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  1. Handbook of univariate and multivariate data analysis with IBM SPSS

    Ho, Robert

    2013-01-01

    Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics and has been updated with the SPSS statistical package for Windows.New to the Second EditionThree new chapters on multiple discriminant analysis, logistic regression, and canonical correlationNew section on how to deal with missing dataCoverage of te

  2. Univariate and multivariate analysis on processing tomato quality under different mulches

    Carmen Moreno

    2014-04-01

    Full Text Available The use of eco-friendly mulch materials as alternatives to the standard polyethylene (PE has become increasingly prevalent worldwide. Consequently, a comparison of mulch materials from different origins is necessary to evaluate their feasibility. Several researchers have compared the effects of mulch materials on each crop variable through univariate analysis (ANOVA. However, it is important to focus on the effect of these materials on fruit quality, because this factor decisively influences the acceptance of the final product by consumers and the industrial sector. This study aimed to analyze the information supplied by a randomized complete block experiment combined over two seasons, a principal component analysis (PCA and a cluster analysis (CA when studying the effects of mulch materials on the quality of processing tomato (Lycopersicon esculentum Mill.. The study focused on the variability in the quality measurements and on the determination of mulch materials with a similar response to them. A comparison of the results from both types of analysis yielded complementary information. ANOVA showed the similarity of certain materials. However, considering the totality of the variables analyzed, the final interpretation was slightly complicated. PCA indicated that the juice color, the fruit firmness and the soluble solid content were the most influential factors in the total variability of a set of 12 juice and fruit variables, and CA allowed us to establish four categories of treatment: plastics (polyethylene - PE, oxo- and biodegradable materials, papers, manual weeding and barley (Hordeum vulgare L. straw. Oxobiodegradable and PE were most closely related based on CA.

  3. Evaluation of genetic diversity among soybean (Glycine max) genotypes using univariate and multivariate analysis.

    Oliveira, M M; Sousa, L B; Reis, M C; Silva Junior, E G; Cardoso, D B O; Hamawaki, O T; Nogueira, A P O

    2017-05-31

    The genetic diversity study has paramount importance in breeding programs; hence, it allows selection and choice of the parental genetic divergence, which have the agronomic traits desired by the breeder. This study aimed to characterize the genetic divergence between 24 soybean genotypes through their agronomic traits, using multivariate clustering methods to select the potential genitors for the promising hybrid combinations. Six agronomic traits evaluated were number of days to flowering and maturity, plant height at flowering and maturity, insertion height of the first pod, and yield. The genetic divergence evaluated by multivariate analysis that esteemed first the Mahalanobis' generalized distance (D 2 ), then the clustering using Tocher's optimization methods, and then the unweighted pair group method with arithmetic average (UPGMA). Tocher's optimization method and the UPGMA agreed with the groups' constitution between each other, the formation of eight distinct groups according Tocher's method and seven distinct groups using UPGMA. The trait number of days for flowering (45.66%) was the most efficient to explain dissimilarity between genotypes, and must be one of the main traits considered by the breeder in the moment of genitors choice in soybean-breeding programs. The genetic variability allowed the identification of dissimilar genotypes and with superior performances. The hybridizations UFU 18 x UFUS CARAJÁS, UFU 15 x UFU 13, and UFU 13 x UFUS CARAJÁS are promising to obtain superior segregating populations, which enable the development of more productive genotypes.

  4. [Retrospective statistical analysis of clinical factors of recurrence in chronic subdural hematoma: correlation between univariate and multivariate analysis].

    Takayama, Motoharu; Terui, Keita; Oiwa, Yoshitsugu

    2012-10-01

    Chronic subdural hematoma is common in elderly individuals and surgical procedures are simple. The recurrence rate of chronic subdural hematoma, however, varies from 9.2 to 26.5% after surgery. The authors studied factors of the recurrence using univariate and multivariate analyses in patients with chronic subdural hematoma We retrospectively reviewed 239 consecutive cases of chronic subdural hematoma who received burr-hole surgery with irrigation and closed-system drainage. We analyzed the relationships between recurrence of chronic subdural hematoma and factors such as sex, age, laterality, bleeding tendency, other complicated diseases, density on CT, volume of the hematoma, residual air in the hematoma cavity, use of artificial cerebrospinal fluid. Twenty-one patients (8.8%) experienced a recurrence of chronic subdural hematoma. Multiple logistic regression found that the recurrence rate was higher in patients with a large volume of the residual air, and was lower in patients using artificial cerebrospinal fluid. No statistical differences were found in bleeding tendency. Techniques to reduce the air in the hematoma cavity are important for good outcome in surgery of chronic subdural hematoma. Also, the use of artificial cerebrospinal fluid reduces recurrence of chronic subdural hematoma. The surgical procedures can be the same for patients with bleeding tendencies.

  5. Evaluation of droplet size distributions using univariate and multivariate approaches.

    Gaunø, Mette Høg; Larsen, Crilles Casper; Vilhelmsen, Thomas; Møller-Sonnergaard, Jørn; Wittendorff, Jørgen; Rantanen, Jukka

    2013-01-01

    Pharmaceutically relevant material characteristics are often analyzed based on univariate descriptors instead of utilizing the whole information available in the full distribution. One example is droplet size distribution, which is often described by the median droplet size and the width of the distribution. The current study was aiming to compare univariate and multivariate approach in evaluating droplet size distributions. As a model system, the atomization of a coating solution from a two-fluid nozzle was investigated. The effect of three process parameters (concentration of ethyl cellulose in ethanol, atomizing air pressure, and flow rate of coating solution) on the droplet size and droplet size distribution using a full mixed factorial design was used. The droplet size produced by a two-fluid nozzle was measured by laser diffraction and reported as volume based size distribution. Investigation of loading and score plots from principal component analysis (PCA) revealed additional information on the droplet size distributions and it was possible to identify univariate statistics (volume median droplet size), which were similar, however, originating from varying droplet size distributions. The multivariate data analysis was proven to be an efficient tool for evaluating the full information contained in a distribution.

  6. Evaluation of droplet size distributions using univariate and multivariate approaches

    Gauno, M.H.; Larsen, C.C.; Vilhelmsen, T.

    2013-01-01

    of the distribution. The current study was aiming to compare univariate and multivariate approach in evaluating droplet size distributions. As a model system, the atomization of a coating solution from a two-fluid nozzle was investigated. The effect of three process parameters (concentration of ethyl cellulose...... in ethanol, atomizing air pressure, and flow rate of coating solution) on the droplet size and droplet size distribution using a full mixed factorial design was used. The droplet size produced by a two-fluid nozzle was measured by laser diffraction and reported as volume based size distribution....... Investigation of loading and score plots from principal component analysis (PCA) revealed additional information on the droplet size distributions and it was possible to identify univariate statistics (volume median droplet size), which were similar, however, originating from varying droplet size distributions...

  7. The pathways for intelligible speech: multivariate and univariate perspectives.

    Evans, S; Kyong, J S; Rosen, S; Golestani, N; Warren, J E; McGettigan, C; Mourão-Miranda, J; Wise, R J S; Scott, S K

    2014-09-01

    An anterior pathway, concerned with extracting meaning from sound, has been identified in nonhuman primates. An analogous pathway has been suggested in humans, but controversy exists concerning the degree of lateralization and the precise location where responses to intelligible speech emerge. We have demonstrated that the left anterior superior temporal sulcus (STS) responds preferentially to intelligible speech (Scott SK, Blank CC, Rosen S, Wise RJS. 2000. Identification of a pathway for intelligible speech in the left temporal lobe. Brain. 123:2400-2406.). A functional magnetic resonance imaging study in Cerebral Cortex used equivalent stimuli and univariate and multivariate analyses to argue for the greater importance of bilateral posterior when compared with the left anterior STS in responding to intelligible speech (Okada K, Rong F, Venezia J, Matchin W, Hsieh IH, Saberi K, Serences JT,Hickok G. 2010. Hierarchical organization of human auditory cortex: evidence from acoustic invariance in the response to intelligible speech. 20: 2486-2495.). Here, we also replicate our original study, demonstrating that the left anterior STS exhibits the strongest univariate response and, in decoding using the bilateral temporal cortex, contains the most informative voxels showing an increased response to intelligible speech. In contrast, in classifications using local "searchlights" and a whole brain analysis, we find greater classification accuracy in posterior rather than anterior temporal regions. Thus, we show that the precise nature of the multivariate analysis used will emphasize different response profiles associated with complex sound to speech processing. © The Author 2013. Published by Oxford University Press.

  8. Comparison between the univariate and multivariate analysis on the partial characterization of the endoglucanase produced in the solid state fermentation by Aspergillus oryzae ATCC 10124.

    de Brito, Aila Riany; Santos Reis, Nadabe Dos; Silva, Tatielle Pereira; Ferreira Bonomo, Renata Cristina; Trovatti Uetanabaro, Ana Paula; de Assis, Sandra Aparecida; da Silva, Erik Galvão Paranhos; Aguiar-Oliveira, Elizama; Oliveira, Julieta Rangel; Franco, Marcelo

    2017-11-26

    Endoglucanase production by Aspergillus oryzae ATCC 10124 cultivated in rice husks or peanut shells was optimized by experimental design as a function of humidity, time, and temperature. The optimum temperature for the endoglucanase activity was estimated by a univariate analysis (one factor at the time) as 50°C (rice husks) and 60°C (peanut shells), however, by a multivariate analysis (synergism of factors), it was determined a different temperature (56°C) for endoglucanase from peanut shells. For the optimum pH, values determined by univariate and multivariate analysis were 5 and 5.2 (rice husk) and 5 and 7.6 (peanut shells). In addition, the best half-lives were observed at 50°C as 22.8 hr (rice husks) and 7.3 hr (peanut shells), also, 80% of residual activities was obtained between 30 and 50°C for both substrates, and the pH stability was improved at 5-7 (rice hulls) and 6-9 (peanut shells). Both endoglucanases obtained presented different characteristics as a result of the versatility of fungi in different substrates.

  9. A comparison between univariate probabilistic and multivariate (logistic regression) methods for landslide susceptibility analysis: the example of the Febbraro valley (Northern Alps, Italy)

    Rossi, M.; Apuani, T.; Felletti, F.

    2009-04-01

    The aim of this paper is to compare the results of two statistical methods for landslide susceptibility analysis: 1) univariate probabilistic method based on landslide susceptibility index, 2) multivariate method (logistic regression). The study area is the Febbraro valley, located in the central Italian Alps, where different types of metamorphic rocks croup out. On the eastern part of the studied basin a quaternary cover represented by colluvial and secondarily, by glacial deposits, is dominant. In this study 110 earth flows, mainly located toward NE portion of the catchment, were analyzed. They involve only the colluvial deposits and their extension mainly ranges from 36 to 3173 m2. Both statistical methods require to establish a spatial database, in which each landslide is described by several parameters that can be assigned using a main scarp central point of landslide. The spatial database is constructed using a Geographical Information System (GIS). Each landslide is described by several parameters corresponding to the value of main scarp central point of the landslide. Based on bibliographic review a total of 15 predisposing factors were utilized. The width of the intervals, in which the maps of the predisposing factors have to be reclassified, has been defined assuming constant intervals to: elevation (100 m), slope (5 °), solar radiation (0.1 MJ/cm2/year), profile curvature (1.2 1/m), tangential curvature (2.2 1/m), drainage density (0.5), lineament density (0.00126). For the other parameters have been used the results of the probability-probability plots analysis and the statistical indexes of landslides site. In particular slope length (0 ÷ 2, 2 ÷ 5, 5 ÷ 10, 10 ÷ 20, 20 ÷ 35, 35 ÷ 260), accumulation flow (0 ÷ 1, 1 ÷ 2, 2 ÷ 5, 5 ÷ 12, 12 ÷ 60, 60 ÷27265), Topographic Wetness Index 0 ÷ 0.74, 0.74 ÷ 1.94, 1.94 ÷ 2.62, 2.62 ÷ 3.48, 3.48 ÷ 6,00, 6.00 ÷ 9.44), Stream Power Index (0 ÷ 0.64, 0.64 ÷ 1.28, 1.28 ÷ 1.81, 1.81 ÷ 4.20, 4.20 ÷ 9

  10. Effect Sizes for Research Univariate and Multivariate Applications

    Grissom, Robert J

    2011-01-01

    Noted for its comprehensive coverage, this greatly expanded new edition now covers the use of univariate and multivariate effect sizes. Many measures and estimators are reviewed along with their application, interpretation, and limitations. Noted for its practical approach, the book features numerous examples using real data for a variety of variables and designs, to help readers apply the material to their own data. Tips on the use of SPSS, SAS, R, and S-Plus are provided. The book's broad disciplinary appeal results from its inclusion of a variety of examples from psychology, medicine, educa

  11. A Java-based fMRI processing pipeline evaluation system for assessment of univariate general linear model and multivariate canonical variate analysis-based pipelines.

    Zhang, Jing; Liang, Lichen; Anderson, Jon R; Gatewood, Lael; Rottenberg, David A; Strother, Stephen C

    2008-01-01

    As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.FEAT and NPAIRS.GLM. In order to overcome these limitations, a Java-based fMRI processing pipeline evaluation system was developed. It integrated YALE (a machine learning environment) into Fiswidgets (a fMRI software environment) to obtain system interoperability and applied an algorithm to measure GLM prediction accuracy. The results demonstrated that the system can evaluate fMRI processing pipelines with univariate GLM and multivariate canonical variates analysis (CVA)-based models on real fMRI data based on prediction accuracy (classification accuracy) and statistical parametric image (SPI) reproducibility. In addition, a preliminary study was performed where four fMRI processing pipelines with GLM and CVA modules such as FSL.FEAT and NPAIRS.CVA were evaluated with the system. The results indicated that (1) the system can compare different fMRI processing pipelines with heterogeneous models (NPAIRS.GLM, NPAIRS.CVA and FSL.FEAT) and rank their performance by automatic performance scoring, and (2) the rank of pipeline performance is highly dependent on the preprocessing operations. These results suggest that the system will be of value for the comparison, validation, standardization and optimization of functional neuroimaging software packages and fMRI processing pipelines.

  12. Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation.

    Cain, Meghan K; Zhang, Zhiyong; Yuan, Ke-Hai

    2017-10-01

    Nonnormality of univariate data has been extensively examined previously (Blanca et al., Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 9(2), 78-84, 2013; Miceeri, Psychological Bulletin, 105(1), 156, 1989). However, less is known of the potential nonnormality of multivariate data although multivariate analysis is commonly used in psychological and educational research. Using univariate and multivariate skewness and kurtosis as measures of nonnormality, this study examined 1,567 univariate distriubtions and 254 multivariate distributions collected from authors of articles published in Psychological Science and the American Education Research Journal. We found that 74 % of univariate distributions and 68 % multivariate distributions deviated from normal distributions. In a simulation study using typical values of skewness and kurtosis that we collected, we found that the resulting type I error rates were 17 % in a t-test and 30 % in a factor analysis under some conditions. Hence, we argue that it is time to routinely report skewness and kurtosis along with other summary statistics such as means and variances. To facilitate future report of skewness and kurtosis, we provide a tutorial on how to compute univariate and multivariate skewness and kurtosis by SAS, SPSS, R and a newly developed Web application.

  13. Comparison of multivariate and univariate statistical process control and monitoring methods

    Leger, R.P.; Garland, WM.J.; Macgregor, J.F.

    1996-01-01

    Work in recent years has lead to the development of multivariate process monitoring schemes which use Principal Component Analysis (PCA). This research compares the performance of a univariate scheme and a multivariate PCA scheme used for monitoring a simple process with 11 measured variables. The multivariate PCA scheme was able to adequately represent the process using two principal components. This resulted in a PCA monitoring scheme which used two charts as opposed to 11 charts for the univariate scheme and therefore had distinct advantages in terms of both data representation, presentation, and fault diagnosis capabilities. (author)

  14. Univariate and Multivariate Specification Search Indices in Covariance Structure Modeling.

    Hutchinson, Susan R.

    1993-01-01

    Simulated population data were used to compare relative performances of the modification index and C. Chou and P. M. Bentler's Lagrange multiplier test (a multivariate generalization of a modification index) for four levels of model misspecification. Both indices failed to recover the true model except at the lowest level of misspecification. (SLD)

  15. Univariate/multivariate genome-wide association scans using data from families and unrelated samples.

    Lei Zhang

    2009-08-01

    Full Text Available As genome-wide association studies (GWAS are becoming more popular, two approaches, among others, could be considered in order to improve statistical power for identifying genes contributing subtle to moderate effects to human diseases. The first approach is to increase sample size, which could be achieved by combining both unrelated and familial subjects together. The second approach is to jointly analyze multiple correlated traits. In this study, by extending generalized estimating equations (GEEs, we propose a simple approach for performing univariate or multivariate association tests for the combined data of unrelated subjects and nuclear families. In particular, we correct for population stratification by integrating principal component analysis and transmission disequilibrium test strategies. The proposed method allows for multiple siblings as well as missing parental information. Simulation studies show that the proposed test has improved power compared to two popular methods, EIGENSTRAT and FBAT, by analyzing the combined data, while correcting for population stratification. In addition, joint analysis of bivariate traits has improved power over univariate analysis when pleiotropic effects are present. Application to the Genetic Analysis Workshop 16 (GAW16 data sets attests to the feasibility and applicability of the proposed method.

  16. Multivariate analysis with LISREL

    Jöreskog, Karl G; Y Wallentin, Fan

    2016-01-01

    This book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice using the LISREL computer program. It presents not only the typical uses of LISREL, such as confirmatory factor analysis and structural equation models, but also several other multivariate analysis topics, including regression (univariate, multivariate, censored, logistic, and probit), generalized linear models, multilevel analysis, and principal component analysis. It provides numerous examples from several disciplines and discusses and interprets the results, illustrated with sections of output from the LISREL program, in the context of the example. The book is intended for masters and PhD students and researchers in the social, behavioral, economic and many other sciences who require a basic understanding of multivariate statistical theory and methods for their analysis of multivariate data. It can also be used as a textbook on various topics of multivariate statistical analysis.

  17. Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China.

    Zhang, Yong; Zhong, Miner; Geng, Nana; Jiang, Yunjian

    2017-01-01

    The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.

  18. Visual classification of very fine-grained sediments: Evaluation through univariate and multivariate statistics

    Hohn, M. Ed; Nuhfer, E.B.; Vinopal, R.J.; Klanderman, D.S.

    1980-01-01

    Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and ??-ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data. ?? 1980 Plenum Publishing Corporation.

  19. An exercise in model validation: Comparing univariate statistics and Monte Carlo-based multivariate statistics

    Weathers, J.B.; Luck, R.; Weathers, J.W.

    2009-01-01

    The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.

  20. An exercise in model validation: Comparing univariate statistics and Monte Carlo-based multivariate statistics

    Weathers, J.B. [Shock, Noise, and Vibration Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: James.Weathers@ngc.com; Luck, R. [Department of Mechanical Engineering, Mississippi State University, 210 Carpenter Engineering Building, P.O. Box ME, Mississippi State, MS 39762-5925 (United States)], E-mail: Luck@me.msstate.edu; Weathers, J.W. [Structural Analysis Group, Northrop Grumman Shipbuilding, P.O. Box 149, Pascagoula, MS 39568 (United States)], E-mail: Jeffrey.Weathers@ngc.com

    2009-11-15

    The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the 'noise level of the validation procedure', which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics.

  1. Validated univariate and multivariate spectrophotometric methods for the determination of pharmaceuticals mixture in complex wastewater

    Riad, Safaa M.; Salem, Hesham; Elbalkiny, Heba T.; Khattab, Fatma I.

    2015-04-01

    Five, accurate, precise, and sensitive univariate and multivariate spectrophotometric methods were developed for the simultaneous determination of a ternary mixture containing Trimethoprim (TMP), Sulphamethoxazole (SMZ) and Oxytetracycline (OTC) in waste water samples collected from different cites either production wastewater or livestock wastewater after their solid phase extraction using OASIS HLB cartridges. In univariate methods OTC was determined at its λmax 355.7 nm (0D), while (TMP) and (SMZ) were determined by three different univariate methods. Method (A) is based on successive spectrophotometric resolution technique (SSRT). The technique starts with the ratio subtraction method followed by ratio difference method for determination of TMP and SMZ. Method (B) is successive derivative ratio technique (SDR). Method (C) is mean centering of the ratio spectra (MCR). The developed multivariate methods are principle component regression (PCR) and partial least squares (PLS). The specificity of the developed methods is investigated by analyzing laboratory prepared mixtures containing different ratios of the three drugs. The obtained results are statistically compared with those obtained by the official methods, showing no significant difference with respect to accuracy and precision at p = 0.05.

  2. The Use of Univariate and Multivariate Analyses in the Geochemical Exploration, Ravanj Lead Mine, Delijan, Iran

    Mostafa Nejadhadad

    2017-11-01

    Full Text Available A geochemical exploration program was applied to recognize the anomalous geochemical haloes at the Ravanj lead mine, Delijan, Iran. Sampling of unweathered rocks were undertaken across rock exposures on a 10 × 10 meter grid (n = 302 as well as the accessible parts of underground mine A (n = 42. First, the threshold values of all elements were determined using the cut-off values used in the exploratory data analysis (EDA method. Then, for further studies, elements with lognormal distributions (Pb, Zn, Ag, As, Cd, Co, Cu, Sb, S, Sr, Th, Ba, Bi, Fe, Ni and Mn were selected. Robustness against outliers is achieved by application of central log ratio transformation to address the closure problems with compositional data prior to principle components analysis (PCA. Results of these analyses show that, in the Ravanj deposit, Pb mineralization is characterized by a Pb-Ba-Ag-Sb ± Zn ± Cd association. The supra-mineralization haloes are characterized by barite and tetrahedrite in a Ba- Th- Ag- Cu- Sb- As- Sr association and sub-mineralization haloes are comprised of pyrite and tetrahedrite, probably reflecting a Fe-Cu-As-Bi-Ni-Co-Mo-Mn association. Using univariate and multivariate geostatistical analyses (e.g., EDA and robust PCA, four anomalies were detected and mapped in Block A of the Ravanj deposit. Anomalies 1 and 2 are around the ancient orebodies. Anomaly 3 is located in a thin bedded limestone-shale intercalation unit that does not show significant mineralization. Drilling of the fourth anomaly suggested a low grade, non-economic Pb mineralization.

  3. Decoding auditory spatial and emotional information encoding using multivariate versus univariate techniques.

    Kryklywy, James H; Macpherson, Ewan A; Mitchell, Derek G V

    2018-04-01

    Emotion can have diverse effects on behaviour and perception, modulating function in some circumstances, and sometimes having little effect. Recently, it was identified that part of the heterogeneity of emotional effects could be due to a dissociable representation of emotion in dual pathway models of sensory processing. Our previous fMRI experiment using traditional univariate analyses showed that emotion modulated processing in the auditory 'what' but not 'where' processing pathway. The current study aims to further investigate this dissociation using a more recently emerging multi-voxel pattern analysis searchlight approach. While undergoing fMRI, participants localized sounds of varying emotional content. A searchlight multi-voxel pattern analysis was conducted to identify activity patterns predictive of sound location and/or emotion. Relative to the prior univariate analysis, MVPA indicated larger overlapping spatial and emotional representations of sound within early secondary regions associated with auditory localization. However, consistent with the univariate analysis, these two dimensions were increasingly segregated in late secondary and tertiary regions of the auditory processing streams. These results, while complimentary to our original univariate analyses, highlight the utility of multiple analytic approaches for neuroimaging, particularly for neural processes with known representations dependent on population coding.

  4. Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model

    Erasmo Cadenas

    2016-02-01

    Full Text Available Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA. This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed using a nonlinear autoregressive exogenous artificial neural network (NARX. This uses the variables: barometric pressure, air temperature, wind direction and solar radiation or relative humidity, as well as delayed wind speed. Both models were developed from two databases from two sites: an hourly average measurements database from La Mata, Oaxaca, Mexico, and a ten minute average measurements database from Metepec, Hidalgo, Mexico. The main objective was to compare the impact of the various meteorological variables on the performance of the multivariate model of wind speed prediction with respect to the high performance univariate linear model. The NARX model gave better results with improvements on the ARIMA model of between 5.5% and 10. 6% for the hourly database and of between 2.3% and 12.8% for the ten minute database for mean absolute error and mean squared error, respectively.

  5. A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia.

    Aboagye-Sarfo, Patrick; Mai, Qun; Sanfilippo, Frank M; Preen, David B; Stewart, Louise M; Fatovich, Daniel M

    2015-10-01

    To develop multivariate vector-ARMA (VARMA) forecast models for predicting emergency department (ED) demand in Western Australia (WA) and compare them to the benchmark univariate autoregressive moving average (ARMA) and Winters' models. Seven-year monthly WA state-wide public hospital ED presentation data from 2006/07 to 2012/13 were modelled. Graphical and VARMA modelling methods were used for descriptive analysis and model fitting. The VARMA models were compared to the benchmark univariate ARMA and Winters' models to determine their accuracy to predict ED demand. The best models were evaluated by using error correction methods for accuracy. Descriptive analysis of all the dependent variables showed an increasing pattern of ED use with seasonal trends over time. The VARMA models provided a more precise and accurate forecast with smaller confidence intervals and better measures of accuracy in predicting ED demand in WA than the ARMA and Winters' method. VARMA models are a reliable forecasting method to predict ED demand for strategic planning and resource allocation. While the ARMA models are a closely competing alternative, they under-estimated future ED demand. Copyright © 2015 Elsevier Inc. All rights reserved.

  6. Influence of microclimatic ammonia levels on productive performance of different broilers' breeds estimated with univariate and multivariate approaches.

    Soliman, Essam S; Moawed, Sherif A; Hassan, Rania A

    2017-08-01

    Birds litter contains unutilized nitrogen in the form of uric acid that is converted into ammonia; a fact that does not only affect poultry performance but also has a negative effect on people's health around the farm and contributes in the environmental degradation. The influence of microclimatic ammonia emissions on Ross and Hubbard broilers reared in different housing systems at two consecutive seasons (fall and winter) was evaluated using a discriminant function analysis to differentiate between Ross and Hubbard breeds. A total number of 400 air samples were collected and analyzed for ammonia levels during the experimental period. Data were analyzed using univariate and multivariate statistical methods. Ammonia levels were significantly higher (p0.05) were found between the two farms in body weight, body weight gain, feed intake, feed conversion ratio, and performance index (PI) of broilers. Body weight; weight gain and PI had increased values (pbroiler breed. Ammonia emissions were positively (although weekly) correlated with the ambient relative humidity (r=0.383; p0.05). Test of significance of discriminant function analysis did not show a classification based on the studied traits suggesting that they cannot been used as predictor variables. The percentage of correct classification was 52% and it was improved after deletion of highly correlated traits to 57%. The study revealed that broiler's growth was negatively affected by increased microclimatic ammonia concentrations and recommended the analysis of broilers' growth performance parameters data using multivariate discriminant function analysis.

  7. Cellulose I crystallinity determination using FT-Raman spectroscopy : univariate and multivariate methods

    Umesh P. Agarwal; Richard S. Reiner; Sally A. Ralph

    2010-01-01

    Two new methods based on FT–Raman spectroscopy, one simple, based on band intensity ratio, and the other using a partial least squares (PLS) regression model, are proposed to determine cellulose I crystallinity. In the simple method, crystallinity in cellulose I samples was determined based on univariate regression that was first developed using the Raman band...

  8. Influence of microclimatic ammonia levels on productive performance of different broilers’ breeds estimated with univariate and multivariate approaches

    Soliman, Essam S.; Moawed, Sherif A.; Hassan, Rania A.

    2017-01-01

    Background and Aim: Birds litter contains unutilized nitrogen in the form of uric acid that is converted into ammonia; a fact that does not only affect poultry performance but also has a negative effect on people’s health around the farm and contributes in the environmental degradation. The influence of microclimatic ammonia emissions on Ross and Hubbard broilers reared in different housing systems at two consecutive seasons (fall and winter) was evaluated using a discriminant function analysis to differentiate between Ross and Hubbard breeds. Materials and Methods: A total number of 400 air samples were collected and analyzed for ammonia levels during the experimental period. Data were analyzed using univariate and multivariate statistical methods. Results: Ammonia levels were significantly higher (p0.05) were found between the two farms in body weight, body weight gain, feed intake, feed conversion ratio, and performance index (PI) of broilers. Body weight; weight gain and PI had increased values (pbroiler breed. Ammonia emissions were positively (although weekly) correlated with the ambient relative humidity (r=0.383; p0.05). Test of significance of discriminant function analysis did not show a classification based on the studied traits suggesting that they cannot been used as predictor variables. The percentage of correct classification was 52% and it was improved after deletion of highly correlated traits to 57%. Conclusion: The study revealed that broiler’s growth was negatively affected by increased microclimatic ammonia concentrations and recommended the analysis of broilers’ growth performance parameters data using multivariate discriminant function analysis. PMID:28919677

  9. Multivariate refined composite multiscale entropy analysis

    Humeau-Heurtier, Anne

    2016-01-01

    Multiscale entropy (MSE) has become a prevailing method to quantify signals complexity. MSE relies on sample entropy. However, MSE may yield imprecise complexity estimation at large scales, because sample entropy does not give precise estimation of entropy when short signals are processed. A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. Nevertheless, RCMSE is for univariate signals only. The simultaneous analysis of multi-channel (multivariate) data often over-performs studies based on univariate signals. We therefore introduce an extension of RCMSE to multivariate data. Applications of multivariate RCMSE to simulated processes reveal its better performances over the standard multivariate MSE. - Highlights: • Multiscale entropy quantifies data complexity but may be inaccurate at large scale. • A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. • Nevertheless, RCMSE is adapted to univariate time series only. • We herein introduce an extension of RCMSE to multivariate data. • It shows better performances than the standard multivariate multiscale entropy.

  10. Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques

    Sfetsos, A. [7 Pirsou Str., Athens (Greece); Coonick, A.H. [Imperial Coll. of Science Technology and Medicine, Dept. of Electrical and Electronic Engineering, London (United Kingdom)

    2000-07-01

    This paper introduces a new approach for the forecasting of mean hourly global solar radiation received by a horizontal surface. In addition to the traditional linear methods, several artificial-intelligence-based techniques are studied. These include linear, feed-forward, recurrent Elman and Radial Basis neural networks alongside the adaptive neuro-fuzzy inference scheme. The problem is examined initially for the univariate case, and is extended to include additional meteorological parameters in the process of estimating the optimum model. The results indicate that the developed artificial intelligence models predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index. The forecasting ability of some models can be further enhanced with the use of additional meteorological parameters. (Author)

  11. Modeling the potential risk factors of bovine viral diarrhea prevalence in Egypt using univariable and multivariable logistic regression analyses

    Abdelfattah M. Selim

    2018-03-01

    Full Text Available Aim: The present cross-sectional study was conducted to determine the seroprevalence and potential risk factors associated with Bovine viral diarrhea virus (BVDV disease in cattle and buffaloes in Egypt, to model the potential risk factors associated with the disease using logistic regression (LR models, and to fit the best predictive model for the current data. Materials and Methods: A total of 740 blood samples were collected within November 2012-March 2013 from animals aged between 6 months and 3 years. The potential risk factors studied were species, age, sex, and herd location. All serum samples were examined with indirect ELIZA test for antibody detection. Data were analyzed with different statistical approaches such as Chi-square test, odds ratios (OR, univariable, and multivariable LR models. Results: Results revealed a non-significant association between being seropositive with BVDV and all risk factors, except for species of animal. Seroprevalence percentages were 40% and 23% for cattle and buffaloes, respectively. OR for all categories were close to one with the highest OR for cattle relative to buffaloes, which was 2.237. Likelihood ratio tests showed a significant drop of the -2LL from univariable LR to multivariable LR models. Conclusion: There was an evidence of high seroprevalence of BVDV among cattle as compared with buffaloes with the possibility of infection in different age groups of animals. In addition, multivariable LR model was proved to provide more information for association and prediction purposes relative to univariable LR models and Chi-square tests if we have more than one predictor.

  12. The use of principal components and univariate charts to control multivariate processes

    Marcela A. G. Machado

    2008-04-01

    Full Text Available In this article, we evaluate the performance of the T² chart based on the principal components (PC X chart and the simultaneous univariate control charts based on the original variables (SU charts or based on the principal components (SUPC charts. The main reason to consider the PC chart lies on the dimensionality reduction. However, depending on the disturbance and on the way the original variables are related, the chart is very slow in signaling, except when all variables are negatively correlated and the principal component is wisely selected. Comparing the SU , the SUPC and the T² charts we conclude that the SU X charts (SUPC charts have a better overall performance when the variables are positively (negatively correlated. We also develop the expression to obtain the power of two S² charts designed for monitoring the covariance matrix. These joint S² charts are, in the majority of the cases, more efficient than the generalized variance chart.Neste artigo, avaliamos o desempenho do gráfico de T² baseado em componentes principais (gráfico PC e dos gráficos de controle simultâneos univariados baseados nas variáveis originais (gráfico SU X ou baseados em componentes principais (gráfico SUPC. A principal razão para o uso do gráfico PC é a redução de dimensionalidade. Entretanto, dependendo da perturbação e da correlação entre as variáveis originais, o gráfico é lento em sinalizar, exceto quando todas as variáveis são negativamente correlacionadas e a componente principal é adequadamente escolhida. Comparando os gráficos SU X, SUPC e T² concluímos que o gráfico SU X (gráfico SUPC tem um melhor desempenho global quando as variáveis são positivamente (negativamente correlacionadas. Desenvolvemos também uma expressão para obter o poder de detecção de dois gráficos de S² projetados para controlar a matriz de covariâncias. Os gráficos conjuntos de S² são, na maioria dos casos, mais eficientes que o gr

  13. A comparison of univariate and multivariate methods for analyzing clinal variation in an invasive species

    Edwards, K.R.; Bastlová, D.; Edwards-Jonášová, Magda; Květ, J.

    2011-01-01

    Roč. 674, č. 1 (2011), s. 119-131 ISSN 0018-8158 Institutional research plan: CEZ:AV0Z60870520 Keywords : common garden * life history traits * local adaptation * principal components analysis * purple loosestrife * redundancy analysis Subject RIV: EH - Ecology, Behaviour Impact factor: 1.784, year: 2011 http://www.springerlink.com/content/71r10n3367m98jxl/

  14. Robust multivariate analysis

    J Olive, David

    2017-01-01

    This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the multivariate normal distribution, the simpler and more applicable large sample theory is given.  The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed by theory.   The robust techniques  are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis.  A simple way to bootstrap confidence regions is also provided. Much of the research on robust multivariate analysis in this book is being published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics. This graduate text is also useful for people who are familiar with the traditional multivariate topics, but want to know more about handling data sets with...

  15. Methods of Multivariate Analysis

    Rencher, Alvin C

    2012-01-01

    Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere."-IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life sit

  16. Comparison of univariate and multivariate calibration for the determination of micronutrients in pellets of plant materials by laser induced breakdown spectrometry

    Batista Braga, Jez Willian; Trevizan, Lilian Cristina; Nunes, Lidiane Cristina; Aparecida Rufini, Iolanda; Santos, Dario; Krug, Francisco Jose

    2010-01-01

    The application of laser induced breakdown spectrometry (LIBS) aiming the direct analysis of plant materials is a great challenge that still needs efforts for its development and validation. In this way, a series of experimental approaches has been carried out in order to show that LIBS can be used as an alternative method to wet acid digestions based methods for analysis of agricultural and environmental samples. The large amount of information provided by LIBS spectra for these complex samples increases the difficulties for selecting the most appropriated wavelengths for each analyte. Some applications have suggested that improvements in both accuracy and precision can be achieved by the application of multivariate calibration in LIBS data when compared to the univariate regression developed with line emission intensities. In the present work, the performance of univariate and multivariate calibration, based on partial least squares regression (PLSR), was compared for analysis of pellets of plant materials made from an appropriate mixture of cryogenically ground samples with cellulose as the binding agent. The development of a specific PLSR model for each analyte and the selection of spectral regions containing only lines of the analyte of interest were the best conditions for the analysis. In this particular application, these models showed a similar performance, but PLSR seemed to be more robust due to a lower occurrence of outliers in comparison to the univariate method. Data suggests that efforts dealing with sample presentation and fitness of standards for LIBS analysis must be done in order to fulfill the boundary conditions for matrix independent development and validation.

  17. MULTIVARIANT AND UNIVARIANT INTERGROUP DIFFERENCES IN THE INVESTIGATED SPECIFIC MOTOR SPACE BETWEEN RESPONDENTS JUNIORS AND SENIORS MEMBERS OF THE MACEDONIAN NATIONAL KARATE TEAM

    Kеnan Аsani

    2013-07-01

    Full Text Available The aim is to establish intergroup multivariant and univariant investigated differences in specific motor space between respondents juniors and seniors members of the Macedonian karate team. The sample of 30 male karate respondents covers juniors on 16,17 and seniors over 18 years.In the research were applied 20 specific motor tests. Based on Graph 1 where it is presented multivariant analysis of variance Manova and Anova can be noted that respondents juniors and seniors, although not belonging to the same population are not different in multivariant understudied area.W. lambda of .19, Rao-wool R - Approximation of 1.91 degrees of freedom df 1 = 20 and df 2 = 9 provides the level of significance of p =, 16. Based on univariant analysis for each variable separately can be seen that has been around intergroup statistically significant difference in seven SMAEGERI (kick in the sack with favoritism leg mae geri for 10 sec., SMAVASI (kick in the sack with favoritism foot mavashi geri by 10 sec., SUSIRO (kick in the sack with favoritism leg ushiro geri for 10 sec., SKIZAME (kick in the sack with favoritism hand kizame cuki for 10 sec., STAPNSR (taping with foot in sagital plane for 15 sec. SUDMNR (hitting a moving target with weaker hand and SUDMPN (hitting a moving target with favoritism foot of twenty applied manifest variables. There are no intergroup differences in multivariant investigated specific - motor space among the respondents juniors and seniors members of the Macedonian karate team. Based on univariant analysis for each variable separately can be seen that has been around intergroup statistically significant difference in seven SMAEGERI (kick in the sack with favoritism leg mae geri for 10 sec., SMAVASI (kick in the sack with favoritism foot mavashi geri by 10 sec., SUSIRO (kick in the sack with favoritism leg ushiro geri for 10 sec., SKIZAME (kick in the sack with favoritism hand kizame cuki for 10 sec., STAPNSR (taping with foot in

  18. Applied multivariate statistical analysis

    Härdle, Wolfgang Karl

    2015-01-01

    Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners.  It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added.  All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior.  All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate ...

  19. Effects of univariate and multivariate regression on the accuracy of hydrogen quantification with laser-induced breakdown spectroscopy

    Ytsma, Cai R.; Dyar, M. Darby

    2018-01-01

    Hydrogen (H) is a critical element to measure on the surface of Mars because its presence in mineral structures is indicative of past hydrous conditions. The Curiosity rover uses the laser-induced breakdown spectrometer (LIBS) on the ChemCam instrument to analyze rocks for their H emission signal at 656.6 nm, from which H can be quantified. Previous LIBS calibrations for H used small data sets measured on standards and/or manufactured mixtures of hydrous minerals and rocks and applied univariate regression to spectra normalized in a variety of ways. However, matrix effects common to LIBS make these calibrations of limited usefulness when applied to the broad range of compositions on the Martian surface. In this study, 198 naturally-occurring hydrous geological samples covering a broad range of bulk compositions with directly-measured H content are used to create more robust prediction models for measuring H in LIBS data acquired under Mars conditions. Both univariate and multivariate prediction models, including partial least square (PLS) and the least absolute shrinkage and selection operator (Lasso), are compared using several different methods for normalization of H peak intensities. Data from the ChemLIBS Mars-analog spectrometer at Mount Holyoke College are compared against spectra from the same samples acquired using a ChemCam-like instrument at Los Alamos National Laboratory and the ChemCam instrument on Mars. Results show that all current normalization and data preprocessing variations for quantifying H result in models with statistically indistinguishable prediction errors (accuracies) ca. ± 1.5 weight percent (wt%) H2O, limiting the applications of LIBS in these implementations for geological studies. This error is too large to allow distinctions among the most common hydrous phases (basalts, amphiboles, micas) to be made, though some clays (e.g., chlorites with ≈ 12 wt% H2O, smectites with 15-20 wt% H2O) and hydrated phases (e.g., gypsum with ≈ 20

  20. Multivariate survival analysis and competing risks

    Crowder, Martin J

    2012-01-01

    Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods. There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.

  1. Multivariate meta-analysis: Potential and promise

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

    2011-01-01

    The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

  2. [A SAS marco program for batch processing of univariate Cox regression analysis for great database].

    Yang, Rendong; Xiong, Jie; Peng, Yangqin; Peng, Xiaoning; Zeng, Xiaomin

    2015-02-01

    To realize batch processing of univariate Cox regression analysis for great database by SAS marco program. We wrote a SAS macro program, which can filter, integrate, and export P values to Excel by SAS9.2. The program was used for screening survival correlated RNA molecules of ovarian cancer. A SAS marco program could finish the batch processing of univariate Cox regression analysis, the selection and export of the results. The SAS macro program has potential applications in reducing the workload of statistical analysis and providing a basis for batch processing of univariate Cox regression analysis.

  3. Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Brain Mapping: Applications to Event-Related Functional MRI, H215O-, and FDG-PET

    James R. Moeller

    2006-01-01

    Full Text Available In brain mapping studies of sensory, cognitive, and motor operations, specific waveforms of dynamic neural activity are predicted based on theoretical models of human information processing. For example in event-related functional MRI (fMRI, the general linear model (GLM is employed in mass-univariate analyses to identify the regions whose dynamic activity closely matches the expected waveforms. By comparison multivariate analyses based on PCA or ICA provide greater flexibility in detecting spatiotemporal properties of experimental data that may strongly support alternative neuroscientific explanations. We investigated conjoint multivariate and mass-univariate analyses that combine the capabilities to (1 verify activation of neural machinery we already understand and (2 discover reliable signatures of new neural machinery. We examined combinations of GLM and PCA that recover latent neural signals (waveforms and footprints with greater accuracy than either method alone. Comparative results are illustrated with analyses of real fMRI data, adding to Monte Carlo simulation support.

  4. Comparative study of the efficiency of computed univariate and multivariate methods for the estimation of the binary mixture of clotrimazole and dexamethasone using two different spectral regions

    Fayez, Yasmin Mohammed; Tawakkol, Shereen Mostafa; Fahmy, Nesma Mahmoud; Lotfy, Hayam Mahmoud; Shehata, Mostafa Abdel-Aty

    2018-04-01

    Three methods of analysis are conducted that need computational procedures by the Matlab® software. The first is the univariate mean centering method which eliminates the interfering signal of the one component at a selected wave length leaving the amplitude measured to represent the component of interest only. The other two multivariate methods named PLS and PCR depend on a large number of variables that lead to extraction of the maximum amount of information required to determine the component of interest in the presence of the other. Good accurate and precise results are obtained from the three methods for determining clotrimazole in the linearity range 1-12 μg/mL and 75-550 μg/mL with dexamethasone acetate 2-20 μg/mL in synthetic mixtures and pharmaceutical formulation using two different spectral regions 205-240 nm and 233-278 nm. The results obtained are compared statistically to each other and to the official methods.

  5. Multivariate data analysis

    Hansen, Michael Adsetts Edberg

    Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set...

  6. Multivariate Generalized Multiscale Entropy Analysis

    Anne Humeau-Heurtier

    2016-11-01

    Full Text Available Multiscale entropy (MSE was introduced in the 2000s to quantify systems’ complexity. MSE relies on (i a coarse-graining procedure to derive a set of time series representing the system dynamics on different time scales; (ii the computation of the sample entropy for each coarse-grained time series. A refined composite MSE (rcMSE—based on the same steps as MSE—also exists. Compared to MSE, rcMSE increases the accuracy of entropy estimation and reduces the probability of inducing undefined entropy for short time series. The multivariate versions of MSE (MMSE and rcMSE (MrcMSE have also been introduced. In the coarse-graining step used in MSE, rcMSE, MMSE, and MrcMSE, the mean value is used to derive representations of the original data at different resolutions. A generalization of MSE was recently published, using the computation of different moments in the coarse-graining procedure. However, so far, this generalization only exists for univariate signals. We therefore herein propose an extension of this generalized MSE to multivariate data. The multivariate generalized algorithms of MMSE and MrcMSE presented herein (MGMSE and MGrcMSE, respectively are first analyzed through the processing of synthetic signals. We reveal that MGrcMSE shows better performance than MGMSE for short multivariate data. We then study the performance of MGrcMSE on two sets of short multivariate electroencephalograms (EEG available in the public domain. We report that MGrcMSE may show better performance than MrcMSE in distinguishing different types of multivariate EEG data. MGrcMSE could therefore supplement MMSE or MrcMSE in the processing of multivariate datasets.

  7. Multivariate analysis: models and method

    Sanz Perucha, J.

    1990-01-01

    Data treatment techniques are increasingly used since computer methods result of wider access. Multivariate analysis consists of a group of statistic methods that are applied to study objects or samples characterized by multiple values. A final goal is decision making. The paper describes the models and methods of multivariate analysis

  8. Multivariate analysis methods in physics

    Wolter, M.

    2007-01-01

    A review of multivariate methods based on statistical training is given. Several multivariate methods useful in high-energy physics analysis are discussed. Selected examples from current research in particle physics are discussed, both from the on-line trigger selection and from the off-line analysis. Also statistical training methods are presented and some new application are suggested [ru

  9. Noncentral Chi-Square versus Normal Distributions in Describing the Likelihood Ratio Statistic: The Univariate Case and Its Multivariate Implication

    Yuan, Ke-Hai

    2008-01-01

    In the literature of mean and covariance structure analysis, noncentral chi-square distribution is commonly used to describe the behavior of the likelihood ratio (LR) statistic under alternative hypothesis. Due to the inaccessibility of the rather technical literature for the distribution of the LR statistic, it is widely believed that the…

  10. Multivariate analysis techniques

    Bendavid, Josh [European Organization for Nuclear Research (CERN), Geneva (Switzerland); Fisher, Wade C. [Michigan State Univ., East Lansing, MI (United States); Junk, Thomas R. [Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)

    2016-01-01

    The end products of experimental data analysis are designed to be simple and easy to understand: hypothesis tests and measurements of parameters. But, the experimental data themselves are voluminous and complex. Furthermore, in modern collider experiments, many petabytes of data must be processed in search of rare new processes which occur together with much more copious background processes that are of less interest to the task at hand. The systematic uncertainties on the background may be larger than the expected signal in many cases. The statistical power of an analysis and its sensitivity to systematic uncertainty can therefore usually both be improved by separating signal events from background events with higher efficiency and purity.

  11. Multivariate data analysis

    Fernandes, A.A.; Antony, M.K.; Somayajulu, Y.K.; Sarma, Y.V.B.; Almeida, A.M.; Mahadevan, R.

    , Head Applied Statistics Unit, Indian Statistical Institute, Calcutta for going through the section on Canonical Correlation Analysis and offering his comments on the same. This report has been prepared using ?Latex? on a ?Linux? platform, viz...., the personal computer Kapila. I wish to thank Mr. Dattaram Shivji for installing ?Latex? and ?GMT? packages on the personal computer. The style file used for preparing this report, has been hacked by me from a Goa University, Ph. D style file prepared by Dr. D...

  12. Practical multivariate analysis

    Afifi, Abdelmonem; Clark, Virginia A

    2011-01-01

    ""First of all, it is very easy to read. … The authors manage to introduce and (at least partially) explain even quite complex concepts, e.g. eigenvalues, in an easy and pedagogical way that I suppose is attractive to readers without deeper statistical knowledge. The text is also sprinkled with references for those who want to probe deeper into a certain topic. Secondly, I personally find the book's emphasis on practical data handling very appealing. … Thirdly, the book gives very nice coverage of regression analysis. … this is a nicely written book that gives a good overview of a large number

  13. Multivariate Multiscale Analysis

    1990-11-08

    The conditions on k in the second half of the statement of the proposition can be somewhat relaxed. In the cases n = 2 and n = 3 the details are given...of Mathematical Func- lions, Dover, New York, N.Y., 1965. [2] Bray and D. C. Solmon, The horocycle transform and harmonic analysis on the Poincare disk...H. Izen, Inversion of the k- plane transform by orthogonal function series expansions, Inverse Problems, 5 (1989), 181-202. [20] J. V. Leahy, K. T

  14. 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.

    2015-01-01

    When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is…

  15. Evaluation of the efficiency of continuous wavelet transform as processing and preprocessing algorithm for resolution of overlapped signals in univariate and multivariate regression analyses; an application to ternary and quaternary mixtures

    Hegazy, Maha A.; Lotfy, Hayam M.; Mowaka, Shereen; Mohamed, Ekram Hany

    2016-07-01

    Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations.

  16. A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data

    Maria Vinaixa

    2012-10-01

    Full Text Available Several metabolomic software programs provide methods for peak picking, retention time alignment and quantification of metabolite features in LC/MS-based metabolomics. Statistical analysis, however, is needed in order to discover those features significantly altered between samples. By comparing the retention time and MS/MS data of a model compound to that from the altered feature of interest in the research sample, metabolites can be then unequivocally identified. This paper reports on a comprehensive overview of a workflow for statistical analysis to rank relevant metabolite features that will be selected for further MS/MS experiments. We focus on univariate data analysis applied in parallel on all detected features. Characteristics and challenges of this analysis are discussed and illustrated using four different real LC/MS untargeted metabolomic datasets. We demonstrate the influence of considering or violating mathematical assumptions on which univariate statistical test rely, using high-dimensional LC/MS datasets. Issues in data analysis such as determination of sample size, analytical variation, assumption of normality and homocedasticity, or correction for multiple testing are discussed and illustrated in the context of our four untargeted LC/MS working examples.

  17. Essentials of multivariate data analysis

    Spencer, Neil H

    2013-01-01

    ""… this text provides an overview at an introductory level of several methods in multivariate data analysis. It contains in-depth examples from one data set woven throughout the text, and a free [Excel] Add-In to perform the analyses in Excel, with step-by-step instructions provided for each technique. … could be used as a text (possibly supplemental) for courses in other fields where researchers wish to apply these methods without delving too deeply into the underlying statistics.""-The American Statistician, February 2015

  18. Multivariate Analysis for the Processing of Signals

    Beattie J.R.

    2014-01-01

    Full Text Available Real-world experiments are becoming increasingly more complex, needing techniques capable of tracking this complexity. Signal based measurements are often used to capture this complexity, where a signal is a record of a sample’s response to a parameter (e.g. time, displacement, voltage, wavelength that is varied over a range of values. In signals the responses at each value of the varied parameter are related to each other, depending on the composition or state sample being measured. Since signals contain multiple information points, they have rich information content but are generally complex to comprehend. Multivariate Analysis (MA has profoundly transformed their analysis by allowing gross simplification of the tangled web of variation. In addition MA has also provided the advantage of being much more robust to the influence of noise than univariate methods of analysis. In recent years, there has been a growing awareness that the nature of the multivariate methods allows exploitation of its benefits for purposes other than data analysis, such as pre-processing of signals with the aim of eliminating irrelevant variations prior to analysis of the signal of interest. It has been shown that exploiting multivariate data reduction in an appropriate way can allow high fidelity denoising (removal of irreproducible non-signals, consistent and reproducible noise-insensitive correction of baseline distortions (removal of reproducible non-signals, accurate elimination of interfering signals (removal of reproducible but unwanted signals and the standardisation of signal amplitude fluctuations. At present, the field is relatively small but the possibilities for much wider application are considerable. Where signal properties are suitable for MA (such as the signal being stationary along the x-axis, these signal based corrections have the potential to be highly reproducible, and highly adaptable and are applicable in situations where the data is noisy or

  19. Handbook of univariate and multivariate data analysis and interpretation with SPSS

    Ho, Robert

    2006-01-01

    Many statistics texts tend to focus more on the theory and mathematics underlying statistical tests than on their applications and interpretation. This can leave readers with little understanding of how to apply statistical tests or how to interpret their findings. While the SPSS statistical software has done much to alleviate the frustrations of social science professionals and students who must analyze data, they still face daunting challenges in selecting the proper tests, executing the tests, and interpreting the test results.With emphasis firmly on such practical matters, this handbook se

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

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

    2009-01-01

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

  1. Multivariate Regression Analysis and Slaughter Livestock,

    AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

  2. Multivariate Methods for Meta-Analysis of Genetic Association Studies.

    Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

    2018-01-01

    Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

  3. Exploratory multivariate analysis by example using R

    Husson, Francois; Pages, Jerome

    2010-01-01

    Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the prin

  4. Factor analysis of multivariate data

    Fernandes, A.A.; Mahadevan, R.

    A brief introduction to factor analysis is presented. A FORTRAN program, which can perform the Q-mode and R-mode factor analysis and the singular value decomposition of a given data matrix is presented in Appendix B. This computer program, uses...

  5. Comparative study between univariate spectrophotometry and multivariate calibration as analytical tools for quantitation of Benazepril alone and in combination with Amlodipine.

    Farouk, M; Elaziz, Omar Abd; Tawakkol, Shereen M; Hemdan, A; Shehata, Mostafa A

    2014-04-05

    Four simple, accurate, reproducible, and selective methods have been developed and subsequently validated for the determination of Benazepril (BENZ) alone and in combination with Amlodipine (AML) in pharmaceutical dosage form. The first method is pH induced difference spectrophotometry, where BENZ can be measured in presence of AML as it showed maximum absorption at 237nm and 241nm in 0.1N HCl and 0.1N NaOH, respectively, while AML has no wavelength shift in both solvents. The second method is the new Extended Ratio Subtraction Method (EXRSM) coupled to Ratio Subtraction Method (RSM) for determination of both drugs in commercial dosage form. The third and fourth methods are multivariate calibration which include Principal Component Regression (PCR) and Partial Least Squares (PLSs). A detailed validation of the methods was performed following the ICH guidelines and the standard curves were found to be linear in the range of 2-30μg/mL for BENZ in difference and extended ratio subtraction spectrophotometric method, and 5-30 for AML in EXRSM method, with well accepted mean correlation coefficient for each analyte. The intra-day and inter-day precision and accuracy results were well within the acceptable limits. Copyright © 2013 Elsevier B.V. All rights reserved.

  6. Multivariate Analysis of Ladle Vibration

    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

  7. A MULTIVARIATE ANALYSIS OF CROATIAN COUNTIES ENTREPRENEURSHIP

    Elza Jurun

    2012-12-01

    Full Text Available In the focus of this paper is a multivariate analysis of Croatian Counties entrepreneurship. Complete data base available by official statistic institutions at national and regional level is used. Modern econometric methodology starting from a comparative analysis via multiple regression to multivariate cluster analysis is carried out as well as the analysis of successful or inefficacious entrepreneurship measured by indicators of efficiency, profitability and productivity. Time horizons of the comparative analysis are in 2004 and 2010. Accelerators of socio-economic development - number of entrepreneur investors, investment in fixed assets and current assets ratio in multiple regression model are analytically filtered between twenty-six independent variables as variables of the dominant influence on GDP per capita in 2010 as dependent variable. Results of multivariate cluster analysis of twentyone Croatian Counties are interpreted also in the sense of three Croatian NUTS 2 regions according to European nomenclature of regional territorial division of Croatia.

  8. Multivariate spectral-analysis of movement-related EEG data

    Andrew, C. M.

    1997-01-01

    The univariate method of event-related desynchronization (ERD) analysis, which quantifies the temporal evolution of power within specific frequency bands from electroencephalographic (EEG) data recorded during a task or event, is extended to an event related multivariate spectral analysis method. With this method, time courses of cross-spectra, phase spectra, coherence spectra, band-averaged coherence values (event-related coherence, ERCoh), partial power spectra and partial coherence spectra are estimated from an ensemble of multivariate event-related EEG trials. This provides a means of investigating relationships between EEG signals recorded over different scalp areas during the performance of a task or the occurrence of an event. The multivariate spectral analysis method is applied to EEG data recorded during three different movement-related studies involving discrete right index finger movements. The first study investigates the impact of the EEG derivation type on the temporal evolution of interhemispheric coherence between activity recorded at electrodes overlying the left and right sensorimotor hand areas during cued finger movement. The question results whether changes in coherence necessarily reflect changes in functional coupling of the cortical structures underlying the recording electrodes. The method is applied to data recorded during voluntary finger movement and a hypothesis, based on an existing global/local model of neocortical dynamics, is formulated to explain the coherence results. The third study applies partial spectral analysis too, and investigates phase relationships of, movement-related data recorded from a full head montage, thereby providing further results strengthening the global/local hypothesis. (author)

  9. Multivariate Analysis of Schools and Educational Policy.

    Kiesling, Herbert J.

    This report describes a multivariate analysis technique that approaches the problems of educational production function analysis by (1) using comparable measures of output across large experiments, (2) accounting systematically for differences in socioeconomic background, and (3) treating the school as a complete system in which different…

  10. Who uses nursing theory? A univariate descriptive analysis of five years' research articles.

    Bond, A Elaine; Eshah, Nidal Farid; Bani-Khaled, Mohammed; Hamad, Atef Omar; Habashneh, Samira; Kataua', Hussein; al-Jarrah, Imad; Abu Kamal, Andaleeb; Hamdan, Falastine Rafic; Maabreh, Roqia

    2011-06-01

    Since the early 1950s, nursing leaders have worked diligently to build the Scientific Discipline of Nursing, integrating Theory, Research and Practice. Recently, the role of theory has again come into question, with some scientists claiming nurses are not using theory to guide their research, with which to improve practice. The purposes of this descriptive study were to determine: (i) Were nursing scientists' research articles in leading nursing journals based on theory? (ii) If so, were the theories nursing theories or borrowed theories? (iii) Were the theories integrated into the studies, or were they used as organizing frameworks? Research articles from seven top ISI journals were analysed, excluding regularly featured columns, meta-analyses, secondary analysis, case studies and literature reviews. The authors used King's dynamic Interacting system and Goal Attainment Theory as an organizing framework. They developed consensus on how to identify the integration of theory, searching the Title, Abstract, Aims, Methods, Discussion and Conclusion sections of each research article, whether quantitative or qualitative. Of 2857 articles published in the seven journals from 2002 to, and including, 2006, 2184 (76%) were research articles. Of the 837 (38%) authors who used theories, 460 (55%) used nursing theories, 377 (45%) used other theories: 776 (93%) of those who used theory integrated it into their studies, including qualitative studies, while 51 (7%) reported they used theory as an organizing framework for their studies. Closer analysis revealed theory principles were implicitly implied, even in research reports that did not explicitly report theory usage. Increasing numbers of nursing research articles (though not percentagewise) continue to be guided by theory, and not always by nursing theory. Newer nursing research methods may not explicitly state the use of nursing theory, though it is implicitly implied. © 2010 The Authors. Scandinavian Journal of Caring

  11. Multivariate Analysis of Industrial Scale Fermentation Data

    Mears, Lisa; Nørregård, Rasmus; Stocks, Stuart M.

    2015-01-01

    Multivariate analysis allows process understanding to be gained from the vast and complex datasets recorded from fermentation processes, however the application of such techniques to this field can be limited by the data pre-processing requirements and data handling. In this work many iterations...

  12. What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis.

    Davis, Tyler; LaRocque, Karen F; Mumford, Jeanette A; Norman, Kenneth A; Wagner, Anthony D; Poldrack, Russell A

    2014-08-15

    Multi-voxel pattern analysis (MVPA) has led to major changes in how fMRI data are analyzed and interpreted. Many studies now report both MVPA results and results from standard univariate voxel-wise analysis, often with the goal of drawing different conclusions from each. Because MVPA results can be sensitive to latent multidimensional representations and processes whereas univariate voxel-wise analysis cannot, one conclusion that is often drawn when MVPA and univariate results differ is that the activation patterns underlying MVPA results contain a multidimensional code. In the current study, we conducted simulations to formally test this assumption. Our findings reveal that MVPA tests are sensitive to the magnitude of voxel-level variability in the effect of a condition within subjects, even when the same linear relationship is coded in all voxels. We also find that MVPA is insensitive to subject-level variability in mean activation across an ROI, which is the primary variance component of interest in many standard univariate tests. Together, these results illustrate that differences between MVPA and univariate tests do not afford conclusions about the nature or dimensionality of the neural code. Instead, targeted tests of the informational content and/or dimensionality of activation patterns are critical for drawing strong conclusions about the representational codes that are indicated by significant MVPA results. Copyright © 2014 Elsevier Inc. All rights reserved.

  13. Univariate characterization of the German business cycle 1955-1994

    Weihs, Claus; Garczarek, Ursula

    2002-01-01

    We present a descriptive analysis of stylized facts for the German business cycle. We demonstrate that simple ad-hoc instructions for identifying univariate rules characterizing the German business cycle 1955-1994 lead to an error rate comparable to standard multivariate methods.

  14. Multivariate two-part statistics for analysis of correlated mass spectrometry data from multiple biological specimens.

    Taylor, Sandra L; Ruhaak, L Renee; Weiss, Robert H; Kelly, Karen; Kim, Kyoungmi

    2017-01-01

    High through-put mass spectrometry (MS) is now being used to profile small molecular compounds across multiple biological sample types from the same subjects with the goal of leveraging information across biospecimens. Multivariate statistical methods that combine information from all biospecimens could be more powerful than the usual univariate analyses. However, missing values are common in MS data and imputation can impact between-biospecimen correlation and multivariate analysis results. We propose two multivariate two-part statistics that accommodate missing values and combine data from all biospecimens to identify differentially regulated compounds. Statistical significance is determined using a multivariate permutation null distribution. Relative to univariate tests, the multivariate procedures detected more significant compounds in three biological datasets. In a simulation study, we showed that multi-biospecimen testing procedures were more powerful than single-biospecimen methods when compounds are differentially regulated in multiple biospecimens but univariate methods can be more powerful if compounds are differentially regulated in only one biospecimen. We provide R functions to implement and illustrate our method as supplementary information CONTACT: sltaylor@ucdavis.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  15. Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data

    Lijun Wang

    2013-01-01

    Full Text Available Brain decoding with functional magnetic resonance imaging (fMRI requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.

  16. Power Estimation in Multivariate Analysis of Variance

    Jean François Allaire

    2007-09-01

    Full Text Available Power is often overlooked in designing multivariate studies for the simple reason that it is believed to be too complicated. In this paper, it is shown that power estimation in multivariate analysis of variance (MANOVA can be approximated using a F distribution for the three popular statistics (Hotelling-Lawley trace, Pillai-Bartlett trace, Wilk`s likelihood ratio. Consequently, the same procedure, as in any statistical test, can be used: computation of the critical F value, computation of the noncentral parameter (as a function of the effect size and finally estimation of power using a noncentral F distribution. Various numerical examples are provided which help to understand and to apply the method. Problems related to post hoc power estimation are discussed.

  17. Some developments in multivariate image analysis

    Kucheryavskiy, Sergey

    be up to several million. The main MIA tool for exploratory analysis is score density plot – all pixels are projected into principal component space and on the corresponding scores plots are colorized according to their density (how many pixels are crowded in the unit area of the plot). Looking...... for and analyzing patterns on these plots and the original image allow to do interactive analysis, to get some hidden information, build a supervised classification model, and much more. In the present work several alternative methods to original principal component analysis (PCA) for building the projection......Multivariate image analysis (MIA), one of the successful chemometric applications, now is used widely in different areas of science and industry. Introduced in late 80s it has became very popular with hyperspectral imaging, where MIA is one of the most efficient tools for exploratory analysis...

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

    D'Amico, E J; Neilands, T B; Zambarano, R

    2001-11-01

    Although power analysis is an important component in the planning and implementation of research designs, it is often ignored. Computer programs for performing power analysis are available, but most have limitations, particularly for complex multivariate designs. An SPSS procedure is presented that can be used for calculating power for univariate, multivariate, and repeated measures models with and without time-varying and time-constant covariates. Three examples provide a framework for calculating power via this method: an ANCOVA, a MANOVA, and a repeated measures ANOVA with two or more groups. The benefits and limitations of this procedure are discussed.

  19. Particulate characterization by PIXE multivariate spectral analysis

    Antolak, Arlyn J.; Morse, Daniel H.; Grant, Patrick G.; Kotula, Paul G.; Doyle, Barney L.; Richardson, Charles B.

    2007-01-01

    Obtaining particulate compositional maps from scanned PIXE (proton-induced X-ray emission) measurements is extremely difficult due to the complexity of analyzing spectroscopic data collected with low signal-to-noise at each scan point (pixel). Multivariate spectral analysis has the potential to analyze such data sets by reducing the PIXE data to a limited number of physically realizable and easily interpretable components (that include both spectral and image information). We have adapted the AXSIA (automated expert spectral image analysis) program, originally developed by Sandia National Laboratories to quantify electron-excited X-ray spectroscopy data, for this purpose. Samples consisting of particulates with known compositions and sizes were loaded onto Mylar and paper filter substrates and analyzed by scanned micro-PIXE. The data sets were processed by AXSIA and the associated principal component spectral data were quantified by converting the weighting images into concentration maps. The results indicate automated, nonbiased, multivariate statistical analysis is useful for converting very large amounts of data into a smaller, more manageable number of compositional components needed for locating individual particles-of-interest on large area collection media

  20. Classification of adulterated honeys by multivariate analysis.

    Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad

    2017-06-01

    In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%). Copyright © 2016 Elsevier Ltd. All rights reserved.

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

    Binod Neupane

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

  2. Multivariate analysis of data in sensory science

    Naes, T; Risvik, E

    1996-01-01

    The state-of-the-art of multivariate analysis in sensory science is described in this volume. Both methods for aggregated and individual sensory profiles are discussed. Processes and results are presented in such a way that they can be understood not only by statisticians but also by experienced sensory panel leaders and users of sensory analysis. The techniques presented are focused on examples and interpretation rather than on the technical aspects, with an emphasis on new and important methods which are possibly not so well known to scientists in the field. Important features of the book are discussions on the relationship among the methods with a strong accent on the connection between problems and methods. All procedures presented are described in relation to sensory data and not as completely general statistical techniques. Sensory scientists, applied statisticians, chemometricians, those working in consumer science, food scientists and agronomers will find this book of value.

  3. Advanced event reweighting using multivariate analysis

    Martschei, D; Feindt, M; Honc, S; Wagner-Kuhr, J

    2012-01-01

    Multivariate analysis (MVA) methods, especially discrimination techniques such as neural networks, are key ingredients in modern data analysis and play an important role in high energy physics. They are usually trained on simulated Monte Carlo (MC) samples to discriminate so called 'signal' from 'background' events and are then applied to data to select real events of signal type. We here address procedures that improve this work flow. This will be the enhancement of data / MC agreement by reweighting MC samples on a per event basis. Then training MVAs on real data using the sPlot technique will be discussed. Finally we will address the construction of MVAs whose discriminator is independent of a certain control variable, i.e. cuts on this variable will not change the discriminator shape.

  4. Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.

    Wang, Yifan; Liu, Aiyi; Mills, James L; Boehnke, Michael; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao; Wu, Colin O; Fan, Ruzong

    2015-05-01

    In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. © 2015 WILEY PERIODICALS, INC.

  5. Hierarchical multivariate covariance analysis of metabolic connectivity.

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

    2014-12-01

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

  6. Multivariate statistical analysis of wildfires in Portugal

    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).

  7. 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.

  8. An Introduction to Applied Multivariate Analysis

    Raykov, Tenko

    2008-01-01

    Focuses on the core multivariate statistics topics which are of fundamental relevance for its understanding. This book emphasis on the topics that are critical to those in the behavioral, social, and educational sciences.

  9. Method for statistical data analysis of multivariate observations

    Gnanadesikan, R

    1997-01-01

    A practical guide for multivariate statistical techniques-- now updated and revised In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data-based interpretations relevant to multivariate analysis in a way that addresses real-world problems arising in many areas of inte

  10. Multivariable nonlinear analysis of foreign exchange rates

    Suzuki, Tomoya; Ikeguchi, Tohru; Suzuki, Masuo

    2003-05-01

    We analyze the multivariable time series of foreign exchange rates. These are price movements that have often been analyzed, and dealing time intervals and spreads between bid and ask prices. Considering dealing time intervals as event timing such as neurons’ firings, we use raster plots (RPs) and peri-stimulus time histograms (PSTHs) which are popular methods in the field of neurophysiology. Introducing special processings to obtaining RPs and PSTHs time histograms for analyzing exchange rates time series, we discover that there exists dynamical interaction among three variables. We also find that adopting multivariables leads to improvements of prediction accuracy.

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

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

    2015-09-01

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

  12. Evaluation of in-line Raman data for end-point determination of a coating process: Comparison of Science-Based Calibration, PLS-regression and univariate data analysis.

    Barimani, Shirin; Kleinebudde, Peter

    2017-10-01

    A multivariate analysis method, Science-Based Calibration (SBC), was used for the first time for endpoint determination of a tablet coating process using Raman data. Two types of tablet cores, placebo and caffeine cores, received a coating suspension comprising a polyvinyl alcohol-polyethylene glycol graft-copolymer and titanium dioxide to a maximum coating thickness of 80µm. Raman spectroscopy was used as in-line PAT tool. The spectra were acquired every minute and correlated to the amount of applied aqueous coating suspension. SBC was compared to another well-known multivariate analysis method, Partial Least Squares-regression (PLS) and a simpler approach, Univariate Data Analysis (UVDA). All developed calibration models had coefficient of determination values (R 2 ) higher than 0.99. The coating endpoints could be predicted with root mean square errors (RMSEP) less than 3.1% of the applied coating suspensions. Compared to PLS and UVDA, SBC proved to be an alternative multivariate calibration method with high predictive power. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Multivariate analysis of 2-DE protein patterns - Practical approaches

    Jacobsen, Charlotte; Jacobsen, Susanne; Grove, H.

    2007-01-01

    Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two...... of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times...

  14. The analysis of multivariate group differences using common principal components

    Bechger, T.M.; Blanca, M.J.; Maris, G.

    2014-01-01

    Although it is simple to determine whether multivariate group differences are statistically significant or not, such differences are often difficult to interpret. This article is about common principal components analysis as a tool for the exploratory investigation of multivariate group differences

  15. 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. © 2015, The International Biometric Society.

  16. Multivariate Analysis and Machine Learning in Cerebral Palsy Research

    Jing Zhang

    2017-12-01

    Full Text Available Cerebral palsy (CP, a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

  17. Multivariate Analysis and Machine Learning in Cerebral Palsy Research.

    Zhang, Jing

    2017-01-01

    Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

  18. Matrix-based introduction to multivariate data analysis

    Adachi, Kohei

    2016-01-01

    This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter. This volume is appropriate for undergraduate students who already have studied introductory statistics. Graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis will also find the book useful, as it is based on modern matrix formulations with a special emphasis on ...

  19. Search for the top quark using multivariate analysis techniques

    Bhat, P.C.

    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 eμ channel and neural networks to the e+jets channel

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

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

    2004-06-01

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

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

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

    2004-01-01

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

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

    Glascock, M. D.; Neff, H. [University of Missouri, Research Reactor Center (United States); Vaughn, K. J. [Pacific Lutheran University, Department of Anthropology (United States)

    2004-06-15

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

  3. EURO AREA FISCAL STRUCTURES. A MULTIVARIATE ANALYSIS

    HURDUZEU Gheorghe

    2014-07-01

    taxes on income of corporations and taxes on income of individuals and households and other current taxes. Actual social contributions were also split into employer’s actual contributions, employee’s social contributions and social contributions of self- and non-employed persons. As the primary data analysis revealed many differences between Euro Area member states, but also similarities concerning various fiscal aggregates, we completed the analysis through multidimensional analysis, with the aims of classifying Euro Area member states into subgroups with similar fiscal structures. Taking into consideration the above mentioned variables, we used cluster analysis in order to determine which member states have similar fiscal structures and which are the main similarities that characterize Euro Area in this respect.

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

    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

  5. EXPLORATORY DATA ANALYSIS AND MULTIVARIATE STRATEGIES FOR REVEALING MULTIVARIATE STRUCTURES IN CLIMATE DATA

    2016-12-01

    Full Text Available This paper is on data analysis strategy in a complex, multidimensional, and dynamic domain. The focus is on the use of data mining techniques to explore the importance of multivariate structures; using climate variables which influences climate change. Techniques involved in data mining exercise vary according to the data structures. The multivariate analysis strategy considered here involved choosing an appropriate tool to analyze a process. Factor analysis is introduced into data mining technique in order to reveal the influencing impacts of factors involved as well as solving for multicolinearity effect among the variables. The temporal nature and multidimensionality of the target variables is revealed in the model using multidimensional regression estimates. The strategy of integrating the method of several statistical techniques, using climate variables in Nigeria was employed. R2 of 0.518 was obtained from the ordinary least square regression analysis carried out and the test was not significant at 5% level of significance. However, factor analysis regression strategy gave a good fit with R2 of 0.811 and the test was significant at 5% level of significance. Based on this study, model building should go beyond the usual confirmatory data analysis (CDA, rather it should be complemented with exploratory data analysis (EDA in order to achieve a desired result.

  6. Multivariate analysis for selecting apple mutants

    Faedi, W.; Bagnara, G.L.; Rosati, P.; Cecchini, M.

    1992-01-01

    The mutlivariate analysis of four year records on several vegetative and productive traits of twenty-one apple mutants (3 of 'Jonathan', 3 of 'Ozark Gold', 14 of 'Mollie's Delicious', 1 of 'Neipling's Early Stayman)' induced by gamma radiations showed that observation of some traits of one-year-old shoots is the most efficient way to reveal compact growing apple mutants. In particular, basal cross-section area, total length and leaf area resulted the most appropriate parameters, while internode length together with conopy height and width are less appropriate. The most interesting mutants we found are: one of 'Mollie's Delicious for the best balance among tree and fruit traits and for high skin color; one of 'Neipling's Early Stayman' with an earlier and more extensively red colored apple than the original clone. (author)

  7. Ischemic risk stratification by means of multivariate analysis of the heart rate variability

    Valencia, José F; Vallverdú, Montserrat; Caminal, Pere; Porta, Alberto; Voss, Andreas; Schroeder, Rico; Vázquez, Rafael; Bayés de Luna, Antonio

    2013-01-01

    In this work, a univariate and multivariate statistical analysis of indexes derived from heart rate variability (HRV) was conducted to stratify patients with ischemic dilated cardiomyopathy (IDC) in cardiac risk groups. Indexes conditional entropy, refined multiscale entropy (RMSE), detrended fluctuation analysis, time and frequency analysis, were applied to the RR interval series (beat-to-beat series), for single and multiscale complexity analysis of the HRV in IDC patients. Also, clinical parameters were considered. Two different end-points after a follow-up of three years were considered: (i) analysis A, with 151 survivor patients as a low risk group and 13 patients that suffered sudden cardiac death as a high risk group; (ii) analysis B, with 192 survivor patients as a low risk group and 30 patients that suffered cardiac mortality as a high risk group. A univariate and multivariate linear discriminant analysis was used as a statistical technique for classifying patients in risk groups. Sensitivity (Sen) and specificity (Spe) were calculated as diagnostic criteria in order to evaluate the performance of the indexes and their linear combinations. Sen and Spe values of 80.0% and 72.9%, respectively, were obtained during daytime by combining one clinical parameter and one index from RMSE, and during nighttime Sen = 80% and Spe = 73.4% were attained by combining one clinical factor and two indexes from RMSE. In particular, relatively long time scales were more relevant for classifying patients into risk groups during nighttime, while during daytime shorter scales performed better. The results suggest that the left atrial size, indexed to body surface and RMSE indexes are those that allow enhanced classification of ischemic patients in their respective risk groups, confirming that a single measurement is not enough to fully characterize ischemic risk patients and the clinical relevance of HRV complexity measures. (paper)

  8. Multivariate Analysis of the Predictors of Survival for Patients with Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization: Focusing on Superselective Chemoembolization

    Ji, Suk Kyeong; Cho, Yun Ku; Ahn, Yong Sik; Kim, Mi Young; Park, Yoon Ok; Kim, Jae Kyun; Kim, Wan Tae

    2008-01-01

    While the prognostic factors of survival for patients with hepatocellular carcinoma (HCC) who underwent transarterial chemoembolization (TACE) are well known, the clinical significance of performing selective TACE for HCC patients has not been clearly documented. We tried to analyze the potential factors of disease-free survival for these patients, including the performance of selective TACE. A total of 151 patients with HCC who underwent TACE were retrospectively analyzed for their disease-free survival (a median follow- up of 23 months, range: 1-88 months). Univariate and multivariate analyses were performed for 20 potential factors by using the Cox proportional hazard model, including 19 baseline factors and one procedure-related factor (conventional versus selective TACE). The parameters that proved to be significant on the univariate analysis were subsequently tested with the multivariate model. Conventional or selective TACE was performed for 40 and 111 patients, respectively. Univariate and multivariate analyses revealed that tumor multiplicity, venous tumor thrombosis and selective TACE were the only three independent significant prognostic factors of disease-free survival (p = 0.002, 0.015 and 0.019, respectively). In our study, selective TACE was a favorable prognostic factor for the disease-free survival of patients with HCC who underwent TACE

  9. Multivariate Volatility Impulse Response Analysis of GFC News Events

    D.E. Allen (David); M.J. McAleer (Michael); R.J. Powell (Robert); A.K. Singh (Abhay)

    2015-01-01

    textabstractThis paper applies the Hafner and Herwartz (2006) (hereafter HH) approach to the analysis of multivariate GARCH models using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock Exchange Index and the FTSE 100 index from the

  10. Multivariate Volatility Impulse Response Analysis of GFC News Events

    D.E. Allen (David); M.J. McAleer (Michael); R.J. Powell (Robert)

    2015-01-01

    markdownabstract__Abstract__ This paper applies the Hafner and Herwartz (2006) (hereafter HH) approach to the analysis of multivariate GARCH models using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock Exchange Index and the

  11. Global Sensitivity Analysis for multivariate output using Polynomial Chaos Expansion

    Garcia-Cabrejo, Oscar; Valocchi, Albert

    2014-01-01

    Many mathematical and computational models used in engineering produce multivariate output that shows some degree of correlation. However, conventional approaches to Global Sensitivity Analysis (GSA) assume that the output variable is scalar. These approaches are applied on each output variable leading to a large number of sensitivity indices that shows a high degree of redundancy making the interpretation of the results difficult. Two approaches have been proposed for GSA in the case of multivariate output: output decomposition approach [9] and covariance decomposition approach [14] but they are computationally intensive for most practical problems. In this paper, Polynomial Chaos Expansion (PCE) is used for an efficient GSA with multivariate output. The results indicate that PCE allows efficient estimation of the covariance matrix and GSA on the coefficients in the approach defined by Campbell et al. [9], and the development of analytical expressions for the multivariate sensitivity indices defined by Gamboa et al. [14]. - Highlights: • PCE increases computational efficiency in 2 approaches of GSA of multivariate output. • Efficient estimation of covariance matrix of output from coefficients of PCE. • Efficient GSA on coefficients of orthogonal decomposition of the output using PCE. • Analytical expressions of multivariate sensitivity indices from coefficients of PCE

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

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

    2014-01-01

    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 s pectral 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

  13. 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.

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

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

    2016-07-01

    A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Code is available at https://github.com/aalto-ics-kepaco anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

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

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

    2017-02-01

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

  16. Multivariate statistical analysis of major and trace element data for ...

    Multivariate statistical analysis of major and trace element data for niobium exploration in the peralkaline granites of the anorogenic ring-complex province of Nigeria. PO Ogunleye, EC Ike, I Garba. Abstract. No Abstract Available Journal of Mining and Geology Vol.40(2) 2004: 107-117. Full Text: EMAIL FULL TEXT EMAIL ...

  17. Metal concentration at surface water using multivariate analysis and ...

    Metal concentration at surface water using multivariate analysis and human health risk assessment. F Azaman, H Juahir, K Yunus, A Azid, S.I. Khalit, A.D. Mustafa, M.A. Amran, C.N.C. Hasnam, M.Z.A.Z. Abidin, M.A.M. Yusri ...

  18. Robust methods for multivariate data analysis A1

    Frosch, Stina; Von Frese, J.; Bro, Rasmus

    2005-01-01

    Outliers may hamper proper classical multivariate analysis, and lead to incorrect conclusions. To remedy the problem of outliers, robust methods are developed in statistics and chemometrics. Robust methods reduce or remove the effect of outlying data points and allow the ?good? data to primarily...... determine the result. This article reviews the most commonly used robust multivariate regression and exploratory methods that have appeared since 1996 in the field of chemometrics. Special emphasis is put on the robust versions of chemometric standard tools like PCA and PLS and the corresponding robust...

  19. TMVA - Toolkit for Multivariate Data Analysis with ROOT Users guide

    Höcker, A; Tegenfeldt, F; Voss, H; Voss, K; Christov, A; Henrot-Versillé, S; Jachowski, M; Krasznahorkay, A; Mahalalel, Y; Prudent, X; Speckmayer, P

    2007-01-01

    Multivariate machine learning techniques for the classification of data from high-energy physics (HEP) experiments have become standard tools in most HEP analyses. The multivariate classifiers themselves have significantly evolved in recent years, also driven by developments in other areas inside and outside science. TMVA is a toolkit integrated in ROOT which hosts a large variety of multivariate classification algorithms. They range from rectangular cut optimisation (using a genetic algorithm) and likelihood estimators, over linear and non-linear discriminants (neural networks), to sophisticated recent developments like boosted decision trees and rule ensemble fitting. TMVA organises the simultaneous training, testing, and performance evaluation of all these classifiers with a user-friendly interface, and expedites the application of the trained classifiers to the analysis of data sets with unknown sample composition.

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

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

    2001-01-01

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

  1. Multivariate statistical analysis of precipitation chemistry in Northwestern Spain

    Prada-Sanchez, J.M.; Garcia-Jurado, I.; Gonzalez-Manteiga, W.; Fiestras-Janeiro, M.G.; Espada-Rios, M.I.; Lucas-Dominguez, T.

    1993-01-01

    149 samples of rainwater were collected in the proximity of a power station in northwestern Spain at three rainwater monitoring stations. The resulting data are analyzed using multivariate statistical techniques. Firstly, the Principal Component Analysis shows that there are three main sources of pollution in the area (a marine source, a rural source and an acid source). The impact from pollution from these sources on the immediate environment of the stations is studied using Factorial Discriminant Analysis. 8 refs., 7 figs., 11 tabs

  2. Multivariate statistical analysis of precipitation chemistry in Northwestern Spain

    Prada-Sanchez, J.M.; Garcia-Jurado, I.; Gonzalez-Manteiga, W.; Fiestras-Janeiro, M.G.; Espada-Rios, M.I.; Lucas-Dominguez, T. (University of Santiago, Santiago (Spain). Faculty of Mathematics, Dept. of Statistics and Operations Research)

    1993-07-01

    149 samples of rainwater were collected in the proximity of a power station in northwestern Spain at three rainwater monitoring stations. The resulting data are analyzed using multivariate statistical techniques. Firstly, the Principal Component Analysis shows that there are three main sources of pollution in the area (a marine source, a rural source and an acid source). The impact from pollution from these sources on the immediate environment of the stations is studied using Factorial Discriminant Analysis. 8 refs., 7 figs., 11 tabs.

  3. PIXE-quantified AXSIA: Elemental mapping by multivariate spectral analysis

    Doyle, B.L.; Provencio, P.P.; Kotula, P.G.; Antolak, A.J.; Ryan, C.G.; Campbell, J.L.; Barrett, K.

    2006-01-01

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

  4. A robust multivariate long run analysis of European electricity prices

    Bruno Bosco; Lucia Parisio; Matteo Pelagatti; Fabio Baldi

    2007-01-01

    This paper analyses the interdependencies existing in wholesale electricity prices in six major European countries. The results of our robust multivariate long run dynamic analysis reveal the presence of four highly integrated central European markets (France, Germany, the Netherlands and Austria). The trend shared by these four electricity markets appears to be common also to gas prices, but not to oil prices. The existence of long term dynamics among electricity prices and between electrici...

  5. Multivariate analysis between air pollutants and meteorological variables in Seoul

    Kim, J.; Lim, J.

    2005-01-01

    Multivariate analysis was conducted to analyze the relationship between air pollutants and meteorological variables measured in Seoul from January 1 to December 31, 1999. The first principal component showed the contrast effect between O 3 and the other pollutants. The second principal component showed the contrast effect between CO, SO 2 , NO 2 , and O 3 , PM 10 , TSP. Based on the cluster analysis, three clusters represented different air pollution levels, seasonal characteristics of air pollutants, and meteorological conditions. Discriminant analysis with air environment index (AEI) was carried out to develop an air pollution index function. (orig.)

  6. Data classification and MTBF prediction with a multivariate analysis approach

    Braglia, Marcello; Carmignani, Gionata; Frosolini, Marco; Zammori, Francesco

    2012-01-01

    The paper presents a multivariate statistical approach that supports the classification of mechanical components, subjected to specific operating conditions, in terms of the Mean Time Between Failure (MTBF). Assessing the influence of working conditions and/or environmental factors on the MTBF is a prerequisite for the development of an effective preventive maintenance plan. However, this task may be demanding and it is generally performed with ad-hoc experimental methods, lacking of statistical rigor. To solve this common problem, a step by step multivariate data classification technique is proposed. Specifically, a set of structured failure data are classified in a meaningful way by means of: (i) cluster analysis, (ii) multivariate analysis of variance, (iii) feature extraction and (iv) predictive discriminant analysis. This makes it possible not only to define the MTBF of the analyzed components, but also to identify the working parameters that explain most of the variability of the observed data. The approach is finally demonstrated on 126 centrifugal pumps installed in an oil refinery plant; obtained results demonstrate the quality of the final discrimination, in terms of data classification and failure prediction.

  7. Multivariant design and multiple criteria analysis of building refurbishments

    Kaklauskas, A.; Zavadskas, E. K.; Raslanas, S. [Faculty of Civil Engineering, Vilnius Gediminas Technical University, Vilnius (Lithuania)

    2005-07-01

    In order to design and realize an efficient building refurbishment, it is necessary to carry out an exhaustive investigation of all solutions that form it. The efficiency level of the considered building's refurbishment depends on a great many of factors, including: cost of refurbishment, annual fuel economy after refurbishment, tentative pay-back time, harmfulness to health of the materials used, aesthetics, maintenance properties, functionality, comfort, sound insulation and longevity, etc. Solutions of an alternative character allow for a more rational and realistic assessment of economic, ecological, legislative, climatic, social and political conditions, traditions and for better the satisfaction of customer requirements. They also enable one to cut down on refurbishment costs. In carrying out the multivariant design and multiple criteria analysis of a building refurbishment much data was processed and evaluated. Feasible alternatives could be as many as 100,000. How to perform a multivariant design and multiple criteria analysis of alternate alternatives based on the enormous amount of information became the problem. Method of multivariant design and multiple criteria of a building refurbishment's analysis were developed by the authors to solve the above problems. In order to demonstrate the developed method, a practical example is presented in this paper. (author)

  8. Value Added Methods: Moving from Univariate to Multivariate Criteria

    Newman, David; Newman, Isadore; Ridenour, Carolyn; Morales, Jennifer

    2014-01-01

    The authors describe five value-added methods (VAM) used in school assessment as the backdrop to their main thesis. Then they review the assumptions underlying measurement and evaluation, the foundation of all assessment systems, including value-added. They discuss the traditional criterion variable used in VAM: a standardized test score. Next,…

  9. 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.

  10. Late rectal toxicity after conformal radiotherapy of prostate cancer (I): multivariate analysis and dose-response

    Skwarchuk, Mark W.; Jackson, Andrew; Zelefsky, Michael J.; Venkatraman, Ennapadam S.; Cowen, Didier M.; Levegruen, Sabine; Burman, Chandra M.; Fuks, Zvi; Leibel, Steven A.; Ling, C. Clifton

    2000-01-01

    Purpose: The purpose of this paper is to use the outcome of a dose escalation protocol for three-dimensional conformal radiation therapy (3D-CRT) of prostate cancer to study the dose-response for late rectal toxicity and to identify anatomic, dosimetric, and clinical factors that correlate with late rectal bleeding in multivariate analysis. Methods and Materials: Seven hundred forty-three patients with T1c-T3 prostate cancer were treated with 3D-CRT with prescribed doses of 64.8 to 81.0 Gy. The 5-year actuarial rate of late rectal toxicity was assessed using Kaplan-Meier statistics. A retrospective dosimetric analysis was performed for patients treated to 70.2 Gy (52 patients) or 75.6 Gy (119 patients) who either exhibited late rectal bleeding (RTOG Grade 2/3) within 30 months after treatment (i.e., 70.2 Gy--13 patients, 75.6 Gy--36 patients) or were nonbleeding for at least 30 months (i.e., 70.2 Gy--39 patients, 75.6 Gy--83 patients). Univariate and multivariate logistic regression was performed to correlate late rectal bleeding with several anatomic, dosimetric, and clinical variables. Results: A dose response for ≥ Grade 2 late rectal toxicity was observed. By multivariate analysis, the following factors were significantly correlated with ≥ Grade 2 late rectal bleeding for patients prescribed 70.2 Gy: 1) enclosure of the outer rectal contour by the 50% isodose on the isocenter slice (i.e., Iso50) (p max (p max

  11. Multivariate statistical analysis a high-dimensional approach

    Serdobolskii, V

    2000-01-01

    In the last few decades the accumulation of large amounts of in­ formation in numerous applications. has stimtllated an increased in­ terest in multivariate analysis. Computer technologies allow one to use multi-dimensional and multi-parametric models successfully. At the same time, an interest arose in statistical analysis with a de­ ficiency of sample data. Nevertheless, it is difficult to describe the recent state of affairs in applied multivariate methods as satisfactory. Unimprovable (dominating) statistical procedures are still unknown except for a few specific cases. The simplest problem of estimat­ ing the mean vector with minimum quadratic risk is unsolved, even for normal distributions. Commonly used standard linear multivari­ ate procedures based on the inversion of sample covariance matrices can lead to unstable results or provide no solution in dependence of data. Programs included in standard statistical packages cannot process 'multi-collinear data' and there are no theoretical recommen­ ...

  12. Identification of multivariate models for noise analysis of nuclear plant

    Zwingelstein, G.C.; Upadhyaya, B.R.

    1979-01-01

    During the normal operation of a pressurized water reactor, neutron noise analysis with multivariate autoregressive procedures in a valuable diagnostic tool to extract dynamic characteristics for incipient failure detection. The first part of the paper will describe in details the equations for estimating the multivariate autoregressive model matrices and the structure of various matrices. The matrices are estimated by solving a set of matrix operations, called Yule-Walker equations. The selection of optimal model order will also be discussed. Once the optimal parameter set is obtained, simple and fast calculations are used to determine the auto power spectral density, cross spectra, coherence function, phase. In addition the spectra may be decomposed into components being contributed from different noise sources. An application using neutron flux data collected on a nuclear plant will illustrate the efficiency of the method

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

    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.

  14. Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

    Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

    2014-01-01

    A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented co...... applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed......A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented...... concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant...

  15. Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

    Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

    2013-01-01

    A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented co...... applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed......A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented...... concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant...

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

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

    2013-12-03

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

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

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

  18. 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.

  19. Multivariate time series analysis with R and financial applications

    Tsay, Ruey S

    2013-01-01

    Since the publication of his first book, Analysis of Financial Time Series, Ruey Tsay has become one of the most influential and prominent experts on the topic of time series. Different from the traditional and oftentimes complex approach to multivariate (MV) time series, this sequel book emphasizes structural specification, which results in simplified parsimonious VARMA modeling and, hence, eases comprehension. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-worl

  20. Categorical speech processing in Broca's area: an fMRI study using multivariate pattern-based analysis.

    Lee, Yune-Sang; Turkeltaub, Peter; Granger, Richard; Raizada, Rajeev D S

    2012-03-14

    Although much effort has been directed toward understanding the neural basis of speech processing, the neural processes involved in the categorical perception of speech have been relatively less studied, and many questions remain open. In this functional magnetic resonance imaging (fMRI) study, we probed the cortical regions mediating categorical speech perception using an advanced brain-mapping technique, whole-brain multivariate pattern-based analysis (MVPA). Normal healthy human subjects (native English speakers) were scanned while they listened to 10 consonant-vowel syllables along the /ba/-/da/ continuum. Outside of the scanner, individuals' own category boundaries were measured to divide the fMRI data into /ba/ and /da/ conditions per subject. The whole-brain MVPA revealed that Broca's area and the left pre-supplementary motor area evoked distinct neural activity patterns between the two perceptual categories (/ba/ vs /da/). Broca's area was also found when the same analysis was applied to another dataset (Raizada and Poldrack, 2007), which previously yielded the supramarginal gyrus using a univariate adaptation-fMRI paradigm. The consistent MVPA findings from two independent datasets strongly indicate that Broca's area participates in categorical speech perception, with a possible role of translating speech signals into articulatory codes. The difference in results between univariate and multivariate pattern-based analyses of the same data suggest that processes in different cortical areas along the dorsal speech perception stream are distributed on different spatial scales.

  1. Micro-Raman Imaging for Biology with Multivariate Spectral Analysis

    Malvaso, Federica

    2015-05-05

    Raman spectroscopy is a noninvasive technique that can provide complex information on the vibrational state of the molecules. It defines the unique fingerprint that allow the identification of the various chemical components within a given sample. The aim of the following thesis work is to analyze Raman maps related to three pairs of different cells, highlighting differences and similarities through multivariate algorithms. The first pair of analyzed cells are human embryonic stem cells (hESCs), while the other two pairs are induced pluripotent stem cells (iPSCs) derived from T lymphocytes and keratinocytes, respectively. Although two different multivariate techniques were employed, ie Principal Component Analysis and Cluster Analysis, the same results were achieved: the iPSCs derived from T-lymphocytes show a higher content of genetic material both compared with the iPSCs derived from keratinocytes and the hESCs . On the other side, equally evident, was that iPS cells derived from keratinocytes assume a molecular distribution very similar to hESCs.

  2. Multivariate analysis of quantitative traits can effectively classify rapeseed germplasm

    Jankulovska Mirjana

    2014-01-01

    Full Text Available In this study, the use of different multivariate approaches to classify rapeseed genotypes based on quantitative traits has been presented. Tree regression analysis, PCA analysis and two-way cluster analysis were applied in order todescribe and understand the extent of genetic variability in spring rapeseed genotype by trait data. The traits which highly influenced seed and oil yield in rapeseed were successfully identified by the tree regression analysis. Principal predictor for both response variables was number of pods per plant (NP. NP and 1000 seed weight could help in the selection of high yielding genotypes. High values for both traits and oil content could lead to high oil yielding genotypes. These traits may serve as indirect selection criteria and can lead to improvement of seed and oil yield in rapeseed. Quantitative traits that explained most of the variability in the studied germplasm were classified using principal component analysis. In this data set, five PCs were identified, out of which the first three PCs explained 63% of the total variance. It helped in facilitating the choice of variables based on which the genotypes’ clustering could be performed. The two-way cluster analysissimultaneously clustered genotypes and quantitative traits. The final number of clusters was determined using bootstrapping technique. This approach provided clear overview on the variability of the analyzed genotypes. The genotypes that have similar performance regarding the traits included in this study can be easily detected on the heatmap. Genotypes grouped in the clusters 1 and 8 had high values for seed and oil yield, and relatively short vegetative growth duration period and those in cluster 9, combined moderate to low values for vegetative growth duration and moderate to high seed and oil yield. These genotypes should be further exploited and implemented in the rapeseed breeding program. The combined application of these multivariate methods

  3. Factors that impact the outcome of endoscopic correction of vesicoureteral reflux: a multivariate analysis.

    Kajbafzadeh, Abdol-Mohammad; Tourchi, Ali; Aryan, Zahra

    2013-02-01

    To identify independent factors that may predict vesicoureteral reflux (VUR) resolution after endoscopic treatment using dextranomer/hyaluronic acid copolymer (Deflux) in children free of anatomical anomalies. A retrospective study was conducted in our pediatric referral center from 1998 to 2011 on children with primary VUR who underwent endoscopic injection of Deflux with or without concomitant autologous blood injection (called HABIT or HIT, respectively). Children with secondary VUR or incomplete records were excluded from the study. Potential factors were divided into three categories including preoperative, intraoperative and postoperative. Success was defined as no sign of VUR on postoperative voiding cystourethrogram. Univariate and multivariate logistic regression models were constructed to identify independent factors that may predict success. Odds ratio (OR) and 95 % confidence interval (95 % CI) for prediction of success were estimated for each factor. From 485 children received Deflux injection, a total of 372 with a mean age of 3.10 years (ranged from 6 months to 12 years) were included in the study and endoscopic management was successful in 322 (86.6 %) of them. Of the patients, 185 (49.7 %) underwent HIT and 187 (50.3 %) underwent HABIT technique. On univariate analysis, VUR grade from preoperative category (OR = 4.79, 95 % CI = 2.22-10.30, p = 0.000), operation technique (OR = 0.33, 95 % CI = 0.17-0.64, p = 0.001) and presence of mound on postoperative sonography (OR = 0.06, 95 % CI = 0.02-0.16, p = 0.000) were associated with success. On multivariate analysis, preoperative VUR grade (OR = 4.85, 95 % CI = 2.49-8.96, p = 0.000) and identification of mound on postoperative sonography (OR = 0.07, 95 % CI = 0.01-0.18, p = 0.000) remained as independent success predictors. Based on this study, successful VUR correction after the endoscopic injection of Deflux can be predicted with respect to preoperative VUR grade and presence of mound after operation.

  4. Environmental Performance in Countries Worldwide: Determinant Factors and Multivariate Analysis

    Isabel Gallego-Alvarez

    2014-11-01

    Full Text Available The aim of this study is to analyze the environmental performance of countries and the variables that can influence it. At the same time, we performed a multivariate analysis using the HJ-biplot, an exploratory method that looks for hidden patterns in the data, obtained from the usual singular value decomposition (SVD of the data matrix, to contextualize the countries grouped by geographical areas and the variables relating to environmental indicators included in the environmental performance index. The sample used comprises 149 countries of different geographic areas. The findings obtained from the empirical analysis emphasize that socioeconomic factors, such as economic wealth and education, as well as institutional factors represented by the style of public administration, in particular control of corruption, are determinant factors of environmental performance in the countries analyzed. In contrast, no effect on environmental performance was found for factors relating to the internal characteristics of a country or political factors.

  5. Multivariate analysis of microarray data: differential expression and differential connection.

    Kiiveri, Harri T

    2011-02-01

    Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression. We use sparse inverse covariance matrices and their associated graphical representation to capture the notion of gene networks. An important issue in using these models is the identification of the pattern of zeroes in the inverse covariance matrix. The limitations of existing methods for doing this are discussed and we provide a workable solution for determining the zero pattern. We then consider a method for estimating the parameters in the inverse covariance matrix which is suitable for very high dimensional matrices. We also show how to construct multivariate tests of hypotheses. These overall multivariate tests can be broken down into two components, the first one being similar to tests for differential expression and the second involving the connections between genes. The methods in this paper enable the extraction of a wealth of information concerning the relationships between genes which can be conveniently represented in graphical form. Differentially expressed genes can be placed in the context of the gene network and places in the gene network where unusual or interesting patterns have emerged can be identified, leading to the formulation of hypotheses for future experimentation.

  6. Multivariate analysis of microarray data: differential expression and differential connection

    Kiiveri Harri T

    2011-02-01

    Full Text Available Abstract Background Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression. Results We use sparse inverse covariance matrices and their associated graphical representation to capture the notion of gene networks. An important issue in using these models is the identification of the pattern of zeroes in the inverse covariance matrix. The limitations of existing methods for doing this are discussed and we provide a workable solution for determining the zero pattern. We then consider a method for estimating the parameters in the inverse covariance matrix which is suitable for very high dimensional matrices. We also show how to construct multivariate tests of hypotheses. These overall multivariate tests can be broken down into two components, the first one being similar to tests for differential expression and the second involving the connections between genes. Conclusion The methods in this paper enable the extraction of a wealth of information concerning the relationships between genes which can be conveniently represented in graphical form. Differentially expressed genes can be placed in the context of the gene network and places in the gene network where unusual or interesting patterns have emerged can be identified, leading to the formulation of hypotheses for future experimentation.

  7. A multivariate analysis of Antarctic sea ice since 1979

    Magalhaes Neto, Newton de; Evangelista, Heitor [Universidade do Estado do Rio de Janeiro (Uerj), LARAMG - Laboratorio de Radioecologia e Mudancas Globais, Maracana, Rio de Janeiro, RJ (Brazil); Tanizaki-Fonseca, Kenny [Universidade do Estado do Rio de Janeiro (Uerj), LARAMG - Laboratorio de Radioecologia e Mudancas Globais, Maracana, Rio de Janeiro, RJ (Brazil); Universidade Federal Fluminense (UFF), Dept. Analise Geoambiental, Inst. de Geociencias, Niteroi, RJ (Brazil); Penello Meirelles, Margareth Simoes [Universidade do Estado do Rio de Janeiro (UERJ)/Geomatica, Maracana, Rio de Janeiro, RJ (Brazil); Garcia, Carlos Eiras [Universidade Federal do Rio Grande (FURG), Laboratorio de Oceanografia Fisica, Rio Grande, RS (Brazil)

    2012-03-15

    Recent satellite observations have shown an increase in the total extent of Antarctic sea ice, during periods when the atmosphere and oceans tend to be warmer surrounding a significant part of the continent. Despite an increase in total sea ice, regional analyses depict negative trends in the Bellingshausen-Amundsen Sea and positive trends in the Ross Sea. Although several climate parameters are believed to drive the formation of Antarctic sea ice and the local atmosphere, a descriptive mechanism that could trigger such differences in trends are still unknown. In this study we employed a multivariate analysis in order to identify the response of the Antarctic sea ice with respect to commonly utilized climate forcings/parameters, as follows: (1) The global air surface temperature, (2) The global sea surface temperature, (3) The atmospheric CO{sub 2} concentration, (4) The South Annular Mode, (5) The Nino 3, (6) The Nino (3 + 4, 7) The Nino 4, (8) The Southern Oscillation Index, (9) The Multivariate ENSO Index, (10) the Total Solar Irradiance, (11) The maximum O{sub 3} depletion area, and (12) The minimum O{sub 3} concentration over Antarctica. Our results indicate that western Antarctic sea ice is simultaneously impacted by several parameters; and that the minimum, mean, and maximum sea ice extent may respond to a separate set of climatic/geochemical parameters. (orig.)

  8. Conversion from laparoscopic to open cholecystectomy: Multivariate analysis of preoperative risk factors

    Khan M

    2005-01-01

    Full Text Available BACKGROUND: Laparoscopic cholecystectomy has become the gold standard in the treatment of symptomatic cholelithiasis. Some patients require conversion to open surgery and several preoperative variables have been identified as risk factors that are helpful in predicting the probability of conversion. However, there is a need to devise a risk-scoring system based on the identified risk factors to (a predict the risk of conversion preoperatively for selected patients, (b prepare the patient psychologically, (c arrange operating schedules accordingly, and (d minimize the procedure-related cost and help overcome financial constraints, which is a significant problem in developing countries. AIM: This study was aimed to evaluate preoperative risk factors for conversion from laparoscopic to open cholecystectomy in our setting. SETTINGS AND DESIGNS: A case control study of patients who underwent laparoscopic surgery from January 1997 to December 2001 was conducted at the Aga Khan University Hospital, Karachi, Pakistan. MATERIALS AND METHODS: All those patients who were converted to open surgery (n = 73 were enrolled as cases. Two controls who had successful laparoscopic surgery (n = 146 were matched with each case for operating surgeon and closest date of surgery. STATISTICAL ANALYSIS USED: Descriptive statistics were computed and, univariate and multivariate analysis was done through multiple logistic regression. RESULTS: The final multivariate model identified two risk factors for conversion: ultrasonographic signs of inflammation (adjusted odds ratio [aOR] = 8.5; 95% confidence interval [CI]: 3.3, 21.9 and age > 60 years (aOR = 8.1; 95% CI: 2.9, 22.2 after adjusting for physical signs, alkaline phosphatase and BMI levels. CONCLUSION: Preoperative risk factors evaluated by the present study confirm the likelihood of conversion. Recognition of these factors is important for understanding the characteristics of patients at a higher risk of conversion.

  9. Multivariate pattern dependence.

    Stefano Anzellotti

    2017-11-01

    Full Text Available When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD: a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS and to the fusiform face area (FFA, using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity.

  10. 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.

  11. Motivation and Self-Regulated Learning: A Multivariate Multilevel Analysis

    Wondimu Ahmed

    2017-09-01

    Full Text Available This study investigated the relationship between motivation and self-regulated learning (SRL in a nationally representative sample of 5245, 15-year-old students in the USA. A multivariate multilevel analysis was conducted to examine the role of three motivational variables (self-efficacy, intrinsic value & instrumental value in predicting three SRL strategies (memorization, elaboration & control. The results showed that compared to self-efficacy, intrinsic value and instrumental value of math were stronger predictors of memorization, elaboration and control strategies. None of the motivational variables had a stronger effect on one strategy than the other. The findings suggest that the development of self-regulatory skills in math can be greatly enhanced by helping students develop positive value of and realistic expectancy for success in math.

  12. Multivariate statistical pattern recognition system for reactor noise analysis

    Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

    1976-01-01

    A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system

  13. Multivariate statistical pattern recognition system for reactor noise analysis

    Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

    1975-01-01

    A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system. 19 references

  14. Multivariate survivorship analysis using two cross-sectional samples.

    Hill, M E

    1999-11-01

    As an alternative to survival analysis with longitudinal data, I introduce a method that can be applied when one observes the same cohort in two cross-sectional samples collected at different points in time. The method allows for the estimation of log-probability survivorship models that estimate the influence of multiple time-invariant factors on survival over a time interval separating two samples. This approach can be used whenever the survival process can be adequately conceptualized as an irreversible single-decrement process (e.g., mortality, the transition to first marriage among a cohort of never-married individuals). Using data from the Integrated Public Use Microdata Series (Ruggles and Sobek 1997), I illustrate the multivariate method through an investigation of the effects of race, parity, and educational attainment on the survival of older women in the United States.

  15. Multivariate statistical analysis of a multi-step industrial processes

    Reinikainen, S.P.; Høskuldsson, Agnar

    2007-01-01

    Monitoring and quality control of industrial processes often produce information on how the data have been obtained. In batch processes, for instance, the process is carried out in stages; some process or control parameters are set at each stage. However, the obtained data might not be utilized...... efficiently, even if this information may reveal significant knowledge about process dynamics or ongoing phenomena. When studying the process data, it may be important to analyse the data in the light of the physical or time-wise development of each process step. In this paper, a unified approach to analyse...... multivariate multi-step processes, where results from each step are used to evaluate future results, is presented. The methods presented are based on Priority PLS Regression. The basic idea is to compute the weights in the regression analysis for given steps, but adjust all data by the resulting score vectors...

  16. HORIZONTAL BRANCH MORPHOLOGY OF GLOBULAR CLUSTERS: A MULTIVARIATE STATISTICAL ANALYSIS

    Jogesh Babu, G.; Chattopadhyay, Tanuka; Chattopadhyay, Asis Kumar; Mondal, Saptarshi

    2009-01-01

    The proper interpretation of horizontal branch (HB) morphology is crucial to the understanding of the formation history of stellar populations. In the present study a multivariate analysis is used (principal component analysis) for the selection of appropriate HB morphology parameter, which, in our case, is the logarithm of effective temperature extent of the HB (log T effHB ). Then this parameter is expressed in terms of the most significant observed independent parameters of Galactic globular clusters (GGCs) separately for coherent groups, obtained in a previous work, through a stepwise multiple regression technique. It is found that, metallicity ([Fe/H]), central surface brightness (μ v ), and core radius (r c ) are the significant parameters to explain most of the variations in HB morphology (multiple R 2 ∼ 0.86) for GGC elonging to the bulge/disk while metallicity ([Fe/H]) and absolute magnitude (M v ) are responsible for GGC belonging to the inner halo (multiple R 2 ∼ 0.52). The robustness is tested by taking 1000 bootstrap samples. A cluster analysis is performed for the red giant branch (RGB) stars of the GGC belonging to Galactic inner halo (Cluster 2). A multi-episodic star formation is preferred for RGB stars of GGC belonging to this group. It supports the asymptotic giant branch (AGB) model in three episodes instead of two as suggested by Carretta et al. for halo GGC while AGB model is suggested to be revisited for bulge/disk GGC.

  17. Multivariate analysis for customer segmentation based on RFM

    Álvaro Julio Cuadros López

    2018-02-01

    Full Text Available Context: To build a successful relationship management (CRM, companies must start with the identification of the true value of customers, as this provides basic information to implement more targeted and customized marketing strategies. The RFM methodology, a classic analysis tool that uses three evaluation parameters, allows companies to understand customer behavior, and to establish customer segments. The addition of a new parameter in the traditional technique is an opportunity to refine the possible outcomes of a customer segmentation since it not only provides a new element of evaluation to identify the most valuable customers, but it also makes it possible to differentiate and get to know customers even better. Method: The article presents a methodology that allows to establish customer segments using an extended RFM method with new variables, selected through multivariate analysis..  Results: The proposed implementation was applied in a company in which variables such as profit, profit percentage, and billing due date were tested. Therefore, it was possible to establish a more detailed customer segmentation than with the classic RFM. Conclusions: the RFM analysis is a method widely used in the industry for its easy understanding and applicability. However, it can be improved with the use of statistical procedures and new variables, which will allow companies to have deeper information about the behavior of the clients, and will facilitate the design of specific marketing strategies.

  18. Time-series panel analysis (TSPA): multivariate modeling of temporal associations in psychotherapy process.

    Ramseyer, Fabian; Kupper, Zeno; Caspar, Franz; Znoj, Hansjörg; Tschacher, Wolfgang

    2014-10-01

    Processes occurring in the course of psychotherapy are characterized by the simple fact that they unfold in time and that the multiple factors engaged in change processes vary highly between individuals (idiographic phenomena). Previous research, however, has neglected the temporal perspective by its traditional focus on static phenomena, which were mainly assessed at the group level (nomothetic phenomena). To support a temporal approach, the authors introduce time-series panel analysis (TSPA), a statistical methodology explicitly focusing on the quantification of temporal, session-to-session aspects of change in psychotherapy. TSPA-models are initially built at the level of individuals and are subsequently aggregated at the group level, thus allowing the exploration of prototypical models. TSPA is based on vector auto-regression (VAR), an extension of univariate auto-regression models to multivariate time-series data. The application of TSPA is demonstrated in a sample of 87 outpatient psychotherapy patients who were monitored by postsession questionnaires. Prototypical mechanisms of change were derived from the aggregation of individual multivariate models of psychotherapy process. In a 2nd step, the associations between mechanisms of change (TSPA) and pre- to postsymptom change were explored. TSPA allowed a prototypical process pattern to be identified, where patient's alliance and self-efficacy were linked by a temporal feedback-loop. Furthermore, therapist's stability over time in both mastery and clarification interventions was positively associated with better outcomes. TSPA is a statistical tool that sheds new light on temporal mechanisms of change. Through this approach, clinicians may gain insight into prototypical patterns of change in psychotherapy. PsycINFO Database Record (c) 2014 APA, all rights reserved.

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

    Tania Dehesh

    2015-01-01

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

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

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

    2015-01-01

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

  1. 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.

    2015-01-01

    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

  2. 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. [School of Mechatronic Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600 Arau, Perlis (Malaysia); Omar, O. [Malaysian Agriculture Research and Development Institute (MARDI), Persiaran MARDI-UPM, 43400 Serdang, Selangor (Malaysia)

    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.

  3. 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.

    2015-05-01

    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.

  4. Elemental content of Vietnamese rice. Part 2. Multivariate data analysis.

    Kokot, S; Phuong, T D

    1999-04-01

    Rice samples were obtained from the Red River region and some other parts of Vietnam as well as from Yanco, Australia. These samples were analysed for 14 elements (P, K, Mg, Ca, Mn, Zn, Fe, Cu, Al, Na, Ni, As, Mo and Cd) by ICP-AES, ICP-MS and FAAS as described in Part 1. This data matrix was then submitted to multivariate data analysis by principal component analysis to investigate the influences of environmental and crop cultivation variables on the elemental content of rice. Results revealed that geographical location, grain variety, seasons and soil conditions are the most likely significant factors causing changes in the elemental content between the rice samples. To assess rice quality according to its elemental content and physio-biological properties, a multicriteria decision making method (PROMETHEE) was applied. With the Vietnamese rice, the sticky rice appeared to contain somewhat higher levels of nutritionally significant elements such as P, K and Mg than the non-sticky rice. Also, rice samples grown during the wet season have better levels of nutritionally significant mineral elements than those of the dry season, but in general, the wet season seemed to provide better overall elemental and physio-biological rice quality.

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

    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.

  6. The Inappropriate Symmetries of Multivariate Statistical Analysis in Geometric Morphometrics.

    Bookstein, Fred L

    In today's geometric morphometrics the commonest multivariate statistical procedures, such as principal component analysis or regressions of Procrustes shape coordinates on Centroid Size, embody a tacit roster of symmetries -axioms concerning the homogeneity of the multiple spatial domains or descriptor vectors involved-that do not correspond to actual biological fact. These techniques are hence inappropriate for any application regarding which we have a-priori biological knowledge to the contrary (e.g., genetic/morphogenetic processes common to multiple landmarks, the range of normal in anatomy atlases, the consequences of growth or function for form). But nearly every morphometric investigation is motivated by prior insights of this sort. We therefore need new tools that explicitly incorporate these elements of knowledge, should they be quantitative, to break the symmetries of the classic morphometric approaches. Some of these are already available in our literature but deserve to be known more widely: deflated (spatially adaptive) reference distributions of Procrustes coordinates, Sewall Wright's century-old variant of factor analysis, the geometric algebra of importing explicit biomechanical formulas into Procrustes space. Other methods, not yet fully formulated, might involve parameterized models for strain in idealized forms under load, principled approaches to the separation of functional from Brownian aspects of shape variation over time, and, in general, a better understanding of how the formalism of landmarks interacts with the many other approaches to quantification of anatomy. To more powerfully organize inferences from the high-dimensional measurements that characterize so much of today's organismal biology, tomorrow's toolkit must rely neither on principal component analysis nor on the Procrustes distance formula, but instead on sound prior biological knowledge as expressed in formulas whose coefficients are not all the same. I describe the problems

  7. Oil price and financial markets: Multivariate dynamic frequency analysis

    Creti, Anna; Ftiti, Zied; Guesmi, Khaled

    2014-01-01

    The aim of this paper is to study the degree of interdependence between oil price and stock market index into two groups of countries: oil-importers and oil-exporters. To this end, we propose a new empirical methodology allowing a time-varying dynamic correlation measure between the stock market index and the oil price series. We use the frequency approach proposed by Priestley and Tong (1973), that is the evolutionary co-spectral analysis. This method allows us to distinguish between short-run and medium-run dependence. In order to complete our study by analysing long-run dependence, we use the cointegration procedure developed by Engle and Granger (1987). We find that interdependence between the oil price and the stock market is stronger in exporters' markets than in the importers' ones. - Highlights: • A new time-varying measure for the stock markets and oil price relationship in different horizons. • We propose a new empirical methodology: multivariate frequency approach. • We propose a comparison between oil importing and exporting countries. • We show that oil is not always countercyclical with respect to stock markets. • When high oil prices originate from supply shocks, oil is countercyclical with stock markets

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

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

    2007-01-01

    We present the preliminary results of a provenance study of obsidians samples from Cerrillos (ca. 800-100 b.c.) using Moessbauer Spectroscopy. The Cerrillos archaeological site, located in the Upper Ica Valley, Peru, is the only Paracas ceremonial center excavated so far. The archaeological data collected suggest the existence of a complex social and economic organization on the south coast of Peru. Provenance research of obsidian provides valuable information about the selection of lithic resources by our ancestors and eventually about the existence of communication routes and exchange networks. We characterized 18 obsidian artifacts samples by Moessbauer spectroscopy from Cerrillos. The spectra, recorded at room temperature using different velocities, are mainly composed of broad asymmetric doublets due to the superposition of at least two quadrupole doublets corresponding to Fe 2+ in two different sites (species A and B), one weak Fe 3+ doublet (specie C) and magnetic components associated to the presence of small particles of magnetite. Multivariate statistical analysis of the Moessbauer data (hyperfine parameters) allows to defined two main groups of obsidians, reflecting different geographical origins.

  9. Multivariate analysis of factors influencing the effect of radiosynovectomy

    Farahati, J.; Schulz, G.; Koerber, C.; Geling, M.; Schmeider, P.; Reiners, Chr.; Wendler, J.; Kenn, W.; Reidemeister, C.

    2002-01-01

    Objective: In this prospective study, the time to remission after radiosynovectomy (RSV) was analyzed and the influence of age, sex, underlying disease, type of joint, and duration of illness on the success rate of RSV was determined. Methods: A total number of 57 patients with rheumatoid arthritis (n = 33) and arthrosis (n = 21) with a total number of 130 treated joints (36 knee, 66 small and 28 medium-size joints) were monitored using visual analogue scales (VAS) from one week before RSV up to four to six months after RSV. The patients had to answer 3 times daily for pain intensity of the treated joint. The time until remission was determined according to the Kaplan-Meier survivorship function. The influence of the prognosis parameters on outcome of RSV was determined by multivariate discriminant analysis. Results: After six months, the probability of pain relief of more than 20% amounted to 78% and was significantly dependent on the age of the patient (p = 0.02) and the duration of illness (p = 0.05), however not on sex (p = 0.17), underlying disease (p = 0.23), and type of joint (p = 0.69). Conclusion: Irrespective of sex, type of joint and underlying disease, a measurable pain relief can be achieved with RSV in 78% of the patients with synovitis, whereby effectiveness is decreasing with increasing age and progress of illness. (orig.) [de

  10. Multivariate analysis of marketing data - applications for bricolage market

    FANARU Mihai

    2017-01-01

    Full Text Available By using concepts and analytical tools for computing, marketing is directly related to the quantitative methods of economic research and other areas where the efficiency of systems performances are studied. Any activity of the company must be programmed and carried out taking into account the consumer. Providing a complete success in business requires the entrepreneur to see the company and its products through the consumers eyes, to act as representative of its clients in order to acquire and satisfy their desires. Through its complex specific activities, marketing aims to provide goods and services the consumers require or right merchandise in the right quantity at the right price at the right time and place. An important consideration in capturing the link between marketing and multivariate statistical analysis is that it provides more powerful instruments that allow researchers to discover relationships between multiple configurations of the relationship between variables, configurations that would otherwise remain hidden or barely visible. In addition, most methods can do this with good accuracy, with the possibility of testing the statistical significance by calculating the level of confidence associated with the link validation to the entire population and not just the investigated sample.

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

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

    2007-02-01

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

  12. Multivariate analysis of magnetic resonance imaging of focal hepatic lesions

    Fujishima, Mamoru; Suemitsu, Ichizou; Sei, Tetsurou; Takeda, Yoshihiro; Hiraki, Yoshio

    1993-01-01

    A total of 124 lesions from 1 to 6 cm in diameter, including 31 cavernous hemangiomas, 32 metastases and 61 hepatocellular carcinomas (HCC) were analyzed to study the usefulness of magnetic resonance imaging (MRI) at 0.5 Tesla to differentiate focal hepatic lesions on the basis of qualitative criteria. Each focal hepatic lesion was assessed for shape, internal architecture and signal intensity relative to normal liver parenchyma. While all cavernous hemangiomas and metastases except one lesion could be detected, detection rate of HCC was significantly inferior to that of the other two diseases. A tumor capsule and a hyperintense focus on T 1 -weighted images were demonstrated in only HCC lesions in strong contrast with the other two diseases; however, metastases with slow-growing characteristics or subacute hematoma may appear as similar images. Cavernous hemangiomas appeared markedly hyperintense on T 2 -weighted images in 23 of 31 lesions, but one metastasis and one HCC had similar images. A multivariate analysis of several MRI resulted in the following mean discriminant scores: cavernous hemangioma, -1.2652; metastasis, 0.1830; and HCC, 0.7138. It appeared to be possible to differentiate the three diseases with 84.4 percent accuracy. (author)

  13. Multivariate statistical analysis - an application to lunar materials

    Deb, M.

    1978-01-01

    The compositional characteristics of clinopyroxenes and spinels - two minerals considered to be very useful in deciphering lunar history, have been studied using the multivariate statistical method of principal component analysis. The mineral-chemical data used are from certain lunar rocks and fines collected by Apollo 11, 12, 14 and 15 and Luna 16 and 20 missions, representing mainly the mare basalts and also non-mare basalts, breccia and rock fragments from the highland regions, in which a large number of these minerals have been analyzed. The correlations noted in the mineral compositions, indicating substitutional relationships, have been interpreted on the basis of available crystal-chemical and petrological informations. Compositional trends for individual specimens have been delineated and compared by producing ''principal latent vector diagrams''. The percent variance of the principal components denoted by the eigenvalues, have been evaluated in terms of the crystallization history of the samples. Some of the major petrogenetic implications of this study concern the role of early formed cumulate phases in the near-surface fractionation of mare basalts, mixing of mineral compositions in the highland regolith and the subsolidus reduction trends in lunar spinels. (auth.)

  14. Micro-Raman Imaging for Biology with Multivariate Spectral Analysis

    Malvaso, Federica

    2015-01-01

    . The aim of the following thesis work is to analyze Raman maps related to three pairs of different cells, highlighting differences and similarities through multivariate algorithms. The first pair of analyzed cells are human embryonic stem cells (h

  15. A comparison of multivariate genome-wide association methods

    Galesloot, Tessel E; Van Steen, Kristel; Kiemeney, Lambertus A L M

    2014-01-01

    Joint association analysis of multiple traits in a genome-wide association study (GWAS), i.e. a multivariate GWAS, offers several advantages over analyzing each trait in a separate GWAS. In this study we directly compared a number of multivariate GWAS methods using simulated data. We focused on six...... methods that are implemented in the software packages PLINK, SNPTEST, MultiPhen, BIMBAM, PCHAT and TATES, and also compared them to standard univariate GWAS, analysis of the first principal component of the traits, and meta-analysis of univariate results. We simulated data (N = 1000) for three...... for scenarios with an opposite sign of genetic and residual correlation. All multivariate analyses resulted in a higher power than univariate analyses, even when only one of the traits was associated with the QTL. Hence, use of multivariate GWAS methods can be recommended, even when genetic correlations between...

  16. Multivariate factor analysis of Girgentana goat milk composition

    Pietro Giaccone

    2010-01-01

    Full Text Available The interpretation of the several variables that contribute to defining milk quality is difficult due to the high degree of  correlation among them. In this case, one of the best methods of statistical processing is factor analysis, which belongs  to the multivariate groups; for our study this particular statistical approach was employed.  A total of 1485 individual goat milk samples from 117 Girgentana goats, were collected fortnightly from January to July,  and analysed for physical and chemical composition, and clotting properties. Milk pH and tritable acidity were within the  normal range for fresh goat milk. Morning milk yield resulted 704 ± 323 g with 3.93 ± 1.23% and 3.48±0.38% for fat  and protein percentages, respectively. The milk urea content was 43.70 ± 8.28 mg/dl. The clotting ability of Girgentana  milk was quite good, with a renneting time equal to 16.96 ± 3.08 minutes, a rate of curd formation of 2.01 ± 1.63 min-  utes and a curd firmness of 25.08 ± 7.67 millimetres.  Factor analysis was performed by applying axis orthogonal rotation (rotation type VARIMAX; the analysis grouped the  milk components into three latent or common factors. The first, which explained 51.2% of the total covariance, was  defined as “slow milks”, because it was linked to r and pH. The second latent factor, which explained 36.2% of the total  covariance, was defined as “milk yield”, because it is positively correlated to the morning milk yield and to the urea con-  tent, whilst negatively correlated to the fat percentage. The third latent factor, which explained 12.6% of the total covari-  ance, was defined as “curd firmness,” because it is linked to protein percentage, a30 and titatrable acidity. With the aim  of evaluating the influence of environmental effects (stage of kidding, parity and type of kidding, factor scores were anal-  ysed with the mixed linear model. Results showed significant effects of the season of

  17. Multivariate analysis of flow cytometric data using decision trees.

    Simon, Svenja; Guthke, Reinhard; Kamradt, Thomas; Frey, Oliver

    2012-01-01

    Characterization of the response of the host immune system is important in understanding the bidirectional interactions between the host and microbial pathogens. For research on the host site, flow cytometry has become one of the major tools in immunology. Advances in technology and reagents allow now the simultaneous assessment of multiple markers on a single cell level generating multidimensional data sets that require multivariate statistical analysis. We explored the explanatory power of the supervised machine learning method called "induction of decision trees" in flow cytometric data. In order to examine whether the production of a certain cytokine is depended on other cytokines, datasets from intracellular staining for six cytokines with complex patterns of co-expression were analyzed by induction of decision trees. After weighting the data according to their class probabilities, we created a total of 13,392 different decision trees for each given cytokine with different parameter settings. For a more realistic estimation of the decision trees' quality, we used stratified fivefold cross validation and chose the "best" tree according to a combination of different quality criteria. While some of the decision trees reflected previously known co-expression patterns, we found that the expression of some cytokines was not only dependent on the co-expression of others per se, but was also dependent on the intensity of expression. Thus, for the first time we successfully used induction of decision trees for the analysis of high dimensional flow cytometric data and demonstrated the feasibility of this method to reveal structural patterns in such data sets.

  18. Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods

    Arenas-Garcia, J.; Petersen, K.; Camps-Valls, G.

    2013-01-01

    correlation analysis (CCA), and orthonormalized PLS (OPLS), as well as their nonlinear extensions derived by means of the theory of reproducing kernel Hilbert spaces (RKHSs). We also review their connections to other methods for classification and statistical dependence estimation and introduce some recent...

  19. Multivariate Analysis for Animal Selection in Experimental Research

    Renan Mercuri Pinto

    2015-02-01

    Full Text Available 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.

  20. Antioxidant activity of Costa Rican propolis: a multivariate analysis approach

    Umana Rojas, Eduardo; Solado, Godofredo; Tamayo-Castillo, Giselle

    2013-01-01

    Propolis is produced by Apis mellifera bees from resins of plants that are found around the apiary. The chemical composition is highly variable and Costa Rica has reported without studies of characterization to define the types of propolis in the country. 119 samples were collected from beekeeping areas of the country. The spectrum of 1 H-NMR and its antioxidant activity against DPPH radical were measured. The spectra have been divided into 243 blocks of 0,04 ppm and processed with the Minitab software for multivariate analysis. 99 of the samples collected were used for construction of models for the valuation of the predictive ability of the model have been used coefficients of determination (R 2 ) of prediction by the software and the remaining 20 samples. The existence of three types of propolis with chemically different metabolomes were determined by principal component analysis (PCA). A prediction model was constructed by analysis of partial least squares (PLS). The prediction model has allowed to classify a propolis according to the level of antioxidant activity (AAO), high (type I and II) or low (type III) from the spectrum of 1 H-NMR. The R 2 has been 0.88 and R 2 prediction of 0, 718 for new samples. The nconiferyl benzoate of group I and nemorosone of the group II as two discriminated antioxidants among the groups I and II were isolated and high concentration levels of these compounds have been differentiated with respect to type III. This has allowed the construction of a linear discriminant model with a success rate of 100% for the samples used for formulation and 92,9 for the prediction of different samples. The classification systems could be applied to the standardization of the quality of propolis from Costa Rica for future medicinal or cosmetic applications that take advantage of its antioxidant properties. Also, the methylated derivative has isolated and identified of the nconiferyl benzoate thereof propolis than was obtained his counterpart

  1. Multivariate genetic divergence among sugarcane clones by multivariate analysis associated with mixed models

    Valéria Rosa Lopes

    2014-02-01

    Full Text Available This work had the aim to evaluate the genetic divergence in sugarcane clones using the methodology of graphic dispersion by principal components analysis associated to linear mixed models, indentifying the more divergent and productive genotypes with more precision, for a subsequent combination. 138 sugarcane clones of the RB97 series of the Sugarcane Breeding Program of the Universidade Federal do Parana, more two standard cultivars were evaluated in three environments, with two replications. The two first components explained 96% of the total variation, sufficiently for explaining the divergence found. The variable that contributed the most to de divergence was kilogram of brix per plot (BKP followed by brix, mass of 10 stalks and number of stalks per plot. The more divergent sugarcane clones were RB975008, RB975112, RB975019, RB975153 and RB975067 and the more productive clones were RB975269, RB977533, RB975102, RB975317 and RB975038.

  2. Impact of functional and structural social relationships on two year depression outcomes: A multivariate analysis.

    Davidson, Sandra K; Dowrick, Christopher F; Gunn, Jane M

    2016-03-15

    High rates of persistent depression highlight the need to identify the risk factors associated with poor depression outcomes and to provide targeted interventions to people at high risk. Although social relationships have been implicated in depression course, interventions targeting social relationships have been disappointing. Possibly, interventions have targeted the wrong elements of relationships. Alternatively, the statistical association between relationships and depression course is not causal, but due to shared variance with other factors. We investigated whether elements of social relationships predict major depressive episode (MDE) when multiple relevant variables are considered. Data is from a longitudinal study of primary care patients with depressive symptoms. 494 participants completed questionnaires at baseline and a depression measure (PHQ-9) two years later. Baseline measures included functional (i.e. quality) and structural (i.e. quantity) social relationships, depression, neuroticism, chronic illness, alcohol abuse, childhood abuse, partner violence and sociodemographic characteristics. Logistic regression with generalised estimating equations was used to estimate the association between social relationships and MDE. Both functional and structural social relationships predicted MDE in univariate analysis. Only functional social relationships remained significant in multivariate analysis (OR: 0.87; 95%CI: 0.79-0.97; p=0.01). Other unique predictors of MDE were baseline depression severity, neuroticism, childhood sexual abuse and intimate partner violence. We did not assess how a person's position in their depression trajectory influenced the association between social relationships and depression. Interventions targeting relationship quality may be part of a personalised treatment plan for people at high risk due of persistent depression due to poor social relationships. Copyright © 2015 Elsevier B.V. All rights reserved.

  3. Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis

    Ye, Lanhan; Song, Kunlin; Shen, Tingting

    2018-01-01

    Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS), coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice). For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV). Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R2 more than 0.97. The limit of detection (LOD) was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR) showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR) performed better in both calibration and prediction sets, where Rc2 and Rp2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice. PMID:29495445

  4. Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis

    Fei Liu

    2018-02-01

    Full Text Available Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS, coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice. For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV. Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R 2 more than 0.97. The limit of detection (LOD was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR performed better in both calibration and prediction sets, where R c 2 and R p 2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice.

  5. A multivariate analysis of factors affecting adoption of improved ...

    This paper analyzes the synergies/tradeoffs involved in the adoption of improved varieties of multiple crops in the mixed crop-livestock production systems of the highlands of Ethiopia A multivariate probit (MVP) model involving a system of four equations for the adoption decision of improved varieties of barley, potatoes, ...

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

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

    2017-03-01

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

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

    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

  8. Multivariable analysis: a practical guide for clinicians and public health researchers

    Katz, Mitchell H

    2011-01-01

    .... It is the perfect introduction for all clinical researchers. It describes how to perform and interpret multivariable analysis, using plain language rather than complex derivations and mathematical formulae...

  9. Multivariate Analysis of Reproductive Risk Factors for Ovarian Cancer in Alexandria, Egypt

    El-Khwsky, F.S.; Maghraby, H.K.; Rostom, Y.A.; Abdel-Rahman, A.H.

    2006-01-01

    Background: Ovarian cancer is the eighth leading cancer in women, as it accounts for 4% of all malignant tumors in females. The incidence of ovarian cancer is up to 10 times higher in western countries than in rural Asian and Africa ones. Different reproductive characteristics, life styles and specific medical conditions are responsible for different pattern and incidence of ovarian cancer worldwide. Material and Methods: A case control study was conducted during the time period from 2000 to 2003 including 172 cases of epithelial ovarian cancer, recently diagnosed and confirmed by histopathology. The patients were accessed at the hospitals currently covered by Alexandria Cancer Registry. In addition, 441 control subjects, comparable by age and address, were randomly selected from patients admitted to the same hospitals for non gynecological, non endocrinal acute diseases. Both cases and controls were subjected to a specific predesigned questionnaire to cover menstrual, reproductive and lifestyle indicators. Univariate and multivariate analysis were conducted and 5% level of significance was adopted. Results: Significantly increased risks were reported with increased number of abortions and increased number of ovarian cycles (OR=1.8, 95% CI (1.7-2.8), and 2.8, 95% CI 2.8 (1.5-5.2), respectively. Similarly, high risks were also reported for increased number of pregnancies, OR= 1.6, 95% CI 1.1-2.4) for I to three pregnancies and 4.2,95% CI 1.2-15.9) for more than four pregnancies On the other hand, decreased risks were reported for those with increased parity compared to nulliparous. Conclusion: Although ovarian cancer is less frequent in our community, yet the significant positive and negative associations between risk factors and ovarian cancer were similar to the results of other studies, apart from the primary prevention program that should be outlined according to prevalence of significant risk factors in the studied local community

  10. Predictors of success after laparoscopic gastric bypass: a multivariate analysis of socioeconomic factors.

    Lutfi, R; Torquati, A; Sekhar, N; Richards, W O

    2006-06-01

    Laparoscopic gastric bypass (LGB) has proven efficacy in causing significant and durable weight loss. However, the degree of postoperative weight loss and metabolic improvement varies greatly among individuals. Our study is aimed to identify independent predictors of successful weight loss after LGB. Socioeconomic demographics were prospectively collected on patients undergoing LGB. Primary endpoint was percent of excess weight loss (EWL) at 1-year follow-up. Insufficient weight loss was defined as EWL or=52.8%. According to this definition, 147 patients (81.7%) achieved successful weight loss 1 year after LGB. On univariate analysis, preoperative BMI had a significant effect on EWL, with patients with BMI vs 61.6%; p = 0.001). Marriage status was also a significant predictor of successful outcome, with single patients achieving a higher percentage of EWL than married patients (89.8% vs 77.7%; p = 0.04). Race had a noticeable but not statistically significant effect, with Caucasian patients achieving a higher percentage of EWL than African Americans (82.9% vs 60%; p = 0.06). Marital status remained an independent predictor of success in the multivariate logistic regression model after adjusting for covariates. Married patients were at more than two times the risk of failure compared to those who were unmarried (OR 2.6; 95% CI: 1.1-6.5, p = 0.04). Weight loss achieved at 1 year after LGB is suboptimal in superobese patients. Single patients with BMI < 50 had the best chance of achieving greater weight loss.

  11. Multivariate data analysis of two-dimensional gel electrophoresis protein patterns from few samples

    Jensen, Kristina Nedenskov; Jessen, Flemming; Jørgensen, Bo

    2008-01-01

    One application of 2D gel electrophoresis is to reveal differences in protein pattern between two or more groups of individuals, attributable to their group membership. Multivariate data analytical methods are useful in pinpointing the spots relevant for discrimination by focusing not only...... on single spot differences, but on the covariance structure between proteins. However, their outcome is dependent on data scaling, and they may fail in producing valid multivariate models due to the much higher number of "irrelevant" spots present in the gels. The case where only few gels are available...... and where the aim is to find as many as possible of the group-dependent proteins seems particularly difficult to handle. The present paper investigates such a case regarding the effect of scaling and of prefiltering by univariate nonparametric statistics on the selection of spots. Besides, a modified...

  12. Use of multivariate extensions of generalized linear models in the analysis of data from clinical trials

    ALONSO ABAD, Ariel; Rodriguez, O.; TIBALDI, Fabian; CORTINAS ABRAHANTES, Jose

    2002-01-01

    In medical studies the categorical endpoints are quite often. Even though nowadays some models for handling this multicategorical variables have been developed their use is not common. This work shows an application of the Multivariate Generalized Linear Models to the analysis of Clinical Trials data. After a theoretical introduction models for ordinal and nominal responses are applied and the main results are discussed. multivariate analysis; multivariate logistic regression; multicategor...

  13. Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs.

    Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G; Shah, Arvind K; Lin, Jianxin

    2013-10-15

    In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the deviance information criterion is used to select the best transformation model. Because the model is quite complex, we develop a novel Monte Carlo Markov chain sampling scheme to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol-lowering drugs where the goal is to jointly model the three-dimensional response consisting of low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). Because the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately; however, a multivariate approach would be more appropriate because these variables are correlated with each other. We carry out a detailed analysis of these data by using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.

  14. Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol lowering drugs

    Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G.; Shah, Arvind K.; Lin, Jianxin

    2013-01-01

    In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the Deviance Information Criterion (DIC) is used to select the best transformation model. Since the model is quite complex, a novel Monte Carlo Markov chain (MCMC) sampling scheme is developed to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional response consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). Since the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate since these variables are correlated with each other. A detailed analysis of these data is carried out using the proposed methodology. PMID:23580436

  15. 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.

  16. Comparison of spectrum normalization techniques for univariate ...

    Laser-induced breakdown spectroscopy; univariate study; normalization models; stainless steel; standard error of prediction. Abstract. Analytical performance of six different spectrum normalization techniques, namelyinternal normalization, normalization with total light, normalization with background along with their ...

  17. Development of a Univariate Membrane-Based Mid-Infrared Method for Protein Quantitation and Total Lipid Content Analysis of Biological Samples

    Ivona Strug

    2014-01-01

    Full Text Available Biological samples present a range of complexities from homogeneous purified protein to multicomponent mixtures. Accurate qualification of such samples is paramount to downstream applications. We describe the development of an MIR spectroscopy-based analytical method offering simultaneous protein quantitation (0.25–5 mg/mL and analysis of total lipid or detergent species, as well as the identification of other biomolecules present in biological samples. The method utilizes a hydrophilic PTFE membrane engineered for presentation of aqueous samples in a dried format compatible with fast infrared analysis. Unlike classical quantification techniques, the reported method is amino acid sequence independent and thus applicable to complex samples of unknown composition. By comparison to existing platforms, this MIR-based method enables direct quantification using minimal sample volume (2 µL; it is well-suited where repeat access and limited sample size are critical parameters. Further, accurate results can be derived without specialized training or knowledge of IR spectroscopy. Overall, the simplified application and analysis system provides a more cost-effective alternative to high-throughput IR systems for research laboratories with minimal throughput demands. In summary, the MIR-based system provides a viable alternative to current protein quantitation methods; it also uniquely offers simultaneous qualification of other components, notably lipids and detergents.

  18. Experimental analysis of multivariate female choice in gray treefrogs (Hyla versicolor): evidence for directional and stabilizing selection.

    Gerhardt, H Carl; Brooks, Robert

    2009-10-01

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

  19. Multivariate image analysis for quality inspection in fish feed production

    Ljungqvist, Martin Georg

    Aquaculture is today one of the fastest growing food producing sectors in the world. Access to good and effective fish feed is a condition for optimised and sustainable aquaculture activity. In the aquaculture industry it is of utmost importance that the fish get feed of proper size and nutrition....... The colour appearance of fish products is important for customers. Salmonid fish get their red colour from a natural pigment called astaxanthin. To ensure a similar red colour of fish in aquaculture astaxanthin is used as an additive coated on the feed pellets. Astaxanthin can either be of natural origin......, or synthesised chemically. Common for both types is that they are relatively expensive in comparison to the other feed ingredients. This thesis investigates multi-variate data collection for visual inspection and optimisation of industrial production in the fish feed industry. Quality parameters focused on here...

  20. A Multivariate Time Series Method for Monte Carlo Reactor Analysis

    Taro Ueki

    2008-01-01

    A robust multivariate time series method has been established for the Monte Carlo calculation of neutron multiplication problems. The method is termed Coarse Mesh Projection Method (CMPM) and can be implemented using the coarse statistical bins for acquisition of nuclear fission source data. A novel aspect of CMPM is the combination of the general technical principle of projection pursuit in the signal processing discipline and the neutron multiplication eigenvalue problem in the nuclear engineering discipline. CMPM enables reactor physicists to accurately evaluate major eigenvalue separations of nuclear reactors with continuous energy Monte Carlo calculation. CMPM was incorporated in the MCNP Monte Carlo particle transport code of Los Alamos National Laboratory. The great advantage of CMPM over the traditional Fission Matrix method is demonstrated for the three space-dimensional modeling of the initial core of a pressurized water reactor

  1. Analysis of preservative-treated wood by multivariate analysis of laser-induced breakdown spectroscopy spectra

    Martin, Madhavi Z.; Labbe, Nicole; Rials, Timothy G.; Wullschleger, Stan D.

    2005-01-01

    In this work, multivariate statistical analysis (MVA) techniques are coupled with laser-induced breakdown spectroscopy (LIBS) to identify preservative types (chromated copper arsenate, ammoniacal copper zinc or alkaline copper quat), and to predict elemental content in preservative-treated wood. The elemental composition of the samples was measured with a standard laboratory method of digestion followed by atomic absorption spectroscopy analysis. The elemental composition was then correlated with the LIBS spectra using projection to latent structures (PLS) models. The correlations for the different elements introduced by different treatments were very strong, with the correlation coefficients generally above 0.9. Additionally, principal component analysis (PCA) was used to differentiate the samples treated with different preservative formulations. The research has focused not only on demonstrating the application of LIBS as a tool for use in the forest products industry, but also considered sampling errors, limits of detection, reproducibility, and accuracy of measurements as they relate to multivariate analysis of this complex wood substrate

  2. Analysis of preservative-treated wood by multivariate analysis of laser-induced breakdown spectroscopy spectra

    Martin, Madhavi Z. [Environmental Sciences Division Oak Ridge National Laboratory, P.O. Box 2008 MS 6422, Oak Ridge TN 37831-6422 (United States); Labbe, Nicole [Forest Products Center, University of Tennessee, 2506 Jacob Drive, Knoxville, TN 37996-4570 (United States)]. E-mail: nlabbe@utk.edu; Rials, Timothy G. [Forest Products Center, University of Tennessee, 2506 Jacob Drive, Knoxville, TN 37996-4570 (United States); Wullschleger, Stan D. [Environmental Sciences Division Oak Ridge National Laboratory, P.O. Box 2008 MS 6422, Oak Ridge TN 37831-6422 (United States)

    2005-08-31

    In this work, multivariate statistical analysis (MVA) techniques are coupled with laser-induced breakdown spectroscopy (LIBS) to identify preservative types (chromated copper arsenate, ammoniacal copper zinc or alkaline copper quat), and to predict elemental content in preservative-treated wood. The elemental composition of the samples was measured with a standard laboratory method of digestion followed by atomic absorption spectroscopy analysis. The elemental composition was then correlated with the LIBS spectra using projection to latent structures (PLS) models. The correlations for the different elements introduced by different treatments were very strong, with the correlation coefficients generally above 0.9. Additionally, principal component analysis (PCA) was used to differentiate the samples treated with different preservative formulations. The research has focused not only on demonstrating the application of LIBS as a tool for use in the forest products industry, but also considered sampling errors, limits of detection, reproducibility, and accuracy of measurements as they relate to multivariate analysis of this complex wood substrate.

  3. MULTIVARIABLE ANALYSIS OF 2,4-D HERBICIDE PHOTOCATALYTIC DEGRADATION

    ANDRÉS F. LÓPEZ-VÁSQUEZ

    2011-01-01

    Full Text Available La degradación del herbicida 2,4-D en suspensiones de TiO2 en agua real fue evaluada bajo condiciones de irradiación artificial. El análisis multivariable de metodología de superficie de respuesta (MSR, se aplicó para evaluar el efecto de variables como la concentración de catalizador y pesticida, el pH y el caudal volumétrico sobre la reacción fotocatalítica en dos fotorreactores catalíticos: placa plana y tubular. La variable de respuesta fue la mineralización del pesticida expresada como porcentaje de degradación de carbono orgánico total (COT después de cuatro horas de irradiación. Para el fotorreactor tubular, los cuatro factores tuvieron la misma significancia sobre la degradación, mientras que para el fotorreactor de placa plana inclinada, sólo la concentración de catalizador y el pH tuvieron significancia. La MSR fue una técnica adecuada para obtener parámetros de operación óptimos de un proceso fotocatalítico con un reactor específico y dentro de un rango de estudio determinado.

  4. Complications from arteriovenous malformation radiosurgery: multivariate analysis and risk modeling

    Flickinger, John C.; Kondziolka, Douglas; Pollock, Bruce E.; Maitz, Ann H.; Lunsford, L. Dade

    1997-01-01

    Purpose/Objective: To assess the relationships of radiosurgery treatment parameters to the development of complications from radiosurgery for arteriovenous malformations (AVM). Methods and Materials: We evaluated follow-up imaging and clinical data in 307 AVM patients who received gamma knife radiosurgery at the University of Pittsburgh between 1987 and 1993. All patients had regular clinical or imaging follow up for a minimum of 2 years (range: 24-96 months, median = 44 months). Results: Post-radiosurgical imaging (PRI) changes developed in 30.5% of patients with regular follow-up magnetic resonance imaging, and were symptomatic in 10.7% of all patients at 7 years. PRI changes resolved within 3 years developed significantly less often (p = 0.0274) in patients with symptoms (52.8%) compared to asymptomatic patients (94.8%). The 7-year actuarial rate for developing persistent symptomatic PRI changes was 5.05%. Multivariate logistic regression modeling found that the 12 Gy volume was the only independent variable that correlated significantly with PRI changes (p < 0.0001) while symptomatic PRI changes were correlated with both 12 Gy volume (p = 0.0013) and AVM location (p 0.0066). Conclusion: Complications from AVM radiosurgery can be predicted with a statistical model relating the risks of developing symptomatic post-radiosurgical imaging changes to 12 Gy treatment volume and location

  5. MULTIVARIATE ANALYSIS OF RISK FACTORS FOR PREMATURITY IN SOUTHERN BRAZIL

    Willian Augusto de Melo

    2014-05-01

    Full Text Available This study assessed the risk factors associated with preterm birth through a cross-sectional study in 4,440 newborns. Examined the factors associated between maternal sociodemographic variables (age, marital status, education and occupation, obstetric (pregnancy and delivery type and number of prenatal visits and neonatal (sex, race/color, birth weight and Apgar. Data were analyzed by multivariate logistic regression technique. Among the 480 (10.8% preterm risk factors were prevalent type of pregnancy (OR=6.48, number of prenatal visits (OR=2.09, Apgar score at first (OR=2.00 and fifth minute (OR=2.14 and birth weight (OR=31.8 indicating that these variables are directly associated with the occurrence of prematurity. The identification of risk factors should be the object of attention of health professionals and services to support effective measures to promote health to the general population; especially for women in fertile included some criteria of gestational risk.

  6. Changes in cod muscle proteins during frozen storage revealed by proteome analysis and multivariate data analysis

    Kjærsgård, Inger Vibeke Holst; Nørrelykke, M.R.; Jessen, Flemming

    2006-01-01

    Multivariate data analysis has been combined with proteomics to enhance the recovery of information from 2-DE of cod muscle proteins during different storage conditions. Proteins were extracted according to 11 different storage conditions and samples were resolved by 2-DE. Data generated by 2-DE...... was subjected to principal component analysis (PCA) and discriminant partial least squares regression (DPLSR). Applying PCA to 2-DE data revealed the samples to form groups according to frozen storage time, whereas differences due to different storage temperatures or chilled storage in modified atmosphere...... light chain 1, 2 and 3, triose-phosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, aldolase A and two ?-actin fragments, and a nuclease diphosphate kinase B fragment to change in concentration, during frozen storage. Application of proteomics, multivariate data analysis and MS/MS to analyse...

  7. Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis.

    Yang, Yan-Qin; Yin, Hong-Xu; Yuan, Hai-Bo; Jiang, Yong-Wen; Dong, Chun-Wang; Deng, Yu-Liang

    2018-01-01

    In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties.

  8. Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis.

    Yan-Qin Yang

    Full Text Available In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME followed by gas chromatography-mass spectrometry (GC-MS was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA and hierarchical clustering analysis (HCA. Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties.

  9. Models and analysis for multivariate failure time data

    Shih, Joanna Huang

    The goal of this research is to develop and investigate models and analytic methods for multivariate failure time data. We compare models in terms of direct modeling of the margins, flexibility of dependency structure, local vs. global measures of association, and ease of implementation. In particular, we study copula models, and models produced by right neutral cumulative hazard functions and right neutral hazard functions. We examine the changes of association over time for families of bivariate distributions induced from these models by displaying their density contour plots, conditional density plots, correlation curves of Doksum et al, and local cross ratios of Oakes. We know that bivariate distributions with same margins might exhibit quite different dependency structures. In addition to modeling, we study estimation procedures. For copula models, we investigate three estimation procedures. the first procedure is full maximum likelihood. The second procedure is two-stage maximum likelihood. At stage 1, we estimate the parameters in the margins by maximizing the marginal likelihood. At stage 2, we estimate the dependency structure by fixing the margins at the estimated ones. The third procedure is two-stage partially parametric maximum likelihood. It is similar to the second procedure, but we estimate the margins by the Kaplan-Meier estimate. We derive asymptotic properties for these three estimation procedures and compare their efficiency by Monte-Carlo simulations and direct computations. For models produced by right neutral cumulative hazards and right neutral hazards, we derive the likelihood and investigate the properties of the maximum likelihood estimates. Finally, we develop goodness of fit tests for the dependency structure in the copula models. We derive a test statistic and its asymptotic properties based on the test of homogeneity of Zelterman and Chen (1988), and a graphical diagnostic procedure based on the empirical Bayes approach. We study the

  10. VC-dimension of univariate decision trees.

    Yildiz, Olcay Taner

    2015-02-01

    In this paper, we give and prove the lower bounds of the Vapnik-Chervonenkis (VC)-dimension of the univariate decision tree hypothesis class. The VC-dimension of the univariate decision tree depends on the VC-dimension values of its subtrees and the number of inputs. Via a search algorithm that calculates the VC-dimension of univariate decision trees exhaustively, we show that our VC-dimension bounds are tight for simple trees. To verify that the VC-dimension bounds are useful, we also use them to get VC-generalization bounds for complexity control using structural risk minimization in decision trees, i.e., pruning. Our simulation results show that structural risk minimization pruning using the VC-dimension bounds finds trees that are more accurate as those pruned using cross validation.

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

    Golay, Jean; Kanevski, Mikhaïl

    2013-04-01

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

  12. Detecting a currency’s dominance using multivariate time series analysis

    Syahidah Yusoff, Nur; Sharif, Shamshuritawati

    2017-09-01

    A currency exchange rate is the price of one country’s currency in terms of another country’s currency. There are four different prices; opening, closing, highest, and lowest can be achieved from daily trading activities. In the past, a lot of studies have been carried out by using closing price only. However, those four prices are interrelated to each other. Thus, the multivariate time series can provide more information than univariate time series. Therefore, the enthusiasm of this paper is to compare the results of two different approaches, which are mean vector and Escoufier’s RV coefficient in constructing similarity matrices of 20 world currencies. Consequently, both matrices are used to substitute the correlation matrix required by network topology. With the help of degree centrality measure, we can detect the currency’s dominance for both networks. The pros and cons for both approaches will be presented at the end of this paper.

  13. Multivariate Analysis Techniques for Optimal Vision System Design

    Sharifzadeh, Sara

    The present thesis considers optimization of the spectral vision systems used for quality inspection of food items. The relationship between food quality, vision based techniques and spectral signature are described. The vision instruments for food analysis as well as datasets of the food items...... used in this thesis are described. The methodological strategies are outlined including sparse regression and pre-processing based on feature selection and extraction methods, supervised versus unsupervised analysis and linear versus non-linear approaches. One supervised feature selection algorithm...... (SSPCA) and DCT based characterization of the spectral diffused reflectance images for wavelength selection and discrimination. These methods together with some other state-of-the-art statistical and mathematical analysis techniques are applied on datasets of different food items; meat, diaries, fruits...

  14. Trochanteric entry femoral nails yield better femoral version and lower revision rates-A large cohort multivariate regression analysis.

    Yoon, Richard S; Gage, Mark J; Galos, David K; Donegan, Derek J; Liporace, Frank A

    2017-06-01

    Intramedullary nailing (IMN) has become the standard of care for the treatment of most femoral shaft fractures. Different IMN options include trochanteric and piriformis entry as well as retrograde nails, which may result in varying degrees of femoral rotation. The objective of this study was to analyze postoperative femoral version between three types of nails and to delineate any significant differences in femoral version (DFV) and revision rates. Over a 10-year period, 417 patients underwent IMN of a diaphyseal femur fracture (AO/OTA 32A-C). Of these patients, 316 met inclusion criteria and obtained postoperative computed tomography (CT) scanograms to calculate femoral version and were thus included in the study. In this study, our main outcome measure was the difference in femoral version (DFV) between the uninjured limb and the injured limb. The effect of the following variables on DFV and revision rates were determined via univariate, multivariate, and ordinal regression analyses: gender, age, BMI, ethnicity, mechanism of injury, operative side, open fracture, and table type/position. Statistical significance was set at pregression analysis revealed that a lower BMI was significantly associated with a lower DFV (p=0.006). Controlling for possible covariables, multivariate analysis yielded a significantly lower DFV for trochanteric entry nails than piriformis or retrograde nails (7.9±6.10 vs. 9.5±7.4 vs. 9.4±7.8°, pregression analysis. However, this is not to state that the other nail types exhibited abnormal DFV. Translation to the clinical impact of a few degrees of DFV is also unknown. Future studies to more in-depth study the intricacies of femoral version may lead to improved technology in addition to potentially improved clinical outcomes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Helicopter Gas Turbine Engine Performance Analysis : A Multivariable Approach

    Arush, Ilan; Pavel, M.D.

    2017-01-01

    Helicopter performance relies heavily on the available output power of the engine(s) installed. A simplistic single-variable analysis approach is often used within the flight-testing community to reduce raw flight-test data in order to predict the available output power under different atmospheric

  16. A multivariate analysis of water quality in lake Naivasha, Kenya

    Ndungu, J.N.; Augustijn, Dionysius C.M.; Hulscher, Suzanne J.M.H.; Fulanda, B.; Kitaka, N.; Mathooko, J.M.

    2014-01-01

    Water quality information in aquatic ecosystems is crucial in setting up guidelines for resource management. This study explores the water quality status and pollution sources in Lake Naivasha, Kenya. Analysis of water quality parameters at seven sampling sites was carried out from water samples

  17. Multivariate data analysis of enzyme production for hydrolysis purposes

    Schmidt, A.S.; Suhr, K.I.

    1999-01-01

    of the structure in the data - possibly combined with analysis of variance (ANOVA). Partial least squares regression (PLSR) showed a clear connection between the two differentdata matrices (the fermentation variables and the hydrolysis variables). Hence, PLSR was suitable for prediction purposes. The hydrolysis...

  18. TMVA(Toolkit for Multivariate Analysis) new architectures design and implementation.

    Zapata Mesa, Omar Andres

    2016-01-01

    Toolkit for Multivariate Analysis(TMVA) is a package in ROOT for machine learning algorithms for classification and regression of the events in the detectors. In TMVA, we are developing new high level algorithms to perform multivariate analysis as cross validation, hyper parameter optimization, variable importance etc... Almost all the algorithms are expensive and designed to process a huge amount of data. It is very important to implement the new technologies on parallel computing to reduce the processing times.

  19. Determination of wheat quality by mass spectrometry and multivariate data analysis

    Gottlieb, D.M.; Schultz, J.; Petersen, M.

    2002-01-01

    Multivariate analysis has been applied as support to proteome analysis in order to implement an easier and faster way of data handling based on separation by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry. The characterisation phase in proteome analysis by means...... of simple visual inspection is a demanding process and also insecure because subjectivity is the controlling element. Multivariate analysis offers, to a considerable extent, objectivity and must therefore be regarded as a neutral way to evaluate results obtained by proteome analysis.Proteome analysis...

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

    Eloísa Urrechaga

    2013-01-01

    Full Text Available 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.

  1. Multivariate Statistical Methods as a Tool of Financial Analysis of Farm Business

    Novák, J.; Sůvová, H.; Vondráček, Jiří

    2002-01-01

    Roč. 48, č. 1 (2002), s. 9-12 ISSN 0139-570X Institutional research plan: AV0Z1030915 Keywords : financial analysis * financial ratios * multivariate statistical methods * correlation analysis * discriminant analysis * cluster analysis Subject RIV: BB - Applied Statistics, Operational Research

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

    Ghirardo, A.; Sørensen, Helle Aagaard; Petersen, M.

    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...... 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....

  3. One Hundred Ways to be Non-Fickian - A Rigorous Multi-Variate Statistical Analysis of Pore-Scale Transport

    Most, Sebastian; Nowak, Wolfgang; Bijeljic, Branko

    2015-04-01

    Fickian transport in groundwater flow is the exception rather than the rule. Transport in porous media is frequently simulated via particle methods (i.e. particle tracking random walk (PTRW) or continuous time random walk (CTRW)). These methods formulate transport as a stochastic process of particle position increments. At the pore scale, geometry and micro-heterogeneities prohibit the commonly made assumption of independent and normally distributed increments to represent dispersion. Many recent particle methods seek to loosen this assumption. Hence, it is important to get a better understanding of the processes at pore scale. For our analysis we track the positions of 10.000 particles migrating through the pore space over time. The data we use come from micro CT scans of a homogeneous sandstone and encompass about 10 grain sizes. Based on those images we discretize the pore structure and simulate flow at the pore scale based on the Navier-Stokes equation. This flow field realistically describes flow inside the pore space and we do not need to add artificial dispersion during the transport simulation. Next, we use particle tracking random walk and simulate pore-scale transport. Finally, we use the obtained particle trajectories to do a multivariate statistical analysis of the particle motion at the pore scale. Our analysis is based on copulas. Every multivariate joint distribution is a combination of its univariate marginal distributions. The copula represents the dependence structure of those univariate marginals and is therefore useful to observe correlation and non-Gaussian interactions (i.e. non-Fickian transport). The first goal of this analysis is to better understand the validity regions of commonly made assumptions. We are investigating three different transport distances: 1) The distance where the statistical dependence between particle increments can be modelled as an order-one Markov process. This would be the Markovian distance for the process, where

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

    Davatzikos, Christos

    2016-10-01

    The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. Copyright © 2016. Published by Elsevier B.V.

  5. 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.

  6. Patterns of regional development in Serbia: A multivariate statistical analysis

    Čoček Ladislav

    2010-01-01

    Full Text Available The primary objective of this paper is to examine patterns of regional development in Serbia and to identify underlying geographical factors of these patterns. Principal component analysis is used to reveal the basic dimensions of regional differentiation. Its results are described in the context of findings from thematically similar research on Central European countries. An area's position in the national settlement system hierarchy has been identified as the strongest determinant of regional differentiation in Serbia. Other strong patterns seem to be connected with macro-geographical position. Success in economic development is most apparent in regions near the metropolitan area of Belgrade, and the general development level, along with a predisposition for agriculture, exhibits a strong north-south polarization. Specific attention is directed at demographic development, which is characterized by a west-east gradient. Central patterns of regional differentiation are similar to those uncovered in previous Central European research. Regional policy in Serbia should, therefore, try to learn from experience within this region to cope with processes and problems that are often quite similar. .

  7. The Pomatocalpa maculosum Complex (Orchidaceae Resolved by Multivariate Morphometric Analysis

    Santi Watthana

    2006-03-01

    Full Text Available Principal components analysis (PCA was employed to analyse the morphological variation among 63 herbarium specimens tentatively identified as Pomatocalpa andamanicum (Hook.f. J. J. Sm., P. koordersii (Rolfe J. J. Sm., P. latifolium (Lindl. J. J. Sm., P. linearifolium Seidenf., P. maculosum (Lindl. J. J. Sm., P. marsupiale (Kraenzl. J. J. Sm., P. naevatum J. J. Sm., or P. siamense (Rolfe ex Downie Summerh. Thirty-seven quantitative and 5 binary characters were included in the analyses. Taxa were delimited according to the observed clustering of specimens in the PCA plots, diagnostic characters were identified, and the correct nomenclature was established through examination of type material. Four species could be recognized viz, P. diffusum Breda (syn. P. latifolium, P. fuscum (Lindl. J. J. Sm. (syn. P. latifolium, P. marsupiale (syn. P. koordersii and P. maculosum. For the latter species, two subspecies could be recognized, viz P. maculosum (Lindl. J. J. Sm. subsp. maculosum (syn. P. maculosum, P. naevatum p.p. and P. maculosum (Lindl. J. J. Sm. subsp. andamanicum (Hook.f. S. Watthana (syn. P. andamanicum, P. linearifolium, P. siamense, P. naevatum p.p.. An identification key and a taxonomic synopsis are provided.

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

    Smit Henriette A

    2008-09-01

    Full Text Available Abstract Background There is mounting evidence that estimates of intakes of a range of dietary nutrients are related to both lung function level and rate of decline, but far less evidence on the relation between lung function and objective measures of serum levels of individual nutrients. The aim of this study was to conduct a comprehensive examination of the independent associations of a wide range of serum markers of nutritional status with lung function, measured as the one-second forced expiratory volume (FEV1. Methods Using data from the Third National Health and Nutrition Examination Survey, a US population-based cross-sectional study, we investigated the relation between 21 serum markers of potentially relevant nutrients and FEV1, with adjustment for potential confounding factors. Systematic approaches were used to guide the analysis. Results In a mutually adjusted model, higher serum levels of antioxidant vitamins (vitamin A, beta-cryptoxanthin, vitamin C, vitamin E, selenium, normalized calcium, chloride, and iron were independently associated with higher levels of FEV1. Higher concentrations of potassium and sodium were associated with lower FEV1. Conclusion Maintaining higher serum concentrations of dietary antioxidant vitamins and selenium is potentially beneficial to lung health. In addition other novel associations found in this study merit further investigation.

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

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

    2009-01-01

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

  10. MUMAL: Multivariate analysis in shotgun proteomics using machine learning techniques

    Cerqueira Fabio R

    2012-10-01

    Full Text Available Abstract Background The shotgun strategy (liquid chromatography coupled with tandem mass spectrometry is widely applied for identification of proteins in complex mixtures. This method gives rise to thousands of spectra in a single run, which are interpreted by computational tools. Such tools normally use a protein database from which peptide sequences are extracted for matching with experimentally derived mass spectral data. After the database search, the correctness of obtained peptide-spectrum matches (PSMs needs to be evaluated also by algorithms, as a manual curation of these huge datasets would be impractical. The target-decoy database strategy is largely used to perform spectrum evaluation. Nonetheless, this method has been applied without considering sensitivity, i.e., only error estimation is taken into account. A recently proposed method termed MUDE treats the target-decoy analysis as an optimization problem, where sensitivity is maximized. This method demonstrates a significant increase in the retrieved number of PSMs for a fixed error rate. However, the MUDE model is constructed in such a way that linear decision boundaries are established to separate correct from incorrect PSMs. Besides, the described heuristic for solving the optimization problem has to be executed many times to achieve a significant augmentation in sensitivity. Results Here, we propose a new method, termed MUMAL, for PSM assessment that is based on machine learning techniques. Our method can establish nonlinear decision boundaries, leading to a higher chance to retrieve more true positives. Furthermore, we need few iterations to achieve high sensitivities, strikingly shortening the running time of the whole process. Experiments show that our method achieves a considerably higher number of PSMs compared with standard tools such as MUDE, PeptideProphet, and typical target-decoy approaches. Conclusion Our approach not only enhances the computational performance, and

  11. Multivariate techniques of analysis for ToF-E recoil spectrometry data

    Whitlow, H J; Bouanani, M E; Persson, L; Hult, M; Jonsson, P; Johnston, P N [Lund Institute of Technology, Solvegatan, (Sweden), Department of Nuclear Physics; Andersson, M [Uppsala Univ. (Sweden). Dept. of Organic Chemistry; Ostling, M; Zaring, C [Royal institute of Technology, Electrum, Kista, (Sweden), Department of Electronics; Johnston, P N; Bubb, I F; Walker, B R; Stannard, W B [Royal Melbourne Inst. of Tech., VIC (Australia); Cohen, D D; Dytlewski, N [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW (Australia)

    1997-12-31

    Multivariate statistical methods are being developed by the Australian -Swedish Recoil Spectrometry Collaboration for quantitative analysis of the wealth of information in Time of Flight (ToF) and energy dispersive Recoil Spectrometry. An overview is presented of progress made in the use of multivariate techniques for energy calibration, separation of mass-overlapped signals and simulation of ToF-E data. 6 refs., 5 figs.

  12. Multivariate techniques of analysis for ToF-E recoil spectrometry data

    Whitlow, H.J.; Bouanani, M.E.; Persson, L.; Hult, M.; Jonsson, P.; Johnston, P.N. [Lund Institute of Technology, Solvegatan, (Sweden), Department of Nuclear Physics; Andersson, M. [Uppsala Univ. (Sweden). Dept. of Organic Chemistry; Ostling, M.; Zaring, C. [Royal institute of Technology, Electrum, Kista, (Sweden), Department of Electronics; Johnston, P.N.; Bubb, I.F.; Walker, B.R.; Stannard, W.B. [Royal Melbourne Inst. of Tech., VIC (Australia); Cohen, D.D.; Dytlewski, N. [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW (Australia)

    1996-12-31

    Multivariate statistical methods are being developed by the Australian -Swedish Recoil Spectrometry Collaboration for quantitative analysis of the wealth of information in Time of Flight (ToF) and energy dispersive Recoil Spectrometry. An overview is presented of progress made in the use of multivariate techniques for energy calibration, separation of mass-overlapped signals and simulation of ToF-E data. 6 refs., 5 figs.

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

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

    2011-01-01

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

  14. Study of ionically modified water performance in carbonate reservoir system by multivariate data analysis

    Sohal, Muhammad Adeel Nassar; Kucheryavskiy, Sergey V.; Thyne, Geoffrey

    2017-01-01

    the critical mechanisms at the pore scale. Better pore scale physico-chemical understanding will guide to formulate accurate reservoir-scale models. This paper presents a comprehensive meta-analysis of the proposed mechanisms using multivariate data analysis. Detailed review of the subject, including...... mechanisms with supporting and contradictory evidence has been presented by Sohal et al. (2016). In this study, the significance of each contributing factor to EOR was quantified and subjected to rigorous multivariate statistical analysis. The analysis was limited because there is no uniform methodology...

  15. A multivariate tobit analysis of highway accident-injury-severity rates.

    Anastasopoulos, Panagiotis Ch; Shankar, Venky N; Haddock, John E; Mannering, Fred L

    2012-03-01

    Relatively recent research has illustrated the potential that tobit regression has in studying factors that affect vehicle accident rates (accidents per distance traveled) on specific roadway segments. Tobit regression has been used because accident rates on specific roadway segments are continuous data that are left-censored at zero (they are censored because accidents may not be observed on all roadway segments during the period over which data are collected). This censoring may arise from a number of sources, one of which being the possibility that less severe crashes may be under-reported and thus may be less likely to appear in crash databases. Traditional tobit-regression analyses have dealt with the overall accident rate (all crashes regardless of injury severity), so the issue of censoring by the severity of crashes has not been addressed. However, a tobit-regression approach that considers accident rates by injury-severity level, such as the rate of no-injury, possible injury and injury accidents per distance traveled (as opposed to all accidents regardless of injury-severity), can potentially provide new insights, and address the possibility that censoring may vary by crash-injury severity. Using five-year data from highways in Washington State, this paper estimates a multivariate tobit model of accident-injury-severity rates that addresses the possibility of differential censoring across injury-severity levels, while also accounting for the possible contemporaneous error correlation resulting from commonly shared unobserved characteristics across roadway segments. The empirical results show that the multivariate tobit model outperforms its univariate counterpart, is practically equivalent to the multivariate negative binomial model, and has the potential to provide a fuller understanding of the factors determining accident-injury-severity rates on specific roadway segments. Published by Elsevier Ltd.

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

    Clegg, Samuel M [Los Alamos National Laboratory; Barefield, James E [Los Alamos National Laboratory; Wiens, Roger C [Los Alamos National Laboratory; Sklute, Elizabeth [MT HOLYOKE COLLEGE; Dyare, Melinda D [MT HOLYOKE COLLEGE

    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.

  17. Comprehensive drought characteristics analysis based on a nonlinear multivariate drought index

    Yang, Jie; Chang, Jianxia; Wang, Yimin; Li, Yunyun; Hu, Hui; Chen, Yutong; Huang, Qiang; Yao, Jun

    2018-02-01

    It is vital to identify drought events and to evaluate multivariate drought characteristics based on a composite drought index for better drought risk assessment and sustainable development of water resources. However, most composite drought indices are constructed by the linear combination, principal component analysis and entropy weight method assuming a linear relationship among different drought indices. In this study, the multidimensional copulas function was applied to construct a nonlinear multivariate drought index (NMDI) to solve the complicated and nonlinear relationship due to its dependence structure and flexibility. The NMDI was constructed by combining meteorological, hydrological, and agricultural variables (precipitation, runoff, and soil moisture) to better reflect the multivariate variables simultaneously. Based on the constructed NMDI and runs theory, drought events for a particular area regarding three drought characteristics: duration, peak, and severity were identified. Finally, multivariate drought risk was analyzed as a tool for providing reliable support in drought decision-making. The results indicate that: (1) multidimensional copulas can effectively solve the complicated and nonlinear relationship among multivariate variables; (2) compared with single and other composite drought indices, the NMDI is slightly more sensitive in capturing recorded drought events; and (3) drought risk shows a spatial variation; out of the five partitions studied, the Jing River Basin as well as the upstream and midstream of the Wei River Basin are characterized by a higher multivariate drought risk. In general, multidimensional copulas provides a reliable way to solve the nonlinear relationship when constructing a comprehensive drought index and evaluating multivariate drought characteristics.

  18. A comparison between multivariate and bivariate analysis used in marketing research

    Constantin, C.

    2012-01-01

    Full Text Available This paper is about an instrumental research conducted in order to compare the information given by two multivariate data analysis in comparison with the usual bivariate analysis. The outcomes of the research reveal that sometimes the multivariate methods use more information from a certain variable, but sometimes they use only a part of the information considered the most important for certain associations. For this reason, a researcher should use both categories of data analysis in order to obtain entirely useful information.

  19. Multivariate Statistical Process Control Charts: An Overview

    Bersimis, Sotiris; Psarakis, Stelios; Panaretos, John

    2006-01-01

    In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as p...

  20. A Study of Effects of MultiCollinearity in the Multivariable Analysis.

    Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; Peter He, Qinghua; Lillard, James W

    2014-10-01

    A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables.

  1. A comparison of bivariate and univariate QTL mapping in livestock populations

    Sorensen Daniel

    2003-11-01

    Full Text Available Abstract This study presents a multivariate, variance component-based QTL mapping model implemented via restricted maximum likelihood (REML. The method was applied to investigate bivariate and univariate QTL mapping analyses, using simulated data. Specifically, we report results on the statistical power to detect a QTL and on the precision of parameter estimates using univariate and bivariate approaches. The model and methodology were also applied to study the effectiveness of partitioning the overall genetic correlation between two traits into a component due to many genes of small effect, and one due to the QTL. It is shown that when the QTL has a pleiotropic effect on two traits, a bivariate analysis leads to a higher statistical power of detecting the QTL and to a more precise estimate of the QTL's map position, in particular in the case when the QTL has a small effect on the trait. The increase in power is most marked in cases where the contributions of the QTL and of the polygenic components to the genetic correlation have opposite signs. The bivariate REML analysis can successfully partition the two components contributing to the genetic correlation between traits.

  2. Application of Multivariable Statistical Techniques in Plant-wide WWTP Control Strategies Analysis

    Flores Alsina, Xavier; Comas, J.; Rodríguez-Roda, I.

    2007-01-01

    The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant...... analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii......) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation...

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

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

    2015-11-01

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

  4. Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

    Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K

    2017-01-01

    The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.

  5. Multivariate Max-Stable Spatial Processes

    Genton, Marc G.

    2014-01-06

    Analysis of spatial extremes is currently based on univariate processes. Max-stable processes allow the spatial dependence of extremes to be modelled and explicitly quantified, they are therefore widely adopted in applications. For a better understanding of extreme events of real processes, such as environmental phenomena, it may be useful to study several spatial variables simultaneously. To this end, we extend some theoretical results and applications of max-stable processes to the multivariate setting to analyze extreme events of several variables observed across space. In particular, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. Then, we define a Poisson process construction in the multivariate setting and introduce multivariate versions of the Smith Gaussian extremevalue, the Schlather extremal-Gaussian and extremal-t, and the BrownResnick models. Inferential aspects of those models based on composite likelihoods are developed. We present results of various Monte Carlo simulations and of an application to a dataset of summer daily temperature maxima and minima in Oklahoma, U.S.A., highlighting the utility of working with multivariate models in contrast to the univariate case. Based on joint work with Simone Padoan and Huiyan Sang.

  6. Multivariate Max-Stable Spatial Processes

    Genton, Marc G.

    2014-01-01

    Analysis of spatial extremes is currently based on univariate processes. Max-stable processes allow the spatial dependence of extremes to be modelled and explicitly quantified, they are therefore widely adopted in applications. For a better understanding of extreme events of real processes, such as environmental phenomena, it may be useful to study several spatial variables simultaneously. To this end, we extend some theoretical results and applications of max-stable processes to the multivariate setting to analyze extreme events of several variables observed across space. In particular, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. Then, we define a Poisson process construction in the multivariate setting and introduce multivariate versions of the Smith Gaussian extremevalue, the Schlather extremal-Gaussian and extremal-t, and the BrownResnick models. Inferential aspects of those models based on composite likelihoods are developed. We present results of various Monte Carlo simulations and of an application to a dataset of summer daily temperature maxima and minima in Oklahoma, U.S.A., highlighting the utility of working with multivariate models in contrast to the univariate case. Based on joint work with Simone Padoan and Huiyan Sang.

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

    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…

  8. Denial-of-service attack detection based on multivariate correlation analysis

    Tan, Zhiyuan; Jamdagni, Aruna; He, Xiangjian; Nanda, Priyadarsi; Liu, Ren Ping; Lu, Bao-Liang; Zhang, Liqing; Kwok, James

    2011-01-01

    The reliability and availability of network services are being threatened by the growing number of Denial-of-Service (DoS) attacks. Effective mechanisms for DoS attack detection are demanded. Therefore, we propose a multivariate correlation analysis approach to investigate and extract second-order

  9. Multivariate quantitative structure-pharmacokinetic relationships (QSPKR) analysis of adenosine A(1) receptor agonists in rat

    Van der Graaf, PH; Nilsson, J; Van Schaick, EA; Danhof, M

    The aim of this study was to investigate the feasibility of a quantitative structure-pharmacokinetic relationships (QSPKR) method based on contemporary three-dimensional (3D) molecular characterization and multivariate statistical analysis. For this purpose, the programs SYBYL/CoMFA, GRID, and

  10. Dynamic factor analysis in the frequency domain: causal modeling of multivariate psychophysiological time series

    Molenaar, P.C.M.

    1987-01-01

    Outlines a frequency domain analysis of the dynamic factor model and proposes a solution to the problem of constructing a causal filter of lagged factor loadings. The method is illustrated with applications to simulated and real multivariate time series. The latter applications involve topographic

  11. Cardiovascular reactivity patterns and pathways to hypertension: a multivariate cluster analysis

    Brindle, R. C.; Ginty, A. T.; Jones, A.; Phillips, A. C.; Roseboom, T. J.; Carroll, D.; Painter, R. C.; de Rooij, S. R.

    2016-01-01

    Substantial evidence links exaggerated mental stress induced blood pressure reactivity to future hypertension, but the results for heart rate reactivity are less clear. For this reason multivariate cluster analysis was carried out to examine the relationship between heart rate and blood pressure

  12. Multivariate data analysis as a tool in advanced quality monitoring in the food production chain

    Bro, R.; van den Berg, F.; Thybo, A.

    2002-01-01

    This paper summarizes some recent advances in mathematical modeling of relevance in advanced quality monitoring in the food production chain. Using chemometrics-multivariate data analysis - it is illustrated how to tackle problems in food science more efficiently and, moreover, solve problems...

  13. Decomposition of multivariate phenotypic means in multigroup genetic covarinace structure analysis

    Dolan, C.V.; Molenaar, P.C.M.; Boomsma, D.I.

    1992-01-01

    Uses D. Sorbom's (1974) method to study differences in latent means in multivariate twin data. By restricting the analysis to a comparison between groups, the results pertain only to the additive contributions of common genetic and environmental factors to the deviation of the group means from what

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

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

    1996-01-01

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

  15. Principal response curves: analysis of time-dependent multivariate responses of biological community to stress

    Brink, van den P.J.; Braak, ter C.J.F.

    1999-01-01

    In this paper a novel multivariate method is proposed for the analysis of community response data from designed experiments repeatedly sampled in time. The long-term effects of the insecticide chlorpyrifos on the invertebrate community and the dissolved oxygen (DO)–pH–alkalinity–conductivity

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

    Ma, Yan; Mazumdar, Madhu

    2011-10-30

    Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting. Copyright © 2011 John Wiley & Sons, Ltd.

  17. Immediate versus delayed intramedullary nailing for open fractures of the tibial shaft: a multivariate analysis of factors affecting deep infection and fracture healing.

    Yokoyama, Kazuhiko; Itoman, Moritoshi; Uchino, Masataka; Fukushima, Kensuke; Nitta, Hiroshi; Kojima, Yoshiaki

    2008-10-01

    The purpose of this study was to evaluate contributing factors affecting deep infection and fracture healing of open tibia fractures treated with locked intramedullary nailing (IMN) by multivariate analysis. We examined 99 open tibial fractures (98 patients) treated with immediate or delayed locked IMN in static fashion from 1991 to 2002. Multivariate analyses following univariate analyses were derived to determine predictors of deep infection, nonunion, and healing time to union. The following predictive variables of deep infection were selected for analysis: age, sex, Gustilo type, fracture grade by AO type, fracture location, timing or method of IMN, reamed or unreamed nailing, debridement time (6 h), method of soft-tissue management, skin closure time (1 week), existence of polytrauma (ISS or =18), existence of floating knee injury, and existence of superficial/pin site infection. The predictive variables of nonunion selected for analysis was the same as those for deep infection, with the addition of deep infection for exchange of pin site infection. The predictive variables of union time selected for analysis was the same as those for nonunion, excluding of location, debridement time, and existence of floating knee and superficial infection. Six (6.1%; type II Gustilo n=1, type IIIB Gustilo n=5) of the 99 open tibial fractures developed deep infections. Multivariate analysis revealed that timing or method of IMN, debridement time, method of soft-tissue management, and existence of superficial or pin site infection significantly correlated with the occurrence of deep infection (Prate in type IIIB + IIIC was significantly higher than those in type I + II and IIIA (P = 0.016). Nonunion occurred in 17 fractures (20.3%, 17/84). Multivariate analysis revealed that Gustilo type, skin closure time, and existence of deep infection significantly correlated with occurrence of nonunion (P < 0.05). Gustilo type and existence of deep infection were significantly correlated

  18. Multivariate Analysis of Multiple Datasets: a Practical Guide for Chemical Ecology.

    Hervé, Maxime R; Nicolè, Florence; Lê Cao, Kim-Anh

    2018-03-01

    Chemical ecology has strong links with metabolomics, the large-scale study of all metabolites detectable in a biological sample. Consequently, chemical ecologists are often challenged by the statistical analyses of such large datasets. This holds especially true when the purpose is to integrate multiple datasets to obtain a holistic view and a better understanding of a biological system under study. The present article provides a comprehensive resource to analyze such complex datasets using multivariate methods. It starts from the necessary pre-treatment of data including data transformations and distance calculations, to the application of both gold standard and novel multivariate methods for the integration of different omics data. We illustrate the process of analysis along with detailed results interpretations for six issues representative of the different types of biological questions encountered by chemical ecologists. We provide the necessary knowledge and tools with reproducible R codes and chemical-ecological datasets to practice and teach multivariate methods.

  19. The studies of post-medieval glass by multivariate and X-ray fluorescence analysis

    Kierzek, J.; Kunicki-Goldfinger, J.

    2002-01-01

    Multivariate statistical analysis of the results obtained by energy dispersive X-ray fluorescence analysis has been used in the study of baroque vessel glasses originated from central Europe. X-ray spectrometry can be applied as a completely non-destructive, non-sampling and multi-element method. It is very useful in the studies of valuable historical artefacts. For the last years, multivariate statistical analysis has been developed as an important tool for the archaeometric purposes. Cluster, principal component and discriminant analysis were applied for the classification of the examined objects. The obtained results show that these statistical tools are very useful and complementary in the studies of historical objects. (author)

  20. Application of instrumental neutron activation analysis and multivariate statistical methods to archaeological Syrian ceramics

    Bakraji, E. H.; Othman, I.; Sarhil, A.; Al-Somel, N.

    2002-01-01

    Instrumental neutron activation analysis (INAA) has been utilized in the analysis of thirty-seven archaeological ceramics fragment samples collected from Tal AI-Wardiate site, Missiaf town, Hamma city, Syria. 36 chemical elements were determined. These elemental concentrations have been processed using two multivariate statistical methods, cluster and factor analysis in order to determine similarities and correlation between the various samples. Factor analysis confirms that samples were correctly classified by cluster analysis. The results showed that samples can be considered to be manufactured using three different sources of raw material. (author)

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

    Cichy, Radoslaw Martin; Pantazis, Dimitrios

    2017-09-01

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

  2. Morphological analysis of enlarged ventricle on CT image, using multivariate analysis

    Iwasaki, Satoru; Kichikawa, Kimihiko; Otsuji, Hideyuki; Fukusumi, Akio; Kobayashi, Yasuo.

    1983-01-01

    Multivariate analysis of enlarged cerebral ventricle on CT was undertaken to study the characteristics of ventricular morphology. Several ventricular segments of enlarged ventricle, defined on the basis of the study of normal group, were linearly measured on CT image. Then the discriminant analysis with the increase and decrease of variable was applied. The following are the results obtained. The error ratio of discrimination between pressure hydrocephalus and cerebral atrophy was 8.4 %, and between obstructive hydrocephalus and communicating hydrocephalus was 11.3 %. Ventricular segments were divided into three groups according to their character of enlargement: (1) the temporal horn and trigone are large in pressure hydrocephalus; (2) the hypothalamic segment of the third ventricle and the body of lateral ventricle are larger in obstructive hydrocephalus than in communicating hydrocephalus; (3) the anterior horn, cellae mediae at the level of the head of caudate nuclei and thalamic segment of the third ventricle are relatively large in cerebral atrophy and communicating hydrocephalus. The hypothalamic segment of the third ventricle assumes a round or oval shape in pressure hydrocephalus but a rectangular or teardrop shape in cerebral atrophy. These findings are contributory to pathological evaluation of ventricular enlargement. (author)

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

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

    2016-01-01

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

  4. Oxidative stability of frozen mackerel batches ― A multivariate data analysis approach

    Helbo Ekgreen, M.; Frosch, Stina; Baron, Caroline Pascale

    2011-01-01

    deterioration and texture changes. The aim was to investigate the correlation between the raw material history and the quality loss observed during frozen storage using relevant multivariate data analysis such as Principal Component Analysis (PCA) and Partial Least Square Analysis (PLS). Preliminary results...... showed that it was possible to differentiate between the different batches depending on their history and that some batches were more oxidised than others. Furthermore, based on the results from the data analysis, critical control points in the entire production chain will be identified and strategies...

  5. Prognostic factors in children and adolescents with acute myeloid leukemia (excluding children with Down syndrome and acute promyelocytic leukemia): univariate and recursive partitioning analysis of patients treated on Pediatric Oncology Group (POG) Study 8821.

    Chang, M; Raimondi, S C; Ravindranath, Y; Carroll, A J; Camitta, B; Gresik, M V; Steuber, C P; Weinstein, H

    2000-07-01

    The purpose of the paper was to define clinical or biological features associated with the risk for treatment failure for children with acute myeloid leukemia. Data from 560 children and adolescents with newly diagnosed acute myeloid leukemia who entered the Pediatric Oncology Group Study 8821 from June 1988 to March 1993 were analyzed by univariate and recursive partitioning methods. Children with Down syndrome or acute promyelocytic leukemia were excluded from the study. Factors examined included age, number of leukocytes, sex, FAB morphologic subtype, cytogenetic findings, and extramedullary disease at the time of diagnosis. The overall event-free survival (EFS) rate at 4 years was 32.7% (s.e. = 2.2%). Age > or =2 years, fewer than 50 x 10(9)/I leukocytes, and t(8;21) or inv(16), and normal chromosomes were associated with higher rates of EFS (P value = 0.003, 0.049, 0.0003, 0.031, respectively), whereas the M5 subtype of AML (P value = 0.0003) and chromosome abnormalities other than t(8;21) and inv(16) were associated with lower rates of EFS (P value = 0.0001). Recursive partitioning analysis defined three groups of patients with widely varied prognoses: female patients with t(8;21), inv(16), or a normal karyotype (n = 89) had the best prognosis (4-year EFS = 55.1%, s.e. = 5.7%); male patients with t(8;21), inv(16) or normal chromosomes (n = 106) had an intermediate prognosis (4-year EFS = 38.1%, s.e. = 5.3%); patients with chromosome abnormalities other than t(8;21) and inv(16) (n = 233) had the worst prognosis (4-year EFS = 27.0%, s.e. = 3.2%). One hundred and thirty-two patients (24%) could not be grouped because of missing cytogenetic data, mainly due to inadequate marrow samples. The results suggest that pediatric patients with acute myeloid leukemia can be categorized into three potential risk groups for prognosis and that differences in sex and chromosomal abnormalities are associated with differences in estimates of EFS. These results are tentative and

  6. Multivariate analysis of risk factors for long-term urethroplasty outcome.

    Breyer, Benjamin N; McAninch, Jack W; Whitson, Jared M; Eisenberg, Michael L; Mehdizadeh, Jennifer F; Myers, Jeremy B; Voelzke, Bryan B

    2010-02-01

    We studied the patient risk factors that promote urethroplasty failure. Records of patients who underwent urethroplasty at the University of California, San Francisco Medical Center between 1995 and 2004 were reviewed. Cox proportional hazards regression analysis was used to identify multivariate predictors of urethroplasty outcome. Between 1995 and 2004, 443 patients of 495 who underwent urethroplasty had complete comorbidity data and were included in analysis. Median patient age was 41 years (range 18 to 90). Median followup was 5.8 years (range 1 month to 10 years). Stricture recurred in 93 patients (21%). Primary estimated stricture-free survival at 1, 3 and 5 years was 88%, 82% and 79%. After multivariate analysis smoking (HR 1.8, 95% CI 1.0-3.1, p = 0.05), prior direct vision internal urethrotomy (HR 1.7, 95% CI 1.0-3.0, p = 0.04) and prior urethroplasty (HR 1.8, 95% CI 1.1-3.1, p = 0.03) were predictive of treatment failure. On multivariate analysis diabetes mellitus showed a trend toward prediction of urethroplasty failure (HR 2.0, 95% CI 0.8-4.9, p = 0.14). Length of urethral stricture (greater than 4 cm), prior urethroplasty and failed endoscopic therapy are predictive of failure after urethroplasty. Smoking and diabetes mellitus also may predict failure potentially secondary to microvascular damage. Copyright 2010 American Urological Association. Published by Elsevier Inc. All rights reserved.

  7. Testing Mean Differences among Groups: Multivariate and Repeated Measures Analysis with Minimal Assumptions.

    Bathke, Arne C; Friedrich, Sarah; Pauly, Markus; Konietschke, Frank; Staffen, Wolfgang; Strobl, Nicolas; Höller, Yvonne

    2018-03-22

    To date, there is a lack of satisfactory inferential techniques for the analysis of multivariate data in factorial designs, when only minimal assumptions on the data can be made. Presently available methods are limited to very particular study designs or assume either multivariate normality or equal covariance matrices across groups, or they do not allow for an assessment of the interaction effects across within-subjects and between-subjects variables. We propose and methodologically validate a parametric bootstrap approach that does not suffer from any of the above limitations, and thus provides a rather general and comprehensive methodological route to inference for multivariate and repeated measures data. As an example application, we consider data from two different Alzheimer's disease (AD) examination modalities that may be used for precise and early diagnosis, namely, single-photon emission computed tomography (SPECT) and electroencephalogram (EEG). These data violate the assumptions of classical multivariate methods, and indeed classical methods would not have yielded the same conclusions with regards to some of the factors involved.

  8. Multivariate phase type distributions - Applications and parameter estimation

    Meisch, David

    The best known univariate probability distribution is the normal distribution. It is used throughout the literature in a broad field of applications. In cases where it is not sensible to use the normal distribution alternative distributions are at hand and well understood, many of these belonging...... and statistical inference, is the multivariate normal distribution. Unfortunately only little is known about the general class of multivariate phase type distribution. Considering the results concerning parameter estimation and inference theory of univariate phase type distributions, the class of multivariate...... projects and depend on reliable cost estimates. The Successive Principle is a group analysis method primarily used for analyzing medium to large projects in relation to cost or duration. We believe that the mathematical modeling used in the Successive Principle can be improved. We suggested a novel...

  9. Myocardial technetium-99m sestamibi single-photon emission tomography as a prognostic tool in coronary artery disease: multivariate analysis in a long-term prospective study

    Zanco, P.; Zampiero, A.; Favero, A.; Borsato, N.; Chierichetti, F.; Rubello, D.; Saitta, B.; Ferlin, G.

    1995-01-01

    A prospective study was started in 1988 and at present 176 consecutive, and thus unselected, patients have been enrolled. All of them have been submitted to stress-rest MIBI SPET for the diagnosis or evaluation of CAD; 147 patients (121 males and 26 females, aged 53±9 years) have completed a surveillance period of at least 36 months following the scintigraphic study (range 36-60 months, mean 43). Sixty-one patients had a documented previous myocardial infarction. The mean pre-test likelihood of CAD was 44% in the patients without prior infarction. The main anamnestic, clinical, EKG and scintigraphic findings were evaluated and statistically correlated with the incidence of ensuing cardiac events using both univariate (chi-square test) and multivariate analysis (logistic regression model). Twenty-nine patients suffered from a cardiac event during the follow-up period (i.e. three cardiac deaths, six myocardial infarctions and 20 cases of unstable angina). Statistical multivariate analysis identified MIBI scan as the only highly significant and independent prognostic predictor. In detail, the most important scintigraphic parameters were the presence of a reversible defect and the extension of the stress perfusion defect. The presence of typical angina proved to be a slightly significant predictor, while no other examined parameter showed a significant correlation with a bad prognosis. In conclusion, MIBI SPET can be considered a useful tool in the risk stratification of CAD patients. (orig.). With 3 tabs

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

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

    2014-08-01

    Acyclovir (ACV) is an antiviral agent. However, its use is limited by adverse side effect, particularly by its nephrotoxicity. Metabonomics technology can provide essential information on the metabolic profiles of biofluids and organs upon drug administration. Therefore, in this study, mass spectrometry-based metabonomics coupled with multivariate data analysis was used to identify the plasma metabolites and metabolic pathways related to nephrotoxicity caused by intraperitoneal injection of low (50mg/kg) and high (100mg/kg) doses of acyclovir. Sixteen biomarkers were identified by metabonomics and nephrotoxicity results revealed the dose-dependent effect of acyclovir on kidney tissues. The present study showed that the top four metabolic pathways interrupted by acyclovir included the metabolisms of arachidonic acid, tryptophan, arginine and proline, and glycerophospholipid. This research proves the established metabonomic approach can provide information on changes in metabolites and metabolic pathways, which can be applied to in-depth research on the mechanism of acyclovir-induced kidney injury. Copyright © 2014 Elsevier B.V. All rights reserved.

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

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

    2010-12-08

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

  12. Clinical patch test data evaluated by multivariate analysis. Danish Contact Dermatitis Group

    Christophersen, J; Menné, T; Tanghøj, P

    1989-01-01

    The aim of the present study was to evaluate the influence of individual explanatory factors, such as sex, age, atopy, test time and presence of diseased skin, on clinical patch test results, by application of multivariate statistical analysis. The study population was 2166 consecutive patients...... patch tested with the standard series of the International Contact Dermatitis Research Group (ICDRG) by members of the Danish Contact Dermatitis Group (DCDG) over a period of 6 months. For the 8 test allergens most often found positive (nickel, fragrance-mix, cobalt, chromate, balsam of Peru, carba......-mix, colophony, and formaldehyde), one or more individual factors were of significance for the risk of being sensitized, except for chromate and formaldehyde. It is concluded that patch test results can be compared only after stratification of the material or by multivariate analysis....

  13. Provenance Study of Archaeological Ceramics from Syria Using XRF Multivariate Statistical Analysis and Thermoluminescence Dating

    Bakraji, Elias Hanna; Abboud, Rana; Issa, Haissm

    2014-01-01

    Thermoluminescence (TL) dating and multivariate statistical methods based on radioisotope X-ray fluorescence analysis have been utilized to date and classify Syrian archaeological ceramics fragment from Tel Jamous site. 54 samples were analyzed by radioisotope X-ray fluorescence; 51 of them come from Tel Jamous archaeological site in Sahel Akkar region, Syria, which fairly represent ceramics belonging to the Middle Bronze Age (2150 to 1600 B.C.) and the remaining three samples come from Mar-T...

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

    M. Agostini

    2013-01-01

    Full Text Available Background: Obesity is currently epidemic in many countries worldwide and is strongly related to diabetes and cardiovascular disease. Mass spectrometry, in particular matrix-assisted laser desorption/ionization time of flight (MALDI-TOF is currently used for detecting different pattern of expressed protein. This study investigated the differences in low molecular weight (LMW peptide profiles between obese and normal-weight subjects in combination with multivariate statistical analysis.

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

    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.

  16. Regression Analysis for Multivariate Dependent Count Data Using Convolved Gaussian Processes

    Sofro, A'yunin; Shi, Jian Qing; Cao, Chunzheng

    2017-01-01

    Research on Poisson regression analysis for dependent data has been developed rapidly in the last decade. One of difficult problems in a multivariate case is how to construct a cross-correlation structure and at the meantime make sure that the covariance matrix is positive definite. To address the issue, we propose to use convolved Gaussian process (CGP) in this paper. The approach provides a semi-parametric model and offers a natural framework for modeling common mean structure and covarianc...

  17. Univaried models in the series of temperature of the air

    Leon Aristizabal Gloria esperanza

    2000-01-01

    The theoretical framework for the study of the air's temperature time series is the theory of stochastic processes, particularly those known as ARIMA, that make it possible to carry out a univaried analysis. ARIMA models are built in order to explain the structure of the monthly temperatures corresponding to the mean, the absolute maximum, absolute minimum, maximum mean and minimum mean temperatures, for four stations in Colombia. By means of those models, the possible evolution of the latter variables is estimated with predictive aims in mind. The application and utility of the models is discussed

  18. Multivariate statistical analysis for x-ray photoelectron spectroscopy spectral imaging: Effect of image acquisition time

    Peebles, D.E.; Ohlhausen, J.A.; Kotula, P.G.; Hutton, S.; Blomfield, C.

    2004-01-01

    The acquisition of spectral images for x-ray photoelectron spectroscopy (XPS) is a relatively new approach, although it has been used with other analytical spectroscopy tools for some time. This technique provides full spectral information at every pixel of an image, in order to provide a complete chemical mapping of the imaged surface area. Multivariate statistical analysis techniques applied to the spectral image data allow the determination of chemical component species, and their distribution and concentrations, with minimal data acquisition and processing times. Some of these statistical techniques have proven to be very robust and efficient methods for deriving physically realistic chemical components without input by the user other than the spectral matrix itself. The benefits of multivariate analysis of the spectral image data include significantly improved signal to noise, improved image contrast and intensity uniformity, and improved spatial resolution - which are achieved due to the effective statistical aggregation of the large number of often noisy data points in the image. This work demonstrates the improvements in chemical component determination and contrast, signal-to-noise level, and spatial resolution that can be obtained by the application of multivariate statistical analysis to XPS spectral images

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

    Giménez, Ana; Gagliardi, Andrés; Ares, Gastón

    2017-09-01

    For most food products, shelf life is determined by changes in their sensory characteristics. A predetermined increase or decrease in the intensity of a sensory characteristic has frequently been used to signal that a product has reached the end of its shelf life. Considering all attributes change simultaneously, the concept of multivariate shelf life allows a single measurement of deterioration that takes into account all these sensory changes at a certain storage time. The aim of the present work was to apply survival analysis to estimate failure criteria in multivariate sensory shelf life testing using two case studies, hamburger buns and orange juice, by modelling the relationship between consumers' rejection of the product and the deterioration index estimated using PCA. In both studies, a panel of 13 trained assessors evaluated the samples using descriptive analysis whereas a panel of 100 consumers answered a "yes" or "no" question regarding intention to buy or consume the product. PC1 explained the great majority of the variance, indicating all sensory characteristics evolved similarly with storage time. Thus, PC1 could be regarded as index of sensory deterioration and a single failure criterion could be estimated through survival analysis for 25 and 50% consumers' rejection. The proposed approach based on multivariate shelf life testing may increase the accuracy of shelf life estimations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Resent state and multivariate analysis of a few juniper forests of baluchistan, pakistan

    Ahmed, M.; Siddiqui, M.F.

    2015-01-01

    Quantitative multivariate investigations were carried out to explore various forms of Juniper trees resulting human disturbances and natural phenomenon. Thirty stands were sampled by point centered quarter method and data were analysed using Wards cluster analysis and Bray-Curtis ordination. On the basis of multivariate analysis eight various forms i.e. healthy, unhealthy, over mature, disturbed, dieback, standing dead, logs and cut stem were recognized. Structural attributes were computed. Highest numbers (130-133 stem ha-1) of logs were recorded from Cautair and Khunk forests. Highest density ha-1 (229 ha-1) of healthy plants was estimated from Tangi Top area while lowest number (24 ha-1) of healthy plants was found from Saraghara area. Multivariate analysis showed five groups in cluster and ordination diagrams. These groups are characterized on the basis of healthy, over mature, disturbed and logged trees of Juniper. Higher number (115, 96, 84, 80 ha-1) of disturbed trees were distributed at Speena Sukher, Srag Kazi, Prang Shella and Tangi Top respectively. Overall density does not show any significant relation with basal area m2 ha-1, degree of slopes and the elevation of the sampling stands. Present study show that each and every Juniper stands are highly disturbed mostly due to human influence, therefore prompt conservational steps should be taken to safe these forests. (author)

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

    Wang, Feng, E-mail: fwang@unu.edu [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Huisman, Jaco [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Stevels, Ab [Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Baldé, Cornelis Peter [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Statistics Netherlands, Henri Faasdreef 312, 2492 JP Den Haag (Netherlands)

    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

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

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

    2013-01-01

    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

  3. Point defect characterization in HAADF-STEM images using multivariate statistical analysis

    Sarahan, Michael C.; Chi, Miaofang; Masiel, Daniel J.; Browning, Nigel D.

    2011-01-01

    Quantitative analysis of point defects is demonstrated through the use of multivariate statistical analysis. This analysis consists of principal component analysis for dimensional estimation and reduction, followed by independent component analysis to obtain physically meaningful, statistically independent factor images. Results from these analyses are presented in the form of factor images and scores. Factor images show characteristic intensity variations corresponding to physical structure changes, while scores relate how much those variations are present in the original data. The application of this technique is demonstrated on a set of experimental images of dislocation cores along a low-angle tilt grain boundary in strontium titanate. A relationship between chemical composition and lattice strain is highlighted in the analysis results, with picometer-scale shifts in several columns measurable from compositional changes in a separate column. -- Research Highlights: → Multivariate analysis of HAADF-STEM images. → Distinct structural variations among SrTiO 3 dislocation cores. → Picometer atomic column shifts correlated with atomic column population changes.

  4. Multivariate methods for analysis of environmental reference materials using laser-induced breakdown spectroscopy

    Shikha Awasthi

    2017-06-01

    Full Text Available Analysis of emission from laser-induced plasma has a unique capability for quantifying the major and minor elements present in any type of samples under optimal analysis conditions. Chemometric techniques are very effective and reliable tools for quantification of multiple components in complex matrices. The feasibility of laser-induced breakdown spectroscopy (LIBS in combination with multivariate analysis was investigated for the analysis of environmental reference materials (RMs. In the present work, different (Certified/Standard Reference Materials of soil and plant origin were analyzed using LIBS and the presence of Al, Ca, Mg, Fe, K, Mn and Si were identified in the LIBS spectra of these materials. Multivariate statistical methods (Partial Least Square Regression and Partial Least Square Discriminant Analysis were employed for quantitative analysis of the constituent elements using the LIBS spectral data. Calibration models were used to predict the concentrations of the different elements of test samples and subsequently, the concentrations were compared with certified concentrations to check the authenticity of models. The non-destructive analytical method namely Instrumental Neutron Activation Analysis (INAA using high flux reactor neutrons and high resolution gamma-ray spectrometry was also used for intercomparison of results of two RMs by LIBS.

  5. Multivariate data analysis approach to understand magnetic properties of perovskite manganese oxides

    Imamura, N.; Mizoguchi, T.; Yamauchi, H.; Karppinen, M.

    2008-01-01

    Here we apply statistical multivariate data analysis techniques to obtain some insights into the complex structure-property relations in antiferromagnetic (AFM) and ferromagnetic (FM) manganese perovskite systems, AMnO 3 . The 131 samples included in the present analyses are described by 21 crystal-structure or crystal-chemical (CS/CC) parameters. Principal component analysis (PCA), carried out separately for the AFM and FM compounds, is used to model and evaluate the various relationships among the magnetic properties and the various CS/CC parameters. Moreover, for the AFM compounds, PLS (partial least squares projections to latent structures) analysis is performed so as to predict the magnitude of the Neel temperature on the bases of the CS/CC parameters. Finally, so-called PLS-DA (PLS discriminant analysis) method is employed to find out the most influential/characteristic CS/CC parameters that differentiate the two classes of compounds from each other. - Graphical abstract: Statistical multivariate data analysis techniques are applied to detect structure-property relations in antiferromagnetic (AFM) and ferromagnetic (FM) manganese perovskites. For AFM compounds, partial least squares projections to latent structures analysis predict the magnitude of the Neel temperature on the bases of structural parameters only. Moreover, AFM and FM compounds are well separated by means of so-called partial least squares discriminant analysis method

  6. Small cell lung cancer with and without superior vena cava syndrome: a multivariate analysis of prognostic factors in 408 cases

    Wuerschmidt, Florian; Buenemann, Henry; Heilmann, Hans-Peter

    1995-01-01

    Purpose: Patients with small cell lung cancer (SCLC) and superior vena cava syndrome (SVCS) are widely believed to have a grave prognosis. The purpose of this study was to determine the prognosis of patients with SCLC and SVCS as compared to SCLC without SVCS. Methods and Materials: A retrospective analysis of 408 cases of SCLC ± SVCS was performed. Three-hundred and sixty showed no clinical signs of SVCS and 43 (11%) had SVCS; in 5 patients no adequate information was available about clinical signs of SVCS. All patients were classified as limited disease cases. About 98% received chemotherapy usually as the first treatment followed by radiotherapy. A median total dose of 46 Gy (range 30 to 70 Gy) was given at 2.0 Gy per fraction five times weekly. A prophylactic cranial irradiation was applied if a complete remission was achieved after chemotherapy or after 30 Gy of irradiation. Kaplan-Meier survival curves are shown and comparisons were made by the log-rank and the Gehan/Wilcoxon test. To adjust for prognostic factors, a proportional hazards analysis was done. Results: Patients without SVCS had 5-year survival rates (± SE) and a median survival time (MST; 95% confidence intervals) of 11% ± 2% and 13.7 months (12.7-14.5) in UICC Stage I to III; in Stage III the figures were 9% ± 2% and 12.6 months (11.2-13.7). In comparison, SCLC with SVCS had 5-year survival rates of 15% ± 7% and MST of 16.1 months (13.8-20.5). The difference was significant in univariate analysis (Stage III disease: p 0.008 by the log-rank test). In a multivariate analysis of all patients, Stage (Stage I + II > III; p = 0.0003), SVCS (yes > no; p = 0.005), and Karnofsky performance status (≤ 70 < 80-100%; p = 0.008) were of significant importance. Conclusions: SVCS is a favorable prognostic sign in SCLC. The treatment should be curatively intended

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

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

    2016-04-01

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

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

    Keenan, Michael R [Albuquerque, NM

    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.

  9. Multivariate Matrix-Exponential Distributions

    Bladt, Mogens; Nielsen, Bo Friis

    2010-01-01

    be written as linear combinations of the elements in the exponential of a matrix. For this reason we shall refer to multivariate distributions with rational Laplace transform as multivariate matrix-exponential distributions (MVME). The marginal distributions of an MVME are univariate matrix......-exponential distributions. We prove a characterization that states that a distribution is an MVME distribution if and only if all non-negative, non-null linear combinations of the coordinates have a univariate matrix-exponential distribution. This theorem is analog to a well-known characterization theorem...

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

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

    2005-02-23

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

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

    Jørgensen, Anna Cecilia; Miroshnyk, Inna; Karjalainen, Milja

    2006-01-01

    of information generated can be overwhelming and the need for more effective data analysis tools is well recognized. The aim of this study was to investigate the use of multivariate data analysis, in particular principal component analysis (PCA), for fast analysis of solid state information. The data sets...... the molecular level interpretation of the structural changes related to the loss of water, as well as interpretation of the phenomena related to the crystallization. The critical temperatures or critical time points were identified easily using the principal component analysis. The variables (diffraction angles...... or wavenumbers) that changed could be identified by the careful interpretation of the loadings plots. The PCA approach provides an effective tool for fast screening of solid state information....

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

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

    2017-03-01

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

  13. Computed ABC Analysis for Rational Selection of Most Informative Variables in Multivariate Data.

    Ultsch, Alfred; Lötsch, Jörn

    2015-01-01

    Multivariate data sets often differ in several factors or derived statistical parameters, which have to be selected for a valid interpretation. Basing this selection on traditional statistical limits leads occasionally to the perception of losing information from a data set. This paper proposes a novel method for calculating precise limits for the selection of parameter sets. The algorithm is based on an ABC analysis and calculates these limits on the basis of the mathematical properties of the distribution of the analyzed items. The limits implement the aim of any ABC analysis, i.e., comparing the increase in yield to the required additional effort. In particular, the limit for set A, the "important few", is optimized in a way that both, the effort and the yield for the other sets (B and C), are minimized and the additional gain is optimized. As a typical example from biomedical research, the feasibility of the ABC analysis as an objective replacement for classical subjective limits to select highly relevant variance components of pain thresholds is presented. The proposed method improved the biological interpretation of the results and increased the fraction of valid information that was obtained from the experimental data. The method is applicable to many further biomedical problems including the creation of diagnostic complex biomarkers or short screening tests from comprehensive test batteries. Thus, the ABC analysis can be proposed as a mathematically valid replacement for traditional limits to maximize the information obtained from multivariate research data.

  14. A comparative multivariate analysis of household energy requirements in Australia, Brazil, Denmark, India and Japan

    Lenzen, M. [University of Sydney (Australia). School of Physics; Wier, M. [Royal Veterinary and Agricultural University, Copenhagen (Denmark). Danish Research Institute of Food Economics; Cohen, C. [Universidade Federal Fluminense, Rio de Janeiro (Brazil). Faculdade de Economia; Hayami, Hitoshi [Keio University, Tokyo (Japan). Keio Economic Observatory; Pachauri, S. [Swiss Federal Institutes of Technology, Zurich (Switzerland). Centre for Energy Policy and Economics; Schaeffer, R. [Universidade Federal do Rio de Janeiro (Brazil). COPPE

    2006-03-01

    In this paper, we appraise sustainable household consumption from a global perspective. Using per capita energy requirements as an indicator of environmental pressure, we focus on the importance of income growth in a cross-country analysis. Our analysis is supported by a detailed within-country analysis encompassing five countries, in which we assess the importance of various socioeconomic-demographic characteristics of household energy requirements. We bring together family expenditure survey data, input-output tables, and energy statistics in a multivariate analysis. Instead of a uniform Kuznet's curve, we find that the effect of increasing income varies considerably across countries, even when controlling for socioeconomic and demographic variations. The latter variables show similar influences, but differing importance across countries. (author)

  15. Use of multivariate analysis to research career advancement of academic librarians

    Filiberto Felipe Martínez Arellano

    2004-01-01

    Full Text Available Diverse variables dealing with credential factors, bureaucratiuc factors, organizational and disciplinary achievements, academic culture factors, social ascribed factors, and institutional factors were stated as explanatory elements of promotion, tenure status, and earnings. A survey was the research instrument for collecting data to test diverse variables dealing with academic librarians rewards and earnings. Since the study attempted to analyze variables in a multivariate context, variable interactions were tested using multiple regression analysis. Findings of this study contribute to a better understanding of those factors influencing career advancement of academic librarians. Likewise, research methodology of this study could be used in Library and Information Science(LIS research.

  16. Application of the techniques of Multivariate analysis in the characterization of germplasm of Quinua

    Garcia A, J.M.; Torres de la Cruz, E.

    2004-01-01

    Its were evaluated 20 lines of Chenopodium quinoa respect characters of agronomical interest finding that nine lines overcame the witness highlighting the lines: 20R1-41, 20R1-10, 20R2-27 that presented near yield to 1.5 ton/ha. The multivariate analysis of main components generated a dendrogram in that is appreciated that at an Euclidean distance of 0.75 its were formed seven groups according to its morphological characteristics and of yield, it highlights the formation of two big groups at a distance of 1.125, that they separate according to the radiation dose (200 and 250 Gy). (Author)

  17. Sensitivity of human auditory cortex to rapid frequency modulation revealed by multivariate representational similarity analysis.

    Joanisse, Marc F; DeSouza, Diedre D

    2014-01-01

    Functional Magnetic Resonance Imaging (fMRI) was used to investigate the extent, magnitude, and pattern of brain activity in response to rapid frequency-modulated sounds. We examined this by manipulating the direction (rise vs. fall) and the rate (fast vs. slow) of the apparent pitch of iterated rippled noise (IRN) bursts. Acoustic parameters were selected to capture features used in phoneme contrasts, however the stimuli themselves were not perceived as speech per se. Participants were scanned as they passively listened to sounds in an event-related paradigm. Univariate analyses revealed a greater level and extent of activation in bilateral auditory cortex in response to frequency-modulated sweeps compared to steady-state sounds. This effect was stronger in the left hemisphere. However, no regions showed selectivity for either rate or direction of frequency modulation. In contrast, multivoxel pattern analysis (MVPA) revealed feature-specific encoding for direction of modulation in auditory cortex bilaterally. Moreover, this effect was strongest when analyses were restricted to anatomical regions lying outside Heschl's gyrus. We found no support for feature-specific encoding of frequency modulation rate. Differential findings of modulation rate and direction of modulation are discussed with respect to their relevance to phonetic discrimination.

  18. Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEG

    Isabella Palamara

    2012-07-01

    Full Text Available An original multivariate multi-scale methodology for assessing the complexity of physiological signals is proposed. The technique is able to incorporate the simultaneous analysis of multi-channel data as a unique block within a multi-scale framework. The basic complexity measure is done by using Permutation Entropy, a methodology for time series processing based on ordinal analysis. Permutation Entropy is conceptually simple, structurally robust to noise and artifacts, computationally very fast, which is relevant for designing portable diagnostics. Since time series derived from biological systems show structures on multiple spatial-temporal scales, the proposed technique can be useful for other types of biomedical signal analysis. In this work, the possibility of distinguish among the brain states related to Alzheimer’s disease patients and Mild Cognitive Impaired subjects from normal healthy elderly is checked on a real, although quite limited, experimental database.

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

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

    2014-01-01

    Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe 0.55 Se 0.45 (T c = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe 1−x Se x structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces

  20. Reduction of the dimensionality and comparative analysis of multivariate radiological data

    Seddeek, M.K.; Kozae, A.M.; Sharshar, T.; Badran, H.M.

    2009-01-01

    Computational methods were used to reduce the dimensionality and to find clusters of multivariate data. The variables were the natural radioactivity contents and the texture characteristics of sand samples. The application of discriminate analysis revealed that samples with high negative values of the former score have the highest contamination with black sand. Principal component analysis (PCA) revealed that radioactivity concentrations alone are sufficient for the classification. Rough set analysis (RSA) showed that the concentration of 238 U, 226 Ra or 232 Th, combined with the concentration of 40 K, can specify the clusters and characteristics of the sand. Both PCA and RSA show that 238 U, 226 Ra and 232 Th behave similarly. RSA revealed that one or two of them can be omitted without degrading predictions.

  1. Simultaneous determination of estrogens (ethinylestradiol and norgestimate) concentrations in human and bovine serum albumin by use of fluorescence spectroscopy and multivariate regression analysis.

    Hordge, LaQuana N; McDaniel, Kiara L; Jones, Derick D; Fakayode, Sayo O

    2016-05-15

    The endocrine disruption property of estrogens necessitates the immediate need for effective monitoring and development of analytical protocols for their analyses in biological and human specimens. This study explores the first combined utility of a steady-state fluorescence spectroscopy and multivariate partial-least-square (PLS) regression analysis for the simultaneous determination of two estrogens (17α-ethinylestradiol (EE) and norgestimate (NOR)) concentrations in bovine serum albumin (BSA) and human serum albumin (HSA) samples. The influence of EE and NOR concentrations and temperature on the emission spectra of EE-HSA EE-BSA, NOR-HSA, and NOR-BSA complexes was also investigated. The binding of EE with HSA and BSA resulted in increase in emission characteristics of HSA and BSA and a significant blue spectra shift. In contrast, the interaction of NOR with HSA and BSA quenched the emission characteristics of HSA and BSA. The observed emission spectral shifts preclude the effective use of traditional univariate regression analysis of fluorescent data for the determination of EE and NOR concentrations in HSA and BSA samples. Multivariate partial-least-squares (PLS) regression analysis was utilized to correlate the changes in emission spectra with EE and NOR concentrations in HSA and BSA samples. The figures-of-merit of the developed PLS regression models were excellent, with limits of detection as low as 1.6×10(-8) M for EE and 2.4×10(-7) M for NOR and good linearity (R(2)>0.994985). The PLS models correctly predicted EE and NOR concentrations in independent validation HSA and BSA samples with a root-mean-square-percent-relative-error (RMS%RE) of less than 6.0% at physiological condition. On the contrary, the use of univariate regression resulted in poor predictions of EE and NOR in HSA and BSA samples, with RMS%RE larger than 40% at physiological conditions. High accuracy, low sensitivity, simplicity, low-cost with no prior analyte extraction or separation

  2. Comparison of spectrum normalization techniques for univariate ...

    2016-02-29

    Feb 29, 2016 ... 1Fuel Chemistry Division, Bhabha Atomic Research Centre, Mumbai 400 085, India. 2Department of ... their three-point smoothing methods were studied using LIBS for quantification of Cr, Mn and Ni ... nique for the qualitative and quantitative analysis of the samples. .... SEP is a type of mean square error.

  3. What makes a pattern? Matching decoding methods to data in multivariate pattern analysis

    Philip A Kragel

    2012-11-01

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

  4. Biodegradable blends of starch/polyvinyl alcohol/glycerol: multivariate analysis of the mechanical properties

    Juliano Zanela

    Full Text Available Abstract The aim of the work was to study the mechanical properties of extruded starch/polyvinyl alcohol (PVA/glycerol biodegradable blends using multivariate analysis. The blends were produced as cylindrical strands by extrusion using PVAs with different hydrolysis degrees and viscosities, at two extrusion temperature profiles (90/170/170/170/170 °C and 90/170/200/200/200 °C and three conditioning relative humidities of the samples (33, 53, and 75%. The mechanical properties showed a great variability according to PVA type, as well as the extrusion temperature profile and the conditioning relative humidity; the tensile strength ranged from 0.42 to 5.40 MPa, elongation at break ranged from 10 to 404% and Young’s modulus ranged from 0.93 to 13.81 MPa. The multivariate analysis was a useful methodology to study the mechanical properties behavior of starch/PVA/glycerol blends, and it can be used as an exploratory technique to select of the more suitable PVA type and extrusion temperature to produce biodegradable materials.

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

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

    2014-07-15

    This work investigates the capability of Fourier-Transform near infrared (FT-NIR) spectroscopy to monitor and assess process parameters in beer fermentation at different operative conditions. For this purpose, the fermentation of wort with two different yeast strains and at different temperatures was monitored for nine days by FT-NIR. To correlate the collected spectra with °Brix, pH and biomass, different multivariate data methodologies were applied. Principal component analysis (PCA), partial least squares (PLS) and locally weighted regression (LWR) were used to assess the relationship between FT-NIR spectra and the abovementioned process parameters that define the beer fermentation. The accuracy and robustness of the obtained results clearly show the suitability of FT-NIR spectroscopy, combined with multivariate data analysis, to be used as a quality control tool in the beer fermentation process. FT-NIR spectroscopy, when combined with LWR, demonstrates to be a perfectly suitable quantitative method to be implemented in the production of beer. Copyright © 2014 Elsevier Ltd. All rights reserved.

  6. Multivariate analysis of attachment of biofouling organisms in response to material surface characteristics.

    Gatley-Montross, Caitlyn M; Finlay, John A; Aldred, Nick; Cassady, Harrison; Destino, Joel F; Orihuela, Beatriz; Hickner, Michael A; Clare, Anthony S; Rittschof, Daniel; Holm, Eric R; Detty, Michael R

    2017-12-29

    Multivariate analyses were used to investigate the influence of selected surface properties (Owens-Wendt surface energy and its dispersive and polar components, static water contact angle, conceptual sign of the surface charge, zeta potentials) on the attachment patterns of five biofouling organisms (Amphibalanus amphitrite, Amphibalanus improvisus, Bugula neritina, Ulva linza, and Navicula incerta) to better understand what surface properties drive attachment across multiple fouling organisms. A library of ten xerogel coatings and a glass standard provided a range of values for the selected surface properties to compare to biofouling attachment patterns. Results from the surface characterization and biological assays were analyzed separately and in combination using multivariate statistical methods. Principal coordinate analysis of the surface property characterization and the biological assays resulted in different groupings of the xerogel coatings. In particular, the biofouling organisms were able to distinguish four coatings that were not distinguishable by the surface properties of this study. The authors used canonical analysis of principal coordinates (CAP) to identify surface properties governing attachment across all five biofouling species. The CAP pointed to surface energy and surface charge as important drivers of patterns in biological attachment, but also suggested that differentiation of the surfaces was influenced to a comparable or greater extent by the dispersive component of surface energy.

  7. Barnyard millet global core collection evaluation in the submontane Himalayan region of India using multivariate analysis

    Salej Sood

    2015-12-01

    Full Text Available Barnyard millet (Echinochloa spp. is one of the most underresearched crops with respect to characterization of genetic resources and genetic enhancement. A total of 95 germplasm lines representing global collection were evaluated in two rainy seasons at Almora, Uttarakhand, India for qualitative and quantitative traits and the data were subjected to multivariate analysis. High variation was observed for days to maturity, five-ear grain weight, and yield components. The first three principal component axes explained 73% of the total multivariate variation. Three major groups were detected by projection of the accessions on the first two principal components. The separation of accessions was based mainly on trait morphology. Almost all Indian and origin-unknown accessions grouped together to form an Echinochloa frumentacea group. Japanese accessions grouped together except for a few outliers to form an Echinochloa esculenta group. The third group contained accessions from Russia, Japan, Cameroon, and Egypt. They formed a separate group on the scatterplot and represented accessions with lower values for all traits except basal tiller number. The interrelationships between the traits indicated that accessions with tall plants, long and broad leaves, longer inflorescences, and greater numbers of racemes should be given priority as donors or parents in varietal development initiatives. Cluster analysis identified two main clusters based on agro-morphological characters.

  8. A method of signal transmission path analysis for multivariate random processes

    Oguma, Ritsuo

    1984-04-01

    A method for noise analysis called ''STP (signal transmission path) analysis'' is presentd as a tool to identify noise sources and their propagation paths in multivariate random proceses. Basic idea of the analysis is to identify, via time series analysis, effective network for the signal power transmission among variables in the system and to make use of its information to the noise analysis. In the present paper, we accomplish this through two steps of signal processings; first, we estimate, using noise power contribution analysis, variables which have large contribution to the power spectrum of interest, and then evaluate the STPs for each pair of variables to identify STPs which play significant role for the generated noise to transmit to the variable under evaluation. The latter part of the analysis is executed through comparison of partial coherence function and newly introduced partial noise power contribution function. This paper presents the procedure of the STP analysis and demonstrates, using simulation data as well as Borssele PWR noise data, its effectiveness for investigation of noise generation and propagation mechanisms. (author)

  9. Probing beer aging chemistry by nuclear magnetic resonance and multivariate analysis

    Rodrigues, J.A.; Barros, A.S.; Carvalho, B.; Brandao, T.; Gil, Ana M.

    2011-01-01

    Graphical abstract: The use of nuclear magnetic resonance (NMR) metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging (at 45 deg. C for up to 18 days) is described. Both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and an aging trend was observed. Inspection of PLS-DA loadings and peak integration revealed the importance of well known markers (e.g. 5-HMF) as well as of other compounds: amino acids, higher alcohols, organic acids, dextrins and some still unassigned spin systems. 2D correlation analysis enabled relevant compound variations to be confirmed and inter-compound correlations to be assessed, thus offering improved insight into the chemical aspects of beer aging. Highlights: · Use of NMR metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging. · Compositional variations evaluated by principal component analysis and partial least squares-discriminant analysis. · Results reveal importance of known markers and other compounds: amino and organic acids, higher alcohols, dextrins. · 2D correlation analysis reveals inter-compound relationships, offering insight into beer aging chemistry. - Abstract: This paper describes the use of nuclear magnetic resonance (NMR) spectroscopy, in tandem with multivariate analysis (MVA), for monitoring the chemical changes occurring in a lager beer exposed to forced aging (at 45 deg. C for up to 18 days). To evaluate the resulting compositional variations, both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and a clear aging trend was observed. Inspection of PLS-DA loadings and peak integration enabled the changing compounds to be identified, revealing the importance of well known markers such as 5-hydroxymethylfurfural (5

  10. Probing beer aging chemistry by nuclear magnetic resonance and multivariate analysis

    Rodrigues, J.A. [CICECO-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal); Barros, A.S. [QOPNA-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal); Carvalho, B.; Brandao, T. [UNICER, Bebidas de Portugal, Leca do Balio, 4466-955, S. Mamede de Infesta (Portugal); Gil, Ana M., E-mail: agil@ua.pt [CICECO-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal)

    2011-09-30

    Graphical abstract: The use of nuclear magnetic resonance (NMR) metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging (at 45 deg. C for up to 18 days) is described. Both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and an aging trend was observed. Inspection of PLS-DA loadings and peak integration revealed the importance of well known markers (e.g. 5-HMF) as well as of other compounds: amino acids, higher alcohols, organic acids, dextrins and some still unassigned spin systems. 2D correlation analysis enabled relevant compound variations to be confirmed and inter-compound correlations to be assessed, thus offering improved insight into the chemical aspects of beer aging. Highlights: {center_dot} Use of NMR metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging. {center_dot} Compositional variations evaluated by principal component analysis and partial least squares-discriminant analysis. {center_dot} Results reveal importance of known markers and other compounds: amino and organic acids, higher alcohols, dextrins. {center_dot} 2D correlation analysis reveals inter-compound relationships, offering insight into beer aging chemistry. - Abstract: This paper describes the use of nuclear magnetic resonance (NMR) spectroscopy, in tandem with multivariate analysis (MVA), for monitoring the chemical changes occurring in a lager beer exposed to forced aging (at 45 deg. C for up to 18 days). To evaluate the resulting compositional variations, both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and a clear aging trend was observed. Inspection of PLS-DA loadings and peak integration enabled the changing compounds to be identified, revealing the importance of well known

  11. Analysis of the stability and accuracy of the discrete least-squares approximation on multivariate polynomial spaces

    Migliorati, Giovanni

    2016-01-01

    We review the main results achieved in the analysis of the stability and accuracy of the discrete leastsquares approximation on multivariate polynomial spaces, with noiseless evaluations at random points, noiseless evaluations at low

  12. A multivariate statistical study with a factor analysis of recent planktonic foraminiferal distribution in the Coromandel Coast of India

    Jayalakshmy, K.V.; Rao, K.K.

    A study of planktonic foraminiferal assemblages from 19 stations in the neritic and oceanic regions off the Coromandel Coast, Bay of Bengal has been made using a multivariate statistical method termed as factor analysis. On the basis of abundance...

  13. [Temporary employment and health: a multivariate analysis of occupational injury risk by job tenure].

    Bena, Antonella; Giraudo, Massimiliano

    2013-01-01

    To study the relationship between job tenure and injury risk, controlling for individual factors and company characteristics. Analysis of incidence and injury risk by job tenure, controlling for gender, age, nationality, economic activity, firm size. Sample of 7% of Italian workers registered in the INPS (National Institute of Social Insurance) database. Private sector employees who worked as blue collars or apprentices. First-time occupational injuries, all occupational injuries, serious occupational injuries. Our findings show an increase in injury risk among those who start a new job and an inverse relationship between job tenure and injury risk. Multivariate analysis confirm these results. Recommendations for improving this situation include the adoption of organizational models that provide periods of mentoring from colleagues already in the company and the assignment to simple and not much hazardous tasks. The economic crisis may exacerbate this problem: it is important for Italy to improve the systems of monitoring relations between temporary employment and health.

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

    Posthuma, D; de Geus, E.J.; Neale, M.C.

    2000-01-01

    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...

  15. imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel.

    Grapov, Dmitry; Newman, John W

    2012-09-01

    Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R's multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010).

  16. Determination of geographic provenance of cotton fibres using multi-isotope profiles and multivariate statistical analysis

    Daeid, N. Nic; Meier-Augenstein, W.; Kemp, H. F.

    2012-04-01

    The analysis of cotton fibres can be particularly challenging within a forensic science context where discrimination of one fibre from another is of importance. Normally cotton fibre analysis examines the morphological structure of the recovered material and compares this with that of a known fibre from a particular source of interest. However, the conventional microscopic and chemical analysis of fibres and any associated dyes is generally unsuccessful because of the similar morphology of the fibres. Analysis of the dyes which may have been applied to the cotton fibre can also be undertaken though this can be difficult and unproductive in terms of discriminating one fibre from another. In the study presented here we have explored the potential for Isotope Ratio Mass Spectrometry (IRMS) to be utilised as an additional tool for cotton fibre analysis in an attempt to reveal further discriminatory information. This work has concentrated on un-dyed cotton fibres of known origin in order to expose the potential of the analytical technique. We report the results of a pilot study aimed at testing the hypothesis that multi-element stable isotope analysis of cotton fibres in conjunction with multivariate statistical analysis of the resulting isotopic abundance data using well established chemometric techniques permits sample provenancing based on the determination of where the cotton was grown and as such will facilitate sample discrimination. To date there is no recorded literature of this type of application of IRMS to cotton samples, which may be of forensic science relevance.

  17. Multivariate Statistical Analysis of Water Quality data in Indian River Lagoon, Florida

    Sayemuzzaman, M.; Ye, M.

    2015-12-01

    The Indian River Lagoon, is part of the longest barrier island complex in the United States, is a region of particular concern to the environmental scientist because of the rapid rate of human development throughout the region and the geographical position in between the colder temperate zone and warmer sub-tropical zone. Thus, the surface water quality analysis in this region always brings the newer information. In this present study, multivariate statistical procedures were applied to analyze the spatial and temporal water quality in the Indian River Lagoon over the period 1998-2013. Twelve parameters have been analyzed on twelve key water monitoring stations in and beside the lagoon on monthly datasets (total of 27,648 observations). The dataset was treated using cluster analysis (CA), principle component analysis (PCA) and non-parametric trend analysis. The CA was used to cluster twelve monitoring stations into four groups, with stations on the similar surrounding characteristics being in the same group. The PCA was then applied to the similar groups to find the important water quality parameters. The principal components (PCs), PC1 to PC5 was considered based on the explained cumulative variances 75% to 85% in each cluster groups. Nutrient species (phosphorus and nitrogen), salinity, specific conductivity and erosion factors (TSS, Turbidity) were major variables involved in the construction of the PCs. Statistical significant positive or negative trends and the abrupt trend shift were detected applying Mann-Kendall trend test and Sequential Mann-Kendall (SQMK), for each individual stations for the important water quality parameters. Land use land cover change pattern, local anthropogenic activities and extreme climate such as drought might be associated with these trends. This study presents the multivariate statistical assessment in order to get better information about the quality of surface water. Thus, effective pollution control/management of the surface

  18. Evaluation of functional outcome of the floating knee injury using multivariate analysis.

    Yokoyama, Kazuhiko; Tsukamoto, Tatsuro; Aoki, Shinichi; Wakita, Ryuji; Uchino, Masataka; Noumi, Takashi; Fukushima, Nobuaki; Itoman, Moritoshi

    2002-11-01

    The objective of this study is to evaluate significant contributing factors affecting the functional prognosis of floating knee injuries using multivariate analysis. A total of 68 floating knee injuries (67 patients) were treated at Kitasato University Hospital from 1986 to 1999. Both the femoral fractures and the tibial fractures were managed surgically by various methods. The functional results of these injuries were evaluated using the grading system of Karlström and Olerud. Follow-up periods ranged from 2 to 19 years (mean 50.2 months) after the original injury. We defined satisfactory (S) outcomes as those cases with excellent or good results and unsatisfactory (US) outcomes as those cases with acceptable or poor results. Logistic regression analysis was used as a multivariate analysis, and the dependent variables were defined as a satisfactory outcome or as an unsatisfactory outcome. The explanatory variables were predicting factors influencing the functional outcome such as age at trauma, gender, severity of soft-tissue injury in the femur and the tibia, AO fracture grade in the femur and the tibia, Fraser type (type I or type II), Injury Severity Score (ISS), and fixation time after injury (less than 1 week or more than 1 week) in the femur and the tibia. The final functional results were as follows: 25 cases had excellent results, 15 cases good results, 16 cases acceptable results, and 12 cases poor results. The predictive logistic regression equation was as follows: Log 1-p/p = 3.12-1.52 x Fraser type - 1.65 x severity of soft-tissue injury in the tibia - 1.31 x fixation time after injury in the tibia - 0.821 x AO fracture grade in the tibia + 1.025 x fixation time after injury in the femur - 0.687 x AO fracture grade in the femur ( p=0.01). Among the variables, Fraser type and the severity of soft-tissue injury in the tibia were significantly related to the final result. The multivariate analysis showed that both the involvement of the knee joint and

  19. Quantitative Evaluation of Hybrid Aspen Xylem and Immunolabeling Patterns Using Image Analysis and Multivariate Statistics

    David Sandquist

    2015-06-01

    Full Text Available A new method is presented for quantitative evaluation of hybrid aspen genotype xylem morphology and immunolabeling micro-distribution. This method can be used as an aid in assessing differences in genotypes from classic tree breeding studies, as well as genetically engineered plants. The method is based on image analysis, multivariate statistical evaluation of light, and immunofluorescence microscopy images of wood xylem cross sections. The selected immunolabeling antibodies targeted five different epitopes present in aspen xylem cell walls. Twelve down-regulated hybrid aspen genotypes were included in the method development. The 12 knock-down genotypes were selected based on pre-screening by pyrolysis-IR of global chemical content. The multivariate statistical evaluations successfully identified comparative trends for modifications in the down-regulated genotypes compared to the unmodified control, even when no definitive conclusions could be drawn from individual studied variables alone. Of the 12 genotypes analyzed, three genotypes showed significant trends for modifications in both morphology and immunolabeling. Six genotypes showed significant trends for modifications in either morphology or immunocoverage. The remaining three genotypes did not show any significant trends for modification.

  20. Study on loss detection algorithms for tank monitoring data using multivariate statistical analysis

    Suzuki, Mitsutoshi; Burr, Tom

    2009-01-01

    Evaluation of solution monitoring data to support material balance evaluation was proposed about a decade ago because of concerns regarding the large throughput planned at Rokkasho Reprocessing Plant (RRP). A numerical study using the simulation code (FACSIM) was done and significant increases in the detection probabilities (DP) for certain types of losses were shown. To be accepted internationally, it is very important to verify such claims using real solution monitoring data. However, a demonstrative study with real tank data has not been carried out due to the confidentiality of the tank data. This paper describes an experimental study that has been started using actual data from the Solution Measurement and Monitoring System (SMMS) in the Tokai Reprocessing Plant (TRP) and the Savannah River Site (SRS). Multivariate statistical methods, such as a vector cumulative sum and a multi-scale statistical analysis, have been applied to the real tank data that have superimposed simulated loss. Although quantitative conclusions have not been derived for the moment due to the difficulty of baseline evaluation, the multivariate statistical methods remain promising for abrupt and some types of protracted loss detection. (author)

  1. Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis.

    Yan, Zhengbing; Kuang, Te-Hui; Yao, Yuan

    2017-09-01

    In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  2. Searching for New Biomarkers and the Use of Multivariate Analysis in Gastric Cancer Diagnostics.

    Kucera, Radek; Smid, David; Topolcan, Ondrej; Karlikova, Marie; Fiala, Ondrej; Slouka, David; Skalicky, Tomas; Treska, Vladislav; Kulda, Vlastimil; Simanek, Vaclav; Safanda, Martin; Pesta, Martin

    2016-04-01

    The first aim of this study was to search for new biomarkers to be used in gastric cancer diagnostics. The second aim was to verify the findings presented in literature on a sample of the local population and investigate the risk of gastric cancer in that population using a multivariant statistical analysis. We assessed a group of 36 patients with gastric cancer and 69 healthy individuals. We determined carcinoembryonic antigen, cancer antigen 19-9, cancer antigen 72-4, matrix metalloproteinases (-1, -2, -7, -8 and -9), osteoprotegerin, osteopontin, prothrombin induced by vitamin K absence-II, pepsinogen I, pepsinogen II, gastrin and Helicobacter pylori for each sample. The multivariate stepwise logistic regression identified the following biomarkers as the best gastric cancer predictors: CEA, CA72-4, pepsinogen I, Helicobacter pylori presence and MMP7. CEA and CA72-4 remain the best markers for gastric cancer diagnostics. We suggest a mathematical model for the assessment of risk of gastric cancer. Copyright© 2016 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

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

    Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

    2016-05-01

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

  4. A Morphometric Survey among Three Iranian Horse Breeds with Multivariate Analysis

    M. Hosseini

    2016-12-01

    Full Text Available Three Iranian horse breeds, Turkoman, Caspian, and Kurdish, are the most important Iranian horse breeds which are well known in all around of the world because of their beauty, versatility, great stamina, and  intelligence. Phenotypic characterization was used to identify and document the diversity within and between distinct breeds, based on their observable attributes. Phenotypic characterization and body biometric in 23 traits were measured in 191 purebred horses belonging to three breeds, i.e. Turkoman (70 horses, Kurdish (77 horses, and Caspian (44 horses.  Caspian breed was  sampled from the Provinces of Alborz and Gilan. Kurdish breed was sampled from the Provinces of Kurdistan, Kermanshah, and Hamadan. Turkoman breed was sampled from the Provinces of Golestan, Markazi, and Isfahan. Multivariate analysis of variance (MANOVA was implemented. In addition, Canonical Discriminate Analysis (CDA, Principal Component Analysis (PCA, and Custer analysis were executed for assessing the relationship among the breeds. All statistical analysis was executed by SAS statistical program. The results of our investigation represented the breeds classification into 3 different classes (Caspian, Turkoman, and Kurdish based on different morphometrical traits. Caspian breed with smaller size in most variables was detached clearly from the others with more distance than Kurdish and Turkoman breeds. The result showed that the most variably trait for classification was Hind Hoof Length. Adaptation with different environments causes difference in morphology and difference among breeds. We can identify and classify domestic population using PCA, CDA, and cluster analysis.

  5. Multivariate innovative approaches to the treatment of the emission of LIBS plasmas. Application to chemical online analysis in a nuclear environment

    El-Rakwe, Maria

    2016-01-01

    Online and in situ analysis is now a strategic development for analytical chemistry. This is especially true in the nuclear field for which the security constraints related to the radioactivity of samples, and the need to minimize waste from analyzes argue for remote measurement techniques without sampling or sample preparation. Laser-Induced Breakdown Spectroscopy (LIBS) technique for elemental analysis of materials based on laser ablation and the optical emission spectroscopy, has these qualities. It is a technique of choice for online analysis. However, processes involved in LIBS, namely laser ablation, atomization, plasma formation and emission, are quite complex and difficult to control because the underlying physical phenomena are coupled and nonlinear. In addition, the analytical performance of the LIBS technique depends strongly on the choice of experimental conditions. Finally, an online analysis system should be as robust as possible face to uncontrolled variations in measurement conditions. The objective of this thesis is to improve control and performance of quantitative analysis by LIBS using multivariate methods capable of handling multi-dimensionality, nonlinearity and the coupling between parameters and data. For this, the work is divided into two parts. First the optimization is carried out using a central composite design to model the relationship between the experimental parameters of laser ablation (pulse energy and beam focusing parameters) and signal detection (delay time) to the physical characteristics of plasma (ablated mass, temperature) and the analytical performance (intensity and repeatability of the signal). The optimization parameters that results is then interpreted as the best compromise for the quantitative analysis between efficiency of laser ablation and plasma heating. Secondly, a multivariate methodology based on MCR-ALS, ICA and PLS techniques, was developed to quantify certain elements in different metallic matrices

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

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

    2010-10-01

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

  7. Multivariate analysis of TOF-SIMS spectra of monolayers on scribed silicon.

    Yang, Li; Lua, Yit-Yian; Jiang, Guilin; Tyler, Bonnie J; Linford, Matthew R

    2005-07-15

    Static time-of-flight secondary ion mass spectrometry (TOF-SIMS) was performed on monolayers on scribed silicon (Si(scr)) derived from 1-alkenes, 1-alkynes, 1-holoalkanes, aldehydes, and acid chlorides. To rapidly determine the variation in the data without introducing user bias, a multivariate analysis was performed. First, principal components analysis (PCA) was done on data obtained from silicon scribed with homologous series of aldehydes and acid chlorides. For this study, the positive ion spectra, the negative ion spectra, and the concatentated (linked) positive and negative ion spectra were preprocessed by normalization, mean centering, and autoscaling. The mean centered data consistently showed the best correlations between the scores on PC1 and the number of carbon atoms in the adsorbate. These correlations were not as strong for the normalized and autoscaled data. After reviewing these methods, it was concluded that mean centering is the best preprocessing method for TOF-SIMS spectra of monolayers on Si(scr). A PCA analysis of all of the positive ion spectra revealed a good correlation between the number of carbon atoms in all of the adsorbates and the scores on PC1. PCA of all of the negative ion spectra and the concatenated positive and negative ion spectra showed a correlation based on the number of carbon atoms in the adsorbate and the class of the adsorbate. These results imply that the positive ion spectra are most sensitive to monolayer thickness, while the negative ion spectra are sensitive to the nature of the substrate-monolayer interface and the monolayer thickness. Loadings show an inverse relationship between (inorganic) fragments that are expected from the substrate and (organic) fragments expected from the monolayer. Multivariate peak intensity ratios were derived. It is also suggested that PCA can be used to detect outlier surfaces. Partial least squares showed a strong correlation between the number of carbon atoms in the adsorbate and the

  8. Banach frames for multivariate alpha-modulation spaces

    Borup, Lasse; Nielsen, Morten

    2006-01-01

    The α-modulation spaces [$Mathematical Term$], form a family of spaces that include the Besov and modulation spaces as special cases. This paper is concerned with construction of Banach frames for α-modulation spaces in the multivariate setting. The frames constructed are unions of independent Ri...... Riesz sequences based on tensor products of univariate brushlet functions, which simplifies the analysis of the full frame. We show that the multivariate α-modulation spaces can be completely characterized by the Banach frames constructed....

  9. Multivariable Analysis of the Association Between Antimicrobial Use and Antimicrobial Resistance in Escherichia coli Isolated from Apparently Healthy Pigs in Japan.

    Makita, Kohei; Goto, Masaki; Ozawa, Manao; Kawanishi, Michiko; Koike, Ryoji; Asai, Tetsuo; Tamura, Yutaka

    2016-01-01

    The objective of this study was to investigate the association between antimicrobial agent use and antimicrobial resistance in Escherichia coli isolated from healthy pigs using data from 2004 to 2007 in the Japanese Veterinary Antimicrobial Resistance Monitoring System (JVARM). Fecal E. coli isolates from 250 pigs (one isolate each from a pig per farm) were examined for antimicrobial resistance. Information on the use of antimicrobials within preceding 6 months and types of farms recorded in JVARM was collected and statistically analyzed against the resistance patterns. In the univariate analysis, associations between both therapeutic and feed additive use of antimicrobials, and resistance to dihydrostreptomycin, gentamicin, kanamycin, ampicillin, cefazolin, ceftiofur, oxytetracycline, chloramphenicol, trimethoprim, nalidixic acid, enrofloxacin, colistin, and bicozamycin, and husbandry factors were investigated. In multivariable analysis, generalized estimating equations were used to control geographical intraclass correlation. Confounding for structurally unrelated associations was tested using generalized linear models. The results suggested direct and cross selections in the associations between use of aminoglycosides in reproduction farms and resistance to kanamycin, use of tetracyclines in larger farms and resistance to oxytetracycline, use of beta-lactams and resistance to ampicillin, use of phenicols and resistance to chloramphenicol, and use of fluoroquinolones and resistance to nalidixic acid and enrofloxacin. Coselection was suggested in the use of tetracyclines and chloramphenicol resistance. The associations between use of beta-lactams and dihydrostreptomycin resistance, use of macrolides and ampicillin and oxytetracycline resistance, and use of colistin and kanamycin resistance were significant, but were confounded by the simultaneous use of homologous antimicrobials.

  10. Characterization of Land Transitions Patterns from Multivariate Time Series Using Seasonal Trend Analysis and Principal Component Analysis

    Benoit Parmentier

    2014-12-01

    Full Text Available Characterizing biophysical changes in land change areas over large regions with short and noisy multivariate time series and multiple temporal parameters remains a challenging task. Most studies focus on detection rather than the characterization, i.e., the manner by which surface state variables are altered by the process of changes. In this study, a procedure is presented to extract and characterize simultaneous temporal changes in MODIS multivariate times series from three surface state variables the Normalized Difference Vegetation Index (NDVI, land surface temperature (LST and albedo (ALB. The analysis involves conducting a seasonal trend analysis (STA to extract three seasonal shape parameters (Amplitude 0, Amplitude 1 and Amplitude 2 and using principal component analysis (PCA to contrast trends in change and no-change areas. We illustrate the method by characterizing trends in burned and unburned pixels in Alaska over the 2001–2009 time period. Findings show consistent and meaningful extraction of temporal patterns related to fire disturbances. The first principal component (PC1 is characterized by a decrease in mean NDVI (Amplitude 0 with a concurrent increase in albedo (the mean and the annual amplitude and an increase in LST annual variability (Amplitude 1. These results provide systematic empirical evidence of surface changes associated with one type of land change, fire disturbances, and suggest that STA with PCA may be used to characterize many other types of land transitions over large landscape areas using multivariate Earth observation time series.

  11. Immediate versus delayed intramedullary nailing for open fractures of the tibial shaft: A multivariate analysis of factors affecting deep infection and fracture healing

    Yokoyama Kazuhiko

    2008-01-01

    Full Text Available Background: The purpose of this study was to evaluate contributing factors affecting deep infection and fracture healing of open tibia fractures treated with locked intramedullary nailing (IMN by multivariate analysis. Materials and Methods: We examined 99 open tibial fractures (98 patients treated with immediate or delayed locked IMN in static fashion from 1991 to 2002. Multivariate analyses following univariate analyses were derived to determine predictors of deep infection, nonunion, and healing time to union. The following predictive variables of deep infection were selected for analysis: age, sex, Gustilo type, fracture grade by AO type, fracture location, timing or method of IMN, reamed or unreamed nailing, debridement time (≤6 h or> 6 h, method of soft-tissue management, skin closure time (≤1 week or> 1 week, existence of polytrauma (ISS< 18 or ISS≥18, existence of floating knee injury, and existence of superficial/pin site infection. The predictive variables of nonunion selected for analysis was the same as those for deep infection, with the addition of deep infection for exchange of pin site infection. The predictive variables of union time selected for analysis was the same as those for nonunion, excluding of location, debridement time, and existence of floating knee and superficial infection. Results: Six (6.1%; type II Gustilo n=1, type IIIB Gustilo n=5 of the 99 open tibial fractures developed deep infections. Multivariate analysis revealed that timing or method of IMN, debridement time, method of soft-tissue management, and existence of superficial or pin site infection significantly correlated with the occurrence of deep infection ( P < 0.0001. In the immediate nailing group alone, the deep infection rate in type IIIB + IIIC was significantly higher than those in type I + II and IIIA ( P = 0.016. Nonunion occurred in 17 fractures (20.3%, 17/84. Multivariate analysis revealed that Gustilo type, skin closure time, and

  12. Spatial and Temporal Assessment on Drug Addiction Using Multivariate Analysis and GIS

    Mohd Ekhwan Toriman; Mohd Ekhwan Toriman; Siti Nor Fazillah Abdullah; Izwan Arif Azizan; Mohd Khairul Amri Kamarudin; Roslan Umar; Nasir Mohamad

    2015-01-01

    There is a need for managing and displaying drug addiction phenomena and trend at both spatial and temporal scales. Spatial and temporal assessment on drug addiction in Terengganu was undertaken to understand the geographical area of district in the same cluster, in addition, identify the hot spot area of this problem and analysis the trend of drug addiction. Data used were topography map of Terengganu and number of drug addicted person in Terengganu by district within 10 years (2004-2013). Number of drug addicted person by district were mapped using Geographic Information system and analysed using a combination of multivariate analysis which is cluster analysis were applied to the database in order to validate the correlation between data in the same cluster. Result showed a cluster analysis for number of drug addiction by district generated three clusters which are Besut and Kuala Terengganu in cluster 1 named moderate drug addicted person (MDA), Dungun, Marang, Setiu and Hulu Terengganu in cluster 2 named lower drug addicted person (LDA) and Kemaman in cluster 3 named high drug addicted person(HDA). This analysis indicates that cluster 3 which is Kemaman is a hot spot area. These results were beneficial for stakeholder to monitor and manage this problem especially in the hot spot area which needs to be emphasized. (author)

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

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

    2011-03-01

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

  14. Physical analysis of multivariate measurements in the Atmospheric high-energy physics experiments within ADEI platform

    Avakyan, K.; Chilingarian, A.; Karapetyan, T.; Chilingaryan, S.

    2017-01-01

    To make transformational scientific progress in Space science and geophysics, the Sun, heliosphere, magnetosphere and different layers of the atmosphere must be studied as a coupled system. Presented paper describes how information on complicated physical processes on Sun, in the heliosphere, magnetosphere and atmosphere can be made immediately assessable for researchers via advanced multivariate visualization system with simple statistical analysis package. Research of the high-energy phenomena in the atmosphere and the atmospheric discharges is of special importance. The relationship between thundercloud electrification, lightning activity, wideband radio emission and particle fluxes have not been yet unambiguously established. One of most intriguing opportunities opening by observation of the high-energy processes in the atmosphere is their relation to lightning initiation. Investigations of the accelerated structures in the geospace plasmas can as well shed light on particle acceleration up to much higher energies in the similar structures of space plasmas in the distant objects of the Universe. (author)

  15. Multivariate analysis and extraction of parameters in resistive RAMs using the Quantum Point Contact model

    Roldán, J. B.; Miranda, E.; González-Cordero, G.; García-Fernández, P.; Romero-Zaliz, R.; González-Rodelas, P.; Aguilera, A. M.; González, M. B.; Jiménez-Molinos, F.

    2018-01-01

    A multivariate analysis of the parameters that characterize the reset process in Resistive Random Access Memory (RRAM) has been performed. The different correlations obtained can help to shed light on the current components that contribute in the Low Resistance State (LRS) of the technology considered. In addition, a screening method for the Quantum Point Contact (QPC) current component is presented. For this purpose, the second derivative of the current has been obtained using a novel numerical method which allows determining the QPC model parameters. Once the procedure is completed, a whole Resistive Switching (RS) series of thousands of curves is studied by means of a genetic algorithm. The extracted QPC parameter distributions are characterized in depth to get information about the filamentary pathways associated with LRS in the low voltage conduction regime.

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

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

    1984-01-01

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

  17. Prediction of chemical, physical and sensory data from process parameters for frozen cod using multivariate analysis

    Bechmann, Iben Ellegaard; Jensen, H.S.; Bøknæs, Niels

    1998-01-01

    Physical, chemical and sensory quality parameters were determined for 115 cod (Gadus morhua) samples stored under varying frozen storage conditions. Five different process parameters (period of frozen storage, frozen storage. temperature, place of catch, season for catching and state of rigor) were...... varied systematically at two levels. The data obtained were evaluated using the multivariate methods, principal component analysis (PCA) and partial least squares (PLS) regression. The PCA models were used to identify which process parameters were actually most important for the quality of the frozen cod....... PLS models that were able to predict the physical, chemical and sensory quality parameters from the process parameters of the frozen raw material were generated. The prediction abilities of the PLS models were good enough to give reasonable results even when the process parameters were characterised...

  18. Analysis of Regularly and Irregularly Sampled Spatial, Multivariate, and Multi-temporal Data

    Nielsen, Allan Aasbjerg

    1994-01-01

    This thesis describes different methods that are useful in the analysis of multivariate data. Some methods focus on spatial data (sampled regularly or irregularly), others focus on multitemporal data or data from multiple sources. The thesis covers selected and not all aspects of relevant data......-variograms are described. As a new way of setting up a well-balanced kriging support the Delaunay triangulation is suggested. Two case studies show the usefulness of 2-D semivariograms of geochemical data from areas in central Spain (with a geologist's comment) and South Greenland, and kriging/cokriging of an undersampled...... are considered as repetitions. Three case studies show the strength of the methods; one uses SPOT High Resolution Visible (HRV) multispectral (XS) data covering economically important pineapple and coffee plantations near Thika, Kiambu District, Kenya, the other two use Landsat Thematic Mapper (TM) data covering...

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

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

    2014-05-01

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

  20. Optimization of Interior Permanent Magnet Motor by Quality Engineering and Multivariate Analysis

    Okada, Yukihiro; Kawase, Yoshihiro

    This paper has described the method of optimization based on the finite element method. The quality engineering and the multivariable analysis are used as the optimization technique. This optimizing method consists of two steps. At Step.1, the influence of parameters for output is obtained quantitatively, at Step.2, the number of calculation by the FEM can be cut down. That is, the optimal combination of the design parameters, which satisfies the required characteristic, can be searched for efficiently. In addition, this method is applied to a design of IPM motor to reduce the torque ripple. The final shape can maintain average torque and cut down the torque ripple 65%. Furthermore, the amount of permanent magnets can be reduced.

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

    Adam, Abdel Majid A.; Eltayeb, Mohamed Ahmed H.

    2012-01-01

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

  2. Multivariate missing data in hydrology - Review and applications

    Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.

    2017-12-01

    Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.

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

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

    2010-08-01

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

  4. Pain in diagnostic hysteroscopy: a multivariate analysis after a randomized, controlled trial.

    Mazzon, Ivan; Favilli, Alessandro; Grasso, Mario; Horvath, Stefano; Bini, Vittorio; Di Renzo, Gian Carlo; Gerli, Sandro

    2014-11-01

    To study which variables are able to influence women's experience of pain during diagnostic hysteroscopy. Multivariate analysis (phase II) after a randomized, controlled trial (phase I). Endoscopic gynecologic center. In phase I, 392 patients were analyzed. Group A: 197 women with carbon dioxide (CO2); group B: 195 women with normal saline. In phase II, 392 patients were assigned to two different groups according to their pain experience as measured by a visual analogue scale (VAS): group VAS>3 (170 patients); group VAS≤3 (222 patients). Free-anesthesia diagnostic hysteroscopy performed using CO2 or normal saline as distension media. Procedure time, VAS score, image quality, and side effects during and after diagnostic hysteroscopy. In phase I the median pain score in group A was 2, whereas in group B it was 3. In phase II the duration of the procedure, nulliparity, and the use of normal saline were significantly correlated with VAS>3. A higher presence of cervical synechiae was observed in the group VAS>3. The multivariate analysis revealed an inverse correlation between parity and a VAS>3, whereas the use of normal saline, the presence of synechiae in the cervical canal, and the duration of the hysteroscopy were all directly correlated to a VAS score>3. Pain in hysteroscopy is significantly related to the presence of cervical synechiae, to the duration of the procedure, and to the use of normal saline; conversely, parity seems to have a protective role. NCT01873391. Copyright © 2014 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

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

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

    2015-03-27

    In the present study a series of 45 metabolite standards belonging to four chemically similar metabolite classes (sugars, amino acids, nucleosides and nucleobases, and amines) was subjected to LC analysis on three HILIC columns under 21 different gradient conditions with the aim to explore whether the retention properties of these analytes are determined from the chemical group they belong. Two multivariate techniques, principal component analysis (PCA) and discriminant analysis (DA), were used for statistical evaluation of the chromatographic data and extraction similarities between chemically related compounds. The total variance explained by the first two principal components of PCA was found to be about 98%, whereas both statistical analyses indicated that all analytes are successfully grouped in four clusters of chemical structure based on the retention obtained in four or at least three chromatographic runs, which, however should be performed on two different HILIC columns. Moreover, leave-one-out cross-validation of the above retention data set showed that the chemical group in which an analyte belongs can be 95.6% correctly predicted when the analyte is subjected to LC analysis under the same four or three experimental conditions as the all set of analytes was run beforehand. That, in turn, may assist with disambiguation of analyte identification in complex biological extracts. Copyright © 2015 Elsevier B.V. All rights reserved.

  6. Dynamics analysis of a boiling water reactor based on multivariable autoregressive modeling

    Oguma, Ritsuo; Matsubara, Kunihiko

    1980-01-01

    The establishment of the highly reliable mathematical model for the dynamic characteristics of a reactor is indispensable for the achievement of safe operation in reactor plants. The authors have tried to model the dynamic characteristics of a reactor based on the identification technique, taking the JPDR (Japan Power Demonstration Reactor) as the object, as one of the technical studies for diagnosing BWR anomaly, and employed the multivariable autoregressive modeling (MAR method) as one of the useful methods for forwarding the analysis. In this paper, the outline of the system analysis by MAR modeling is explained, and the identification experiments and their analysis results performed in the phase 4 of the power increase test of the JPDR are described. The authors evaluated the results of identification based on only reactor noises, making reference to the results of identification in the case of exciting the system by applying artificial irregular disturbance, in order to clarify the extent in which the modeling is possible by reactor noises only. However, some difficulties were encountered. The largest problem is the one concerning the separation and identification of the noise sources exciting the variables from the dynamic characteristics among the variables. If the effective technique can be obtained to this problem, the approach by the identification technique based on the probability model might be a powerful tool in the field of reactor noise analysis and the development of diagnosis technics. (Wakatsuki, Y.)

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

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

    2015-02-25

    A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety. Copyright © 2014 Elsevier B.V. All rights reserved.

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

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

    2015-02-01

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

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

    Soh, Shiau-Chian; Abdullah, Md Pauzi

    2007-01-01

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

  10. CoSMoMVPA: multi-modal multivariate pattern analysis of neuroimaging datain Matlab / GNU Octave

    Nikolaas N Oosterhof

    2016-07-01

    Full Text Available Recent years have seen an increase in the popularity of multivariate pattern (MVP analysis of functional magnetic resonance (fMRI data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens.CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species.It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques.CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality

  11. Newly Graduated Nurses' Competence and Individual and Organizational Factors: A Multivariate Analysis.

    Numminen, Olivia; Leino-Kilpi, Helena; Isoaho, Hannu; Meretoja, Riitta

    2015-09-01

    To study the relationships between newly graduated nurses' (NGNs') perceptions of their professional competence, and individual and organizational work-related factors. A multivariate, quantitative, descriptive, correlation design was applied. Data collection took place in November 2012 with a national convenience sample of 318 NGNs representing all main healthcare settings in Finland. Five instruments measured NGNs' perceptions of their professional competence, occupational commitment, empowerment, practice environment, and its ethical climate, with additional questions on turnover intentions, job satisfaction, and demographics. Descriptive statistics summarized the demographic data, and inferential statistics multivariate path analysis modeling estimated the relationships between the variables. The strongest relationship was found between professional competence and empowerment, competence explaining 20% of the variance of empowerment. The explanatory power of competence regarding practice environment, ethical climate of the work unit, and occupational commitment, and competence's associations with turnover intentions, job satisfaction, and age, were statistically significant but considerably weaker. Higher competence and satisfaction with quality of care were associated with more positive perceptions of practice environment and its ethical climate as well as higher empowerment and occupational commitment. Apart from its association with empowerment, competence seems to be a rather independent factor in relation to the measured work-related factors. Further exploration would deepen the knowledge of this relationship, providing support for planning educational and developmental programs. Research on other individual and organizational factors is warranted to shed light on factors associated with professional competence in providing high-quality and safe care as well as retaining new nurses in the workforce. The study sheds light on the strength and direction of

  12. Studies on multivariate autoregressive analysis using synthesized reactor noise-like data for optimal modelling

    Ciftcioglu, O.; Hoogenboom, J.E.; Dam, H. van

    1988-01-01

    Studies on the multivariate autoregressive (MAR) analysis are carried out for the choice of the parameters for modelling the data obtained from various sensors optimally. Accordingly, the roles of the parameters on the analysis results are identified and the related ambiguities are reduced. Experimental investigations are carried out by means of synthesized reactor noise-like data obtained from a digital simulator providing simulated stochastic signals of an operating nuclear reactor so that the simulator constitutes a favourable tool for the present studies aimed. As the system is well defined with its known structure, precise comparison of the MAR analysis results with the true values is performed. With the help of the information gained through the studies carried out, conditions to be taken care of for optimal signal processing in MAR modelling are determined. Although the parameters involved are related among themselves and they have to be given different values suitable for the particular application in hand, some criteria, namely memory-time and sample length-time play an essential role in AR modelling and they are found to be applicable to each individual case commonly, for the establishment of the optimality.

  13. Studies on multivariate autoregressive analysis using synthesized reactor noise-like data for optimal modelling

    Ciftcioglu, O.

    1988-01-01

    Studies on the multivariate autoregressive (MAR) analysis are carried out for the choice of the parameters for modelling the data obtained from various sensors optimally. Accordingly, the roles of the parameters on the analysis results are identified and the related ambiguities are reduced. Experimental investigations are carried out by means of synthesized reactor noise-like data obtained from a digital simulator providing simulated stochastic signals of an operating nuclear reactor so that the simulator constitutes a favourable tool for the present studies aimed. As the system is well defined with its known structure, precise comparison of the MAR analysis results with the true values is performed. With the help of the information gained through the studies carried out, conditions to be taken care of for optimal signal processing in MAR modelling are determined. Although the parameters involved are related among themselves and they have to be given different values suitable for the particular application in hand, some criteria, namely memory-time and sample length-time play an essential role in AR modelling and they are found to be applicable to each individual case commonly, for the establishment of the optimality. (author)

  14. Classification of emotions by multivariate analysis and individual differences of nuclear power plant operators' emotion

    Hasegawa, Naoko; Yoshimura, Seiichi

    1999-01-01

    The purpose of this study is the development of a simulation model which expresses operators' emotion under plant emergency. This report shows the classification of emotions by multivariate analysis and investigation results conducted to clarify individual differences of activated emotion influenced by personal traits. Although a former investigation was conducted to classify emotions into five basic emotions proposed by Johnson-Laird, the basic emotions was not based on real data. For the development of more realistic and accurate simulation model, it is necessary to recognize basic emotion and to classify emotions into them. As a result of analysis by qualification method 3 and cluster analysis, four basic clusters were clarified, i.e., Emotion expressed towards objects, Emotion affected by objects, Pleasant emotion, and Surprise. Moreover, 51 emotions were ranked in the order according to their similarities in each cluster. An investigation was conducted to clarify individual differences in emotion process using 87 plant operators. The results showed the differences of emotion depending on the existence of operators' foresight, cognitive style, experience in operation, and consciousness of attribution to an operating team. For example, operators with low self-efficacy, short experience or low consciousness of attribution to a team, feel more intensive emotion under plant emergency and more affected by severe plant conditions. The model which can express individual differences will be developed utilizing and converting these data hereafter. (author)

  15. Application of Multivariate Statistical Analysis to Biomarkers in Se-Turkey Crude Oils

    Gürgey, K.; Canbolat, S.

    2017-11-01

    Twenty-four crude oil samples were collected from the 24 oil fields distributed in different districts of SE-Turkey. API and Sulphur content (%), Stable Carbon Isotope, Gas Chromatography (GC), and Gas Chromatography-Mass Spectrometry (GC-MS) data were used to construct a geochemical data matrix. The aim of this study is to examine the genetic grouping or correlations in the crude oil samples, hence the number of source rocks present in the SE-Turkey. To achieve these aims, two of the multivariate statistical analysis techniques (Principle Component Analysis [PCA] and Cluster Analysis were applied to data matrix of 24 samples and 8 source specific biomarker variables/parameters. The results showed that there are 3 genetically different oil groups: Batman-Nusaybin Oils, Adıyaman-Kozluk Oils and Diyarbakir Oils, in addition to a one mixed group. These groupings imply that at least, three different source rocks are present in South-Eastern (SE) Turkey. Grouping of the crude oil samples appears to be consistent with the geographic locations of the oils fields, subsurface stratigraphy as well as geology of the area.

  16. APPLICATION OF MULTIVARIATE STATISTICAL ANALYSIS TO BIOMARKERS IN SE-TURKEY CRUDE OILS

    K. Gürgey

    2017-11-01

    Full Text Available Twenty-four crude oil samples were collected from the 24 oil fields distributed in different districts of SE-Turkey. API and Sulphur content (%, Stable Carbon Isotope, Gas Chromatography (GC, and Gas Chromatography-Mass Spectrometry (GC-MS data were used to construct a geochemical data matrix. The aim of this study is to examine the genetic grouping or correlations in the crude oil samples, hence the number of source rocks present in the SE-Turkey. To achieve these aims, two of the multivariate statistical analysis techniques (Principle Component Analysis [PCA] and Cluster Analysis were applied to data matrix of 24 samples and 8 source specific biomarker variables/parameters. The results showed that there are 3 genetically different oil groups: Batman-Nusaybin Oils, Adıyaman-Kozluk Oils and Diyarbakir Oils, in addition to a one mixed group. These groupings imply that at least, three different source rocks are present in South-Eastern (SE Turkey. Grouping of the crude oil samples appears to be consistent with the geographic locations of the oils fields, subsurface stratigraphy as well as geology of the area.

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

    Myoung, Se Hun; Kim, Jin-Koo

    2016-03-01

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

  18. Classification of emotions by multivariate analysis and individual differences of nuclear power plant operators` emotion

    Hasegawa, Naoko; Yoshimura, Seiichi [Central Research Inst. of Electric Power Industry, Tokyo (Japan)

    1999-03-01

    The purpose of this study is the development of a simulation model which expresses operators` emotion under plant emergency. This report shows the classification of emotions by multivariate analysis and investigation results conducted to clarify individual differences of activated emotion influenced by personal traits. Although a former investigation was conducted to classify emotions into five basic emotions proposed by Johnson-Laird, the basic emotions was not based on real data. For the development of more realistic and accurate simulation model, it is necessary to recognize basic emotion and to classify emotions into them. As a result of analysis by qualification method 3 and cluster analysis, four basic clusters were clarified, i.e., Emotion expressed towards objects, Emotion affected by objects, Pleasant emotion, and Surprise. Moreover, 51 emotions were ranked in the order according to their similarities in each cluster. An investigation was conducted to clarify individual differences in emotion process using 87 plant operators. The results showed the differences of emotion depending on the existence of operators` foresight, cognitive style, experience in operation, and consciousness of attribution to an operating team. For example, operators with low self-efficacy, short experience or low consciousness of attribution to a team, feel more intensive emotion under plant emergency and more affected by severe plant conditions. The model which can express individual differences will be developed utilizing and converting these data hereafter. (author)

  19. Automated pre-processing and multivariate vibrational spectra analysis software for rapid results in clinical settings

    Bhattacharjee, T.; Kumar, P.; Fillipe, L.

    2018-02-01

    Vibrational spectroscopy, especially FTIR and Raman, has shown enormous potential in disease diagnosis, especially in cancers. Their potential for detecting varied pathological conditions are regularly reported. However, to prove their applicability in clinics, large multi-center multi-national studies need to be undertaken; and these will result in enormous amount of data. A parallel effort to develop analytical methods, including user-friendly software that can quickly pre-process data and subject them to required multivariate analysis is warranted in order to obtain results in real time. This study reports a MATLAB based script that can automatically import data, preprocess spectra— interpolation, derivatives, normalization, and then carry out Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) of the first 10 PCs; all with a single click. The software has been verified on data obtained from cell lines, animal models, and in vivo patient datasets, and gives results comparable to Minitab 16 software. The software can be used to import variety of file extensions, asc, .txt., .xls, and many others. Options to ignore noisy data, plot all possible graphs with PCA factors 1 to 5, and save loading factors, confusion matrices and other parameters are also present. The software can provide results for a dataset of 300 spectra within 0.01 s. We believe that the software will be vital not only in clinical trials using vibrational spectroscopic data, but also to obtain rapid results when these tools get translated into clinics.

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

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

    2016-01-15

    Multivariate analysis was used to identify the variables affecting the performance of pilot-scale activated sludge (AS) reactors treating old leachate from a landfill and from domestic wastewater. Raw leachate was pre-treated using air stripping to partially remove the total ammoniacal nitrogen (TAN). The control AS reactor (AS-0%) was loaded only with domestic wastewater, whereas the other reactor was loaded with mixtures containing leachate at volumetric ratios of 2 and 5%. The best removal efficiencies were obtained for a ratio of 2%, as follows: 70 ± 4% for total suspended solids (TSS), 70 ± 3% for soluble chemical oxygen demand (SCOD), 70 ± 4% for dissolved organic carbon (DOC), and 51 ± 9% for the leachate slowly biodegradable organic matter (SBOM). Fourier transform infrared (FTIR) spectroscopic analysis confirmed that most of the SBOM was removed by partial biodegradation rather than dilution or adsorption of organics in the sludge. Nitrification was approximately 80% in the AS-0% and AS-2% reactors. No significant accumulation of heavy metals was observed for any of the tested volumetric ratios. Principal component analysis (PCA) and partial least squares (PLS) indicated that the data dimension could be reduced and that TAN, SCOD, DOC and nitrification efficiency were the main variables that affected the performance of the AS reactors. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. Multivariate Empirical Mode Decomposition Based Signal Analysis and Efficient-Storage in Smart Grid

    Liu, Lu [University of Tennessee, Knoxville (UTK); Albright, Austin P [ORNL; Rahimpour, Alireza [University of Tennessee, Knoxville (UTK); Guo, Jiandong [University of Tennessee, Knoxville (UTK); Qi, Hairong [University of Tennessee, Knoxville (UTK); Liu, Yilu [University of Tennessee (UTK) and Oak Ridge National Laboratory (ORNL)

    2017-01-01

    Wide-area-measurement systems (WAMSs) are used in smart grid systems to enable the efficient monitoring of grid dynamics. However, the overwhelming amount of data and the severe contamination from noise often impede the effective and efficient data analysis and storage of WAMS generated measurements. To solve this problem, we propose a novel framework that takes advantage of Multivariate Empirical Mode Decomposition (MEMD), a fully data-driven approach to analyzing non-stationary signals, dubbed MEMD based Signal Analysis (MSA). The frequency measurements are considered as a linear superposition of different oscillatory components and noise. The low-frequency components, corresponding to the long-term trend and inter-area oscillations, are grouped and compressed by MSA using the mean shift clustering algorithm. Whereas, higher-frequency components, mostly noise and potentially part of high-frequency inter-area oscillations, are analyzed using Hilbert spectral analysis and they are delineated by statistical behavior. By conducting experiments on both synthetic and real-world data, we show that the proposed framework can capture the characteristics, such as trends and inter-area oscillation, while reducing the data storage requirements

  2. Rapid thyroid dysfunction screening based on serum surface-enhanced Raman scattering and multivariate statistical analysis

    Tian, Dayong; Lü, Guodong; Zhai, Zhengang; Du, Guoli; Mo, Jiaqing; Lü, Xiaoyi

    2018-01-01

    In this paper, serum surface-enhanced Raman scattering and multivariate statistical analysis are used to investigate a rapid screening technique for thyroid function diseases. At present, the detection of thyroid function has become increasingly important, and it is urgently necessary to develop a rapid and portable method for the detection of thyroid function. Our experimental results show that, by using the Silmeco-based enhanced Raman signal, the signal strength greatly increases and the characteristic peak appears obviously. It is also observed that the Raman spectra of normal and anomalous thyroid function human serum are significantly different. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was used to diagnose thyroid dysfunction, and the diagnostic accuracy was 87.4%. The use of serum surface-enhanced Raman scattering technology combined with PCA-LDA shows good diagnostic performance for the rapid detection of thyroid function. By means of Raman technology, it is expected that a portable device for the rapid detection of thyroid function will be developed.

  3. Multivariate Analysis of Some Pine Forested Areas of Azad Kashmir-Pakistan

    Bokhari, T.Z.; Liu, Y.; Li, Q.; Malik, S.A.; Ahmed, M.; Siddiqui, M.F.; Khan, Z.U.

    2016-01-01

    Floristic composition and communities in Azad Kashmir area of Pakistan were studied by using multivariate analysis. Quantitative sampling from thirty one sites was carried out in different coniferous forests of Azad Kashmir in order to analyze the effects of past earthquakes and landslides on vegetation of these areas. Though coniferous forests were highly disturbed either naturally or anthropogenic activities, therefore sampling was preferred to those forests which were near fault line. Trees were sampled using Point Centered Quarter (PCQ) method. Results of cluster analysis (using Ward's method) yielded six groups dominated by different conifer species. Group I and V were dominated by Pinus wallichiana while this species was co-dominant in group III. Other groups showed the dominance of different conifer species i.e. Cedrus deodara, Pinus roxburghii, Picea smithiana and Abies pindrow. Both the cluster analysis and ordination techniques (by two dimensional non-metric multidimensional scaling) classify and ordinate the structure of various groups indicating interrelationship among different species. The groups of trees were readily be superimposed on NMS ordination axes; they were well classified and well separated out in ordination. The present research revealed that these forests had diverse and asymmetric structure due to natural anthropogenic disturbances and overgrazing, which were key factors in addition to natural disturbances. However, some of the forests showed considerably stable structure due to less human interference. (author)

  4. Aortic and Hepatic Contrast Enhancement During Hepatic-Arterial and Portal Venous Phase Computed Tomography Scanning: Multivariate Linear Regression Analysis Using Age, Sex, Total Body Weight, Height, and Cardiac Output.

    Masuda, Takanori; Nakaura, Takeshi; Funama, Yoshinori; Higaki, Toru; Kiguchi, Masao; Imada, Naoyuki; Sato, Tomoyasu; Awai, Kazuo

    We evaluated the effect of the age, sex, total body weight (TBW), height (HT) and cardiac output (CO) of patients on aortic and hepatic contrast enhancement during hepatic-arterial phase (HAP) and portal venous phase (PVP) computed tomography (CT) scanning. This prospective study received institutional review board approval; prior informed consent to participate was obtained from all 168 patients. All were examined using our routine protocol; the contrast material was 600 mg/kg iodine. Cardiac output was measured with a portable electrical velocimeter within 5 minutes of starting the CT scan. We calculated contrast enhancement (per gram of iodine: [INCREMENT]HU/gI) of the abdominal aorta during the HAP and of the liver parenchyma during the PVP. We performed univariate and multivariate linear regression analysis between all patient characteristics and the [INCREMENT]HU/gI of aortic- and liver parenchymal enhancement. Univariate linear regression analysis demonstrated statistically significant correlations between the [INCREMENT]HU/gI and the age, sex, TBW, HT, and CO (all P linear regression analysis showed that only the TBW and CO were of independent predictive value (P linear regression analysis only the TBW and CO were significantly correlated with aortic and liver parenchymal enhancement; the age, sex, and HT were not. The CO was the only independent factor affecting aortic and liver parenchymal enhancement at hepatic CT when the protocol was adjusted for the TBW.

  5. Collision prediction models using multivariate Poisson-lognormal regression.

    El-Basyouny, Karim; Sayed, Tarek

    2009-07-01

    This paper advocates the use of multivariate Poisson-lognormal (MVPLN) regression to develop models for collision count data. The MVPLN approach presents an opportunity to incorporate the correlations across collision severity levels and their influence on safety analyses. The paper introduces a new multivariate hazardous location identification technique, which generalizes the univariate posterior probability of excess that has been commonly proposed and applied in the literature. In addition, the paper presents an alternative approach for quantifying the effect of the multivariate structure on the precision of expected collision frequency. The MVPLN approach is compared with the independent (separate) univariate Poisson-lognormal (PLN) models with respect to model inference, goodness-of-fit, identification of hot spots and precision of expected collision frequency. The MVPLN is modeled using the WinBUGS platform which facilitates computation of posterior distributions as well as providing a goodness-of-fit measure for model comparisons. The results indicate that the estimates of the extra Poisson variation parameters were considerably smaller under MVPLN leading to higher precision. The improvement in precision is due mainly to the fact that MVPLN accounts for the correlation between the latent variables representing property damage only (PDO) and injuries plus fatalities (I+F). This correlation was estimated at 0.758, which is highly significant, suggesting that higher PDO rates are associated with higher I+F rates, as the collision likelihood for both types is likely to rise due to similar deficiencies in roadway design and/or other unobserved factors. In terms of goodness-of-fit, the MVPLN model provided a superior fit than the independent univariate models. The multivariate hazardous location identification results demonstrated that some hazardous locations could be overlooked if the analysis was restricted to the univariate models.

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

    Tan Boon Ann

    1987-06-01

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

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

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

    2016-12-01

    The hemicellulose composition of a pulp significantly affects its chemical and physical properties and thus represents an important process control variable. However, complicated steps of sample preparation make standard methods for the carbohydrate analysis of pulp samples, such as high performance liquid chromatography (HPLC), expensive and time-consuming. In contrast, pulp analysis by attenuated total internal reflection Fourier transform infrared spectroscopy (ATR FT-IR) requires little sample preparation. Here we show that ATR FT-IR with discrete wavelet transform (DWT) and standard normal variate (SNV) spectral preprocessing offers a convenient means for the qualitative and quantitative analysis of hemicelluloses in bleached kraft pulp and alkaline treated kraft pulp. The pulp samples investigated include bleached softwood kraft pulps, bleached hardwood kraft pulps, and their mixtures, as obtained from Canadian industry mills or blended in a lab, and bleached kraft pulp samples treated with 0-6% NaOH solutions. In the principal component analysis (PCA) of these spectra, we find the potential both to differentiate all pulps on the basis of hemicellulose compositions and to distinguish bleached hardwood pulps by species. Partial least squares (PLS) multivariate analysis gives a 0.442 wt% root mean square errors of prediction (RMSEP) for the prediction of xylan content and 0.233 wt% RMSEP for the prediction of mannan content. These data all support the idea that ATR FT-IR has a great potential to rapidly and accurately predict the content of xylan and mannan for bleached kraft pulps (softwood, hardwood, and their mixtures) in industry. However, the prediction of xylan and mannan concentrations presented a difficulty for pulp samples with modified cellulose crystalline structure. © The Author(s) 2016.

  8. An analysis of longitudinal data with nonignorable dropout using the truncated multivariate normal distribution

    Jolani, Shahab

    2014-01-01

    For a vector of multivariate normal when some elements, but not necessarily all, are truncated, we derive the moment generating function and obtain expressions for the first two moments involving the multivariate hazard gradient. To show one of many applications of these moments, we then extend the

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

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

    2013-01-01

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

  10. Real time computer control of a nonlinear Multivariable System via Linearization and Stability Analysis

    Raza, K.S.M.

    2004-01-01

    This paper demonstrates that if a complicated nonlinear, non-square, state-coupled multi variable system is smartly linearized and subjected to a thorough stability analysis then we can achieve our design objectives via a controller which will be quite simple (in term of resource usage and execution time) and very efficient (in terms of robustness). Further the aim is to implement this controller via computer in a real time environment. Therefore first a nonlinear mathematical model of the system is achieved. An intelligent work is done to decouple the multivariable system. Linearization and stability analysis techniques are employed for the development of a linearized and mathematically sound control law. Nonlinearities like the saturation in actuators are also been catered. The controller is then discretized using Runge-Kutta integration. Finally the discretized control law is programmed in a computer in a real time environment. The programme is done in RT -Linux using GNU C for the real time realization of the control scheme. The real time processes, like sampling and controlled actuation, and the non real time processes, like graphical user interface and display, are programmed as different tasks. The issue of inter process communication, between real time and non real time task is addressed quite carefully. The results of this research pursuit are presented graphically. (author)

  11. Analysis of Surface Water Pollution in the Kinta River Using Multivariate Technique

    Hamza Ahmad Isiyaka; Hafizan Juahir

    2015-01-01

    This study aims to investigate the spatial variation in the characteristics of water quality monitoring sites, identify the most significant parameters and the major possible sources of pollution, and apportion the source category in the Kinta River. 31 parameters collected from eight monitoring sites for eight years (2006-2013) were employed. The eight monitoring stations were spatially grouped into three independent clusters in a dendrogram. A drastic reduction in the number of monitored parameters from 31 to eight and nine significant parameters (P<0.05) was achieved using the forward stepwise and backward stepwise discriminate analysis (DA). Principal component analysis (PCA) accounted for more than 76 % in the total variance and attributes the source of pollution to anthropogenic and natural processes. The source apportionment using a combined multiple linear regression and principal component scores indicates that 41 % of the total pollution load is from rock weathering and untreated waste water, 26 % from waste discharge, 24 % from surface runoff and 7 % from faecal waste. This study proposes a reduction in the number of monitoring stations and parameters for a cost effective and time management in the monitoring processes and multivariate technique can provide a simple representation of complex and dynamic water quality characteristics. (author)

  12. Noise source analysis of nuclear ship Mutsu plant using multivariate autoregressive model

    Hayashi, K.; Shimazaki, J.; Shinohara, Y.

    1996-01-01

    The present study is concerned with the noise sources in N.S. Mutsu reactor plant. The noise experiments on the Mutsu plant were performed in order to investigate the plant dynamics and the effect of sea condition and and ship motion on the plant. The reactor noise signals as well as the ship motion signals were analyzed by a multivariable autoregressive (MAR) modeling method to clarify the noise sources in the reactor plant. It was confirmed from the analysis results that most of the plant variables were affected mainly by a horizontal component of the ship motion, that is the sway, through vibrations of the plant structures. Furthermore, the effect of ship motion on the reactor power was evaluated through the analysis of wave components extracted by a geometrical transform method. It was concluded that the amplitude of the reactor power oscillation was about 0.15% in normal sea condition, which was small enough for safe operation of the reactor plant. (authors)

  13. imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel

    Grapov, Dmitry; Newman, John W.

    2012-01-01

    Summary: Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R's multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Availability and implementation: Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010). Contact: John.Newman@ars.usda.gov Supplementary Information: Installation instructions, tutorials and users manual are available at http://sourceforge.net/projects/imdev/. PMID:22815358

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

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

    2016-04-01

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

  15. Identification of Chemical Attribution Signatures of Fentanyl Syntheses Using Multivariate Statistical Analysis of Orthogonal Analytical Data

    Mayer, B. P. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Mew, D. A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); DeHope, A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Spackman, P. E. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Williams, A. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-09-24

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

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

    S. Gaitan

    2015-06-01

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

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

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

    2015-06-01

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

  18. SPICE: exploration and analysis of post-cytometric complex multivariate datasets.

    Roederer, Mario; Nozzi, Joshua L; Nason, Martha C

    2011-02-01

    Polychromatic flow cytometry results in complex, multivariate datasets. To date, tools for the aggregate analysis of these datasets across multiple specimens grouped by different categorical variables, such as demographic information, have not been optimized. Often, the exploration of such datasets is accomplished by visualization of patterns with pie charts or bar charts, without easy access to statistical comparisons of measurements that comprise multiple components. Here we report on algorithms and a graphical interface we developed for these purposes. In particular, we discuss thresholding necessary for accurate representation of data in pie charts, the implications for display and comparison of normalized versus unnormalized data, and the effects of averaging when samples with significant background noise are present. Finally, we define a statistic for the nonparametric comparison of complex distributions to test for difference between groups of samples based on multi-component measurements. While originally developed to support the analysis of T cell functional profiles, these techniques are amenable to a broad range of datatypes. Published 2011 Wiley-Liss, Inc.

  19. Multivariate analysis in relation to breeding system in opium popy, Papaver somniferum L.

    Singh S.P.

    2004-01-01

    Full Text Available The opium poppy (Papaver somniferum L. is an important medicinal plant of great pharmacopoel uses. 101 germplasm lines of different eco-geographical origin maintained at National Botanical Research Institute, Lucknow were evaluated to study the genetic divergence for seed yield/plant, opium yield/plant and its 8 component traits following multivariate and canonical analysis. The genotypes were grouped in 13 clusters and confirmed by canonical analysis. Sixty eight percent genotypes (69/101 were genetically close to each other and grouped in 6 clusters (II, III, IV, V, VIII, XII while apparent diversity was noticed for 32 percent (32/101 of the genotypes who diversed into rest 7 clusters (I, VI, VII, IX, X, XI, XIII. Inter cluster distance ranged from 47.28 to 234.55. The maximum was between IX and X followed by VII and IX (208.30 and IX and XI (205.53. The genotypes in cluster IX, X. XI, and XII had greater potential as breeding stock by virtue of high mean values of one or more component characters and high statistical distance among them. Based on findings of high cluster mean of component trait and inter-cluster distance among clusters, a breeding plan has been discussed.

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

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

    2013-11-27

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

  1. Multivariate Analysis for Quantification of Plutonium(IV) in Nitric Acid Based on Absorption Spectra

    Lines, Amanda M. [Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States; Adami, Susan R. [Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States; Sinkov, Sergey I. [Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States; Lumetta, Gregg J. [Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States; Bryan, Samuel A. [Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, United States

    2017-08-09

    Development of more effective, reliable, and fast methods for monitoring process streams is a growing opportunity for analytical applications. Many fields can benefit from on-line monitoring, including the nuclear fuel cycle where improved methods for monitoring radioactive materials will facilitate maintenance of proper safeguards and ensure safe and efficient processing of materials. On-line process monitoring with a focus on optical spectroscopy can provide a fast, non-destructive method for monitoring chemical species. However, identification and quantification of species can be hindered by the complexity of the solutions if bands overlap or show condition-dependent spectral features. Plutonium (IV) is one example of a species which displays significant spectral variation with changing nitric acid concentration. Single variate analysis (i.e. Beer’s Law) is difficult to apply to the quantification of Pu(IV) unless the nitric acid concentration is known and separate calibration curves have been made for all possible acid strengths. Multivariate, or chemometric, analysis is an approach that allows for the accurate quantification of Pu(IV) without a priori knowledge of nitric acid concentration.

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

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

    2016-08-01

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

  3. Application of Multivariate Statistical Analysis in Evaluation of Surface River Water Quality of a Tropical River

    Teck-Yee Ling

    2017-01-01

    Full Text Available The present study evaluated the spatial variations of surface water quality in a tropical river using multivariate statistical techniques, including cluster analysis (CA and principal component analysis (PCA. Twenty physicochemical parameters were measured at 30 stations along the Batang Baram and its tributaries. The water quality of the Batang Baram was categorized as “slightly polluted” where the chemical oxygen demand and total suspended solids were the most deteriorated parameters. The CA grouped the 30 stations into four clusters which shared similar characteristics within the same cluster, representing the upstream, middle, and downstream regions of the main river and the tributaries from the middle to downstream regions of the river. The PCA has determined a reduced number of six principal components that explained 83.6% of the data set variance. The first PC indicated that the total suspended solids, turbidity, and hydrogen sulphide were the dominant polluting factors which is attributed to the logging activities, followed by the five-day biochemical oxygen demand, total phosphorus, organic nitrogen, and nitrate-nitrogen in the second PC which are related to the discharges from domestic wastewater. The components also imply that logging activities are the major anthropogenic activities responsible for water quality variations in the Batang Baram when compared to the domestic wastewater discharge.

  4. Multivariate cluster analysis of dynamic iodine-123 iodobenzamide SPET dopamine D2receptor images in schizophrenia

    Acton, P.D.; Pilowsky, L.S.; Costa, D.C.; Ell, P.J.

    1997-01-01

    This paper describes the application of a multivariate statistical technique to investigate striatal dopamine D 2 receptor concentrations measured by iodine-123 iodobenzamide ( 123 I-IBZM) single-photon emission tomography (SPET). This technique enables the automatic segmentation of dynamic nuclear medicine images based on the underlying time-activity curves present in the data. Once the time-activity curves have been extracted, each pixel can be mapped back on to the underlying distribution, considerably reducing image noise. Cluster analysis has been verified using computer simulations and phantom studies. The technique has been applied to SPET images of dopamine D 2 receptors in a total of 20 healthy and 20 schizophrenic volunteers (22 male, 18 female), using the ligand 123 I-IBZM. Following automatic image segmentation, the concentration of striatal dopamine D 2 receptors shows a significant left-sided asymmetry in male schizophrenics compared with male controls. The mean left-minus-right laterality index for controls is -1.52 (95% CI -3.72-0.66) and for patients 4.04 (95% CI 1.07-7.01). Analysis of variance shows a case-by-sex-by-side interaction, with F=10.01, P=0.005. We can now demonstrate that the previously observed male sex-specific D 2 receptor asymmetry in schizophrenia, which had failed to attain statistical significance, is valid. Cluster analysis of dynamic nuclear medicine studies provides a powerful tool for automatic segmentation and noise reduction of the images, removing much of the subjectivity inherent in region-of-interest analysis. The observed striatal D 2 asymmetry could reflect long hypothesized disruptions in dopamine-rich cortico-striatal-limbic circuits in schizophrenic males. (orig.). With 4 figs., 2 tabs

  5. Investigation of rectal complication after RALS-therapy for uterine cervix cancer using multivariate analysis

    Inoue, Takehiro; Inoue, Toshihiko; Suzuki, Takaichiro

    1983-01-01

    Rectal injury is one of the major side effects after radiation therapy for carcinoma of the uterine cervix. According to our previous reports, the cases of rectal complication were mainly related to the measured rectal dose in half of patients, and the other causes were related to the following factors; such as diabetes mellitus, hemorrhagic tendency, syphilis and so on. Concerning to rectal complication, these factors were investigated by means of the discriminant analysis, one of the multivariate analyses, in this paper. Twenty-eight factors as to radiation dose, laboratory tests and physical condition of patients were analyzed. From August 1978 through January 1980, 52 cases of previously untreated carcinoma of the uterine cervix were treated using RALS, remotely controlled high dose rate intracavitary radiotherapy, at our department. The data from 49 out of 52 cases were available for the discriminant analysis. By m eans of this analysis, it was found that these factors, such as the dose of whole pelvic irradiation, Point A dose of RALS, measured rectal dose by RALS, WGC-Z and TPHA were important factors for occurence of rectal complication. According to the discriminant score, 46 out of 49 cases (94 %) could be correctly discriminated. There were two cases of false positive and one false negative. Form February 1980 through July 1980, 27 cases of previously untreated carcinoma of the uterine cervix were treated at our department. The obtained discriminant function was applied to these 27 cases, and 24 out of 27 cases (89 %) were correctly predicted. There were two cases of false positive, and one of false negative. Discriminant analysis is useful for the prediction of rectal complication after radiation therapy for carcinoma of the uterine cervix. (J.P.N.)

  6. Multivariate regression analysis for determining short-term values of radon and its decay products from filter measurements

    Kraut, W.; Schwarz, W.; Wilhelm, A.

    1994-01-01

    A multivariate regression analysis is applied to decay measurements of α-resp. β-filter activcity. Activity concentrations for Po-218, Pb-214 and Bi-214, resp. for the Rn-222 equilibrium equivalent concentration are obtained explicitly. The regression analysis takes into account properly the variances of the measured count rates and their influence on the resulting activity concentrations. (orig.) [de

  7. Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review

    Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

    2017-01-01

    Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0–0.10 m, or 0–0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component

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

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

    2017-01-01

    Rapidly evolving technical and regulatory landscapes of the pharmaceutical product development necessitates risk management with application of multivariate analysis using Process Analytical Technology (PAT) and Quality by Design (QbD). Poorly soluble, high dose drug, Satranidazole was optimally nanoprecipitated (SAT-NP) employing principles of Formulation by Design (FbD). The potential risk factors influencing the critical quality attributes (CQA) of SAT-NP were identified using Ishikawa diagram. Plackett-Burman screening design was adopted to screen the eight critical formulation and process parameters influencing the mean particle size, zeta potential and dissolution efficiency at 30min in pH7.4 dissolution medium. Pareto charts (individual and cumulative) revealed three most critical factors influencing CQA of SAT-NP viz. aqueous stabilizer (Polyvinyl alcohol), release modifier (Eudragit® S 100) and volume of aqueous phase. The levels of these three critical formulation attributes were optimized by FbD within established design space to minimize mean particle size, poly dispersity index, and maximize encapsulation efficiency of SAT-NP. Lenth's and Bayesian analysis along with mathematical modeling of results allowed identification and quantification of critical formulation attributes significantly active on the selected CQAs. The optimized SAT-NP exhibited mean particle size; 216nm, polydispersity index; 0.250, zeta potential; -3.75mV and encapsulation efficiency; 78.3%. The product was lyophilized using mannitol to form readily redispersible powder. X-ray diffraction analysis confirmed the conversion of crystalline SAT to amorphous form. In vitro release of SAT-NP in gradually pH changing media showed 95%) in pH7.4 in next 3h, indicative of burst release after a lag time. This investigation demonstrated effective application of risk management and QbD tools in developing site-specific release SAT-NP by nanoprecipitation. Copyright © 2016 Elsevier B.V. All

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

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

    2017-03-01

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

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

    Djorgovski, S. George

    1994-01-01

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

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

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

    2013-03-01

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

  12. Multivariate analysis on unilateral cleft lip and palate treatment outcome by EUROCRAN index: A retrospective study.

    Yew, Ching Ching; Alam, Mohammad Khursheed; Rahman, Shaifulizan Abdul

    2016-10-01

    This study is to evaluate the dental arch relationship and palatal morphology of unilateral cleft lip and palate patients by using EUROCRAN index, and to assess the factors that affect them using multivariate statistical analysis. A total of one hundred and seven patients from age five to twelve years old with non-syndromic unilateral cleft lip and palate were included in the study. These patients have received cheiloplasty and one stage palatoplasty surgery but yet to receive alveolar bone grafting procedure. Five assessors trained in the use of the EUROCRAN index underwent calibration exercise and ranked the dental arch relationships and palatal morphology of the patients' study models. For intra-rater agreement, the examiners scored the models twice, with two weeks interval in between sessions. Variable factors of the patients were collected and they included gender, site, type and, family history of unilateral cleft lip and palate; absence of lateral incisor on cleft side, cheiloplasty and palatoplasty technique used. Associations between various factors and dental arch relationships were assessed using logistic regression analysis. Dental arch relationship among unilateral cleft lip and palate in local population had relatively worse scoring than other parts of the world. Crude logistics regression analysis did not demonstrate any significant associations among the various socio-demographic factors, cheiloplasty and palatoplasty techniques used with the dental arch relationship outcome. This study has limitations that might have affected the results, example: having multiple operators performing the surgeries and the inability to access the influence of underlying genetic predisposed cranio-facial variability. These may have substantial influence on the treatment outcome. The factors that can affect unilateral cleft lip and palate treatment outcome is multifactorial in nature and remained controversial in general. Copyright © 2016 Elsevier Ireland Ltd. All

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

    Djorgovski, S. G.

    1994-01-01

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

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

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

    2003-09-01

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

  15. Rapid analysis of pharmaceutical drugs using LIBS coupled with multivariate analysis.

    Tiwari, P K; Awasthi, S; Kumar, R; Anand, R K; Rai, P K; Rai, A K

    2018-02-01

    Type 2 diabetes drug tablets containing voglibose having dose strengths of 0.2 and 0.3 mg of various brands have been examined, using laser-induced breakdown spectroscopy (LIBS) technique. The statistical methods such as the principal component analysis (PCA) and the partial least square regression analysis (PLSR) have been employed on LIBS spectral data for classifying and developing the calibration models of drug samples. We have developed the ratio-based calibration model applying PLSR in which relative spectral intensity ratios H/C, H/N and O/N are used. Further, the developed model has been employed to predict the relative concentration of element in unknown drug samples. The experiment has been performed in air and argon atmosphere, respectively, and the obtained results have been compared. The present model provides rapid spectroscopic method for drug analysis with high statistical significance for online control and measurement process in a wide variety of pharmaceutical industrial applications.

  16. Discrete Fourier Transform-Based Multivariate Image Analysis: Application to Modeling of Aromatase Inhibitory Activity.

    Barigye, Stephen J; Freitas, Matheus P; Ausina, Priscila; Zancan, Patricia; Sola-Penna, Mauro; Castillo-Garit, Juan A

    2018-02-12

    We recently generalized the formerly alignment-dependent multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) method through the application of the discrete Fourier transform (DFT), allowing for its application to noncongruent and structurally diverse chemical compound data sets. Here we report the first practical application of this method in the screening of molecular entities of therapeutic interest, with human aromatase inhibitory activity as the case study. We developed an ensemble classification model based on the two-dimensional (2D) DFT MIA-QSAR descriptors, with which we screened the NCI Diversity Set V (1593 compounds) and obtained 34 chemical compounds with possible aromatase inhibitory activity. These compounds were docked into the aromatase active site, and the 10 most promising compounds were selected for in vitro experimental validation. Of these compounds, 7419 (nonsteroidal) and 89 201 (steroidal) demonstrated satisfactory antiproliferative and aromatase inhibitory activities. The obtained results suggest that the 2D-DFT MIA-QSAR method may be useful in ligand-based virtual screening of new molecular entities of therapeutic utility.

  17. Multivariate statistical analysis of electron energy-loss spectroscopy in anisotropic materials

    Hu Xuerang; Sun Yuekui; Yuan Jun

    2008-01-01

    Recently, an expression has been developed to take into account the complex dependence of the fine structure in core-level electron energy-loss spectroscopy (EELS) in anisotropic materials on specimen orientation and spectral collection conditions [Y. Sun, J. Yuan, Phys. Rev. B 71 (2005) 125109]. One application of this expression is the development of a phenomenological theory of magic-angle electron energy-loss spectroscopy (MAEELS), which can be used to extract the isotropically averaged spectral information for materials with arbitrary anisotropy. Here we use this expression to extract not only the isotropically averaged spectral information, but also the anisotropic spectral components, without the restriction of MAEELS. The application is based on a multivariate statistical analysis of core-level EELS for anisotropic materials. To demonstrate the applicability of this approach, we have conducted a study on a set of carbon K-edge spectra of multi-wall carbon nanotube (MWCNT) acquired with energy-loss spectroscopic profiling (ELSP) technique and successfully extracted both the averaged and dichroic spectral components of the wrapped graphite-like sheets. Our result shows that this can be a practical alternative to MAEELS for the study of electronic structure of anisotropic materials, in particular for those nanostructures made of layered materials

  18. Multivariate data analysis for finding the relevant fatty acids contributing to the melting fractions of cream

    Buldo, Patrizia; Larsen, Mette Krogh; Wiking, Lars

    2013-01-01

    :0 and palmitoleic acid (C16:1) in milk fat, whereas it decreased the amount of stearic acid (C18:0) and C18:1 trans fatty acid. Average data on the melting behaviour of cream separated the farms into two groups where the main differences in feeding were the amounts of maize silage and rapeseed cake used. CONCLUSION......BACKGROUND: The melting behaviour and fatty acid composition of cream from a total of 33 cows from four farms were analysed. Multivariate data analysis was used to identify the fatty acids that contributed most to the melting points and to differentiate between creams from different practical...... feeding regimes. RESULTS: It was demonstrated that the melting point of the medium melting fraction of milk fat was positively correlated with palmitic acid (C16:0), whereas it was negatively correlated with oleic acid (C18:1 cis9), conjugated linoleic acid (CLA cis9 trans11), vaccenic acid (C18:1 trans11...

  19. Evaluation of herbicides photodegradation by photo-Fenton process using multivariate analysis

    Paterlini, W.C.; Nogueira, R.F.P. [Inst. of Chemistry, Sao Paulo State Univ., R. Prof. Francisco Degni s/n, Araraquara, SP (Brazil)

    2003-07-01

    The photodegradation of herbicides in aqueous medium by photo-Fenton process using ferrioxalate complex (FeOx) as a source of Fe{sup 2+} was evaluated under blacklight irradiation. The commercial products of the herbicides tebuthiuron, 2,4-D and diuron were used. Multivariate analysis was used to evaluate the role of two variables in the photodegradation process, FeOx and hydrogen peroxide concentrations, and to define the concentration ranges that result in the most efficient photodegradation of the herbicides. The photodegradation of the herbicides was followed by monitoring the decrease of the original compounds concentration by HPLC, by the determination of remaining total organic carbon content (TOC), and by the chloride ion release. Under optimised conditions, 20 minutes irradiation was enough to remove 92.7% of TOC for 2,4 D and 89.5% for diuron. Complete dechlorination of these compounds was achieved after 10 minutes of irradiation. It was observed that the initial concentration of these compounds and tebuthiuron was reduced to less than 15% after only 1 minute of irradiation. (orig.)

  20. Predicting biomaterial property-dendritic cell phenotype relationships from the multivariate analysis of responses to polymethacrylates

    Kou, Peng Meng; Pallassana, Narayanan; Bowden, Rebeca; Cunningham, Barry; Joy, Abraham; Kohn, Joachim; Babensee, Julia E.

    2011-01-01

    Dendritic cells (DCs) play a critical role in orchestrating the host responses to a wide variety of foreign antigens and are essential in maintaining immune tolerance. Distinct biomaterials have been shown to differentially affect the phenotype of DCs, which suggested that biomaterials may be used to modulate immune response towards the biologic component in combination products. The elucidation of biomaterial property-DC phenotype relationships is expected to inform rational design of immuno-modulatory biomaterials. In this study, DC response to a set of 12 polymethacrylates (pMAs) was assessed in terms of surface marker expression and cytokine profile. Principal component analysis (PCA) determined that surface carbon correlated with enhanced DC maturation, while surface oxygen was associated with an immature DC phenotype. Partial square linear regression, a multivariate modeling approach, was implemented and successfully predicted biomaterial-induced DC phenotype in terms of surface marker expression from biomaterial properties with R2prediction = 0.76. Furthermore, prediction of DC phenotype was effective based on only theoretical chemical composition of the bulk polymers with R2prediction = 0.80. These results demonstrated that immune cell response can be predicted from biomaterial properties, and computational models will expedite future biomaterial design and selection. PMID:22136715

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

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

    2014-01-01

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

  2. Safety and effectiveness of olanzapine in monotherapy: a multivariate analysis of a naturalistic study.

    Ciudad, Antonio; Gutiérrez, Miguel; Cañas, Fernando; Gibert, Juan; Gascón, Josep; Carrasco, José-Luis; Bobes, Julio; Gómez, Juan-Carlos; Alvarez, Enrique

    2005-07-01

    This study investigated safety and effectiveness of olanzapine in monotherapy compared with conventional antipsychotics in treatment of acute inpatients with schizophrenia. This was a prospective, comparative, nonrandomized, open-label, multisite, observational study of Spanish inpatients with an acute episode of schizophrenia. Data included safety assessments with an extrapyramidal symptoms (EPS) questionnaire and the report of spontaneous adverse events, plus clinical assessments with the Brief Psychiatric Rating Scale (BPRS) and the Clinical Global Impressions-Severity of Illness (CGI-S). A multivariate methodology was used to more adequately determine which factors can influence safety and effectiveness of olanzapine in monotherapy. 339 patients treated with olanzapine in monotherapy (OGm) and 385 patients treated with conventional antipsychotics (CG) were included in the analysis. Treatment-emergent EPS were significantly higher in the CG (pOGm (p=0.005). Logistic regression analyses revealed that the only variable significantly correlated with treatment-emergent EPS and clinical response was treatment strategy, with patients in OGm having 1.5 times the probability of obtaining a clinical response and patients in CG having 5 times the risk of developing EPS. In this naturalistic study olanzapine in monotherapy was better-tolerated and at least as effective as conventional antipsychotics.

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

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

    2016-01-01

    The chemical composition and in vitro antimicrobial activities of Dittrichia graveolens (L.) Greuter essential oil was studied. Moreover, using agglomerative hierarchical cluster (AHC) and principal component analyses (PCA), the interrelationships of the D. graveolens essential-oil profiles characterized so far (including the sample from this study) were investigated. To evaluate the chemical composition of the essential oil, GC-FID and GC/MS analyses were performed. Altogether, 54 compounds were identified, accounting for 92.9% of the total oil composition. The D. graveolens oil belongs to the monoterpenoid chemotype, with monoterpenoids comprising 87.4% of the totally identified compounds. The major components were borneol (43.6%) and bornyl acetate (38.3%). Multivariate analysis showed that the compounds borneol and bornyl acetate exerted the greatest influence on the spatial differences in the composition of the reported oils. The antimicrobial activity against five bacterial and one fungal strain was determined using a disk-diffusion assay. The studied essential oil was active only against Gram-positive bacteria. Copyright © 2016 Verlag Helvetica Chimica Acta AG, Zürich.

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

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

    2007-01-01

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

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

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

    2015-11-15

    Infrared spectroscopic techniques and chemometric methods were used to study oxidation of olive, sunflower and rapeseed oils. Accelerated oxidative degradation of oils at 60°C was monitored using peroxide values and FT-MIR ATR and FT-NIR transmittance spectroscopy. Principal component analysis (PCA) facilitated visualization and interpretation of spectral changes occurring during oxidation. Multivariate curve resolution (MCR) method found three spectral components in the NIR and MIR spectral matrix, corresponding to the oxidation products, and saturated and unsaturated structures. Good quantitative relation was found between peroxide value and contribution of oxidation products evaluated using MCR--based on NIR (R(2) = 0.890), MIR (R(2) = 0.707) and combined NIR and MIR (R(2) = 0.747) data. Calibration models for prediction peroxide value established using partial least squares (PLS) regression were characterized for MIR (R(2) = 0.701, RPD = 1.7), NIR (R(2) = 0.970, RPD = 5.3), and combined NIR and MIR data (R(2) = 0.954, RPD = 3.1). Copyright © 2015 Elsevier Ltd. All rights reserved.

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

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

    2015-11-10

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

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

    Ryan, Elaine A.; Farquharson, Michael J.

    2007-11-01

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

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

    Ryan, Elaine A; Farquharson, Michael J [School of Allied Health Sciences, City University, Charterhouse Square, London EC1M 6PA (United Kingdom)

    2007-11-21

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

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

    Charlene Steinhausen

    2016-09-01

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

  10. Correlations among behavior, performance and environment in broiler breeders using multivariate analysis

    DF Pereira

    2007-12-01

    Full Text Available Animal welfare issues have received much attention not only to supply farmed animal requirements, but also to ethical and cultural public concerns. Daily collected information, as well as the systematic follow-up of production stages, produces important statistical data for production assessment and control, as well as for improvement possibilities. In this scenario, this research study analyzed behavioral, production, and environmental data using Main Component Multivariable Analysis, which correlated observed behaviors, recorded using video cameras and electronic identification, with performance parameters of female broiler breeders. The aim was to start building a system to support decision-making in broiler breeder housing, based on bird behavioral parameters. Birds were housed in an environmental chamber, with three pens with different controlled environments. Bird sensitivity to environmental conditions were indicated by their behaviors, stressing the importance of behavioral observations for modern poultry management. A strong association between performance parameters and the behavior "at the nest", suggesting that this behavior may be used to predict productivity. The behaviors of "ruffling feathers", "opening wings", "preening", and "at the drinker" were negatively correlated with environmental temperature, suggesting that the increase of in the frequency of these behaviors indicate improvement of thermal welfare.

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

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

    2014-01-01

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

  12. Quality assessment of pharmaceutical tablet samples using Fourier transform near infrared spectroscopy and multivariate analysis

    Kandpal, Lalit Mohan; Tewari, Jagdish; Gopinathan, Nishanth; Stolee, Jessica; Strong, Rick; Boulas, Pierre; Cho, Byoung-Kwan

    2017-09-01

    Determination of the content uniformity, assessed by the amount of an active pharmaceutical ingredient (API), and hardness of pharmaceutical materials is important for achieving a high-quality formulation and to ensure the intended therapeutic effects of the end-product. In this work, Fourier transform near infrared (FT-NIR) spectroscopy was used to determine the content uniformity and hardness of a pharmaceutical mini-tablet and standard tablet samples. Tablet samples were scanned using an FT-NIR instrument and tablet spectra were collected at wavelengths of 1000-2500 nm. Furthermore, multivariate analysis was applied to extract the relationship between the FT-NIR spectra and the measured parameters. The results of FT-NIR spectroscopy for API and hardness prediction were as precise as the reference high-performance liquid chromatography and mechanical hardness tests. For the prediction of mini-tablet API content, the highest coefficient of determination for the prediction (R2p) was found to be 0.99 with a standard error of prediction (SEP) of 0.72 mg. Moreover, the standard tablet hardness measurement had a R2p value of 0.91 with an SEP of 0.25 kg. These results suggest that FT-NIR spectroscopy is an alternative and accurate nondestructive measurement tool for the detection of the chemical and physical properties of pharmaceutical samples.

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

    Beiras, Ricardo; Durán, Iria

    2014-12-01

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

  14. Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flow.

    Gao, Zhong-Ke; Zhang, Xin-Wang; Jin, Ning-De; Marwan, Norbert; Kurths, Jürgen

    2013-09-01

    Characterizing complex patterns arising from horizontal oil-water two-phase flows is a contemporary and challenging problem of paramount importance. We design a new multisector conductance sensor and systematically carry out horizontal oil-water two-phase flow experiments for measuring multivariate signals of different flow patterns. We then infer multivariate recurrence networks from these experimental data and investigate local cross-network properties for each constructed network. Our results demonstrate that a cross-clustering coefficient from a multivariate recurrence network is very sensitive to transitions among different flow patterns and recovers quantitative insights into the flow behavior underlying horizontal oil-water flows. These properties render multivariate recurrence networks particularly powerful for investigating a horizontal oil-water two-phase flow system and its complex interacting components from a network perspective.

  15. Multivariate analysis of the Scutellinia umbrorum complex (Pezizales, Ascomycetes) from five ecotopes in the Netherlands

    Paal, Jaanus; Kullman, Bellis; Huijser, Henk A.

    1998-01-01

    The multivariate structure of the Scutellinia umbrorum complex ( Pezizales, Ascomycetes), based on the morphometrical parameters of 81 specimens from five ecotopes in the Netherlands, was analysed. According to conventional expert estimation, five putative taxa resp. species were established: S.

  16. Analysis of multi-species point patterns using multivariate log Gaussian Cox processes

    Waagepetersen, Rasmus; Guan, Yongtao; Jalilian, Abdollah

    Multivariate log Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far only been applied in bivariate cases. In this paper we move beyond the bivariate case in order to model multi-species point patterns of tree locations. In particular we address t...... of the data. The selected number of common latent fields provides an index of complexity of the multivariate covariance structure. Hierarchical clustering is used to identify groups of species with similar patterns of dependence on the common latent fields.......Multivariate log Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far only been applied in bivariate cases. In this paper we move beyond the bivariate case in order to model multi-species point patterns of tree locations. In particular we address...... the problems of identifying parsimonious models and of extracting biologically relevant information from the fitted models. The latent multivariate Gaussian field is decomposed into components given in terms of random fields common to all species and components which are species specific. This allows...

  17. A multivariate surface-based analysis of the putamen in premature newborns: regional differences within the ventral striatum.

    Jie Shi

    Full Text Available Many children born preterm exhibit frontal executive dysfunction, behavioral problems including attentional deficit/hyperactivity disorder and attention related learning disabilities. Anomalies in regional specificity of cortico-striato-thalamo-cortical circuits may underlie deficits in these disorders. Nonspecific volumetric deficits of striatal structures have been documented in these subjects, but little is known about surface deformation in these structures. For the first time, here we found regional surface morphological differences in the preterm neonatal ventral striatum. We performed regional group comparisons of the surface anatomy of the striatum (putamen and globus pallidus between 17 preterm and 19 term-born neonates at term-equivalent age. We reconstructed striatal surfaces from manually segmented brain magnetic resonance images and analyzed them using our in-house conformal mapping program. All surfaces were registered to a template with a new surface fluid registration method. Vertex-based statistical comparisons between the two groups were performed via four methods: univariate and multivariate tensor-based morphometry, the commonly used medial axis distance, and a combination of the last two statistics. We found statistically significant differences in regional morphology between the two groups that are consistent across statistics, but more extensive for multivariate measures. Differences were localized to the ventral aspect of the striatum. In particular, we found abnormalities in the preterm anterior/inferior putamen, which is interconnected with the medial orbital/prefrontal cortex and the midline thalamic nuclei including the medial dorsal nucleus and pulvinar. These findings support the hypothesis that the ventral striatum is vulnerable, within the cortico-stiato-thalamo-cortical neural circuitry, which may underlie the risk for long-term development of frontal executive dysfunction, attention deficit hyperactivity

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

    Mohammad Khursheed Alam

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

  19. Multivariate analysis of flow cytometric data using decision trees

    Svenja eSimon

    2012-04-01

    Full Text Available Characterization of the response of the host immune system is important in understanding the bidirectional interactions between the host and microbial pathogens. For research on the host site, flow cytometry has become one of the major tools in immunology. Advances in technology and reagents allow now the simultaneous assessment of multiple markers on a single cell level generating multidimensional data sets that require multivariate statistical analysis. We explored the explanatory power of the supervised machine learning method called 'induction of decision trees' in flow cytometric data. In order to examine whether the production of a certain cytokine is depended on other cytokines, datasets from intracellular staining for six cytokines with complex patterns of co-expression were analyzed by induction of decision trees. After weighting the data according to their class probabilities, we created a total of 13,392 different decision trees for each given cytokine with different parameter settings. For a more realistic estimation of the decision trees's quality, we used stratified 5-fold cross-validation and chose the 'best' tree according to a combination of different quality criteria. While some of the decision trees reflected previously known co-expression patterns, we found that the expression of some cytokines was not only dependent on the co-expression of others per se, but was also dependent on the intensity of expression. Thus, for the first time we successfully used induction of decision trees for the analysis of high dimensional flow cytometric data and demonstrated the feasibility of this method to reveal structural patterns in such data sets.

  20. Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring.

    Eide, Ingvar; Westad, Frank

    2018-01-01

    A pilot study demonstrating real-time environmental monitoring with automated multivariate analysis of multi-sensor data submitted online has been performed at the cabled LoVe Ocean Observatory located at 258 m depth 20 km off the coast of Lofoten-Vesterålen, Norway. The major purpose was efficient monitoring of many variables simultaneously and early detection of changes and time-trends in the overall response pattern before changes were evident in individual variables. The pilot study was performed with 12 sensors from May 16 to August 31, 2015. The sensors provided data for chlorophyll, turbidity, conductivity, temperature (three sensors), salinity (calculated from temperature and conductivity), biomass at three different depth intervals (5-50, 50-120, 120-250 m), and current speed measured in two directions (east and north) using two sensors covering different depths with overlap. A total of 88 variables were monitored, 78 from the two current speed sensors. The time-resolution varied, thus the data had to be aligned to a common time resolution. After alignment, the data were interpreted using principal component analysis (PCA). Initially, a calibration model was established using data from May 16 to July 31. The data on current speed from two sensors were subject to two separate PCA models and the score vectors from these two models were combined with the other 10 variables in a multi-block PCA model. The observations from August were projected on the calibration model consecutively one at a time and the result was visualized in a score plot. Automated PCA of multi-sensor data submitted online is illustrated with an attached time-lapse video covering the relative short time period used in the pilot study. Methods for statistical validation, and warning and alarm limits are described. Redundant sensors enable sensor diagnostics and quality assurance. In a future perspective, the concept may be used in integrated environmental monitoring.

  1. Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring.

    Ingvar Eide

    Full Text Available A pilot study demonstrating real-time environmental monitoring with automated multivariate analysis of multi-sensor data submitted online has been performed at the cabled LoVe Ocean Observatory located at 258 m depth 20 km off the coast of Lofoten-Vesterålen, Norway. The major purpose was efficient monitoring of many variables simultaneously and early detection of changes and time-trends in the overall response pattern before changes were evident in individual variables. The pilot study was performed with 12 sensors from May 16 to August 31, 2015. The sensors provided data for chlorophyll, turbidity, conductivity, temperature (three sensors, salinity (calculated from temperature and conductivity, biomass at three different depth intervals (5-50, 50-120, 120-250 m, and current speed measured in two directions (east and north using two sensors covering different depths with overlap. A total of 88 variables were monitored, 78 from the two current speed sensors. The time-resolution varied, thus the data had to be aligned to a common time resolution. After alignment, the data were interpreted using principal component analysis (PCA. Initially, a calibration model was established using data from May 16 to July 31. The data on current speed from two sensors were subject to two separate PCA models and the score vectors from these two models were combined with the other 10 variables in a multi-block PCA model. The observations from August were projected on the calibration model consecutively one at a time and the result was visualized in a score plot. Automated PCA of multi-sensor data submitted online is illustrated with an attached time-lapse video covering the relative short time period used in the pilot study. Methods for statistical validation, and warning and alarm limits are described. Redundant sensors enable sensor diagnostics and quality assurance. In a future perspective, the concept may be used in integrated environmental monitoring.

  2. A multivariate analysis of age-related differences in functional networks supporting conflict resolution.

    Salami, Alireza; Rieckmann, Anna; Fischer, Håkan; Bäckman, Lars

    2014-02-01

    Functional neuroimaging studies demonstrate age-related differences in recruitment of a large-scale attentional network during interference resolution, especially within dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC). These alterations in functional responses have been frequently observed despite equivalent task performance, suggesting age-related reallocation of neural resources, although direct evidence for a facilitating effect in aging is sparse. We used the multi-source interference task and multivariate partial-least-squares to investigate age-related differences in the neuronal signature of conflict resolution, and their behavioral implications in younger and older adults. There were interference-related increases in activity, involving fronto-parietal and basal ganglia networks that generalized across age. In addition an age-by-task interaction was observed within a distributed network, including DLPFC and ACC, with greater activity during interference in the old. Next, we combined brain-behavior and functional connectivity analyses to investigate whether compensatory brain changes were present in older adults, using DLPFC and ACC as regions of interest (i.e. seed regions). This analysis revealed two networks differentially related to performance across age groups. A structural analysis revealed age-related gray-matter losses in regions facilitating performance in the young, suggesting that functional reorganization may partly reflect structural alterations in aging. Collectively, these findings suggest that age-related structural changes contribute to reductions in the efficient recruitment of a youth-like interference network, which cascades into instantiation of a different network facilitating conflict resolution in elderly people. © 2013. Published by Elsevier Inc. All rights reserved.

  3. Risk factors for pedicled flap necrosis in hand soft tissue reconstruction: a multivariate logistic regression analysis.

    Gong, Xu; Cui, Jianli; Jiang, Ziping; Lu, Laijin; Li, Xiucun

    2018-03-01

    Few clinical retrospective studies have reported the risk factors of pedicled flap necrosis in hand soft tissue reconstruction. The aim of this study was to identify non-technical risk factors associated with pedicled flap perioperative necrosis in hand soft tissue reconstruction via a multivariate logistic regression analysis. For patients with hand soft tissue reconstruction, we carefully reviewed hospital records and identified 163 patients who met the inclusion criteria. The characteristics of these patients, flap transfer procedures and postoperative complications were recorded. Eleven predictors were identified. The correlations between pedicled flap necrosis and risk factors were analysed using a logistic regression model. Of 163 skin flaps, 125 flaps survived completely without any complications. The pedicled flap necrosis rate in hands was 11.04%, which included partial flap necrosis (7.36%) and total flap necrosis (3.68%). Soft tissue defects in fingers were noted in 68.10% of all cases. The logistic regression analysis indicated that the soft tissue defect site (P = 0.046, odds ratio (OR) = 0.079, confidence interval (CI) (0.006, 0.959)), flap size (P = 0.020, OR = 1.024, CI (1.004, 1.045)) and postoperative wound infection (P < 0.001, OR = 17.407, CI (3.821, 79.303)) were statistically significant risk factors for pedicled flap necrosis of the hand. Soft tissue defect site, flap size and postoperative wound infection were risk factors associated with pedicled flap necrosis in hand soft tissue defect reconstruction. © 2017 Royal Australasian College of Surgeons.

  4. Correlation analysis of energy indicators for sustainable development using multivariate statistical techniques

    Carneiro, Alvaro Luiz Guimaraes; Santos, Francisco Carlos Barbosa dos

    2007-01-01

    Energy is an essential input for social development and economic growth. The production and use of energy cause environmental degradation at all levels, being local, regional and global such as, combustion of fossil fuels causing air pollution; hydropower often causes environmental damage due to the submergence of large areas of land; and global climate change associated with the increasing concentration of greenhouse gases in the atmosphere. As mentioned in chapter 9 of Agenda 21, the Energy is essential to economic and social development and improved quality of life. Much of the world's energy, however, is currently produced and consumed in ways that could not be sustained if technologies were remain constant and if overall quantities were to increase substantially. All energy sources will need to be used in ways that respect the atmosphere, human health, and the environment as a whole. The energy in the context of sustainable development needs a set of quantifiable parameters, called indicators, to measure and monitor important changes and significant progress towards the achievement of the objectives of sustainable development policies. The indicators are divided into four dimensions: social, economic, environmental and institutional. This paper shows a methodology of analysis using Multivariate Statistical Technique that provide the ability to analyse complex sets of data. The main goal of this study is to explore the correlation analysis among the indicators. The data used on this research work, is an excerpt of IBGE (Instituto Brasileiro de Geografia e Estatistica) data census. The core indicators used in this study follows The IAEA (International Atomic Energy Agency) framework: Energy Indicators for Sustainable Development. (author)

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

    Tomita, Tsuneyuki [Division of Radiology, Osaka Red Cross Hospital, Osaka (Japan); Nakamura, Mitsuhiro, E-mail: m_nkmr@kuhp.kyoto-u.ac.jp [Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto (Japan); Hirose, Yoshinori; Kitsuda, Kenji; Notogawa, Takuya; Miki, Katsuhito [Division of Radiology, Osaka Red Cross Hospital, Osaka (Japan); Nakamura, Kiyonao; Ishigaki, Takashi [Department of Radiation Oncology, Osaka Red Cross Hospital, Osaka (Japan)

    2014-01-01

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

  6. Application of multivariate techniques to analytical data on Aegean ceramics

    Bieber, A.M.; Brooks, D.W.; Harbottle, G.; Sayre, E.V.

    1976-01-01

    The general problems of data collection and handling for multivariate elemental analyses of ancient pottery are considered including such specific questions as the level of analytical precision required, the number and type of elements to be determined and the need for comprehensive multivariate statistical analysis of the collected data in contrast to element by element statistical analysis. The multivariate statistical procedures of clustering in a multidimensional space and determination of the numerical probabilities of specimens belonging to a group through calculation of the Mahalanobis distances for these specimens in multicomponent space are described together with supporting univariate statistical procedures used at Brookhaven. The application of these techniques to the data on Late Bronze Age Aegean pottery (largely previously analysed at Oxford and Brookhaven with some new specimens considered) have resulted in meaningful subdivisions of previously established groups. (author)

  7. Multivariate analysis in the frequency mastery applied to the Laguna Verde Central

    Castillo D, R.; Ortiz V, J.; Calleros M, G.

    2006-01-01

    The noise analysis is an auxiliary tool in the detection of abnormal operation conditions of equipment, instruments or systems that affect to the dynamic behavior of the reactor. The spectral density of normalized power has usually been used (NPSD, by its initials in English), to watch over the behavior of some components of the reactor, for example, the jet pumps, the recirculation pumps, valves of flow control in the recirculation knots, etc. The behavior change is determined by individual analysis of the NPSD of the signals of the components in study. An alternative analysis that can allow to obtain major information on the component under surveillance is the multivariate autoregressive analysis (MAR, by its initials in English), which allows to know the relationship that exists among diverse signals of the reactor systems, in the time domain. In the space of the frequency, the relative contribution of power (RPC for their initials in English) it quantifies the influence of the variables of the systems on a variable of interest. The RPC allows, therefore that for a peak shown in the NPSD of a variable, it can be determine the influence from other variables to that frequency of interest. This facilitates, in principle, the pursuit of the important physical parameters during an event, and to study their interrelation. In this work, by way of example of the application of the RPC, two events happened in the Laguna Verde Central are analyzed: the rods blockade alarms by high scale in the monitors of average power, in which it was presents a power peak of 12% of width peak to peak, and the power oscillations event. The main obtained result of the analysis of the control rods blockade alarm event was that it was detected that the power peak observed in the signals of the average power monitors was caused by the movement of the valve of flow control of recirculation of the knot B. In the other oscillation event the results its show the mechanism of the oscillation of

  8. Multivariate analysis of dopaminergic gene variants as risk factors of heroin dependence.

    Andrea Vereczkei

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

  9. Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

    Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

    2017-12-01

    Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis

  10. A multivariate analysis of pre-, peri-, and post-transplant factors affecting outcome after pediatric liver transplantation.

    McDiarmid, Sue V; Anand, Ravinder; Martz, Karen; Millis, Michael J; Mazariegos, George

    2011-07-01

    The purpose of this study was to identify significant, independent factors that predicted 6 month patient and graft survival after pediatric liver transplantation. The Studies of Pediatric Liver Transplantation (SPLIT) is a multicenter database established in 1995, of currently more than 4000 US and Canadian children undergoing liver transplantation. Previous published analyses from this data have examined specific factors influencing outcome. This study analyzes a comprehensive range of factors that may influence outcome from the time of listing through the peri- and postoperative period. A total of 42 pre-, peri- and posttransplant variables evaluated in 2982 pediatric recipients of a first liver transplant registered in SPLIT significant at the univariate level were included in multivariate models. In the final model combining all baseline and posttransplant events, posttransplant complications had the highest relative risk of death or graft loss. Reoperation for any cause increased the risk for both patient and graft loss by 11 fold and reoperation exclusive of specific complications by 4 fold. Vascular thromboses, bowel perforation, septicemia, and retransplantation, each independently increased the risk of patient and graft loss by 3 to 4 fold. The only baseline factor with a similarly high relative risk for patient and graft loss was recipient in the intensive care unit (ICU) intubated at transplant. A significant center effect was also found but did not change the impact of the highly significant factors already identified. We conclude that the most significant factors predicting patient and graft loss at 6 months in children listed for transplant are posttransplant surgical complications.

  11. Plasma metabolic profiling analysis of toxicity induced by brodifacoum using metabonomics coupled with multivariate data analysis.

    Yan, Hui; Qiao, Zheng; Shen, Baohua; Xiang, Ping; Shen, Min

    2016-10-01

    Brodifacoum is one of the most widely used rodenticides for rodent control and eradication; however, human and animal poisoning due to primary and secondary exposure has been reported since its development. Although numerous studies have described brodifacoum induced toxicity, the precise mechanism still needs to be explored. Gas chromatography mass spectrometry (GC-MS) coupled with an ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) was applied to characterize the metabolic profile of brodifacoum induced toxicity and discover potential biomarkers in rat plasma. The toxicity of brodifacoum was dose-dependent, and the high-dose group obviously manifested toxicity with subcutaneous hemorrhage. The blood brodifacoum concentration showed a positive relation to the ingestion dose in toxicological analysis. Significant changes of twenty-four metabolites were identified and considered as potential toxicity biomarkers, primarily involving glucose metabolism, lipid metabolism and amino acid metabolism associated with anticoagulant activity, nephrotoxicity and hepatic damage. MS-based metabonomics analysis in plasma samples is helpful to search for potential poisoning biomarkers and to understand the underlying mechanisms of brodifacoum induced toxicity. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  12. Multivariable analysis of a failure event of pressure regulator in a BWR; Analisis multivariable de un evento de falla del regulador de presion en un BWR

    Castillo D, R.; Ortiz V, J. [ININ, Carretera Mexico-Toluca s/n, 52750 Ocoyoacac, Estado de Mexico (Mexico); Calleros M, G. [Comision Federal de Electricidad, Central Nucleoelectrica Laguna Verde, Carretera Cardel-Nautla, Km. 43.5, Veracruz (Mexico)], e-mail: rogelio.castillo@inin.gob.mx

    2009-10-15

    The boiling water reactors can experiment three types of instabilities: one caused by the controllers failure of plant, another renowned instability by reactivity and the last knew as thermal hydraulics instability. An event of pressure regulator failure of electro-hydraulic control of Unit 1 of nuclear power plant of Laguna Verde was analyzed, which caused power oscillations that were increasing their magnitude in the time course. The event has been analyzed using the Fourier transformation in short time for time-frequency analysis and for the frequency domain be employment the power spectral density. Both techniques reported a resonance to oscillation frequency of 0.055 Hz in the power spectrum, this frequency is of observed order of magnitude when fail the reactor control systems. However, these analysis did not allow to study the interrelation of event signals. Of the previous studies, were obtained power spectral densities containing picks and valleys related with the dynamic behaviour of reactor, which includes the control systems performance. For a pick or present valley to a specific frequency in the power spectrum for one of previous variables, can determine the influence of other variables on the pick or valley by relative contribution of power. This method was established in a developed program of name Noise, which uses a multivariable autoregressive model to obtain the autoregressive coefficients, and starting from them the relative contribution of power is determined. Basically two important results were obtained, the first is related with the influence of feed water flow on the other variables to the frequency of 0.055 Hz, the second is related with the instability by reactivity and confirms that this way was not excited during the event. (Author)

  13. Multivariate analysis in the frequency mastery applied to the Laguna Verde Central; Analisis multivariable en el dominio de la frecuencia aplicado a la Central Laguna Verde

    Castillo D, R.; Ortiz V, J. [ININ, 52045 Ocoyoacac, Estado de Mexico (Mexico); Calleros M, G. [CFE, Central Nucleoelectrica de Laguna Verde, carretera Nautla-Cardel Km. 42.5, Alto Lucero, Veracruz (Mexico)]. e-mail: rcd@nuclear.inin.mx

    2006-07-01

    The noise analysis is an auxiliary tool in the detection of abnormal operation conditions of equipment, instruments or systems that affect to the dynamic behavior of the reactor. The spectral density of normalized power has usually been used (NPSD, by its initials in English), to watch over the behavior of some components of the reactor, for example, the jet pumps, the recirculation pumps, valves of flow control in the recirculation knots, etc. The behavior change is determined by individual analysis of the NPSD of the signals of the components in study. An alternative analysis that can allow to obtain major information on the component under surveillance is the multivariate autoregressive analysis (MAR, by its initials in English), which allows to know the relationship that exists among diverse signals of the reactor systems, in the time domain. In the space of the frequency, the relative contribution of power (RPC for their initials in English) it quantifies the influence of the variables of the systems on a variable of interest. The RPC allows, therefore that for a peak shown in the NPSD of a variable, it can be determine the influence from other variables to that frequency of interest. This facilitates, in principle, the pursuit of the important physical parameters during an event, and to study their interrelation. In this work, by way of example of the application of the RPC, two events happened in the Laguna Verde Central are analyzed: the rods blockade alarms by high scale in the monitors of average power, in which it was presents a power peak of 12% of width peak to peak, and the power oscillations event. The main obtained result of the analysis of the control rods blockade alarm event was that it was detected that the power peak observed in the signals of the average power monitors was caused by the movement of the valve of flow control of recirculation of the knot B. In the other oscillation event the results its show the mechanism of the oscillation of

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

    chan, S.

    2013-12-01

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

  15. Search for the Higgs Boson in the H→ ZZ(*)→4μ Channel in CMS Using a Multivariate Analysis

    Alonso Diaz, A.

    2007-01-01

    This note presents a Higgs boson search analysis in the CMS detector of the LHC accelerator (CERN, Geneva, Switzerland) in the H→ ZZ ( *)→4μ channel, using a multivariate method. This analysis, based in a Higgs boson mass dependent likelihood, constructed from discriminant variables, provides a significant improvement of the Higgs boson discovery potential in a wide mass range with respect to the official analysis published by CMS, based in orthogonal cuts independent of the Higgs boson mass. (Author) 8 refs

  16. UNCOVERING THE FORMATION OF ULTRACOMPACT DWARF GALAXIES BY MULTIVARIATE STATISTICAL ANALYSIS

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

    2012-01-01

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

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

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

    2008-01-01

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

  18. "Life history space": a multivariate analysis of life history variation in extant and extinct Malagasy lemurs.

    Catlett, Kierstin K; Schwartz, Gary T; Godfrey, Laurie R; Jungers, William L

    2010-07-01

    Studies of primate life history variation are constrained by the fact that all large-bodied extant primates are haplorhines. However, large-bodied strepsirrhines recently existed. If we can extract life history information from their skeletons, these species can contribute to our understanding of primate life history variation. This is particularly important in light of new critiques of the classic "fast-slow continuum" as a descriptor of variation in life history profiles across mammals in general. We use established dental histological methods to estimate gestation length and age at weaning for five extinct lemur species. On the basis of these estimates, we reconstruct minimum interbirth intervals and maximum reproductive rates. We utilize principal components analysis to create a multivariate "life history space" that captures the relationships among reproductive parameters and brain and body size in extinct and extant lemurs. Our data show that, whereas large-bodied extinct lemurs can be described as "slow" in some fashion, they also varied greatly in their life history profiles. Those with relatively large brains also weaned their offspring late and had long interbirth intervals. These were not the largest of extinct lemurs. Thus, we distinguish size-related life history variation from variation that linked more strongly to ecological factors. Because all lemur species larger than 10 kg, regardless of life history profile, succumbed to extinction after humans arrived in Madagascar, we argue that large body size increased the probability of extinction independently of reproductive rate. We also provide some evidence that, among lemurs, brain size predicts reproductive rate better than body size. (c) 2010 Wiley-Liss, Inc.

  19. Screening and selection of tomato genotypes/cultivars for drought tolerance using multivariate analysis

    Shamim, F.; Waheed, A.; Saqlan, S.M.; Athar, H.U.R.

    2014-01-01

    Drought is one of the most important abiotic stresses reducing crop growth and yield of tomato. Development of water stress tolerant cultivars through screening and selection is one important strategy to overcome this problem. In the present study, seeds of 120 local and exotic lines of tomato were allowed to germinate at varying levels of polyethylene glycol (PEG8000) induced water stress (PEG8000 0, 2.5%, 5.0% and 7.5%) for two weeks. Increasing PEG concentrations in the growth medium (water stress) caused a consistent decrease in seed germination percentage and seedling growth of all tomato cultivars. Moreover, a significant amount of genetic variability was found in all attributes of 120 genotypes of tomato. All lines/cultivars of tomato were ranked on the basis of relative water stress tolerance using 13 morphometric traits and categorized in four groups (tolerant, moderately tolerant, moderately sensitive, and sensitive) through multivariate analysis. Of 120 lines, 18, 25, 29 and 48 lines were ranked as tolerant, moderately tolerant, moderately sensitive and sensitive respectively. The germination percentage or speeds of germination were not found as effective indicator of genotypic differences for water stress at the seedling stage. Moreover, degree of water stress tolerance at the germination and seedling growth stage did not maintain in all tomato lines. Thus, it is not certain whether such variation is detectable at the later vegetative or reproductive growth stages. This needs to be further investigated. Overall, lines 19905, 19906, LA0716, and LA0722 were found to be water stress tolerant at least at early growth stages. (author)

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

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

    2016-01-01

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

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

    Peng Fang

    2015-01-01

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

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

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

    2015-01-01

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

  3. UNCOVERING THE FORMATION OF ULTRACOMPACT DWARF GALAXIES BY MULTIVARIATE STATISTICAL ANALYSIS

    Chattopadhyay, Tanuka [Department of Applied Mathematics, Calcutta University, 92 A.P.C. Road, Calcutta 700009 (India); Sharina, Margarita [Special Astrophysical Observatory, Russian Academy of Sciences, N. Arkhyz, KCh R 369167 (Russian Federation); Davoust, Emmanuel [IRAP, Universite de Toulouse, CNRS, 14 Avenue Edouard Belin, 31400 Toulouse (France); De, Tuli; Chattopadhyay, Asis Kumar, E-mail: tanuka@iucaa.ernet.in, E-mail: sme@sao.ru, E-mail: davoust@ast.obs-mip.fr, E-mail: akcstat@caluniv.ac.in [Department of Statistics, Calcutta University, 35 B.C. Road, Calcutta 700019 (India)

    2012-05-10

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

  4. Accelerating policy decisions to adopt haemophilus influenzae type B vaccine: a global, multivariable analysis.

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

    2010-03-16

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

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

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

    2016-02-01

    Magnetic resonance imaging (MRI) studies have revealed brain structural abnormalities in obsessive-compulsive disorder (OCD) patients, involving both gray matter (GM) and white matter (WM). However, the results of previous publications were based on average differences between groups, which limited their usages in clinical practice. Therefore, the aim of this study was to examine whether the application of multivariate pattern analysis (MVPA) to high-dimensional structural images would allow accurate discrimination between OCD patients and healthy control subjects (HCS). High-resolution T1-weighted images were acquired from 33 OCD patients and 33 demographically matched HCS in a 3.0 T scanner. Differences in GM and WM volume between OCD and HCS were examined using two types of well-established MVPA techniques: support vector machine (SVM) and Gaussian process classifier (GPC). We also drew a receiver operating characteristic (ROC) curve to evaluate the performance of each classifier. The classification accuracies for both classifiers using GM and WM anatomy were all above 75%. The highest classification accuracy (81.82%, P<0.001) was achieved with the SVM classifier using WM information. Regional brain anomalies with high discriminative power were based on three distributed networks including the fronto-striatal circuit, the temporo-parieto-occipital junction and the cerebellum. Our study illustrated that both GM and WM anatomical features may be useful in differentiating OCD patients from HCS. WM volume using the SVM approach showed the highest accuracy in our population for revealing group differences, which suggested its potential diagnostic role in detecting highly enriched OCD patients at the level of the individual. Copyright © 2015 Elsevier B.V. and ECNP. All rights reserved.

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

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

    2013-06-01

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

  7. Comparative urine analysis by liquid chromatography-mass spectrometry and multivariate statistics : Method development, evaluation, and application to proteinuria

    Kemperman, Ramses F. J.; Horvatovich, Peter L.; Hoekman, Berend; Reijmers, Theo H.; Muskiet, Frits A. J.; Bischoff, Rainer

    2007-01-01

    We describe a platform for the comparative profiling of urine using reversed-phase liquid chromatography-mass spectrometry (LC-MS) and multivariate statistical data analysis. Urinary compounds were separated by gradient elution and subsequently detected by electrospray Ion-Trap MS. The lower limit

  8. Analysis of the stability and accuracy of the discrete least-squares approximation on multivariate polynomial spaces

    Migliorati, Giovanni

    2016-01-05

    We review the main results achieved in the analysis of the stability and accuracy of the discrete leastsquares approximation on multivariate polynomial spaces, with noiseless evaluations at random points, noiseless evaluations at low-discrepancy point sets, and noisy evaluations at random points.

  9. Correlation of aqueous solubility of salts of benzylamine with experimentally and theoretically derived parameters. A multivariate data analysis approach

    Parshad, Henrik; Frydenvang, Karla Andrea; Liljefors, Tommy

    2002-01-01

    Twenty two salts of benzylamine and p-substituted benzoic acids were prepared and characterized. The p-substituent was varied with regard to electronic, hydrophobic, and steric effects as well as hydrogen bonding potential. A multivariate data analysis was used to describe the relationship between...

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

    Joo, Soohyung; Kipp, Margaret E. I.

    2015-01-01

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

  11. Fourier transform infrared spectroscopic imaging and multivariate regression for prediction of proteoglycan content of articular cartilage.

    Lassi Rieppo

    Full Text Available Fourier Transform Infrared (FT-IR spectroscopic imaging has been earlier applied for the spatial estimation of the collagen and the proteoglycan (PG contents of articular cartilage (AC. However, earlier studies have been limited to the use of univariate analysis techniques. Current analysis methods lack the needed specificity for collagen and PGs. The aim of the present study was to evaluate the suitability of partial least squares regression (PLSR and principal component regression (PCR methods for the analysis of the PG content of AC. Multivariate regression models were compared with earlier used univariate methods and tested with a sample material consisting of healthy and enzymatically degraded steer AC. Chondroitinase ABC enzyme was used to increase the variation in PG content levels as compared to intact AC. Digital densitometric measurements of Safranin O-stained sections provided the reference for PG content. The results showed that multivariate regression models predict PG content of AC significantly better than earlier used absorbance spectrum (i.e. the area of carbohydrate region with or without amide I normalization or second derivative spectrum univariate parameters. Increased molecular specificity favours the use of multivariate regression models, but they require more knowledge of chemometric analysis and extended laboratory resources for gathering reference data for establishing the models. When true molecular specificity is required, the multivariate models should be used.

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

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

    2016-01-13

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

  13. Analysis, Simulation and Prediction of Multivariate Random Fields with Package RandomFields

    Martin Schlather

    2015-02-01

    Full Text Available Modeling of and inference on multivariate data that have been measured in space, such as temperature and pressure, are challenging tasks in environmental sciences, physics and materials science. We give an overview over and some background on modeling with cross- covariance models. The R package RandomFields supports the simulation, the parameter estimation and the prediction in particular for the linear model of coregionalization, the multivariate Matrn models, the delay model, and a spectrum of physically motivated vector valued models. An example on weather data is considered, illustrating the use of RandomFields for parameter estimation and prediction.

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

    Torres, A. F.

    2011-12-01

    two excellent tools from the Learning Machine field know as the Wavelet Decomposition Analysis (WDA) and the Multivariate Relevance Vector Machine (MVRVM) to forecast the results obtained from the SEBAL algorithm using LandSat imagery and soil moisture maps. The predictive capability of this novel hybrid WDA-RVM actual evapotranspiration forecasting technique is tested by comparing the crop water requirements and delivered crop water in the Lower Sevier River Basin, Utah, for the period 2007-2011. This location was selected because of their success increasing the efficiency of water use and control along the entire irrigation system. Research is currently on going to assess the efficacy of the WDA-RVM technique along the irrigation season, which is required to enhance the water use efficiency and minimize climate change impact on the Sevier River Basin.

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

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

    2009-05-30

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

  16. Prognostic factorsin inoperable adenocarcinoma of the lung: A multivariate regression analysis of 259 patiens

    Sørensen, Jens Benn; Badsberg, Jens Henrik; Olsen, Jens

    1989-01-01

    The prognostic factors for survival in advanced adenocarcinoma of the lung were investigated in a consecutive series of 259 patients treated with chemotherapy. Twenty-eight pretreatment variables were investigated by use of Cox's multivariate regression model, including histological subtypes and ...

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

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

    2008-01-01

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

  18. Multivariate statistical treatment of PIXE analysis of some traditional Chinese medicines

    Xiaofeng Zhang; Jianguo Ma; Junfa Qin; Lun Xiao

    1991-01-01

    Elements in two kinds of 30 traditional Chinese medicines were analyzed by PIXE method, and the data were treated by multivariate statistical methods. The results show that these two kinds of traditional Chinese medicines are almost separable according to their elemental contents. The results are congruous with the traditional Chinese medicine practice. (author) 7 refs.; 2 figs.; 2 tabs

  19. Multivariate statistical study with a factor analysis of foraminiferal fauna from the Chilka Lake, India

    Jayalakshmy, K.V.; Rao, K.K.

    Harbour, En- gland: a reappraisal using multivariate tech- niques. J. Paleontol., 43 (3) : 660-675. Imbrie, J. and F.B. Phleger. 1963. Analisis por vectores de los foraminiferos bentonicos del area de San Diego, California. Soc. Geol. Mex., Bol., 26...

  20. A multivariate decision tree analysis of biophysical factors in tropical forest fire occurrence

    Rey S. Ofren; Edward Harvey

    2000-01-01

    A multivariate decision tree model was used to quantify the relative importance of complex hierarchical relationships between biophysical variables and the occurrence of tropical forest fires. The study site is the Huai Kha Kbaeng wildlife sanctuary, a World Heritage Site in northwestern Thailand where annual fires are common and particularly destructive. Thematic...

  1. Defining climate zones in México City using multivariate analysis

    Estrada, Feporrua; Martínez-Arroyo, A.; Fernández-Eguiarte, A.; Luyando, E.; Gay, C.

    2009-01-01

    Spatial variability in the climate of México City was studied using multivariate methods to analyze 30 years of meteorological data from 37 stations (from the Servicio Meteorológico Nacional) located within the city. Although it covers relatively small area, México City encompasses considerable

  2. Regression Is a Univariate General Linear Model Subsuming Other Parametric Methods as Special Cases.

    Vidal, Sherry

    Although the concept of the general linear model (GLM) has existed since the 1960s, other univariate analyses such as the t-test and the analysis of variance models have remained popular. The GLM produces an equation that minimizes the mean differences of independent variables as they are related to a dependent variable. From a computer printout…

  3. Effects of Video Games and Online Chat on Mathematics Performance in High School: An Approach of Multivariate Data Analysis

    Lina Wu; Wenyi Lu; Ye Li

    2016-01-01

    Regarding heavy video game players for boys and super online chat lovers for girls as a symbolic phrase in the current adolescent culture, this project of data analysis verifies the displacement effect on deteriorating mathematics performance. To evaluate correlation or regression coefficients between a factor of playing video games or chatting online and mathematics performance compared with other factors, we use multivariate analysis technique and take gender difference into account. We fin...

  4. Intelligent Prediction of Soccer Technical Skill on Youth Soccer Player's Relative Performance Using Multivariate Analysis and Artificial Neural Network Techniques

    Abdullah, M. R; Maliki, A. B. H. M; Musa, R. M; Kosni, N. A; Juahir, H

    2016-01-01

    This study aims to predict the potential pattern of soccer technical skill on Malaysia youth soccer players relative performance using multivariate analysis and artificial neural network techniques. 184 male youth soccer players were recruited in Malaysia soccer academy (average age = 15.2±2.0) underwent to, physical fitness test, anthropometric, maturity, motivation and the level of skill related soccer. Unsupervised pattern recognition of principal component analysis (PCA) was used to ident...

  5. New Riemannian Priors on the Univariate Normal Model

    Salem Said

    2014-07-01

    Full Text Available The current paper introduces new prior distributions on the univariate normal model, with the aim of applying them to the classification of univariate normal populations. These new prior distributions are entirely based on the Riemannian geometry of the univariate normal model, so that they can be thought of as “Riemannian priors”. Precisely, if {pθ ; θ ∈ Θ} is any parametrization of the univariate normal model, the paper considers prior distributions G( θ - , γ with hyperparameters θ - ∈ Θ and γ > 0, whose density with respect to Riemannian volume is proportional to exp(−d2(θ, θ - /2γ2, where d2(θ, θ - is the square of Rao’s Riemannian distance. The distributions G( θ - , γ are termed Gaussian distributions on the univariate normal model. The motivation for considering a distribution G( θ - , γ is that this distribution gives a geometric representation of a class or cluster of univariate normal populations. Indeed, G( θ - , γ has a unique mode θ - (precisely, θ - is the unique Riemannian center of mass of G( θ - , γ, as shown in the paper, and its dispersion away from θ - is given by γ.  Therefore, one thinks of members of the class represented by G( θ - , γ as being centered around θ - and  lying within a typical  distance determined by γ. The paper defines rigorously the Gaussian distributions G( θ - , γ and describes an algorithm for computing maximum likelihood estimates of their hyperparameters. Based on this algorithm and on the Laplace approximation, it describes how the distributions G( θ - , γ can be used as prior distributions for Bayesian classification of large univariate normal populations. In a concrete application to texture image classification, it is shown that  this  leads  to  an  improvement  in  performance  over  the  use  of  conjugate  priors.

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

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

    2017-01-01

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

  7. Univariate normalization of bispectrum using Hölder's inequality.

    Shahbazi, Forooz; Ewald, Arne; Nolte, Guido

    2014-08-15

    Considering that many biological systems including the brain are complex non-linear systems, suitable methods capable of detecting these non-linearities are required to study the dynamical properties of these systems. One of these tools is the third order cummulant or cross-bispectrum, which is a measure of interfrequency interactions between three signals. For convenient interpretation, interaction measures are most commonly normalized to be independent of constant scales of the signals such that its absolute values are bounded by one, with this limit reflecting perfect coupling. Although many different normalization factors for cross-bispectra were suggested in the literature these either do not lead to bounded measures or are themselves dependent on the coupling and not only on the scale of the signals. In this paper we suggest a normalization factor which is univariate, i.e., dependent only on the amplitude of each signal and not on the interactions between signals. Using a generalization of Hölder's inequality it is proven that the absolute value of this univariate bicoherence is bounded by zero and one. We compared three widely used normalizations to the univariate normalization concerning the significance of bicoherence values gained from resampling tests. Bicoherence values are calculated from real EEG data recorded in an eyes closed experiment from 10 subjects. The results show slightly more significant values for the univariate normalization but in general, the differences are very small or even vanishing in some subjects. Therefore, we conclude that the normalization factor does not play an important role in the bicoherence values with regard to statistical power, although a univariate normalization is the only normalization factor which fulfills all the required conditions of a proper normalization. Copyright © 2014 Elsevier B.V. All rights reserved.

  8. Multivariate analysis of various factors affecting background liver and mediastinal standardized uptake values

    Kuruva, Manohar; Mittal, Bhagwant Rai; Abrar, Mohammed Labeeb; Kashyap, Raghava; Bhattacharya, Anish

    2012-01-01

    Standardized uptake value (SUV) is the most commonly used semi-quantitative PET parameter. Various response assessment criteria grade the tumor uptake relative to liver or mediastinal uptake. However various factors can affect the background SUV values. This prospective study was carried out to assess the variability of liver and mediastinal SUVs normalized to lean body mass (SUL-L, SUL-M), body surface area (SUB-L, SUB-M), and body weight (SUW-L, SUW-M) and their dependence on various factors which can affect SUV values. Eighty-eight patients who underwent F-18 FDG PET/CT for various oncological indications were prospectively included in this study. SUVs of liver and mediastinum were calculated by ROIs drawn as suggested by Wahl, et al., in PERCIST 1.0 criteria. Multivariate linear regression analysis was done to assess for the various factors influencing the SUVs of liver and mediastinum. Factors assessed were age, sex, weight, blood glucose level, diabetic status, and uptake period. A P value less than 0.01 was considered significant. SUL-L, SUL-M, SUB-L, SUB-M, SUW-L, SUW-M were not affected significantly by age, sex, blood glucose levels, diabetic status. The uptake period had a statistically significant effect on SUL-L (P = 0.007) and SUW-L (P = 0.008) with a progressive decrease with increasing uptake time. Body weight showed a statistically significant effect on SUW-L (P = 0.001) while SUL-L and SUB-L were not dependent on weight. SUB-L was least dependent on weight (P = 0.851) when compared with SUL-L (P = 0.425). However SUL-L was also not affected statistically significantly by variations in body weight (P = 0.425). Mediastinal SUVs were not significantly affected by any of the factors. As mediastinal SUVs are not affected significantly by any of the factors, it can be considered as background when wide variations occur in uptake times or weight of the patient when comparing two PET/CT studies to evaluate response

  9. Characterization of used nuclear fuel with multivariate analysis for process monitoring

    Dayman, Kenneth J., E-mail: kenneth.dayman@gmail.com [University of Texas at Austin, Austin, TX 78758 (United States); Coble, Jamie B.; Orton, Christopher R.; Schwantes, Jon M. [Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, Richland, WA 99354 (United States)

    2014-01-21

    This paper presents initial development of a reactor-type classifier that is used to select a reactor-specific partial least squares model to predict used nuclear fuel burnup. Nuclide activities for prototypic used fuel samples were generated in ORIGEN-ARP and used to investigate techniques to characterize used nuclear fuel in terms of reactor type (pressurized or boiling water reactor) and burnup. A variety of reactor type classification algorithms, including k-nearest neighbors, linear and quadratic discriminant analyses, and support vector machines, were evaluated to differentiate used fuel from pressurized and boiling water reactors. Then, reactor type-specific partial least squares models were developed to predict the burnup of the fuel. Using these reactor type-specific models instead of a model trained for all light water reactors improved the accuracy of burnup predictions. The developed classification and prediction models were combined and applied to a large dataset that included eight fuel assembly designs, two of which were not used in training the models, and spanned the range of the initial {sup 235}U enrichment, cooling time, and burnup values expected of future commercial used fuel for reprocessing. Error rates were consistent across the range of considered enrichment, cooling time, and burnup values. Average absolute relative errors in burnup predictions for validation data both within and outside the training space were 0.0574% and 0.0597%, respectively. The errors seen in this work are artificially low, because the models were trained, optimized, and tested on simulated, noise-free data. However, these results indicate that the developed models may generalize well to new data and that the proposed approach constitutes a viable first step in developing a fuel characterization algorithm based on gamma spectra. -- Highlights: • We investigate characterization of used nuclear fuel with multivariate analysis. • PWR and BWR fuels expected in

  10. Accelerated fractionation in cancers of the esophagus: a multivariate analysis on 102 patients

    Girinsky, T.; Marsiglia, H.; Auperin, A.

    1995-01-01

    for T1, T2, T3 are 67%, 43%, and 25% respectively. Late toxicity included esophageal stricture and pulmonary fibrosis in 8% and 9% of the patients respectively. Multivariate analysis found that stage, the Karnofsky index, chemotherapy, and overall treatment time were independent prognostic factors for the cause specific survival. Conclusions: Accelerated fractionation for cancer of the esophagus seems to be useful in small tumors. Chemotherapy was found to be beneficial to patients when cause specific survival was the end point. Overall radiation treatment time was also an independent prognostic factor for the same end point

  11. Accelerated fractionation in cancers of the esophagus: a multivariate analysis on 102 patients

    Girinsky, T; Marsiglia, H; Auperin, A

    1995-07-01

    for T1, T2, T3 are 67%, 43%, and 25% respectively. Late toxicity included esophageal stricture and pulmonary fibrosis in 8% and 9% of the patients respectively. Multivariate analysis found that stage, the Karnofsky index, chemotherapy, and overall treatment time were independent prognostic factors for the cause specific survival. Conclusions: Accelerated fractionation for cancer of the esophagus seems to be useful in small tumors. Chemotherapy was found to be beneficial to patients when cause specific survival was the end point. Overall radiation treatment time was also an independent prognostic factor for the same end point.

  12. Advances in the analysis of energy commodities and of multivariate dependence structures

    Schlueter, Stephan

    2011-01-27

    In the first chapter of the dissertation a new stochastic long-term/short-term model for short-term electricity prices is introduced and applied to four major European indices. Evidence is given that all time series contain certain periodic patterns, and it is shown how to use the wavelet transform for filtering purpose. The wavelet transform is also applied to separate the long-term trend from the short-term oscillation in the seasonal-adjusted log-prices. Moreover, dynamic volatility is found in all time series, which is incorporated by using a bivariate GARCH model with constant correlation. The residuals are modeled using the normal-inverse Gaussian distribution. In the second chapter an overview over different wavelet based time series forecasting methods is given. The methods are tested on four data sets, each with its own characteristics. Eventually, it can be seen that using wavelets does improve the forecasting quality, especially for longer time horizons than one day ahead. However, there is no single superior method; the performance depends on the data set and the forecasting time horizon. In the third chapter a new formula for extreme Student t quantiles is derived. The derivation is based on the proof for the Gaussian quantile and on the fact that the Student t distribution arises as the limit of a variance-mixture of normals. In the fourth chapter a theoretical framework and a solved example for valuing a European gas storage facility is presented. For modeling the gas price a mean reverting process with GARCH volatility is chosen. Based on this process dynamic programming methods are applied to derive partial differential equations for valuing the storage facility. As an example a storage site in Epe, Germany, is chosen. In this context the effects of multiple contract types for renting a storage site are investigated and a sensitivity analysis is performed. In the fifth chapter multivariate copula models are discussed. Using three different four

  13. The value of external beam radiation in pathologic node positive prostate cancer: a multivariate analysis

    Morris, Astrid D.; Zietman, Anthony L.; Althausen, Alex F.; Heney, Niall M.; Kaufman, Donald S.; Shipley, William U.

    1997-01-01

    Purpose: The goal of this study was to evaluate the effect of local/regional treatment, particularly external beam radiation alone versus radical prostatectomy and radiation therapy in patients with pathologic node positive prostate cancer on survival. The effect of delayed vs. immediate endocrine therapy on patients treated with radiation alone was also examined. Methods: Medical records of all 116 patients who received their initial treatment at the Massachusetts General Hospital between 1980 and 1996 for adenocarcinoma of the prostate with pathologic confirmed nodal metastasis and no distant disease were reviewed. The mean follow up was 5.5 years. Disease specific survival, time to PSA failure on endocrine therapy, and time to first intervention were evaluated. PSA failure was defined as two consecutive post-nadir rises following the first use of endocrine therapy. Intervention was defined as any surgical or radiotherapeutic procedure required for relief of symptoms related to local/regional recurrence. Survival comparisons were made between any local/regional treatment vs. none, radiation therapy alone vs. prostatectomy with radiation therapy, and immediate vs. delayed endocrine therapy. The effect of the different treatment options on survival were compared using multivariate Cox proportional hazard models to simultaneously adjust for patient and tumor characteristics (tumor stage, Gleason grade, number of positive nodes) that might influence survival. Results: The combined patient population had a 5 year disease specific survival of 74% and a 10 year disease specific survival of 48%. The comparison groups for local/regional treatment had the following adjusted outcomes. In a subgroup analysis of patients with clinical T1-T2 and clinical T3-T4 disease, local/regional treatment continued to confer a disease specific survival advantage over no local regional treatment in both subgroups (p=0.05 and p=0.02, respectively). PSA failure on endocrine therapy was

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

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

    2009-01-01

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

  15. Multivariate analysis of factors influencing the effect of radiosynovectomy; Multivariate Analyse der Einflussfaktoren auf die Wirkung der Radiosynoviorthese bei entzuendlichen Gelenkerkrankungen

    Farahati, J.; Schulz, G.; Koerber, C.; Geling, M.; Schmeider, P.; Reiners, Chr. [Wuerzburg Univ. (Germany). Klinik fuer Nuklearmedizin; Wendler, J. [Erlangen-Nuernberg Univ. (Germany). Klinik fuer Innere Medizin III; Kenn, W. [Wuerzburg Univ. (Germany). Inst. fuer Roentgendiagnostik; Reidemeister, C. [Wuerzburg Univ. (Germany). Klinik fuer Innere Medizin

    2002-04-01

    Objective: In this prospective study, the time to remission after radiosynovectomy (RSV) was analyzed and the influence of age, sex, underlying disease, type of joint, and duration of illness on the success rate of RSV was determined. Methods: A total number of 57 patients with rheumatoid arthritis (n = 33) and arthrosis (n = 21) with a total number of 130 treated joints (36 knee, 66 small and 28 medium-size joints) were monitored using visual analogue scales (VAS) from one week before RSV up to four to six months after RSV. The patients had to answer 3 times daily for pain intensity of the treated joint. The time until remission was determined according to the Kaplan-Meier survivorship function. The influence of the prognosis parameters on outcome of RSV was determined by multivariate discriminant analysis. Results: After six months, the probability of pain relief of more than 20% amounted to 78% and was significantly dependent on the age of the patient (p = 0.02) and the duration of illness (p = 0.05), however not on sex (p = 0.17), underlying disease (p = 0.23), and type of joint (p = 0.69). Conclusion: Irrespective of sex, type of joint and underlying disease, a measurable pain relief can be achieved with RSV in 78% of the patients with synovitis, whereby effectiveness is decreasing with increasing age and progress of illness. (orig.) [German] Ziel: In dieser prospektiven Studie wurde die Zeit bis zur Remission nach einer Radiosynoviorthese (RSO) untersucht. Ebenso wurde der Einfluss von Alter, Geschlecht, Grunderkrankung, Gelenktyp und Erkrankungsdauer auf die Erfolgsrate der RSO ermittelt. Methodik: Bei insgesamt 57 Patienten mit rheumatoider Arthritis (n = 33) und Arthritis bei aktivierter Arthrose (n = 24) wurden 130 Gelenke (36 Kniegelenke, 66 kleine und 28 mittelgrosse Gelenke) behandelt. Die Patienten wurden unter Verwendung so genannter visueller Analogskalen eine Woche vor RSO und vier bis sechs Monate danach 3-mal taeglich zur Schmerzintensitaet des

  16. What are the Predictive Factors of the Cure and Complication Rates for Midurethral Slings in the Treatment of Stress Urinary Incontinence in Women: A Multicenter and Multivariate Analysis Study

    Sinharib Çitgez

    2017-09-01

    Full Text Available Objective: Predictive factors that could affect the cure and complication rates of midurethral slings (MUS in the treatment of stress urinary incontinence (SUI were investigated. Materials and Methods: A total of 594 women (outside-in transobturator in 285, inside-out transobturator in 91, and retropubic in 218 with SUI and who had undergone MUS were evaluated. The median age was 53.9 (27-82 years. Univariate analyses were done using chi-square test, Student’s t-test and the Mann-Whitney U test. Multivariate analyses were done using logistic regression analysis to determine predictive factors affecting cure and complication rates. Results: The mean follow-up time was 48 months. The subjective cure rate was 84% and complication rate was 11.2%. On univariate and multivariate analyses, the cure rate was found to be increased in younger patients [odds ratio (OR: 0.97, 95% confidence interval (CI: 0.95-0.99, p=0.038] and in patients with pure SUI (OR: 2.17, 95% CI: 1.31-3.60, p=0.002. The type of surgery was the only statistically significant parameter affecting the complication rate, which was significantly higher in retropubic MUS procedure (OR: 6.28, 95% CI: 3.51-11.22, p<0.001. Conclusion: MUS is an effective and safe surgical procedure in the treatment of SUI. In this study, age and type of incontinence were the only significant predictive factors affecting the cure rate. Our study suggests that retropubic approach could be considered a risk factor for complication after MUS.

  17. Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review.

    Maione, Camila; Barbosa, Rommel Melgaço

    2018-01-24

    Rice is one of the most important staple foods around the world. Authentication of rice is one of the most addressed concerns in the present literature, which includes recognition of its geographical origin and variety, certification of organic rice and many other issues. Good results have been achieved by multivariate data analysis and data mining techniques when combined with specific parameters for ascertaining authenticity and many other useful characteristics of rice, such as quality, yield and others. This paper brings a review of the recent research projects on discrimination and authentication of rice using multivariate data analysis and data mining techniques. We found that data obtained from image processing, molecular and atomic spectroscopy, elemental fingerprinting, genetic markers, molecular content and others are promising sources of information regarding geographical origin, variety and other aspects of rice, being widely used combined with multivariate data analysis techniques. Principal component analysis and linear discriminant analysis are the preferred methods, but several other data classification techniques such as support vector machines, artificial neural networks and others are also frequently present in some studies and show high performance for discrimination of rice.

  18. Renewable energy consumption and economic growth in Argentina. A multivariate co-integration analysis

    Khobai, Hlalefang

    2018-01-01

    This paper applied the ARDL bounds test approach and the VECM test technique to examine the long run relationship and direction of causality between renewable energy consumption and economic growth in Argentina. Quarterly time series data was employed in this study covering a period between 1990 and 2014. Trade openness, capital and employment were included in the study to form a multivariate framework. The results established that there is a long run relationship between the variables. The V...

  19. Univariate and Bivariate Empirical Mode Decomposition for Postural Stability Analysis

    Jacques Duchêne

    2008-05-01

    Full Text Available The aim of this paper was to compare empirical mode decomposition (EMD and two new extended methods of  EMD named complex empirical mode decomposition (complex-EMD and bivariate empirical mode decomposition (bivariate-EMD. All methods were used to analyze stabilogram center of pressure (COP time series. The two new methods are suitable to be applied to complex time series to extract complex intrinsic mode functions (IMFs before the Hilbert transform is subsequently applied on the IMFs. The trace of the analytic IMF in the complex plane has a circular form, with each IMF having its own rotation frequency. The area of the circle and the average rotation frequency of IMFs represent efficient indicators of the postural stability status of subjects. Experimental results show the effectiveness of these indicators to identify differences in standing posture between groups.

  20. Assessing earthworm and sewage sludge impacts on microbiological and biochemical soil quality using multivariate analysis

    Hanye Jafari Vafa

    2017-06-01

    with soil matrix.Heavy metals concentrations were found to be below the maximum permissible limits for municipal sewage sludge. After applying sewage sludge treatments, the pots were irrigated (70% soil field capacity for three months to achieve a relative equilibrium condition in the soil. Eight adult earthworms with fully-developed clitellum were added to each pot. In the pots with both earthworm species, 4 specimen of each earthworm species was added. At the end of the experiment (90 days, soil samples were collected from treatments and were separately air-dried for chemical analysis or kept fresh and stored (4oC for microbial analysis. Finally, data obtained from the study were analyzed using multivariate analysis. Results and Discussion: Factor analysis led to the selection of three factors with eigen value greater than 1. The first, second and third factors were accounted for 62, 17.7 and 9.2% of the variability in soil data, respectively. The three factors together explained 89% of the original variability (i.e., variance in the soil dataset. Consequently, three factors were retained to represent the original variability of the dataset. The first factor had 16 highly weighted variables with a negative loading for soil pH and positive loadings for other variables. The first factor, which included most soil indicators as input variables, clearly separated sewage sludge treatments. Most of the soil microbial characteristics were increased by sewage sludge application due to the high contents of organic matter and nutrients in sewage sludge, as well as low concentrations of heavy metals. Fungal respiration, bacterial respiration and microbial biomass carbon loaded heavily on the second factor with a negative loading for fungal respiration and positive loadings for bacterial respiration and microbial biomass carbon. The second factor, which included microbial biomass and community composition, noticeably discriminated earthworm treatments. In sewage sludge treatments

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

    Yu, P.

    2008-01-01

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

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

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

    2015-02-01

    Alicyclobacillus is a causative agent of spoilage in pasteurized and heat-treated apple juice products. Differentiating between this genus and the closely related Bacillus is crucially important. In this study, Fourier transform infrared spectroscopy (FT-IR) was used to identify and discriminate between 4 Alicyclobacillus strains and 4 Bacillus isolates inoculated individually into apple juice. Loading plots over the range of 1350 and 1700 cm(-1) reflected the most distinctive biochemical features of Bacillus and Alicyclobacillus. Multivariate statistical methods (for example, principal component analysis and soft independent modeling of class analogy) were used to analyze the spectral data. Distinctive separation of spectral samples was observed. This study demonstrates that FT-IR spectroscopy in combination with multivariate analysis could serve as a rapid and effective tool for fruit juice industry to differentiate between Bacillus and Alicyclobacillus and to distinguish between species belonging to these 2 genera. © 2015 Institute of Food Technologists®

  3. Multivariate analysis in the evaluation of the antinociceptive activity of irradiated essential oil of nutmeg

    Santos, Marcelo C.; Lima, Keila S.C.; Oliveira, Sergio E.M.; Lima, Antonio L.S.; Silva, Jose C.C.; Silva, Otniel F.

    2013-01-01

    saline solution, unirradiated oil and samples irradiated with 1.0, 3.0 and 5.0 kGy were compared. In this in vivo experiment the essential oil showed significant antinociceptive activity, with its results varying non-linearly with the radiation doses. The best result was achieved in the dose of 5.0 kGy, inhibiting 92,65% of the contortions. With the obtained results a multivariate analysis was performed, indicating which bioactive molecules of the essential oil were relevant in the antinociceptive activity. (author)

  4. Multivariate analysis in the evaluation of the antinociceptive activity of irradiated essential oil of nutmeg

    Santos, Marcelo C.; Lima, Keila S.C.; Oliveira, Sergio E.M.; Lima, Antonio L.S., E-mail: marcelocdossantos@yahoo.com.br [Instituto Militar de Engenharia (IME), Rio de Janeiro, RJ (Brazil); Silva, Jose C.C., E-mail: pinto@peq.coppe.ufrj.br [Coordenacao do Programas de Pos-Graduacao em Engenharia (COPPE/UFRJ), Rio de Janeiro, RJ (Brazil); Silva, Otniel F., E-mail: otniel.freitas@embrapa.br [Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA), Rio de Janeiro, RJ (Brazil)

    2013-07-01

    saline solution, unirradiated oil and samples irradiated with 1.0, 3.0 and 5.0 kGy were compared. In this in vivo experiment the essential oil showed significant antinociceptive activity, with its results varying non-linearly with the radiation doses. The best result was achieved in the dose of 5.0 kGy, inhibiting 92,65% of the contortions. With the obtained results a multivariate analysis was performed, indicating which bioactive molecules of the essential oil were relevant in the antinociceptive activity. (author)

  5. A multivariate time series approach to modeling and forecasting demand in the emergency department.

    Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L

    2009-02-01

    The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

  6. Which DTW Method Applied to Marine Univariate Time Series Imputation

    Phan , Thi-Thu-Hong; Caillault , Émilie; Lefebvre , Alain; Bigand , André

    2017-01-01

    International audience; Missing data are ubiquitous in any domains of applied sciences. Processing datasets containing missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Therefore, the aim of this paper is to build a framework for filling missing values in univariate time series and to perform a comparison of different similarity metrics used for the imputation task. This allows to suggest the most suitable methods for the imp...

  7. Univariate decision tree induction using maximum margin classification

    Yıldız, Olcay Taner

    2012-01-01

    In many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we propose a new decision tree learning algorithm called univariate margin tree where, for each continuous attribute, the best split is found using convex optimization. Our simulation results on 47 data sets show that the novel margin tree classifier performs at least as good as C4.5 and linear discriminant tree (LDT) with a similar time complexity. F...

  8. Acceleration techniques in the univariate Lipschitz global optimization

    Sergeyev, Yaroslav D.; Kvasov, Dmitri E.; Mukhametzhanov, Marat S.; De Franco, Angela

    2016-10-01

    Univariate box-constrained Lipschitz global optimization problems are considered in this contribution. Geometric and information statistical approaches are presented. The novel powerful local tuning and local improvement techniques are described in the contribution as well as the traditional ways to estimate the Lipschitz constant. The advantages of the presented local tuning and local improvement techniques are demonstrated using the operational characteristics approach for comparing deterministic global optimization algorithms on the class of 100 widely used test functions.

  9. Batch-to-Batch Quality Consistency Evaluation of Botanical Drug Products Using Multivariate Statistical Analysis of the Chromatographic Fingerprint

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

    2013-01-01

    Botanical drug products have batch-to-batch quality variability due to botanical raw materials and the current manufacturing process. The rational evaluation and control of product quality consistency are essential to ensure the efficacy and safety. Chromatographic fingerprinting is an important and widely used tool to characterize the chemical composition of botanical drug products. Multivariate statistical analysis has showed its efficacy and applicability in the quality evaluation of many ...

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

    Buttigieg, Pier Luigi; Ramette, Alban Nicolas

    2014-01-01

    The application of multivariate statistical analyses has become a consistent feature in microbial ecology. However, many microbial ecologists are still in the process of developing a deep understanding of these methods and appreciating their limitations. As a consequence, staying abreast of progress and debate in this arena poses an additional challenge to many microbial ecologists. To address these issues, we present the GUide to STatistical Analysis in Microbial Ecology (GUSTA ME): a dynami...

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

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

    2017-02-01

    Drug permeation through the intercellular lipids, which pack around and between corneocytes, may be enhanced by increasing the thermodynamic activity of the active in a formulation. However, this may also result in unwanted drug crystallisation on and in the skin. In this work, we explore the combination of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the skin. Ex vivo permeation studies of saturated solutions of diclofenac sodium (DF Na) in two vehicles, propylene glycol (PG) and dimethyl sulphoxide (DMSO), were carried out in porcine ear skin. Tape stripping and ATR-FTIR spectroscopy were conducted simultaneously to collect spectral data as a function of skin depth. Multivariate data analysis was applied to visualise and categorise the spectral data in the region of interest (1700-1500cm -1 ) containing the carboxylate (COO - ) asymmetric stretching vibrations of DF Na. Spectral data showed the redshifts of the COO - asymmetric stretching vibrations for DF Na in the solution compared with solid drug. Similar shifts were evident following application of saturated solutions of DF Na to porcine skin samples. Multivariate data analysis categorised the spectral data based on the spectral differences and drug crystallisation was found to be confined to the upper layers of the skin. This proof-of-concept study highlights the utility of ATR-FTIR spectroscopy in combination with multivariate data analysis as a simple and rapid approach in the investigation of drug deposition in the skin. The approach described here will be extended to the study of other actives for topical application to the skin. Copyright © 2016 Elsevier B.V. All rights reserved.

  12. Validation of models with multivariate output

    Rebba, Ramesh; Mahadevan, Sankaran

    2006-01-01

    This paper develops metrics for validating computational models with experimental data, considering uncertainties in both. A computational model may generate multiple response quantities and the validation experiment might yield corresponding measured values. Alternatively, a single response quantity may be predicted and observed at different spatial and temporal points. Model validation in such cases involves comparison of multiple correlated quantities. Multiple univariate comparisons may give conflicting inferences. Therefore, aggregate validation metrics are developed in this paper. Both classical and Bayesian hypothesis testing are investigated for this purpose, using multivariate analysis. Since, commonly used statistical significance tests are based on normality assumptions, appropriate transformations are investigated in the case of non-normal data. The methodology is implemented to validate an empirical model for energy dissipation in lap joints under dynamic loading

  13. The iron bars from the ‘Gresham Ship’: employing multivariate statistics to further slag inclusion analysis of ferrous objects

    Birch, Thomas; Martinón-Torres, Marcos

    2015-01-01

    An assemblage of post-medieval iron bars was found with the Princes Channel wreck, salvaged from the Thames Estuary in 2003. They were recorded and studied, with a focus on metallography and slag inclusion analysis. The investigation provided an opportunity to explore the use of multivariate...... statistical techniques to analyse slag inclusion data. Cluster analysis supplemented by principal components analysis revealed two groups of iron, probably originating from different smelting systems, which were compared to those observed macroscopically and through metallography. The analyses reveal...

  14. Replacement tunnelled dialysis catheters for haemodialysis access: Same site, new site, or exchange — A multivariate analysis and risk score

    Tapping, C.R.; Scott, P.M.; Lakshminarayan, R.; Ettles, D.F.; Robinson, G.J.

    2012-01-01

    Aim: To identify variables related to complications following tunnelled dialysis catheter (TDC) replacement and stratifying the risk to reduce morbidity in patients with end-stage renal disease. Materials and methods: One hundred and forty TDCs (Split Cath, medCOMP) were replaced in 140 patients over a 5 year period. Multiple variables were retrospectively collected and analysed to stratify the risk and to predict patients who were more likely to suffer from complications. Multivariate regression analysis was used to identify variables predictive of complications. Results: There were six immediate complications, 42 early complications, and 37 late complications. Multivariate analysis revealed that variables significantly associated to complications were: female sex (p = 0.003; OR 2.9); previous TDC in the same anatomical position in the past (p = 0.014; OR 4.1); catheter exchange (p = 0.038; OR 3.8); haemoglobin 15 s (p = 0.002; OR 4.1); and C-reactive protein >50 mg/l (p = 0.007; OR 4.6). A high-risk score, which used the values from the multivariate analysis, predicted 100% of the immediate complications, 95% of the early complications, and 68% of the late complications. Conclusion: Patients can now be scored prior to TDC replacement. A patient with a high-risk score can be optimized to reduce the chance of complications. Further prospective studies to confirm that rotating the site of TDC reduces complications are warranted as this has implications for current guidelines.

  15. A Scheme for Initial Exploratory Data Analysis of Multivariate Image Data

    Hilger, Klaus Baggesen; Nielsen, Allan Aasbjerg; Larsen, Rasmus

    2001-01-01

    A new scheme is proposed for handling initial exploratory analyses of multivariate image data. The method is invariant to linear transformations of the original data and is useful for data fusion of multisource measurements. The scheme includes dimensionality reduction followed by unsupervised...... clustering of the data. A transformation is proposed which maximizes autocorrelation by projection onto subspaces with signal-to-noise ratio dependent variance. We apply the traditional fuzzy c-means algorithm and introduce two additional memberships enhancing the textural awareness of the algorithm. Cluster...

  16. An uncertain journey around the tails of multivariate hydrological distributions

    Serinaldi, Francesco

    2013-10-01

    Moving from univariate to multivariate frequency analysis, this study extends the Klemeš' critique of the widespread belief that the increasingly refined mathematical structures of probability functions increase the accuracy and credibility of the extrapolated upper tails of the fitted distribution models. In particular, we discuss key aspects of multivariate frequency analysis applied to hydrological data such as the selection of multivariate design events (i.e., appropriate subsets or scenarios of multiplets that exhibit the same joint probability to be used in design applications) and the assessment of the corresponding uncertainty. Since these problems are often overlooked or treated separately, and sometimes confused, we attempt to clarify properties, advantages, shortcomings, and reliability of results of frequency analysis. We suggest a selection method of multivariate design events with prescribed joint probability based on simple Monte Carlo simulations that accounts for the uncertainty affecting the inference results and the multivariate extreme quantiles. It is also shown that the exploration of the p-level probability regions of a joint distribution returns a set of events that is a subset of the p-level scenarios resulting from an appropriate assessment of the sampling uncertainty, thus tending to overlook more extreme and potentially dangerous events with the same (uncertain) joint probability. Moreover, a quantitative assessment of the uncertainty of multivariate quantiles is provided by introducing the concept of joint confidence intervals. From an operational point of view, the simulated event sets describing the distribution of the multivariate p-level quantiles can be used to perform multivariate risk analysis under sampling uncertainty. As an example of the practical implications of this study, we analyze two case studies already presented in the literature.

  17. Measures of precision for dissimilarity-based multivariate analysis of ecological communities.

    Anderson, Marti J; Santana-Garcon, Julia

    2015-01-01

    Ecological studies require key decisions regarding the appropriate size and number of sampling units. No methods currently exist to measure precision for multivariate assemblage data when dissimilarity-based analyses are intended to follow. Here, we propose a pseudo multivariate dissimilarity-based standard error (MultSE) as a useful quantity for assessing sample-size adequacy in studies of ecological communities. Based on sums of squared dissimilarities, MultSE measures variability in the position of the centroid in the space of a chosen dissimilarity measure under repeated sampling for a given sample size. We describe a novel double resampling method to quantify uncertainty in MultSE values with increasing sample size. For more complex designs, values of MultSE can be calculated from the pseudo residual mean square of a permanova model, with the double resampling done within appropriate cells in the design. R code functions for implementing these techniques, along with ecological examples, are provided. © 2014 The Authors. Ecology Letters published by John Wiley & Sons Ltd and CNRS.

  18. Path analysis and multi-criteria decision making: an approach for multivariate model selection and analysis in health.

    Vasconcelos, A G; Almeida, R M; Nobre, F F

    2001-08-01

    This paper introduces an approach that includes non-quantitative factors for the selection and assessment of multivariate complex models in health. A goodness-of-fit based methodology combined with fuzzy multi-criteria decision-making approach is proposed for model selection. Models were obtained using the Path Analysis (PA) methodology in order to explain the interrelationship between health determinants and the post-neonatal component of infant mortality in 59 municipalities of Brazil in the year 1991. Socioeconomic and demographic factors were used as exogenous variables, and environmental, health service and agglomeration as endogenous variables. Five PA models were developed and accepted by statistical criteria of goodness-of fit. These models were then submitted to a group of experts, seeking to characterize their preferences, according to predefined criteria that tried to evaluate model relevance and plausibility. Fuzzy set techniques were used to rank the alternative models according to the number of times a model was superior to ("dominated") the others. The best-ranked model explained above 90% of the endogenous variables variation, and showed the favorable influences of income and education levels on post-neonatal mortality. It also showed the unfavorable effect on mortality of fast population growth, through precarious dwelling conditions and decreased access to sanitation. It was possible to aggregate expert opinions in model evaluation. The proposed procedure for model selection allowed the inclusion of subjective information in a clear and systematic manner.

  19. Applied multivariate statistics with R

    Zelterman, Daniel

    2015-01-01

    This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays, linear algebra, univariate, bivariate and multivariate normal distributions, factor methods, linear regression, discrimination and classification, clustering, time series models, and additional methods. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. As an example of the approach, the text examines a sample from the B...

  20. Recent trends in application of multivariate curve resolution approaches for improving gas chromatography-mass spectrometry analysis of essential oils.

    Jalali-Heravi, Mehdi; Parastar, Hadi

    2011-08-15

    Essential oils (EOs) are valuable natural products that are popular nowadays in the world due to their effects on the health conditions of human beings and their role in preventing and curing diseases. In addition, EOs have a broad range of applications in foods, perfumes, cosmetics and human nutrition. Among different techniques for analysis of EOs, gas chromatography-mass spectrometry (GC-MS) is the most important one in recent years. However, there are some fundamental problems in GC-MS analysis including baseline drift, spectral background, noise, low S/N (signal to noise) ratio, changes in the peak shapes and co-elution. Multivariate curve resolution (MCR) approaches cope with ongoing challenges and are able to handle these problems. This review focuses on the application of MCR techniques for improving GC-MS analysis of EOs published between January 2000 and December 2010. In the first part, the importance of EOs in human life and their relevance in analytical chemistry is discussed. In the second part, an insight into some basics needed to understand prospects and limitations of the MCR techniques are given. In the third part, the significance of the combination of the MCR approaches with GC-MS analysis of EOs is highlighted. Furthermore, the commonly used algorithms for preprocessing, chemical rank determination, local rank analysis and multivariate resolution in the field of EOs analysis are reviewed. Copyright © 2011 Elsevier B.V. All rights reserved.