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
Multivariate Analysis in Metabolomics
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
Method of multivariate spectral analysis
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).
Multivariate data analysis of proteome data.
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
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.
Multivariate Longitudinal Analysis with Bivariate Correlation Test.
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
Multivariate Longitudinal Analysis with Bivariate Correlation Test
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
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.
Biological Sequence Analysis with Multivariate String Kernels.
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
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
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
Multivariate multiscale entropy for brain consciousness analysis.
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
Classical least squares multivariate spectral analysis
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.
Hybrid least squares multivariate spectral analysis methods
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.
Hybrid least squares multivariate spectral analysis methods
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.
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.
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.
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…
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.
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.
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.
Multivariate Analysis of Genotype-Phenotype Association.
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
Augmented classical least squares multivariate spectral analysis
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.
Augmented Classical Least Squares Multivariate Spectral Analysis
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.
Augmented Classical Least Squares Multivariate Spectral Analysis
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.
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.
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
Apparatus and system for multivariate spectral analysis
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.
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).
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
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.
Multivariate analysis of environmental data for two hydrographic basins
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.
Regional dissociated heterochrony in multivariate analysis.
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
Heavy flavor identification using multivariate analysis at H1
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.
Search for the top quark using multivariate analysis techniques
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.
The statistical analysis of multivariate serological frequency data.
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
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.
Multivariate concentration determination using principal component regression with residual analysis
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
Multivariate analysis of TLD orientation effects
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.
Are propensity scores really superior to standard multivariable analysis?
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
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.
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.
Multivariate calibration applied to the quantitative analysis of infrared spectra
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.
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
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…
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.
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…
Multivariate geometry as an approach to algal community analysis
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.
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.
Multivariate analysis of pathophysiological factors in reflux oesophagitis.
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
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)
Multivariate analysis of prognostic factors in early stage Hodgkin's disease
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.
Multivariate meta-analysis using individual participant data.
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
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.
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.
Dating violence, social learning theory, and gender: a multivariate analysis.
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
Multivariate statistical analysis of atom probe tomography data.
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
Multivariate statistical analysis of atom probe tomography data
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.
Comparing G: multivariate analysis of genetic variation in multiple populations.
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
Automatic Prosodic Analysis to Identify Mild Dementia.
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
Automatic Prosodic Analysis to Identify Mild Dementia
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
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.
Asymmetric measures of association, closed data, and multivariate analysis
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.
Automatic analysis and classification of surface electromyography.
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
Advanced multivariate analysis to assess remediation of hydrocarbons in soils.
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
Decoding neural representational spaces using multivariate pattern analysis.
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
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
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.
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.
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.
Automatic analysis of computation in biochemical reactions.
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
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.
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.
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.
Semi-automatic analysis of fire debris
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
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.
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.
Toxicological evaluation of complex mixtures: fingerprinting and multivariate analysis.
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
Monitoring Quality of Biotherapeutic Products Using Multivariate Data Analysis.
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
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.
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.
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.
High dimensional data analysis using multivariate generalized spatial quantiles
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
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.
Cocaine dependence and thalamic functional connectivity: a multivariate pattern analysis.
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
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.
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…
Classification of Malaysia aromatic rice using multivariate statistical analysis
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.
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.
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.
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.
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.
Automatic variance analysis of multistage care pathways.
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
ADS-Demo Fuel Rod Performance: Multivariate Statistical Analysis
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)
A multivariate analysis of arctic climate in GCMs
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.
Adaptive multiscale entropy analysis of multivariate neural data.
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
Prognostic factors of adult metastatic renal carcinoma: a multivariate analysis.
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
Atmospheric conditions, lunar phases, and childbirth: a multivariate analysis.
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
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.
Multivariate multiscale entropy: a tool for complexity analysis of multichannel data.
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
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.
Multivariate Analysis for Animal Selection in Experimental Research
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
Multivariate meta-analysis of mixed outcomes: a Bayesian approach.
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
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
Correlative and multivariate analysis of increased radon concentration in underground laboratory.
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
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.
Turnover intention in new graduate nurses: a multivariate analysis
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
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…
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),…
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…
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".
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
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.
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…
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…
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
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.
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.
Multivariate pattern analysis of FMRI in breast cancer survivors and healthy women.
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
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.
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.
Systematic wavelength selection for improved multivariate spectral analysis
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.
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
Multivariate longitudinal data analysis with mixed effects hidden Markov models.
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
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
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.
Suicidal ideation among Canadian youth: a multivariate analysis.
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
A regularized multivariate regression approach for eQTL analysis
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
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).
Automatic Analysis of Critical Incident Reports: Requirements and Use Cases.
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
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
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.
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.
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…
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…
Profiling School Shooters: Automatic Text-Based Analysis
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
Profiling School Shooters: Automatic Text-Based Analysis.
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
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
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…
HRMAS-NMR spectroscopy and multivariate analysis meat characterisation.
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
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.
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…
Seasonal foods of coyotes in southeastern Idaho: a multivariate analysis
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.
Effectiveness of an Automatic Tracking Software in Underwater Motion Analysis
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
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.
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.
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.
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.
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…
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
Multivariate analysis of Buteo nest site selection in Washington
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.
Characterization of Nuclear Fuel using Multivariate Statistical Analysis
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.
Multivariable Discriminant Analysis for the Differential Diagnosis of Microcytic Anemia
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
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.
The analysis of multivariate longitudinal data: A review
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
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.
Multivariate analysis of parameters related to lake acidification in Quebec
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.
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.
Quantitative multivariate analysis of dynamic multicellular morphogenic trajectories.
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
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
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
Multivariate analysis of regional differentials of nuptiality in Bangladesh.
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
Multivariate analysis relating oil shale geochemical properties to NMR relaxometry
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.
Multivariate analysis of somatosensory evoked potential parameters in normal adults.
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
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
Integrated Analysis of Tropical Trees Growth: A Multivariate Approach
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
Multivariate analysis of noise in genetic regulatory networks.
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
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
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
A multivariate analysis approach for the Imaging Atmospheric Cerenkov Telescopes System H.E.S.S
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.
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...
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…
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)
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…
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…
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…
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…
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…
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.
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
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.
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.
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
Development of an automatic identification algorithm for antibiogram analysis.
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
Automatic Generation of User Material Subroutines for Biomechanical Growth Analysis
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
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).
Facilitator control as automatic behavior: A verbal behavior analysis
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
An integrated spatial signature analysis and automatic defect classification system
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.
Multivariate analysis of intracranial pressure (ICP) signal using principal component analysis.
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
Analysis of worldwide earthquake mortality using multivariate demographic and seismic data.
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
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.
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.
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
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
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.
Automatic 3-D grayscale volume matching and shape analysis.
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
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.
Chemical structure of wood charcoal by infrared spectroscopy and multivariate analysis.
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
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.
[Characterization of flowability of pharmaceutical powders based on multivariate analysis method].
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
Feature++: Automatic Feature Construction for Clinical Data Analysis.
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
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.
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.
Spectral compression algorithms for the analysis of very large multivariate images
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.
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.
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.
Rapid automatic keyword extraction for information retrieval and analysis
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.
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
Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis
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
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.
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
Craniometrical estimation of the native Japanese Mishima cattle, using multivariate analysis.
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
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.
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.
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
Breast Density Analysis Using an Automatic Density Segmentation Algorithm.
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
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.
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.
Spatial compression algorithm for the analysis of very large multivariate images
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.
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.
Severe pneumonia in the elderly: a multivariate analysis of risk factors.
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
Severe pneumonia in the elderly: a multivariate analysis of risk factors
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
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.
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.
Automatic Visual Tracking and Social Behaviour Analysis with Multiple Mice
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
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
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
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
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
DWT features performance analysis for automatic speech recognition of Urdu.
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
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
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
Risk factors for baclofen pump infection in children: a multivariate analysis.
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
Multivariate Classification of Original and Fake Perfumes by Ion Analysis and Ethanol Content.
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
The automaticity of emotional Stroop: a meta-analysis.
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
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.
A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis
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
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 ...
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.
Multivariate genetic analysis of brain structure in an extended twin design.
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
PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data
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
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
Application of multivariate statistical methods to the analysis of ancient Turkish potsherds
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.
Applications of multivariate analysis to precious metal exploration in the western US
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.
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
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
Detection of counterfeit Viagra® by Raman microspectroscopy imaging and multivariate analysis.
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
Automatic Beam Path Analysis of Laser Wakefield Particle Acceleration Data
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.
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
A versatile multivariate image analysis pipeline reveals features of Xenopus extract spindles.
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