was demonstrated in complex interactions which tax the information processing abilities of the human observer. PMID:14120152

  5. 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

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

    PubMed

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

    2016-07-01

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

  7. 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. PMID:26765094

  8. 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. PMID:22524958

  9. Structuring Lecture Videos by Automatic Projection Screen Localization and Analysis.

    PubMed

    Li, Kai; Wang, Jue; Wang, Haoqian; Dai, Qionghai

    2015-06-01

    We present a fully automatic system for extracting the semantic structure of a typical academic presentation video, which captures the whole presentation stage with abundant camera motions such as panning, tilting, and zooming. Our system automatically detects and tracks both the projection screen and the presenter whenever they are visible in the video. By analyzing the image content of the tracked screen region, our system is able to detect slide progressions and extract a high-quality, non-occluded, geometrically-compensated image for each slide, resulting in a list of representative images that reconstruct the main presentation structure. Afterwards, our system recognizes text content and extracts keywords from the slides, which can be used for keyword-based video retrieval and browsing. Experimental results show that our system is able to generate more stable and accurate screen localization results than commonly-used object tracking methods. Our system also extracts more accurate presentation structures than general video summarization methods, for this specific type of video. PMID:26357345

  10. Synchrotron-Based Microspectroscopic Analysis of Molecular and Biopolymer Structures Using Multivariate Techniques and Advanced Multi-Components Modeling

    SciTech Connect

    Yu, P.

    2008-01-01

    More recently, advanced synchrotron radiation-based bioanalytical technique (SRFTIRM) has been applied as a novel non-invasive analysis tool to study molecular, functional group and biopolymer chemistry, nutrient make-up and structural conformation in biomaterials. This novel synchrotron technique, taking advantage of bright synchrotron light (which is million times brighter than sunlight), is capable of exploring the biomaterials at molecular and cellular levels. However, with the synchrotron RFTIRM technique, a large number of molecular spectral data are usually collected. The objective of this article was to illustrate how to use two multivariate statistical techniques: (1) agglomerative hierarchical cluster analysis (AHCA) and (2) principal component analysis (PCA) and two advanced multicomponent modeling methods: (1) Gaussian and (2) Lorentzian multi-component peak modeling for molecular spectrum analysis of bio-tissues. The studies indicated that the two multivariate analyses (AHCA, PCA) are able to create molecular spectral corrections by including not just one intensity or frequency point of a molecular spectrum, but by utilizing the entire spectral information. Gaussian and Lorentzian modeling techniques are able to quantify spectral omponent peaks of molecular structure, functional group and biopolymer. By application of these four statistical methods of the multivariate techniques and Gaussian and Lorentzian modeling, inherent molecular structures, functional group and biopolymer onformation between and among biological samples can be quantified, discriminated and classified with great efficiency.

  11. 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

  12. 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

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

    NASA Astrophysics Data System (ADS)

    Tamea, Stefania; Laio, Francesco; Ridolfi, Luca

    2014-05-01

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

  14. Infrared Spectroscopy with Multivariate Analysis Potentially Facilitates the Segregation of Different Types of Prostate Cell

    PubMed Central

    German, Matthew J.; Hammiche, Azzedine; Ragavan, Narasimhan; Tobin, Mark J.; Cooper, Leanne J.; Matanhelia, Shyam S.; Hindley, Andrew C.; Nicholson, Caroline M.; Fullwood, Nigel J.; Pollock, Hubert M.; Martin, Francis L.

    2006-01-01

    The prostate gland is conventionally divided into zones or regions. This morphology is of clinical significance as prostate cancer (CaP) occurs mainly in the peripheral zone (PZ). We obtained tissue sets consisting of paraffin-embedded blocks of cancer-free transition zone (TZ) and PZ and adjacent CaP from patients (n = 6) who had undergone radical retropubic prostatectomy; a seventh tissue set of snap-frozen PZ and TZ was obtained from a CaP-free gland removed after radical cystoprostatectomy. Paraffin-embedded tissue slices were sectioned (10-μm thick) and mounted on suitable windows to facilitate infrared (IR) spectra acquisition before being dewaxed and air dried; cryosections were dessicated on BaF2 windows. Spectra were collected employing synchrotron Fourier-transform infrared (FTIR) microspectroscopy in transmission mode or attenuated total reflection-FTIR (ATR) spectroscopy. Epithelial cell and stromal IR spectra were subjected to principal component analysis to determine whether wavenumber-absorbance relationships expressed as single points in “hyperspace” might on the basis of multivariate distance reveal biophysical differences between cells in situ in different tissue regions. After spectroscopic analysis, plotted clusters and their loadings curves highlighted marked variation in the spectral region containing DNA/RNA bands (≈1490–1000 cm−1). By interrogating the intrinsic dimensionality of IR spectra in this small cohort sample, we found that TZ epithelial cells appeared to align more closely with those of CaP while exhibiting marked structural differences compared to PZ epithelium. IR spectra of PZ stroma also suggested that these cells are structurally more different to CaP than those located in the TZ. Because the PZ exhibits a higher occurrence of CaP, other factors (e.g., hormone exposure) may modulate the growth kinetics of initiated epithelial cells in this region. The results of this pilot study surprisingly indicate that TZ

  15. 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…

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

    ERIC Educational Resources Information Center

    Cardon, Lon R.; And Others

    1992-01-01

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

  17. Finite element fatigue analysis of rectangular clutch spring of automatic slack adjuster

    NASA Astrophysics Data System (ADS)

    Xu, Chen-jie; Luo, Zai; Hu, Xiao-feng; Jiang, Wen-song

    2015-02-01

    The failure of rectangular clutch spring of automatic slack adjuster directly affects the work of automatic slack adjuster. We establish the structural mechanics model of automatic slack adjuster rectangular clutch spring based on its working principle and mechanical structure. In addition, we upload such structural mechanics model to ANSYS Workbench FEA system to predict the fatigue life of rectangular clutch spring. FEA results show that the fatigue life of rectangular clutch spring is 2.0403×105 cycle under the effect of braking loads. In the meantime, fatigue tests of 20 automatic slack adjusters are carried out on the fatigue test bench to verify the conclusion of the structural mechanics model. The experimental results show that the mean fatigue life of rectangular clutch spring is 1.9101×105, which meets the results based on the finite element analysis using ANSYS Workbench FEA system.

  18. Development and Uncertainty Analysis of an Automatic Testing System for Diffusion Pump Performance

    NASA Astrophysics Data System (ADS)

    Zhang, S. W.; Liang, W. S.; Zhang, Z. J.

    A newly developed automatic testing system used in laboratory for diffusion pump performance measurement is introduced in this paper. By using two optical fiber sensors to indicate the oil level in glass-buret and a needle valve driven by a stepper motor to regulate the pressure in the test dome, the system can automatically test the ultimate pressure and pumping speed of a diffusion pump in accordance with ISO 1608. The uncertainty analysis theory is applied to analyze pumping speed measurement results. Based on the test principle and system structure, it is studied how much influence each component and test step contributes to the final uncertainty. According to differential method, the mathematical model for systematic uncertainty transfer function is established. Finally, by case study, combined uncertainties of manual operation and automatic operation are compared with each other (6.11% and 5.87% respectively). The reasonableness and practicality of this newly developed automatic testing system is proved.

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

    NASA Astrophysics Data System (ADS)

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

    1993-07-01

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

  20. 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.

  1. Identification of frozen salt solutions combining LIBS and multivariate analysis methods

    NASA Astrophysics Data System (ADS)

    Schröder, S.; Pavlov, S.; Jessberger, E.; Hübers, H.

    2012-12-01

    considerably complicates differentiation between salts with the same type of cation. The focus in this study was on the capability of different multivariate analysis (MVA) techniques applied to LIBS data to discriminate between salts with cations of the same kind in frozen salt solutions. With principal components analysis (PCA) the data were analyzed with the aim of separating the LIBS spectra into groups and revealing the most important lines in the spectra for discrimination and identification of the type of salt. PCA performance is improved by selecting the most relevant lines with emphasis on the sulfur and chlorine lines and additionally averaging the spectra before analysis. A subsequent local PCA can improve the discrimination ability for a sulfate and a chloride with the same type of cation. Soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) were performed. While SIMCA worked well for the pressed salt samples, its application to the spectra of the frozen salt solutions was not successful. A PLS-DA of the LIBS spectra of salts with the same cation is capable of distinguishing sulfate, chloride, and perchlorate. The results of this work demonstrate that LIBS is a suitable analytical technique for the investigation and identification of salts and frozen salt solutions under Martian atmospheric conditions.

  2. Development of a System for Automatic Facial Expression Analysis

    NASA Astrophysics Data System (ADS)

    Diago, Luis A.; Kitaoka, Tetsuko; Hagiwara, Ichiro

    Automatic recognition of facial expressions can be an important component of natural human-machine interactions. While a lot of samples are desirable for estimating more accurately the feelings of a person (e.g. likeness) about a machine interface, in real world situation, only a small number of samples must be obtained because the high cost in collecting emotions from observed person. This paper proposes a system that solves this problem conforming to individual differences. A new method is developed for facial expression classification based on the combination of Holographic Neural Networks (HNN) and Type-2 Fuzzy Logic. For the recognition of emotions induced by facial expressions, compared with former HNN and Support Vector Machines (SVM) classifiers, proposed method achieved the best generalization performance using less learning time than SVM classifiers.

  3. Mathematical morphology for TOFD image analysis and automatic crack detection.

    PubMed

    Merazi-Meksen, Thouraya; Boudraa, Malika; Boudraa, Bachir

    2014-08-01

    The aim of this work is to automate the interpretation of ultrasonic images during the non-destructive testing (NDT) technique called time-of-flight diffraction (TOFD) to aid in decision making. In this paper, the mathematical morphology approach is used to extract relevant pixels corresponding to the presence of a discontinuity, and a pattern recognition technique is used to characterize the discontinuity. The watershed technique is exploited to determine the region of interest and image background is removed using an erosion process, thereby improving the detection of connected shapes present in the image. Remaining shapes, are finally reduced to curves using a skeletonization technique. In the case of crack defects, the curve formed by such pixels has a parabolic form that can be automatically detected using the randomized Hough transform. PMID:24709071

  4. Automatic K scaling by means of fractal and harmonic analysis

    NASA Astrophysics Data System (ADS)

    de Santis, A.; Chiappini, M.

    1992-08-01

    The K index indicates the level of magnetic perturbation with respect to the normal diurnal variation. Usually K is taken manually from magnetograms, and the involved operations are consequently rather subjective. When data are available in digital form, it is possible to derive the K index automatically, using computer algorithms. This work applies a new combined technique based on both fractal and harmonic analyses. While the latter is often used in K determination, the former provides a substantially novel approach. One year (1989) of K observations at L'Aquila observatory has been used as a basis for comparison between hand and computer estimations of K. Agreements which have been found are comparable with those expected from two different operators.

  5. 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

  6. 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. PMID:26789008

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

    PubMed

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

    2016-10-01

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

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

    PubMed Central

    Dehesh, Tania; Zare, Najaf; Ayatollahi, Seyyed Mohammad Taghi

    2015-01-01

    Background. Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach. Methods. We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study. Result. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. Conclusion. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients. PMID:26413142

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

    PubMed

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

    2015-11-01

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

  10. 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.

  11. 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

  12. Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification

    PubMed Central

    Gaonkar, Bilwaj; Davatzikos, Christos

    2013-01-01

    Multivariate pattern analysis (MVPA) methods such as support vector machines (SVMs) have been increasingly applied to fMRI and sMRI analyses, enabling the detection of distinctive imaging patterns. However, identifying brain regions that significantly contribute to the classification/group separation requires computationally expensive permutation testing. In this paper we show that the results of SVM-permutation testing can be analytically approximated. This approximation leads to more than a thousand fold speed up of the permutation testing procedure, thereby rendering it feasible to perform such tests on standard computers. The speed up achieved makes SVM based group difference analysis competitive with standard univariate group difference analysis methods. PMID:23583748

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

    PubMed Central

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

    2016-01-01

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

  14. Formal Specification and Automatic Analysis of Business Processes under Authorization Constraints: An Action-Based Approach

    NASA Astrophysics Data System (ADS)

    Armando, Alessandro; Giunchiglia, Enrico; Ponta, Serena Elisa

    We present an approach to the formal specification and automatic analysis of business processes under authorization constraints based on the action language \\cal{C}. The use of \\cal{C} allows for a natural and concise modeling of the business process and the associated security policy and for the automatic analysis of the resulting specification by using the Causal Calculator (CCALC). Our approach improves upon previous work by greatly simplifying the specification step while retaining the ability to perform a fully automatic analysis. To illustrate the effectiveness of the approach we describe its application to a version of a business process taken from the banking domain and use CCALC to determine resource allocation plans complying with the security policy.

  15. [Assessment of drinking water quality in Avellino (Italy) by multivariate analysis].

    PubMed

    Mainolfi, Pietro; Prudente, Michelina Elisa; Ambrosone, Edoardo; Nunziata, Ferdinando; Mainolfi, Chiara; Galgano, Erberto

    2014-01-01

    The aim of this study was to assess the quality of the drinking water supply in the district of Avellino (Italy), by evaluating physico-chemical parameters and presence of contaminants. A multivariate approach was used to analyse data and to evaluate compliance to norms and standards. Study results indicate that statistical modeling is a powerful descriptive method to identify qualitative temporal trends in water quality and, if repeated in time, allows an evaluation of representativeness of the sampling points. PMID:25715892

  16. Analysis of natural red dyes (cochineal) in textiles of historical importance using HPLC and multivariate data analysis.

    PubMed

    Serrano, Ana; Sousa, Micaela M; Hallett, Jessica; Lopes, João A; Oliveira, M Conceição

    2011-08-01

    A new analytical approach based on high-performance liquid chromatography with diode array detector (HPLC-DAD) and multivariate data analysis was applied and assessed for analyzing the red dye extracted from cochineal insects, used in precious historical textiles. The most widely used method of analysis involves quantification of specific minor compounds (markers), using HPLC-DAD. However, variation in the cochineal markers concentration, use of aggressive dye extraction methods and poor resolution of HPLC chromatograms can compromise the identification of the precise insect species used in the textiles. In this study, a soft extraction method combined with a new dye recovery treatment was developed, capable of yielding HPLC chromatograms with good resolution, for the first time, for historical cochineal-dyed textiles. After principal components analysis (PCA) and mass spectrometry (MS), it was possible to identify the cochineal species used in these textiles, in contrast to the accepted method of analysis. In order to compare both methodologies, 7 cochineal species and 63 historical cochineal insect specimens were analyzed using the two approaches, and then compared with the results for 15 historical textiles in order to assess their applicability to real complex samples. The methodology developed here was shown to provide more accurate and consistent information than the traditional method. Almost all of the historical textiles were dyed with Porphyrophora sp. insects. These results emphasize the importance of adopting the proposed methodology for future research on cochineal (and related red dyes). Mild extraction methods and HPLC-DAD/MS(n) analysis yield distinctive profiles, which, in combination with a PCA reference database, are a powerful tool for identifying red insect dyes. PMID:21626194

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

    PubMed

    Buttigieg, Pier Luigi; Ramette, Alban

    2014-12-01

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

  18. Inheritance of nitrogen use efficiency in inbred progenies of tropical maize based on multivariate diallel analysis.

    PubMed

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

    2014-01-01

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

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

    PubMed Central

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

    2014-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Gómez Araújo, Iván; Laier, Jose Elias

    2015-09-01

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

  1. Automatic classification for pathological prostate images based on fractal analysis.

    PubMed

    Huang, Po-Whei; Lee, Cheng-Hsiung

    2009-07-01

    Accurate grading for prostatic carcinoma in pathological images is important to prognosis and treatment planning. Since human grading is always time-consuming and subjective, this paper presents a computer-aided system to automatically grade pathological images according to Gleason grading system which is the most widespread method for histological grading of prostate tissues. We proposed two feature extraction methods based on fractal dimension to analyze variations of intensity and texture complexity in regions of interest. Each image can be classified into an appropriate grade by using Bayesian, k-NN, and support vector machine (SVM) classifiers, respectively. Leave-one-out and k-fold cross-validation procedures were used to estimate the correct classification rates (CCR). Experimental results show that 91.2%, 93.7%, and 93.7% CCR can be achieved by Bayesian, k-NN, and SVM classifiers, respectively, for a set of 205 pathological prostate images. If our fractal-based feature set is optimized by the sequential floating forward selection method, the CCR can be promoted up to 94.6%, 94.2%, and 94.6%, respectively, using each of the above three classifiers. Experimental results also show that our feature set is better than the feature sets extracted from multiwavelets, Gabor filters, and gray-level co-occurrence matrix methods because it has a much smaller size and still keeps the most powerful discriminating capability in grading prostate images. PMID:19164082

  2. Statistical language analysis for automatic exfiltration event detection.

    SciTech Connect

    Robinson, David Gerald

    2010-04-01

    This paper discusses the recent development a statistical approach for the automatic identification of anomalous network activity that is characteristic of exfiltration events. This approach is based on the language processing method eferred to as latent dirichlet allocation (LDA). Cyber security experts currently depend heavily on a rule-based framework for initial detection of suspect network events. The application of the rule set typically results in an extensive list of uspect network events that are then further explored manually for suspicious activity. The ability to identify anomalous network events is heavily dependent on the experience of the security personnel wading through the network log. Limitations f this approach are clear: rule-based systems only apply to exfiltration behavior that has previously been observed, and experienced cyber security personnel are rare commodities. Since the new methodology is not a discrete rule-based pproach, it is more difficult for an insider to disguise the exfiltration events. A further benefit is that the methodology provides a risk-based approach that can be implemented in a continuous, dynamic or evolutionary fashion. This permits uspect network activity to be identified early with a quantifiable risk associated with decision making when responding to suspicious activity.

  3. Towards automatic music transcription: note extraction based on independent subspace analysis

    NASA Astrophysics Data System (ADS)

    Wellhausen, Jens; Hoynck, Michael

    2005-01-01

    Due to the increasing amount of music available electronically the need of automatic search, retrieval and classification systems for music becomes more and more important. In this paper an algorithm for automatic transcription of polyphonic piano music into MIDI data is presented, which is a very interesting basis for database applications, music analysis and music classification. The first part of the algorithm performs a note accurate temporal audio segmentation. In the second part, the resulting segments are examined using Independent Subspace Analysis to extract sounding notes. Finally, the results are used to build a MIDI file as a new representation of the piece of music which is examined.

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

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

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

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

    PubMed Central

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

    2013-01-01

    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. PMID:24558311

  6. 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. PMID:26630554

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  8. Multivariate meta-analysis of prognostic factor studies with multiple cut-points and/or methods of measurement.

    PubMed

    Riley, Richard D; Elia, Eleni G; Malin, Gemma; Hemming, Karla; Price, Malcolm P

    2015-07-30

    A prognostic factor is any measure that is associated with the risk of future health outcomes in those with existing disease. Often, the prognostic ability of a factor is evaluated in multiple studies. However, meta-analysis is difficult because primary studies often use different methods of measurement and/or different cut-points to dichotomise continuous factors into 'high' and 'low' groups; selective reporting is also common. We illustrate how multivariate random effects meta-analysis models can accommodate multiple prognostic effect estimates from the same study, relating to multiple cut-points and/or methods of measurement. The models account for within-study and between-study correlations, which utilises more information and reduces the impact of unreported cut-points and/or measurement methods in some studies. The applicability of the approach is improved with individual participant data and by assuming a functional relationship between prognostic effect and cut-point to reduce the number of unknown parameters. The models provide important inferential results for each cut-point and method of measurement, including the summary prognostic effect, the between-study variance and a 95% prediction interval for the prognostic effect in new populations. Two applications are presented. The first reveals that, in a multivariate meta-analysis using published results, the Apgar score is prognostic of neonatal mortality but effect sizes are smaller at most cut-points than previously thought. In the second, a multivariate meta-analysis of two methods of measurement provides weak evidence that microvessel density is prognostic of mortality in lung cancer, even when individual participant data are available so that a continuous prognostic trend is examined (rather than cut-points). PMID:25924725

  9. Automatic differentiation for design sensitivity analysis of structural systems using multiple processors

    NASA Technical Reports Server (NTRS)

    Nguyen, Duc T.; Storaasli, Olaf O.; Qin, Jiangning; Qamar, Ramzi

    1994-01-01

    An automatic differentiation tool (ADIFOR) is incorporated into a finite element based structural analysis program for shape and non-shape design sensitivity analysis of structural systems. The entire analysis and sensitivity procedures are parallelized and vectorized for high performance computation. Small scale examples to verify the accuracy of the proposed program and a medium scale example to demonstrate the parallel vector performance on multiple CRAY C90 processors are included.

  10. 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

  11. Automatic photolaryngoscope for vibration analysis of vocal cords

    NASA Astrophysics Data System (ADS)

    Igielski, J.; Kujawinska, Malgorzata; Pawlowski, Z.

    1995-05-01

    The vibration analysis of vocal cords gives information about the functioning of speech organs as well as about some illness within human organism. The analysis is usually performed by electroglottography or stroboscopic methods. The authors present the new opto-mechanical and electronic system of photolaryngoscope. The instrument uses laser diode light for illumination of vocal cords. The light reflected from the vibrating cord surface is detected electronically and analyzed. The further mathematical analysis of glottograms by autoregression method with covariance or by periodogram method is performed in order to define new criteria for medical interpretation of results.

  12. Alleviating Search Uncertainty through Concept Associations: Automatic Indexing, Co-Occurrence Analysis, and Parallel Computing.

    ERIC Educational Resources Information Center

    Chen, Hsinchun; Martinez, Joanne; Kirchhoff, Amy; Ng, Tobun D.; Schatz, Bruce R.

    1998-01-01

    Grounded on object filtering, automatic indexing, and co-occurrence analysis, an experiment was performed using a parallel supercomputer to analyze over 400,000 abstracts in an INSPEC computer engineering collection. A user evaluation revealed that system-generated thesauri were better than the human-generated INSPEC subject thesaurus in concept…

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

    Davatzikos, Christos

    2016-10-01

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

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

    PubMed

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

    2015-08-01

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

  16. Four Simultaneous Component Models for the Analysis of Multivariate Time Series from More Than One Subject To Model Intraindividual and Interindividual Differences.

    ERIC Educational Resources Information Center

    Timmerman, Marieke E.; Kiers, Henk A. L.

    2003-01-01

    Discusses a class of four simultaneous component models for the explanatory analysis of multivariate time series collected from more than one subject simultaneously. Shows how the models can be ordered hierarchically and illustrates their use through an empirical example. (SLD)

  17. Automatic Method of Supernovae Classification by Modeling Human Procedure of Spectrum Analysis

    NASA Astrophysics Data System (ADS)

    Módolo, Marcelo; Rosa, Reinaldo; Guimaraes, Lamartine N. F.

    2016-07-01

    The classification of a recently discovered supernova must be done as quickly as possible in order to define what information will be captured and analyzed in the following days. This classification is not trivial and only a few experts astronomers are able to perform it. This paper proposes an automatic method that models the human procedure of classification. It uses Multilayer Perceptron Neural Networks to analyze the supernovae spectra. Experiments were performed using different pre-processing and multiple neural network configurations to identify the classic types of supernovae. Significant results were obtained indicating the viability of using this method in places that have no specialist or that require an automatic analysis.

  18. System for Automatic Detection and Analysis of Targets in FMICW Radar Signal

    NASA Astrophysics Data System (ADS)

    Rejfek, Luboš; Mošna, Zbyšek; Urbář, Jaroslav; Koucká Knížová, Petra

    2016-01-01

    This paper presents the automatic system for the processing of the signals from the frequency modulated interrupted continuous wave (FMICW) radar and describes methods for the primary signal processing. Further, we present methods for the detection of the targets in strong noise. These methods are tested both on the real and simulated signals. The real signals were measured using the developed at the IAP CAS experimental prototype of FMICW radar with operational frequency 35.4 GHz. The measurement campaign took place at the TU Delft, the Netherlands. The obtained results were used for development of the system for the automatic detection and analysis of the targets measured by the FMICW radar.

  19. Phase analysis in single-chain variable fragment production by recombinant Pichia pastoris based on proteomics combined with multivariate statistics.

    PubMed

    Fujiki, Yuya; Kumada, Yoichi; Kishimoto, Michimasa

    2015-08-01

    The proteomics technique, which consists of two-dimensional gel electrophoresis (2-DE), peptide mass fingerprinting (PMF), gel image analysis, and multivariate statistics, was applied to the phase analysis of a fed-batch culture for the production of a single-chain variable fragment (scFv) of an anti-C-reactive protein (CRP) antibody by Pichia pastoris. The time courses of the fed-batch culture were separated into three distinct phases: the growth phase of the batch process, the growth phase of the fed-batch process, and the production phase of the fed-batch process. Multivariate statistical analysis using 2-DE gel image analysis data clearly showed the change in the culture phase and provided information concerning the protein expression, which suggested a metabolic change related to cell growth and production during the fed-batch culture. Furthermore, specific proteins, such as alcohol oxidase, which is strongly related to scFv expression, and proteinase A, which could biodegrade scFv in the latter phases of production, were identified via the PMF method. The proteomics technique provided valuable information about the effect of the methanol concentration on scFv production. PMID:25636980

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

    PubMed

    Arsuaga, J L; Carretero, J M

    1994-02-01

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

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

    PubMed Central

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

    2014-01-01

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

  2. Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis.

    PubMed

    Zijdenbos, Alex P; Forghani, Reza; Evans, Alan C

    2002-10-01

    The quantitative analysis of magnetic resonance imaging (MRI) data has become increasingly important in both research and clinical studies aiming at human brain development, function, and pathology. Inevitably, the role of quantitative image analysis in the evaluation of drug therapy will increase, driven in part by requirements imposed by regulatory agencies. However, the prohibitive length of time involved and the significant intraand inter-rater variability of the measurements obtained from manual analysis of large MRI databases represent major obstacles to the wider application of quantitative MRI analysis. We have developed a fully automatic "pipeline" image analysis framework and have successfully applied it to a number of large-scale, multicenter studies (more than 1,000 MRI scans). This pipeline system is based on robust image processing algorithms, executed in a parallel, distributed fashion. This paper describes the application of this system to the automatic quantification of multiple sclerosis lesion load in MRI, in the context of a phase III clinical trial. The pipeline results were evaluated through an extensive validation study, revealing that the obtained lesion measurements are statistically indistinguishable from those obtained by trained human observers. Given that intra- and inter-rater measurement variability is eliminated by automatic analysis, this system enhances the ability to detect small treatment effects not readily detectable through conventional analysis techniques. While useful for clinical trial analysis in multiple sclerosis, this system holds widespread potential for applications in other neurological disorders, as well as for the study of neurobiology in general. PMID:12585710

  3. CAD system for automatic analysis of CT perfusion maps

    NASA Astrophysics Data System (ADS)

    Hachaj, T.; Ogiela, M. R.

    2011-03-01

    In this article, authors present novel algorithms developed for the computer-assisted diagnosis (CAD) system for analysis of dynamic brain perfusion, computer tomography (CT) maps, cerebral blood flow (CBF), and cerebral blood volume (CBV). Those methods perform both quantitative analysis [detection and measurement and description with brain anatomy atlas (AA) of potential asymmetries/lesions] and qualitative analysis (semantic interpretation of visualized symptoms). The semantic interpretation (decision about type of lesion: ischemic/hemorrhagic, is the brain tissue at risk of infraction or not) of visualized symptoms is done by, so-called, cognitive inference processes allowing for reasoning on character of pathological regions based on specialist image knowledge. The whole system is implemented in.NET platform (C# programming language) and can be used on any standard PC computer with.NET framework installed.

  4. Differentiation of betamethasone and dexamethasone using liquid chromatography/positive electrospray tandem mass spectrometry and multivariate statistical analysis.

    PubMed

    Antignac, Jean-Philippe; Le Bizec, Bruno; Monteau, Fabrice; Andre, François

    2002-01-01

    Betamethasone and dexamethasone are two corticosteroids differing in the stereoisomery of their C-16 methyl group. These two compounds are imperfectly separated by reversed-phase liquid chromatography and their mass spectra are very similar, leading to a difficult unambiguous identification according to European criteria. A method is proposed for differentiating betamethasone and dexamethasone using liquid chromatography/tandem mass spectrometry and multivariate statistical analysis. Multiple analysis of variance was used for the justification and the selection of diagnostic ions. Principal component analysis permitted the suitability of the approach to be tested on a large number of samples. Discriminant factorial analysis was finally performed to build a decisional model based on the six most significant ions. This novel utilization of mass spectrometric data appeared efficient for the unambiguous identification of the target analytes in urine samples. PMID:11813313

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

    NASA Astrophysics Data System (ADS)

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

    2006-03-01

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

  6. Multivariate analysis of the volumetric capnograph for PaCO2 estimation

    PubMed Central

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

    2015-01-01

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

  7. Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing.

    PubMed

    Van Hertem, T; Bahr, C; Schlageter Tello, A; Viazzi, S; Steensels, M; Romanini, C E B; Lokhorst, C; Maltz, E; Halachmi, I; Berckmans, D

    2016-09-01

    The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ2, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific

  8. Rapid Automatized Naming and Reading Performance: A Meta-Analysis

    ERIC Educational Resources Information Center

    Araújo, Susana; Reis, Alexandra; Petersson, Karl Magnus; Faísca, Luís

    2015-01-01

    Evidence that rapid naming skill is associated with reading ability has become increasingly prevalent in recent years. However, there is considerable variation in the literature concerning the magnitude of this relationship. The objective of the present study was to provide a comprehensive analysis of the evidence on the relationship between rapid…

  9. The symbolic computation and automatic analysis of trajectories

    NASA Technical Reports Server (NTRS)

    Grossman, Robert

    1991-01-01

    Research was generally done on computation of trajectories of dynamical systems, especially control systems. Algorithms were further developed for rewriting expressions involving differential operators. The differential operators involved arise in the local analysis of nonlinear control systems. An initial design was completed of the system architecture for software to analyze nonlinear control systems using data base computing.

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

    PubMed

    Wallace, Jack; Champagne, Pascale; Hall, Geof

    2016-06-01

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

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

    PubMed

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

    2009-12-01

    Despite the indisputable significance of identification of modern analogs for Paleoecology research, relatively few studies attempted to integrate modern and fossil samples on paleoenvironmental reconstructions. In Palynology, this general pattern is not different from other fields of Paleoecology. This study demonstrates the practical application of modern pollen deposition data on paleoenvironmental reconstructions based on fossil pollen by using multivariate analysis. The main goal of this study was to use Detrended Correspondence Analysis (DCA) to compare pollen samples from two sediment cores collected at Lagoa Salgada, a coastal lagoon located at northeastern Rio de Janeiro State. Furthermore, modern surface samples were also statistically compared with samples from both cores, providing new paleoecological insights. DCA demonstrated that samples from both cores are more similar than previously expected, and that a strong pattern, related to a paleoenvironmental event, is present within the fossil data, clearly identifying in the scatter plot samples that represent pre- and post-environmental change. Additionally, it became apparent that modern vegetation and environmental conditions were established in this region 2500 years before present (BP). Multivariate Analysis allowed a more reliable integration of modern and fossil pollen data, proving to be a powerful tool in Paleoecology studies that should be employed more often on paleoclimate and paleoenvironmental reconstructions. PMID:19893900

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

    PubMed

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

    1990-06-01

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

  13. Novel Strategy for Non-Targeted Isotope-Assisted Metabolomics by Means of Metabolic Turnover and Multivariate Analysis

    PubMed Central

    Nakayama, Yasumune; Tamada, Yoshihiro; Tsugawa, Hiroshi; Bamba, Takeshi; Fukusaki, Eiichiro

    2014-01-01

    Isotope-labeling is a useful technique for understanding cellular metabolism. Recent advances in metabolomics have extended the capability of isotope-assisted studies to reveal global metabolism. For instance, isotope-assisted metabolomics technology has enabled the mapping of a global metabolic network, estimation of flux at branch points of metabolic pathways, and assignment of elemental formulas to unknown metabolites. Furthermore, some data processing tools have been developed to apply these techniques to a non-targeted approach, which plays an important role in revealing unknown or unexpected metabolism. However, data collection and integration strategies for non-targeted isotope-assisted metabolomics have not been established. Therefore, a systematic approach is proposed to elucidate metabolic dynamics without targeting pathways by means of time-resolved isotope tracking, i.e., “metabolic turnover analysis”, as well as multivariate analysis. We applied this approach to study the metabolic dynamics in amino acid perturbation of Saccharomyces cerevisiae. In metabolic turnover analysis, 69 peaks including 35 unidentified peaks were investigated. Multivariate analysis of metabolic turnover successfully detected a pathway known to be inhibited by amino acid perturbation. In addition, our strategy enabled identification of unknown peaks putatively related to the perturbation. PMID:25257997

  14. 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. PMID:21227435

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

    PubMed

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

    2016-05-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1974-01-01

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

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

    PubMed

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

    2012-05-15

    Over the past century farming activity has intensified worldwide, characterized by an increasing dependence on external inputs and on land conversion. Although the intensification of agriculture has increased productivity, the sustainability of agroecosystems has also been compromised. The objective of this study is to build multivariate relationships between farm structural characteristics and farm performance to highlight the relative costs and benefits of four main farming systems in Central Italy: organic, conventional, mixed and non-mixed farms. Results show that the relationship between cropping diversity and agroecological sustainability is associated to a mixed versus non-mixed farm management dichotomy, not to organic or conventional farming practices. The presence of livestock appears to have played an important role as an economic lever for diversifying the farm cropping system. PMID:22265812

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

    NASA Technical Reports Server (NTRS)

    Kriegler, F. J.

    1974-01-01

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

  19. Reverse inference of memory retrieval processes underlying metacognitive monitoring of learning using multivariate pattern analysis.

    PubMed

    Stiers, Peter; Falbo, Luciana; Goulas, Alexandros; van Gog, Tamara; de Bruin, Anique

    2016-05-15

    Monitoring of learning is only accurate at some time after learning. It is thought that immediate monitoring is based on working memory, whereas later monitoring requires re-activation of stored items, yielding accurate judgements. Such interpretations are difficult to test because they require reverse inference, which presupposes specificity of brain activity for the hidden cognitive processes. We investigated whether multivariate pattern classification can provide this specificity. We used a word recall task to create single trial examples of immediate and long term retrieval and trained a learning algorithm to discriminate them. Next, participants performed a similar task involving monitoring instead of recall. The recall-trained classifier recognized the retrieval patterns underlying immediate and long term monitoring and classified delayed monitoring examples as long-term retrieval. This result demonstrates the feasibility of decoding cognitive processes, instead of their content. PMID:26883066

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

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

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

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

    SciTech Connect

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

    1980-06-01

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

  2. Multivariable flight control synthesis and literal robustness analysis for an aeroelastic vehicle

    NASA Technical Reports Server (NTRS)

    Schmidt, David K.; Newman, Brett

    1990-01-01

    An integrated flight/aeroelastic control law is developed analytically for a hypothetical large supersonic transport aircraft in which the first aeroelastic mode frequency of the fuselage (6 rad/sec) is near the short-period mode (2 rad/sec). The approach employed is based on a linear-quadratic-regulator (LQR) formulation (yielding model-following state-feedback gains), followed by asymptotic loop-transfer recovery of LQR robustness (to produce an output-feedback control law). The derivation is outlined, and numerical results comparing the performance and multivariate stability robustness of the present controller with those of a classical controller are presented in graphs. The two controllers are shown to have similar characteristics, even with respect to the sources of limitations on robustness.

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

    NASA Astrophysics Data System (ADS)

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

    2010-06-01

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

  4. Automatic forensic analysis of automotive paints using optical microscopy.

    PubMed

    Thoonen, Guy; Nys, Bart; Vander Haeghen, Yves; De Roy, Gilbert; Scheunders, Paul

    2016-02-01

    The timely identification of vehicles involved in an accident, such as a hit-and-run situation, bears great importance in forensics. To this end, procedures have been defined for analyzing car paint samples that combine techniques such as visual analysis and Fourier transform infrared spectroscopy. This work proposes a new methodology in order to automate the visual analysis using image retrieval. Specifically, color and texture information is extracted from a microscopic image of a recovered paint sample, and this information is then compared with the same features for a database of paint types, resulting in a shortlist of candidate paints. In order to demonstrate the operation of the methodology, a test database has been set up and two retrieval experiments have been performed. The first experiment quantifies the performance of the procedure for retrieving exact matches, while the second experiment emulates the real-life situation of paint samples that experience changes in color and texture over time. PMID:26774250

  5. Automatic data processing and analysis system for monitoring region around a planned nuclear power plant

    NASA Astrophysics Data System (ADS)

    Tiira, Timo; Kaisko, Outi; Kortström, Jari; Vuorinen, Tommi; Uski, Marja; Korja, Annakaisa

    2015-04-01

    The site of a new planned nuclear power plant is located in Pyhäjoki, eastern coast of the Bay of Bothnia. The area is characterized by low-active intraplate seismicity, with earthquake magnitudes rarely exceeding 4.0. IAEA guidelines state that when a nuclear power plant site is evaluated a network of sensitive seismographs having a recording capability for micro-earthquakes should be installed to acquire more detailed information on potential seismic sources. The operation period of the network should be long enough to obtain a comprehensive earthquake catalogue for seismotectonic interpretation. A near optimal configuration of ten seismograph stations will be installed around the site. A central station, including 3-C high-frequency and strong motion seismographs, is located in the site area. In addition, the network comprises nine high-frequency 3-C stations within a distance of 50 km from the central station. The network is dense enough to fulfil the requirements of azimuthal coverage better than 180o and automatic event location capability down to ~ ML -0.1 within a radius of 25 km from the site. Automatic processing and analysis of the planned seismic network is presented. Following the IAEA guidelines, real-time monitoring of the site area is integrated with the automatic detection and location process operated by the Institute of Seismology, University of Helsinki. In addition interactive data analysis is needed. At the end of year 2013 5 stations have been installed. The automatic analysis utilizes also 7 near by stations of national seismic networks of Finland and Sweden. During this preliminary phase several small earthquakes have been detected. The detection capability and location accuracy of the automatic analysis is estimated using chemical explosions at 15 known sites.

  6. A framework for automatic heart sound analysis without segmentation

    PubMed Central

    2011-01-01

    Background A new framework for heart sound analysis is proposed. One of the most difficult processes in heart sound analysis is segmentation, due to interference form murmurs. Method Equal number of cardiac cycles were extracted from heart sounds with different heart rates using information from envelopes of autocorrelation functions without the need to label individual fundamental heart sounds (FHS). The complete method consists of envelope detection, calculation of cardiac cycle lengths using auto-correlation of envelope signals, features extraction using discrete wavelet transform, principal component analysis, and classification using neural network bagging predictors. Result The proposed method was tested on a set of heart sounds obtained from several on-line databases and recorded with an electronic stethoscope. Geometric mean was used as performance index. Average classification performance using ten-fold cross-validation was 0.92 for noise free case, 0.90 under white noise with 10 dB signal-to-noise ratio (SNR), and 0.90 under impulse noise up to 0.3 s duration. Conclusion The proposed method showed promising results and high noise robustness to a wide range of heart sounds. However, more tests are needed to address any bias that may have been introduced by different sources of heart sounds in the current training set, and to concretely validate the method. Further work include building a new training set recorded from actual patients, then further evaluate the method based on this new training set. PMID:21303558

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

    PubMed Central

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

    2014-01-01

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

  8. Theory and algorithms of an efficient fringe analysis technology for automatic measurement applications.

    PubMed

    Juarez-Salazar, Rigoberto; Guerrero-Sanchez, Fermin; Robledo-Sanchez, Carlos

    2015-06-10

    Some advances in fringe analysis technology for phase computing are presented. A full scheme for phase evaluation, applicable to automatic applications, is proposed. The proposal consists of: a fringe-pattern normalization method, Fourier fringe-normalized analysis, generalized phase-shifting processing for inhomogeneous nonlinear phase shifts and spatiotemporal visibility, and a phase-unwrapping method by a rounding-least-squares approach. The theoretical principles of each algorithm are given. Numerical examples and an experimental evaluation are presented. PMID:26192836

  9. Development of automatic movement analysis system for a small laboratory animal using image processing

    NASA Astrophysics Data System (ADS)

    Nagatomo, Satoshi; Kawasue, Kikuhito; Koshimoto, Chihiro

    2013-03-01

    Activity analysis in a small laboratory animal is an effective procedure for various bioscience fields. The simplest way to obtain animal activity data is just observation and recording manually, even though this is labor intensive and rather subjective. In order to analyze animal movement automatically and objectivity, expensive equipment is usually needed. In the present study, we develop animal activity analysis system by means of a template matching method with video recorded movements in laboratory animal at a low cost.

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

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

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

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

    PubMed

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

    2016-01-15

    Tannins are polyphenolic compounds of complex structures formed by secondary metabolism in several plants. These polyphenolic compounds have different applications, such as drugs, anti-corrosion agents, flocculants, and tanning agents. This study analyses six different type of polyphenolic extracts by Fourier transform infrared spectroscopy (FTIR) combined with multivariate analysis. Through both principal component analysis (PCA) and hierarchical cluster analysis (HCA), we observed well-defined separation between condensed (quebracho and black wattle) and hydrolysable (valonea, chestnut, myrobalan, and tara) tannins. For hydrolysable tannins, it was also possible to observe the formation of two different subgroups between samples of chestnut and valonea and between samples of tara and myrobalan. Among all samples analysed, the chestnut and valonea showed the greatest similarity, indicating that these extracts contain equivalent chemical compositions and structure and, therefore, similar properties. PMID:26296253

  12. Biosignal Analysis to Assess Mental Stress in Automatic Driving of Trucks: Palmar Perspiration and Masseter Electromyography

    PubMed Central

    Zheng, Rencheng; Yamabe, Shigeyuki; Nakano, Kimihiko; Suda, Yoshihiro

    2015-01-01

    Nowadays insight into human-machine interaction is a critical topic with the large-scale development of intelligent vehicles. Biosignal analysis can provide a deeper understanding of driver behaviors that may indicate rationally practical use of the automatic technology. Therefore, this study concentrates on biosignal analysis to quantitatively evaluate mental stress of drivers during automatic driving of trucks, with vehicles set at a closed gap distance apart to reduce air resistance to save energy consumption. By application of two wearable sensor systems, a continuous measurement was realized for palmar perspiration and masseter electromyography, and a biosignal processing method was proposed to assess mental stress levels. In a driving simulator experiment, ten participants completed automatic driving with 4, 8, and 12 m gap distances from the preceding vehicle, and manual driving with about 25 m gap distance as a reference. It was found that mental stress significantly increased when the gap distances decreased, and an abrupt increase in mental stress of drivers was also observed accompanying a sudden change of the gap distance during automatic driving, which corresponded to significantly higher ride discomfort according to subjective reports. PMID:25738768

  13. Biosignal analysis to assess mental stress in automatic driving of trucks: palmar perspiration and masseter electromyography.

    PubMed

    Zheng, Rencheng; Yamabe, Shigeyuki; Nakano, Kimihiko; Suda, Yoshihiro

    2015-01-01

    Nowadays insight into human-machine interaction is a critical topic with the large-scale development of intelligent vehicles. Biosignal analysis can provide a deeper understanding of driver behaviors that may indicate rationally practical use of the automatic technology. Therefore, this study concentrates on biosignal analysis to quantitatively evaluate mental stress of drivers during automatic driving of trucks, with vehicles set at a closed gap distance apart to reduce air resistance to save energy consumption. By application of two wearable sensor systems, a continuous measurement was realized for palmar perspiration and masseter electromyography, and a biosignal processing method was proposed to assess mental stress levels. In a driving simulator experiment, ten participants completed automatic driving with 4, 8, and 12 m gap distances from the preceding vehicle, and manual driving with about 25 m gap distance as a reference. It was found that mental stress significantly increased when the gap distances decreased, and an abrupt increase in mental stress of drivers was also observed accompanying a sudden change of the gap distance during automatic driving, which corresponded to significantly higher ride discomfort according to subjective reports. PMID:25738768

  14. Toward automatic computer aided dental X-ray analysis using level set method.

    PubMed

    Li, Shuo; Fevens, Thomas; Krzyzak, Adam; Jin, Chao; Li, Song

    2005-01-01

    A Computer Aided Dental X-rays Analysis (CADXA) framework is proposed to semi-automatically detect areas of bone loss and root decay in digital dental X-rays. In this framework, first, a new proposed competitive coupled level set method is proposed to segment the image into three pathologically meaningful regions using two coupled level set functions. Tailored for the dental clinical environment, the segmentation stage uses a trained support vector machine (SVM) classifier to provide initial contours. Then, based on the segmentation results, an analysis scheme is applied. First, the scheme builds an uncertainty map from which those areas with bone loss will be automatically detected. Secondly, the scheme employs a method based on the SVM and the average intensity profile to isolate the teeth and detect root decay. Experimental results show that our proposed framework is able to automatically detect the areas of bone loss and, when given the orientation of the teeth, it is able to automatically detect the root decay with a seriousness level marked for diagnosis. PMID:16685904

  15. Social Risk and Depression: Evidence from Manual and Automatic Facial Expression Analysis

    PubMed Central

    Girard, Jeffrey M.; Cohn, Jeffrey F.; Mahoor, Mohammad H.; Mavadati, Seyedmohammad; Rosenwald, Dean P.

    2014-01-01

    Investigated the relationship between change over time in severity of depression symptoms and facial expression. Depressed participants were followed over the course of treatment and video recorded during a series of clinical interviews. Facial expressions were analyzed from the video using both manual and automatic systems. Automatic and manual coding were highly consistent for FACS action units, and showed similar effects for change over time in depression severity. For both systems, when symptom severity was high, participants made more facial expressions associated with contempt, smiled less, and those smiles that occurred were more likely to be accompanied by facial actions associated with contempt. These results are consistent with the “social risk hypothesis” of depression. According to this hypothesis, when symptoms are severe, depressed participants withdraw from other people in order to protect themselves from anticipated rejection, scorn, and social exclusion. As their symptoms fade, participants send more signals indicating a willingness to affiliate. The finding that automatic facial expression analysis was both consistent with manual coding and produced the same pattern of depression effects suggests that automatic facial expression analysis may be ready for use in behavioral and clinical science. PMID:24598859

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

    PubMed

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

    2015-01-01

    Fermentanomics is an emerging field of research and involves understanding the underlying controlled process variables and their effect on process yield and product quality. Although major advancements have occurred in process analytics over the past two decades, accurate real-time measurement of significant quality attributes for a biotech product during production culture is still not feasible. Researchers have used an amalgam of process models and analytical measurements for monitoring and process control during production. This article focuses on using multivariate data analysis as a tool for monitoring the internal bioreactor dynamics, the metabolic state of the cell, and interactions among them during culture. Quality attributes of the monoclonal antibody product that were monitored include glycosylation profile of the final product along with process attributes, such as viable cell density and level of antibody expression. These were related to process variables, raw materials components of the chemically defined hybridoma media, concentration of metabolites formed during the course of the culture, aeration-related parameters, and supplemented raw materials such as glucose, methionine, threonine, tryptophan, and tyrosine. This article demonstrates the utility of multivariate data analysis for correlating the product quality attributes (especially glycosylation) to process variables and raw materials (especially amino acid supplements in cell culture media). The proposed approach can be applied for process optimization to increase product expression, improve consistency of product quality, and target the desired quality attribute profile. PMID:26280800

  17. Multivariate analysis of PRISMA optimized TLC image for predicting antioxidant activity and identification of contributing compounds from Pereskia bleo.

    PubMed

    Sharif, K M; Rahman, M M; Azmir, J; Khatib, A; Sabina, E; Shamsudin, S H; Zaidul, I S M

    2015-12-01

    Multivariate analysis of thin-layer chromatography (TLC) images was modeled to predict antioxidant activity of Pereskia bleo leaves and to identify the contributing compounds of the activity. TLC was developed in optimized mobile phase using the 'PRISMA' optimization method and the image was then converted to wavelet signals and imported for multivariate analysis. An orthogonal partial least square (OPLS) model was developed consisting of a wavelet-converted TLC image and 2,2-diphynyl-picrylhydrazyl free radical scavenging activity of 24 different preparations of P. bleo as the x- and y-variables, respectively. The quality of the constructed OPLS model (1 + 1 + 0) with one predictive and one orthogonal component was evaluated by internal and external validity tests. The validated model was then used to identify the contributing spot from the TLC plate that was then analyzed by GC-MS after trimethylsilyl derivatization. Glycerol and amine compounds were mainly found to contribute to the antioxidant activity of the sample. An alternative method to predict the antioxidant activity of a new sample of P. bleo leaves has been developed. PMID:26033701

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

    SciTech Connect

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

    1986-01-01

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

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

    PubMed

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

    2014-08-01

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

  20. Automatic Fatigue Detection of Drivers through Yawning Analysis

    NASA Astrophysics Data System (ADS)

    Azim, Tayyaba; Jaffar, M. Arfan; Ramzan, M.; Mirza, Anwar M.

    This paper presents a non-intrusive fatigue detection system based on the video analysis of drivers. The focus of the paper is on how to detect yawning which is an important cue for determining driver's fatigue. Initially, the face is located through Viola-Jones face detection method in a video frame. Then, a mouth window is extracted from the face region, in which lips are searched through spatial fuzzy c-means (s-FCM) clustering. The degree of mouth openness is extracted on the basis of mouth features, to determine driver's yawning state. If the yawning state of the driver persists for several consecutive frames, the system concludes that the driver is non-vigilant due to fatigue and is thus warned through an alarm. The system reinitializes when occlusion or misdetection occurs. Experiments were carried out using real data, recorded in day and night lighting conditions, and with users belonging to different race and gender.