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Additive genetic variation and evolvability of a multivariate trait can be increased by epistatic gene action.

PubMed

Griswold, Cortland K

2015-12-21

Epistatic gene action occurs when mutations or alleles interact to produce a phenotype. Theoretically and empirically it is of interest to know whether gene interactions can facilitate the evolution of diversity. In this paper, we explore how epistatic gene action affects the additive genetic component or heritable component of multivariate trait variation, as well as how epistatic gene action affects the evolvability of multivariate traits. The analysis involves a sexually reproducing and recombining population. Our results indicate that under stabilizing selection conditions a population with a mixed additive and epistatic genetic architecture can have greater multivariate additive genetic variation and evolvability than a population with a purely additive genetic architecture. That greater multivariate additive genetic variation can occur with epistasis is in contrast to previous theory that indicated univariate additive genetic variation is decreased with epistasis under stabilizing selection conditions. In a multivariate setting, epistasis leads to less relative covariance among individuals in their genotypic, as well as their breeding values, which facilitates the maintenance of additive genetic variation and increases a population׳s evolvability. Our analysis involves linking the combinatorial nature of epistatic genetic effects to the ancestral graph structure of a population to provide insight into the consequences of epistasis on multivariate trait variation and evolution. Copyright © 2015 Elsevier Ltd. All rights reserved.
Multi-variant study of obesity risk genes in African Americans: The Jackson Heart Study.

PubMed

Liu, Shijian; Wilson, James G; Jiang, Fan; Griswold, Michael; Correa, Adolfo; Mei, Hao

2016-11-30

Genome-wide association study (GWAS) has been successful in identifying obesity risk genes by single-variant association analysis. For this study, we designed steps of analysis strategy and aimed to identify multi-variant effects on obesity risk among candidate genes. Our analyses were focused on 2137 African American participants with body mass index measured in the Jackson Heart Study and 657 common single nucleotide polymorphisms (SNPs) genotyped at 8 GWAS-identified obesity risk genes. Single-variant association test showed that no SNPs reached significance after multiple testing adjustment. The following gene-gene interaction analysis, which was focused on SNPs with unadjusted p-value<0.10, identified 6 significant multi-variant associations. Logistic regression showed that SNPs in these associations did not have significant linear interactions; examination of genetic risk score evidenced that 4 multi-variant associations had significant additive effects of risk SNPs; and haplotype association test presented that all multi-variant associations contained one or several combinations of particular alleles or haplotypes, associated with increased obesity risk. Our study evidenced that obesity risk genes generated multi-variant effects, which can be additive or non-linear interactions, and multi-variant study is an important supplement to existing GWAS for understanding genetic effects of obesity risk genes. Copyright © 2016 Elsevier B.V. All rights reserved.
Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.
Multivariate meta-analysis: potential and promise.

PubMed

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

2011-09-10

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

NASA Astrophysics Data System (ADS)

Yamanishi, Junsuke; Iwase, Shigeru; Ishida, Nobuyuki; Fujita, Daisuke

2018-01-01

We applied principal component analysis (PCA) to two-dimensional tunneling spectroscopy (2DTS) data obtained on a Si(111)-(7 × 7) surface to explore the effectiveness of multivariate analysis for interpreting 2DTS data. We demonstrated that several components that originated mainly from specific atoms at the Si(111)-(7 × 7) surface can be extracted by PCA. Furthermore, we showed that hidden components in the tunneling spectra can be decomposed (peak separation), which is difficult to achieve with normal 2DTS analysis without the support of theoretical calculations. Our analysis showed that multivariate analysis can be an additional powerful way to analyze 2DTS data and extract hidden information from a large amount of spectroscopic data.
Multivariate Analysis, Retrieval, and Storage System (MARS). Volume 1: MARS System and Analysis Techniques

NASA Technical Reports Server (NTRS)

Hague, D. S.; Vanderberg, J. D.; Woodbury, N. W.

1974-01-01

A method for rapidly examining the probable applicability of weight estimating formulae to a specific aerospace vehicle design is presented. The Multivariate Analysis Retrieval and Storage System (MARS) is comprised of three computer programs which sequentially operate on the weight and geometry characteristics of past aerospace vehicles designs. Weight and geometric characteristics are stored in a set of data bases which are fully computerized. Additional data bases are readily added to the MARS system and/or the existing data bases may be easily expanded to include additional vehicles or vehicle characteristics.
Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy

NASA Astrophysics Data System (ADS)

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

2015-02-01

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

NASA Technical Reports Server (NTRS)

Bloom, S. C.

1984-01-01

The observations of surface pressure are quite sparse over oceanic areas. An effort to improve the analysis of surface pressure over oceans through the development of a multivariate surface analysis scheme which makes use of surface pressure and wind data is discussed. Although the present research used ship winds, future versions of this analysis scheme could utilize winds from additional sources, such as satellite scatterometer data.
Multivariate meta-analysis: Potential and promise

PubMed Central

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
Multivariate Quantitative Chemical Analysis

NASA Technical Reports Server (NTRS)

Kinchen, David G.; Capezza, Mary

1995-01-01

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

PubMed

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

2015-02-25

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

PubMed Central

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. PMID:29312134
Multivariate Analysis and Machine Learning in Cerebral Palsy Research.

PubMed

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.
Multivariate optimum interpolation of surface pressure and surface wind over oceans

NASA Technical Reports Server (NTRS)

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

1984-01-01

The present multivariate analysis method for surface pressure and winds incorporates ship wind observations into the analysis of surface pressure. For the specific case of 0000 GMT, on February 3, 1979, the additional data resulted in a global rms difference of 0.6 mb; individual maxima as larse as 5 mb occurred over the North Atlantic and East Pacific Oceans. These differences are noted to be smaller than the analysis increments to the first-guess fields.
Optimal Multicomponent Analysis Using the Generalized Standard Addition Method.

ERIC Educational Resources Information Center

Raymond, Margaret; And Others

1983-01-01

Describes an experiment on the simultaneous determination of chromium and magnesium by spectophotometry modified to include the Generalized Standard Addition Method computer program, a multivariate calibration method that provides optimal multicomponent analysis in the presence of interference and matrix effects. Provides instructions for…
Compositional differences among Chinese soy sauce types studied by (13)C NMR spectroscopy coupled with multivariate statistical analysis.

PubMed

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

2016-09-01

Soy sauce a well known seasoning all over the world, especially in Asia, is available in global market in a wide range of types based on its purpose and the processing methods. Its composition varies with respect to the fermentation processes and addition of additives, preservatives and flavor enhancers. A comprehensive (1)H NMR based study regarding the metabonomic variations of soy sauce to differentiate among different types of soy sauce available on the global market has been limited due to the complexity of the mixture. In present study, (13)C NMR spectroscopy coupled with multivariate statistical data analysis like principle component analysis (PCA), and orthogonal partial least square-discriminant analysis (OPLS-DA) was applied to investigate metabonomic variations among different types of soy sauce, namely super light, super dark, red cooking and mushroom soy sauce. The main additives in soy sauce like glutamate, sucrose and glucose were easily distinguished and quantified using (13)C NMR spectroscopy which were otherwise difficult to be assigned and quantified due to serious signal overlaps in (1)H NMR spectra. The significantly higher concentration of sucrose in dark, red cooking and mushroom flavored soy sauce can directly be linked to the addition of caramel in soy sauce. Similarly, significantly higher level of glutamate in super light as compared to super dark and mushroom flavored soy sauce may come from the addition of monosodium glutamate. The study highlights the potentiality of (13)C NMR based metabonomics coupled with multivariate statistical data analysis in differentiating between the types of soy sauce on the basis of level of additives, raw materials and fermentation procedures. Copyright © 2016 Elsevier B.V. All rights reserved.
Multivariate analysis of progressive thermal desorption coupled gas chromatography-mass spectrometry.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Van Benthem, Mark Hilary; Mowry, Curtis Dale; Kotula, Paul Gabriel

Thermal decomposition of poly dimethyl siloxane compounds, Sylgard{reg_sign} 184 and 186, were examined using thermal desorption coupled gas chromatography-mass spectrometry (TD/GC-MS) and multivariate analysis. This work describes a method of producing multiway data using a stepped thermal desorption. The technique involves sequentially heating a sample of the material of interest with subsequent analysis in a commercial GC/MS system. The decomposition chromatograms were analyzed using multivariate analysis tools including principal component analysis (PCA), factor rotation employing the varimax criterion, and multivariate curve resolution. The results of the analysis show seven components related to offgassing of various fractions of siloxanes that varymore » as a function of temperature. Thermal desorption coupled with gas chromatography-mass spectrometry (TD/GC-MS) is a powerful analytical technique for analyzing chemical mixtures. It has great potential in numerous analytic areas including materials analysis, sports medicine, in the detection of designer drugs; and biological research for metabolomics. Data analysis is complicated, far from automated and can result in high false positive or false negative rates. We have demonstrated a step-wise TD/GC-MS technique that removes more volatile compounds from a sample before extracting the less volatile compounds. This creates an additional dimension of separation before the GC column, while simultaneously generating three-way data. Sandia's proven multivariate analysis methods, when applied to these data, have several advantages over current commercial options. It also has demonstrated potential for success in finding and enabling identification of trace compounds. Several challenges remain, however, including understanding the sources of noise in the data, outlier detection, improving the data pretreatment and analysis methods, developing a software tool for ease of use by the chemist, and demonstrating our belief that this multivariate analysis will enable superior differentiation capabilities. In addition, noise and system artifacts challenge the analysis of GC-MS data collected on lower cost equipment, ubiquitous in commercial laboratories. This research has the potential to affect many areas of analytical chemistry including materials analysis, medical testing, and environmental surveillance. It could also provide a method to measure adsorption parameters for chemical interactions on various surfaces by measuring desorption as a function of temperature for mixtures. We have presented results of a novel method for examining offgas products of a common PDMS material. Our method involves utilizing a stepped TD/GC-MS data acquisition scheme that may be almost totally automated, coupled with multivariate analysis schemes. This method of data generation and analysis can be applied to a number of materials aging and thermal degradation studies.« less
In situ X-ray diffraction analysis of (CF x) n batteries: signal extraction by multivariate analysis

DOE PAGES

Rodriguez, Mark A.; Keenan, Michael R.; Nagasubramanian, Ganesan

2007-11-10

In this study, (CF x) n cathode reaction during discharge has been investigated using in situ X-ray diffraction (XRD). Mathematical treatment of the in situ XRD data set was performed using multivariate curve resolution with alternating least squares (MCR–ALS), a technique of multivariate analysis. MCR–ALS analysis successfully separated the relatively weak XRD signal intensity due to the chemical reaction from the other inert cell component signals. The resulting dynamic reaction component revealed the loss of (CF x) n cathode signal together with the simultaneous appearance of LiF by-product intensity. Careful examination of the XRD data set revealed an additional dynamicmore » component which may be associated with the formation of an intermediate compound during the discharge process.« less
Quality-by-design case study: investigation of the role of poloxamer in immediate-release tablets by experimental design and multivariate data analysis.

PubMed

Kaul, Goldi; Huang, Jun; Chatlapalli, Ramarao; Ghosh, Krishnendu; Nagi, Arwinder

2011-12-01

The role of poloxamer 188, water and binder addition rate, on retarding dissolution in immediate-release tablets of a model drug from BCS class II was investigated by means of multivariate data analysis (MVDA) combined with design of experiments (DOE). While the DOE analysis yielded important clues into the cause-and-effect relationship between the responses and design factors, multivariate data analysis of the 40+ variables provided additional information on slowdown in tablet dissolution. A steep dependence of both tablet dissolution and disintegration on the poloxamer and less so on other design variables was observed. Poloxamer was found to increase dissolution rates in granules as expected of surfactants in general but retard dissolution in tablets. The unexpected effect of poloxamer in tablets was accompanied by an increase in tablet-disintegration-time-mediated slowdown of tablet dissolution and by a surrogate binding effect of poloxamer at higher concentrations. It was additionally realized through MVDA that poloxamer in tablets either acts as a binder by itself or promotes binder action of the binder povidone resulting in increased intragranular cohesion. Additionally, poloxamer was found to mediate tablet dissolution on stability as well. In contrast to tablet dissolution at release (time zero), poloxamer appeared to increase tablet dissolution in a concentration-dependent manner on accelerated open-dish stability. Substituting polysorbate 80 as an alternate surfactant in place of poloxamer in the formulation was found to stabilize tablet dissolution.
Multivariate missing data in hydrology - Review and applications

NASA Astrophysics Data System (ADS)

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.

Identification of Reliable Components in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS): a Data-Driven Approach across Metabolic Processes.

PubMed

Motegi, Hiromi; Tsuboi, Yuuri; Saga, Ayako; Kagami, Tomoko; Inoue, Maki; Toki, Hideaki; Minowa, Osamu; Noda, Tetsuo; Kikuchi, Jun

2015-11-04

There is an increasing need to use multivariate statistical methods for understanding biological functions, identifying the mechanisms of diseases, and exploring biomarkers. In addition to classical analyses such as hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis, various multivariate strategies, including independent component analysis, non-negative matrix factorization, and multivariate curve resolution, have recently been proposed. However, determining the number of components is problematic. Despite the proposal of several different methods, no satisfactory approach has yet been reported. To resolve this problem, we implemented a new idea: classifying a component as "reliable" or "unreliable" based on the reproducibility of its appearance, regardless of the number of components in the calculation. Using the clustering method for classification, we applied this idea to multivariate curve resolution-alternating least squares (MCR-ALS). Comparisons between conventional and modified methods applied to proton nuclear magnetic resonance ((1)H-NMR) spectral datasets derived from known standard mixtures and biological mixtures (urine and feces of mice) revealed that more plausible results are obtained by the modified method. In particular, clusters containing little information were detected with reliability. This strategy, named "cluster-aided MCR-ALS," will facilitate the attainment of more reliable results in the metabolomics datasets.
Clinical management provided by board-certificated physiatrists in early rehabilitation is a significant determinant of functional improvement in acute stroke patients: a retrospective analysis of Japan rehabilitation database.

PubMed

Kinoshita, Shoji; Kakuda, Wataru; Momosaki, Ryo; Yamada, Naoki; Sugawara, Hidekazu; Watanabe, Shu; Abo, Masahiro

2015-05-01

Early rehabilitation for acute stroke patients is widely recommended. We tested the hypothesis that clinical outcome of stroke patients who receive early rehabilitation managed by board-certificated physiatrists (BCP) is generally better than that provided by other medical specialties. Data of stroke patients who underwent early rehabilitation in 19 acute hospitals between January 2005 and December 2013 were collected from the Japan Rehabilitation Database and analyzed retrospectively. Multivariate linear regression analysis using generalized estimating equations method was performed to assess the association between Functional Independence Measure (FIM) effectiveness and management provided by BCP in early rehabilitation. In addition, multivariate logistic regression analysis was also performed to assess the impact of management provided by BCP in acute phase on discharge destination. After setting the inclusion criteria, data of 3838 stroke patients were eligible for analysis. BCP provided early rehabilitation in 814 patients (21.2%). Both the duration of daily exercise time and the frequency of regular conferencing were significantly higher for patients managed by BCP than by other specialties. Although the mortality rate was not different, multivariate regression analysis showed that FIM effectiveness correlated significantly and positively with the management provided by BCP (coefficient, .35; 95% confidence interval [CI], .012-.059; P < .005). In addition, multivariate logistic analysis identified clinical management by BCP as a significant determinant of home discharge (odds ratio, 1.24; 95% CI, 1.08-1.44; P < .005). Our retrospective cohort study demonstrated that clinical management provided by BCP in early rehabilitation can lead to functional recovery of acute stroke. Copyright © 2015 National Stroke Association. Published by Elsevier Inc. All rights reserved.
Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

Multivariate analysis refers to statistical techniques that simultaneously look at three or more variables in relation to the subjects under investigation with the aim of identifying or clarifying the relationships between them. These techniques have been broadly classified as dependence techniques, which explore the relationship between one or more dependent variables and their independent predictors, and interdependence techniques, that make no such distinction but treat all variables equally in a search for underlying relationships. Multiple linear regression models a situation where a single numerical dependent variable is to be predicted from multiple numerical independent variables. Logistic regression is used when the outcome variable is dichotomous in nature. The log-linear technique models count type of data and can be used to analyze cross-tabulations where more than two variables are included. Analysis of covariance is an extension of analysis of variance (ANOVA), in which an additional independent variable of interest, the covariate, is brought into the analysis. It tries to examine whether a difference persists after "controlling" for the effect of the covariate that can impact the numerical dependent variable of interest. Multivariate analysis of variance (MANOVA) is a multivariate extension of ANOVA used when multiple numerical dependent variables have to be incorporated in the analysis. Interdependence techniques are more commonly applied to psychometrics, social sciences and market research. Exploratory factor analysis and principal component analysis are related techniques that seek to extract from a larger number of metric variables, a smaller number of composite factors or components, which are linearly related to the original variables. Cluster analysis aims to identify, in a large number of cases, relatively homogeneous groups called clusters, without prior information about the groups. The calculation intensive nature of multivariate analysis has so far precluded most researchers from using these techniques routinely. The situation is now changing with wider availability, and increasing sophistication of statistical software and researchers should no longer shy away from exploring the applications of multivariate methods to real-life data sets.
Effect of sexual steroids on boar kinematic sperm subpopulations.

PubMed

Ayala, E M E; Aragón, M A

2017-11-01

Here, we show the effects of sexual steroids, progesterone, testosterone, or estradiol on motility parameters of boar sperm. Sixteen commercial seminal doses, four each of four adult boars, were analyzed using computer assisted sperm analysis (CASA). Mean values of motility parameters were analyzed by bivariate and multivariate statistics. Principal component analysis (PCA), followed by hierarchical clustering, was applied on data of motility parameters, provided automatically as intervals by the CASA system. Effects of sexual steroids were described in the kinematic subpopulations identified from multivariate statistics. Mean values of motility parameters were not significantly changed after addition of sexual steroids. Multivariate graphics showed that sperm subpopulations were not sensitive to the addition of either testosterone or estradiol, but sperm subpopulations responsive to progesterone were found. Distribution of motility parameters were wide in controls but sharpened at distinct concentrations of progesterone. We conclude that kinematic sperm subpopulations responsive to progesterone are present in boar semen, and these subpopulations are masked in evaluations of mean values of motility parameters. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.
Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

PubMed

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 (PCA) and cluster analysis (CA). Copyright © 2017 Elsevier Ltd. All rights reserved.
Immediate versus delayed intramedullary nailing for open fractures of the tibial shaft: a multivariate analysis of factors affecting deep infection and fracture healing.

PubMed

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 (< or =6 h or >6 h), method of soft-tissue management, skin closure time (< or =1 week or >1 week), existence of polytrauma (ISS< 18 or 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 (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 existence of deep infection significantly correlated with occurrence of nonunion (P < 0.05). Gustilo type and existence of deep infection were significantly correlated with healing time to union on multivariate analysis (r(2) = 0.263, P = 0.0001). Multivariate analyses for open tibial fractures treated with IMN showed that IMN after EF (especially in existence of pin site infection) was at high risk of deep infection, and that debridement within 6 h and appropriate soft-tissue managements were also important factor in preventing deep infections. These analyses postulated that both the Gustilo type and the existence of deep infection is related with fracture healing in open fractures treated with IMN. In addition, immediate IMN for type IIIB and IIIC is potentially risky, and canal reaming did not increase the risk of complication for open tibial fractures treated with IMN.
Distributions of Characteristic Roots in Multivariate Analysis

DTIC Science & Technology

1976-07-01

stiidied by various authors, have been briefly discussed. Such distributional ies of four test criteria and a few less important ones which are...functions h. -nots have further been discussed in view of the power comparisons made in co. ion wich tests of three multivariate hypotheses. In addition...one- sample case has also been considered in terms of distributional aspects of the ch. roots and criteria for tests of two hypotheses on the
Combination of multivariate curve resolution and multivariate classification techniques for comprehensive high-performance liquid chromatography-diode array absorbance detection fingerprints analysis of Salvia reuterana extracts.

PubMed

Hakimzadeh, Neda; Parastar, Hadi; Fattahi, Mohammad

2014-01-24

In this study, multivariate curve resolution (MCR) and multivariate classification methods are proposed to develop a new chemometric strategy for comprehensive analysis of high-performance liquid chromatography-diode array absorbance detection (HPLC-DAD) fingerprints of sixty Salvia reuterana samples from five different geographical regions. Different chromatographic problems occurred during HPLC-DAD analysis of S. reuterana samples, such as baseline/background contribution and noise, low signal-to-noise ratio (S/N), asymmetric peaks, elution time shifts, and peak overlap are handled using the proposed strategy. In this way, chromatographic fingerprints of sixty samples are properly segmented to ten common chromatographic regions using local rank analysis and then, the corresponding segments are column-wise augmented for subsequent MCR analysis. Extended multivariate curve resolution-alternating least squares (MCR-ALS) is used to obtain pure component profiles in each segment. In general, thirty-one chemical components were resolved using MCR-ALS in sixty S. reuterana samples and the lack of fit (LOF) values of MCR-ALS models were below 10.0% in all cases. Pure spectral profiles are considered for identification of chemical components by comparing their resolved spectra with the standard ones and twenty-four components out of thirty-one components were identified. Additionally, pure elution profiles are used to obtain relative concentrations of chemical components in different samples for multivariate classification analysis by principal component analysis (PCA) and k-nearest neighbors (kNN). Inspection of the PCA score plot (explaining 76.1% of variance accounted for three PCs) showed that S. reuterana samples belong to four clusters. The degree of class separation (DCS) which quantifies the distance separating clusters in relation to the scatter within each cluster is calculated for four clusters and it was in the range of 1.6-5.8. These results are then confirmed by kNN. In addition, according to the PCA loading plot and kNN dendrogram of thirty-one variables, five chemical constituents of luteolin-7-o-glucoside, salvianolic acid D, rosmarinic acid, lithospermic acid and trijuganone A are identified as the most important variables (i.e., chemical markers) for clusters discrimination. Finally, the effect of different chemical markers on samples differentiation is investigated using counter-propagation artificial neural network (CP-ANN) method. It is concluded that the proposed strategy can be successfully applied for comprehensive analysis of chromatographic fingerprints of complex natural samples. Copyright © 2013 Elsevier B.V. All rights reserved.
Multivariable Parametric Cost Model for Ground Optical Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2005-01-01

A parametric cost model for ground-based telescopes is developed using multivariable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction-limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature are examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e., multi-telescope phased-array systems). Additionally, single variable models Based on aperture diameter are derived.
Spectroscopic analysis and control

DOEpatents

Tate; , James D.; Reed, Christopher J.; Domke, Christopher H.; Le, Linh; Seasholtz, Mary Beth; Weber, Andy; Lipp, Charles

2017-04-18

Apparatus for spectroscopic analysis which includes a tunable diode laser spectrometer having a digital output signal and a digital computer for receiving the digital output signal from the spectrometer, the digital computer programmed to process the digital output signal using a multivariate regression algorithm. In addition, a spectroscopic method of analysis using such apparatus. Finally, a method for controlling an ethylene cracker hydrogenator.
Fibroblast Growth Factor 2-A Predictor of Outcome for Patients Irradiated for Stage II-III Non-Small-Cell Lung Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rades, Dirk, E-mail: Rades.Dirk@gmx.net; Setter, Cornelia; Dahl, Olav

2012-01-01

Purpose: The prognostic value of the tumor cell expression of the fibroblast growth factor 2 (FGF-2) in patients with non-small-cell lung cancer (NSCLC) is unclear. The present study investigated the effect of tumor cell expression of FGF-2 on the outcome of 60 patients irradiated for Stage II-III NSCLC. Methods and Materials: The effect of FGF-2 expression and 13 additional factors on locoregional control (LRC), metastasis-free survival (MFS), and overall survival (OS) were retrospectively evaluated. These additional factors included age, gender, Karnofsky performance status, histologic type, histologic grade, T and N category, American Joint Committee on Cancer stage, surgery, chemotherapy, pack-years,more » smoking during radiotherapy, and hemoglobin during radiotherapy. Locoregional failure was identified by endoscopy or computed tomography. Univariate analyses were performed with the Kaplan-Meier method and the Wilcoxon test and multivariate analyses with the Cox proportional hazard model. Results: On univariate analysis, improved LRC was associated with surgery (p = .017), greater hemoglobin levels (p = .036), and FGF-2 negativity (p <.001). On multivariate analysis of LRC, surgery (relative risk [RR], 2.44; p = .037), and FGF-2 expression (RR, 5.06; p <.001) maintained significance. On univariate analysis, improved MFS was associated with squamous cell carcinoma (p = .020), greater hemoglobin levels (p = .007), and FGF-2 negativity (p = .001). On multivariate analysis of MFS, the hemoglobin levels (RR, 2.65; p = .019) and FGF-2 expression (RR, 3.05; p = .004) were significant. On univariate analysis, improved OS was associated with a lower N category (p = .048), greater hemoglobin levels (p <.001), and FGF-2 negativity (p <.001). On multivariate analysis of OS, greater hemoglobin levels (RR, 4.62; p = .002) and FGF-2 expression (RR, 3.25; p = .002) maintained significance. Conclusions: Tumor cell expression of FGF-2 appeared to be an independent negative predictor of LRC, MFS, and OS.« less
A novel second-order standard addition analytical method based on data processing with multidimensional partial least-squares and residual bilinearization.

PubMed

Lozano, Valeria A; Ibañez, Gabriela A; Olivieri, Alejandro C

2009-10-05

In the presence of analyte-background interactions and a significant background signal, both second-order multivariate calibration and standard addition are required for successful analyte quantitation achieving the second-order advantage. This report discusses a modified second-order standard addition method, in which the test data matrix is subtracted from the standard addition matrices, and quantitation proceeds via the classical external calibration procedure. It is shown that this novel data processing method allows one to apply not only parallel factor analysis (PARAFAC) and multivariate curve resolution-alternating least-squares (MCR-ALS), but also the recently introduced and more flexible partial least-squares (PLS) models coupled to residual bilinearization (RBL). In particular, the multidimensional variant N-PLS/RBL is shown to produce the best analytical results. The comparison is carried out with the aid of a set of simulated data, as well as two experimental data sets: one aimed at the determination of salicylate in human serum in the presence of naproxen as an additional interferent, and the second one devoted to the analysis of danofloxacin in human serum in the presence of salicylate.
High-throughput investigation of single and binary protein adsorption isotherms in anion exchange chromatography employing multivariate analysis.

PubMed

Field, Nicholas; Konstantinidis, Spyridon; Velayudhan, Ajoy

2017-08-11

The combination of multi-well plates and automated liquid handling is well suited to the rapid measurement of the adsorption isotherms of proteins. Here, single and binary adsorption isotherms are reported for BSA, ovalbumin and conalbumin on a strong anion exchanger over a range of pH and salt levels. The impact of the main experimental factors at play on the accuracy and precision of the adsorbed protein concentrations is quantified theoretically and experimentally. In addition to the standard measurement of liquid concentrations before and after adsorption, the amounts eluted from the wells are measured directly. This additional measurement corroborates the calculation based on liquid concentration data, and improves precision especially under conditions of weak or moderate interaction strength. The traditional measurement of multicomponent isotherms is limited by the speed of HPLC analysis; this analytical bottleneck is alleviated by careful multivariate analysis of UV spectra. Copyright © 2017. Published by Elsevier B.V.
Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space.

PubMed

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.
Does investor ownership of nursing homes compromise the quality of care?

PubMed

Harrington, C; Woolhandler, S; Mullan, J; Carrillo, H; Himmelstein, D U

2001-09-01

Two thirds of nursing homes are investor owned. This study examined whether investor ownership affects quality. We analyzed 1998 data from state inspections of 13,693 nursing facilities. We used a multivariate model and controlled for case mix, facility characteristics, and location. Investor-owned facilities averaged 5.89 deficiencies per home, 46.5% higher than nonprofit facilities and 43.0% higher than public facilities. In multivariate analysis, investor ownership predicted 0.679 additional deficiencies per home; chain ownership predicted an additional 0.633 deficiencies. Nurse staffing was lower at investor-owned nursing homes. Investor-owned nursing homes provide worse care and less nursing care than do not-for-profit or public homes.
Multivariable Parametric Cost Model for Ground Optical: Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2004-01-01

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature were examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter were derived.
A systematic review of the relationship factor between women and health professionals within the multivariant analysis of maternal satisfaction.

PubMed

Macpherson, Ignacio; Roqué-Sánchez, María V; Legget Bn, Finola O; Fuertes, Ferran; Segarra, Ignacio

2016-10-01

personalised support provided to women by health professionals is one of the prime factors attaining women's satisfaction during pregnancy and childbirth. However the multifactorial nature of 'satisfaction' makes difficult to assess it. Statistical multivariate analysis may be an effective technique to obtain in depth quantitative evidence of the importance of this factor and its interaction with the other factors involved. This technique allows us to estimate the importance of overall satisfaction in its context and suggest actions for healthcare services. systematic review of studies that quantitatively measure the personal relationship between women and healthcare professionals (gynecologists, obstetricians, nurse, midwifes, etc.) regarding maternity care satisfaction. The literature search focused on studies carried out between 1970 and 2014 that used multivariate analyses and included the woman-caregiver relationship as a factor of their analysis. twenty-four studies which applied various multivariate analysis tools to different periods of maternity care (antenatal, perinatal, post partum) were selected. The studies included discrete scale scores and questionnaires from women with low-risk pregnancies. The "personal relationship" factor appeared under various names: care received, personalised treatment, professional support, amongst others. The most common multivariate techniques used to assess the percentage of variance explained and the odds ratio of each factor were principal component analysis and logistic regression. the data, variables and factor analysis suggest that continuous, personalised care provided by the usual midwife and delivered within a family or a specialised setting, generates the highest level of satisfaction. In addition, these factors foster the woman's psychological and physiological recovery, often surpassing clinical action (e.g. medicalization and hospital organization) and/or physiological determinants (e.g. pain, pathologies, etc.). Copyright © 2016 Elsevier Ltd. All rights reserved.
Longitudinal assessment of treatment effects on pulmonary ventilation using 1H/3He MRI multivariate templates

NASA Astrophysics Data System (ADS)

Tustison, Nicholas J.; Contrella, Benjamin; Altes, Talissa A.; Avants, Brian B.; de Lange, Eduard E.; Mugler, John P.

2013-03-01

The utitlity of pulmonary functional imaging techniques, such as hyperpolarized 3He MRI, has encouraged their inclusion in research studies for longitudinal assessment of disease progression and the study of treatment effects. We present methodology for performing voxelwise statistical analysis of ventilation maps derived from hyper polarized 3He MRI which incorporates multivariate template construction using simultaneous acquisition of IH and 3He images. Additional processing steps include intensity normalization, bias correction, 4-D longitudinal segmentation, and generation of expected ventilation maps prior to voxelwise regression analysis. Analysis is demonstrated on a cohort of eight individuals with diagnosed cystic fibrosis (CF) undergoing treatment imaged five times every two weeks with a prescribed treatment schedule.
Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.
Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

PubMed Central

Xu, Rui; Zhen, Zonglei; Liu, Jia

2010-01-01

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies. PMID:21152081

Multivariate and geo-spatial approach for seawater quality of Chidiyatappu Bay, south Andaman Islands, India.

PubMed

Jha, Dilip Kumar; Vinithkumar, Nambali Valsalan; Sahu, Biraja Kumar; Dheenan, Palaiya Sukumaran; Das, Apurba Kumar; Begum, Mehmuna; Devi, Marimuthu Prashanthi; Kirubagaran, Ramalingam

2015-07-15

Chidiyatappu Bay is one of the least disturbed marine environments of Andaman & Nicobar Islands, the union territory of India. Oceanic flushing from southeast and northwest direction is prevalent in this bay. Further, anthropogenic activity is minimal in the adjoining environment. Considering the pristine nature of this bay, seawater samples collected from 12 sampling stations covering three seasons were analyzed. Principal Component Analysis (PCA) revealed 69.9% of total variance and exhibited strong factor loading for nitrite, chlorophyll a and phaeophytin. In addition, analysis of variance (ANOVA-one way), regression analysis, box-whisker plots and Geographical Information System based hot spot analysis further simplified and supported multivariate results. The results obtained are important to establish reference conditions for comparative study with other similar ecosystems in the region. Copyright © 2015 Elsevier Ltd. All rights reserved.
Does Investor Ownership of Nursing Homes Compromise the Quality of Care?

PubMed Central

Harrington, Charlene; Woolhandler, Steffie; Mullan, Joseph; Carrillo, Helen; Himmelstein, David U.

2001-01-01

Objectives. Two thirds of nursing homes are investor owned. This study examined whether investor ownership affects quality. Methods. We analyzed 1998 data from state inspections of 13 693 nursing facilities. We used a multivariate model and controlled for case mix, facility characteristics, and location. Results. Investor-owned facilities averaged 5.89 deficiencies per home, 46.5% higher than nonprofit facilities and 43.0% higher than public facilities. In multivariate analysis, investor ownership predicted 0.679 additional deficiencies per home; chain ownership predicted an additional 0.633 deficiencies. Nurse staffing was lower at investor-owned nursing homes. Conclusions. Investor-owned nursing homes provide worse care and less nursing care than do not-for-profit or public homes. PMID:11527781
Use of direct gradient analysis to uncover biological hypotheses in 16s survey data and beyond.

PubMed

Erb-Downward, John R; Sadighi Akha, Amir A; Wang, Juan; Shen, Ning; He, Bei; Martinez, Fernando J; Gyetko, Margaret R; Curtis, Jeffrey L; Huffnagle, Gary B

2012-01-01

This study investigated the use of direct gradient analysis of bacterial 16S pyrosequencing surveys to identify relevant bacterial community signals in the midst of a "noisy" background, and to facilitate hypothesis-testing both within and beyond the realm of ecological surveys. The results, utilizing 3 different real world data sets, demonstrate the utility of adding direct gradient analysis to any analysis that draws conclusions from indirect methods such as Principal Component Analysis (PCA) and Principal Coordinates Analysis (PCoA). Direct gradient analysis produces testable models, and can identify significant patterns in the midst of noisy data. Additionally, we demonstrate that direct gradient analysis can be used with other kinds of multivariate data sets, such as flow cytometric data, to identify differentially expressed populations. The results of this study demonstrate the utility of direct gradient analysis in microbial ecology and in other areas of research where large multivariate data sets are involved.
Extending Inferential Group Analysis in Type 2 Diabetic Patients with Multivariate GLM Implemented in SPM8.

PubMed

Ferreira, Fábio S; Pereira, João M S; Duarte, João V; Castelo-Branco, Miguel

2017-01-01

Although voxel based morphometry studies are still the standard for analyzing brain structure, their dependence on massive univariate inferential methods is a limiting factor. A better understanding of brain pathologies can be achieved by applying inferential multivariate methods, which allow the study of multiple dependent variables, e.g. different imaging modalities of the same subject. Given the widespread use of SPM software in the brain imaging community, the main aim of this work is the implementation of massive multivariate inferential analysis as a toolbox in this software package. applied to the use of T1 and T2 structural data from diabetic patients and controls. This implementation was compared with the traditional ANCOVA in SPM and a similar multivariate GLM toolbox (MRM). We implemented the new toolbox and tested it by investigating brain alterations on a cohort of twenty-eight type 2 diabetes patients and twenty-six matched healthy controls, using information from both T1 and T2 weighted structural MRI scans, both separately - using standard univariate VBM - and simultaneously, with multivariate analyses. Univariate VBM replicated predominantly bilateral changes in basal ganglia and insular regions in type 2 diabetes patients. On the other hand, multivariate analyses replicated key findings of univariate results, while also revealing the thalami as additional foci of pathology. While the presented algorithm must be further optimized, the proposed toolbox is the first implementation of multivariate statistics in SPM8 as a user-friendly toolbox, which shows great potential and is ready to be validated in other clinical cohorts and modalities.
Extending Inferential Group Analysis in Type 2 Diabetic Patients with Multivariate GLM Implemented in SPM8

PubMed Central

Ferreira, Fábio S.; Pereira, João M.S.; Duarte, João V.; Castelo-Branco, Miguel

2017-01-01

Background: Although voxel based morphometry studies are still the standard for analyzing brain structure, their dependence on massive univariate inferential methods is a limiting factor. A better understanding of brain pathologies can be achieved by applying inferential multivariate methods, which allow the study of multiple dependent variables, e.g. different imaging modalities of the same subject. Objective: Given the widespread use of SPM software in the brain imaging community, the main aim of this work is the implementation of massive multivariate inferential analysis as a toolbox in this software package. applied to the use of T1 and T2 structural data from diabetic patients and controls. This implementation was compared with the traditional ANCOVA in SPM and a similar multivariate GLM toolbox (MRM). Method: We implemented the new toolbox and tested it by investigating brain alterations on a cohort of twenty-eight type 2 diabetes patients and twenty-six matched healthy controls, using information from both T1 and T2 weighted structural MRI scans, both separately – using standard univariate VBM - and simultaneously, with multivariate analyses. Results: Univariate VBM replicated predominantly bilateral changes in basal ganglia and insular regions in type 2 diabetes patients. On the other hand, multivariate analyses replicated key findings of univariate results, while also revealing the thalami as additional foci of pathology. Conclusion: While the presented algorithm must be further optimized, the proposed toolbox is the first implementation of multivariate statistics in SPM8 as a user-friendly toolbox, which shows great potential and is ready to be validated in other clinical cohorts and modalities. PMID:28761571
Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes.

PubMed

Achana, Felix A; Cooper, Nicola J; Bujkiewicz, Sylwia; Hubbard, Stephanie J; Kendrick, Denise; Jones, David R; Sutton, Alex J

2014-07-21

Network meta-analysis (NMA) enables simultaneous comparison of multiple treatments while preserving randomisation. When summarising evidence to inform an economic evaluation, it is important that the analysis accurately reflects the dependency structure within the data, as correlations between outcomes may have implication for estimating the net benefit associated with treatment. A multivariate NMA offers a framework for evaluating multiple treatments across multiple outcome measures while accounting for the correlation structure between outcomes. The standard NMA model is extended to multiple outcome settings in two stages. In the first stage, information is borrowed across outcomes as well across studies through modelling the within-study and between-study correlation structure. In the second stage, we make use of the additional assumption that intervention effects are exchangeable between outcomes to predict effect estimates for all outcomes, including effect estimates on outcomes where evidence is either sparse or the treatment had not been considered by any one of the studies included in the analysis. We apply the methods to binary outcome data from a systematic review evaluating the effectiveness of nine home safety interventions on uptake of three poisoning prevention practices (safe storage of medicines, safe storage of other household products, and possession of poison centre control telephone number) in households with children. Analyses are conducted in WinBUGS using Markov Chain Monte Carlo (MCMC) simulations. Univariate and the first stage multivariate models produced broadly similar point estimates of intervention effects but the uncertainty around the multivariate estimates varied depending on the prior distribution specified for the between-study covariance structure. The second stage multivariate analyses produced more precise effect estimates while enabling intervention effects to be predicted for all outcomes, including intervention effects on outcomes not directly considered by the studies included in the analysis. Accounting for the dependency between outcomes in a multivariate meta-analysis may or may not improve the precision of effect estimates from a network meta-analysis compared to analysing each outcome separately.
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.

PubMed

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.
Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging.

PubMed

Falahati, Farshad; Westman, Eric; Simmons, Andrew

2014-01-01

Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Advances in medical imaging and medical image analysis have provided a means to generate and extract valuable neuroimaging information. Automatic classification techniques provide tools to analyze this information and observe inherent disease-related patterns in the data. In particular, these classifiers have been used to discriminate AD patients from healthy control subjects and to predict conversion from mild cognitive impairment to AD. In this paper, recent studies are reviewed that have used machine learning and multivariate analysis in the field of AD research. The main focus is on studies that used structural magnetic resonance imaging (MRI), but studies that included positron emission tomography and cerebrospinal fluid biomarkers in addition to MRI are also considered. A wide variety of materials and methods has been employed in different studies, resulting in a range of different outcomes. Influential factors such as classifiers, feature extraction algorithms, feature selection methods, validation approaches, and cohort properties are reviewed, as well as key MRI-based and multi-modal based studies. Current and future trends are discussed.
Site 765: Sedimentology

USGS Publications Warehouse

,

1990-01-01

Various techniques were used to decipher the sedimentation history of Site 765, including Markov chain analysis of facies transitions, XRD analysis of clay and other minerals, and multivariate analysis of smear-slide data, in addition to the standard descriptive procedures employed by the shipboard sedimentologist. This chapter presents brief summaries of methodology and major findings of these three techniques, a summary of the sedimentation history, and a discussion of trends in sedimentation through time.
Bringing Reading-to-Write and Writing-Only Assessment Tasks Together: A Generalizability Analysis

ERIC Educational Resources Information Center

Gebril, Atta

2010-01-01

Integrated tasks are currently employed in a number of L2 exams since they are perceived as an addition to the writing-only task type. Given this trend, the current study investigates composite score generalizability of both reading-to-write and writing-only tasks. For this purpose, a multivariate generalizability analysis is used to investigate…
Multivariate approaches for stability control of the olive oil reference materials for sensory analysis - part II: applications.

PubMed

Valverde-Som, Lucia; Ruiz-Samblás, Cristina; Rodríguez-García, Francisco P; Cuadros-Rodríguez, Luis

2018-02-09

The organoleptic quality of virgin olive oil depends on positive and negative sensory attributes. These attributes are related to volatile organic compounds and phenolic compounds that represent the aroma and taste (flavour) of the virgin olive oil. The flavour is the characteristic that can be measured by a taster panel. However, as for any analytical measuring device, the tasters, individually, and the panel, as a whole, should be harmonized and validated and proper olive oil standards are needed. In the present study, multivariate approaches are put into practice in addition to the rules to build a multivariate control chart from chromatographic volatile fingerprinting and chemometrics. Fingerprinting techniques provide analytical information without identify and quantify the analytes. This methodology is used to monitor the stability of sensory reference materials. The similarity indices have been calculated to build multivariate control chart with two olive oils certified reference materials that have been used as examples to monitor their stabilities. This methodology with chromatographic data could be applied in parallel with the 'panel test' sensory method to reduce the work of sensory analysis. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.
Multivariate analysis of toxicity experimental results of environmental endpoints. (FutureToxII)

EPA Science Inventory

The toxicity of hundreds of chemicals have been assessed in laboratory animal studies through EPA chemical regulation and toxicological research. Currently, over 5000 laboratory animal toxicity studies have been collected in the Toxicity Reference Database (ToxRefDB). In addition...
Linear, multivariable robust control with a mu perspective

NASA Technical Reports Server (NTRS)

Packard, Andy; Doyle, John; Balas, Gary

1993-01-01

The structured singular value is a linear algebra tool developed to study a particular class of matrix perturbation problems arising in robust feedback control of multivariable systems. These perturbations are called linear fractional, and are a natural way to model many types of uncertainty in linear systems, including state-space parameter uncertainty, multiplicative and additive unmodeled dynamics uncertainty, and coprime factor and gap metric uncertainty. The structured singular value theory provides a natural extension of classical SISO robustness measures and concepts to MIMO systems. The structured singular value analysis, coupled with approximate synthesis methods, make it possible to study the tradeoff between performance and uncertainty that occurs in all feedback systems. In MIMO systems, the complexity of the spatial interactions in the loop gains make it difficult to heuristically quantify the tradeoffs that must occur. This paper examines the role played by the structured singular value (and its computable bounds) in answering these questions, as well as its role in the general robust, multivariable control analysis and design problem.
Optimization of Interior Permanent Magnet Motor by Quality Engineering and Multivariate Analysis

NASA Astrophysics Data System (ADS)

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

NASA Technical Reports Server (NTRS)

Djorgovski, George

1993-01-01

The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multiparameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resource.
Multivariate statistical analysis software technologies for astrophysical research involving large data bases

NASA Technical Reports Server (NTRS)

Djorgovski, Stanislav

1992-01-01

The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multi parameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resources.
The Statistical Consulting Center for Astronomy (SCCA)

NASA Technical Reports Server (NTRS)

Akritas, Michael

2001-01-01

The process by which raw astronomical data acquisition is transformed into scientifically meaningful results and interpretation typically involves many statistical steps. Traditional astronomy limits itself to a narrow range of old and familiar statistical methods: means and standard deviations; least-squares methods like chi(sup 2) minimization; and simple nonparametric procedures such as the Kolmogorov-Smirnov tests. These tools are often inadequate for the complex problems and datasets under investigations, and recent years have witnessed an increased usage of maximum-likelihood, survival analysis, multivariate analysis, wavelet and advanced time-series methods. The Statistical Consulting Center for Astronomy (SCCA) assisted astronomers with the use of sophisticated tools, and to match these tools with specific problems. The SCCA operated with two professors of statistics and a professor of astronomy working together. Questions were received by e-mail, and were discussed in detail with the questioner. Summaries of those questions and answers leading to new approaches were posted on the Web (www.state.psu.edu/ mga/SCCA). In addition to serving individual astronomers, the SCCA established a Web site for general use that provides hypertext links to selected on-line public-domain statistical software and services. The StatCodes site (www.astro.psu.edu/statcodes) provides over 200 links in the areas of: Bayesian statistics; censored and truncated data; correlation and regression, density estimation and smoothing, general statistics packages and information; image analysis; interactive Web tools; multivariate analysis; multivariate clustering and classification; nonparametric analysis; software written by astronomers; spatial statistics; statistical distributions; time series analysis; and visualization tools. StatCodes has received a remarkable high and constant hit rate of 250 hits/week (over 10,000/year) since its inception in mid-1997. It is of interest to scientists both within and outside of astronomy. The most popular sections are multivariate techniques, image analysis, and time series analysis. Hundreds of copies of the ASURV, SLOPES and CENS-TAU codes developed by SCCA scientists were also downloaded from the StatCodes site. In addition to formal SCCA duties, SCCA scientists continued a variety of related activities in astrostatistics, including refereeing of statistically oriented papers submitted to the Astrophysical Journal, talks in meetings including Feigelson's talk to science journalists entitled "The reemergence of astrostatistics" at the American Association for the Advancement of Science meeting, and published papers of astrostatistical content.
Applications of modern statistical methods to analysis of data in physical science

NASA Astrophysics Data System (ADS)

Wicker, James Eric

Modern methods of statistical and computational analysis offer solutions to dilemmas confronting researchers in physical science. Although the ideas behind modern statistical and computational analysis methods were originally introduced in the 1970's, most scientists still rely on methods written during the early era of computing. These researchers, who analyze increasingly voluminous and multivariate data sets, need modern analysis methods to extract the best results from their studies. The first section of this work showcases applications of modern linear regression. Since the 1960's, many researchers in spectroscopy have used classical stepwise regression techniques to derive molecular constants. However, problems with thresholds of entry and exit for model variables plagues this analysis method. Other criticisms of this kind of stepwise procedure include its inefficient searching method, the order in which variables enter or leave the model and problems with overfitting data. We implement an information scoring technique that overcomes the assumptions inherent in the stepwise regression process to calculate molecular model parameters. We believe that this kind of information based model evaluation can be applied to more general analysis situations in physical science. The second section proposes new methods of multivariate cluster analysis. The K-means algorithm and the EM algorithm, introduced in the 1960's and 1970's respectively, formed the basis of multivariate cluster analysis methodology for many years. However, several shortcomings of these methods include strong dependence on initial seed values and inaccurate results when the data seriously depart from hypersphericity. We propose new cluster analysis methods based on genetic algorithms that overcomes the strong dependence on initial seed values. In addition, we propose a generalization of the Genetic K-means algorithm which can accurately identify clusters with complex hyperellipsoidal covariance structures. We then use this new algorithm in a genetic algorithm based Expectation-Maximization process that can accurately calculate parameters describing complex clusters in a mixture model routine. Using the accuracy of this GEM algorithm, we assign information scores to cluster calculations in order to best identify the number of mixture components in a multivariate data set. We will showcase how these algorithms can be used to process multivariate data from astronomical observations.
Multivariate analysis of the impacts of the turbine fuel JP-4 in a microcosm toxicity test with implications for the evaluation of ecosystem dynamics and risk assessment.

PubMed

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

1993-12-01

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

PubMed Central

Gayowski, Timothy; Marino, Ignazio R.; Singh, Nina; Doyle, Howard; Wagener, Marilyn; Fung, John J.; Starzl, Thomas E.

2010-01-01

Background One of the most controversial areas in patient selection and donor allocation is the high-risk patient. Risk factors for mortality and major infectious morbidity were prospectively analyzed in consecutive United States veterans undergoing liver transplantation under primary tacrolimus-based immunosuppression. Methods Twenty-eight pre-liver transplant, operative, and posttransplant risk factors were examined univariately and multivariately in 140 consecutive liver transplants in 130 veterans (98% male; mean age, 47.3 years). Results Eighty-two percent of the patients had post-necrotic cirrhosis due to viral hepatitis or ethanol (20% ethanol alone), and only 12% had cholestatic liver disease. Ninety-eight percent of the patients were hospitalized at the time of transplantation (66% United Network for Organ Sharing [UNOS] 2, 32% UNOS 1). Major bacterial infection, posttransplant dialysis, additional immunosuppression, readmission to intensive care unit (P=0.0001 for all), major fungal infection, posttransplant abdominal surgery, posttransplant intensive care unit stay length of stay (P<0.005 for all), donor age, pretransplant dialysis, and creatinine (P<0.05 for all) were significantly associated with mortality by univariate analysis. Underlying liver disease, cytomegalovirus infection and disease, portal vein thrombosis, UNOS status, Childs-Pugh score, patient age, pretransplant bilirubin, ischemia time, and operative blood loss were not significant predictors of mortality. Patients with hepatitis C (HCV) and recurrent HCV had a trend towards higher mortality (P=0.18). By multivariate analysis, donor age, any major infection, additional immunosuppression, post-transplant dialysis, and subsequent transplantation were significant independent predictors of mortality (P<0.05). Major infectious morbidity was associated with HCV recurrence (P=0.003), posttransplant dialysis (P=0.001), pretransplant creatinine, donor age, median blood loss, intensive care unit length of stay, additional immunosuppression, and biopsy-proven rejection (P<0.05 for all). By multivariate analysis, intensive care unit length of stay and additional immunosuppression were significant independent predictors of infectious morbidity (P<0.03). HCV recurrence was of borderline significance (P=0.07). Conclusions Biologic and physiologic parameters appear to be more powerful predictors of mortality and morbidity after liver transplantation. Both donor and recipient variables need to be considered for early and late outcome analysis and risk assessment modeling. PMID:9500623

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

PubMed

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

2013-10-15

Consumer demand for pomegranate juice has considerably grown, during the last years, for its potential health benefits. Since it is an expensive functional food, cheaper fruit juices addition (i.e., grape and apple juices) or its simple dilution, or polyphenols subtraction are deceptively used. At present, time-consuming analyses are used to control the quality of this product. Furthermore these analyses are expensive and require well-trained analysts. Thus, the purpose of this study was to propose a high-speed and easy-to-use shortcut. Based on UV-VIS spectroscopy and chemometrics, a screening method is proposed to quickly screening some common fillers of pomegranate juice that could decrease the antiradical scavenging capacity of pure products. The analytical method was applied to laboratory prepared juices, to commercial juices and to representative experimental mixtures at different levels of water and filler juices. The outcomes were evaluated by means of multivariate exploratory analysis. The results indicate that the proposed strategy can be a useful screening tool to assess addition of filler juices and water to pomegranate juices. Copyright © 2012 Elsevier Ltd. All rights reserved.
Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials.

PubMed

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

2013-05-01

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

PubMed Central

Kaya, Koray D.; Karakülah, Gökhan; Yakıcıer, Cengiz M.; Acar, Aybar C.; Konu, Özlen

2011-01-01

microRNA expression and sequence analysis database (http://konulab.fen.bilkent.edu.tr/mirna/) (mESAdb) is a regularly updated database for the multivariate analysis of sequences and expression of microRNAs from multiple taxa. mESAdb is modular and has a user interface implemented in PHP and JavaScript and coupled with statistical analysis and visualization packages written for the R language. The database primarily comprises mature microRNA sequences and their target data, along with selected human, mouse and zebrafish expression data sets. mESAdb analysis modules allow (i) mining of microRNA expression data sets for subsets of microRNAs selected manually or by motif; (ii) pair-wise multivariate analysis of expression data sets within and between taxa; and (iii) association of microRNA subsets with annotation databases, HUGE Navigator, KEGG and GO. The use of existing and customized R packages facilitates future addition of data sets and analysis tools. Furthermore, the ability to upload and analyze user-specified data sets makes mESAdb an interactive and expandable analysis tool for microRNA sequence and expression data. PMID:21177657
mESAdb: microRNA expression and sequence analysis database.

PubMed

Kaya, Koray D; Karakülah, Gökhan; Yakicier, Cengiz M; Acar, Aybar C; Konu, Ozlen

2011-01-01

microRNA expression and sequence analysis database (http://konulab.fen.bilkent.edu.tr/mirna/) (mESAdb) is a regularly updated database for the multivariate analysis of sequences and expression of microRNAs from multiple taxa. mESAdb is modular and has a user interface implemented in PHP and JavaScript and coupled with statistical analysis and visualization packages written for the R language. The database primarily comprises mature microRNA sequences and their target data, along with selected human, mouse and zebrafish expression data sets. mESAdb analysis modules allow (i) mining of microRNA expression data sets for subsets of microRNAs selected manually or by motif; (ii) pair-wise multivariate analysis of expression data sets within and between taxa; and (iii) association of microRNA subsets with annotation databases, HUGE Navigator, KEGG and GO. The use of existing and customized R packages facilitates future addition of data sets and analysis tools. Furthermore, the ability to upload and analyze user-specified data sets makes mESAdb an interactive and expandable analysis tool for microRNA sequence and expression data.
Factors affecting survival outcomes of patients with non-metastatic Ewing's sarcoma family tumors in the spine: a retrospective analysis of 63 patients in a single center.

PubMed

Wan, Wei; Lou, Yan; Hu, Zhiqi; Wang, Ting; Li, Jinsong; Tang, Yu; Wu, Zhipeng; Xu, Leqin; Yang, Xinghai; Song, Dianwen; Xiao, Jianru

2017-01-01

Little information has been published in the literature regarding survival outcomes of patients with Ewing's sarcoma family tumors (ESFTs) of the spine. The purpose of this study is to explore factors that may affect the prognosis of patients with non-metastatic spinal ESFTs. A retrospective analysis of survival outcomes was performed in patients with non-metastatic spinal ESFTs. Univariate and multivariate analyses were employed to identify prognostic factors for recurrence and survival. Recurrence-free survival (RFS) and overall survival (OS) were defined as the date of surgery to the date of local relapse and death. Kaplan-Meier methods were applied to estimate RFS and OS. Log-rank test was used to analyze single factors for RFS and OS. Factors with p values ≤0.1 were subjected to multivariate analysis. A total of 63 patients with non-metastatic spinal ESFTs were included in this study. The mean follow-up period was 35.1 months (range 1-155). Postoperative recurrence was detected in 25 patients, and distant metastasis and death occurred in 22 and 36 patients respectively. The result of multivariate analysis suggested that age older than 25 years and neoadjuvant chemotherapy were favorable independent prognostic factors for RFS and OS. In addition, total en-bloc resection, postoperative chemotherapy, radiotherapy and non-distant metastasis were favorable independent prognostic factors for OS. Age older than 25 years and neoadjuvant chemotherapy are favorable prognostic factors for both RFS and OS. In addition, total en-bloc resection, postoperative chemotherapy, radiotherapy and non-distant metastasis are closely associated with favorable survival.
Trend Detection and Bivariate Frequency Analysis for Nonstrationary Rainfall Data

NASA Astrophysics Data System (ADS)

Joo, K.; Kim, H.; Shin, J. Y.; Heo, J. H.

2017-12-01

Multivariate frequency analysis has been developing for hydro-meteorological data such as rainfall, flood, and drought. Particularly, the copula has been used as a useful tool for multivariate probability model which has no limitation on deciding marginal distributions. The time-series rainfall data can be characterized to rainfall event by inter-event time definition (IETD) and each rainfall event has a rainfall depth and rainfall duration. In addition, nonstationarity in rainfall event has been studied recently due to climate change and trend detection of rainfall event is important to determine the data has nonstationarity or not. With the rainfall depth and duration of a rainfall event, trend detection and nonstationary bivariate frequency analysis has performed in this study. 62 stations from Korea Meteorological Association (KMA) over 30 years of hourly recorded data used in this study and the suitability of nonstationary copula for rainfall event has examined by the goodness-of-fit test.
Simultaneous quantitative analysis of olmesartan, amlodipine and hydrochlorothiazide in their combined dosage form utilizing classical and alternating least squares based chemometric methods.

PubMed

Darwish, Hany W; Bakheit, Ahmed H; Abdelhameed, Ali S

2016-03-01

Simultaneous spectrophotometric analysis of a multi-component dosage form of olmesartan, amlodipine and hydrochlorothiazide used for the treatment of hypertension has been carried out using various chemometric methods. Multivariate calibration methods include classical least squares (CLS) executed by net analyte processing (NAP-CLS), orthogonal signal correction (OSC-CLS) and direct orthogonal signal correction (DOSC-CLS) in addition to multivariate curve resolution-alternating least squares (MCR-ALS). Results demonstrated the efficiency of the proposed methods as quantitative tools of analysis as well as their qualitative capability. The three analytes were determined precisely using the aforementioned methods in an external data set and in a dosage form after optimization of experimental conditions. Finally, the efficiency of the models was validated via comparison with the partial least squares (PLS) method in terms of accuracy and precision.
Comparative Study of Elemental Nutrients in Organic and Conventional Vegetables Using Laser-Induced Breakdown Spectroscopy (LIBS).

PubMed

Bhatt, Chet R; Alfarraj, Bader; Ghany, Charles T; Yueh, Fang Y; Singh, Jagdish P

2017-04-01

In this study, the laser-induced breakdown spectroscopy (LIBS) technique was used to identify and compare the presence of major nutrient elements in organic and conventional vegetables. Different parts of cauliflowers and broccolis were used as working samples. Laser-induced breakdown spectra from these samples were acquired at optimum values of laser energy, gate delay, and gate width. Both univariate and multivariate analyses were performed for the comparison of these organic and conventional vegetable flowers. Principal component analysis (PCA) was taken into account for multivariate analysis while for univariate analysis, the intensity of selected atomic lines of different elements and their intensity ratio with some reference lines of organic cauliflower and broccoli samples were compared with those of conventional ones. In addition, different parts of the cauliflower and broccoli were compared in terms of intensity and intensity ratio of elemental lines.
Application of reflectance spectroscopies (FTIR-ATR & FT-NIR) coupled with multivariate methods for robust in vivo detection of begomovirus infection in papaya leaves

NASA Astrophysics Data System (ADS)

Haq, Quazi M. I.; Mabood, Fazal; Naureen, Zakira; Al-Harrasi, Ahmed; Gilani, Sayed A.; Hussain, Javid; Jabeen, Farah; Khan, Ajmal; Al-Sabari, Ruqaya S. M.; Al-khanbashi, Fatema H. S.; Al-Fahdi, Amira A. M.; Al-Zaabi, Ahoud K. A.; Al-Shuraiqi, Fatma A. M.; Al-Bahaisi, Iman M.

2018-06-01

Nucleic acid & serology based methods have revolutionized plant disease detection, however, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic infection, in addition, they need at least 1-2 days for sample harvesting, processing, and analysis. In this study, two reflectance spectroscopies i.e. Near Infrared reflectance spectroscopy (NIR) and Fourier-Transform-Infrared spectroscopy with Attenuated Total Reflection (FT-IR, ATR) coupled with multivariate exploratory methods like Principle Component Analysis (PCA) and Partial least square discriminant analysis (PLS-DA) have been deployed to detect begomovirus infection in papaya leaves. The application of those techniques demonstrates that they are very useful for robust in vivo detection of plant begomovirus infection. These methods are simple, sensitive, reproducible, precise, and do not require any lengthy samples preparation procedures.
Assessment of need of patients with schizophrenia: a study in Vellore, India.

PubMed

Ernest, Sharmila; Nagarajan, Guru; Jacob, K S

2013-12-01

and aims: There is a dearth of studies investigating the prevalence and factors associated with unmet needs in people with schizophrenia from low- and middle-income countries. We aimed to study prevalence and risk factors for unmet need. A case-control study design was employed. One hundred and one (101) consecutive patients attending a psychiatric hospital were assessed using Camberwell Assessment of Need Short version (CANSAS) and Positive and Negative Syndrome Scale (PANSS). Multivariate analysis was employed to adjust for confounders. The majority of patients had many unmet needs. These unmet needs were significantly associated with lower education, poverty and persistent psychopathology on multivariate analysis. Unmet needs are associated with poverty, lower education and persistent psychopathology. There is a need to manage unmet needs, in addition to addressing psychopathology and poverty.
Simple and Multivariate Relationships Between Spiritual Intelligence with General Health and Happiness.

PubMed

Amirian, Mohammad-Elyas; Fazilat-Pour, Masoud

2016-08-01

The present study examined simple and multivariate relationships of spiritual intelligence with general health and happiness. The employed method was descriptive and correlational. King's Spiritual Quotient scales, GHQ-28 and Oxford Happiness Inventory, are filled out by a sample consisted of 384 students, which were selected using stratified random sampling from the students of Shahid Bahonar University of Kerman. Data are subjected to descriptive and inferential statistics including correlations and multivariate regressions. Bivariate correlations support positive and significant predictive value of spiritual intelligence toward general health and happiness. Further analysis showed that among the Spiritual Intelligence' subscales, Existential Critical Thinking Predicted General Health and Happiness, reversely. In addition, happiness was positively predicted by generation of personal meaning and transcendental awareness. The findings are discussed in line with the previous studies and the relevant theoretical background.
Method for factor analysis of GC/MS data

DOEpatents

Van Benthem, Mark H; Kotula, Paul G; Keenan, Michael R

2012-09-11

The method of the present invention provides a fast, robust, and automated multivariate statistical analysis of gas chromatography/mass spectroscopy (GC/MS) data sets. The method can involve systematic elimination of undesired, saturated peak masses to yield data that follow a linear, additive model. The cleaned data can then be subjected to a combination of PCA and orthogonal factor rotation followed by refinement with MCR-ALS to yield highly interpretable results.
[Risk factors for anorexia in children].

PubMed

Liu, Wei-Xiao; Lang, Jun-Feng; Zhang, Qin-Feng

2016-11-01

To investigate the risk factors for anorexia in children, and to reduce the prevalence of anorexia in children. A questionnaire survey and a case-control study were used to collect the general information of 150 children with anorexia (case group) and 150 normal children (control group). Univariate analysis and multivariate logistic stepwise regression analysis were performed to identify the risk factors for anorexia in children. The results of the univariate analysis showed significant differences between the case and control groups in the age in months when supplementary food were added, feeding pattern, whether they liked meat, vegetables and salty food, whether they often took snacks and beverages, whether they liked to play while eating, and whether their parents asked them to eat food on time (P<0.05). The results of the multivariate logistic regression analysis showed that late addition of supplementary food (OR=5.408), high frequency of taking snacks and/or drinks (OR=11.813), and eating while playing (OR=6.654) were major risk factors for anorexia in children. Liking of meat (OR=0.093) and vegetables (OR=0.272) and eating on time required by parents (OR=0.079) were protective factors against anorexia in children. Timely addition of supplementary food, a proper diet, and development of children's proper eating and living habits can reduce the incidence of anorexia in children.
Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes

PubMed Central

2014-01-01

Background Network meta-analysis (NMA) enables simultaneous comparison of multiple treatments while preserving randomisation. When summarising evidence to inform an economic evaluation, it is important that the analysis accurately reflects the dependency structure within the data, as correlations between outcomes may have implication for estimating the net benefit associated with treatment. A multivariate NMA offers a framework for evaluating multiple treatments across multiple outcome measures while accounting for the correlation structure between outcomes. Methods The standard NMA model is extended to multiple outcome settings in two stages. In the first stage, information is borrowed across outcomes as well across studies through modelling the within-study and between-study correlation structure. In the second stage, we make use of the additional assumption that intervention effects are exchangeable between outcomes to predict effect estimates for all outcomes, including effect estimates on outcomes where evidence is either sparse or the treatment had not been considered by any one of the studies included in the analysis. We apply the methods to binary outcome data from a systematic review evaluating the effectiveness of nine home safety interventions on uptake of three poisoning prevention practices (safe storage of medicines, safe storage of other household products, and possession of poison centre control telephone number) in households with children. Analyses are conducted in WinBUGS using Markov Chain Monte Carlo (MCMC) simulations. Results Univariate and the first stage multivariate models produced broadly similar point estimates of intervention effects but the uncertainty around the multivariate estimates varied depending on the prior distribution specified for the between-study covariance structure. The second stage multivariate analyses produced more precise effect estimates while enabling intervention effects to be predicted for all outcomes, including intervention effects on outcomes not directly considered by the studies included in the analysis. Conclusions Accounting for the dependency between outcomes in a multivariate meta-analysis may or may not improve the precision of effect estimates from a network meta-analysis compared to analysing each outcome separately. PMID:25047164
Identification of trace additives in polymer materials by attenuated total reflection Fourier transform infrared mapping coupled with multivariate curve resolution

NASA Astrophysics Data System (ADS)

Li, Qian; Tang, Yongjiao; Yan, Zhiwei; Zhang, Pudun

2017-06-01

Although multivariate curve resolution (MCR) has been applied to the analysis of Fourier transform infrared (FTIR) imaging, it is still problematic to determine the number of components. The reported methods at present tend to cause the components of low concentration missed. In this paper a new idea was proposed to resolve this problem. First, MCR calculation was repeated by increasing the number of components sequentially, then each retrieved pure spectrum of as-resulted MCR component was directly compared with a real-world pixel spectrum of the local high concentration in the corresponding MCR map. One component was affirmed only if the characteristic bands of the MCR component had been included in its pixel spectrum. This idea was applied to attenuated total reflection (ATR)/FTIR mapping for identifying the trace additives in blind polymer materials and satisfactory results were acquired. The successful demonstration of this novel approach opens up new possibilities for analyzing additives in polymer materials.
Multivariate pattern analysis for MEG: A comparison of dissimilarity measures.

PubMed

Guggenmos, Matthias; Sterzer, Philipp; Cichy, Radoslaw Martin

2018-06-01

Multivariate pattern analysis (MVPA) methods such as decoding and representational similarity analysis (RSA) are growing rapidly in popularity for the analysis of magnetoencephalography (MEG) data. However, little is known about the relative performance and characteristics of the specific dissimilarity measures used to describe differences between evoked activation patterns. Here we used a multisession MEG data set to qualitatively characterize a range of dissimilarity measures and to quantitatively compare them with respect to decoding accuracy (for decoding) and between-session reliability of representational dissimilarity matrices (for RSA). We tested dissimilarity measures from a range of classifiers (Linear Discriminant Analysis - LDA, Support Vector Machine - SVM, Weighted Robust Distance - WeiRD, Gaussian Naïve Bayes - GNB) and distances (Euclidean distance, Pearson correlation). In addition, we evaluated three key processing choices: 1) preprocessing (noise normalisation, removal of the pattern mean), 2) weighting decoding accuracies by decision values, and 3) computing distances in three different partitioning schemes (non-cross-validated, cross-validated, within-class-corrected). Four main conclusions emerged from our results. First, appropriate multivariate noise normalization substantially improved decoding accuracies and the reliability of dissimilarity measures. Second, LDA, SVM and WeiRD yielded high peak decoding accuracies and nearly identical time courses. Third, while using decoding accuracies for RSA was markedly less reliable than continuous distances, this disadvantage was ameliorated by decision-value-weighting of decoding accuracies. Fourth, the cross-validated Euclidean distance provided unbiased distance estimates and highly replicable representational dissimilarity matrices. Overall, we strongly advise the use of multivariate noise normalisation as a general preprocessing step, recommend LDA, SVM and WeiRD as classifiers for decoding and highlight the cross-validated Euclidean distance as a reliable and unbiased default choice for RSA. Copyright © 2018 Elsevier Inc. All rights reserved.
A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses.

PubMed

Buttigieg, Pier Luigi; Ramette, Alban

2014-12-01

The application of multivariate statistical analyses has become a consistent feature in microbial ecology. However, many microbial ecologists are still in the process of developing a deep understanding of these methods and appreciating their limitations. As a consequence, staying abreast of progress and debate in this arena poses an additional challenge to many microbial ecologists. To address these issues, we present the GUide to STatistical Analysis in Microbial Ecology (GUSTA ME): a dynamic, web-based resource providing accessible descriptions of numerous multivariate techniques relevant to microbial ecologists. A combination of interactive elements allows users to discover and navigate between methods relevant to their needs and examine how they have been used by others in the field. We have designed GUSTA ME to become a community-led and -curated service, which we hope will provide a common reference and forum to discuss and disseminate analytical techniques relevant to the microbial ecology community. © 2014 The Authors. FEMS Microbiology Ecology published by John Wiley & Sons Ltd on behalf of Federation of European Microbiological Societies.
Multivariate analysis in thoracic research.

PubMed

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

2015-03-01

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

ERIC Educational Resources Information Center

McCormick, Alexander C.; Staklis, Sandra

This study examined the addition and expansion of graduate programs at primarily undergraduate colleges. The primary approach of the study was quantitative, consisting of descriptive and multivariate analysis of master's degree programs at colleges that were classified in 1994 as Baccalaureate Colleges. Data came from the 1994 and 2000 Carnegie…
The Irvine, Beatties, and Bresnahan (IBB) Forelimb Recovery Scale: An Assessment of Reliability and Validity

PubMed Central

Irvine, Karen-Amanda; Ferguson, Adam R.; Mitchell, Kathleen D.; Beattie, Stephanie B.; Lin, Amity; Stuck, Ellen D.; Huie, J. Russell; Nielson, Jessica L.; Talbott, Jason F.; Inoue, Tomoo; Beattie, Michael S.; Bresnahan, Jacqueline C.

2014-01-01

The IBB scale is a recently developed forelimb scale for the assessment of fine control of the forelimb and digits after cervical spinal cord injury [SCI; (1)]. The present paper describes the assessment of inter-rater reliability and face, concurrent and construct validity of this scale following SCI. It demonstrates that the IBB is a reliable and valid scale that is sensitive to severity of SCI and to recovery over time. In addition, the IBB correlates with other outcome measures and is highly predictive of biological measures of tissue pathology. Multivariate analysis using principal component analysis (PCA) demonstrates that the IBB is highly predictive of the syndromic outcome after SCI (2), and is among the best predictors of bio-behavioral function, based on strong construct validity. Altogether, the data suggest that the IBB, especially in concert with other measures, is a reliable and valid tool for assessing neurological deficits in fine motor control of the distal forelimb, and represents a powerful addition to multivariate outcome batteries aimed at documenting recovery of function after cervical SCI in rats. PMID:25071704

Comparative study of anthocyanin and volatile compounds content of four varieties of Mexican roselle (Hibiscus sabdariffa L.) by multivariable analysis.

PubMed

Camelo-Méndez, G A; Ragazzo-Sánchez, J A; Jiménez-Aparicio, A R; Vanegas-Espinoza, P E; Paredes-López, O; Del Villar-Martínez, A A

2013-09-01

Anthocyanins are a group of water-soluble pigments that provide red, purple or blue color to the leaves, flowers, and fruits. In addition, benefits have been attributed to hypertension and cardiovascular diseases. This study compared the content of total anthocyanins and volatile compounds in aqueous and ethanolic extracts of four varieties of Mexican roselle, with different levels of pigmentation. The multivariable analysis of categorical data demonstrated that ethanol was the best solvent for the extraction of both anthocyanins and volatile compounds. The concentration of anthocyanin in pigmented varieties ranged from 17.3 to 32.2 mg of cyanidin 3-glucoside/g dry weight, while volatile compounds analysis showed that geraniol was the main compound in extracts from the four varieties. The principal component analysis (PCA) allowed description of results with 77.38% of variance establishing a clear grouping for each variety in addition to similarities among some of these varieties. These results were validated by the confusion matrix obtained in the classification by the factorial discriminate analysis (FDA); it can be useful for roselle varieties classification. Small differences in anthocyanin and volatile compounds content could be detected, and it may be of interest for the food industry in order to classify a new individual into one of several groups using different variables at once.
Correlative and multivariate analysis of increased radon concentration in underground laboratory.

PubMed

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

2014-11-01

The results of analysis using correlative and multivariate methods, as developed for data analysis in high-energy physics and implemented in the Toolkit for Multivariate Analysis software package, of the relations of the variation of increased radon concentration with climate variables in shallow underground laboratory is presented. Multivariate regression analysis identified a number of multivariate methods which can give a good evaluation of increased radon concentrations based on climate variables. The use of the multivariate regression methods will enable the investigation of the relations of specific climate variable with increased radon concentrations by analysis of regression methods resulting in 'mapped' underlying functional behaviour of radon concentrations depending on a wide spectrum of climate variables. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

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

PubMed Central

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

2016-01-01

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

PubMed

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

2016-01-01

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

ERIC Educational Resources Information Center

Grochowalski, Joseph H.

2015-01-01

Component Universe Score Profile analysis (CUSP) is introduced in this paper as a psychometric alternative to multivariate profile analysis. The theoretical foundations of CUSP analysis are reviewed, which include multivariate generalizability theory and constrained principal components analysis. Because CUSP is a combination of generalizability…
FAILURE OF RADIOACTIVE IODINE IN TREATMENT OF HYPERTHYROIDISM

PubMed Central

Schneider, David F.; Sonderman, Philip E.; Jones, Michaela F.; Ojomo, Kristin A.; Chen, Herbert; Jaume, Juan C.; Elson, Diane F.; Perlman, Scott B.; Sippel, Rebecca S.

2015-01-01

Introduction Persistent or recurrent hyperthyroidism after treatment with radioactive iodine (RAI) is common, and many patients require either additional doses or surgery before they are cured. The purpose of this study was to identify patterns and predictors of failure of RAI in patients with hyperthyroidism. Methods We conducted a retrospective review of patients treated with RAI from 2007–2010. Failure of RAI was defined as receipt of additional dose(s) and/or total thyroidectomy. Using a Cox proportional hazards model, we conducted univariate analysis to identify factors associated with failure of RAI. A final multivariate model was then constructed with significant (p < 0.05) variables from the univariate analysis. Results Of the 325 patients analyzed, 74 patients (22.8%) failed initial RAI treatment. 53 (71.6%) received additional RAI, 13 (17.6%) received additional RAI followed by surgery, and the remaining 8 (10.8%) were cured after thyroidectomy. The percentage of patients who failed decreased in a step-wise fashion as RAI dose increased. Similarly, the incidence of failure increased as the presenting T3 level increased. Sensitivity analysis revealed that RAI doses < 12.5 mCi were associated with failure while initial T3 and free T4 levels of at least 4.5 pg/mL and 2.3 ng/dL, respectively, were associated with failure. In the final multivariate analysis, higher T4 (HR 1.13, 95% CI 1.02–1.26, p=0.02) and methimazole treatment (HR 2.55, 95% CI 1.22–5.33, p=0.01) were associated with failure. Conclusions Laboratory values at presentation can predict which patients with hyperthyroidism are at risk for failing RAI treatment. Higher doses of RAI or surgical referral may prevent the need for repeat RAI in selected patients. PMID:25001092
Failure of radioactive iodine in the treatment of hyperthyroidism.

PubMed

Schneider, David F; Sonderman, Philip E; Jones, Michaela F; Ojomo, Kristin A; Chen, Herbert; Jaume, Juan C; Elson, Diane F; Perlman, Scott B; Sippel, Rebecca S

2014-12-01

Persistent or recurrent hyperthyroidism after treatment with radioactive iodine (RAI) is common and many patients require either additional doses or surgery before they are cured. The purpose of this study was to identify patterns and predictors of failure of RAI in patients with hyperthyroidism. We conducted a retrospective review of patients treated with RAI from 2007 to 2010. Failure of RAI was defined as receipt of additional dose(s) and/or total thyroidectomy. Using a Cox proportional hazards model, we conducted univariate analysis to identify factors associated with failure of RAI. A final multivariate model was then constructed with significant (p < 0.05) variables from the univariate analysis. Of the 325 patients analyzed, 74 patients (22.8 %) failed initial RAI treatment, 53 (71.6 %) received additional RAI, 13 (17.6 %) received additional RAI followed by surgery, and the remaining 8 (10.8 %) were cured after thyroidectomy. The percentage of patients who failed decreased in a stepwise fashion as RAI dose increased. Similarly, the incidence of failure increased as the presenting T3 level increased. Sensitivity analysis revealed that RAI doses <12.5 mCi were associated with failure while initial T3 and free T4 levels of at least 4.5 pg/mL and 2.3 ng/dL, respectively, were associated with failure. In the final multivariate analysis, higher T4 (hazard ratio [HR] 1.13; 95 % confidence interval [CI] 1.02-1.26; p = 0.02) and methimazole treatment (HR 2.55; 95 % CI 1.22-5.33; p = 0.01) were associated with failure. Laboratory values at presentation can predict which patients with hyperthyroidism are at risk for failing RAI treatment. Higher doses of RAI or surgical referral may prevent the need for repeat RAI in selected patients.
Multivariate analysis and extraction of parameters in resistive RAMs using the Quantum Point Contact model

NASA Astrophysics Data System (ADS)

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.
Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data.

PubMed

Zhou, Hua; Blangero, John; Dyer, Thomas D; Chan, Kei-Hang K; Lange, Kenneth; Sobel, Eric M

2017-04-01

Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even datasets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Pedigrees selected for extreme trait values often segregate a single gene with strong effect. Finally, many pedigrees are available as an important legacy from the era of linkage analysis. Unfortunately, pedigree likelihoods are notoriously hard to compute. In this paper, we reexamine the computational bottlenecks and implement ultra-fast pedigree-based GWAS analysis. Kinship coefficients can either be based on explicitly provided pedigrees or automatically estimated from dense markers. Our strategy (a) works for random sample data, pedigree data, or a mix of both; (b) entails no loss of power; (c) allows for any number of covariate adjustments, including correction for population stratification; (d) allows for testing SNPs under additive, dominant, and recessive models; and (e) accommodates both univariate and multivariate quantitative traits. On a typical personal computer (six CPU cores at 2.67 GHz), analyzing a univariate HDL (high-density lipoprotein) trait from the San Antonio Family Heart Study (935,392 SNPs on 1,388 individuals in 124 pedigrees) takes less than 2 min and 1.5 GB of memory. Complete multivariate QTL analysis of the three time-points of the longitudinal HDL multivariate trait takes less than 5 min and 1.5 GB of memory. The algorithm is implemented as the Ped-GWAS Analysis (Option 29) in the Mendel statistical genetics package, which is freely available for Macintosh, Linux, and Windows platforms from http://genetics.ucla.edu/software/mendel. © 2016 WILEY PERIODICALS, INC.
Multivariate temporal pattern analysis applied to the study of rat behavior in the elevated plus maze: methodological and conceptual highlights.

PubMed

Casarrubea, M; Magnusson, M S; Roy, V; Arabo, A; Sorbera, F; Santangelo, A; Faulisi, F; Crescimanno, G

2014-08-30

Aim of this article is to illustrate the application of a multivariate approach known as t-pattern analysis in the study of rat behavior in elevated plus maze. By means of this multivariate approach, significant relationships among behavioral events in the course of time can be described. Both quantitative and t-pattern analyses were utilized to analyze data obtained from fifteen male Wistar rats following a trial 1-trial 2 protocol. In trial 2, in comparison with the initial exposure, mean occurrences of behavioral elements performed in protected zones of the maze showed a significant increase counterbalanced by a significant decrease of mean occurrences of behavioral elements in unprotected zones. Multivariate t-pattern analysis, in trial 1, revealed the presence of 134 t-patterns of different composition. In trial 2, the temporal structure of behavior become more simple, being present only 32 different t-patterns. Behavioral strings and stripes (i.e. graphical representation of each t-pattern onset) of all t-patterns were presented both for trial 1 and trial 2 as well. Finally, percent distributions in the three zones of the maze show a clear-cut increase of t-patterns in closed arm and a significant reduction in the remaining zones. Results show that previous experience deeply modifies the temporal structure of rat behavior in the elevated plus maze. In addition, this article, by highlighting several conceptual, methodological and illustrative aspects on the utilization of t-pattern analysis, could represent a useful background to employ such a refined approach in the study of rat behavior in elevated plus maze. Copyright © 2014 Elsevier B.V. All rights reserved.
[Determination of the Plant Origin of Licorice Oil Extract, a Natural Food Additive, by Principal Component Analysis Based on Chemical Components].

PubMed

Tada, Atsuko; Ishizuki, Kyoko; Sugimoto, Naoki; Yoshimatsu, Kayo; Kawahara, Nobuo; Suematsu, Takako; Arifuku, Kazunori; Fukai, Toshio; Tamura, Yukiyoshi; Ohtsuki, Takashi; Tahara, Maiko; Yamazaki, Takeshi; Akiyama, Hiroshi

2015-01-01

"Licorice oil extract" (LOE) (antioxidant agent) is described in the notice of Japanese food additive regulations as a material obtained from the roots and/or rhizomes of Glycyrrhiza uralensis, G. inflata or G. glabra. In this study, we aimed to identify the original Glycyrrhiza species of eight food additive products using LC/MS. Glabridin, a characteristic compound in G. glabra, was specifically detected in seven products, and licochalcone A, a characteristic compound in G. inflata, was detected in one product. In addition, Principal Component Analysis (PCA) (a kind of multivariate analysis) using the data of LC/MS or (1)H-NMR analysis was performed. The data of thirty-one samples, including LOE products used as food additives, ethanol extracts of various Glycyrrhiza species and commercially available Glycyrrhiza species-derived products were assessed. Based on the PCA results, the majority of LOE products was confirmed to be derived from G. glabra. This study suggests that PCA using (1)H-NMR analysis data is a simple and useful method to identify the plant species of origin of natural food additive products.
Multivariate spatial models of excess crash frequency at area level: case of Costa Rica.

PubMed

Aguero-Valverde, Jonathan

2013-10-01

Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data. This paper aims to extent the concept of safety performance functions to be used in areal models of crash frequency. A multivariate spatial model is used for that purpose and compared to its univariate counterpart. Full Bayes hierarchical approach is used to estimate the models of crash frequency at canton level for Costa Rica. An intrinsic multivariate conditional autoregressive model is used for modeling spatial random effects. The results show that the multivariate spatial model performs better than its univariate counterpart in terms of the penalized goodness-of-fit measure Deviance Information Criteria. Additionally, the effects of the spatial smoothing due to the multivariate spatial random effects are evident in the estimation of excess equivalent property damage only crashes. Copyright © 2013 Elsevier Ltd. All rights reserved.
Origin Discrimination of Osmanthus fragrans var. thunbergii Flowers using GC-MS and UPLC-PDA Combined with Multivariable Analysis Methods.

PubMed

Zhou, Fei; Zhao, Yajing; Peng, Jiyu; Jiang, Yirong; Li, Maiquan; Jiang, Yuan; Lu, Baiyi

2017-07-01

Osmanthus fragrans flowers are used as folk medicine and additives for teas, beverages and foods. The metabolites of O. fragrans flowers from different geographical origins were inconsistent in some extent. Chromatography and mass spectrometry combined with multivariable analysis methods provides an approach for discriminating the origin of O. fragrans flowers. To discriminate the Osmanthus fragrans var. thunbergii flowers from different origins with the identified metabolites. GC-MS and UPLC-PDA were conducted to analyse the metabolites in O. fragrans var. thunbergii flowers (in total 150 samples). Principal component analysis (PCA), soft independent modelling of class analogy analysis (SIMCA) and random forest (RF) analysis were applied to group the GC-MS and UPLC-PDA data. GC-MS identified 32 compounds common to all samples while UPLC-PDA/QTOF-MS identified 16 common compounds. PCA of the UPLC-PDA data generated a better clustering than PCA of the GC-MS data. Ten metabolites (six from GC-MS and four from UPLC-PDA) were selected as effective compounds for discrimination by PCA loadings. SIMCA and RF analysis were used to build classification models, and the RF model, based on the four effective compounds (caffeic acid derivative, acteoside, ligustroside and compound 15), yielded better results with the classification rate of 100% in the calibration set and 97.8% in the prediction set. GC-MS and UPLC-PDA combined with multivariable analysis methods can discriminate the origin of Osmanthus fragrans var. thunbergii flowers. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Multivariate evaluation of the effectiveness of treatment efficacy of cypermethrin against sea lice (Lepeophtheirus salmonis) in Atlantic salmon (Salmo salar)

PubMed Central

2013-01-01

Background The sea louse Lepeophtheirus salmonis is the most important ectoparasite of farmed Atlantic salmon (Salmo salar) in Norwegian aquaculture. Control of sea lice is primarily dependent on the use of delousing chemotherapeutants, which are both expensive and toxic to other wildlife. The method most commonly used for monitoring treatment effectiveness relies on measuring the percentage reduction in the mobile stages of Lepeophtheirus salmonis only. However, this does not account for changes in the other sea lice stages and may result in misleading or incomplete interpretation regarding the effectiveness of treatment. With the aim of improving the evaluation of delousing treatments, we explored multivariate analyses of bath treatments using the topical pyrethroid, cypermethrin, in salmon pens at five Norwegian production sites. Results Conventional univariate analysis indicated reductions of over 90% in mobile stages at all sites. In contrast, multivariate analyses indicated differing treatment effectiveness between sites (p-value < 0.01) based on changes in the proportion and abundance of the chalimus and PAAM (pre-adult and adult males) stages. Low water temperatures and shortened intervals between sampling after treatment may account for the differences in the composition of chalimus and PAAM stage groups following treatment. Using multivariate analysis, such factors could be separated from those which were attributable to inadequate treatment or chemotherapeutant failure. Conclusions Multivariate analyses for evaluation of treatment effectiveness against multiple life cycle stages of L. salmonis yield additional information beyond that derivable from univariate methods. This can aid in the identification of causes of apparent treatment failure in salmon aquaculture. PMID:24354936
Multivariate Models for Normal and Binary Responses in Intervention Studies

ERIC Educational Resources Information Center

Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen

2016-01-01

Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…
PHI and PCA3 improve the prognostic performance of PRIAS and Epstein criteria in predicting insignificant prostate cancer in men eligible for active surveillance.

PubMed

Cantiello, Francesco; Russo, Giorgio Ivan; Cicione, Antonio; Ferro, Matteo; Cimino, Sebastiano; Favilla, Vincenzo; Perdonà, Sisto; De Cobelli, Ottavio; Magno, Carlo; Morgia, Giuseppe; Damiano, Rocco

2016-04-01

To assess the performance of prostate health index (PHI) and prostate cancer antigen 3 (PCA3) when added to the PRIAS or Epstein criteria in predicting the presence of pathologically insignificant prostate cancer (IPCa) in patients who underwent radical prostatectomy (RP) but eligible for active surveillance (AS). An observational retrospective study was performed in 188 PCa patients treated with laparoscopic or robot-assisted RP but eligible for AS according to Epstein or PRIAS criteria. Blood and urinary specimens were collected before initial prostate biopsy for PHI and PCA3 measurements. Multivariate logistic regression analyses and decision curve analysis were carried out to identify predictors of IPCa using the updated ERSPC definition. At the multivariate analyses, the inclusion of both PCA3 and PHI significantly increased the accuracy of the Epstein multivariate model in predicting IPCa with an increase of 17 % (AUC = 0.77) and of 32 % (AUC = 0.92), respectively. The inclusion of both PCA3 and PHI also increased the predictive accuracy of the PRIAS multivariate model with an increase of 29 % (AUC = 0.87) and of 39 % (AUC = 0.97), respectively. DCA revealed that the multivariable models with the addition of PHI or PCA3 showed a greater net benefit and performed better than the reference models. In a direct comparison, PHI outperformed PCA3 performance resulting in higher net benefit. In a same cohort of patients eligible for AS, the addition of PHI and PCA3 to Epstein or PRIAS models improved their prognostic performance. PHI resulted in greater net benefit in predicting IPCa compared to PCA3.
Small-Noise Analysis and Symmetrization of Implicit Monte Carlo Samplers

DOE PAGES

Goodman, Jonathan; Lin, Kevin K.; Morzfeld, Matthias

2015-07-06

Implicit samplers are algorithms for producing independent, weighted samples from multivariate probability distributions. These are often applied in Bayesian data assimilation algorithms. We use Laplace asymptotic expansions to analyze two implicit samplers in the small noise regime. Our analysis suggests a symmetrization of the algorithms that leads to improved implicit sampling schemes at a relatively small additional cost. Here, computational experiments confirm the theory and show that symmetrization is effective for small noise sampling problems.
Bioprospecting Chemical Diversity and Bioactivity in a Marine Derived Aspergillus terreus.

PubMed

Adpressa, Donovon A; Loesgen, Sandra

2016-02-01

A comparative metabolomic study of a marine derived fungus (Aspergillus terreus) grown under various culture conditions is presented. The fungus was grown in eleven different culture conditions using solid agar, broth cultures, or grain based media (OSMAC). Multivariate analysis of LC/MS data from the organic extracts revealed drastic differences in the metabolic profiles and guided our subsequent isolation efforts. The compound 7-desmethylcitreoviridin was isolated and identified, and is fully described for the first time. In addition, 16 known fungal metabolites were also isolated and identified. All compounds were elucidated by detailed spectroscopic analysis and tested for antibacterial activities against five human pathogens and tested for cytotoxicity. This study demonstrates that LC/MS based multivariate analysis provides a simple yet powerful tool to analyze the metabolome of a single fungal strain grown under various conditions. This approach allows environmentally-induced changes in metabolite expression to be rapidly visualized, and uses these differences to guide the discovery of new bioactive molecules. Copyright © 2016 Verlag Helvetica Chimica Acta AG, Zürich.
Bioinformatics Identification of Modules of Transcription Factor Binding Sites in Alzheimer's Disease-Related Genes by In Silico Promoter Analysis and Microarrays

PubMed Central

Augustin, Regina; Lichtenthaler, Stefan F.; Greeff, Michael; Hansen, Jens; Wurst, Wolfgang; Trümbach, Dietrich

2011-01-01

The molecular mechanisms and genetic risk factors underlying Alzheimer's disease (AD) pathogenesis are only partly understood. To identify new factors, which may contribute to AD, different approaches are taken including proteomics, genetics, and functional genomics. Here, we used a bioinformatics approach and found that distinct AD-related genes share modules of transcription factor binding sites, suggesting a transcriptional coregulation. To detect additional coregulated genes, which may potentially contribute to AD, we established a new bioinformatics workflow with known multivariate methods like support vector machines, biclustering, and predicted transcription factor binding site modules by using in silico analysis and over 400 expression arrays from human and mouse. Two significant modules are composed of three transcription factor families: CTCF, SP1F, and EGRF/ZBPF, which are conserved between human and mouse APP promoter sequences. The specific combination of in silico promoter and multivariate analysis can identify regulation mechanisms of genes involved in multifactorial diseases. PMID:21559189

Deconstructing multivariate decoding for the study of brain function.

PubMed

Hebart, Martin N; Baker, Chris I

2017-08-04

Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function. Copyright © 2017. Published by Elsevier Inc.
Psychosocial distress in patients with thyroid cancer.

PubMed

Buchmann, Luke; Ashby, Shaelene; Cannon, Richard B; Hunt, Jason P

2015-04-01

The purpose of this study is to evaluate levels of psychosocial distress in thyroid cancer patients. An analysis of factors contributing to levels of distress is included. Individual retrospective cohort study. Head and neck cancer clinic at the Huntsman Cancer Institute. A total of 118 newly diagnosed thyroid cancer patients were included in the study. Univariate and multivariate analyses evaluated levels of and factors contributing to distress. Almost half (43.3%) of patients had significant distress. Those with self-reported psychiatric history, use of antidepressant medication, and history of radiation treatment had higher levels of distress. On multivariate analysis, patient endorsement of emotional issues predicted a higher distress level. Thyroid cancer patients have high distress levels. Identification of thyroid cancer patients with high distress levels is important to offer additional support during cancer therapy. © American Academy of Otolaryngology—Head and Neck Surgery Foundation 2015.
Time-varying correlations in global real estate markets: A multivariate GARCH with spatial effects approach

NASA Astrophysics Data System (ADS)

Gu, Huaying; Liu, Zhixue; Weng, Yingliang

2017-04-01

The present study applies the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) with spatial effects approach for the analysis of the time-varying conditional correlations and contagion effects among global real estate markets. A distinguishing feature of the proposed model is that it can simultaneously capture the spatial interactions and the dynamic conditional correlations compared with the traditional MGARCH models. Results reveal that the estimated dynamic conditional correlations have exhibited significant increases during the global financial crisis from 2007 to 2009, thereby suggesting contagion effects among global real estate markets. The analysis further indicates that the returns of the regional real estate markets that are in close geographic and economic proximities exhibit strong co-movement. In addition, evidence of significantly positive leverage effects in global real estate markets is also determined. The findings have significant implications on global portfolio diversification opportunities and risk management practices.
Geographically Sourcing Cocaine's Origin - Delineation of the Nineteen Major Coca Growing Regions in South America.

PubMed

Mallette, Jennifer R; Casale, John F; Jordan, James; Morello, David R; Beyer, Paul M

2016-03-23

Previously, geo-sourcing to five major coca growing regions within South America was accomplished. However, the expansion of coca cultivation throughout South America made sub-regional origin determinations increasingly difficult. The former methodology was recently enhanced with additional stable isotope analyses ((2)H and (18)O) to fully characterize cocaine due to the varying environmental conditions in which the coca was grown. An improved data analysis method was implemented with the combination of machine learning and multivariate statistical analysis methods to provide further partitioning between growing regions. Here, we show how the combination of trace cocaine alkaloids, stable isotopes, and multivariate statistical analyses can be used to classify illicit cocaine as originating from one of 19 growing regions within South America. The data obtained through this approach can be used to describe current coca cultivation and production trends, highlight trafficking routes, as well as identify new coca growing regions.
Geographically Sourcing Cocaine’s Origin - Delineation of the Nineteen Major Coca Growing Regions in South America

NASA Astrophysics Data System (ADS)

Mallette, Jennifer R.; Casale, John F.; Jordan, James; Morello, David R.; Beyer, Paul M.

2016-03-01

Previously, geo-sourcing to five major coca growing regions within South America was accomplished. However, the expansion of coca cultivation throughout South America made sub-regional origin determinations increasingly difficult. The former methodology was recently enhanced with additional stable isotope analyses (2H and 18O) to fully characterize cocaine due to the varying environmental conditions in which the coca was grown. An improved data analysis method was implemented with the combination of machine learning and multivariate statistical analysis methods to provide further partitioning between growing regions. Here, we show how the combination of trace cocaine alkaloids, stable isotopes, and multivariate statistical analyses can be used to classify illicit cocaine as originating from one of 19 growing regions within South America. The data obtained through this approach can be used to describe current coca cultivation and production trends, highlight trafficking routes, as well as identify new coca growing regions.
Multivariate Longitudinal Analysis with Bivariate Correlation Test

PubMed Central

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

2016-01-01

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

PubMed

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

2016-01-01

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

NASA Astrophysics Data System (ADS)

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.
Hot spots of multivariate extreme anomalies in Earth observations

NASA Astrophysics Data System (ADS)

Flach, M.; Sippel, S.; Bodesheim, P.; Brenning, A.; Denzler, J.; Gans, F.; Guanche, Y.; Reichstein, M.; Rodner, E.; Mahecha, M. D.

2016-12-01

Anomalies in Earth observations might indicate data quality issues, extremes or the change of underlying processes within a highly multivariate system. Thus, considering the multivariate constellation of variables for extreme detection yields crucial additional information over conventional univariate approaches. We highlight areas in which multivariate extreme anomalies are more likely to occur, i.e. hot spots of extremes in global atmospheric Earth observations that impact the Biosphere. In addition, we present the year of the most unusual multivariate extreme between 2001 and 2013 and show that these coincide with well known high impact extremes. Technically speaking, we account for multivariate extremes by using three sophisticated algorithms adapted from computer science applications. Namely an ensemble of the k-nearest neighbours mean distance, a kernel density estimation and an approach based on recurrences is used. However, the impact of atmosphere extremes on the Biosphere might largely depend on what is considered to be normal, i.e. the shape of the mean seasonal cycle and its inter-annual variability. We identify regions with similar mean seasonality by means of dimensionality reduction in order to estimate in each region both the `normal' variance and robust thresholds for detecting the extremes. In addition, we account for challenges like heteroscedasticity in Northern latitudes. Apart from hot spot areas, those anomalies in the atmosphere time series are of particular interest, which can only be detected by a multivariate approach but not by a simple univariate approach. Such an anomalous constellation of atmosphere variables is of interest if it impacts the Biosphere. The multivariate constellation of such an anomalous part of a time series is shown in one case study indicating that multivariate anomaly detection can provide novel insights into Earth observations.
A Multivariate Genome-Wide Association Analysis of 10 LDL Subfractions, and Their Response to Statin Treatment, in 1868 Caucasians

PubMed Central

Shim, Heejung; Chasman, Daniel I.; Smith, Joshua D.; Mora, Samia; Ridker, Paul M.; Nickerson, Deborah A.; Krauss, Ronald M.; Stephens, Matthew

2015-01-01

We conducted a genome-wide association analysis of 7 subfractions of low density lipoproteins (LDLs) and 3 subfractions of intermediate density lipoproteins (IDLs) measured by gradient gel electrophoresis, and their response to statin treatment, in 1868 individuals of European ancestry from the Pharmacogenomics and Risk of Cardiovascular Disease study. Our analyses identified four previously-implicated loci (SORT1, APOE, LPA, and CETP) as containing variants that are very strongly associated with lipoprotein subfractions (log10Bayes Factor > 15). Subsequent conditional analyses suggest that three of these (APOE, LPA and CETP) likely harbor multiple independently associated SNPs. Further, while different variants typically showed different characteristic patterns of association with combinations of subfractions, the two SNPs in CETP show strikingly similar patterns - both in our original data and in a replication cohort - consistent with a common underlying molecular mechanism. Notably, the CETP variants are very strongly associated with LDL subfractions, despite showing no association with total LDLs in our study, illustrating the potential value of the more detailed phenotypic measurements. In contrast with these strong subfraction associations, genetic association analysis of subfraction response to statins showed much weaker signals (none exceeding log10Bayes Factor of 6). However, two SNPs (in APOE and LPA) previously-reported to be associated with LDL statin response do show some modest evidence for association in our data, and the subfraction response proles at the LPA SNP are consistent with the LPA association, with response likely being due primarily to resistance of Lp(a) particles to statin therapy. An additional important feature of our analysis is that, unlike most previous analyses of multiple related phenotypes, we analyzed the subfractions jointly, rather than one at a time. Comparisons of our multivariate analyses with standard univariate analyses demonstrate that multivariate analyses can substantially increase power to detect associations. Software implementing our multivariate analysis methods is available at http://stephenslab.uchicago.edu/software.html. PMID:25898129
Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY
Two-pole microring weight banks.

PubMed

Tait, Alexander N; Wu, Allie X; Ferreira de Lima, Thomas; Nahmias, Mitchell A; Shastri, Bhavin J; Prucnal, Paul R

2018-05-15

Weighted addition is an elemental multi-input to single-output operation that can be implemented with high-performance photonic devices. Microring (MRR) weight banks bring programmable weighted addition to silicon photonics. Prior work showed that their channel limits are affected by coherent inter-channel effects that occur uniquely in weight banks. We fabricate two-pole designs that exploit this inter-channel interference in a way that is robust to dynamic tuning and fabrication variation. Scaling analysis predicts a channel count improvement of 3.4-fold, which is substantially greater than predicted by incoherent analysis used in conventional MRR devices. Advances in weight bank design expand the potential of reconfigurable analog photonic networks and multivariate microwave photonics.
Impact of liver volume and liver function on posthepatectomy liver failure after portal vein embolization- A multivariable cohort analysis.

PubMed

Alizai, Patrick H; Haelsig, Annabel; Bruners, Philipp; Ulmer, Florian; Klink, Christian D; Dejong, Cornelis H C; Neumann, Ulf P; Schmeding, Maximilian

2018-01-01

Liver failure remains a life-threatening complication after liver resection, and is difficult to predict preoperatively. This retrospective cohort study evaluated different preoperative factors in regard to their impact on posthepatectomy liver failure (PHLF) after extended liver resection and previous portal vein embolization (PVE). Patient characteristics, liver function and liver volumes of patients undergoing PVE and subsequent liver resection were analyzed. Liver function was determined by the LiMAx test (enzymatic capacity of cytochrome P450 1A2). Factors associated with the primary end point PHLF (according to ISGLS definition) were identified through multivariable analysis. Secondary end points were 30-day mortality and morbidity. 95 patients received PVE, of which 64 patients underwent major liver resection. PHLF occurred in 7 patients (11%). Calculated postoperative liver function was significantly lower in patients with PHLF than in patients without PHLF (67 vs. 109 μg/kg/h; p = 0.01). Other factors associated with PHLF by univariable analysis were age, future liver remnant, MELD score, ASA score, renal insufficiency and heart insufficiency. By multivariable analysis, future liver remnant was the only factor significantly associated with PHLF (p = 0.03). Mortality and morbidity rates were 4.7% and 29.7% respectively. Future liver remnant is the only preoperative factor with a significant impact on PHLF. Assessment of preoperative liver function may additionally help identify patients at risk for PHLF.
Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.
Effect of duration of denervation on outcomes of ansa-recurrent laryngeal nerve reinnervation.

PubMed

Li, Meng; Chen, Shicai; Wang, Wei; Chen, Donghui; Zhu, Minhui; Liu, Fei; Zhang, Caiyun; Li, Yan; Zheng, Hongliang

2014-08-01

To investigate the efficacy of laryngeal reinnervation with ansa cervicalis among unilateral vocal fold paralysis (UVFP) patients with different denervation durations. We retrospectively reviewed 349 consecutive UVFP cases of delayed ansa cervicalis to the recurrent laryngeal nerve (RLN) anastomosis. Potential influencing factors were analyzed in multivariable logistic regression analysis. Stratification analysis performed was aimed at one of the identified significant variables: denervation duration. Videostroboscopy, perceptual evaluation, acoustic analysis, maximum phonation time (MPT), and laryngeal electromyography (EMG) were performed preoperatively and postoperatively. Gender, age, preoperative EMG status and denervation duration were analyzed in multivariable logistic regression analysis. Stratification analysis was performed on denervation duration, which was divided into three groups according to the interval between RLN injury and reinnervation: group A, 6 to 12 months; group B, 12 to 24 months; and group C, > 24 months. Age, preoperative EMG, and denervation duration were identified as significant variables in multivariable logistic regression analysis. Stratification analysis on denervation duration showed significant differences between group A and C and between group B and C (P < 0.05)-but showed no significant difference between group A and B (P > 0.05) with regard to parameters overall grade, jitter, shimmer, noise-to-harmonics ratio, MPT, and postoperative EMG. In addition, videostroboscopic and laryngeal EMG data, perceptual and acoustic parameters, and MPT values were significantly improved postoperatively in each denervation duration group (P < 0.01). Although delayed laryngeal reinnervation is proved valid for UVFP, surgical outcome is better if the procedure is performed within 2 years after nerve injury than that over 2 years. © 2014 The American Laryngological, Rhinological and Otological Society, Inc.
Occupational and Biomass Exposure in Chronic Obstructive Pulmonary Disease: Results of a Cross-Sectional Analysis of the On-Sint Study.

PubMed

López-Campos, José Luis; Fernández-Villar, Alberto; Calero-Acuña, Carmen; Represas-Represas, Cristina; López-Ramírez, Cecilia; Fernández, Virginia Leiro; Casamor, Ricard

2017-01-01

Although tobacco smoke is the main risk factor for chronic obstructive pulmonary disease (COPD), other inhaled toxics have also been associated with the disease. The present study analyzes data from exposure to these substances in a cohort of patients with COPD and assesses their impact on the clinical presentation of the disease. This is a cross-sectional analysis of the Clinical presentation, diagnosis and course of chronic obstructive pulmonary disease (On-Sint) study. All patients were smokers or ex-smokers as per protocol. In addition, during the inclusion visit patients were enquired about their occupational and biomass exposure history. The clinical features of patients with and without an added risk factor to tobacco were compared and those significant were entered in a multivariate logistic regression analysis, expressed as odds ratio (OR). The sample size was 1214 patients with COPD, of which 1012 (83.4%) had tobacco as the only risk factor and 202 (16.6%) had additional ones, mainly 174 (14.3%) with occupational gases and 32 (2.6%) with biomass exposure. The geographical distribution of this exposure showed a preference for the northern parts of the country and the East coast. The biomass exposure was rather low. Male gender (OR: 2.180), CAT score (OR: 1.036) and the use of long-term oxygen therapy (OR: 1.642) were associated with having an additional risk factor in the multivariate analysis. Occupational exposures are more common than biomass in Spain. COPD caused by tobacco plus other inhalants has some differential features and a more impaired quality of life. Copyright © 2016 SEPAR. Publicado por Elsevier España, S.L.U. All rights reserved.
Multivariate Statistical Approach Applied to Sediment Source Tracking Through Quantification and Mineral Identification, Cheyenne River, South Dakota

NASA Astrophysics Data System (ADS)

Valder, J.; Kenner, S.; Long, A.

2008-12-01

Portions of the Cheyenne River are characterized as impaired by the U.S. Environmental Protection Agency because of water-quality exceedences. The Cheyenne River watershed includes the Black Hills National Forest and part of the Badlands National Park. Preliminary analysis indicates that the Badlands National Park is a major contributor to the exceedances of the water-quality constituents for total dissolved solids and total suspended solids. Water-quality data have been collected continuously since 2007, and in the second year of collection (2008), monthly grab and passive sediment samplers are being used to collect total suspended sediment and total dissolved solids in both base-flow and runoff-event conditions. In addition, sediment samples from the river channel, including bed, bank, and floodplain, have been collected. These samples are being analyzed at the South Dakota School of Mines and Technology's X-Ray Diffraction Lab to quantify the mineralogy of the sediments. A multivariate statistical approach (including principal components, least squares, and maximum likelihood techniques) is applied to the mineral percentages that were characterized for each site to identify the contributing source areas that are causing exceedances of sediment transport in the Cheyenne River watershed. Results of the multivariate analysis demonstrate the likely sources of solids found in the Cheyenne River samples. A further refinement of the methods is in progress that utilizes a conceptual model which, when applied with the multivariate statistical approach, provides a better estimate for sediment sources.
Multiple Hypothesis Testing for Experimental Gingivitis Based on Wilcoxon Signed Rank Statistics

PubMed Central

Preisser, John S.; Sen, Pranab K.; Offenbacher, Steven

2011-01-01

Dental research often involves repeated multivariate outcomes on a small number of subjects for which there is interest in identifying outcomes that exhibit change in their levels over time as well as to characterize the nature of that change. In particular, periodontal research often involves the analysis of molecular mediators of inflammation for which multivariate parametric methods are highly sensitive to outliers and deviations from Gaussian assumptions. In such settings, nonparametric methods may be favored over parametric ones. Additionally, there is a need for statistical methods that control an overall error rate for multiple hypothesis testing. We review univariate and multivariate nonparametric hypothesis tests and apply them to longitudinal data to assess changes over time in 31 biomarkers measured from the gingival crevicular fluid in 22 subjects whereby gingivitis was induced by temporarily withholding tooth brushing. To identify biomarkers that can be induced to change, multivariate Wilcoxon signed rank tests for a set of four summary measures based upon area under the curve are applied for each biomarker and compared to their univariate counterparts. Multiple hypothesis testing methods with choice of control of the false discovery rate or strong control of the family-wise error rate are examined. PMID:21984957
Multivariate calibration in Laser-Induced Breakdown Spectroscopy quantitative analysis: The dangers of a 'black box' approach and how to avoid them

NASA Astrophysics Data System (ADS)

Safi, A.; Campanella, B.; Grifoni, E.; Legnaioli, S.; Lorenzetti, G.; Pagnotta, S.; Poggialini, F.; Ripoll-Seguer, L.; Hidalgo, M.; Palleschi, V.

2018-06-01

The introduction of multivariate calibration curve approach in Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis has led to a general improvement of the LIBS analytical performances, since a multivariate approach allows to exploit the redundancy of elemental information that are typically present in a LIBS spectrum. Software packages implementing multivariate methods are available in the most diffused commercial and open source analytical programs; in most of the cases, the multivariate algorithms are robust against noise and operate in unsupervised mode. The reverse of the coin of the availability and ease of use of such packages is the (perceived) difficulty in assessing the reliability of the results obtained which often leads to the consideration of the multivariate algorithms as 'black boxes' whose inner mechanism is supposed to remain hidden to the user. In this paper, we will discuss the dangers of a 'black box' approach in LIBS multivariate analysis, and will discuss how to overcome them using the chemical-physical knowledge that is at the base of any LIBS quantitative analysis.
Early experiences building a software quality prediction model

NASA Technical Reports Server (NTRS)

Agresti, W. W.; Evanco, W. M.; Smith, M. C.

1990-01-01

Early experiences building a software quality prediction model are discussed. The overall research objective is to establish a capability to project a software system's quality from an analysis of its design. The technical approach is to build multivariate models for estimating reliability and maintainability. Data from 21 Ada subsystems were analyzed to test hypotheses about various design structures leading to failure-prone or unmaintainable systems. Current design variables highlight the interconnectivity and visibility of compilation units. Other model variables provide for the effects of reusability and software changes. Reported results are preliminary because additional project data is being obtained and new hypotheses are being developed and tested. Current multivariate regression models are encouraging, explaining 60 to 80 percent of the variation in error density of the subsystems.

Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data.

PubMed

Grootswagers, Tijl; Wardle, Susan G; Carlson, Thomas A

2017-04-01

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to "decode" different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.
Predictors of condom use and refusal among the population of Free State province in South Africa

PubMed Central

2012-01-01

Background This study investigated the extent and predictors of condom use and condom refusal in the Free State province in South Africa. Methods Through a household survey conducted in the Free Sate province of South Africa, 5,837 adults were interviewed. Univariate and multivariate survey logistic regressions and classification trees (CT) were used for analysing two response variables ‘ever used condom’ and ‘ever refused condom’. Results Eighty-three per cent of the respondents had ever used condoms, of which 38% always used them; 61% used them during the last sexual intercourse and 9% had ever refused to use them. The univariate logistic regression models and CT analysis indicated that a strong predictor of condom use was its perceived need. In the CT analysis, this variable was followed in importance by ‘knowledge of correct use of condom’, condom availability, young age, being single and higher education. ‘Perceived need’ for condoms did not remain significant in the multivariate analysis after controlling for other variables. The strongest predictor of condom refusal, as shown by the CT, was shame associated with condoms followed by the presence of sexual risk behaviour, knowing one’s HIV status, older age and lacking knowledge of condoms (i.e., ability to prevent sexually transmitted diseases and pregnancy, availability, correct and consistent use and existence of female condoms). In the multivariate logistic regression, age was not significant for condom refusal while affordability and perceived need were additional significant variables. Conclusions The use of complementary modelling techniques such as CT in addition to logistic regressions adds to a better understanding of condom use and refusal. Further improvement in correct and consistent use of condoms will require targeted interventions. In addition to existing social marketing campaigns, tailored approaches should focus on establishing the perceived need for condom-use and improving skills for correct use. They should also incorporate interventions to reduce the shame associated with condoms and individual counselling of those likely to refuse condoms. PMID:22639964
Multivariate analysis: A statistical approach for computations

NASA Astrophysics Data System (ADS)

Michu, Sachin; Kaushik, Vandana

2014-10-01

Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.
Multivariate Cluster Analysis.

ERIC Educational Resources Information Center

McRae, Douglas J.

Procedures for grouping students into homogeneous subsets have long interested educational researchers. The research reported in this paper is an investigation of a set of objective grouping procedures based on multivariate analysis considerations. Four multivariate functions that might serve as criteria for adequate grouping are given and…
Childhood adversities and first onset of psychiatric disorders in a national sample of adolescents

PubMed Central

McLaughlin, Katie A.; Green, Jennifer Greif; Gruber, Michael J.; Sampson, Nancy A.; Zaslavsky, Alan M.; Kessler, Ronald C.

2012-01-01

Context Although childhood adversities (CAs) are known to be highly co-occurring, most research examines their associations with mental disorders one at a time. Recent evidence from adult studies suggests, though, that the associations of multiple CAs with mental disorders are non-additive, arguing for the importance of multivariate analysis of multiple CAs. No attempt has yet been made to carry out a similar kind of analysis among children or adolescents. Objective To examine the multivariate associations of 12 CAs with first onset of mental disorders in a national sample of US adolescents. Design US national survey of adolescents (ages 13–17) assessing DSM-IV anxiety, mood, behavior, and substance disorders and CAs. The CAs include parental loss (death, divorce, other separations), maltreatment (physical, sexual, and emotional abuse, neglect), parental maladjustment (psychopathology, substance abuse, criminality, violence) and economic adversity. Setting Dual-frame household-school samples. Participants 6,483 adolescents-parent pairs. Main outcome measure Lifetime DSM-IV disorders assessed with the WHO Composite International Diagnostic Interview. Results 58.3% of adolescents reported at least one CA, among whom 59.7% reported multiple CAs. CAs reflecting maladaptive family functioning (MFF) were more strongly associated than other CAs with disorder onsets. The best-fitting model included terms for type and number of CAs and distinguished between MFF and Other CAs. CAs predicted behavior disorders most strongly and fear disorders least strongly. The joint associations of multiple CAs were sub-additive. The population-attributable risk proportions for disorder classes ranged from 15.7% for fear disorders to 40.7% for behavior disorders. CAs were associated with 28.2% of all onsets. Conclusions CAs are common, highly co-occurring, and strongly associated with onset of mental disorders among US adolescents. The sub-additive multivariate associations of CAs with disorder onsets have implications for targeting interventions to reduce exposure to CAs and to mitigate the harmful effects of CAs to improve population mental health. PMID:23117636
Comparative forensic soil analysis of New Jersey state parks using a combination of simple techniques with multivariate statistics.

PubMed

Bonetti, Jennifer; Quarino, Lawrence

2014-05-01

This study has shown that the combination of simple techniques with the use of multivariate statistics offers the potential for the comparative analysis of soil samples. Five samples were obtained from each of twelve state parks across New Jersey in both the summer and fall seasons. Each sample was examined using particle-size distribution, pH analysis in both water and 1 M CaCl2 , and a loss on ignition technique. Data from each of the techniques were combined, and principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for multivariate data transformation. Samples from different locations could be visually differentiated from one another using these multivariate plots. Hold-one-out cross-validation analysis showed error rates as low as 3.33%. Ten blind study samples were analyzed resulting in no misclassifications using Mahalanobis distance calculations and visual examinations of multivariate plots. Seasonal variation was minimal between corresponding samples, suggesting potential success in forensic applications. © 2014 American Academy of Forensic Sciences.
Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

PubMed Central

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

2012-01-01

Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I2, which we call . We also provide a multivariate H2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22763950
Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models

PubMed Central

Baldwin, Scott A.; Imel, Zac E.; Braithwaite, Scott R.; Atkins, David C.

2014-01-01

Objective Multilevel models have become a standard data analysis approach in intervention research. Although the vast majority of intervention studies involve multiple outcome measures, few studies use multivariate analysis methods. The authors discuss multivariate extensions to the multilevel model that can be used by psychotherapy researchers. Method and Results Using simulated longitudinal treatment data, the authors show how multivariate models extend common univariate growth models and how the multivariate model can be used to examine multivariate hypotheses involving fixed effects (e.g., does the size of the treatment effect differ across outcomes?) and random effects (e.g., is change in one outcome related to change in the other?). An online supplemental appendix provides annotated computer code and simulated example data for implementing a multivariate model. Conclusions Multivariate multilevel models are flexible, powerful models that can enhance clinical research. PMID:24491071
Applications of multivariate modeling to neuroimaging group analysis: A comprehensive alternative to univariate general linear model

PubMed Central

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

2014-01-01

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

PubMed

Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

2016-05-01

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

PubMed

Aursand, Marit; Standal, Inger B; Praël, Angelika; McEvoy, Lesley; Irvine, Joe; Axelson, David E

2009-05-13

(13)C nuclear magnetic resonance (NMR) in combination with multivariate data analysis was used to (1) discriminate between farmed and wild Atlantic salmon ( Salmo salar L.), (2) discriminate between different geographical origins, and (3) verify the origin of market samples. Muscle lipids from 195 Atlantic salmon of known origin (wild and farmed salmon from Norway, Scotland, Canada, Iceland, Ireland, the Faroes, and Tasmania) in addition to market samples were analyzed by (13)C NMR spectroscopy and multivariate analysis. Both probabilistic neural networks (PNN) and support vector machines (SVM) provided excellent discrimination (98.5 and 100.0%, respectively) between wild and farmed salmon. Discrimination with respect to geographical origin was somewhat more difficult, with correct classification rates ranging from 82.2 to 99.3% by PNN and SVM, respectively. In the analysis of market samples, five fish labeled and purchased as wild salmon were classified as farmed salmon (indicating mislabeling), and there were also some discrepancies between the classification and the product declaration with regard to geographical origin.
Comprehensive analysis of Polygoni Multiflori Radix of different geographical origins using ultra-high-performance liquid chromatography fingerprints and multivariate chemometric methods.

PubMed

Sun, Li-Li; Wang, Meng; Zhang, Hui-Jie; Liu, Ya-Nan; Ren, Xiao-Liang; Deng, Yan-Ru; Qi, Ai-Di

2018-01-01

Polygoni Multiflori Radix (PMR) is increasingly being used not just as a traditional herbal medicine but also as a popular functional food. In this study, multivariate chemometric methods and mass spectrometry were combined to analyze the ultra-high-performance liquid chromatograph (UPLC) fingerprints of PMR from six different geographical origins. A chemometric strategy based on multivariate curve resolution-alternating least squares (MCR-ALS) and three classification methods is proposed to analyze the UPLC fingerprints obtained. Common chromatographic problems, including the background contribution, baseline contribution, and peak overlap, were handled by the established MCR-ALS model. A total of 22 components were resolved. Moreover, relative species concentrations were obtained from the MCR-ALS model, which was used for multivariate classification analysis. Principal component analysis (PCA) and Ward's method have been applied to classify 72 PMR samples from six different geographical regions. The PCA score plot showed that the PMR samples fell into four clusters, which related to the geographical location and climate of the source areas. The results were then corroborated by Ward's method. In addition, according to the variance-weighted distance between cluster centers obtained from Ward's method, five components were identified as the most significant variables (chemical markers) for cluster discrimination. A counter-propagation artificial neural network has been applied to confirm and predict the effects of chemical markers on different samples. Finally, the five chemical markers were identified by UPLC-quadrupole time-of-flight mass spectrometer. Components 3, 12, 16, 18, and 19 were identified as 2,3,5,4'-tetrahydroxy-stilbene-2-O-β-d-glucoside, emodin-8-O-β-d-glucopyranoside, emodin-8-O-(6'-O-acetyl)-β-d-glucopyranoside, emodin, and physcion, respectively. In conclusion, the proposed method can be applied for the comprehensive analysis of natural samples. Copyright © 2016. Published by Elsevier B.V.
Analysis techniques for multivariate root loci. [a tool in linear control systems

NASA Technical Reports Server (NTRS)

Thompson, P. M.; Stein, G.; Laub, A. J.

1980-01-01

Analysis and techniques are developed for the multivariable root locus and the multivariable optimal root locus. The generalized eigenvalue problem is used to compute angles and sensitivities for both types of loci, and an algorithm is presented that determines the asymptotic properties of the optimal root locus.
Methods for presentation and display of multivariate data

NASA Technical Reports Server (NTRS)

Myers, R. H.

1981-01-01

Methods for the presentation and display of multivariate data are discussed with emphasis placed on the multivariate analysis of variance problems and the Hotelling T(2) solution in the two-sample case. The methods utilize the concepts of stepwise discrimination analysis and the computation of partial correlation coefficients.
A Primer on Multivariate Analysis of Variance (MANOVA) for Behavioral Scientists

ERIC Educational Resources Information Center

Warne, Russell T.

2014-01-01

Reviews of statistical procedures (e.g., Bangert & Baumberger, 2005; Kieffer, Reese, & Thompson, 2001; Warne, Lazo, Ramos, & Ritter, 2012) show that one of the most common multivariate statistical methods in psychological research is multivariate analysis of variance (MANOVA). However, MANOVA and its associated procedures are often not…
Ground-Based Telescope Parametric Cost Model

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes

2004-01-01

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis, The model includes both engineering and performance parameters. While diameter continues to be the dominant cost driver, other significant factors include primary mirror radius of curvature and diffraction limited wavelength. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e.. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter are derived. This analysis indicates that recent mirror technology advances have indeed reduced the historical telescope cost curve.
Antimicrobial resistance: the major contribution of poor governance and corruption to this growing problem.

PubMed

Collignon, Peter; Athukorala, Prema-Chandra; Senanayake, Sanjaya; Khan, Fahad

2015-01-01

To determine how important governmental, social, and economic factors are in driving antibiotic resistance compared to the factors usually considered the main driving factors-antibiotic usage and levels of economic development. A retrospective multivariate analysis of the variation of antibiotic resistance in Europe in terms of human antibiotic usage, private health care expenditure, tertiary education, the level of economic advancement (per capita GDP), and quality of governance (corruption). The model was estimated using a panel data set involving 7 common human bloodstream isolates and covering 28 European countries for the period 1998-2010. Only 28% of the total variation in antibiotic resistance among countries is attributable to variation in antibiotic usage. If time effects are included the explanatory power increases to 33%. However when the control of corruption indicator is included as an additional variable, 63% of the total variation in antibiotic resistance is now explained by the regression. The complete multivariate regression only accomplishes an additional 7% in terms of goodness of fit, indicating that corruption is the main socioeconomic factor that explains antibiotic resistance. The income level of a country appeared to have no effect on resistance rates in the multivariate analysis. The estimated impact of corruption was statistically significant (p< 0.01). The coefficient indicates that an improvement of one unit in the corruption indicator is associated with a reduction in antibiotic resistance by approximately 0.7 units. The estimated coefficient of private health expenditure showed that one unit reduction is associated with a 0.2 unit decrease in antibiotic resistance. These findings support the hypothesis that poor governance and corruption contributes to levels of antibiotic resistance and correlate better than antibiotic usage volumes with resistance rates. We conclude that addressing corruption and improving governance will lead to a reduction in antibiotic resistance.
Differentiating organically and conventionally grown oregano using ultraperformance liquid chromatography mass spectrometry (UPLC-MS), headspace gas chromatography with flame ionization detection (headspace-GC-FID), and flow injection mass spectrum (FIMS) fingerprints combined with multivariate data analysis.

PubMed

Gao, Boyan; Qin, Fang; Ding, Tingting; Chen, Yineng; Lu, Weiying; Yu, Liangli Lucy

2014-08-13

Ultraperformance liquid chromatography mass spectrometry (UPLC-MS), flow injection mass spectrometry (FIMS), and headspace gas chromatography (headspace-GC) combined with multivariate data analysis techniques were examined and compared in differentiating organically grown oregano from that grown conventionally. It is the first time that headspace-GC fingerprinting technology is reported in differentiating organically and conventionally grown spice samples. The results also indicated that UPLC-MS, FIMS, and headspace-GC-FID fingerprints with OPLS-DA were able to effectively distinguish oreganos under different growing conditions, whereas with PCA, only FIMS fingerprint could differentiate the organically and conventionally grown oregano samples. UPLC fingerprinting provided detailed information about the chemical composition of oregano with a longer analysis time, whereas FIMS finished a sample analysis within 1 min. On the other hand, headspace GC-FID fingerprinting required no sample pretreatment, suggesting its potential as a high-throughput method in distinguishing organically and conventionally grown oregano samples. In addition, chemical components in oregano were identified by their molecular weight using QTOF-MS and headspace-GC-MS.
Combining fibre optic Raman spectroscopy and tactile resonance measurement for tissue characterization

NASA Astrophysics Data System (ADS)

Candefjord, Stefan; Nyberg, Morgan; Jalkanen, Ville; Ramser, Kerstin; Lindahl, Olof A.

2010-12-01

Tissue characterization is fundamental for identification of pathological conditions. Raman spectroscopy (RS) and tactile resonance measurement (TRM) are two promising techniques that measure biochemical content and stiffness, respectively. They have potential to complement the golden standard--histological analysis. By combining RS and TRM, complementary information about tissue content can be obtained and specific drawbacks can be avoided. The aim of this study was to develop a multivariate approach to compare RS and TRM information. The approach was evaluated on measurements at the same points on porcine abdominal tissue. The measurement points were divided into five groups by multivariate analysis of the RS data. A regression analysis was performed and receiver operating characteristic (ROC) curves were used to compare the RS and TRM data. TRM identified one group efficiently (area under ROC curve 0.99). The RS data showed that the proportion of saturated fat was high in this group. The regression analysis showed that stiffness was mainly determined by the amount of fat and its composition. We concluded that RS provided additional, important information for tissue identification that was not provided by TRM alone. The results are promising for development of a method combining RS and TRM for intraoperative tissue characterization.
Are prostatic calculi independent predictive factors of lower urinary tract symptoms?

PubMed

Park, Sung-Woo; Nam, Jong-Kil; Lee, Sang-Don; Chung, Moon-Kee

2010-03-01

We determined the correlation between prostatic calculi and lower urinary tract symptoms (LUTS), as well as the predisposing factors of prostatic calculi. Of the 1 527 patients who presented at our clinic for LUTS, 802 underwent complete evaluations, including transrectal ultrasonography, voided bladder-3 specimen and international prostatic symptoms score (IPSS). A total of 335 patients with prostatic calculi and 467 patients without prostatic calculi were divided into calculi and no calculi groups, respectively. Predictive factors of severe LUTS and prostatic calculi were determined using uni/multivariate analysis. The overall IPSS score was 15.7 +/- 9.2 and 14.1 +/- 9.2 in the calculi and no calculi group, respectively (P = 0.013). The maximum flow rate was 12.1 +/- 6.9 and 14.2 +/- 8.2 mL s(-1) in the calculi and no calculi group, respectively (P = 0.003). On univariate analysis for predicting factors of severe LUTS, differences on age (P = 0.042), prostatic calculi (P = 0.048) and prostatitis (P = 0.018) were statistically significant. However, on multivariate analysis, no factor was significant. On multivariate analysis for predisposing factors of prostatic calculi, differences on age (P < 0.001) and prostate volume (P = 0.001) were significant. To our knowledge, patients who have prostatic calculi complain of more severe LUTS. However, prostatic calculi are not an independent predictive factor of severe LUTS. Therefore, men with prostatic calculi have more severe LUTS not only because of prostatic calculi but also because of age and other factors. In addition, old age and large prostate volume are independent predisposing factors for prostatic calculi.

The impact of lungs from diabetic donors on lung transplant recipients†.

PubMed

Ambur, Vishnu; Taghavi, Sharven; Jayarajan, Senthil; Kadakia, Sagar; Zhao, Huaqing; Gomez-Abraham, Jesus; Toyoda, Yoshiya

2017-02-01

We attempted to determine if transplants of lungs from diabetic donors (DDs) is associated with increased mortality of recipients in the modern era of the lung allocation score (LAS). The United Network for Organ Sharing (UNOS) database was queried for all adult lung transplant recipients from 2006 to 2014. Patients receiving a lung from a DD were compared to those receiving a transplant from a non-DD. Multivariate Cox regression analysis using variables associated with mortality was used to examine survival. A total of 13 159 adult lung transplants were performed between January 2006 and June 2014: 4278 (32.5%) were single-lung transplants (SLT) and 8881 (67.5%) were double-lung transplants (DLT). The log-rank test demonstrated a lower median survival in the DD group (5.6 vs 5.0 years, P = 0.003). We performed additional analysis by dividing this initial cohort into two cohorts by transplant type. On multivariate analysis, receiving an SLT from a DD was associated with increased mortality (HR 1.28, 95% CI 1.07–1.54, P = 0.011). Interestingly, multivariate analysis demonstrated no difference in mortality rates for patients receiving a DLT from a DD (HR 1.12, 95% CI 0.97–1.30, P = 0.14). DLT with DDs can be performed safely without increased mortality, but SLT using DDs results in worse survival and post-transplant outcomes. Preference should be given to DLT when using lungs from donors with diabetes. © The Author 2016. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.
Interactions between macromolecule-bound antioxidants and Trolox during liposome autoxidation: A multivariate approach.

PubMed

Çelik, Ecem Evrim; Rubio, Jose Manuel Amigo; Andersen, Mogens L; Gökmen, Vural

2017-12-15

The interactions between free and macromolecule-bound antioxidants were investigated in order to evaluate their combined effects on the antioxidant environment. Dietary fiber (DF), protein and lipid-bound antioxidants, obtained from whole wheat, soybean and olive oil products, respectively and Trolox were used for this purpose. Experimental studies were carried out in autoxidizing liposome medium by monitoring the development of fluorescent products formed by lipid oxidation. Chemometric methods were used both at experimental design and multivariate data analysis stages. Comparison of the simple addition effects of Trolox and bound antioxidants with measured values on lipid oxidation revealed synergetic interactions for DF and refined olive oil-bound antioxidants, and antagonistic interactions for protein and extra virgin olive oil-bound antioxidants with Trolox. A generalized version of logistic function was successfully used for modelling the oxidation curve of liposomes. Principal component analysis revealed two separate phases of liposome autoxidation. Copyright © 2017 Elsevier Ltd. All rights reserved.
Geographically Sourcing Cocaine’s Origin – Delineation of the Nineteen Major Coca Growing Regions in South America

PubMed Central

Mallette, Jennifer R.; Casale, John F.; Jordan, James; Morello, David R.; Beyer, Paul M.

2016-01-01

Previously, geo-sourcing to five major coca growing regions within South America was accomplished. However, the expansion of coca cultivation throughout South America made sub-regional origin determinations increasingly difficult. The former methodology was recently enhanced with additional stable isotope analyses (2H and 18O) to fully characterize cocaine due to the varying environmental conditions in which the coca was grown. An improved data analysis method was implemented with the combination of machine learning and multivariate statistical analysis methods to provide further partitioning between growing regions. Here, we show how the combination of trace cocaine alkaloids, stable isotopes, and multivariate statistical analyses can be used to classify illicit cocaine as originating from one of 19 growing regions within South America. The data obtained through this approach can be used to describe current coca cultivation and production trends, highlight trafficking routes, as well as identify new coca growing regions. PMID:27006288
Exploratory analysis of TOF-SIMS data from biological surfaces

NASA Astrophysics Data System (ADS)

Vaidyanathan, Seetharaman; Fletcher, John S.; Henderson, Alex; Lockyer, Nicholas P.; Vickerman, John C.

2008-12-01

The application of multivariate analytical tools enables simplification of TOF-SIMS datasets so that useful information can be extracted from complex spectra and images, especially those that do not give readily interpretable results. There is however a challenge in understanding the outputs from such analyses. The problem is complicated when analysing images, given the additional dimensions in the dataset. Here we demonstrate how the application of simple pre-processing routines can enable the interpretation of TOF-SIMS spectra and images. For the spectral data, TOF-SIMS spectra used to discriminate bacterial isolates associated with urinary tract infection were studied. Using different criteria for picking peaks before carrying out PC-DFA enabled identification of the discriminatory information with greater certainty. For the image data, an air-dried salt stressed bacterial sample, discussed in another paper by us in this issue, was studied. Exploration of the image datasets with and without normalisation prior to multivariate analysis by PCA or MAF resulted in different regions of the image being highlighted by the techniques.
On Models for Binomial Data with Random Numbers of Trials

PubMed Central

Comulada, W. Scott; Weiss, Robert E.

2010-01-01

Summary A binomial outcome is a count s of the number of successes out of the total number of independent trials n = s + f, where f is a count of the failures. The n are random variables not fixed by design in many studies. Joint modeling of (s, f) can provide additional insight into the science and into the probability π of success that cannot be directly incorporated by the logistic regression model. Observations where n = 0 are excluded from the binomial analysis yet may be important to understanding how π is influenced by covariates. Correlation between s and f may exist and be of direct interest. We propose Bayesian multivariate Poisson models for the bivariate response (s, f), correlated through random effects. We extend our models to the analysis of longitudinal and multivariate longitudinal binomial outcomes. Our methodology was motivated by two disparate examples, one from teratology and one from an HIV tertiary intervention study. PMID:17688514
Quality by design case study: an integrated multivariate approach to drug product and process development.

PubMed

Huang, Jun; Kaul, Goldi; Cai, Chunsheng; Chatlapalli, Ramarao; Hernandez-Abad, Pedro; Ghosh, Krishnendu; Nagi, Arwinder

2009-12-01

To facilitate an in-depth process understanding, and offer opportunities for developing control strategies to ensure product quality, a combination of experimental design, optimization and multivariate techniques was integrated into the process development of a drug product. A process DOE was used to evaluate effects of the design factors on manufacturability and final product CQAs, and establish design space to ensure desired CQAs. Two types of analyses were performed to extract maximal information, DOE effect & response surface analysis and multivariate analysis (PCA and PLS). The DOE effect analysis was used to evaluate the interactions and effects of three design factors (water amount, wet massing time and lubrication time), on response variables (blend flow, compressibility and tablet dissolution). The design space was established by the combined use of DOE, optimization and multivariate analysis to ensure desired CQAs. Multivariate analysis of all variables from the DOE batches was conducted to study relationships between the variables and to evaluate the impact of material attributes/process parameters on manufacturability and final product CQAs. The integrated multivariate approach exemplifies application of QbD principles and tools to drug product and process development.
Applying Multivariate Discrete Distributions to Genetically Informative Count Data.

PubMed

Kirkpatrick, Robert M; Neale, Michael C

2016-03-01

We present a novel method of conducting biometric analysis of twin data when the phenotypes are integer-valued counts, which often show an L-shaped distribution. Monte Carlo simulation is used to compare five likelihood-based approaches to modeling: our multivariate discrete method, when its distributional assumptions are correct, when they are incorrect, and three other methods in common use. With data simulated from a skewed discrete distribution, recovery of twin correlations and proportions of additive genetic and common environment variance was generally poor for the Normal, Lognormal and Ordinal models, but good for the two discrete models. Sex-separate applications to substance-use data from twins in the Minnesota Twin Family Study showed superior performance of two discrete models. The new methods are implemented using R and OpenMx and are freely available.
Estimating an Effect Size in One-Way Multivariate Analysis of Variance (MANOVA)

ERIC Educational Resources Information Center

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

2009-01-01

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

NASA Astrophysics Data System (ADS)

D'Souza, Adora M.; Abidin, Anas Zainul; Leistritz, Lutz; Wismüller, Axel

2016-03-01

We demonstrate an approach to measure the information flow between each pair of time series in resting-state functional MRI (fMRI) data of the human brain and subsequently recover its underlying network structure. By integrating dimensionality reduction into predictive time series modeling, large-scale Granger Causality (lsGC) analysis method can reveal directed information flow suggestive of causal influence at an individual voxel level, unlike other multivariate approaches. This method quantifies the influence each voxel time series has on every other voxel time series in a multivariate sense and hence contains information about the underlying dynamics of the whole system, which can be used to reveal functionally connected networks within the brain. To identify such networks, we perform non-metric network clustering, such as accomplished by the Louvain method. We demonstrate the effectiveness of our approach to recover the motor and visual cortex from resting state human brain fMRI data and compare it with the network recovered from a visuomotor stimulation experiment, where the similarity is measured by the Dice Coefficient (DC). The best DC obtained was 0.59 implying a strong agreement between the two networks. In addition, we thoroughly study the effect of dimensionality reduction in lsGC analysis on network recovery. We conclude that our approach is capable of detecting causal influence between time series in a multivariate sense, which can be used to segment functionally connected networks in the resting-state fMRI.
Dangers in Using Analysis of Covariance Procedures.

ERIC Educational Resources Information Center

Campbell, Kathleen T.

Problems associated with the use of analysis of covariance (ANCOVA) as a statistical control technique are explained. Three problems relate to the use of "OVA" methods (analysis of variance, analysis of covariance, multivariate analysis of variance, and multivariate analysis of covariance) in general. These are: (1) the wasting of information when…
The role of whole brain radiation therapy in the management of melanoma brain metastases

PubMed Central

2014-01-01

Background Brain metastases are common in patients with melanoma, and optimal management is not well defined. As melanoma has traditionally been thought of as “radioresistant,” the role of whole brain radiation therapy (WBRT) in particular is unclear. We conducted this retrospective study to identify prognostic factors for patients treated with stereotactic radiosurgery (SRS) for melanoma brain metastases and to investigate the role of additional up-front treatment with whole brain radiation therapy (WBRT). Methods We reviewed records of 147 patients who received SRS as part of initial management of their melanoma brain metastases from January 2000 through June 2010. Overall survival (OS) and time to distant intracranial progression were calculated using the Kaplan-Meier method. Prognostic factors were evaluated using the Cox proportional hazards model. Results WBRT was employed with SRS in 27% of patients and as salvage in an additional 22%. Age at SRS > 60 years (hazard ratio [HR] 0.64, p = 0.05), multiple brain metastases (HR 1.90, p = 0.008), and omission of up-front WBRT (HR 2.24, p = 0.005) were associated with distant intracranial progression on multivariate analysis. Extensive extracranial metastases (HR 1.86, p = 0.0006), Karnofsky Performance Status (KPS) ≤ 80% (HR 1.58, p = 0.01), and multiple brain metastases (HR 1.40, p = 0.06) were associated with worse OS on univariate analysis. Extensive extracranial metastases (HR 1.78, p = 0.001) and KPS (HR 1.52, p = 0.02) remained significantly associated with OS on multivariate analysis. In patients with absent or stable extracranial disease, multiple brain metastases were associated with worse OS (multivariate HR 5.89, p = 0.004), and there was a trend toward an association with worse OS when up-front WBRT was omitted (multivariate HR 2.56, p = 0.08). Conclusions Multiple brain metastases and omission of up-front WBRT (particularly in combination) are associated with distant intracranial progression. Improvement in intracranial disease control may be especially important in the subset of patients with absent or stable extracranial disease, where the competing risk of death from extracranial disease is low. These results are hypothesis generating and require confirmation from ongoing randomized trials. PMID:24954062
A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.

PubMed

Meeker, Daniella; Jiang, Xiaoqian; Matheny, Michael E; Farcas, Claudiu; D'Arcy, Michel; Pearlman, Laura; Nookala, Lavanya; Day, Michele E; Kim, Katherine K; Kim, Hyeoneui; Boxwala, Aziz; El-Kareh, Robert; Kuo, Grace M; Resnic, Frederic S; Kesselman, Carl; Ohno-Machado, Lucila

2015-11-01

Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.
Utility of Intermediate-Delay Washout CT Images for Differentiation of Malignant and Benign Adrenal Lesions: A Multivariate Analysis.

PubMed

Ng, Chaan S; Altinmakas, Emre; Wei, Wei; Ghosh, Payel; Li, Xiao; Grubbs, Elizabeth G; Perrier, Nancy D; Lee, Jeffrey E; Prieto, Victor G; Hobbs, Brian P

2018-06-27

The objective of this study was to identify features that impact the diagnostic performance of intermediate-delay washout CT for distinguishing malignant from benign adrenal lesions. This retrospective study evaluated 127 pathologically proven adrenal lesions (82 malignant, 45 benign) in 126 patients who had undergone portal venous phase and intermediate-delay washout CT (1-3 minutes after portal venous phase) with or without unenhanced images. Unenhanced images were available for 103 lesions. Quantitatively, lesion CT attenuation on unenhanced (UA) and delayed (DL) images, absolute and relative percentage of enhancement washout (APEW and RPEW, respectively), descriptive CT features (lesion size, margin characteristics, heterogeneity or homogeneity, fat, calcification), patient demographics, and medical history were evaluated for association with lesion status using multiple logistic regression with stepwise model selection. Area under the ROC curve (A z ) was calculated from both univariate and multivariate analyses. The predictive diagnostic performance of multivariate evaluations was ascertained through cross-validation. A z for DL, APEW, RPEW, and UA was 0.751, 0.795, 0.829, and 0.839, respectively. Multivariate analyses yielded the following significant CT quantitative features and associated A z when combined: RPEW and DL (A z = 0.861) when unenhanced images were not available and APEW and UA (A z = 0.889) when unenhanced images were available. Patient demographics and presence of a prior malignancy were additional significant factors, increasing A z to 0.903 and 0.927, respectively. The combined predictive classifier, without and with UA available, yielded 85.7% and 87.3% accuracies with cross-validation, respectively. When appropriately combined with other CT features, washout derived from intermediate-delay CT with or without additional clinical data has potential utility in differentiating malignant from benign adrenal lesions.
Resilience following spinal cord injury: A prospective controlled study investigating the influence of the provision of group cognitive behavior therapy during inpatient rehabilitation.

PubMed

Guest, Rebecca; Craig, Ashley; Nicholson Perry, Kathryn; Tran, Yvonne; Ephraums, Catherine; Hales, Alison; Dezarnaulds, Annalisa; Crino, Rocco; Middleton, James

2015-11-01

To examine change in resilience in people with spinal cord injury (SCI) when group cognitive behavior therapy (GCBT) was added to routine psychosocial rehabilitation (RPR). A prospective repeated-measures cohort design was used to determine the efficacy of the addition of GCBT (n = 50). The control group consisted of individuals receiving RPR, which included access to individual CBT (ICBT) when required (n = 38). Groups were assessed on 3 occasions: soon after admission, within 2 weeks of discharge, and 6-months postdischarge. Measures included sociodemographic, injury, and psychosocial factors. The outcome variable was resilience, considered an important outcome measure for recovery. To adjust for baseline differences in self-efficacy, depressive mood and anxiety between the 2 groups, these factors were entered into a repeated measures multivariate analysis of covariance (MANCOVA) as covariates. Latent class analysis was used to determine the best-fitting model of resilience trajectories for both groups. The MANCOVA indicated that the addition of GCBT to psychosocial rehabilitation did not result in improved resilience compared with the ICBT group. Trajectory data indicated over 60% were demonstrating acceptable resilience irrespective of group. Changes in resilience mean scores suggest the addition of GCBT adds little to resilience outcomes. Latent class modeling indicated both groups experienced similar trajectories of improvement and deterioration. Results highlight the importance of conducting multivariate modeling analysis that isolates subgroups of related cases over time to understand complex trajectories. Further research is needed to clarify individual differences in CBT intervention preference as well as other factors which impact on resilience. (c) 2015 APA, all rights reserved).
Preoperative Radiotherapy of Advanced Rectal Cancer With Capecitabine and Oxaliplatin With or Without Cetuximab: A Pooled Analysis of Three Prospective Phase I-II Trials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weiss, Christian, E-mail: christian.weiss@kgu.d; Arnold, Dirk; Dellas, Kathrin

2010-10-01

Purpose: A pooled analysis of three prospective trials of preoperative radiochemotherapy (RCT) for rectal cancer by using oxaliplatin and capecitabine with or without cetuximab was performed to evaluate the impact of additional cetuximab on pathologic complete response (pCR) rates and tumor regression (TRG) grades. Methods and Materials: Of 202 patients, 172 patients met the inclusion criteria (primary tumor stage II/III, M0). All patients received concurrent RCT, and 46 patients received additional cetuximab therapy. A correlation of pretreatment clinicopathologic factors and cetuximab treatment with early pCR rates (TRG > 50%) was performed with univariate and multivariate analyses. Toxicity data were recordedmore » for all patients. Results: Of 172 patients, 24 (14%) patients achieved a pCR, and 84 of 172 (71%) patients showed a TRG of >50% in the surgical specimen assessment after preoperative treatment. Age, gender, and T/N stages, as well as localization of the tumor, were not associated with pCR or good TRG. The pCR rate was 16% after preoperative RCT alone and 9% with concurrent cetuximab therapy (p = 0.32). A significantly reduced TRG of >50% was found after RCT with cetuximab compared to RCT alone (p = 0.0035). This was validated by a multivariate analysis with all available clinical factors (p = 0.0037). Acute toxicity and surgical complications were not increased with additional cetuximab. Conclusions: Triple therapy with RCT and cetuximab seems to be feasible, with no unexpected toxicity. Early response assessment (TRG), however, suggests subadditive interaction. A longer follow-up (and finally randomized trials) is needed to draw any firm conclusions with respect to local and distant failure rates.« less
The Impact of Adjuvant Postoperative Radiation Therapy and Chemotherapy on Survival After Esophagectomy for Esophageal Carcinoma.

PubMed

Wong, Andrew T; Shao, Meng; Rineer, Justin; Lee, Anna; Schwartz, David; Schreiber, David

2017-06-01

The objective of this study was to analyze the impact on overall survival (OS) from the addition of postoperative radiation with or without chemotherapy after esophagectomy, using a large, hospital-based dataset. Previous retrospective studies have suggested an OS advantage for postoperative chemoradiation over surgery alone, although prospective data are lacking. The National Cancer Data Base was queried to select patients diagnosed with stage pT3-4Nx-0M0 or pT1-4N1-3M0 esophageal carcinoma (squamous cell or adenocarcinoma) from 1998 to 2011 treated with definitive esophagectomy ± postoperative radiation and/or chemotherapy. OS was analyzed using the Kaplan-Meier method and compared using the log-rank test. Multivariate Cox regression analysis was used to identify covariates associated with OS. There were 4893 patients selected, of whom 1153 (23.6%) received postoperative radiation. Most patients receiving radiation also received sequential/concomitant chemotherapy (89.9%). For the entire cohort, postoperative radiation was associated with a statistically significant but modest absolute improvement in survival (hazard ratio 0.77; 95% CI, 0.71-0.83; P < 0.001). On subgroup analysis, postoperative radiation was associated with improved OS for patients with node-positive disease (3-yr OS 34.3 % vs 27.8%, P < 0.001) or positive margins (3-yr OS 36.4% vs 18.0%, P < 0.001). When chemotherapy usage was incorporated, sequential chemotherapy was associated with the best survival (P < 0.001). Multivariate analysis revealed that the addition of chemotherapy to radiation therapy, whether sequentially or concurrently, was a strong prognostic factor for OS. In this hospital-based study, the addition of postoperative chemoradiation (either sequentially or concomitantly) after esophagectomy was associated with improved OS for patients with node-positive disease or positive margins.
The intervals method: a new approach to analyse finite element outputs using multivariate statistics

PubMed Central

De Esteban-Trivigno, Soledad; Püschel, Thomas A.; Fortuny, Josep

2017-01-01

Background In this paper, we propose a new method, named the intervals’ method, to analyse data from finite element models in a comparative multivariate framework. As a case study, several armadillo mandibles are analysed, showing that the proposed method is useful to distinguish and characterise biomechanical differences related to diet/ecomorphology. Methods The intervals’ method consists of generating a set of variables, each one defined by an interval of stress values. Each variable is expressed as a percentage of the area of the mandible occupied by those stress values. Afterwards these newly generated variables can be analysed using multivariate methods. Results Applying this novel method to the biological case study of whether armadillo mandibles differ according to dietary groups, we show that the intervals’ method is a powerful tool to characterize biomechanical performance and how this relates to different diets. This allows us to positively discriminate between specialist and generalist species. Discussion We show that the proposed approach is a useful methodology not affected by the characteristics of the finite element mesh. Additionally, the positive discriminating results obtained when analysing a difficult case study suggest that the proposed method could be a very useful tool for comparative studies in finite element analysis using multivariate statistical approaches. PMID:29043107
Multivariate moment closure techniques for stochastic kinetic models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lakatos, Eszter, E-mail: e.lakatos13@imperial.ac.uk; Ale, Angelique; Kirk, Paul D. W.

2015-09-07

Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporallymore » evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.« less
Marital status is an independent prognostic factor for tracheal cancer patients: an analysis of the SEER database.

PubMed

Li, Mu; Dai, Chen-Yang; Wang, Yu-Ning; Chen, Tao; Wang, Long; Yang, Ping; Xie, Dong; Mao, Rui; Chen, Chang

2016-11-22

Although marital status is an independent prognostic factor in many cancers, its prognostic impact on tracheal cancer has not yet been determined. The goal of this study was to examine the relationship between marital status and survival in patients with tracheal cancer. Compared with unmarried patients (42.67%), married patients (57.33%) had better 5-year OS (25.64% vs. 35.89%, p = 0.009) and 5-year TCSS (44.58% vs. 58.75%, p = 0.004). Results of multivariate analysis indicated that marital status is an independent prognostic factor, with married patients showing better OS (hazard ratio [HR] = 0.78, 95% confidence interval [CI] 0.64-0.95, p = 0.015) and TCSS (HR = 0.70, 95% CI 0.54-0.91, p = 0.008). In addition, subgroup analysis suggested that marital status plays a more important role in the TCSS of patients with non-low-grade malignant tumors (HR = 0.71, 95% CI 0.53-0.93, p = 0.015). We extracted 600 cases from the Surveillance, Epidemiology, and End Results (SEER) database. Variables were compared by Pearson chi-squared test, t-test, log-rank test, and multivariate Cox regression analysis. Overall survival (OS) and tracheal cancer-specific survival (TCSS) were compared between subgroups with different pathologic features and tumor stages. Marital status is an independent prognostic factor for survival in patients with tracheal cancer. For that reason, additional social support may be needed for unmarried patients, especially those with non-low-grade malignant tumors.
Rapid differentiation of Chinese hop varieties (Humulus lupulus) using volatile fingerprinting by HS-SPME-GC-MS combined with multivariate statistical analysis.

PubMed

Liu, Zechang; Wang, Liping; Liu, Yumei

2018-01-18

Hops impart flavor to beer, with the volatile components characterizing the various hop varieties and qualities. Fingerprinting, especially flavor fingerprinting, is often used to identify 'flavor products' because inconsistencies in the description of flavor may lead to an incorrect definition of beer quality. Compared to flavor fingerprinting, volatile fingerprinting is simpler and easier. We performed volatile fingerprinting using head space-solid phase micro-extraction gas chromatography-mass spectrometry combined with similarity analysis and principal component analysis (PCA) for evaluating and distinguishing between three major Chinese hops. Eighty-four volatiles were identified, which were classified into seven categories. Volatile fingerprinting based on similarity analysis did not yield any obvious result. By contrast, hop varieties and qualities were identified using volatile fingerprinting based on PCA. The potential variables explained the variance in the three hop varieties. In addition, the dendrogram and principal component score plot described the differences and classifications of hops. Volatile fingerprinting plus multivariate statistical analysis can rapidly differentiate between the different varieties and qualities of the three major Chinese hops. Furthermore, this method can be used as a reference in other fields. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

Rapid discrimination of sea buckthorn berries from different H. rhamnoides subspecies by multi-step IR spectroscopy coupled with multivariate data analysis

NASA Astrophysics Data System (ADS)

Liu, Yue; Zhang, Ying; Zhang, Jing; Fan, Gang; Tu, Ya; Sun, Suqin; Shen, Xudong; Li, Qingzhu; Zhang, Yi

2018-03-01

As an important ethnic medicine, sea buckthorn was widely used to prevent and treat various diseases due to its nutritional and medicinal properties. According to the Chinese Pharmacopoeia, sea buckthorn was originated from H. rhamnoides, which includes five subspecies distributed in China. Confusion and misidentification usually occurred due to their similar morphology, especially in dried and powdered forms. Additionally, these five subspecies have vital differences in quality and physiological efficacy. This paper focused on the quick classification and identification method of sea buckthorn berry powders from five H. rhamnoides subspecies using multi-step IR spectroscopy coupled with multivariate data analysis. The holistic chemical compositions revealed by the FT-IR spectra demonstrated that flavonoids, fatty acids and sugars were the main chemical components. Further, the differences in FT-IR spectra regarding their peaks, positions and intensities were used to identify H. rhamnoides subspecies samples. The discrimination was achieved using principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). The results showed that the combination of multi-step IR spectroscopy and chemometric analysis offered a simple, fast and reliable method for the classification and identification of the sea buckthorn berry powders from different H. rhamnoides subspecies.
Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis.

PubMed

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

PubMed Central

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. PMID:29494626
Multivariate pattern dependence

PubMed Central

Saxe, Rebecca

2017-01-01

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. PMID:29155809
Characterization of Rheumatoid Arthritis Subtypes Using Symptom Profiles, Clinical Chemistry and Metabolomics Measurements

PubMed Central

van der Kooij, Anita J.; Reijmers, Theo H.; Schroën, Yan; Wang, Mei; Xu, Zhiliang; Wang, Xinchang; Kong, Hongwei; Xu, Guowang; Hankemeier, Thomas; Meulman, Jacqueline J.; van der Greef, Jan

2012-01-01

Objective The aim is to characterize subgroups or phenotypes of rheumatoid arthritis (RA) patients using a systems biology approach. The discovery of subtypes of rheumatoid arthritis patients is an essential research area for the improvement of response to therapy and the development of personalized medicine strategies. Methods In this study, 39 RA patients are phenotyped using clinical chemistry measurements, urine and plasma metabolomics analysis and symptom profiles. In addition, a Chinese medicine expert classified each RA patient as a Cold or Heat type according to Chinese medicine theory. Multivariate data analysis techniques are employed to detect and validate biochemical and symptom relationships with the classification. Results The questionnaire items ‘Red joints’, ‘Swollen joints’, ‘Warm joints’ suggest differences in the level of inflammation between the groups although c-reactive protein (CRP) and rheumatoid factor (RHF) levels were equal. Multivariate analysis of the urine metabolomics data revealed that the levels of 11 acylcarnitines were lower in the Cold RA than in the Heat RA patients, suggesting differences in muscle breakdown. Additionally, higher dehydroepiandrosterone sulfate (DHEAS) levels in Heat patients compared to Cold patients were found suggesting that the Cold RA group has a more suppressed hypothalamic-pituitary-adrenal (HPA) axis function. Conclusion Significant and relevant biochemical differences are found between Cold and Heat RA patients. Differences in immune function, HPA axis involvement and muscle breakdown point towards opportunities to tailor disease management strategies to each of the subgroups RA patient. PMID:22984493
Self-Critical, and Robust, Procedures for the Analysis of Multivariate Normal Data.

DTIC Science & Technology

1982-06-01

Influence Functions The influence function is the most important tt of qual- itative zobustness since many other robustness characteristics of an estimator...may be derived from it. The influence function characterizes the (asymptotic) response of an estimator to an additional observation as a function of...the influence function be bounded. It is also advantageous, in our opinion, if the influence functions are re-descending to zero. The influence function for
Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing

DOE PAGES

Steed, Chad A.; Halsey, William; Dehoff, Ryan; ...

2017-02-16

Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. Here, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Our specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system thatmore » allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. The techniques described are applicable to the analysis of any quantitative time series, though the focus of this paper is on additive manufacturing.« less
Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A.; Halsey, William; Dehoff, Ryan

Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. Here, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Our specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system thatmore » allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. The techniques described are applicable to the analysis of any quantitative time series, though the focus of this paper is on additive manufacturing.« less
Effects of Green Tea Gargling on the Prevention of Influenza Infection: An Analysis Using Bayesian Approaches.

PubMed

Ide, Kazuki; Kawasaki, Yohei; Akutagawa, Maiko; Yamada, Hiroshi

2017-02-01

The aim of this study is to analyze the data obtained from a randomized trial on the prevention of influenza by gargling with green tea, which gave nonsignificant results based on frequentist approaches, by using Bayesian approaches. The posterior proportion, with 95% credible interval (CrI), of influenza in each group was calculated. The Bayesian index θ is the probability that a hypothesis is true. In this case, θ is the probability that the hypothesis that green tea gargling reduced influenza compared with water gargling is true. Univariate and multivariate logistic regression analyses were also performed by using the Markov chain Monte Carlo method. The full analysis set included 747 participants. During the study period, influenza occurred in 44 participants (5.9%). The difference between the two independent binominal proportions was -0.019 (95% CrI, -0.054 to 0.015; θ = 0.87). The partial regression coefficients in the univariate analysis were -0.35 (95% CrI, -1.00 to 0.24) with use of a uniform prior and -0.34 (95% CrI, -0.96 to 0.27) with use of a Jeffreys prior. In the multivariate analysis, the values were -0.37 (95% CrI, -0.96 to 0.30) and -0.36 (95% CrI, -1.03 to 0.21), respectively. The difference between the two independent binominal proportions was less than 0, and θ was greater than 0.85. Therefore, green tea gargling may slightly reduce influenza compared with water gargling. This analysis suggests that green tea gargling can be an additional preventive measure for use with other pharmaceutical and nonpharmaceutical measures and indicates the need for additional studies to confirm the effect of green tea gargling.
Chemical Discrimination of Cortex Phellodendri amurensis and Cortex Phellodendri chinensis by Multivariate Analysis Approach.

PubMed

Sun, Hui; Wang, Huiyu; Zhang, Aihua; Yan, Guangli; Han, Ying; Li, Yuan; Wu, Xiuhong; Meng, Xiangcai; Wang, Xijun

2016-01-01

As herbal medicines have an important position in health care systems worldwide, their current assessment, and quality control are a major bottleneck. Cortex Phellodendri chinensis (CPC) and Cortex Phellodendri amurensis (CPA) are widely used in China, however, how to identify species of CPA and CPC has become urgent. In this study, multivariate analysis approach was performed to the investigation of chemical discrimination of CPA and CPC. Principal component analysis showed that two herbs could be separated clearly. The chemical markers such as berberine, palmatine, phellodendrine, magnoflorine, obacunone, and obaculactone were identified through the orthogonal partial least squared discriminant analysis, and were identified tentatively by the accurate mass of quadruple-time-of-flight mass spectrometry. A total of 29 components can be used as the chemical markers for discrimination of CPA and CPC. Of them, phellodenrine is significantly higher in CPC than that of CPA, whereas obacunone and obaculactone are significantly higher in CPA than that of CPC. The present study proves that multivariate analysis approach based chemical analysis greatly contributes to the investigation of CPA and CPC, and showed that the identified chemical markers as a whole should be used to discriminate the two herbal medicines, and simultaneously the results also provided chemical information for their quality assessment. Multivariate analysis approach was performed to the investigate the herbal medicineThe chemical markers were identified through multivariate analysis approachA total of 29 components can be used as the chemical markers. UPLC-Q/TOF-MS-based multivariate analysis method for the herbal medicine samples Abbreviations used: CPC: Cortex Phellodendri chinensis, CPA: Cortex Phellodendri amurensis, PCA: Principal component analysis, OPLS-DA: Orthogonal partial least squares discriminant analysis, BPI: Base peaks ion intensity.
A comparison study on detection of key geochemical variables and factors through three different types of factor analysis

NASA Astrophysics Data System (ADS)

Hoseinzade, Zohre; Mokhtari, Ahmad Reza

2017-10-01

Large numbers of variables have been measured to explain different phenomena. Factor analysis has widely been used in order to reduce the dimension of datasets. Additionally, the technique has been employed to highlight underlying factors hidden in a complex system. As geochemical studies benefit from multivariate assays, application of this method is widespread in geochemistry. However, the conventional protocols in implementing factor analysis have some drawbacks in spite of their advantages. In the present study, a geochemical dataset including 804 soil samples collected from a mining area in central Iran in order to search for MVT type Pb-Zn deposits was considered to outline geochemical analysis through various fractal methods. Routine factor analysis, sequential factor analysis, and staged factor analysis were applied to the dataset after opening the data with (additive logratio) alr-transformation to extract mineralization factor in the dataset. A comparison between these methods indicated that sequential factor analysis has more clearly revealed MVT paragenesis elements in surface samples with nearly 50% variation in F1. In addition, staged factor analysis has given acceptable results while it is easy to practice. It could detect mineralization related elements while larger factor loadings are given to these elements resulting in better pronunciation of mineralization.
metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.

PubMed

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

PubMed Central

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

2016-01-01

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

PubMed Central

Ohki, Takamasa; Sato, Koki; Yamada, Tomoharu; Yamagami, Mari; Ito, Daisaku; Kawanishi, Koki; Kojima, Kentaro; Seki, Michiharu; Toda, Nobuo; Tagawa, Kazumi

2015-01-01

AIM: To elucidate the efficacies of tolvaptan (TLV) as a treatment for refractory ascites compared with conventional treatment. METHODS: We retrospectively enrolled 120 refractory ascites patients between January 1, 2009 and September 31, 2014. Sixty patients were treated with oral TLV at a starting dose of 3.75 mg/d in addition to sodium restriction (> 7 g/d), albumin infusion (10-20 g/wk), and standard diuretic therapy (20-60 mg/d furosemide and 25-50 mg/d spironolactone) and 60 patients with large volume paracentesis in addition to sodium restriction (less than 7 g/d), albumin infusion (10-20 g/wk), and standard diuretic therapy (20-120 mg/d furosemide and 25-150 mg/d spironolactone). Patient demographics and laboratory data, including liver function, were not matched due to the small number of patients. Continuous variables were analyzed by unpaired t-test or paired t-test. Fisher’s exact test was applied in cases comparing two nominal variables. We analyzed factors affecting clinical outcomes using receiver operating characteristic curves and multivariate regression analysis. We also used multivariate Cox’s proportional hazard regression analysis to elucidate the risk factors that contributed to the increased incidence of ascites. RESULTS: TLV was effective in 38 (63.3%) patients. The best cut-off values for urine output and reduced urine osmolality as measures of refractory ascites improvement were > 1800 mL within the first 24 h and > 30%, respectively. Multivariate regression analysis indicated that > 25% reduced urine osmolality [odds ratio (OR) = 20.7; P < 0.01] and positive hepatitis C viral antibodies (OR = 5.93; P = 0.05) were positively correlated with an improvement of refractory ascites, while the total bilirubin level per 1.0 mg/dL (OR = 0.57; P = 0.02) was negatively correlated with improvement. In comparing the TLV group and controls, only the serum sodium level was significantly lower in the TLV group (133 mEq/L vs 136 mEq/L; P = 0.02). However, there were no significant differences in the other parameters between the two groups. The cumulative incidence rate was significantly higher in the control group with a median incidence time of 30 d in the TLV group and 20 d in the control group (P = 0.01). Cox hazard proportional multivariate analysis indicated that the use of TLV (OR = 0.58; P < 0.01), uncontrolled liver neoplasms (OR = 1.92; P < 0.01), total bilirubin level per 1.0 mg/dL (OR = 1.10; P < 0.01), and higher sodium level per 1.0 mEq/L (OR = 0.94; P < 0.01) were independent factors that contributed to incidence. CONCLUSION: Administration of TLV results in better control of refractory ascites and reduced the incidence of additional invasive procedures or hospitalization compared with conventional ascites treatments. PMID:26140088
Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students

ERIC Educational Resources Information Center

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

2011-01-01

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

PubMed

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

2017-03-01

The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate statistics. Integr Environ Assess Manag 2017;13:387-395. © 2016 SETAC. © 2016 SETAC.
A power analysis for multivariate tests of temporal trend in species composition.

PubMed

Irvine, Kathryn M; Dinger, Eric C; Sarr, Daniel

2011-10-01

Long-term monitoring programs emphasize power analysis as a tool to determine the sampling effort necessary to effectively document ecologically significant changes in ecosystems. Programs that monitor entire multispecies assemblages require a method for determining the power of multivariate statistical models to detect trend. We provide a method to simulate presence-absence species assemblage data that are consistent with increasing or decreasing directional change in species composition within multiple sites. This step is the foundation for using Monte Carlo methods to approximate the power of any multivariate method for detecting temporal trends. We focus on comparing the power of the Mantel test, permutational multivariate analysis of variance, and constrained analysis of principal coordinates. We find that the power of the various methods we investigate is sensitive to the number of species in the community, univariate species patterns, and the number of sites sampled over time. For increasing directional change scenarios, constrained analysis of principal coordinates was as or more powerful than permutational multivariate analysis of variance, the Mantel test was the least powerful. However, in our investigation of decreasing directional change, the Mantel test was typically as or more powerful than the other models.
Fourier Transform Infrared Spectroscopy (FTIR) and Multivariate Analysis for Identification of Different Vegetable Oils Used in Biodiesel Production

PubMed Central

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

2013-01-01

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

PubMed Central

Gasparrini, A; Armstrong, B; Kenward, M G

2012-01-01

In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22807043
Changes in Concurrent Risk of Warm and Dry Years under Impact of Climate Change

NASA Astrophysics Data System (ADS)

Sarhadi, A.; Wiper, M.; Touma, D. E.; Ausín, M. C.; Diffenbaugh, N. S.

2017-12-01

Anthropogenic global warming has changed the nature and the risk of extreme climate phenomena. The changing concurrence of multiple climatic extremes (warm and dry years) may result in intensification of undesirable consequences for water resources, human and ecosystem health, and environmental equity. The present study assesses how global warming influences the probability that warm and dry years co-occur in a global scale. In the first step of the study a designed multivariate Mann-Kendall trend analysis is used to detect the areas in which the concurrence of warm and dry years has increased in the historical climate records and also climate models in the global scale. The next step investigates the concurrent risk of the extremes under dynamic nonstationary conditions. A fully generalized multivariate risk framework is designed to evolve through time under dynamic nonstationary conditions. In this methodology, Bayesian, dynamic copulas are developed to model the time-varying dependence structure between the two different climate extremes (warm and dry years). The results reveal an increasing trend in the concurrence risk of warm and dry years, which are in agreement with the multivariate trend analysis from historical and climate models. In addition to providing a novel quantification of the changing probability of compound extreme events, the results of this study can help decision makers develop short- and long-term strategies to prepare for climate stresses now and in the future.

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

ERIC Educational Resources Information Center

Gaotlhobogwe, Michael; Laugharne, Janet; Durance, Isabelle

2011-01-01

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

PubMed

Thulin, M

2016-09-10

Testing whether the mean vector of a multivariate set of biomarkers differs between several populations is an increasingly common problem in medical research. Biomarker data is often left censored because some measurements fall below the laboratory's detection limit. We investigate how such censoring affects multivariate two-sample and one-way multivariate analysis of variance tests. Type I error rates, power and robustness to increasing censoring are studied, under both normality and non-normality. Parametric tests are found to perform better than non-parametric alternatives, indicating that the current recommendations for analysis of censored multivariate data may have to be revised. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
A non-iterative extension of the multivariate random effects meta-analysis.

PubMed

Makambi, Kepher H; Seung, Hyunuk

2015-01-01

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

PubMed

Kim, Siwon; Yoon, Dahye; Lee, Dong-Kye; Yoon, Changshin; Kim, Suhkmann

2017-07-01

Identification of suspected automobile-related lubricants could provide valuable information in forensic cases. We examined that automobile lubricants might exhibit the chemometric characteristics to their individual usages. To compare the degree of clustering in the plots, we co-plotted general industrial oils that were highly dissimilar with automobile lubricants in additive compositions. 1 H-NMR spectroscopy was used with multivariate statistics as a tool for grouping, clustering, and identification of automobile lubricants in laboratory conditions. We analyzed automobile lubricants including automobile engine oils, automobile transmission oils, automobile gear oils, and motorcycle oils. In contrast to the general industrial oils, automobile lubricants showed relatively high tendencies of clustering to their usages. Our pilot study demonstrated that the comparison of known and questioned samples to their usages might be possible in forensic fields. © 2017 American Academy of Forensic Sciences.
Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705
A refined method for multivariate meta-analysis and meta-regression.

PubMed

Jackson, Daniel; Riley, Richard D

2014-02-20

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects' standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. Copyright © 2013 John Wiley & Sons, Ltd.
Are prostatic calculi independent predictive factors of lower urinary tract symptoms?

PubMed Central

Park, Sung-Woo; Nam, Jong-Kil; Lee, Sang-Don; Chung, Moon-Kee

2010-01-01

We determined the correlation between prostatic calculi and lower urinary tract symptoms (LUTS), as well as the predisposing factors of prostatic calculi. Of the 1 527 patients who presented at our clinic for LUTS, 802 underwent complete evaluations, including transrectal ultrasonography, voided bladder-3 specimen and international prostatic symptoms score (IPSS). A total of 335 patients with prostatic calculi and 467 patients without prostatic calculi were divided into calculi and no calculi groups, respectively. Predictive factors of severe LUTS and prostatic calculi were determined using uni/multivariate analysis. The overall IPSS score was 15.7 ± 9.2 and 14.1 ± 9.2 in the calculi and no calculi group, respectively (P = 0.013). The maximum flow rate was 12.1 ± 6.9 and 14.2 ± 8.2 mL s−1 in the calculi and no calculi group, respectively (P = 0.003). On univariate analysis for predicting factors of severe LUTS, differences on age (P = 0.042), prostatic calculi (P = 0.048) and prostatitis (P = 0.018) were statistically significant. However, on multivariate analysis, no factor was significant. On multivariate analysis for predisposing factors of prostatic calculi, differences on age (P < 0.001) and prostate volume (P = 0.001) were significant. To our knowledge, patients who have prostatic calculi complain of more severe LUTS. However, prostatic calculi are not an independent predictive factor of severe LUTS. Therefore, men with prostatic calculi have more severe LUTS not only because of prostatic calculi but also because of age and other factors. In addition, old age and large prostate volume are independent predisposing factors for prostatic calculi. PMID:19966831
Association of left subclavian artery coverage without revascularization and spinal cord ischemia in patients undergoing thoracic endovascular aortic repair: A Vascular Quality Initiative® analysis.

PubMed

Teixeira, Pedro Gr; Woo, Karen; Beck, Adam W; Scali, Salvatore T; Weaver, Fred A

2017-12-01

Objectives Investigate the impact of left subclavian artery coverage without revascularization on spinal cord ischemia development in patients undergoing thoracic endovascular aortic repair. Methods The Vascular Quality Initiative thoracic endovascular aortic repair module (April 2011-July 2014) was analyzed. Patients undergoing left subclavian artery coverage were divided into two groups according to revascularization status. The association between left subclavian artery revascularization with the primary outcome of spinal cord ischemia and the secondary outcome of stroke was assessed with multivariable analysis adjusting for between-group baseline differences. Results The left subclavian artery was covered in 508 (24.6%) of the 2063 thoracic endovascular aortic repairs performed. Among patients with left subclavian artery coverage, 58.9% underwent revascularization. Spinal cord ischemia incidence was 12.1% in the group without revascularization compared to 8.5% in the group undergoing left subclavian artery revascularization (odds ratio (95%CI): 1.48(0.82-2.68), P = 0.189). Multivariable analysis adjustment identified an independent association between left subclavian artery coverage without revascularization and the incidence of spinal cord ischemia (adjusted odds ratio (95%CI): 2.29(1.03-5.14), P = 0.043). Although the incidence of stroke was also higher for the group with a covered and nonrevascularized left subclavian artery (12.1% versus 8.5%), this difference was not statistically significant after multivariable analysis (adjusted odds ratio (95%CI): 1.55(0.74-3.26), P = 0.244). Conclusion For patients undergoing left subclavian artery coverage during thoracic endovascular aortic repair, the addition of a revascularization procedure was associated with a significantly lower incidence of spinal cord ischemia.
Spatial characterization of dissolved trace elements and heavy metals in the upper Han River (China) using multivariate statistical techniques.

PubMed

Li, Siyue; Zhang, Quanfa

2010-04-15

A data matrix (4032 observations), obtained during a 2-year monitoring period (2005-2006) from 42 sites in the upper Han River is subjected to various multivariate statistical techniques including cluster analysis, principal component analysis (PCA), factor analysis (FA), correlation analysis and analysis of variance to determine the spatial characterization of dissolved trace elements and heavy metals. Our results indicate that waters in the upper Han River are primarily polluted by Al, As, Cd, Pb, Sb and Se, and the potential pollutants include Ba, Cr, Hg, Mn and Ni. Spatial distribution of trace metals indicates the polluted sections mainly concentrate in the Danjiang, Danjiangkou Reservoir catchment and Hanzhong Plain, and the most contaminated river is in the Hanzhong Plain. Q-model clustering depends on geographical location of sampling sites and groups the 42 sampling sites into four clusters, i.e., Danjiang, Danjiangkou Reservoir region (lower catchment), upper catchment and one river in headwaters pertaining to water quality. The headwaters, Danjiang and lower catchment, and upper catchment correspond to very high polluted, moderate polluted and relatively low polluted regions, respectively. Additionally, PCA/FA and correlation analysis demonstrates that Al, Cd, Mn, Ni, Fe, Si and Sr are controlled by natural sources, whereas the other metals appear to be primarily controlled by anthropogenic origins though geogenic source contributing to them. 2009 Elsevier B.V. All rights reserved.
Elimination of chromatographic and mass spectrometric problems in GC-MS analysis of Lavender essential oil by multivariate curve resolution techniques: Improving the peak purity assessment by variable size moving window-evolving factor analysis.

PubMed

Jalali-Heravi, Mehdi; Moazeni-Pourasil, Roudabeh Sadat; Sereshti, Hassan

2015-03-01

In analysis of complex natural matrices by gas chromatography-mass spectrometry (GC-MS), many disturbing factors such as baseline drift, spectral background, homoscedastic and heteroscedastic noise, peak shape deformation (non-Gaussian peaks), low S/N ratio and co-elution (overlapped and/or embedded peaks) lead the researchers to handle them to serve time, money and experimental efforts. This study aimed to improve the GC-MS analysis of complex natural matrices utilizing multivariate curve resolution (MCR) methods. In addition, to assess the peak purity of the two-dimensional data, a method called variable size moving window-evolving factor analysis (VSMW-EFA) is introduced and examined. The proposed methodology was applied to the GC-MS analysis of Iranian Lavender essential oil, which resulted in extending the number of identified constituents from 56 to 143 components. It was found that the most abundant constituents of the Iranian Lavender essential oil are α-pinene (16.51%), camphor (10.20%), 1,8-cineole (9.50%), bornyl acetate (8.11%) and camphene (6.50%). This indicates that the Iranian type Lavender contains a relatively high percentage of α-pinene. Comparison of different types of Lavender essential oils showed the composition similarity between Iranian and Italian (Sardinia Island) Lavenders. Published by Elsevier B.V.
Development of Pattern Recognition Techniques for the Evaluation of Toxicant Impacts to Multispecies Systems

DTIC Science & Technology

1993-06-18

the exception. In the Standardized Aquatic Microcosm and the Mixed Flask Culture (MFC) microcosms, multivariate analysis and clustering methods...rule rather than the exception. In the Standardized Aquatic Microcosm and the Mixed Flask Culture (MFC) microcosms, multivariate analysis and...experiments using two microcosm protocols. We use nonmetric clustering, a multivariate pattern recognition technique developed by Matthews and Heame (1991
Simultaneous evaluation of superoxide content and mitochondrial membrane potential in stallion semen samples provides additional information about sperm quality.

PubMed

Johannisson, A; Figueiredo, M I; Al-Kass, Z; Morrell, J M

2018-05-01

An improved fertility prediction for stallions is of importance for equine breeding. Here, we investigate the potential of a combined staining of stallion spermatozoa for superoxide and mitochondrial membrane potential (MMP) for this purpose. Semen samples were analysed immediately after arrival at the laboratory, as well as after 24 h. Superoxide was measured by MitoSOXRed, while MMP was measured with JC-1. Menadione was used to stimulate superoxide production. In addition, other parameters of sperm quality, namely motility, membrane integrity, chromatin integrity, sperm kinematics and Hoechst 33258 exclusion were measured and correlated to superoxide production and MMP. Both bivariate correlations between measured parameters as well as multivariate analysis were performed. Measured values in the superoxide/MMP assay did not correlate with other parameters. However, there was a strong negative correlation (r = 0.96 after 0 h, r = 0.95 after 24 h) between membrane integrity and chromatin integrity. Moderate positive correlations were found between motility parameters and membrane integrity, as well as moderate negative correlations between motility parameters and chromatin integrity. The multivariate analysis revealed that membrane integrity, chromatin integrity and motility contributed to the first principal component, while the second was influenced by superoxide/MMP parameters as well as sperm kinematics. Storage of samples for 24 h decreased motility, chromatin integrity and membrane integrity. In conclusion, combined measurement of superoxide and MMP provides additional information not obtained by other assays of sperm quality. Copyright © 2018 Elsevier B.V. All rights reserved.
Revisiting Hemicraniectomy: Late Decompressive Hemicraniectomy for Malignant Middle Cerebral Artery Stroke and the Role of Infarct Growth Rate

PubMed Central

Akhtar, Naveed; Salam, Abdul; Alboudi, Ayman; Kamran, Kainat; Ahmed, Arsalan; Khan, Rabia A.; Mirza, Mohsin K.; Inshasi, Jihad

2017-01-01

Objective and Methods. The outcome in late decompressive hemicraniectomy in malignant middle cerebral artery stroke and the optimal timings of surgery has not been addressed by the randomized trials and pooled analysis. Retrospective, multicenter, cross-sectional study to measure outcome following DHC under 48 or over 48 hours using the modified Rankin scale [mRS] and dichotomized as favorable ≤4 or unfavorable >4 at three months. Results. In total, 137 patients underwent DHC. Functional outcome analyzed as mRS 0–4 versus mRS 5-6 showed no difference in this split between early and late operated on patients [P = 0.140] and mortality [P = 0.975]. Multivariate analysis showed that age ≥ 55 years, MCA with additional infarction, septum pellucidum deviation ≥1 cm, and uncal herniation were independent predictors of poor functional outcome at three months. In the “best” multivariate model, second infarct growth rate [IGR2] >7.5 ml/hr, MCA with additional infarction, and patients with temporal lobe involvement were independently associated with surgery under 48 hours. Both first infarct growth rate [IGR1] and second infarct growth rate [IGR2] were nearly double [P < 0.001] in patients with early surgery [under 48 hours]. Conclusions. The outcome and mortality in malignant middle cerebral artery stroke patients operated on over 48 hours of stroke onset were comparable to those of patients operated on less than 48 hours after stroke onset. Our data identifies IGR, temporal lobe involvement, and middle cerebral artery with additional infarct as independent predictors for early surgery. PMID:28409051
Revisiting Hemicraniectomy: Late Decompressive Hemicraniectomy for Malignant Middle Cerebral Artery Stroke and the Role of Infarct Growth Rate.

PubMed

Kamran, Saadat; Akhtar, Naveed; Salam, Abdul; Alboudi, Ayman; Kamran, Kainat; Ahmed, Arsalan; Khan, Rabia A; Mirza, Mohsin K; Inshasi, Jihad; Shuaib, Ashfaq

2017-01-01

Objective and Methods. The outcome in late decompressive hemicraniectomy in malignant middle cerebral artery stroke and the optimal timings of surgery has not been addressed by the randomized trials and pooled analysis. Retrospective, multicenter, cross-sectional study to measure outcome following DHC under 48 or over 48 hours using the modified Rankin scale [mRS] and dichotomized as favorable ≤4 or unfavorable >4 at three months. Results. In total, 137 patients underwent DHC. Functional outcome analyzed as mRS 0-4 versus mRS 5-6 showed no difference in this split between early and late operated on patients [ P = 0.140] and mortality [ P = 0.975]. Multivariate analysis showed that age ≥ 55 years, MCA with additional infarction, septum pellucidum deviation ≥1 cm, and uncal herniation were independent predictors of poor functional outcome at three months. In the "best" multivariate model, second infarct growth rate [IGR2] >7.5 ml/hr, MCA with additional infarction, and patients with temporal lobe involvement were independently associated with surgery under 48 hours. Both first infarct growth rate [IGR1] and second infarct growth rate [IGR2] were nearly double [ P < 0.001] in patients with early surgery [under 48 hours]. Conclusions. The outcome and mortality in malignant middle cerebral artery stroke patients operated on over 48 hours of stroke onset were comparable to those of patients operated on less than 48 hours after stroke onset. Our data identifies IGR, temporal lobe involvement, and middle cerebral artery with additional infarct as independent predictors for early surgery.
Impact of FAB classification on predicting outcome in acute myeloid leukemia, not otherwise specified, patients undergoing allogeneic stem cell transplantation in CR1: An analysis of 1690 patients from the acute leukemia working party of EBMT.

PubMed

Canaani, Jonathan; Beohou, Eric; Labopin, Myriam; Socié, Gerard; Huynh, Anne; Volin, Liisa; Cornelissen, Jan; Milpied, Noel; Gedde-Dahl, Tobias; Deconinck, Eric; Fegueux, Nathalie; Blaise, Didier; Mohty, Mohamad; Nagler, Arnon

2017-04-01

The French, American, and British (FAB) classification system for acute myeloid leukemia (AML) is extensively used and is incorporated into the AML, not otherwise specified (NOS) category in the 2016 WHO edition of myeloid neoplasm classification. While recent data proposes that FAB classification does not provide additional prognostic information for patients for whom NPM1 status is available, it is unknown whether FAB still retains a current prognostic role in predicting outcome of AML patients undergoing allogeneic stem cell transplantation. Using the European Society of Blood and Bone Marrow Transplantation registry we analyzed outcome of 1690 patients transplanted in CR1 to determine if FAB classification provides additional prognostic value. Multivariate analysis revealed that M6/M7 patients had decreased leukemia free survival (hazard ratio (HR) of 1.41, 95% confidence interval (CI), 1.01-1.99; P = .046) in addition to increased nonrelapse mortality (NRM) rates (HR, 1.79; 95% CI, 1.06-3.01; P = .028) compared with other FAB types. In the NPM1 wt AML, NOS cohort, FAB M6/M7 was also associated with increased NRM (HR, 2.17; 95% CI, 1.14-4.16; P = .019). Finally, in FLT3-ITD + patients, multivariate analyses revealed that specific FAB types were tightly associated with adverse outcome. In conclusion, FAB classification may predict outcome following transplantation in AML, NOS patients. © 2017 Wiley Periodicals, Inc.
Composition-driven Cu-speciation and reducibility in Cu-CHA zeolite catalysts: a multivariate XAS/FTIR approach to complexity† †Electronic supplementary information (ESI) available: Sample description and synthesis details, experimental setup for in situ XAS and FTIR spectroscopy, details on the MCR-ALS method, details on DFT-assisted XANES simulations, details on the determination of N pure by PCA, MCR-ALS results for downsized and upsized component spaces, additional information to support the assignment of theoretical XANES curves, details on EXAFS analysis, details on IR spectral deconvolution. See DOI: 10.1039/c7sc02266b Click here for additional data file.

PubMed Central

Martini, A.; Lomachenko, K. A.; Pankin, I. A.; Negri, C.; Berlier, G.; Beato, P.; Falsig, H.; Bordiga, S.; Lamberti, C.

2017-01-01

The small pore Cu-CHA zeolite is attracting increasing attention as a versatile platform to design novel single-site catalysts for deNOx applications and for the direct conversion of methane to methanol. Understanding at the atomic scale how the catalyst composition influences the Cu-species formed during thermal activation is a key step to unveil the relevant composition–activity relationships. Herein, we explore by in situ XAS the impact of Cu-CHA catalyst composition on temperature-dependent Cu-speciation and reducibility. Advanced multivariate analysis of in situ XANES in combination with DFT-assisted simulation of XANES spectra and multi-component EXAFS fits as well as in situ FTIR spectroscopy of adsorbed N2 allow us to obtain unprecedented quantitative structural information on the complex dynamics during the speciation of Cu-sites inside the framework of the CHA zeolite. PMID:29147509
Chemical studies of H chondrites. 6: Antarctic/non-Antarctic compositional differences revisited

NASA Astrophysics Data System (ADS)

Wolf, Stephen F.; Lipschutz, Michael E.

1995-02-01

We report data for the trace elements Au, Co, Sb, Ga, Rb, Ag, Se, Cs, Te, Zn, Cd, Bi, T1, and In (ordered by putative volatility during nebular condensation and accretion) determined by radiochemical neutron activation analysis of 14 additional H5 and H6 chondrite falls. Data for the 10 most volatile elements (Rb to In) treated by the multivariate techniques of linear discriminant analysis and logistic regression in these and 44 other falls are compared with those of 59 H4-6 chondrites from Antarctica. Various populations are tested by the multivariate techniques, using the previously developed method of randomization-simulation to assess significance levels. An earlier conclusion, based on fewer examples, that H4-6 chondrite falls are compositionally distinguishable from the Antarctic suite is verified by the additional data. This distinctiveness is highly significant because of the presence of samples from Victoria Land in the Antarctic population, which differ compositionally from falls beyond any reasonable doubt. However, it cannot be proven unequivocally that falls and Antarctic samples from Queen Maud Land are compositionally distinguishable. Trivial causes (e.g., analyst bias, weathering) cannot explain the Victoria Land (Antarctic)/non-Antarctic compositional difference for paradigmatic H4-6 chondrites. This seems to reflect a time-dependent variation of near-Earth meteoroid source regions differing in average thermal history.
Chemical studies of H chondrites. 6: Antarctic/non-Antarctic compositional differences revisited

NASA Technical Reports Server (NTRS)

Wolf, Stephen F.; Lipschutz, Michael E.

1995-01-01

We report data for the trace elements Au, Co, Sb, Ga, Rb, Ag, Se, Cs, Te, Zn, Cd, Bi, T1, and In (ordered by putative volatility during nebular condensation and accretion) determined by radiochemical neutron activation analysis of 14 additional H5 and H6 chondrite falls. Data for the 10 most volatile elements (Rb to In) treated by the multivariate techniques of linear discriminant analysis and logistic regression in these and 44 other falls are compared with those of 59 H4-6 chondrites from Antarctica. Various populations are tested by the multivariate techniques, using the previously developed method of randomization-simulation to assess significance levels. An earlier conclusion, based on fewer examples, that H4-6 chondrite falls are compositionally distinguishable from the Antarctic suite is verified by the additional data. This distinctiveness is highly significant because of the presence of samples from Victoria Land in the Antarctic population, which differ compositionally from falls beyond any reasonable doubt. However, it cannot be proven unequivocally that falls and Antarctic samples from Queen Maud Land are compositionally distinguishable. Trivial causes (e.g., analyst bias, weathering) cannot explain the Victoria Land (Antarctic)/non-Antarctic compositional difference for paradigmatic H4-6 chondrites. This seems to reflect a time-dependent variation of near-Earth meteoroid source regions differing in average thermal history.
Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture

PubMed Central

Pauli, Duke; Ziegler, Greg; Ren, Min; Jenks, Matthew A.; Hunsaker, Douglas J.; Zhang, Min; Baxter, Ivan; Gore, Michael A.

2018-01-01

To mitigate the effects of heat and drought stress, a better understanding of the genetic control of physiological responses to these environmental conditions is needed. To this end, we evaluated an upland cotton (Gossypium hirsutum L.) mapping population under water-limited and well-watered conditions in a hot, arid environment. The elemental concentrations (ionome) of seed samples from the population were profiled in addition to those of soil samples taken from throughout the field site to better model environmental variation. The elements profiled in seeds exhibited moderate to high heritabilities, as well as strong phenotypic and genotypic correlations between elements that were not altered by the imposed irrigation regimes. Quantitative trait loci (QTL) mapping results from a Bayesian classification method identified multiple genomic regions where QTL for individual elements colocalized, suggesting that genetic control of the ionome is highly interrelated. To more fully explore this genetic architecture, multivariate QTL mapping was implemented among groups of biochemically related elements. This analysis revealed both additional and pleiotropic QTL responsible for coordinated control of phenotypic variation for elemental accumulation. Machine learning algorithms that utilized only ionomic data predicted the irrigation regime under which genotypes were evaluated with very high accuracy. Taken together, these results demonstrate the extent to which the seed ionome is genetically interrelated and predictive of plant physiological responses to adverse environmental conditions. PMID:29437829
Reparametrization-based estimation of genetic parameters in multi-trait animal model using Integrated Nested Laplace Approximation.

PubMed

Mathew, Boby; Holand, Anna Marie; Koistinen, Petri; Léon, Jens; Sillanpää, Mikko J

2016-02-01

A novel reparametrization-based INLA approach as a fast alternative to MCMC for the Bayesian estimation of genetic parameters in multivariate animal model is presented. Multi-trait genetic parameter estimation is a relevant topic in animal and plant breeding programs because multi-trait analysis can take into account the genetic correlation between different traits and that significantly improves the accuracy of the genetic parameter estimates. Generally, multi-trait analysis is computationally demanding and requires initial estimates of genetic and residual correlations among the traits, while those are difficult to obtain. In this study, we illustrate how to reparametrize covariance matrices of a multivariate animal model/animal models using modified Cholesky decompositions. This reparametrization-based approach is used in the Integrated Nested Laplace Approximation (INLA) methodology to estimate genetic parameters of multivariate animal model. Immediate benefits are: (1) to avoid difficulties of finding good starting values for analysis which can be a problem, for example in Restricted Maximum Likelihood (REML); (2) Bayesian estimation of (co)variance components using INLA is faster to execute than using Markov Chain Monte Carlo (MCMC) especially when realized relationship matrices are dense. The slight drawback is that priors for covariance matrices are assigned for elements of the Cholesky factor but not directly to the covariance matrix elements as in MCMC. Additionally, we illustrate the concordance of the INLA results with the traditional methods like MCMC and REML approaches. We also present results obtained from simulated data sets with replicates and field data in rice.

Influence of Pelvicaliceal Anatomy on Stone Clearance After Flexible Ureteroscopy and Holmium Laser Lithotripsy for Large Renal Stones.

PubMed

Inoue, Takaaki; Murota, Takashi; Okada, Shinsuke; Hamamoto, Shuzo; Muguruma, Kouei; Kinoshita, Hidefumi; Matsuda, Tadashi

2015-09-01

This study was performed to evaluate the impact of pelvicaliceal anatomy on stone clearance in patients with remnant fragments in the lower pole after flexible ureteroscopy and holmium laser lithotripsy (fURSL) for renal stones >15 mm. This retrospective study included 67 patients with radiopaque residual fragments (>2 mm) in the lower pole after fURSL for large renal stones (>15 mm). The preoperative infundibular length (IL), infundibular width (IW), infundibulopelvic angle (IPA), and caliceal pelvic height (CPH) were measured using intravenous urography. Multivariate analysis was performed to determine whether any of these measurements affected stone clearance. Of the 67 patients, 55 (82.1%) were stone free (SF) 3 months after fURSL. The anatomic factors significantly favorable for an SF status were a short IL, broad IW, wide IPA, and low CPH. On multivariate analysis, the IPA had a significant influence on an SF status after fURSL (p=0.010). An IPA <30° was a negative risk factor (p=0.019). Postoperative complications occurred in nine patients (13.4%), including Clavien grade I complications in two patients (2.9%), grade II in six patients (8.9%), and grade IIIa in one patient (1.8%). Almost all complications were minor. An IPA <30° is the only negative risk factor for stone clearance after fURSL for large renal stones according to our multivariate analysis. Additional studies are required to further evaluate the characteristics of the pelvicaliceal anatomy influencing stone clearance.
Brain natriuretic peptide predicts functional outcome in ischemic stroke

PubMed Central

Rost, Natalia S; Biffi, Alessandro; Cloonan, Lisa; Chorba, John; Kelly, Peter; Greer, David; Ellinor, Patrick; Furie, Karen L

2011-01-01

Background Elevated serum levels of brain natriuretic peptide (BNP) have been associated with cardioembolic (CE) stroke and increased post-stroke mortality. We sought to determine whether BNP levels were associated with functional outcome after ischemic stroke. Methods We measured BNP in consecutive patients aged ≥18 years admitted to our Stroke Unit between 2002–2005. BNP quintiles were used for analysis. Stroke subtypes were assigned using TOAST criteria. Outcomes were measured as 6-month modified Rankin Scale score (“good outcome” = 0–2 vs. “poor”) as well as mortality. Multivariate logistic regression was used to assess association between the quintiles of BNP and outcomes. Predictive performance of BNP as compared to clinical model alone was assessed by comparing ROC curves. Results Of 569 ischemic stroke patients, 46% were female; mean age was 67.9 ± 15 years. In age- and gender-adjusted analysis, elevated BNP was associated with lower ejection fraction (p<0.0001) and left atrial dilatation (p<0.001). In multivariate analysis, elevated BNP decreased the odds of good functional outcome (OR 0.64, 95%CI 0.41–0.98) and increased the odds of death (OR 1.75, 95%CI 1.36–2.24) in these patients. Addition of BNP to multivariate models increased their predictive performance for functional outcome (p=0.013) and mortality (p<0.03) after CE stroke. Conclusions Serum BNP levels are strongly associated with CE stroke and functional outcome at 6 months after ischemic stroke. Inclusion of BNP improved prediction of mortality in patients with CE stroke. PMID:22116811
Multivariate Analysis of Schools and Educational Policy.

ERIC Educational Resources Information Center

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…
Multispectral UV imaging for surface analysis of MUPS tablets with special focus on the pellet distribution.

PubMed

Novikova, Anna; Carstensen, Jens M; Rades, Thomas; Leopold, Prof Dr Claudia S

2016-12-30

In the present study the applicability of multispectral UV imaging in combination with multivariate image analysis for surface evaluation of MUPS tablets was investigated with respect to the differentiation of the API pellets from the excipients matrix, estimation of the drug content as well as pellet distribution, and influence of the coating material and tablet thickness on the predictive model. Different formulations consisting of coated drug pellets with two coating polymers (Aquacoat ® ECD and Eudragit ® NE 30 D) at three coating levels each were compressed to MUPS tablets with various amounts of coated pellets and different tablet thicknesses. The coated drug pellets were clearly distinguishable from the excipients matrix using a partial least squares approach regardless of the coating layer thickness and coating material used. Furthermore, the number of the detected drug pellets on the tablet surface allowed an estimation of the true drug content in the respective MUPS tablet. In addition, the pellet distribution in the MUPS formulations could be estimated by UV image analysis of the tablet surface. In conclusion, this study revealed that UV imaging in combination with multivariate image analysis is a promising approach for the automatic quality control of MUPS tablets during the manufacturing process. Copyright Â© 2016 Elsevier B.V. All rights reserved.
Multivariate statistical analysis: Principles and applications to coorbital streams of meteorite falls

NASA Technical Reports Server (NTRS)

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

1993-01-01

Multivariate statistical analysis techniques (linear discriminant analysis and logistic regression) can provide powerful discrimination tools which are generally unfamiliar to the planetary science community. Fall parameters were used to identify a group of 17 H chondrites (Cluster 1) that were part of a coorbital stream which intersected Earth's orbit in May, from 1855 - 1895, and can be distinguished from all other H chondrite falls. Using multivariate statistical techniques, it was demonstrated that a totally different criterion, labile trace element contents - hence thermal histories - or 13 Cluster 1 meteorites are distinguishable from those of 45 non-Cluster 1 H chondrites. Here, we focus upon the principles of multivariate statistical techniques and illustrate their application using non-meteoritic and meteoritic examples.
Dominance Genetic Variance for Traits Under Directional Selection in Drosophila serrata

PubMed Central

Sztepanacz, Jacqueline L.; Blows, Mark W.

2015-01-01

In contrast to our growing understanding of patterns of additive genetic variance in single- and multi-trait combinations, the relative contribution of nonadditive genetic variance, particularly dominance variance, to multivariate phenotypes is largely unknown. While mechanisms for the evolution of dominance genetic variance have been, and to some degree remain, subject to debate, the pervasiveness of dominance is widely recognized and may play a key role in several evolutionary processes. Theoretical and empirical evidence suggests that the contribution of dominance variance to phenotypic variance may increase with the correlation between a trait and fitness; however, direct tests of this hypothesis are few. Using a multigenerational breeding design in an unmanipulated population of Drosophila serrata, we estimated additive and dominance genetic covariance matrices for multivariate wing-shape phenotypes, together with a comprehensive measure of fitness, to determine whether there is an association between directional selection and dominance variance. Fitness, a trait unequivocally under directional selection, had no detectable additive genetic variance, but significant dominance genetic variance contributing 32% of the phenotypic variance. For single and multivariate morphological traits, however, no relationship was observed between trait–fitness correlations and dominance variance. A similar proportion of additive and dominance variance was found to contribute to phenotypic variance for single traits, and double the amount of additive compared to dominance variance was found for the multivariate trait combination under directional selection. These data suggest that for many fitness components a positive association between directional selection and dominance genetic variance may not be expected. PMID:25783700
Multivariate analysis in the pharmaceutical industry: enabling process understanding and improvement in the PAT and QbD era.

PubMed

Ferreira, Ana P; Tobyn, Mike

2015-01-01

In the pharmaceutical industry, chemometrics is rapidly establishing itself as a tool that can be used at every step of product development and beyond: from early development to commercialization. This set of multivariate analysis methods allows the extraction of information contained in large, complex data sets thus contributing to increase product and process understanding which is at the core of the Food and Drug Administration's Process Analytical Tools (PAT) Guidance for Industry and the International Conference on Harmonisation's Pharmaceutical Development guideline (Q8). This review is aimed at providing pharmaceutical industry professionals an introduction to multivariate analysis and how it is being adopted and implemented by companies in the transition from "quality-by-testing" to "quality-by-design". It starts with an introduction to multivariate analysis and the two methods most commonly used: principal component analysis and partial least squares regression, their advantages, common pitfalls and requirements for their effective use. That is followed with an overview of the diverse areas of application of multivariate analysis in the pharmaceutical industry: from the development of real-time analytical methods to definition of the design space and control strategy, from formulation optimization during development to the application of quality-by-design principles to improve manufacture of existing commercial products.
Examining a Comprehensive Model of Disaster-Related Posttraumatic Stress Disorder in Systematically Studied Survivors of 10 Disasters

PubMed Central

Oliver, Julianne; Pandya, Anand

2012-01-01

Objectives. Using a comprehensive disaster model, we examined predictors of posttraumatic stress disorder (PTSD) in combined data from 10 different disasters. Methods. The combined sample included data from 811 directly exposed survivors of 10 disasters between 1987 and 1995. We used consistent methods across all 10 disaster samples, including full diagnostic assessment. Results. In multivariate analyses, predictors of PTSD were female gender, younger age, Hispanic ethnicity, less education, ever-married status, predisaster psychopathology, disaster injury, and witnessing injury or death; exposure through death or injury to friends or family members and witnessing the disaster aftermath did not confer additional PTSD risk. Intentionally caused disasters associated with PTSD in bivariate analysis did not independently predict PTSD in multivariate analysis. Avoidance and numbing symptoms represented a PTSD marker. Conclusions. Despite confirming some previous research findings, we found no associations between PTSD and disaster typology. Prospective research is needed to determine whether early avoidance and numbing symptoms identify individuals likely to develop PTSD later. Our findings may help identify at-risk populations for treatment research. PMID:22897543
Comparative artificial neural network and partial least squares models for analysis of Metronidazole, Diloxanide, Spiramycin and Cliquinol in pharmaceutical preparations.

PubMed

Elkhoudary, Mahmoud M; Abdel Salam, Randa A; Hadad, Ghada M

2014-09-15

Metronidazole (MNZ) is a widely used antibacterial and amoebicide drug. Therefore, it is important to develop a rapid and specific analytical method for the determination of MNZ in mixture with Spiramycin (SPY), Diloxanide (DIX) and Cliquinol (CLQ) in pharmaceutical preparations. This work describes simple, sensitive and reliable six multivariate calibration methods, namely linear and nonlinear artificial neural networks preceded by genetic algorithm (GA-ANN) and principle component analysis (PCA-ANN) as well as partial least squares (PLS) either alone or preceded by genetic algorithm (GA-PLS) for UV spectrophotometric determination of MNZ, SPY, DIX and CLQ in pharmaceutical preparations with no interference of pharmaceutical additives. The results manifest the problem of nonlinearity and how models like ANN can handle it. Analytical performance of these methods was statistically validated with respect to linearity, accuracy, precision and specificity. The developed methods indicate the ability of the previously mentioned multivariate calibration models to handle and solve UV spectra of the four components' mixtures using easy and widely used UV spectrophotometer. Copyright © 2014 Elsevier B.V. All rights reserved.
Prediction of Gestational Diabetes through NMR Metabolomics of Maternal Blood.

PubMed

Pinto, Joana; Almeida, Lara M; Martins, Ana S; Duarte, Daniela; Barros, António S; Galhano, Eulália; Pita, Cristina; Almeida, Maria do Céu; Carreira, Isabel M; Gil, Ana M

2015-06-05

Metabolic biomarkers of pre- and postdiagnosis gestational diabetes mellitus (GDM) were sought, using nuclear magnetic resonance (NMR) metabolomics of maternal plasma and corresponding lipid extracts. Metabolite differences between controls and disease were identified through multivariate analysis of variable selected (1)H NMR spectra. For postdiagnosis GDM, partial least squares regression identified metabolites with higher dependence on normal gestational age evolution. Variable selection of NMR spectra produced good classification models for both pre- and postdiagnostic GDM. Prediagnosis GDM was accompanied by cholesterol increase and minor increases in lipoproteins (plasma), fatty acids, and triglycerides (extracts). Small metabolite changes comprised variations in glucose (up regulated), amino acids, betaine, urea, creatine, and metabolites related to gut microflora. Most changes were enhanced upon GDM diagnosis, in addition to newly observed changes in low-Mw compounds. GDM prediction seems possible exploiting multivariate profile changes rather than a set of univariate changes. Postdiagnosis GDM is successfully classified using a 26-resonance plasma biomarker. Plasma and extracts display comparable classification performance, the former enabling direct and more rapid analysis. Results and putative biochemical hypotheses require further confirmation in larger cohorts of distinct ethnicities.
Alignment-Independent Comparisons of Human Gastrointestinal Tract Microbial Communities in a Multidimensional 16S rRNA Gene Evolutionary Space▿

PubMed Central

Rudi, Knut; Zimonja, Monika; Kvenshagen, Bente; Rugtveit, Jarle; Midtvedt, Tore; Eggesbø, Merete

2007-01-01

We present a novel approach for comparing 16S rRNA gene clone libraries that is independent of both DNA sequence alignment and definition of bacterial phylogroups. These steps are the major bottlenecks in current microbial comparative analyses. We used direct comparisons of taxon density distributions in an absolute evolutionary coordinate space. The coordinate space was generated by using alignment-independent bilinear multivariate modeling. Statistical analyses for clone library comparisons were based on multivariate analysis of variance, partial least-squares regression, and permutations. Clone libraries from both adult and infant gastrointestinal tract microbial communities were used as biological models. We reanalyzed a library consisting of 11,831 clones covering complete colons from three healthy adults in addition to a smaller 390-clone library from infant feces. We show that it is possible to extract detailed information about microbial community structures using our alignment-independent method. Our density distribution analysis is also very efficient with respect to computer operation time, meeting the future requirements of large-scale screenings to understand the diversity and dynamics of microbial communities. PMID:17337554
Evaluation of the combined solar TiO2/photo-Fenton process using multivariate analysis.

PubMed

Nogueira, R F P; Trovó, A G; Paterlini, W C

2004-01-01

The effect of combining the photocatalytic processes using TiO2 and the photo-Fenton reaction with Fe3+ or ferrioxalate as a source of Fe2+ was investigated in the degradation of 4-chlorophenol (4CP) and dichloroacetic acid (DCA) using solar irradiation. Multivariate analysis was used to evaluate the role of three variables: iron, H2O2 and TiO2 concentrations. The results show that TiO2 plays a minor role when compared to iron and H2O2 in the solar degradation of 4CP and DCA in the studied conditions. However, its presence can improve TOC removal when H2O2 is totally consumed. Iron and peroxide play major roles, especially when Fe(NO3)3 is used in the degradation of 4CP. No significant synergistic effect was observed by the addition of TiO2 in this process. On the other hand, synergistic effects were observed between FeOx and TiO2 and between H2O2 and TiO2 in the degradation of DCA.
Comparative artificial neural network and partial least squares models for analysis of Metronidazole, Diloxanide, Spiramycin and Cliquinol in pharmaceutical preparations

NASA Astrophysics Data System (ADS)

Elkhoudary, Mahmoud M.; Abdel Salam, Randa A.; Hadad, Ghada M.

2014-09-01

Metronidazole (MNZ) is a widely used antibacterial and amoebicide drug. Therefore, it is important to develop a rapid and specific analytical method for the determination of MNZ in mixture with Spiramycin (SPY), Diloxanide (DIX) and Cliquinol (CLQ) in pharmaceutical preparations. This work describes simple, sensitive and reliable six multivariate calibration methods, namely linear and nonlinear artificial neural networks preceded by genetic algorithm (GA-ANN) and principle component analysis (PCA-ANN) as well as partial least squares (PLS) either alone or preceded by genetic algorithm (GA-PLS) for UV spectrophotometric determination of MNZ, SPY, DIX and CLQ in pharmaceutical preparations with no interference of pharmaceutical additives. The results manifest the problem of nonlinearity and how models like ANN can handle it. Analytical performance of these methods was statistically validated with respect to linearity, accuracy, precision and specificity. The developed methods indicate the ability of the previously mentioned multivariate calibration models to handle and solve UV spectra of the four components’ mixtures using easy and widely used UV spectrophotometer.
Rituximab is associated with improved survival in Burkitt lymphoma: a retrospective analysis from two US academic medical centers.

PubMed

Wildes, Tanya M; Farrington, Laura; Yeung, Cecilia; Harrington, Alexandra M; Foyil, Kelley V; Liu, Jingxia; Kreisel, Friederike; Bartlett, Nancy L; Fenske, Timothy S

2014-02-01

Burkitt lymphoma (BL) is a rare, highly aggressive B-cell malignancy treated most successfully with brief-duration, high-intensity chemotherapeutic regimens. The benefit of the addition of rituximab to these regimens remains uncertain. We sought to examine the effectiveness of chemotherapy with and without rituximab in patients with BL. This study is a retrospective cohort study of all adult patients with BL diagnosed and treated with modern, dose-intense chemotherapeutic regimens from 1998-2008 at two tertiary care institutions. All cases were confirmed by application of WHO 2008 criteria by hematopathologists. Medical records were reviewed for patient-, disease-, and treatment- related factors as well as treatment response and survival. Factors associated with survival were analyzed using Cox proportional hazards modeling. A total of 35 patients were analyzed: 18 patients received rituximab with chemotherapy (R-chemo) and 17 received chemotherapy (chemo) alone. The median age was 42 (range 20-74 years); 57% were male; 71% had Ann Arbor Stage IV disease; 33% had central nervous system involvement; 78% had an Eastern Cooperative Oncology Group (ECOG) performance status of 0-1. R-chemo was associated with significantly longer overall survival (OS) than chemo alone (5-year OS 70% and 29%, respectively, p = 0.040). On multivariate regression analysis, poor performance status and central nervous system involvement were associated with poorer survival. The addition of rituximab to chemotherapy was associated with improved OS in patients with Burkitt lymphoma. Poor performance status and central nervous system involvement were prognostically significant on multivariate analysis.
Tafamidis delays disease progression in patients with early stage transthyretin familial amyloid polyneuropathy: additional supportive analyses from the pivotal trial.

PubMed

Keohane, Denis; Schwartz, Jeffrey; Gundapaneni, Balarama; Stewart, Michelle; Amass, Leslie

2017-03-01

Tafamidis, a non-NSAID highly specific transthyretin stabilizer, delayed neurologic disease progression as measured by Neuropathy Impairment Score-Lower Limbs (NIS-LL) in an 18-month, double-blind, placebo-controlled randomized trial in 128 patients with early-stage transthyretin V30M familial amyloid polyneuropathy (ATTRV30M-FAP). The current post hoc analyses aimed to further evaluate the effects of tafamidis in delaying ATTRV30M-FAP progression in this trial. Pre-specified, repeated-measures analysis of change from baseline in NIS-LL in this trial (ClinicalTrials.gov NCT00409175) was repeated with addition of baseline as covariate and multiple imputation analysis for missing data by treatment group. Change in NIS-LL plus three small-fiber nerve tests (NIS-LL + Σ3) and NIS-LL plus seven nerve tests (NIS-LL + Σ7) were assessed without baseline as covariate. Treatment outcomes over the NIS-LL, Σ3, Σ7, modified body mass index and Norfolk Quality of Life-Diabetic Neuropathy Total Quality of Life Score were also examined using multivariate analysis techniques. Neuropathy progression based on NIS-LL change from baseline to Month 18 remained significantly reduced for tafamidis versus placebo in the baseline-adjusted and multiple imputation analyses. NIS-LL + Σ3 and NIS-LL + Σ7 captured significant treatment group differences. Multivariate analyses provided strong statistical evidence for a superior tafamidis treatment effect. These supportive analyses confirm that tafamidis delays neurologic progression in early-stage ATTRV30M-FAP. NCT00409175.
Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

NASA Astrophysics Data System (ADS)

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

2004-06-01

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

PubMed Central

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

2015-01-01

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. PMID:25664257
A Study of Effects of MultiCollinearity in the Multivariable Analysis.

PubMed

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

PubMed

Olswold, Curtis; de Andrade, Mariza

2003-12-31

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

NASA Astrophysics Data System (ADS)

Matsunaga, Yasuhiro; Fuchigami, Sotaro; Kidera, Akinori

2009-03-01

Multivariate frequency domain analysis (MFDA) is proposed to characterize collective vibrational dynamics of protein obtained by a molecular dynamics (MD) simulation. MFDA performs principal component analysis (PCA) for a bandpass filtered multivariate time series using the multitaper method of spectral estimation. By applying MFDA to MD trajectories of bovine pancreatic trypsin inhibitor, we determined the collective vibrational modes in the frequency domain, which were identified by their vibrational frequencies and eigenvectors. At near zero temperature, the vibrational modes determined by MFDA agreed well with those calculated by normal mode analysis. At 300 K, the vibrational modes exhibited characteristic features that were considerably different from the principal modes of the static distribution given by the standard PCA. The influences of aqueous environments were discussed based on two different sets of vibrational modes, one derived from a MD simulation in water and the other from a simulation in vacuum. Using the varimax rotation, an algorithm of the multivariate statistical analysis, the representative orthogonal set of eigenmodes was determined at each vibrational frequency.

Imaging of polysaccharides in the tomato cell wall with Raman microspectroscopy

PubMed Central

2014-01-01

Background The primary cell wall of fruits and vegetables is a structure mainly composed of polysaccharides (pectins, hemicelluloses, cellulose). Polysaccharides are assembled into a network and linked together. It is thought that the percentage of components and of plant cell wall has an important influence on mechanical properties of fruits and vegetables. Results In this study the Raman microspectroscopy technique was introduced to the visualization of the distribution of polysaccharides in cell wall of fruit. The methodology of the sample preparation, the measurement using Raman microscope and multivariate image analysis are discussed. Single band imaging (for preliminary analysis) and multivariate image analysis methods (principal component analysis and multivariate curve resolution) were used for the identification and localization of the components in the primary cell wall. Conclusions Raman microspectroscopy supported by multivariate image analysis methods is useful in distinguishing cellulose and pectins in the cell wall in tomatoes. It presents how the localization of biopolymers was possible with minimally prepared samples. PMID:24917885
A refined method for multivariate meta-analysis and meta-regression

PubMed Central

Jackson, Daniel; Riley, Richard D

2014-01-01

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects’ standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:23996351
Predictors of workplace sexual health policy at sex work establishments in the Philippines.

PubMed

Withers, M; Dornig, K; Morisky, D E

2007-09-01

Based on the literature, we identified manager and establishment characteristics that we hypothesized are related to workplace policies that support HIV protective behavior. We developed a sexual health policy index consisting of 11 items as our outcome variable. We utilized both bivariate and multivariate analysis of variance. The significant variables in our bivariate analyses (establishment type, number of employees, manager age, and membership in manager association) were entered into a multivariate regression model. The model was significant (p<.01), and predicted 42) of the variability in the development and management of a workplace sexual health policy supportive of condom use. The significant predictors were number of employees and establishment type. In addition to individually-focused CSW interventions, HIV prevention programs should target managers and establishment policies. Future HIV prevention programs may need to focus on helping smaller establishments, in particular those with less employees, to build capacity and develop sexual health policy guidelines.
Motivations for genetic testing for lung cancer risk among young smokers.

PubMed

O'Neill, Suzanne C; Lipkus, Isaac M; Sanderson, Saskia C; Shepperd, James; Docherty, Sharron; McBride, Colleen M

2013-11-01

To examine why young people might want to undergo genetic susceptibility testing for lung cancer despite knowing that tested gene variants are associated with small increases in disease risk. The authors used a mixed-method approach to evaluate motives for and against genetic testing and the association between these motivations and testing intentions in 128 college students who smoke. Exploratory factor analysis yielded four reliable factors: Test Scepticism, Test Optimism, Knowledge Enhancement and Smoking Optimism. Test Optimism and Knowledge Enhancement correlated positively with intentions to test in bivariate and multivariate analyses (ps<0.001). Test Scepticism correlated negatively with testing intentions in multivariate analyses (p<0.05). Open-ended questions assessing testing motivations generally replicated themes of the quantitative survey. In addition to learning about health risks, young people may be motivated to seek genetic testing for reasons, such as gaining knowledge about new genetic technologies more broadly.
Edge-Preserving Image Smoothing Constraint in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) of Hyperspectral Data.

PubMed

Hugelier, Siewert; Vitale, Raffaele; Ruckebusch, Cyril

2018-03-01

This article explores smoothing with edge-preserving properties as a spatial constraint for the resolution of hyperspectral images with multivariate curve resolution-alternating least squares (MCR-ALS). For each constrained component image (distribution map), irrelevant spatial details and noise are smoothed applying an L 1 - or L 0 -norm penalized least squares regression, highlighting in this way big changes in intensity of adjacent pixels. The feasibility of the constraint is demonstrated on three different case studies, in which the objects under investigation are spatially clearly defined, but have significant spectral overlap. This spectral overlap is detrimental for obtaining a good resolution and additional spatial information should be provided. The final results show that the spatial constraint enables better image (map) abstraction, artifact removal, and better interpretation of the results obtained, compared to a classical MCR-ALS analysis of hyperspectral images.
A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

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

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213
Using multivariate generalizability theory to assess the effect of content stratification on the reliability of a performance assessment.

PubMed

Keller, Lisa A; Clauser, Brian E; Swanson, David B

2010-12-01

In recent years, demand for performance assessments has continued to grow. However, performance assessments are notorious for lower reliability, and in particular, low reliability resulting from task specificity. Since reliability analyses typically treat the performance tasks as randomly sampled from an infinite universe of tasks, these estimates of reliability may not be accurate. For tests built according to a table of specifications, tasks are randomly sampled from different strata (content domains, skill areas, etc.). If these strata remain fixed in the test construction process, ignoring this stratification in the reliability analysis results in an underestimate of "parallel forms" reliability, and an overestimate of the person-by-task component. This research explores the effect of representing and misrepresenting the stratification appropriately in estimation of reliability and the standard error of measurement. Both multivariate and univariate generalizability studies are reported. Results indicate that the proper specification of the analytic design is essential in yielding the proper information both about the generalizability of the assessment and the standard error of measurement. Further, illustrative D studies present the effect under a variety of situations and test designs. Additional benefits of multivariate generalizability theory in test design and evaluation are also discussed.
Multivariate curve-resolution analysis of pesticides in water samples from liquid chromatographic-diode array data.

PubMed

Maggio, Rubén M; Damiani, Patricia C; Olivieri, Alejandro C

2011-01-30

Liquid chromatographic-diode array detection data recorded for aqueous mixtures of 11 pesticides show the combined presence of strongly coeluting peaks, distortions in the time dimension between experimental runs, and the presence of potential interferents not modeled by the calibration phase in certain test samples. Due to the complexity of these phenomena, data were processed by a second-order multivariate algorithm based on multivariate curve resolution and alternating least-squares, which allows one to successfully model both the spectral and retention time behavior for all sample constituents. This led to the accurate quantitation of all analytes in a set of validation samples: aldicarb sulfoxide, oxamyl, aldicarb sulfone, methomyl, 3-hydroxy-carbofuran, aldicarb, propoxur, carbofuran, carbaryl, 1-naphthol and methiocarb. Limits of detection in the range 0.1-2 μg mL(-1) were obtained. Additionally, the second-order advantage for several analytes was achieved in samples containing several uncalibrated interferences. The limits of detection for all analytes were decreased by solid phase pre-concentration to values compatible to those officially recommended, i.e., in the order of 5 ng mL(-1). Copyright Â© 2010 Elsevier B.V. All rights reserved.
An exploratory examination of the associations among racial and ethnic discrimination, racial climate, and trauma-related symptoms in a college student population.

PubMed

Pieterse, Alex L; Carter, Robert T; Evans, Sarah A; Walter, Rebecca A

2010-07-01

In this study, we examined the association among perceptions of racial and/or ethnic discrimination, racial climate, and trauma-related symptoms among 289 racially diverse college undergraduates. Study measures included the Perceived Stress Scale, the Perceived Ethnic Discrimination Questionnaire, the Posttraumatic Stress Disorder Checklist-Civilian Version, and the Racial Climate Scale. Results of a multivariate analysis of variance (MANOVA) indicated that Asian and Black students reported more frequent experiences of discrimination than did White students. Additionally, the MANOVA indicated that Black students perceived the campus racial climate as being more negative than did White and Asian students. A hierarchical regression analysis showed that when controlling for generic life stress, perceptions of discrimination contributed an additional 10% of variance in trauma-related symptoms for Black students, and racial climate contributed an additional 7% of variance in trauma symptoms for Asian students. (c) 2010 APA, all rights reserved.
Antimicrobial Resistance: The Major Contribution of Poor Governance and Corruption to This Growing Problem

PubMed Central

Collignon, Peter; Athukorala, Prema-chandra; Senanayake, Sanjaya; Khan, Fahad

2015-01-01

Objectives To determine how important governmental, social, and economic factors are in driving antibiotic resistance compared to the factors usually considered the main driving factors—antibiotic usage and levels of economic development. Design A retrospective multivariate analysis of the variation of antibiotic resistance in Europe in terms of human antibiotic usage, private health care expenditure, tertiary education, the level of economic advancement (per capita GDP), and quality of governance (corruption). The model was estimated using a panel data set involving 7 common human bloodstream isolates and covering 28 European countries for the period 1998–2010. Results Only 28% of the total variation in antibiotic resistance among countries is attributable to variation in antibiotic usage. If time effects are included the explanatory power increases to 33%. However when the control of corruption indicator is included as an additional variable, 63% of the total variation in antibiotic resistance is now explained by the regression. The complete multivariate regression only accomplishes an additional 7% in terms of goodness of fit, indicating that corruption is the main socioeconomic factor that explains antibiotic resistance. The income level of a country appeared to have no effect on resistance rates in the multivariate analysis. The estimated impact of corruption was statistically significant (p< 0.01). The coefficient indicates that an improvement of one unit in the corruption indicator is associated with a reduction in antibiotic resistance by approximately 0.7 units. The estimated coefficient of private health expenditure showed that one unit reduction is associated with a 0.2 unit decrease in antibiotic resistance. Conclusions These findings support the hypothesis that poor governance and corruption contributes to levels of antibiotic resistance and correlate better than antibiotic usage volumes with resistance rates. We conclude that addressing corruption and improving governance will lead to a reduction in antibiotic resistance. PMID:25786027
Heritability of somatotype components: a multivariate analysis.

PubMed

Peeters, M W; Thomis, M A; Loos, R J F; Derom, C A; Fagard, R; Claessens, A L; Vlietinck, R F; Beunen, G P

2007-08-01

To study the genetic and environmental determination of variation in Heath-Carter somatotype (ST) components (endomorphy, mesomorphy and ectomorphy). Multivariate path analysis on twin data. Eight hundred and three members of 424 adult Flemish twin pairs (18-34 years of age). The results indicate the significance of sex differences and the significance of the covariation between the three ST components. After age-regression, variation of the population in ST components and their covariation is explained by additive genetic sources of variance (A), shared (familial) environment (C) and unique environment (E). In men, additive genetic sources of variance explain 28.0% (CI 8.7-50.8%), 86.3% (71.6-90.2%) and 66.5% (37.4-85.1%) for endomorphy, mesomorphy and ectomorphy, respectively. For women, corresponding values are 32.3% (8.9-55.6%), 82.0% (67.7-87.7%) and 70.1% (48.9-81.8%). For all components in men and women, more than 70% of the total variation was explained by sources of variance shared between the three components, emphasising the importance of analysing the ST in a multivariate way. The findings suggest that the high heritabilities for mesomorphy and ectomorphy reported in earlier twin studies in adolescence are maintained in adulthood. For endomorphy, which represents a relative measure of subcutaneous adipose tissue, however, the results suggest heritability may be considerably lower than most values reported in earlier studies on adolescent twins. The heritability is also lower than values reported for, for example, body mass index (BMI), which next to the weight of organs and adipose tissue also includes muscle and bone tissue. Considering the differences in heritability between musculoskeletal robustness (mesomorphy) and subcutaneous adipose tissue (endomorphy) it may be questioned whether studying the genetics of BMI will eventually lead to a better understanding of the genetics of fatness, obesity and overweight.
Iron Status in Chronic Heart Failure: Impact on Symptoms, Functional Class and Submaximal Exercise Capacity.

PubMed

Enjuanes, Cristina; Bruguera, Jordi; Grau, María; Cladellas, Mercé; Gonzalez, Gina; Meroño, Oona; Moliner-Borja, Pedro; Verdú, José M; Farré, Nuria; Comín-Colet, Josep

2016-03-01

To evaluate the effect of iron deficiency and anemia on submaximal exercise capacity in patients with chronic heart failure. We undertook a single-center cross-sectional study in a group of stable patients with chronic heart failure. At recruitment, patients provided baseline information and completed a 6-minute walk test to evaluate submaximal exercise capacity and exercise-induced symptoms. At the same time, blood samples were taken for serological evaluation. Iron deficiency was defined as ferritin < 100 ng/mL or transferrin saturation < 20% when ferritin is < 800 ng/mL. Additional markers of iron status were also measured. A total of 538 heart failure patients were eligible for inclusion, with an average age of 71 years and 33% were in New York Heart Association class III/IV. The mean distance walked in the test was 285 ± 101 meters among those with impaired iron status, vs 322 ± 113 meters (P=.002). Symptoms during the test were more frequent in iron deficiency patients (35% vs 27%; P=.028) and the most common symptom reported was fatigue. Multivariate logistic regression analyses showed that increased levels of soluble transferrin receptor indicating abnormal iron status were independently associated with advanced New York Heart Association class (P < .05). Multivariable analysis using generalized additive models, soluble transferrin receptor and ferritin index, both biomarkers measuring iron status, showed a significant, independent and linear association with submaximal exercise capacity (P=.03 for both). In contrast, hemoglobin levels were not significantly associated with 6-minute walk test distance in the multivariable analysis. In patients with chronic heart failure, iron deficiency but not anemia was associated with impaired submaximal exercise capacity and symptomatic functional limitation. Copyright © 2015 Sociedad Española de Cardiología. Published by Elsevier España, S.L.U. All rights reserved.
Out-of-pocket fertility patient expense: data from a multicenter prospective infertility cohort.

PubMed

Wu, Alex K; Odisho, Anobel Y; Washington, Samuel L; Katz, Patricia P; Smith, James F

2014-02-01

The high costs of fertility care may deter couples from seeking care. Urologists often are asked about the costs of these treatments. To our knowledge previous studies have not addressed the direct out-of-pocket costs to couples. We characterized these expenses in patients seeking fertility care. Couples were prospectively recruited from 8 community and academic reproductive endocrinology clinics. Each participating couple completed face-to-face or telephone interviews and cost diaries at study enrollment, and 4, 10 and 18 months of care. We determined overall out-of-pocket costs, in addition to relationships between out-of-pocket costs and treatment type, clinical outcomes and socioeconomic characteristics on multivariate linear regression analysis. A total of 332 couples completed cost diaries and had data available on treatment and outcomes. Average age was 36.8 and 35.6 years in men and women, respectively. Of this cohort 19% received noncycle based therapy, 4% used ovulation induction medication only, 22% underwent intrauterine insemination and 55% underwent in vitro fertilization. The median overall out-of-pocket expense was $5,338 (IQR 1,197-19,840). Couples using medication only had the lowest median out-of-pocket expenses at $912 while those using in vitro fertilization had the highest at $19,234. After multivariate adjustment the out-of-pocket expense was not significantly associated with successful pregnancy. On multivariate analysis couples treated with in vitro fertilization spent an average of $15,435 more than those treated with intrauterine insemination. Couples spent about $6,955 for each additional in vitro fertilization cycle. These data provide real-world estimates of out-of-pocket costs, which can be used to help couples plan for expenses that they may incur with treatment. Copyright © 2014 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.
MDAS: an integrated system for metabonomic data analysis.

PubMed

Liu, Juan; Li, Bo; Xiong, Jiang-Hui

2009-03-01

Metabonomics, the latest 'omics' research field, shows great promise as a tool in biomarker discovery, drug efficacy and toxicity analysis, disease diagnosis and prognosis. One of the major challenges now facing researchers is how to process this data to yield useful information about a biological system, e.g., the mechanism of diseases. Traditional methods employed in metabonomic data analysis use multivariate analysis methods developed independently in chemometrics research. Additionally, with the development of machine learning approaches, some methods such as SVMs also show promise for use in metabonomic data analysis. Aside from the application of general multivariate analysis and machine learning methods to this problem, there is also a need for an integrated tool customized for metabonomic data analysis which can be easily used by biologists to reveal interesting patterns in metabonomic data.In this paper, we present a novel software tool MDAS (Metabonomic Data Analysis System) for metabonomic data analysis which integrates traditional chemometrics methods and newly introduced machine learning approaches. MDAS contains a suite of functional models for metabonomic data analysis and optimizes the flow of data analysis. Several file formats can be accepted as input. The input data can be optionally preprocessed and can then be processed with operations such as feature analysis and dimensionality reduction. The data with reduced dimensionalities can be used for training or testing through machine learning models. The system supplies proper visualization for data preprocessing, feature analysis, and classification which can be a powerful function for users to extract knowledge from the data. MDAS is an integrated platform for metabonomic data analysis, which transforms a complex analysis procedure into a more formalized and simplified one. The software package can be obtained from the authors.
Multivariate time series analysis of neuroscience data: some challenges and opportunities.

PubMed

Pourahmadi, Mohsen; Noorbaloochi, Siamak

2016-04-01

Neuroimaging data may be viewed as high-dimensional multivariate time series, and analyzed using techniques from regression analysis, time series analysis and spatiotemporal analysis. We discuss issues related to data quality, model specification, estimation, interpretation, dimensionality and causality. Some recent research areas addressing aspects of some recurring challenges are introduced. Copyright © 2015 Elsevier Ltd. All rights reserved.
Evaluating Measurement of Dynamic Constructs: Defining a Measurement Model of Derivatives

PubMed Central

Estabrook, Ryne

2015-01-01

While measurement evaluation has been embraced as an important step in psychological research, evaluating measurement structures with longitudinal data is fraught with limitations. This paper defines and tests a measurement model of derivatives (MMOD), which is designed to assess the measurement structure of latent constructs both for analyses of between-person differences and for the analysis of change. Simulation results indicate that MMOD outperforms existing models for multivariate analysis and provides equivalent fit to data generation models. Additional simulations show MMOD capable of detecting differences in between-person and within-person factor structures. Model features, applications and future directions are discussed. PMID:24364383
Data analysis techniques

NASA Technical Reports Server (NTRS)

Park, Steve

1990-01-01

A large and diverse number of computational techniques are routinely used to process and analyze remotely sensed data. These techniques include: univariate statistics; multivariate statistics; principal component analysis; pattern recognition and classification; other multivariate techniques; geometric correction; registration and resampling; radiometric correction; enhancement; restoration; Fourier analysis; and filtering. Each of these techniques will be considered, in order.
Chemical structure of wood charcoal by infrared spectroscopy and multivariate analysis

Treesearch

Nicole Labbe; David Harper; Timothy Rials; Thomas Elder

2006-01-01

In this work, the effect of temperature on charcoal structure and chemical composition is investigated for four tree species. Wood charcoal carbonized at various temperatures is analyzed by mid infrared spectroscopy coupled with multivariate analysis and by thermogravimetric analysis to characterize the chemical composition during the carbonization process. The...
Root Cause Analysis of Quality Defects Using HPLC-MS Fingerprint Knowledgebase for Batch-to-batch Quality Control of Herbal Drugs.

PubMed

Yan, Binjun; Fang, Zhonghua; Shen, Lijuan; Qu, Haibin

2015-01-01

The batch-to-batch quality consistency of herbal drugs has always been an important issue. To propose a methodology for batch-to-batch quality control based on HPLC-MS fingerprints and process knowledgebase. The extraction process of Compound E-jiao Oral Liquid was taken as a case study. After establishing the HPLC-MS fingerprint analysis method, the fingerprints of the extract solutions produced under normal and abnormal operation conditions were obtained. Multivariate statistical models were built for fault detection and a discriminant analysis model was built using the probabilistic discriminant partial-least-squares method for fault diagnosis. Based on multivariate statistical analysis, process knowledge was acquired and the cause-effect relationship between process deviations and quality defects was revealed. The quality defects were detected successfully by multivariate statistical control charts and the type of process deviations were diagnosed correctly by discriminant analysis. This work has demonstrated the benefits of combining HPLC-MS fingerprints, process knowledge and multivariate analysis for the quality control of herbal drugs. Copyright © 2015 John Wiley & Sons, Ltd.
Multivariate analysis: greater insights into complex systems

USDA-ARS?s Scientific Manuscript database

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

Association between serum CA 19-9 and metabolic syndrome: A cross-sectional study.

PubMed

Du, Rui; Cheng, Di; Lin, Lin; Sun, Jichao; Peng, Kui; Xu, Yu; Xu, Min; Chen, Yuhong; Bi, Yufang; Wang, Weiqing; Lu, Jieli; Ning, Guang

2017-11-01

Increasing evidence suggests that serum CA 19-9 is associated with abnormal glucose metabolism. However, data on the association between CA 19-9 and metabolic syndrome is limited. The aim of the present study was to investigate the association between serum CA 19-9 and metabolic syndrome. A cross-sectional study was conducted on 3641 participants aged ≥40 years from the Songnan Community, Baoshan District in Shanghai, China. Logistic regression analysis was used to evaluate the association between serum CA 19-9 and metabolic syndrome. Multivariate logistic regression analysis showed that compared with participants in the first tertile of serum CA 19-9, those in the second and third tertiles had increased odds ratios (OR) for prevalent metabolic syndrome (multivariate adjusted OR 1.46 [95% confidence interval {CI} 1.11-1.92] and 1.51 [95% CI 1.14-1.98]; P trend = 0.005). In addition, participants with elevated serum CA 19-9 (≥37 U/mL) had an increased risk of prevalent metabolic syndrome compared with those with serum CA 19-9 < 37 U/mL (multivariate adjusted OR 2.10; 95% CI 1.21-3.65). Serum CA 19-9 is associated with an increased risk of prevalent metabolic syndrome. In order to confirm this association and identify potential mechanisms, prospective cohort and mechanic studies should be performed. © 2017 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd.
Dominance genetic variance for traits under directional selection in Drosophila serrata.

PubMed

Sztepanacz, Jacqueline L; Blows, Mark W

2015-05-01

In contrast to our growing understanding of patterns of additive genetic variance in single- and multi-trait combinations, the relative contribution of nonadditive genetic variance, particularly dominance variance, to multivariate phenotypes is largely unknown. While mechanisms for the evolution of dominance genetic variance have been, and to some degree remain, subject to debate, the pervasiveness of dominance is widely recognized and may play a key role in several evolutionary processes. Theoretical and empirical evidence suggests that the contribution of dominance variance to phenotypic variance may increase with the correlation between a trait and fitness; however, direct tests of this hypothesis are few. Using a multigenerational breeding design in an unmanipulated population of Drosophila serrata, we estimated additive and dominance genetic covariance matrices for multivariate wing-shape phenotypes, together with a comprehensive measure of fitness, to determine whether there is an association between directional selection and dominance variance. Fitness, a trait unequivocally under directional selection, had no detectable additive genetic variance, but significant dominance genetic variance contributing 32% of the phenotypic variance. For single and multivariate morphological traits, however, no relationship was observed between trait-fitness correlations and dominance variance. A similar proportion of additive and dominance variance was found to contribute to phenotypic variance for single traits, and double the amount of additive compared to dominance variance was found for the multivariate trait combination under directional selection. These data suggest that for many fitness components a positive association between directional selection and dominance genetic variance may not be expected. Copyright © 2015 by the Genetics Society of America.
Comparison of connectivity analyses for resting state EEG data

NASA Astrophysics Data System (ADS)

Olejarczyk, Elzbieta; Marzetti, Laura; Pizzella, Vittorio; Zappasodi, Filippo

2017-06-01

Objective. In the present work, a nonlinear measure (transfer entropy, TE) was used in a multivariate approach for the analysis of effective connectivity in high density resting state EEG data in eyes open and eyes closed. Advantages of the multivariate approach in comparison to the bivariate one were tested. Moreover, the multivariate TE was compared to an effective linear measure, i.e. directed transfer function (DTF). Finally, the existence of a relationship between the information transfer and the level of brain synchronization as measured by phase synchronization value (PLV) was investigated. Approach. The comparison between the connectivity measures, i.e. bivariate versus multivariate TE, TE versus DTF, TE versus PLV, was performed by means of statistical analysis of indexes based on graph theory. Main results. The multivariate approach is less sensitive to false indirect connections with respect to the bivariate estimates. The multivariate TE differentiated better between eyes closed and eyes open conditions compared to DTF. Moreover, the multivariate TE evidenced non-linear phenomena in information transfer, which are not evidenced by the use of DTF. We also showed that the target of information flow, in particular the frontal region, is an area of greater brain synchronization. Significance. Comparison of different connectivity analysis methods pointed to the advantages of nonlinear methods, and indicated a relationship existing between the flow of information and the level of synchronization of the brain.
Prognostic Significance of POLE Proofreading Mutations in Endometrial Cancer

PubMed Central

Church, David N.; Stelloo, Ellen; Nout, Remi A.; Valtcheva, Nadejda; Depreeuw, Jeroen; ter Haar, Natalja; Noske, Aurelia; Amant, Frederic; Wild, Peter J.; Lambrechts, Diether; Jürgenliemk-Schulz, Ina M.; Jobsen, Jan J.; Smit, Vincent T. H. B. M.; Creutzberg, Carien L.; Bosse, Tjalling

2015-01-01

Background: Current risk stratification in endometrial cancer (EC) results in frequent over- and underuse of adjuvant therapy, and may be improved by novel biomarkers. We examined whether POLE proofreading mutations, recently reported in about 7% of ECs, predict prognosis. Methods: We performed targeted POLE sequencing in ECs from the PORTEC-1 and -2 trials (n = 788), and analyzed clinical outcome according to POLE status. We combined these results with those from three additional series (n = 628) by meta-analysis to generate multivariable-adjusted, pooled hazard ratios (HRs) for recurrence-free survival (RFS) and cancer-specific survival (CSS) of POLE-mutant ECs. All statistical tests were two-sided. Results: POLE mutations were detected in 48 of 788 (6.1%) ECs from PORTEC-1 and-2 and were associated with high tumor grade (P < .001). Women with POLE-mutant ECs had fewer recurrences (6.2% vs 14.1%) and EC deaths (2.3% vs 9.7%), though, in the total PORTEC cohort, differences in RFS and CSS were not statistically significant (multivariable-adjusted HR = 0.43, 95% CI = 0.13 to 1.37, P = .15; HR = 0.19, 95% CI = 0.03 to 1.44, P = .11 respectively). However, of 109 grade 3 tumors, 0 of 15 POLE-mutant ECs recurred, compared with 29 of 94 (30.9%) POLE wild-type cancers; reflected in statistically significantly greater RFS (multivariable-adjusted HR = 0.11, 95% CI = 0.001 to 0.84, P = .03). In the additional series, there were no EC-related events in any of 33 POLE-mutant ECs, resulting in a multivariable-adjusted, pooled HR of 0.33 for RFS (95% CI = 0.12 to 0.91, P = .03) and 0.26 for CSS (95% CI = 0.06 to 1.08, P = .06). Conclusion: POLE proofreading mutations predict favorable EC prognosis, independently of other clinicopathological variables, with the greatest effect seen in high-grade tumors. This novel biomarker may help to reduce overtreatment in EC. PMID:25505230
Independent and additive interaction between polymorphisms of tumor necrosis factor α-308 and lymphotoxin α+252 on risk of hepatocellular carcinoma related to hepatitis B.

PubMed

Tsai, Jung-Fa; Chen, Shinn-Chern; Lin, Zu-Yau; Dai, Chia-Yen; Huang, Jee-Fu; Yu, Min-Lung; Chuang, Wan-Long

2017-09-01

This case-control study was aimed to assess the effect of genetic variants of tumor necrosis factor (TNF) α-308 and lymphotoxin (LT) α+252 on development of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). Their gene-gene interaction was also investigated. We enrolled 200 pairs of age- and sex-matched patients with cirrhotic HBV-HCC and unrelated patients with HBV-cirrhosis alone. Polymorphisms of TNFα-308 and LTα+252 were genotyped. Synergy index was used to calculate interaction between the variant genotypes. The results indicated that the frequency distribution of the variant genotypes (TNFα-308 G/A and LTα+252 G/G) in patients with HCC were significantly higher than those in patients with cirrhosis alone. Multivariate analysis indicated that TNFα-308 G/A (odds ratio [OR], 2.34) and LTα+252 G/G (OR, 2.04) were independent risk factors for HCC. By the clinical characteristics of study population, multivariate analysis demonstrated that independent factors associated with harboring the variant genotypes included cirrhosis with Child-Pugh C (OR = 6.47 in cases and OR = 11.56 in controls) and thrombocytopenia (OR = 8.86 in cases and OR = 7.74 in controls). Calculation of synergy index (SI) indicated that there are additive interaction between TNFα-308 G/A and LTα+252 G/G on risk of HCC (SI = 1.29). There are independent and additive interactions between TNFα-308 G/A and LTα+252 G/G on risk for HBV-HCC. They correlated with advanced hepatic fibrosis and severe liver damage, which might contribute to a higher risk for HCC. Copyright © 2017 Kaohsiung Medical University. Published by Elsevier Taiwan. All rights reserved.
Prevalence and determinants of albuminuria in a cohort of diabetic patients in Lebanon.

PubMed

Taleb, Nadine; Salti, Haytham; Al-Mokaddam, Mona; Merheb, Marie; Salti, Ibrahim; Nasrallah, Mona

2008-01-01

Few data are available on the extent of albuminuria in diabetic populations in the Middle East generally and in Lebanon specifically. We conducted this study to determine the prevalence of albuminuria and its major risk factors in a cohort of diabetic patients in Lebanon. Diabetic patients followed in the outpatient department at the American University of Beirut Medical Center (AUBMC) were included in a prospective observational study. AUBMC is a tertiary referral center and the outpatient department typically handles patients of low socioeconomic status with advanced disease. Patients were classified according to their urinary albumin-to-creatinine ratio (ACR) as having normoalbuminuria (ACR<30 mg/g creatinine), microalbuminuria (ACR=30 to <300 mg/g creatinine), or macroalbuminuria (ACR > or =300 mg/g creatinine). The three groups were compared to analyze the association between albuminuria and its risk factors. In addition, independent predictors of albuminuria were determined using multivariate logistic regression and presented as an odds ratio. Microalbuminuria and macroalbuminuria were present in 33.3% and 12.7% of 222 patients (mean age 56.4 years, mean deviation of diabetes 8.6 years, 58.7% women, 43.8% obese), respectively. Factors significantly associated with microalbuminuria included glycemic control, insulin use, and total and LDL cholesterol. Those associated with macroalbuminuria included in addition to glycemic control and insulin use, duration of diabetes, hypertension, elevated mean arterial pressure (MAP), and presence of neuropathy, retinopathy and peripheral vascular disease by bivariate analysis. Only glycemic control was an independent risk factor for both in addition to MAP and retinopathy for macroalbuminuria by multivariate analysis. Albuminuria is highly prevalent among this cohort of diabetic patients in Lebanon. Both glycemic control and blood pressure need to be better targeted in its management.
MULTIVARIATE CURVE RESOLUTION OF NMR SPECTROSCOPY METABONOMIC DATA

EPA Science Inventory

Sandia National Laboratories is working with the EPA to evaluate and develop mathematical tools for analysis of the collected NMR spectroscopy data. Initially, we have focused on the use of Multivariate Curve Resolution (MCR) also known as molecular factor analysis (MFA), a tech...
Multivariate curve resolution based chromatographic peak alignment combined with parallel factor analysis to exploit second-order advantage in complex chromatographic measurements.

PubMed

Parastar, Hadi; Akvan, Nadia

2014-03-13

In the present contribution, a new combination of multivariate curve resolution-correlation optimized warping (MCR-COW) with trilinear parallel factor analysis (PARAFAC) is developed to exploit second-order advantage in complex chromatographic measurements. In MCR-COW, the complexity of the chromatographic data is reduced by arranging the data in a column-wise augmented matrix, analyzing using MCR bilinear model and aligning the resolved elution profiles using COW in a component-wise manner. The aligned chromatographic data is then decomposed using trilinear model of PARAFAC in order to exploit pure chromatographic and spectroscopic information. The performance of this strategy is evaluated using simulated and real high-performance liquid chromatography-diode array detection (HPLC-DAD) datasets. The obtained results showed that the MCR-COW can efficiently correct elution time shifts of target compounds that are completely overlapped by coeluted interferences in complex chromatographic data. In addition, the PARAFAC analysis of aligned chromatographic data has the advantage of unique decomposition of overlapped chromatographic peaks to identify and quantify the target compounds in the presence of interferences. Finally, to confirm the reliability of the proposed strategy, the performance of the MCR-COW-PARAFAC is compared with the frequently used methods of PARAFAC, COW-PARAFAC, multivariate curve resolution-alternating least squares (MCR-ALS), and MCR-COW-MCR. In general, in most of the cases the MCR-COW-PARAFAC showed an improvement in terms of lack of fit (LOF), relative error (RE) and spectral correlation coefficients in comparison to the PARAFAC, COW-PARAFAC, MCR-ALS and MCR-COW-MCR results. Copyright © 2014 Elsevier B.V. All rights reserved.
Prostate weight: an independent predictor for positive surgical margins during robotic-assisted laparoscopic radical prostatectomy.

PubMed

Msezane, Lambda P; Gofrit, Ofer N; Lin, Shang; Shalhav, Arieh L; Zagaja, Gregory P; Zorn, Kevin C

2007-10-01

Pre-operative prediction of pathological stage represents the cornerstone of prostate cancer management. Patient counseling is routinely based on pre-operative PSA, Gleason score and clinical stage. In this study, we evaluated whether prostate weight (PW) is an independent predictor of extracapsular extension (ECE) and positive surgical margin (PSM). Between February 2003 and November 2006, 709 men underwent robotic-assisted laparoscopic radical prostatectomy (RLRP). Pre-operative parameters (patient age, pre-operative PSA, biopsy Gleason score, clinical stage) as well as pathological data (prostate weight, pathological stage) were prospectively gathered after internal-review board (IRB) approval. Evaluation of the influence of these variables on ECE and PSM outcomes were assessed using both univariate and multivariate logistic regression analysis. Mean overall patient age, pre-operative PSA and PW were 59.6 years, 6.5 ng/ml and 52.9 g (range 5.5 g-198.7 g), respectively. Of the 393, 209 and 107 men with PW < 50 g, 50 g-< 70 g and < 70 g, ECE was observed in 20.1%, 15.3% and 9.3%, respectively (p = 0.015). In the same patient cohorts, PSM was observed in 25.4%, 14.4% and 7.5%, respectively (p < 0.001). In a multivariate logistic regression analysis, PW, in addition to pre-operative PSA, biopsy Gleason score and clinical stage, was an independent risk factor for ECE (p < 0.001). Similarly, in multi-variate analysis, PW was observed to be a risk factor for PSM (p < 0.001). PW is an independent predictor of both ECE and PSM, with an inverse relationship having been demonstrated between both variables. PW should be considered when counseling patients with prostate cancer treatment.
Prognostic factors in prostate cancer patients treated by radical external beam radiotherapy.

PubMed

Garibaldi, Elisabetta; Gabriele, Domenico; Maggio, Angelo; Delmastro, Elena; Garibaldi, Monica; Russo, Filippo; Bresciani, Sara; Stasi, Michele; Gabriele, Pietro

2017-09-01

The aim of this paper was to analyze, retrospectively, in prostate cancer patients treated in our Centre with external beam radiotherapy, the prognostic factors and their impact on the outcome in terms of cancer-specific survival (CSS), biochemical disease-free survival (BDFS) and clinical disease-free survival (CDFS). From October 1999 and March 2012, 1080 patients were treated with radiotherapy at our Institution: 87% of them were classified as ≤cT2, 83% had a Gleason Score (GS) ≤7, their mean of iPSA was 18 ng/mL, and the rate of clinical positive nodes was 1%. The mean follow-up was 81 months. The statistically significant prognostic factors for all groups of patients at both, univariate and multivariate analysis, were the GS and the iPSA. In intermediate- and high- or very-high-risk patients at multivariate analysis other prognostic factors for CSS were positive nodes on computed tomography (CT) scan and rectal preparation during the treatment; for BDFS, the prognostic factors were patient risk classification, positive lymph nodes on CT scan and rectal/bladder preparation; for CDFS, the prognostic factors were the number of positive core on biopsy (P=0.003), positive lymph nodes on CT scan, and radiotherapy (RT) dose. In high/very-high risk patient group at multivariate analysis other prognostic factors for CSS were clinical/radiological stage and RT dose, for BDFS they were adjuvant hormone therapy, clinical/radiological stage, and RT dose >77.7 Gy, and for CDFS they were clinical/radiological stage and RT dose >77.7 Gy. The results of this study confirm the prognostic factors described in the recent literature, with the addition of rectal/bladder preparation, generally known for its effect on toxicity but not yet on outcome.
Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267
Drunk driving detection based on classification of multivariate time series.

PubMed

Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.
The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

PubMed

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

2015-12-30

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

PubMed Central

Hebart, Martin N.; Görgen, Kai; Haynes, John-Dylan

2015-01-01

The multivariate analysis of brain signals has recently sparked a great amount of interest, yet accessible and versatile tools to carry out decoding analyses are scarce. Here we introduce The Decoding Toolbox (TDT) which represents a user-friendly, powerful and flexible package for multivariate analysis of functional brain imaging data. TDT is written in Matlab and equipped with an interface to the widely used brain data analysis package SPM. The toolbox allows running fast whole-brain analyses, region-of-interest analyses and searchlight analyses, using machine learning classifiers, pattern correlation analysis, or representational similarity analysis. It offers automatic creation and visualization of diverse cross-validation schemes, feature scaling, nested parameter selection, a variety of feature selection methods, multiclass capabilities, and pattern reconstruction from classifier weights. While basic users can implement a generic analysis in one line of code, advanced users can extend the toolbox to their needs or exploit the structure to combine it with external high-performance classification toolboxes. The toolbox comes with an example data set which can be used to try out the various analysis methods. Taken together, TDT offers a promising option for researchers who want to employ multivariate analyses of brain activity patterns. PMID:25610393
Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis.

PubMed

Flores, X; Comas, J; Roda, I R; Jiménez, L; Gernaey, K V

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 of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.
The relationship of self-concept and perceived athletic competence to physical activity level and gender among Turkish early adolescents.

PubMed

Aşçi, F H; Koşar, S N; Işler, A K

2001-01-01

The purpose of this study was to examine the self-concept and perceived athletic competence of Turkish early adolescents in relation to physical activity level and gender. Self-concept was assessed using the Piers-Harris Children's Self-Concept Scale, and perceived athletic competence was assessed by means of the Athletic Competence subscale of Harter's Self-Perception Profile for Children. In addition, the Weekly Activity Checklist was used for assessing physical activity level. Males and females were assigned to low and high physical activity level groups based on their mean scores. Multivariate analysis of variance revealed significant main effects for gender and physical activity level, but there was no significant gender by physical activity interaction. Univariate analysis demonstrated a significant main effect for physical activity level on perceived athletic competence but not global self-concept. In addition, univariate analysis did not reveal a significant difference in either global self-concept or perceived athletic competence with respect to gender.
Moving beyond Univariate Post-Hoc Testing in Exercise Science: A Primer on Descriptive Discriminate Analysis

ERIC Educational Resources Information Center

Barton, Mitch; Yeatts, Paul E.; Henson, Robin K.; Martin, Scott B.

2016-01-01

There has been a recent call to improve data reporting in kinesiology journals, including the appropriate use of univariate and multivariate analysis techniques. For example, a multivariate analysis of variance (MANOVA) with univariate post hocs and a Bonferroni correction is frequently used to investigate group differences on multiple dependent…
Risk factors for an additional port in single-incision laparoscopic cholecystectomy in patients with cholecystitis.

PubMed

Araki, Kenichiro; Shirabe, Ken; Watanabe, Akira; Kubo, Norio; Sasaki, Shigeru; Suzuki, Hideki; Asao, Takayuki; Kuwano, Hiroyuki

2017-01-01

Although single-incision laparoscopic cholecystectomy is now widely performed in patients with cholecystitis, some cases require an additional port to complete the procedure. In this study, we focused on risk factor of additional port in this surgery. We performed single-incision cholecystectomy in 75 patients with acute cholecystitis or after cholecystitis between 2010 and 2014 at Gunma University Hospital. Surgical indications followed the TG13 guidelines. Our standard procedure for single-incision cholecystectomy routinely uses two needlescopic devices. We used logistic regression analysis to identify the risk factors associated with use of an additional full-size port (5 or 10 mm). Surgical outcome was acceptable without biliary injury. Nine patients (12.0%) required an additional port, and one patient (1.3%) required conversion to open cholecystectomy because of severe adhesions around the cystic duct and common bile duct. In multivariate analysis, high C-reactive protein (CRP) values (>7.0 mg/dl) during cholecystitis attacks were significantly correlated with the need for an additional port (P = 0.009), with a sensitivity of 55.6%, specificity of 98.5%, and accuracy of 93.3%. This study indicated that the severe inflammation indicated by high CRP values during cholecystitis attacks predicts the need for an additional port. J. Med. Invest. 64: 245-249, August, 2017.
Jaundice: an important, poorly recognized risk factor for diminished survival in patients with adenocarcinoma of the head of the pancreas

PubMed Central

Strasberg, Steven M; Gao, Feng; Sanford, Dominic; Linehan, David C; Hawkins, William G; Fields, Ryan; Carpenter, Danielle H; Brunt, Elizabeth M; Phillips, Carolyn

2014-01-01

Objectives: Jaundice impairs cellular immunity, an important defence against the dissemination of cancer. Jaundice is a common mode of presentation in pancreatic head adenocarcinoma. The purpose of this study was to determine whether there is an association between preoperative jaundice and survival in patients who have undergone resection of such tumours. Methods: Thirty possible survival risk factors were evaluated in a database of over 400 resected patients. Univariate analysis was used to determine odds ratio for death. All factors for which a P-value of <0.30 was obtained were entered into a multivariate analysis using the Cox model with backward selection. Results: Preoperative jaundice, age, positive node status, poor differentiation and lymphatic invasion were significant indicators of poor outcome in multivariate analysis. Absence of jaundice was a highly favourable prognostic factor. Interaction emerged between jaundice and nodal status. The benefit conferred by the absence of jaundice was restricted to patients in whom negative node status was present. Five-year overall survival in this group was 66%. Jaundiced patients who underwent preoperative stenting had a survival advantage. Conclusions: Preoperative jaundice is a negative risk factor in adenocarcinoma of the pancreas. Additional studies are required to determine the exact mechanism for this effect. PMID:23600768
FTIR microspectroscopy for rapid screening and monitoring of polyunsaturated fatty acid production in commercially valuable marine yeasts and protists.

PubMed

Vongsvivut, Jitraporn; Heraud, Philip; Gupta, Adarsha; Puri, Munish; McNaughton, Don; Barrow, Colin J

2013-10-21

The increase in polyunsaturated fatty acid (PUFA) consumption has prompted research into alternative resources other than fish oil. In this study, a new approach based on focal-plane-array Fourier transform infrared (FPA-FTIR) microspectroscopy and multivariate data analysis was developed for the characterisation of some marine microorganisms. Cell and lipid compositions in lipid-rich marine yeasts collected from the Australian coast were characterised in comparison to a commercially available PUFA-producing marine fungoid protist, thraustochytrid. Multivariate classification methods provided good discriminative accuracy evidenced from (i) separation of the yeasts from thraustochytrids and distinct spectral clusters among the yeasts that conformed well to their biological identities, and (ii) correct classification of yeasts from a totally independent set using cross-validation testing. The findings further indicated additional capability of the developed FPA-FTIR methodology, when combined with partial least squares regression (PLSR) analysis, for rapid monitoring of lipid production in one of the yeasts during the growth period, which was achieved at a high accuracy compared to the results obtained from the traditional lipid analysis based on gas chromatography. The developed FTIR-based approach when coupled to programmable withdrawal devices and a cytocentrifugation module would have strong potential as a novel online monitoring technology suited for bioprocessing applications and large-scale production.

Comparison of Portable and Bench-Top Spectrometers for Mid-Infrared Diffuse Reflectance Measurements of Soils.

PubMed

Hutengs, Christopher; Ludwig, Bernard; Jung, András; Eisele, Andreas; Vohland, Michael

2018-03-27

Mid-infrared (MIR) spectroscopy has received widespread interest as a method to complement traditional soil analysis. Recently available portable MIR spectrometers additionally offer potential for on-site applications, given sufficient spectral data quality. We therefore tested the performance of the Agilent 4300 Handheld FTIR (DRIFT spectra) in comparison to a Bruker Tensor 27 bench-top instrument in terms of (i) spectral quality and measurement noise quantified by wavelet analysis; (ii) accuracy of partial least squares (PLS) calibrations for soil organic carbon (SOC), total nitrogen (N), pH, clay and sand content with a repeated cross-validation analysis; and (iii) key spectral regions for these soil properties identified with a Monte Carlo spectral variable selection approach. Measurements and multivariate calibrations with the handheld device were as good as or slightly better than Bruker equipped with a DRIFT accessory, but not as accurate as with directional hemispherical reflectance (DHR) data collected with an integrating sphere. Variations in noise did not markedly affect the accuracy of multivariate PLS calibrations. Identified key spectral regions for PLS calibrations provided a good match between Agilent and Bruker DHR data, especially for SOC and N. Our findings suggest that portable FTIR instruments are a viable alternative for MIR measurements in the laboratory and offer great potential for on-site applications.
Multidisciplinary therapy for patients with locally oligo-recurrent pelvic malignancies.

PubMed

Sole, Claudio V; Calvo, Felipe A; de Sierra, Pedro Alvarez; Herranz, Rafael; Gonzalez-Bayon, Luis; García-Sabrido, Jose Luis

2014-07-01

To analyze prognostic factors and long-term outcomes in patients with locally recurrent pelvic cancer (LRPC) treated with a multidisciplinary approach. From January 1995 to December 2011, 81 patients [rectal (47 %); gynecologic (39 %); retroperitoneal sarcoma (14 %)] underwent extended surgery [multiorgan (58 %), bone (35 %), vascular (9 %), soft tissue (63 %)] and intraoperative electron beam radiation therapy (IOERT) to treat recurrent tumors in the pelvic region. Thirty-five patients (43 %) received external beam radiotherapy (EBRT). Survival was estimated using the Kaplan-Meier method, and risk factors were identified using univariate and multivariate analysis. Median follow-up was 39 months (6-189 months); the 1- 3- and 5-year rates of locoregional control (LRC) were 83, 53, and 41 %, respectively. Univariate Cox proportional hazard analysis revealed worse LRC in patients who did not receive integrated EBRT as rescue treatment of pelvic recurrence (p = 0.003) or underwent non-radical resection (p = 0.01). In the multivariate analysis EBRT, non-radical resection, and tumor fragmentation retained significance (p = 0.002, p = 0.004, and p = 0.05, respectively). Radical resection, absence of tumor fragmentation and addition of EBRT for rescue are associated with improved LRC in patients with LRPC. Our results suggest that this group can benefit from EBRT combined with extended surgical resection and IOERT.
Anger Expression Types and Interpersonal Problems in Nurses.

PubMed

Han, Aekyung; Won, Jongsoon; Kim, Oksoo; Lee, Sang E

2015-06-01

The purpose of this study was to investigate the anger expression types in nurses and to analyze the differences between the anger expression types and interpersonal problems. The data were collected from 149 nurses working in general hospitals with 300 beds or more in Seoul or Gyeonggi province, Korea. For anger expression type, the anger expression scale from the Korean State-Trait Anger Expression Inventory was used. For interpersonal problems, the short form of the Korean Inventory of Interpersonal Problems Circumplex Scales was used. Data were analyzed using descriptive statistics, cluster analysis, multivariate analysis of variance, and Duncan's multiple comparisons test. Three anger expression types in nurses were found: low-anger expression, anger-in, and anger-in/control type. From the results of multivariate analysis of variance, there were significant differences between anger expression types and interpersonal problems (Wilks lambda F = 3.52, p < .001). Additionally, anger-in/control type was found to have the most difficulty with interpersonal problems by Duncan's post hoc test (p < .050). Based on this research, the development of an anger expression intervention program for nurses is recommended to establish the means of expressing the suppressed emotions, which would help the nurses experience less interpersonal problems. Copyright © 2015. Published by Elsevier B.V.
Exploring laser-induced breakdown spectroscopy for nuclear materials analysis and in-situ applications

NASA Astrophysics Data System (ADS)

Martin, Madhavi Z.; Allman, Steve; Brice, Deanne J.; Martin, Rodger C.; Andre, Nicolas O.

2012-08-01

Laser-induced breakdown spectroscopy (LIBS) has been used to determine the limits of detection of strontium (Sr) and cesium (Cs), common nuclear fission products. Additionally, detection limits were determined for cerium (Ce), often used as a surrogate for radioactive plutonium in laboratory studies. Results were obtained using a laboratory instrument with a Nd:YAG laser at fundamental wavelength of 1064 nm, frequency doubled to 532 nm with energy of 50 mJ/pulse. The data was compared for different concentrations of Sr and Ce dispersed in a CaCO3 (white) and carbon (black) matrix. We have addressed the sampling errors, limits of detection, reproducibility, and accuracy of measurements as they relate to multivariate analysis in pellets that were doped with the different elements at various concentrations. These results demonstrate that LIBS technique is inherently well suited for in situ analysis of nuclear materials in hot cells. Three key advantages are evident: (1) small samples (mg) can be evaluated; (2) nuclear materials can be analyzed with minimal sample preparation; and (3) samples can be remotely analyzed very rapidly (ms-seconds). Our studies also show that the methods can be made quantitative. Very robust multivariate models have been used to provide quantitative measurement and statistical evaluation of complex materials derived from our previous research on wood and soil samples.
Biodemographic Modeling of the Links Between Fertility Motivation and Fertility Outcomes in the NLSY79

PubMed Central

MILLER, WARREN B.; BARD, DAVID E.; PASTA, DAVID J.; RODGERS, JOSEPH LEE

2010-01-01

In spite of long-held beliefs that traits related to reproductive success tend to become fixed by evolution with little or no genetic variation, there is now considerable evidence that the natural variation of fertility within populations is genetically influenced and that a portion of that influence is related to the motivational precursors to fertility. We conduct a two-stage analysis to examine these inferences in a time-ordered multivariate context. First, using data from the National Longitudinal Survey of Youth, 1979, and LISREL analysis, we develop a structural equation model in which five hypothesized motivational precursors to fertility, measured in 1979–1982, predict both a child-timing and a child-number outcome, measured in 2002. Second, having chosen two time-ordered sequences of six variables from the SEM to represent our phenotypic models, we use Mx to conduct both univariate and multivariate behavioral genetic analyses with the selected variables. Our results indicate that one or more genes acting within a gene network have additive effects that operate through child-number desires to affect both the timing of the next child born and the final number of children born, that one or more genes acting through a separate network may have additive effects operating through gender role attitudes to produce downstream effects on the two fertility outcomes, and that no genetic variance is associated with either child-timing intentions or educational intentions. PMID:20608103
MGAS: a powerful tool for multivariate gene-based genome-wide association analysis.

PubMed

Van der Sluis, Sophie; Dolan, Conor V; Li, Jiang; Song, Youqiang; Sham, Pak; Posthuma, Danielle; Li, Miao-Xin

2015-04-01

Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype-phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis. MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype-phenotype models. MGAS is freely available in KGG v3.0 (http://statgenpro.psychiatry.hku.hk/limx/kgg/download.php). Access to the metabolic dataset can be requested at dbGaP (https://dbgap.ncbi.nlm.nih.gov/). The R-simulation code is available from http://ctglab.nl/people/sophie_van_der_sluis. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.
What matters? Assessing and developing inquiry and multivariable reasoning skills in high school chemistry

NASA Astrophysics Data System (ADS)

Daftedar Abdelhadi, Raghda Mohamed

Although the Next Generation Science Standards (NGSS) present a detailed set of Science and Engineering Practices, a finer grained representation of the underlying skills is lacking in the standards document. Therefore, it has been reported that teachers are facing challenges deciphering and effectively implementing the standards, especially with regards to the Practices. This analytical study assessed the development of high school chemistry students' (N = 41) inquiry, multivariable causal reasoning skills, and metacognition as a mediator for their development. Inquiry tasks based on concepts of element properties of the periodic table as well as reaction kinetics required students to conduct controlled thought experiments, make inferences, and declare predictions of the level of the outcome variable by coordinating the effects of multiple variables. An embedded mixed methods design was utilized for depth and breadth of understanding. Various sources of data were collected including students' written artifacts, audio recordings of in-depth observational groups and interviews. Data analysis was informed by a conceptual framework formulated around the concepts of coordinating theory and evidence, metacognition, and mental models of multivariable causal reasoning. Results of the study indicated positive change towards conducting controlled experimentation, making valid inferences and justifications. Additionally, significant positive correlation between metastrategic and metacognitive competencies, and sophistication of experimental strategies, signified the central role metacognition played. Finally, lack of consistency in indicating effective variables during the multivariable prediction task pointed towards the fragile mental models of multivariable causal reasoning the students had. Implications for teacher education, science education policy as well as classroom research methods are discussed. Finally, recommendations for developing reform-based chemistry curricula based on the Practices are presented.
Multivariate methods to visualise colour-space and colour discrimination data.

PubMed

Hastings, Gareth D; Rubin, Alan

2015-01-01

Despite most modern colour spaces treating colour as three-dimensional (3-D), colour data is usually not visualised in 3-D (and two-dimensional (2-D) projection-plane segments and multiple 2-D perspective views are used instead). The objectives of this article are firstly, to introduce a truly 3-D percept of colour space using stereo-pairs, secondly to view colour discrimination data using that platform, and thirdly to apply formal statistics and multivariate methods to analyse the data in 3-D. This is the first demonstration of the software that generated stereo-pairs of RGB colour space, as well as of a new computerised procedure that investigated colour discrimination by measuring colour just noticeable differences (JND). An initial pilot study and thorough investigation of instrument repeatability were performed. Thereafter, to demonstrate the capabilities of the software, five colour-normal and one colour-deficient subject were examined using the JND procedure and multivariate methods of data analysis. Scatter plots of responses were meaningfully examined in 3-D and were useful in evaluating multivariate normality as well as identifying outliers. The extent and direction of the difference between each JND response and the stimulus colour point was calculated and appreciated in 3-D. Ellipsoidal surfaces of constant probability density (distribution ellipsoids) were fitted to response data; the volumes of these ellipsoids appeared useful in differentiating the colour-deficient subject from the colour-normals. Hypothesis tests of variances and covariances showed many statistically significant differences between the results of the colour-deficient subject and those of the colour-normals, while far fewer differences were found when comparing within colour-normals. The 3-D visualisation of colour data using stereo-pairs, as well as the statistics and multivariate methods of analysis employed, were found to be unique and useful tools in the representation and study of colour. Many additional studies using these methods along with the JND and other procedures have been identified and will be reported in future publications. © 2014 The Authors Ophthalmic & Physiological Optics © 2014 The College of Optometrists.
Supporting employees' work-family needs improves health care quality: Longitudinal evidence from long-term care.

PubMed

Okechukwu, Cassandra A; Kelly, Erin L; Bacic, Janine; DePasquale, Nicole; Hurtado, David; Kossek, Ellen; Sembajwe, Grace

2016-05-01

We analyzed qualitative and quantitative data from U.S.-based employees in 30 long-term care facilities. Analysis of semi-structured interviews from 154 managers informed quantitative analyses. Quantitative data include 1214 employees' scoring of their supervisors and their organizations on family supportiveness (individual scores and aggregated to facility level), and three outcomes: (1), care quality indicators assessed at facility level (n = 30) and collected monthly for six months after employees' data collection; (2), employees' dichotomous survey response on having additional off-site jobs; and (3), proportion of employees with additional jobs at each facility. Thematic analyses revealed that managers operate within the constraints of an industry that simultaneously: (a) employs low-wage employees with multiple work-family challenges, and (b) has firmly institutionalized goals of prioritizing quality of care and minimizing labor costs. Managers universally described providing work-family support and prioritizing care quality as antithetical to each other. Concerns surfaced that family-supportiveness encouraged employees to work additional jobs off-site, compromising care quality. Multivariable linear regression analysis of facility-level data revealed that higher family-supportive supervision was associated with significant decreases in residents' incidence of all pressure ulcers (-2.62%) and other injuries (-9.79%). Higher family-supportive organizational climate was associated with significant decreases in all falls (-17.94%) and falls with injuries (-7.57%). Managers' concerns about additional jobs were not entirely unwarranted: multivariable logistic regression of employee-level data revealed that among employees with children, having family-supportive supervision was associated with significantly higher likelihood of additional off-site jobs (RR 1.46, 95%CI 1.08-1.99), but family-supportive organizational climate was associated with lower likelihood (RR 0.76, 95%CI 0.59-0.99). However, proportion of workers with additional off-site jobs did not significantly predict care quality at facility levels. Although managers perceived providing work-family support and ensuring high care quality as conflicting goals, results suggest that family-supportiveness is associated with better care quality. Copyright © 2016 Elsevier Ltd. All rights reserved.
Supporting employees’ work-family needs improves health care quality: longitudinal evidence from long-term care

PubMed Central

Okechukwu, Cassandra A.; Kelly, Erin L.; Bacic, Janine; DePasquale, Nicole; Hurtado, David; Kossek, Ellen; Sembajwe, Grace

2016-01-01

We analyzed qualitative and quantitative data from U.S.-based employees in 30 long-term care facilities. Analysis of semi-structured interviews from 154 managers informed quantitative analyses. Quantitative data include 1,214 employees’ scoring of their supervisors and their organizations on family supportiveness (individual scores and aggregated to facility level), and three outcomes: (1), care quality indicators assessed at facility level (n=30) and collected monthly for six months after employees’ data collection; (2), employees’ dichotomous survey response on having additional off-site jobs; and (3), proportion of employees with additional jobs at each facility. Thematic analyses revealed that managers operate within the constraints of an industry that simultaneously: (a) employs low-wage employees with multiple work-family challenges, and (b) has firmly institutionalized goals of prioritizing quality of care and minimizing labor costs. Managers universally described providing work-family support and prioritizing care quality as antithetical to each other. Concerns surfaced that family-supportiveness encouraged employees to work additional jobs off-site, compromising care quality. Multivariable linear regression analysis of facility-level data revealed that higher family-supportive supervision was associated with significant decreases in residents’ incidence of all pressure ulcers (−2.62%) and other injuries (−9.79%). Higher family-supportive organizational climate was associated with significant decreases in all falls (−17.94%) and falls with injuries (−7.57%). Managers’ concerns about additional jobs were not entirely unwarranted: multivariable logistic regression of employee-level data revealed that among employees with children, having family-supportive supervision was associated with significantly higher likelihood of additional off-site jobs (RR 1.46, 95%CI 1.08-1.99), but family-supportive organizational climate was associated with lower likelihood (RR 0.76, 95%CI 0.59-0.99). However, proportion of workers with additional off-site jobs did not significantly predict care quality at facility levels. Although managers perceived providing work-family support and ensuring high care quality as conflicting goals, results suggest that family-supportiveness is associated with better care quality. PMID:27082022
Reduced miR-300 expression predicts poor prognosis in patients with laryngeal squamous cell carcinoma.

PubMed

He, F-Y; Liu, H-J; Guo, Q; Sheng, J-L

2017-02-01

miR-300 has been demonstrated to play an important role in the progression of several tumors, but its role in tumorigenesis of laryngeal squamous cell carcinoma (LSCC) is still unclear. The purpose of this study was to explore miR-300 expression in LSCC patients and analyze its association with clinicopathological factors and prognosis. In the present study, we measured the expression level of miR-300 in LSCC tissues by RT-PCR. Associations between miRNA-300 expressions and various clinicopathological characteristics were analyzed. Patient survival and their differences were determined by Kaplan-Meier method and log-rank test. The univariate and multivariate analysis were performed using the Cox proportional hazard analysis. miR-300 expression was significantly increased in LSCC tissues compared with that in adjacent non-cancerous tissues (p < 0.01). In addition, lymph node metastasis (p = 0.004) and TNM stage (p = 0.001) were obvious influence factors for the expression of miR-300. More importantly, Kaplan-Meier analysis showed that LSCC patients with low miR-300 expression tended to have shorter overall survival (p < 0.001). Finally, multivariate analysis revealed that miR-300 expression was an independent prognostic factor for LSCC patients. Our results pointed to miR-300 as a powerful prognostic marker in LSCC and as a novel target for tumor-suppressive therapy.
Intratumoral heterogeneity analysis reveals hidden associations between protein expression losses and patient survival in clear cell renal cell carcinoma

PubMed Central

Devarajan, Karthik; Parsons, Theodore; Wang, Qiong; O'Neill, Raymond; Solomides, Charalambos; Peiper, Stephen C.; Testa, Joseph R.; Uzzo, Robert; Yang, Haifeng

2017-01-01

Intratumoral heterogeneity (ITH) is a prominent feature of kidney cancer. It is not known whether it has utility in finding associations between protein expression and clinical parameters. We used ITH that is detected by immunohistochemistry (IHC) to aid the association analysis between the loss of SWI/SNF components and clinical parameters.160 ccRCC tumors (40 per tumor stage) were used to generate tissue microarray (TMA). Four foci from different regions of each tumor were selected. IHC was performed against PBRM1, ARID1A, SETD2, SMARCA4, and SMARCA2. Statistical analyses were performed to correlate biomarker losses with patho-clinical parameters. Categorical variables were compared between groups using Fisher's exact tests. Univariate and multivariable analyses were used to correlate biomarker changes and patient survivals. Multivariable analyses were performed by constructing decision trees using the classification and regression trees (CART) methodology. IHC detected widespread ITH in ccRCC tumors. The statistical analysis of the “Truncal loss” (root loss) found additional correlations between biomarker losses and tumor stages than the traditional “Loss in tumor (total)”. Losses of SMARCA4 or SMARCA2 significantly improved prognosis for overall survival (OS). Losses of PBRM1, ARID1A or SETD2 had the opposite effect. Thus “Truncal Loss” analysis revealed hidden links between protein losses and patient survival in ccRCC. PMID:28445125
Multivariate meta-analysis using individual participant data

PubMed Central

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

2016-01-01

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

DOEpatents

Haaland, David M.

2002-01-01

A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following estimation or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The "hybrid" method herein means a combination of an initial classical least squares analysis calibration step with subsequent analysis by an inverse multivariate analysis method. A "spectral shape" herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The "shape" can be continuous, discontinuous, or even discrete points illustrative of the particular effect.
Multivariate generalized multifactor dimensionality reduction to detect gene-gene interactions

PubMed Central

2013-01-01

Background Recently, one of the greatest challenges in genome-wide association studies is to detect gene-gene and/or gene-environment interactions for common complex human diseases. Ritchie et al. (2001) proposed multifactor dimensionality reduction (MDR) method for interaction analysis. MDR is a combinatorial approach to reduce multi-locus genotypes into high-risk and low-risk groups. Although MDR has been widely used for case-control studies with binary phenotypes, several extensions have been proposed. One of these methods, a generalized MDR (GMDR) proposed by Lou et al. (2007), allows adjusting for covariates and applying to both dichotomous and continuous phenotypes. GMDR uses the residual score of a generalized linear model of phenotypes to assign either high-risk or low-risk group, while MDR uses the ratio of cases to controls. Methods In this study, we propose multivariate GMDR, an extension of GMDR for multivariate phenotypes. Jointly analysing correlated multivariate phenotypes may have more power to detect susceptible genes and gene-gene interactions. We construct generalized estimating equations (GEE) with multivariate phenotypes to extend generalized linear models. Using the score vectors from GEE we discriminate high-risk from low-risk groups. We applied the multivariate GMDR method to the blood pressure data of the 7,546 subjects from the Korean Association Resource study: systolic blood pressure (SBP) and diastolic blood pressure (DBP). We compare the results of multivariate GMDR for SBP and DBP to the results from separate univariate GMDR for SBP and DBP, respectively. We also applied the multivariate GMDR method to the repeatedly measured hypertension status from 5,466 subjects and compared its result with those of univariate GMDR at each time point. Results Results from the univariate GMDR and multivariate GMDR in two-locus model with both blood pressures and hypertension phenotypes indicate best combinations of SNPs whose interaction has significant association with risk for high blood pressures or hypertension. Although the test balanced accuracy (BA) of multivariate analysis was not always greater than that of univariate analysis, the multivariate BAs were more stable with smaller standard deviations. Conclusions In this study, we have developed multivariate GMDR method using GEE approach. It is useful to use multivariate GMDR with correlated multiple phenotypes of interests. PMID:24565370
Integrative Exploratory Analysis of Two or More Genomic Datasets.

PubMed

Meng, Chen; Culhane, Aedin

2016-01-01

Exploratory analysis is an essential step in the analysis of high throughput data. Multivariate approaches such as correspondence analysis (CA), principal component analysis, and multidimensional scaling are widely used in the exploratory analysis of single dataset. Modern biological studies often assay multiple types of biological molecules (e.g., mRNA, protein, phosphoproteins) on a same set of biological samples, thereby creating multiple different types of omics data or multiassay data. Integrative exploratory analysis of these multiple omics data is required to leverage the potential of multiple omics studies. In this chapter, we describe the application of co-inertia analysis (CIA; for analyzing two datasets) and multiple co-inertia analysis (MCIA; for three or more datasets) to address this problem. These methods are powerful yet simple multivariate approaches that represent samples using a lower number of variables, allowing a more easily identification of the correlated structure in and between multiple high dimensional datasets. Graphical representations can be employed to this purpose. In addition, the methods simultaneously project samples and variables (genes, proteins) onto the same lower dimensional space, so the most variant variables from each dataset can be selected and associated with samples, which can be further used to facilitate biological interpretation and pathway analysis. We applied CIA to explore the concordance between mRNA and protein expression in a panel of 60 tumor cell lines from the National Cancer Institute. In the same 60 cell lines, we used MCIA to perform a cross-platform comparison of mRNA gene expression profiles obtained on four different microarray platforms. Last, as an example of integrative analysis of multiassay or multi-omics data we analyzed transcriptomic, proteomic, and phosphoproteomic data from pluripotent (iPS) and embryonic stem (ES) cell lines.
Power analysis for multivariate and repeated measures designs: a flexible approach using the SPSS MANOVA procedure.

PubMed

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

2001-11-01

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

DTIC Science & Technology

1982-12-20

of Likelihood Criteria for I)fferent Hypotheses," in P. A. Krishnaiah (Ed.), Multivariate Analysis-Il, New York: Academic Press. [5] Fisher, R. A...Methods of Simultaneous Inference in MANOVA," in P. R. Krishnaiah (Ed.), rultivariate Analysis-Il, New York: Academic Press. [8) Kendall, M. G. (1966...1982), Applied Multivariate Statisti- cal-Analysis, Englewood Cliffs: Prentice-Mall, Inc. [1U] Krishnaiah , P. R. (1969), "Simultaneous Test
Docking and multivariate methods to explore HIV-1 drug-resistance: a comparative analysis

NASA Astrophysics Data System (ADS)

Almerico, Anna Maria; Tutone, Marco; Lauria, Antonino

2008-05-01

In this paper we describe a comparative analysis between multivariate and docking methods in the study of the drug resistance to the reverse transcriptase and the protease inhibitors. In our early papers we developed a simple but efficient method to evaluate the features of compounds that are less likely to trigger resistance or are effective against mutant HIV strains, using the multivariate statistical procedures PCA and DA. In the attempt to create a more solid background for the prediction of susceptibility or resistance, we carried out a comparative analysis between our previous multivariate approach and molecular docking study. The intent of this paper is not only to find further support to the results obtained by the combined use of PCA and DA, but also to evidence the structural features, in terms of molecular descriptors, similarity, and energetic contributions, derived from docking, which can account for the arising of drug-resistance against mutant strains.
SUGGESTIONS FOR OPTIMIZED PLANNING OF MULTIVARIATE MONITORING OF ATMOSPHERIC POLLUTION

EPA Science Inventory

Recent work in factor analysis of multivariate data sets has shown that variables with little signal should not be included in the factor analysis. Work also shows that rotational ambiguity is reduced if sources impacting a receptor have both large and small contributions. Thes...

Multivariate Meta-Analysis Using Individual Participant Data

ERIC Educational Resources Information Center

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

2015-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is…
Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol lowering drugs

PubMed Central

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

PubMed

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.
Stochastic modelling of temperatures affecting the in situ performance of a solar-assisted heat pump: The multivariate approach and physical interpretation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Loveday, D.L.; Craggs, C.

Box-Jenkins-based multivariate stochastic modeling is carried out using data recorded from a domestic heating system. The system comprises an air-source heat pump sited in the roof space of a house, solar assistance being provided by the conventional tile roof acting as a radiation absorber. Multivariate models are presented which illustrate the time-dependent relationships between three air temperatures - at external ambient, at entry to, and at exit from, the heat pump evaporator. Using a deterministic modeling approach, physical interpretations are placed on the results of the multivariate technique. It is concluded that the multivariate Box-Jenkins approach is a suitable techniquemore » for building thermal analysis. Application to multivariate Box-Jenkins approach is a suitable technique for building thermal analysis. Application to multivariate model-based control is discussed, with particular reference to building energy management systems. It is further concluded that stochastic modeling of data drawn from a short monitoring period offers a means of retrofitting an advanced model-based control system in existing buildings, which could be used to optimize energy savings. An approach to system simulation is suggested.« less
Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review.

PubMed

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.
Predictors of decompressive hemicraniectomy in malignant middle cerebral artery stroke.

PubMed

Kamran, Saadat; Salam, Abdul; Akhtar, Naveed; D'soza, Atlantic; Shuaib, Ashfaq

2018-04-12

Identification of factors in malignant middle cerebral artery (MMCA) stroke patients that may be useful in selecting patients for DHC. This study was a retrospective multicenter study of patients referred for DHC based on the criteria of the randomized control trials of DHC in MMCA stroke. Demographic, clinical, and radiology data were analyzed. Patients who underwent DHC were compared to those who survived without surgery. Two hundred three patients with MMCA strokes were identified: 137 underwent DHC, 47 survived without DHC, and 19 refused surgery and died. Multivariate analysis identified the following factors determining DHC in MMCA stroke: age < 55 years (OR 8.5, 95% CI 3.3-22.1, P < 0.001), MCA with involvement of additional vascular territories (anterior cerebral artery, posterior cerebral artery (OR 4.8, 95% CI 1.5-14.9, P = 0.007), septum pellucidum displacement ≥ 7.5 mm (OR 4.8, 95% CI 1.9-11.7, P = 0.001), diabetes (OR 3.7, 95% CI 1.3-10.6, P = 0.012), infarct growth rate (IGR) ml/h (OR 1.11, 95% CI 1.02-1.2, P = 0.015), and temporal lobe involvement (OR 2.5, 95% CI 1.01-6.1, P = 0.048). The internal validation of the multivariate logistic regression model using bootstrapping analysis showed marginal bias. Among patients with MMCA infarctions, an increased possibility of DHC is associated with younger age, MCA with additional infarction, septum pellucidum deviation of > 7.5 mm, diabetes, IGR, and temporal lobe involvement. The presence of these risk factors identifies those MMCA stroke patients who may require DHC. Bootstrapping analysis indicated the model is good enough to predict the outcome in general population.
Role of Adjuvant Chemoradiation Therapy in Adenocarcinomas of the Ampulla of Vater

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krishnan, Sunil; Rana, Vishal; Evans, Douglas B.

2008-03-01

Purpose: The role of adjuvant chemoradiation therapy (CRT) in the treatment of ampullary cancers remains undefined. We retrospectively compared treatment outcomes in patients treated with pancreaticoduodenectomy alone versus those who received additional adjuvant CRT. Methods and Materials: Between May 1990 and January 2006, 54 of 96 patients with ampullary adenocarcinoma who underwent potentially curative pancreaticoduodenectomy also received adjuvant CRT. The median preoperative radiation dose was 45 Gy (range, 30-50.4 Gy) and median postoperative dose was 50.4 Gy (range, 45-55.8 Gy). Concurrent chemotherapy included primarily 5-fluorouracil (52%) and capecitabine (43%). Median follow-up was 31 months. Univariate and multivariate statistical methodologies weremore » used to determine significant prognostic factors for local control (LC), distant control (DC), and overall survival (OS). Results: Actuarial 5-year LC, DC, and OS were 77%, 69%, and 64%, respectively. On univariate analysis, age, gender, race/ethnicity, tumor grade, use of adjuvant treatment, and sequencing of adjuvant therapy were not significantly associated with LC, DC, or OS. However, on univariate analysis, T3/T4 tumor stage was prognostic for poorer LC and OS (p = 0.02 and p < 0.001, respectively); node-positive disease was prognostic for poorer LC (p = 0.03). On multivariate analysis, T3/T4 tumor stage was independently prognostic for decreased OS (p = 0.002). Among these patients (n = 34), those who received adjuvant CRT had a trend toward improved OS (median, 35.2 vs. 16.5 months; p = 0.06). Conclusions: Ampullary cancers have a distinctly better treatment outcome than pancreatic adenocarcinomas. Higher primary tumor stage (T3/T4), an independent adverse risk factor for poorer treatment outcomes, may warrant the addition of adjuvant CRT to pancreaticoduodenectomy.« less
Exploring public databases to characterize urban flood risks in Amsterdam

NASA Astrophysics Data System (ADS)

Gaitan, Santiago; ten Veldhuis, Marie-claire; van de Giesen, Nick

2015-04-01

Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to decide upon investment to reduce their impacts. Obvious flooding factors affecting flood risk include sewer systems performance and urban topography. 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 and socioeconomic characteristics may help to explain probability and impacts of urban flooding. Several public databases were analyzed: complaints about flooding made by citizens, rainfall depths (15 min and 100 Ha spatio-temporal resolution), grids describing number of inhabitants, income, and housing price (1Ha and 25Ha resolution); and buildings age. Data analysis was done using Python and GIS programming, and included spatial indexing of data, cluster analysis, and multivariate regression on the complaints. Complaints were used as a proxy to characterize flooding impacts. The cluster analysis, run for all the variables except the complaints, grouped part of the grid-cells of central Amsterdam into a highly differentiated group, covering 10% of the analyzed area, and accounting for 25% of registered complaints. The configuration of the analyzed variables in central Amsterdam coincides with a high complaint count. Remaining complaints were evenly dispersed along other groups. An adjusted R2 of 0.38 in the multivariate regression suggests that explaining power can improve if additional variables are considered. While rainfall intensity explained 4% of the incidence of complaints, population density and building age significantly explained around 20% each. Data mining of public databases proved to be a valuable tool to identify factors explaining variability in occurrence of urban pluvial flooding, though additional variables must be considered to fully explain flood risk variability.
Impact of the branched-chain amino acid to tyrosine ratio and branched-chain amino acid granule therapy in patients with hepatocellular carcinoma: A propensity score analysis.

PubMed

Tada, Toshifumi; Kumada, Takashi; Toyoda, Hidenori; Kiriyama, Seiki; Tanikawa, Makoto; Hisanaga, Yasuhiro; Kanamori, Akira; Kitabatake, Shusuke; Yama, Tsuyoki

2015-09-01

It has been reported that the branched-chain amino acid (BCAA) to tyrosine ratio (BTR) is a useful indicator of liver function and BCAA therapy is associated with a decreased incidence of hepatocellular carcinoma (HCC). However, there has not been sufficient research on the relationship between BTR and the effects of BCAA therapy after initial treatment of HCC. We investigated the impact of BTR and BCAA therapy on survival in patients with HCC. A total of 315 patients with HCC who were treated (n = 66) or not treated (n = 249) with BCAA were enrolled; of these, 66 were selected from each group using propensity score matching. Survival from liver-related mortality was analyzed. In patients who did not receive BCAA therapy (n = 249), multivariate analysis for factors associated with survival indicated that low BTR (≤ 4.4) was independently associated with poor prognosis in patients with HCC (hazard ratio, 1.880; 95% confidence interval, 1.125-3.143; P = 0.016). In addition, among patients selected by propensity score matching (n = 132), multivariate analysis indicated that BCAA therapy was independently associated with good prognosis in patients with HCC (hazard ratio, 0.524; 95% confidence interval, 0.282-0.973; P = 0.041). BTR was not significantly associated with survival. Intervention involving BCAA therapy improved survival in patients with HCC versus untreated controls, regardless of BTR. In addition, low BTR was associated with poor prognosis in patients who did not receive BCAA therapy. © 2015 Journal of Gastroenterology and Hepatology Foundation and Wiley Publishing Asia Pty Ltd.
Polytopic vector analysis in igneous petrology: Application to lunar petrogenesis

NASA Technical Reports Server (NTRS)

Shervais, John W.; Ehrlich, R.

1993-01-01

Lunar samples represent a heterogeneous assemblage of rocks with complex inter-relationships that are difficult to decipher using standard petrogenetic approaches. These inter-relationships reflect several distinct petrogenetic trends as well as thermomechanical mixing of distinct components. Additional complications arise from the unequal quality of chemical analyses and from the fact that many samples (e.g., breccia clasts) are too small to be representative of the system from which they derived. Polytopic vector analysis (PVA) is a multi-variate procedure used as a tool for exploratory data analysis. PVA allows the analyst to classify samples and clarifies relationships among heterogenous samples with complex petrogenetic histories. It differs from orthogonal factor analysis in that it uses non-orthogonal multivariate sample vectors to extract sample endmember compositions. The output from a Q-mode (sample based) factor analysis is the initial step in PVA. The Q-mode analysis, using criteria established by Miesch and Klovan and Miesch, is used to determine the number of endmembers in the data system. The second step involves determination of endmembers and mixing proportions with all output expressed in the same geochemical variable as the input. The composition of endmembers is derived by analysis of the variability of the data set. Endmembers need not be present in the data set, nor is it necessary for their composition to be known a priori. A set of any endmembers defines a 'polytope' or classification figure (triangle for a three component system, tetrahedron for a four component system, a 'five-tope' in four dimensions for five component system, et cetera).
Big-Data RHEED analysis for understanding epitaxial film growth processes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vasudevan, Rama K; Tselev, Alexander; Baddorf, Arthur P

Reflection high energy electron diffraction (RHEED) has by now become a standard tool for in-situ monitoring of film growth by pulsed laser deposition and molecular beam epitaxy. Yet despite the widespread adoption and wealth of information in RHEED image, most applications are limited to observing intensity oscillations of the specular spot, and much additional information on growth is discarded. With ease of data acquisition and increased computation speeds, statistical methods to rapidly mine the dataset are now feasible. Here, we develop such an approach to the analysis of the fundamental growth processes through multivariate statistical analysis of RHEED image sequence.more » This approach is illustrated for growth of LaxCa1-xMnO3 films grown on etched (001) SrTiO3 substrates, but is universal. The multivariate methods including principal component analysis and k-means clustering provide insight into the relevant behaviors, the timing and nature of a disordered to ordered growth change, and highlight statistically significant patterns. Fourier analysis yields the harmonic components of the signal and allows separation of the relevant components and baselines, isolating the assymetric nature of the step density function and the transmission spots from the imperfect layer-by-layer (LBL) growth. These studies show the promise of big data approaches to obtaining more insight into film properties during and after epitaxial film growth. Furthermore, these studies open the pathway to use forward prediction methods to potentially allow significantly more control over growth process and hence final film quality.« less
Estimating the net effect of progesterone elevation on the day of hCG on live birth rates after IVF: a cohort analysis of 3296 IVF cycles.

PubMed

Venetis, Christos A; Kolibianakis, Efstratios M; Bosdou, Julia K; Lainas, George T; Sfontouris, Ioannis A; Tarlatzis, Basil C; Lainas, Tryfon G

2015-03-01

What is the proper way of assessing the effect of progesterone elevation (PE) on the day of hCG on live birth in women undergoing fresh embryo transfer after in vitro fertilization (IVF) using GnRH analogues and gonadotrophins? This study indicates that a multivariable approach, where the effect of the most important confounders is controlled for, can lead to markedly different results regarding the association between PE on the day of hCG and live birth rates after IVF when compared with the bivariate analysis that has been typically used in the relevant literature up to date. PE on the day of hCG is associated with decreased pregnancy rates in fresh IVF cycles. Evidence for this comes from observational studies that mostly failed to control for potential confounders. This is a retrospective analysis of a cohort of fresh IVF/intracytoplasmic sperm injection cycles (n = 3296) performed in a single IVF centre during the period 2001-2013. Patients in whom ovarian stimulation was performed with gonadotrophins and GnRH analogues. Natural cycles and cycles where stimulation involved the administration of clomiphene were excluded. In order to reflect routine clinical practice, no other exclusion criteria were imposed on this dataset. The primary outcome measure for this study was live birth defined as the delivery of a live infant after 24 weeks of gestation. We compared the association between PE on the day of hCG (defined as P > 1.5 ng/ml) and live birth rates calculated by simple bivariate analyses with that derived from multivariable logistic regression. The multivariable analysis controlled for female age, number of oocytes retrieved, number of embryos transferred, developmental stage of embryos at transfer (cleavage versus blastocyst), whether at least one good-quality embryo was transferred, the woman's body mass index, the total dose of FSH administered during ovarian stimulation and the type of GnRH analogues used (agonists versus antagonists) during ovarian stimulation. In addition, an interaction analysis was performed in order to assess whether the ovarian response (<6, 6-18, >18 oocytes) has a moderating effect on the association of PE on the day of hCG with live birth rates after IVF. Live birth rates were not significantly different between cycles with and those without PE when a bivariate analysis was performed [odds ratio (OR): 0.78, 95% confidence interval (CI): 0.56-1.09]. However, when a multivariable analysis was performed, controlling for the effect of the aforementioned confounders, live birth rates (OR: 0.68, 95% CI: 0.48-0.97) were significantly decreased in the group with PE on the day of hCG. The number of oocytes retrieved was the most potent confounder, causing a 29.4% reduction in the OR for live birth between the two groups compared. Furthermore, a moderating effect of ovarian response on the association between PE and live birth rates was not supported in the present analysis since no interaction was detected between PE and the type of ovarian response (<6, 6-18, >18 oocytes). This is a retrospective analysis of data collected during a 12-year period, and although the effect of the most important confounders was controlled for in the multivariable analysis, the presence of residual bias cannot be excluded. This analysis highlights the need for a multivariable approach when researchers or clinicians aim to evaluate the impact of PE on pregnancy rates in their own clinical setting. Failure to do so might explain why many past studies have failed to identify the detrimental effect of PE in fresh IVF cycles. None. © The Author 2015. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Nutritional Intervention: A Secondary Analysis of Its Effect on Malnourished Colombian Pre-Schoolers.

ERIC Educational Resources Information Center

Bejar, Isaac I.

1981-01-01

Effects of nutritional supplementation on physical development of malnourished children was analyzed by univariate and multivariate methods for the analysis of repeated measures. Results showed that the nutritional treatment was successful, but it was necessary to resort to the multivariate approach. (Author/GK)
A Multivariate Descriptive Model of Motivation for Orthodontic Treatment.

ERIC Educational Resources Information Center

Hackett, Paul M. W.; And Others

1993-01-01

Motivation for receiving orthodontic treatment was studied among 109 young adults, and a multivariate model of the process is proposed. The combination of smallest scale analysis and Partial Order Scalogram Analysis by base Coordinates (POSAC) illustrates an interesting methodology for health treatment studies and explores motivation for dental…
Exploring Pattern of Socialisation Conditions and Human Development by Nonlinear Multivariate Analysis.

ERIC Educational Resources Information Center

Grundmann, Matthias

Following the assumptions of ecological socialization research, adequate analysis of socialization conditions must take into account the multilevel and multivariate structure of social factors that impact on human development. This statement implies that complex models of family configurations or of socialization factors are needed to explain the…
Univariate Analysis of Multivariate Outcomes in Educational Psychology.

ERIC Educational Resources Information Center

Hubble, L. M.

1984-01-01

The author examined the prevalence of multiple operational definitions of outcome constructs and an estimate of the incidence of Type I error rates when univariate procedures were applied to multiple variables in educational psychology. Multiple operational definitions of constructs were advocated and wider use of multivariate analysis was…
Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

ERIC Educational Resources Information Center

Warner, Rebecca M.

2007-01-01

This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…
Evaluation of Meterorite Amono Acid Analysis Data Using Multivariate Techniques

NASA Technical Reports Server (NTRS)

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

1999-01-01

The amino acid distributions in the Murchison carbonaceous chondrite, Mars meteorite ALH84001, and ice from the Allan Hills region of Antarctica are shown, using a multivariate technique known as Principal Component Analysis (PCA), to be statistically distinct from the average amino acid compostion of 101 terrestrial protein superfamilies.
MULTIVARIATE ANALYSIS ON LEVELS OF SELECTED METALS, PARTICULATE MATTER, VOC, AND HOUSEHOLD CHARACTERISTICS AND ACTIVITIES FROM THE MIDWESTERN STATES NHEXAS

EPA Science Inventory

Microenvironmental and biological/personal monitoring information were collected during the National Human Exposure Assessment Survey (NHEXAS), conducted in the six states comprising U.S. EPA Region Five. They have been analyzed by multivariate analysis techniques with general ...
Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

PubMed

Ma, Yan; Mazumdar, Madhu

2011-10-30

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

Marital status independently predicts testis cancer survival--an analysis of the SEER database.

PubMed

Abern, Michael R; Dude, Annie M; Coogan, Christopher L

2012-01-01

Previous reports have shown that married men with malignancies have improved 10-year survival over unmarried men. We sought to investigate the effect of marital status on 10-year survival in a U.S. population-based cohort of men with testis cancer. We examined 30,789 cases of testis cancer reported to the Surveillance, Epidemiology, and End Results (SEER 17) database between 1973 and 2005. All staging were converted to the 1997 AJCC TNM system. Patients less than 18 years of age at time of diagnosis were excluded. A subgroup analysis of patients with stages I or II non-seminomatous germ cell tumors (NSGCT) was performed. Univariate analysis using t-tests and χ(2) tests compared characteristics of patients separated by marital status. Multivariate analysis was performed using a Cox proportional hazard model to generate Kaplan-Meier survival curves, with all-cause and cancer-specific mortality as the primary endpoints. 20,245 cases met the inclusion criteria. Married men were more likely to be older (38.9 vs. 31.4 years), Caucasian (94.4% vs. 92.1%), stage I (73.1% vs. 61.4%), and have seminoma as the tumor histology (57.3% vs. 43.4%). On multivariate analysis, married status (HR 0.58, P < 0.001) and Caucasian race (HR 0.66, P < 0.001) independently predicted improved overall survival, while increased age (HR 1.05, P < 0.001), increased stage (HR 1.53-6.59, P < 0.001), and lymphoid (HR 4.05, P < 0.001), or NSGCT (HR 1.89, P < 0.001) histology independently predicted death. Similarly, on multivariate analysis, married status (HR 0.60, P < 0.001) and Caucasian race (HR 0.57, P < 0.001) independently predicted improved testis cancer-specific survival, while increased age (HR 1.03, P < 0.001), increased stage (HR 2.51-15.67, P < 0.001), and NSGCT (HR 2.54, P < 0.001) histology independently predicted testis cancer-specific death. A subgroup analysis of men with stages I or II NSGCT revealed similar predictors of all-cause survival as the overall cohort, with retroperitoneal lymph node dissection (RPLND) as an additional independent predictor of overall survival (HR 0.59, P = 0.001), despite equal rates of the treatment between married and unmarried men (44.8% vs. 43.4%, P = 0.33). Marital status is an independent predictor of improved overall and cancer-specific survival in men with testis cancer. In men with stages I or II NSGCT, RPLND is an additional predictor of improved overall survival. Marital status does not appear to influence whether men undergo RPLND. Copyright © 2012 Elsevier Inc. All rights reserved.
Distributed effects of methylphenidate on the network structure of the resting brain: a connectomic pattern classification analysis.

PubMed

Sripada, Chandra Sekhar; Kessler, Daniel; Welsh, Robert; Angstadt, Michael; Liberzon, Israel; Phan, K Luan; Scott, Clayton

2013-11-01

Methylphenidate is a psychostimulant medication that produces improvements in functions associated with multiple neurocognitive systems. To investigate the potentially distributed effects of methylphenidate on the brain's intrinsic network architecture, we coupled resting state imaging with multivariate pattern classification. In a within-subject, double-blind, placebo-controlled, randomized, counterbalanced, cross-over design, 32 healthy human volunteers received either methylphenidate or placebo prior to two fMRI resting state scans separated by approximately one week. Resting state connectomes were generated by placing regions of interest at regular intervals throughout the brain, and these connectomes were submitted for support vector machine analysis. We found that methylphenidate produces a distributed, reliably detected, multivariate neural signature. Methylphenidate effects were evident across multiple resting state networks, especially visual, somatomotor, and default networks. Methylphenidate reduced coupling within visual and somatomotor networks. In addition, default network exhibited decoupling with several task positive networks, consistent with methylphenidate modulation of the competitive relationship between these networks. These results suggest that connectivity changes within and between large-scale networks are potentially involved in the mechanisms by which methylphenidate improves attention functioning. Copyright © 2013 Elsevier Inc. All rights reserved.
CRISP: Catheterization RISk score for Pediatrics: A Report from the Congenital Cardiac Interventional Study Consortium (CCISC).

PubMed

Nykanen, David G; Forbes, Thomas J; Du, Wei; Divekar, Abhay A; Reeves, Jaxk H; Hagler, Donald J; Fagan, Thomas E; Pedra, Carlos A C; Fleming, Gregory A; Khan, Danyal M; Javois, Alexander J; Gruenstein, Daniel H; Qureshi, Shakeel A; Moore, Phillip M; Wax, David H

2016-02-01

We sought to develop a scoring system that predicts the risk of serious adverse events (SAE's) for individual pediatric patients undergoing cardiac catheterization procedures. Systematic assessment of risk of SAE in pediatric catheterization can be challenging in view of a wide variation in procedure and patient complexity as well as rapidly evolving technology. A 10 component scoring system was originally developed based on expert consensus and review of the existing literature. Data from an international multi-institutional catheterization registry (CCISC) between 2008 and 2013 were used to validate this scoring system. In addition we used multivariate methods to further refine the original risk score to improve its predictive power of SAE's. Univariate analysis confirmed the strong correlation of each of the 10 components of the original risk score with SAE attributed to a pediatric cardiac catheterization (P < 0.001 for all variables). Multivariate analysis resulted in a modified risk score (CRISP) that corresponds to an increase in value of area under a receiver operating characteristic curve (AUC) from 0.715 to 0.741. The CRISP score predicts risk of occurrence of an SAE for individual patients undergoing pediatric cardiac catheterization procedures. © 2015 Wiley Periodicals, Inc.
Trends in Fatalities From Distracted Driving in the United States, 1999 to 2008

PubMed Central

Stimpson, Jim P.

2010-01-01

Objectives. We examined trends in distracted driving fatalities and their relation to cell phone use and texting volume. Methods. The Fatality Analysis Reporting System (FARS) records data on all road fatalities that occurred on public roads in the United States from 1999 to 2008. We studied trends in distracted driving fatalities, driver and crash characteristics, and trends in cell phone use and texting volume. We used multivariate regression analysis to estimate the relation between state-level distracted driving fatalities and texting volumes. Results. After declining from 1999 to 2005, fatalities from distracted driving increased 28% after 2005, rising from 4572 fatalities to 5870 in 2008. Crashes increasingly involved male drivers driving alone in collisions with roadside obstructions in urban areas. By use of multivariate analyses, we predicted that increasing texting volumes resulted in more than 16 000 additional road fatalities from 2001 to 2007. Conclusions. Distracted driving is a growing public safety hazard. Specifically, the dramatic rise in texting volume since 2005 appeared to be contributing to an alarming rise in distracted driving fatalities. Legislation enacting texting bans should be paired with effective enforcement to deter drivers from using cell phones while driving. PMID:20864709
Trends in fatalities from distracted driving in the United States, 1999 to 2008.

PubMed

Wilson, Fernando A; Stimpson, Jim P

2010-11-01

We examined trends in distracted driving fatalities and their relation to cell phone use and texting volume. The Fatality Analysis Reporting System (FARS) records data on all road fatalities that occurred on public roads in the United States from 1999 to 2008. We studied trends in distracted driving fatalities, driver and crash characteristics, and trends in cell phone use and texting volume. We used multivariate regression analysis to estimate the relation between state-level distracted driving fatalities and texting volumes. After declining from 1999 to 2005, fatalities from distracted driving increased 28% after 2005, rising from 4572 fatalities to 5870 in 2008. Crashes increasingly involved male drivers driving alone in collisions with roadside obstructions in urban areas. By use of multivariate analyses, we predicted that increasing texting volumes resulted in more than 16,000 additional road fatalities from 2001 to 2007. Distracted driving is a growing public safety hazard. Specifically, the dramatic rise in texting volume since 2005 appeared to be contributing to an alarming rise in distracted driving fatalities. Legislation enacting texting bans should be paired with effective enforcement to deter drivers from using cell phones while driving.
Prognostic significance of interventricular septal thickness in patients with AL amyloidosis.

PubMed

Cho, Hyunsoo; Kim, Soo-Jeong; Shim, Chi Young; Hong, Geu-Ru; Ha, Jong-Won; Kim, Yu Ri; Yang, Woo Ick; Chung, Haerim; Jang, Ji Eun; Cheong, June-Won; Min, Yoo Hong; Kim, Jin Seok

2017-09-01

The major prognostic determinant of immunoglobulin light chain (AL) amyloidosis is cardiac involvement. However, the role of interventricular septal thickness (IVST), which reflects the extent of cardiac involvement, remains unclear. Therefore, we analyzed 77 patients with newly diagnosed AL amyloidosis and evaluated the prognostic role of IVST. Fifty patients (64.9%) had cardiac involvement and 17 patients (22.1%) showed IVST >15mm. Among all patients, the revised Mayo Clinic Stage III-IV and IVST >15mm were independently associated with inferior overall survival (OS) in a multivariable analysis. IVST >15mm was also adversely prognostic for OS in a subgroup of advanced-stage (revised Mayo Clinic stage III-IV) patients in a multivariable analysis (P<0.001). Furthermore, advanced-stage patients with IVST >15mm did not show survival benefit from treatment with bortezomib-based regimens and/or autologous stem-cell transplantation (ASCT). Our study demonstrated that IVST >15mm is adversely prognostic independent of the revised Mayo Clinic staging system in patients with AL amyloidosis. In addition, the degree of IVST might be used as a useful prognostic indicator that can guide the management of patients with AL amyloidosis especially at an advanced stage. Copyright © 2017 Elsevier Ltd. All rights reserved.
Impact of interleukin-10 polymorphisms (-1082 and -3575) on the survival of patients with lymphoid neoplasms.

PubMed

Domingo-Domènech, Eva; Benavente, Yolanda; González-Barca, Eva; Montalban, Carlos; Gumà, Josep; Bosch, Ramón; Wang, Sophia S; Lan, Qing; Whitby, Denise; Fernández de Sevilla, Alberto; Rothman, Nathaniel; de Sanjosé, Sílvia

2007-11-01

Single-nucleotide polymorphisms (SNP) in interleukin-10 (IL-10) genes can influence immune responses, which may affect the outcome of patients with lymphoid neoplasms. The aim of this study was to explore the association between polymorphisms of IL-10-(1082A>G) and IL-10-(3575T>A) with the overall survival in patients with lymphoid neoplasms. We analyzed two IL-10 SNP (-1082 and -3575) in 472 consecutive cases with lymphoid neoplasms. Genotypes were tested for association with overall survival and classical prognostic factors by multivariate analysis. Haplotype analysis was carried out using the haplostats package implemented in R software. The implications for survival of patients with lymphoma were evaluated using multivariate analysis. Lymphoma patients with the IL-10-(3575T>A) genotype had a better overall survival (p= 0.002), as did the subgroup with non-Hodgkin's lymphoma (NHL) (p=0.05). Patients with the IL10(-1082GG) genotype had a better median overall survival (p=0.05). When both genotypes were included in a multivariate analysis, IL-10(-3575AA) genotype was the only independent prognostic factor for survival (HR=0.20, 95%CI 0.05-0.92). Patients with the IL-10(-1082) and (-3575) G-A/G-A diplotype had a longer overall survival (p=0.003) and this combination appeared to be an independent prognostic factor for survival (HR:0.26; 95%CI 0.08-0.83). The IL-10(-3575A/A) genotype was identified as a marker of favorable survival. Because the IL-10(-1082) and (-3575) G-A/G-A diplotype was also identified as an indicator of longer survival, we cannot exclude the potential additive role of the IL-10(-1082GG) genotype. These results need to be replicated in larger series and examined in different NHL subtypes.
Hypofractionated Whole-Brain Radiotherapy for Multiple Brain Metastases From Transitional Cell Carcinoma of the Bladder

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rades, Dirk, E-mail: Rades.Dirk@gmx.ne; Department of Radiation Oncology, University of Hamburg; Meyners, Thekla

2010-10-01

Purpose: Brain metastases in bladder cancer patients are extremely rare. Most patients with multiple lesions receive longer-course whole-brain radiotherapy (WBRT) with 10 x 3 Gy/2 weeks or 20 x 2 Gy/4 weeks. Because its radiosensitivity is relatively low, metastases from bladder cancer may be treated better with hypofractionated radiotherapy. This study compared short-course hypofractionated WBRT (5 x 4 Gy/1 week) to longer-course WBRT. Methods and Materials: Data for 33 patients receiving WBRT alone for multiple brain metastases from transitional cell bladder carcinoma were retrospectively analyzed. Short-course WBRT with 5 x 4 Gy (n = 12 patients) was compared to longer-coursemore » WBRT with 10 x 3 Gy/20 x 2 Gy (n = 21 patients) for overall survival (OS) and local (intracerebral) control (LC). Five additional potential prognostic factors were investigated: age, gender, Karnofsky performance score (KPS), number of brain metastases, and extracranial metastases. The Bonferroni correction for multiple tests was used to adjust the p values derived from the multivariate analysis. p values of <0.025 were considered significant. Results: At 6 months, OS was 42% after 5 x 4 Gy and 24% after 10 x 3/20 x 2 Gy (p = 0.31). On univariate analysis, improved OS was associated with less than four brain metastases (p = 0.021) and almost associated with a lack of extracranial metastases (p = 0.057). On multivariate analysis, both factors were not significant. At 6 months, LC was 83% after 5 x 4 Gy and 27% after 10 x 3/20 x 2 Gy (p = 0.035). Improved LC was almost associated with a KPS of {>=}70 (p = 0.051). On multivariate analysis, WBRT regimen was almost significant (p = 0.036). KPS showed a trend (p = 0.07). Conclusions: Short-course WBRT with 5 x 4 Gy should be seriously considered for most patients with multiple brain metastases from bladder cancer, as it resulted in improved LC.« less
A Comparison Study of Multivariate Fixed Models and Gene Association with Multiple Traits (GAMuT) for Next-Generation Sequencing

PubMed Central

Chiu, Chi-yang; Jung, Jeesun; Wang, Yifan; Weeks, Daniel E.; Wilson, Alexander F.; Bailey-Wilson, Joan E.; Amos, Christopher I.; Mills, James L.; Boehnke, Michael; Xiong, Momiao; Fan, Ruzong

2016-01-01

In this paper, extensive simulations are performed to compare two statistical methods to analyze multiple correlated quantitative phenotypes: (1) approximate F-distributed tests of multivariate functional linear models (MFLM) and additive models of multivariate analysis of variance (MANOVA), and (2) Gene Association with Multiple Traits (GAMuT) for association testing of high-dimensional genotype data. It is shown that approximate F-distributed tests of MFLM and MANOVA have higher power and are more appropriate for major gene association analysis (i.e., scenarios in which some genetic variants have relatively large effects on the phenotypes); GAMuT has higher power and is more appropriate for analyzing polygenic effects (i.e., effects from a large number of genetic variants each of which contributes a small amount to the phenotypes). MFLM and MANOVA are very flexible and can be used to perform association analysis for: (i) rare variants, (ii) common variants, and (iii) a combination of rare and common variants. Although GAMuT was designed to analyze rare variants, it can be applied to analyze a combination of rare and common variants and it performs well when (1) the number of genetic variants is large and (2) each variant contributes a small amount to the phenotypes (i.e., polygenes). MFLM and MANOVA are fixed effect models which perform well for major gene association analysis. GAMuT can be viewed as an extension of sequence kernel association tests (SKAT). Both GAMuT and SKAT are more appropriate for analyzing polygenic effects and they perform well not only in the rare variant case, but also in the case of a combination of rare and common variants. Data analyses of European cohorts and the Trinity Students Study are presented to compare the performance of the two methods. PMID:27917525
Classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.

2002-01-01

An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.
Company profile: PGXIS Ltd.

PubMed

McCarthy, Alun

2011-09-01

Pharmacogenomic Innovative Solutions Ltd (PGXIS) was established in 2007 by a group of pharmacogenomic (PGx) experts to make their expertise available to biotechnology and pharmaceutical companies. PGXIS has subsequently established a network of experts to broaden its access to relevant PGx knowledge and technologies. In addition, it has developed a novel multivariate analysis method called Taxonomy3 which is both a data integration tool and a targeting tool. Together with siRNA methodology from CytoPathfinder Inc., PGXIS now has an extensive range of diverse PGx methodologies focused on enhancing drug development.
MANOVA vs nonlinear mixed effects modeling: The comparison of growth patterns of female and male quail

NASA Astrophysics Data System (ADS)

Gürcan, Eser Kemal

2017-04-01

The most commonly used methods for analyzing time-dependent data are multivariate analysis of variance (MANOVA) and nonlinear regression models. The aim of this study was to compare some MANOVA techniques and nonlinear mixed modeling approach for investigation of growth differentiation in female and male Japanese quail. Weekly individual body weight data of 352 male and 335 female quail from hatch to 8 weeks of age were used to perform analyses. It is possible to say that when all the analyses are evaluated, the nonlinear mixed modeling is superior to the other techniques because it also reveals the individual variation. In addition, the profile analysis also provides important information.
Application of MCR-ALS to reveal intermediate conformations in the thermally induced α-β transition of poly-L-lysine monitored by FT-IR spectroscopy

NASA Astrophysics Data System (ADS)

Alcaráz, Mirta R.; Schwaighofer, Andreas; Goicoechea, Héctor; Lendl, Bernhard

2017-10-01

Temperature-induced conformational transitions of poly-L-lysine were monitored with Fourier-transform infrared (FT-IR) spectroscopy between 10 °C and 70 °C. Chemometric analysis of dynamic IR spectra was performed by multivariate curve analysis-alternating least squares (MCR-ALS) of the amide I‧ and amide II‧ spectral region. With this approach, the pure spectral and concentration profiles of the conformational transition were obtained. Beside the initial α-helical, the intermediate random coil/extended helices and the final β-sheet structure, an additional intermediate PLL conformation was identified and attributed to a transient β-sheet structure.
Multivariate Analysis of Genotype-Phenotype Association.

PubMed

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

2016-04-01

With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated-in terms of effect size-with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype-phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype-phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype-phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype-phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3-the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the genotype-phenotype map has important consequences for gene identification and may shed light on the evolvability of organisms. Copyright © 2016 by the Genetics Society of America.
Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
Time Series Model Identification by Estimating Information.

DTIC Science & Technology

1982-11-01

principle, Applications of Statistics, P. R. Krishnaiah , ed., North-Holland: Amsterdam, 27-41. Anderson, T. W. (1971). The Statistical Analysis of Time Series...E. (1969). Multiple Time Series Modeling, Multivariate Analysis II, edited by P. Krishnaiah , Academic Press: New York, 389-409. Parzen, E. (1981...Newton, H. J. (1980). Multiple Time Series Modeling, II Multivariate Analysis - V, edited by P. Krishnaiah , North Holland: Amsterdam, 181-197. Shibata, R
Genomic Analysis of Complex Microbial Communities in Wounds

DTIC Science & Technology

2012-01-01

thoroughly in the ecology literature. Permutation Multivariate Analysis of Variance ( PerMANOVA ). We used PerMANOVA to test the null-hypothesis of no...difference between the bacterial communities found within a single wound compared to those from different patients (α = 0.05). PerMANOVA is a...permutation-based version of the multivariate analysis of variance (MANOVA). PerMANOVA uses the distances between samples to partition variance and
Does placental inflammation relate to brain lesions and volume in preterm infants?

PubMed

Reiman, Milla; Kujari, Harry; Maunu, Jonna; Parkkola, Riitta; Rikalainen, Hellevi; Lapinleimu, Helena; Lehtonen, Liisa; Haataja, Leena

2008-05-01

To evaluate the association between histologic inflammation of placenta and brain findings in ultrasound examinations and regional brain volumes in magnetic resonance imaging in very-low-birth-weight (VLBW) or in very preterm infants. VLBW or very preterm infants (n = 121) were categorized into 3 groups according to the most pathologic brain finding on ultrasound examinations until term. The brain magnetic resonance imaging performed at term was analyzed for regional brain volumes. The placentas were analyzed for histologic inflammatory findings. Histologic chorioamnionitis on the fetal side correlated to brain lesions in univariate but not in multivariate analyses. Low gestational age was the only significant risk factor for brain lesions in multivariate analysis (P < .0001). Histologic chorioamnionitis was not associated with brain volumes in multivariate analyses. Female sex, low gestational age, and low birth weight z score correlated to smaller volumes in total brain tissue (P = .001, P = .0002, P < .0001, respectively) and cerebellum (P = .047, P = .003, P = .001, respectively). In addition, low gestational age and low-birth-weight z score correlated to a smaller combined volume of basal ganglia and thalami (P = .0002). Placental inflammation does not appear to correlate to brain lesions or smaller regional brain volumes in VLBW or in very preterm infants at term age.
Multivariate optical computing using a digital micromirror device for fluorescence and Raman spectroscopy.

PubMed

Smith, Zachary J; Strombom, Sven; Wachsmann-Hogiu, Sebastian

2011-08-29

A multivariate optical computer has been constructed consisting of a spectrograph, digital micromirror device, and photomultiplier tube that is capable of determining absolute concentrations of individual components of a multivariate spectral model. We present experimental results on ternary mixtures, showing accurate quantification of chemical concentrations based on integrated intensities of fluorescence and Raman spectra measured with a single point detector. We additionally show in simulation that point measurements based on principal component spectra retain the ability to classify cancerous from noncancerous T cells.
Hybrid least squares multivariate spectral analysis methods

DOEpatents

Haaland, David M.

2004-03-23

A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following prediction or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The hybrid method herein means a combination of an initial calibration step with subsequent analysis by an inverse multivariate analysis method. A spectral shape herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The shape can be continuous, discontinuous, or even discrete points illustrative of the particular effect.

Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis.

PubMed

White, Jon; Sofat, Reecha; Hemani, Gibran; Shah, Tina; Engmann, Jorgen; Dale, Caroline; Shah, Sonia; Kruger, Felix A; Giambartolomei, Claudia; Swerdlow, Daniel I; Palmer, Tom; McLachlan, Stela; Langenberg, Claudia; Zabaneh, Delilah; Lovering, Ruth; Cavadino, Alana; Jefferis, Barbara; Finan, Chris; Wong, Andrew; Amuzu, Antoinette; Ong, Ken; Gaunt, Tom R; Warren, Helen; Davies, Teri-Louise; Drenos, Fotios; Cooper, Jackie; Ebrahim, Shah; Lawlor, Debbie A; Talmud, Philippa J; Humphries, Steve E; Power, Christine; Hypponen, Elina; Richards, Marcus; Hardy, Rebecca; Kuh, Diana; Wareham, Nicholas; Ben-Shlomo, Yoav; Day, Ian N; Whincup, Peter; Morris, Richard; Strachan, Mark W J; Price, Jacqueline; Kumari, Meena; Kivimaki, Mika; Plagnol, Vincent; Whittaker, John C; Smith, George Davey; Dudbridge, Frank; Casas, Juan P; Holmes, Michael V; Hingorani, Aroon D

2016-04-01

Increased circulating plasma urate concentration is associated with an increased risk of coronary heart disease, but the extent of any causative effect of urate on risk of coronary heart disease is still unclear. In this study, we aimed to clarify any causal role of urate on coronary heart disease risk using Mendelian randomisation analysis. We first did a fixed-effects meta-analysis of the observational association of plasma urate and risk of coronary heart disease. We then used a conventional Mendelian randomisation approach to investigate the causal relevance using a genetic instrument based on 31 urate-associated single nucleotide polymorphisms (SNPs). To account for potential pleiotropic associations of certain SNPs with risk factors other than urate, we additionally did both a multivariable Mendelian randomisation analysis, in which the genetic associations of SNPs with systolic and diastolic blood pressure, HDL cholesterol, and triglycerides were included as covariates, and an Egger Mendelian randomisation (MR-Egger) analysis to estimate a causal effect accounting for unmeasured pleiotropy. In the meta-analysis of 17 prospective observational studies (166 486 individuals; 9784 coronary heart disease events) a 1 SD higher urate concentration was associated with an odds ratio (OR) for coronary heart disease of 1·07 (95% CI 1·04-1·10). The corresponding OR estimates from the conventional, multivariable adjusted, and Egger Mendelian randomisation analysis (58 studies; 198 598 individuals; 65 877 events) were 1·18 (95% CI 1·08-1·29), 1·10 (1·00-1·22), and 1·05 (0·92-1·20), respectively, per 1 SD increment in plasma urate. Conventional and multivariate Mendelian randomisation analysis implicates a causal role for urate in the development of coronary heart disease, but these estimates might be inflated by hidden pleiotropy. Egger Mendelian randomisation analysis, which accounts for pleiotropy but has less statistical power, suggests there might be no causal effect. These results might help investigators to determine the priority of trials of urate lowering for the prevention of coronary heart disease compared with other potential interventions. UK National Institute for Health Research, British Heart Foundation, and UK Medical Research Council. Copyright © 2016 White et al. Open Access article distributed under the terms of CC BY. Published by Elsevier Ltd.. All rights reserved.
Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis

PubMed Central

White, Jon; Sofat, Reecha; Hemani, Gibran; Shah, Tina; Engmann, Jorgen; Dale, Caroline; Shah, Sonia; Kruger, Felix A; Giambartolomei, Claudia; Swerdlow, Daniel I; Palmer, Tom; McLachlan, Stela; Langenberg, Claudia; Zabaneh, Delilah; Lovering, Ruth; Cavadino, Alana; Jefferis, Barbara; Finan, Chris; Wong, Andrew; Amuzu, Antoinette; Ong, Ken; Gaunt, Tom R; Warren, Helen; Davies, Teri-Louise; Drenos, Fotios; Cooper, Jackie; Ebrahim, Shah; Lawlor, Debbie A; Talmud, Philippa J; Humphries, Steve E; Power, Christine; Hypponen, Elina; Richards, Marcus; Hardy, Rebecca; Kuh, Diana; Wareham, Nicholas; Ben-Shlomo, Yoav; Day, Ian N; Whincup, Peter; Morris, Richard; Strachan, Mark W J; Price, Jacqueline; Kumari, Meena; Kivimaki, Mika; Plagnol, Vincent; Whittaker, John C; Smith, George Davey; Dudbridge, Frank; Casas, Juan P; Holmes, Michael V; Hingorani, Aroon D

2016-01-01

Summary Background Increased circulating plasma urate concentration is associated with an increased risk of coronary heart disease, but the extent of any causative effect of urate on risk of coronary heart disease is still unclear. In this study, we aimed to clarify any causal role of urate on coronary heart disease risk using Mendelian randomisation analysis. Methods We first did a fixed-effects meta-analysis of the observational association of plasma urate and risk of coronary heart disease. We then used a conventional Mendelian randomisation approach to investigate the causal relevance using a genetic instrument based on 31 urate-associated single nucleotide polymorphisms (SNPs). To account for potential pleiotropic associations of certain SNPs with risk factors other than urate, we additionally did both a multivariable Mendelian randomisation analysis, in which the genetic associations of SNPs with systolic and diastolic blood pressure, HDL cholesterol, and triglycerides were included as covariates, and an Egger Mendelian randomisation (MR-Egger) analysis to estimate a causal effect accounting for unmeasured pleiotropy. Findings In the meta-analysis of 17 prospective observational studies (166 486 individuals; 9784 coronary heart disease events) a 1 SD higher urate concentration was associated with an odds ratio (OR) for coronary heart disease of 1·07 (95% CI 1·04–1·10). The corresponding OR estimates from the conventional, multivariable adjusted, and Egger Mendelian randomisation analysis (58 studies; 198 598 individuals; 65 877 events) were 1·18 (95% CI 1·08–1·29), 1·10 (1·00–1·22), and 1·05 (0·92–1·20), respectively, per 1 SD increment in plasma urate. Interpretation Conventional and multivariate Mendelian randomisation analysis implicates a causal role for urate in the development of coronary heart disease, but these estimates might be inflated by hidden pleiotropy. Egger Mendelian randomisation analysis, which accounts for pleiotropy but has less statistical power, suggests there might be no causal effect. These results might help investigators to determine the priority of trials of urate lowering for the prevention of coronary heart disease compared with other potential interventions. Funding UK National Institute for Health Research, British Heart Foundation, and UK Medical Research Council. PMID:26781229
Multivariate geomorphic analysis of forest streams: Implications for assessment of land use impacts on channel condition

Treesearch

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...
Modeling Associations among Multivariate Longitudinal Categorical Variables in Survey Data: A Semiparametric Bayesian Approach

ERIC Educational Resources Information Center

Tchumtchoua, Sylvie; Dey, Dipak K.

2012-01-01

This paper proposes a semiparametric Bayesian framework for the analysis of associations among multivariate longitudinal categorical variables in high-dimensional data settings. This type of data is frequent, especially in the social and behavioral sciences. A semiparametric hierarchical factor analysis model is developed in which the…
Use of Multivariate Linkage Analysis for Dissection of a Complex Cognitive Trait

PubMed Central

Marlow, Angela J.; Fisher, Simon E.; Francks, Clyde; MacPhie, I. Laurence; Cherny, Stacey S.; Richardson, Alex J.; Talcott, Joel B.; Stein, John F.; Monaco, Anthony P.; Cardon, Lon R.

2003-01-01

Replication of linkage results for complex traits has been exceedingly difficult, owing in part to the inability to measure the precise underlying phenotype, small sample sizes, genetic heterogeneity, and statistical methods employed in analysis. Often, in any particular study, multiple correlated traits have been collected, yet these have been analyzed independently or, at most, in bivariate analyses. Theoretical arguments suggest that full multivariate analysis of all available traits should offer more power to detect linkage; however, this has not yet been evaluated on a genomewide scale. Here, we conduct multivariate genomewide analyses of quantitative-trait loci that influence reading- and language-related measures in families affected with developmental dyslexia. The results of these analyses are substantially clearer than those of previous univariate analyses of the same data set, helping to resolve a number of key issues. These outcomes highlight the relevance of multivariate analysis for complex disorders for dissection of linkage results in correlated traits. The approach employed here may aid positional cloning of susceptibility genes in a wide spectrum of complex traits. PMID:12587094
The association between body mass index and severe biliary infections: a multivariate analysis.

PubMed

Stewart, Lygia; Griffiss, J McLeod; Jarvis, Gary A; Way, Lawrence W

2012-11-01

Obesity has been associated with worse infectious disease outcomes. It is a risk factor for cholesterol gallstones, but little is known about associations between body mass index (BMI) and biliary infections. We studied this using factors associated with biliary infections. A total of 427 patients with gallstones were studied. Gallstones, bile, and blood (as applicable) were cultured. Illness severity was classified as follows: none (no infection or inflammation), systemic inflammatory response syndrome (fever, leukocytosis), severe (abscess, cholangitis, empyema), or multi-organ dysfunction syndrome (bacteremia, hypotension, organ failure). Associations between BMI and biliary bacteria, bacteremia, gallstone type, and illness severity were examined using bivariate and multivariate analysis. BMI inversely correlated with pigment stones, biliary bacteria, bacteremia, and increased illness severity on bivariate and multivariate analysis. Obesity correlated with less severe biliary infections. BMI inversely correlated with pigment stones and biliary bacteria; multivariate analysis showed an independent correlation between lower BMI and illness severity. Most patients with severe biliary infections had a normal BMI, suggesting that obesity may be protective in biliary infections. This study examined the correlation between BMI and biliary infection severity. Published by Elsevier Inc.
Multivariate meta-analysis using individual participant data.

PubMed

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

2015-06-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. © 2014 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.
Multivariate Analysis As a Support for Diagnostic Flowcharts in Allergic Bronchopulmonary Aspergillosis: A Proof-of-Concept Study.

PubMed

Vitte, Joana; Ranque, Stéphane; Carsin, Ania; Gomez, Carine; Romain, Thomas; Cassagne, Carole; Gouitaa, Marion; Baravalle-Einaudi, Mélisande; Bel, Nathalie Stremler-Le; Reynaud-Gaubert, Martine; Dubus, Jean-Christophe; Mège, Jean-Louis; Gaudart, Jean

2017-01-01

Molecular-based allergy diagnosis yields multiple biomarker datasets. The classical diagnostic score for allergic bronchopulmonary aspergillosis (ABPA), a severe disease usually occurring in asthmatic patients and people with cystic fibrosis, comprises succinct immunological criteria formulated in 1977: total IgE, anti- Aspergillus fumigatus ( Af ) IgE, anti- Af "precipitins," and anti- Af IgG. Progress achieved over the last four decades led to multiple IgE and IgG(4) Af biomarkers available with quantitative, standardized, molecular-level reports. These newly available biomarkers have not been included in the current diagnostic criteria, either individually or in algorithms, despite persistent underdiagnosis of ABPA. Large numbers of individual biomarkers may hinder their use in clinical practice. Conversely, multivariate analysis using new tools may bring about a better chance of less diagnostic mistakes. We report here a proof-of-concept work consisting of a three-step multivariate analysis of Af IgE, IgG, and IgG4 biomarkers through a combination of principal component analysis, hierarchical ascendant classification, and classification and regression tree multivariate analysis. The resulting diagnostic algorithms might show the way for novel criteria and improved diagnostic efficiency in Af -sensitized patients at risk for ABPA.
Forensic analysis of dyed textile fibers.

PubMed

Goodpaster, John V; Liszewski, Elisa A

2009-08-01

Textile fibers are a key form of trace evidence, and the ability to reliably associate or discriminate them is crucial for forensic scientists worldwide. While microscopic and instrumental analysis can be used to determine the composition of the fiber itself, additional specificity is gained by examining fiber color. This is particularly important when the bulk composition of the fiber is relatively uninformative, as it is with cotton, wool, or other natural fibers. Such analyses pose several problems, including extremely small sample sizes, the desire for nondestructive techniques, and the vast complexity of modern dye compositions. This review will focus on more recent methods for comparing fiber color by using chromatography, spectroscopy, and mass spectrometry. The increasing use of multivariate statistics and other data analysis techniques for the differentiation of spectra from dyed fibers will also be discussed.
Multivariate analysis of longitudinal rates of change.

PubMed

Bryan, Matthew; Heagerty, Patrick J

2016-12-10

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

PubMed

Besley, John C; Oh, Sang-Hwa

2014-05-01

This study involves the analysis of three waves of survey data about nuclear energy using a probability-based online panel of respondents in the United States. Survey waves included an initial baseline survey conducted in early 2010, a follow-up survey conducted in 2010 following the Deepwater Horizon oil spill in the Gulf of Mexico, and an additional follow-up conducted just after the 2011 Fukushima, Japan, nuclear accident. The central goal is to assess the degree to which changes in public views following an accident are contingent on individual attention and respondent predispositions. Such results would provide real-world evidence of motivated reasoning. The primary analysis focuses on the impact of Fukushima and how the impact of individual attention to energy issues is moderated by both environmental views and political ideology over time. The analysis uses both mean comparisons and multivariate statistics to test key relationships. Additional variables common in the study of emerging technologies are included in the analysis, including demographics, risk and benefit perceptions, and views about the fairness of decisionmakers in both government and the private sector. © 2013 Society for Risk Analysis.
Comparison of pure laparoscopic versus open left hemihepatectomy by multivariate analysis: a retrospective cohort study.

PubMed

Cho, Hwui-Dong; Kim, Ki-Hun; Hwang, Shin; Ahn, Chul-Soo; Moon, Deok-Bog; Ha, Tae-Yong; Song, Gi-Won; Jung, Dong-Hwan; Park, Gil-Chun; Lee, Sung-Gyu

2018-02-01

To compare the outcomes of pure laparoscopic left hemihepatectomy (LLH) versus open left hemihepatectomy (OLH) for benign and malignant conditions using multivariate analysis. All consecutive cases of LLH and OLH between October 2007 and December 2013 in a tertiary referral hospital were enrolled in this retrospective cohort study. All surgical procedures were performed by one surgeon. The LLH and OLH groups were compared in terms of patient demographics, preoperative data, clinical perioperative outcomes, and tumor characteristics in patients with malignancy. Multivariate analysis of the prognostic factors associated with severe complications was then performed. The LLH group (n = 62) had a significantly shorter postoperative hospital stay than the OLH group (n = 118) (9.53 ± 3.30 vs 14.88 ± 11.36 days, p < 0.001). Multivariate analysis revealed that the OLH group had >4 times the risk of the LLH group in terms of developing severe complications (Clavien-Dindo grade ≥III) (odds ratio 4.294, 95% confidence intervals 1.165-15.832, p = 0.029). LLH was a safe and feasible procedure for selected patients. LLH required shorter hospital stay and resulted in less operative blood loss. Multivariate analysis revealed that LLH was associated with a lower risk of severe complications compared to OLH. The authors suggest that LLH could be a reasonable treatment option for selected patients.
Multivariate selection and intersexual genetic constraints in a wild bird population.

PubMed

Poissant, J; Morrissey, M B; Gosler, A G; Slate, J; Sheldon, B C

2016-10-01

When selection differs between the sexes for traits that are genetically correlated between the sexes, there is potential for the effect of selection in one sex to be altered by indirect selection in the other sex, a situation commonly referred to as intralocus sexual conflict (ISC). While potentially common, ISC has rarely been studied in wild populations. Here, we studied ISC over a set of morphological traits (wing length, tarsus length, bill depth and bill length) in a wild population of great tits (Parus major) from Wytham Woods, UK. Specifically, we quantified the microevolutionary impacts of ISC by combining intra- and intersex additive genetic (co)variances and sex-specific selection estimates in a multivariate framework. Large genetic correlations between homologous male and female traits combined with evidence for sex-specific multivariate survival selection suggested that ISC could play an appreciable role in the evolution of this population. Together, multivariate sex-specific selection and additive genetic (co)variance for the traits considered accounted for additive genetic variance in fitness that was uncorrelated between the sexes (cross-sex genetic correlation = -0.003, 95% CI = -0.83, 0.83). Gender load, defined as the reduction in a population's rate of adaptation due to sex-specific effects, was estimated at 50% (95% CI = 13%, 86%). This study provides novel insights into the evolution of sexual dimorphism in wild populations and illustrates how quantitative genetics and selection analyses can be combined in a multivariate framework to quantify the microevolutionary impacts of ISC. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.
Does investor-ownership of nursing homes compromise the quality of care?

PubMed

Harrington, Charlene; Woolhandler, Steffie; Mullan, Joseph; Carrillo, Helen; Himmelstein, David U

2002-01-01

Quality problems have long plagued the nursing home industry. While two-thirds of U.S. nursing homes are investor-owned, few studies have examined the impact of investor-ownership on the quality of care. The authors analyzed 1998 data from inspections of 13,693 nursing facilities representing virtually all U.S. nursing homes. They grouped deficiency citations issued by inspectors into three categories ("quality of care," "quality of life," and "other") and compared deficiency rates in investor-owned, nonprofit, and public nursing homes. A multivariate model was used to control for case mix, percentage of residents covered by Medicaid, whether the facility was hospital-based, whether it was a skilled nursing facility for Medicare only, chain ownership, and location by state. The study also assessed nurse staffing. The authors found that investor-owned nursing homes provide worse care and less nursing care than nonprofit or public homes. Investor-owned facilities averaged 5.89 deficiencies per home, 46.5 percent higher than nonprofit and 43.0 percent higher than public facilities, and also had more of each category of deficiency. In the multivariate analysis, investor-ownership predicted 0.679 additional deficiencies per home; chain-ownership predicted an additional 0.633 deficiencies per home. Nurse staffing ratios were markedly lower at investor-owned homes.
FABP4 and Cardiovascular Events in Peripheral Arterial Disease.

PubMed

Höbaus, Clemens; Herz, Carsten Thilo; Pesau, Gerfried; Wrba, Thomas; Koppensteiner, Renate; Schernthaner, Gerit-Holger

2018-05-01

Fatty acid-binding protein 4 (FABP4) is a possible biomarker of atherosclerosis. We evaluated FABP4 levels, for the first time, in patients with peripheral artery disease (PAD) and the possible association between baseline FABP4 levels and cardiovascular events over time. Patients (n = 327; mean age 69 ± 10 years) with stable PAD were enrolled in this study. Serum FABP4 was measured by bead-based multiplex assay. Cardiovascular events were analyzed by FABP4 tertiles using Kaplan-Meier and Cox regression analyses after 5 years. Serum FABP4 levels showed a significant association with the classical 3-point major adverse cardiovascular event (MACE) end point (including death, nonlethal myocardial infarction, or nonfatal stroke) in patients with PAD ( P = .038). A standard deviation increase of FABP4 resulted in a hazard ratio (HR) of 1.33 (95% confidence interval [95% CI]: 1.03-1.71) for MACE. This association increased (HR: 1.47, 95% CI: 1.03-1.71) after multivariable adjustment ( P = .020). Additionally, in multivariable linear regression analysis, FABP4 was linked to estimated glomerular filtration rate ( P < .001), gender ( P = .005), fasting triglycerides ( P = .048), and body mass index ( P < .001). Circulating FABP4 may be a useful additional biomarker to evaluate patients with stable PAD at risk of major cardiovascular complications.
Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation.

PubMed

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.
[Value of Neutrophil/Lymphocyte Ratio and Platelet/Lymphocyte Ratio for Prognostic Evaluation of Diffuse Large B-cell Lymphoma].

PubMed

Ni, Jing; Wang, Yong-Qing; Zhang, Ying-Ping; Wu, Wei; Zeng, Qing-Shu; Yang, Ming-Zhen; Xia, Rui-Xiang

2016-04-01

To investigate the predictive value of neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) for the patients with diffuse large B-cell lymphoma (DLBCL). The clinical data of 57 DLBCL patients admitted in the First Affiliated hospital of Anhui Medical University were analyzed retrospectively. According to ROC curve, the cut-off value for NLR and PLR was deterimined, and the patients were divided into high and low NLR/PLR groups before first chamotherapy. Then the relation of NLR and PLR with overall survival (OS) and progression-free survival (PFS) was analyzed by univariate and multivariate COX regression. The optimal cut-off value for NLR and PLR was 2.915 and 270.27, respectively. NLR at the diagnosis was found to be an independent predictor for OS and PFS by univariate and multivariate analysis, while the PLR was an independent predictor for PFS, but did not affect the OS. NLR and PLR may provide additional prognostic information for DLBCL patients.
Characterizing backcountry camping impacts in Great Smoky Mountains National Park

USGS Publications Warehouse

Leung, Y.-F.; Marion, J.L.

1999-01-01

This investigates resource impacts on backcounty campsites in the Great Smoky Mountains National Park, USA. Study objectives were to enhance our understanding of camping impacts and to improve campsite impact assessment procedures by means of multivariate techniques. Three-hundred and eight campsites at designated backcountry campgrounds, and 69 additional unofficial campsites were assessed. Factor analysis of 195 established campsites on eight impact indicator variables revealed three dimensions of campsite impact: area disturbance, soil and groundcover damage, and tree-related damage. Four distinctive backcountry campsite types were identified, three of which were derived from cluster analyses of factor scores. These four backcountry campsite types characterize the intensity and areal extent of resource impacts, and they vary in locational and environmental attributes. At an aggregate level, different campsite types contributed unequally to the cumulative level of impact. The dimensional structure and typology developed in this study demonstrates that campsite impacts can be viewed and examined holistically with the use of multivariate methods. Implications for assessment procedures, management and further research are discussed.
Awareness and Acceptability of Pre-exposure HIV Prophylaxis Among Men Who have Sex with Men in Baltimore.

PubMed

Fallon, Susan A; Park, Ju Nyeong; Ogbue, Christine Powell; Flynn, Colin; German, Danielle

2017-05-01

This paper assessed characteristics associated with awareness of and willingness to take pre-exposure prophylaxis (PrEP) among Baltimore men who have sex with men (MSM). We used data from BESURE-MSM3, a venue-based cross-sectional HIV surveillance study conducted among MSM in 2011. Multivariate regression was used to identify characteristics associated with PrEP knowledge and acceptability among 399 participants. Eleven percent had heard of PrEP, 48% would be willing to use PrEP, and none had previously used it. In multivariable analysis, black race and perceived discrimination against those with HIV were significantly associated with decreased awareness, and those who perceived higher HIV discrimination reported higher acceptability of PrEP. Our findings indicate a need for further education about the potential utility of PrEP in addition to other prevention methods among MSM. HIV prevention efforts should address the link between discrimination and potential PrEP use, especially among men of color.
A Statistical Discrimination Experiment for Eurasian Events Using a Twenty-Seven-Station Network

DTIC Science & Technology

1980-07-08

to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...the weight assigned to each variable whenever a new one is added. Jennrich, R. I. (1977). Stepwise discriminant analysis , in Statistical Methods for

Is Heart Rate Variability Better Than Routine Vital Signs for Prehospital Identification of Major Hemorrhage

DTIC Science & Technology

2015-01-01

different PRBC transfusion volumes. We performed multivariate regression analysis using HRV metrics and routine vital signs to test the hypothesis that...study sponsors did not have any role in the study design, data collection, analysis and interpretation of data, report writing, or the decision to...primary outcome was hemorrhagic injury plus different PRBC transfusion volumes. We performed multivariate regression analysis using HRV metrics and
Nonlinear multivariate and time series analysis by neural network methods

NASA Astrophysics Data System (ADS)

Hsieh, William W.

2004-03-01

Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression and classification. More recently, neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA), and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA, and NLSSA techniques and their applications to various data sets of the atmosphere and the ocean (especially for the El Niño-Southern Oscillation and the stratospheric quasi-biennial oscillation). These data sets reveal that the linear methods are often too simplistic to describe real-world systems, with a tendency to scatter a single oscillatory phenomenon into numerous unphysical modes or higher harmonics, which can be largely alleviated in the new nonlinear paradigm.
Analysis and assessment on heavy metal sources in the coastal soils developed from alluvial deposits using multivariate statistical methods.

PubMed

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

2009-05-30

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

PubMed

Alkarkhi, Abbas F M; Ramli, Saifullah Bin; Easa, Azhar Mat

2009-01-01

Major (sodium, potassium, calcium, magnesium) and minor elements (iron, copper, zinc, manganese) and one heavy metal (lead) of Cavendish banana flour and Dream banana flour were determined, and data were analyzed using multivariate statistical techniques of factor analysis and discriminant analysis. Factor analysis yielded four factors explaining more than 81% of the total variance: the first factor explained 28.73%, comprising magnesium, sodium, and iron; the second factor explained 21.47%, comprising only manganese and copper; the third factor explained 15.66%, comprising zinc and lead; while the fourth factor explained 15.50%, comprising potassium. Discriminant analysis showed that magnesium and sodium exhibited a strong contribution in discriminating the two types of banana flour, affording 100% correct assignation. This study presents the usefulness of multivariate statistical techniques for analysis and interpretation of complex mineral content data from banana flour of different varieties.
PYCHEM: a multivariate analysis package for python.

PubMed

Jarvis, Roger M; Broadhurst, David; Johnson, Helen; O'Boyle, Noel M; Goodacre, Royston

2006-10-15

We have implemented a multivariate statistical analysis toolbox, with an optional standalone graphical user interface (GUI), using the Python scripting language. This is a free and open source project that addresses the need for a multivariate analysis toolbox in Python. Although the functionality provided does not cover the full range of multivariate tools that are available, it has a broad complement of methods that are widely used in the biological sciences. In contrast to tools like MATLAB, PyChem 2.0.0 is easily accessible and free, allows for rapid extension using a range of Python modules and is part of the growing amount of complementary and interoperable scientific software in Python based upon SciPy. One of the attractions of PyChem is that it is an open source project and so there is an opportunity, through collaboration, to increase the scope of the software and to continually evolve a user-friendly platform that has applicability across a wide range of analytical and post-genomic disciplines. http://sourceforge.net/projects/pychem
Borrowing of strength and study weights in multivariate and network meta-analysis.

PubMed

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

2017-12-01

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

PubMed

Lin, Tsung-I; Lachos, Victor H; Wang, Wan-Lun

2018-05-08

The multivariate linear mixed model (MLMM) has emerged as an important analytical tool for longitudinal data with multiple outcomes. However, the analysis of multivariate longitudinal data could be complicated by the presence of censored measurements because of a detection limit of the assay in combination with unavoidable missing values arising when subjects miss some of their scheduled visits intermittently. This paper presents a generalization of the MLMM approach, called the MLMM-CM, for a joint analysis of the multivariate longitudinal data with censored and intermittent missing responses. A computationally feasible expectation maximization-based procedure is developed to carry out maximum likelihood estimation within the MLMM-CM framework. Moreover, the asymptotic standard errors of fixed effects are explicitly obtained via the information-based method. We illustrate our methodology by using simulated data and a case study from an AIDS clinical trial. Experimental results reveal that the proposed method is able to provide more satisfactory performance as compared with the traditional MLMM approach. Copyright © 2018 John Wiley & Sons, Ltd.
Borrowing of strength and study weights in multivariate and network meta-analysis

PubMed Central

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

2016-01-01

Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of ‘borrowing of strength’. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis). PMID:26546254
A framework for multivariate data-based at-site flood frequency analysis: Essentiality of the conjugal application of parametric and nonparametric approaches

NASA Astrophysics Data System (ADS)

Vittal, H.; Singh, Jitendra; Kumar, Pankaj; Karmakar, Subhankar

2015-06-01

In watershed management, flood frequency analysis (FFA) is performed to quantify the risk of flooding at different spatial locations and also to provide guidelines for determining the design periods of flood control structures. The traditional FFA was extensively performed by considering univariate scenario for both at-site and regional estimation of return periods. However, due to inherent mutual dependence of the flood variables or characteristics [i.e., peak flow (P), flood volume (V) and flood duration (D), which are random in nature], analysis has been further extended to multivariate scenario, with some restrictive assumptions. To overcome the assumption of same family of marginal density function for all flood variables, the concept of copula has been introduced. Although, the advancement from univariate to multivariate analyses drew formidable attention to the FFA research community, the basic limitation was that the analyses were performed with the implementation of only parametric family of distributions. The aim of the current study is to emphasize the importance of nonparametric approaches in the field of multivariate FFA; however, the nonparametric distribution may not always be a good-fit and capable of replacing well-implemented multivariate parametric and multivariate copula-based applications. Nevertheless, the potential of obtaining best-fit using nonparametric distributions might be improved because such distributions reproduce the sample's characteristics, resulting in more accurate estimations of the multivariate return period. Hence, the current study shows the importance of conjugating multivariate nonparametric approach with multivariate parametric and copula-based approaches, thereby results in a comprehensive framework for complete at-site FFA. Although the proposed framework is designed for at-site FFA, this approach can also be applied to regional FFA because regional estimations ideally include at-site estimations. The framework is based on the following steps: (i) comprehensive trend analysis to assess nonstationarity in the observed data; (ii) selection of the best-fit univariate marginal distribution with a comprehensive set of parametric and nonparametric distributions for the flood variables; (iii) multivariate frequency analyses with parametric, copula-based and nonparametric approaches; and (iv) estimation of joint and various conditional return periods. The proposed framework for frequency analysis is demonstrated using 110 years of observed data from Allegheny River at Salamanca, New York, USA. The results show that for both univariate and multivariate cases, the nonparametric Gaussian kernel provides the best estimate. Further, we perform FFA for twenty major rivers over continental USA, which shows for seven rivers, all the flood variables followed nonparametric Gaussian kernel; whereas for other rivers, parametric distributions provide the best-fit either for one or two flood variables. Thus the summary of results shows that the nonparametric method cannot substitute the parametric and copula-based approaches, but should be considered during any at-site FFA to provide the broadest choices for best estimation of the flood return periods.
Bivariate versus multivariate smart spectrophotometric calibration methods for the simultaneous determination of a quaternary mixture of mosapride, pantoprazole and their degradation products.

PubMed

Hegazy, M A; Yehia, A M; Moustafa, A A

2013-05-01

The ability of bivariate and multivariate spectrophotometric methods was demonstrated in the resolution of a quaternary mixture of mosapride, pantoprazole and their degradation products. The bivariate calibrations include bivariate spectrophotometric method (BSM) and H-point standard addition method (HPSAM), which were able to determine the two drugs, simultaneously, but not in the presence of their degradation products, the results showed that simultaneous determinations could be performed in the concentration ranges of 5.0-50.0 microg/ml for mosapride and 10.0-40.0 microg/ml for pantoprazole by bivariate spectrophotometric method and in the concentration ranges of 5.0-45.0 microg/ml for both drugs by H-point standard addition method. Moreover, the applied multivariate calibration methods were able for the determination of mosapride, pantoprazole and their degradation products using concentration residuals augmented classical least squares (CRACLS) and partial least squares (PLS). The proposed multivariate methods were applied to 17 synthetic samples in the concentration ranges of 3.0-12.0 microg/ml mosapride, 8.0-32.0 microg/ml pantoprazole, 1.5-6.0 microg/ml mosapride degradation products and 2.0-8.0 microg/ml pantoprazole degradation products. The proposed bivariate and multivariate calibration methods were successfully applied to the determination of mosapride and pantoprazole in their pharmaceutical preparations.
Kernel canonical-correlation Granger causality for multiple time series

NASA Astrophysics Data System (ADS)

Wu, Guorong; Duan, Xujun; Liao, Wei; Gao, Qing; Chen, Huafu

2011-04-01

Canonical-correlation analysis as a multivariate statistical technique has been applied to multivariate Granger causality analysis to infer information flow in complex systems. It shows unique appeal and great superiority over the traditional vector autoregressive method, due to the simplified procedure that detects causal interaction between multiple time series, and the avoidance of potential model estimation problems. However, it is limited to the linear case. Here, we extend the framework of canonical correlation to include the estimation of multivariate nonlinear Granger causality for drawing inference about directed interaction. Its feasibility and effectiveness are verified on simulated data.
Multivariate geometry as an approach to algal community analysis

USGS Publications Warehouse

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

1973-01-01

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

PubMed

Loeb, Stacy; Shin, Sanghyuk S; Broyles, Dennis L; Wei, John T; Sanda, Martin; Klee, George; Partin, Alan W; Sokoll, Lori; Chan, Daniel W; Bangma, Chris H; van Schaik, Ron H N; Slawin, Kevin M; Marks, Leonard S; Catalona, William J

2017-07-01

To examine the use of the Prostate Health Index (PHI) as a continuous variable in multivariable risk assessment for aggressive prostate cancer in a large multicentre US study. The study population included 728 men, with prostate-specific antigen (PSA) levels of 2-10 ng/mL and a negative digital rectal examination, enrolled in a prospective, multi-site early detection trial. The primary endpoint was aggressive prostate cancer, defined as biopsy Gleason score ≥7. First, we evaluated whether the addition of PHI improves the performance of currently available risk calculators (the Prostate Cancer Prevention Trial [PCPT] and European Randomised Study of Screening for Prostate Cancer [ERSPC] risk calculators). We also designed and internally validated a new PHI-based multivariable predictive model, and created a nomogram. Of 728 men undergoing biopsy, 118 (16.2%) had aggressive prostate cancer. The PHI predicted the risk of aggressive prostate cancer across the spectrum of values. Adding PHI significantly improved the predictive accuracy of the PCPT and ERSPC risk calculators for aggressive disease. A new model was created using age, previous biopsy, prostate volume, PSA and PHI, with an area under the curve of 0.746. The bootstrap-corrected model showed good calibration with observed risk for aggressive prostate cancer and had net benefit on decision-curve analysis. Using PHI as part of multivariable risk assessment leads to a significant improvement in the detection of aggressive prostate cancer, potentially reducing harms from unnecessary prostate biopsy and overdiagnosis. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.
Compound effects of temperature and precipitation in making droughts more frequent in Marathwada, India

NASA Astrophysics Data System (ADS)

Mondal, A.; Zachariah, M.; Achutarao, K. M.; Otto, F. E. L.

2017-12-01

The Marathwada region in Maharashtra, India is known to suffer significantly from agrarian crisis including farmer suicides resulting from persistent droughts. Drought monitoring in India is commonly based on univariate indicators that consider the deficiency in precipitation alone. However, droughts may involve complex interplay of multiple physical variables, necessitating an integrated, multivariate approach to analyse their behaviour. In this study, we compare the behaviour of drought characteristics in Marathwada in the recent years as compared to the first half of the twentieth century, using a joint precipitation and temperature-based Multivariate Standardized Drought Index (MSDI). Drought events in the recent times are found to exhibit exceptional simultaneous anomalies of high temperature and precipitation deficits in this region, though studies on precipitation alone show that these events are within the range of historically observed variability. Additionally, we also develop multivariate copula-based Severity-Duration-Frequency (SDF) relationships for droughts in this region and compare their natures pre- and post- 1950. Based on multivariate return periods considering both temperature and precipitation anomalies, as well as the severity and duration of droughts, it is found that droughts have become more frequent in the post-1950 period. Based on precipitation alone, such an observation cannot be made. This emphasizes the sensitivity of droughts to temperature and underlines the importance of considering compound effects of temperature and precipitation in order to avoid an underestimation of drought risk. This observation-based analysis is the first step towards investigating the causal mechanisms of droughts, their evolutions and impacts in this region, particularly those influenced by anthropogenic climate change.
Comparison of Optimum Interpolation and Cressman Analyses

NASA Technical Reports Server (NTRS)

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

1984-01-01

The objective of this investigation is to develop a state-of-the-art optimum interpolation (O/I) objective analysis procedure for use in numerical weather prediction studies. A three-dimensional multivariate O/I analysis scheme has been developed. Some characteristics of the GLAS O/I compared with those of the NMC and ECMWF systems are summarized. Some recent enhancements of the GLAS scheme include a univariate analysis of water vapor mixing ratio, a geographically dependent model prediction error correlation function and a multivariate oceanic surface analysis.
Comparison of Optimum Interpolation and Cressman Analyses

NASA Technical Reports Server (NTRS)

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

1985-01-01

The development of a state of the art optimum interpolation (O/I) objective analysis procedure for use in numerical weather prediction studies was investigated. A three dimensional multivariate O/I analysis scheme was developed. Some characteristics of the GLAS O/I compared with those of the NMC and ECMWF systems are summarized. Some recent enhancements of the GLAS scheme include a univariate analysis of water vapor mixing ratio, a geographically dependent model prediction error correlation function and a multivariate oceanic surface analysis.
Prognostic Significance of Tumor Necrosis in Hilar Cholangiocarcinoma.

PubMed

Atanasov, Georgi; Schierle, Katrin; Hau, Hans-Michael; Dietel, Corinna; Krenzien, Felix; Brandl, Andreas; Wiltberger, Georg; Englisch, Julianna Paulina; Robson, Simon C; Reutzel-Selke, Anja; Pascher, Andreas; Jonas, Sven; Pratschke, Johann; Benzing, Christian; Schmelzle, Moritz

2017-02-01

Tumor necrosis and peritumoral fibrosis have both been suggested to have a prognostic value in selected solid tumors. However, little is known regarding their influence on tumor progression and prognosis in hilar cholangiocarcinoma (HC). Surgically resected tumor specimens of HC (n = 47) were analyzed for formation of necrosis and extent of peritumoral fibrosis. Tumor necrosis and grade of fibrosis were assessed histologically and correlated with clinicopathological characteristics, tumor recurrence, and patients' survival. Univariate Kaplan-Meier analysis and a stepwise multivariable Cox regression model were applied. Mild peritumoral fibrosis was evident in 12 tumor samples, moderate peritumoral fibrosis in 20, and high-grade fibrosis in 15. Necrosis was evident in 19 of 47 tumor samples. Patients with tumors characterized by necrosis showed a significantly decreased 5-year recurrence-free survival (37.9 vs. 25.7 %; p < .05) and a significantly decreased 5-year overall survival (42.6 vs. 12.4 %; p < .05), when compared with patients with tumors showing no necrosis. R status, tumor recurrence, and tumor necrosis were of prognostic value in the univariate analysis (all p < .05). Multivariate survival analysis confirmed tumor necrosis (p = .038) as the only independent prognostic variable. The assessment of tumor necrosis appears as a valuable additional prognostic tool in routine histopathological evaluation of HC. These observations might have implications for monitoring and more individualized multimodal therapeutic strategies.
The prognostic role of CD68 and FoxP3 expression in patients with primary central nervous system lymphoma.

PubMed

Cho, Hyunsoo; Kim, Se Hoon; Kim, Soo-Jeong; Chang, Jong Hee; Yang, Woo Ick; Suh, Chang-Ok; Cheong, June-Won; Kim, Yu Ri; Lee, Jung Yeon; Jang, Ji Eun; Kim, Yundeok; Min, Yoo Hong; Kim, Jin Seok

2017-07-01

The prognostic role of CD68 and FoxP3 in primary central nervous system lymphoma (PCNSL) has not been evaluated. Thus, we examined the prognostic significance of CD68 and FoxP3 expression in tumor samples of 76 newly diagnosed immunocompetent PCNSL patients. All patients were treated initially with high-dose methotrexate (HD-MTX)-based chemotherapy, and 16 (21.1%) patients received upfront autologous stem cell transplantation (ASCT) consolidation. High expression of CD68 (>55 cells/high-power field) or FoxP3 (>15 cells/high-power field) was observed in 10 patients, respectively. High CD68 expression was associated with inferior overall survival (OS) and progression-free survival (PFS) in multivariate analysis (P = 0.023 and P = 0.021, respectively). In addition, we performed subgroup analysis based on upfront ASCT. High CD68 expression was also associated with inferior OS and PFS in multivariate analysis (P = 0.013 and P < 0.001, respectively) among patients who did not receive upfront ASCT (n = 60), but not in patients who received upfront ASCT. The expression of FoxP3 was not significantly associated with survival. Therefore, we identified a prognostic significance of high CD68 expression in PCNSL, which suggests a need for further clinical trials and biological studies on the role of PCNSL tumor microenvironment.
Factors Influencing the Appearance of Oxaliplatin-Induced Allergy.

PubMed

Nishihara, Masayuki; Nishikura, Kyoko; Morikawa, Norimichi; Yokoyama, Shota

2017-01-01

Several studies reported that the administration of oxaliplatin often induced allergy, but few studies have analyzed the pathogenesis. In this study, we examined the relationship between the incidence of allergy and status of oxaliplatin administration, patient background, laboratory data, or combined drugs. The subjects were 144 patients with colorectal or gastric cancer in whom oxaliplatin administration was started and completed between 2010 and 2016. They were divided into 2 groups: allergy and non-allergy groups. We extracted important factors influencing its appearance using multivariate analysis, and analyzed items of which the influence was suggested, using receiver operating characteristic (ROC) analysis. In 11 patients (7.6%), allergy appeared. The median frequency of appearance was 9 times (range: 5-13), being similar to that previously reported. On multivariate analysis, albumin (Alb) was extracted as an important factor. The cut-off value of Alb for the risk of allergy was 4.1 g/dL. An increase in the number of protein conjugates may have increased the risk of functioning as a hapten. Furthermore, the results suggested that the more frequency of oxaliplatin administration might increase the incidence of allergy, although it was not extracted as an important factor. In addition to young and female patients, as previously indicated, careful follow-up may be necessary for those with an Alb level of ≥4.1 g/dL especially after the 6th course.
GC-MS-based metabolite profiling of Cosmos caudatus leaves possessing alpha-glucosidase inhibitory activity.

PubMed

Javadi, Neda; Abas, Faridah; Abd Hamid, Azizah; Simoh, Sanimah; Shaari, Khozirah; Ismail, Intan Safinar; Mediani, Ahmed; Khatib, Alfi

2014-06-01

Cosmos caudatus, which is known as "Ulam Raja," is an herbal plant used in Malaysia to enhance vitality. This study focused on the evaluation of the α-glucosidase inhibitory activity of different ethanolic extracts of C. caudatus. Six series of samples extracted with water, 20%, 40%, 60%, 80%, and 100% ethanol (EtOH) were employed. Gas chromatography-mass spectrometry (GC-MS) and orthogonal partial least-squares (OPLS) analysis was used to correlate bioactivity of different extracts to different metabolite profiles of C. caudatus. The obtained OPLS scores indicated a distinct and remarkable separation into 6 clusters, which were indicative of the 6 different ethanol concentrations. GC-MS can be integrated with multivariate data analysis to identify compounds that inhibit α-glucosidase activity. In addition, catechin, α-linolenic acid, α-D-glucopyranoside, and vitamin E compounds were identified and indicate the potential α-glucosidase inhibitory activity of this herb. GC-MS and multivariate data analysis was applied to discriminate Cosmos caudatus samples extracted with water and different ratio of ethanol. Orthogonal partial least-squares (OPLS) model developed was used to determine the major metabolites contributed to α-glucosidase inhibitory activity. This approach also has the ability to predict the bioactivity of a new set of extracts based on a developed validated regression model that is important for quality control of the herb preparation. © 2014 Institute of Food Technologists®

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

ERIC Educational Resources Information Center

Anderson, John R.

2012-01-01

Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…
Functional Path Analysis as a Multivariate Technique in Developing a Theory of Participation in Adult Education.

ERIC Educational Resources Information Center

Martin, James L.

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

ERIC Educational Resources Information Center

Finch, W. Holmes

2016-01-01

Multivariate analysis of variance (MANOVA) is widely used in educational research to compare means on multiple dependent variables across groups. Researchers faced with the problem of missing data often use multiple imputation of values in place of the missing observations. This study compares the performance of 2 methods for combining p values in…
Web-Based Tools for Modelling and Analysis of Multivariate Data: California Ozone Pollution Activity

ERIC Educational Resources Information Center

Dinov, Ivo D.; Christou, Nicolas

2011-01-01

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

ERIC Educational Resources Information Center

Kim, Soyoung; Olejnik, Stephen

2005-01-01

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

Treesearch

Wilbur A. Farr; John S. Hard

1987-01-01

A multivariate analysis of climate was used to delineate 10 significantly different groups of climatic stations along the southern coast of Alaska based on latitude, longitude, seasonal temperatures and precipitation, frost-free periods, and total number of growing degree days. The climatic stations were too few to delineate this rugged, mountainous region into...
Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study

PubMed Central

Neupane, Binod; Beyene, Joseph

2015-01-01

In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data in the endpoint are imputed with null effects and quite large variance. PMID:26196398
Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study.

PubMed

Neupane, Binod; Beyene, Joseph

2015-01-01

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

PubMed Central

Hesemann, Jennifer; Lauer, Emily; Ziska, Stephen; Noonan, Kenneth; Nemeth, Blaise; Scott-Schwoerer, Jessica; McCarty, Catherine; Rasmussen, Kristen; Goldberg, Jacob M.; Sund, Sarah; Eickhoff, Jens; Raggio, Cathleen L.; Giampietro, Philip F.

2014-01-01

Study Design A retrospective chart review of cases with congenital vertebral malformations (CVM) and controls with normal spine morphology. Objective To determine the relative contribution of maternal environmental factors (MEF) during pregnancy including maternal insulin dependent diabetes mellitus, valproic acid, alcohol, smoking, hyperthermia, twin gestation, assisted reproductive technology, in-vitro fertilization and maternal clomiphene usage to CVM development. Summary of Background Data Congenital vertebral malformations (CVM) represent defects in formation and segmentation of somites occurring with an estimated incidence of between 0.13–0.50 per 1000 live births. CVM may be associated with congenital scoliosis, Klippel-Feil syndrome, hemifacial microsomia and VACTERL syndromes, and represent significant morbidity due to pain and cosmetic disfigurement. Methods A multicenter retrospective chart review of 229 cases with CVM and 267 controls with normal spine morphology between the ages of 1–50 years was performed in order to obtain the odds ratio (OR) of MEF related to CVM among cases vs. controls. CVM due to an underlying syndrome associated with a known gene mutation or chromosome etiology were excluded. An imputation based analysis was performed in which subjects with no documentation of MEF history were treated as no maternal exposure.” Univariate and multivariate analysis was conducted to calculate the OR. Results Of the 229 total cases, 104 cases had single or multiple CVM without additional congenital malformations (CM) (Group 1) and 125 cases had single or multiple CVM and additional CM (Group 2). Nineteen percent of total cases had an identified MEF. The OR (95% CI, P-value) for MEF history for Group 1 was 6.0 (2.4–15.1, P<0.001) in the univariate analysis. The OR for MEF history in Group 2 was 9.1 (95%CI, P-value) (3.8–21.6, P<0.001) in the univariate analysis. The results were confirmed in the multivariate analysis, after adjusting for age, gender, and institution. Discussion These results support a hypothesis for an association between the above MEF during pregnancy and CVM and have implications for development of prevention strategies. Further prospective studies are needed to quantify association between CVM and specific MEF. PMID:23446706
MULTIVARIATE ANALYSES (CONONICAL CORRELATION AND PARTIAL LEAST SQUARE, PLS) TO MODEL AND ASSESS THE ASSOCIATION OF LANDSCAPE METRICS TO SURFACE WATER CHEMICAL AND BIOLOGICAL PROPERTIES USING SAVANNAH RIVER BASIN DATA.

EPA Science Inventory

Many multivariate methods are used in describing and predicting relation; each has its unique usage of categorical and non-categorical data. In multivariate analysis of variance (MANOVA), many response variables (y's) are related to many independent variables that are categorical...
Multivariate Density Estimation and Remote Sensing

NASA Technical Reports Server (NTRS)

Scott, D. W.

1983-01-01

Current efforts to develop methods and computer algorithms to effectively represent multivariate data commonly encountered in remote sensing applications are described. While this may involve scatter diagrams, multivariate representations of nonparametric probability density estimates are emphasized. The density function provides a useful graphical tool for looking at data and a useful theoretical tool for classification. This approach is called a thunderstorm data analysis.
Comprehensive drought characteristics analysis based on a nonlinear multivariate drought index

NASA Astrophysics Data System (ADS)

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.
Effect of Contact Damage on the Strength of Ceramic Materials.

DTIC Science & Technology

1982-10-01

variables that are important to erosion, and a multivariate , linear regression analysis is used to fit the data to the dimensional analysis. The...of Equations 7 and 8 by a multivariable regression analysis (room tem- perature data) Exponent Regression Standard error Computed coefficient of...1980) 593. WEAVER, Proc. Brit. Ceram. Soc. 22 (1973) 125. 39. P. W. BRIDGMAN, "Dimensional Analaysis ", (Yale 18. R. W. RICE, S. W. FREIMAN and P. F
Toward high value sensing: monolayer-protected metal nanoparticles in multivariable gas and vapor sensors.

PubMed

Potyrailo, Radislav A

2017-08-29

For detection of gases and vapors in complex backgrounds, "classic" analytical instruments are an unavoidable alternative to existing sensors. Recently a new generation of sensors, known as multivariable sensors, emerged with a fundamentally different perspective for sensing to eliminate limitations of existing sensors. In multivariable sensors, a sensing material is designed to have diverse responses to different gases and vapors and is coupled to a multivariable transducer that provides independent outputs to recognize these diverse responses. Data analytics tools provide rejection of interferences and multi-analyte quantitation. This review critically analyses advances of multivariable sensors based on ligand-functionalized metal nanoparticles also known as monolayer-protected nanoparticles (MPNs). These MPN sensing materials distinctively stand out from other sensing materials for multivariable sensors due to their diversity of gas- and vapor-response mechanisms as provided by organic and biological ligands, applicability of these sensing materials for broad classes of gas-phase compounds such as condensable vapors and non-condensable gases, and for several principles of signal transduction in multivariable sensors that result in non-resonant and resonant electrical sensors as well as material- and structure-based photonic sensors. Such features should allow MPN multivariable sensors to be an attractive high value addition to existing analytical instrumentation.
Solving chromatographic challenges in comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry using multivariate curve resolution-alternating least squares.

PubMed

Parastar, Hadi; Radović, Jagoš R; Bayona, Josep M; Tauler, Roma

2013-07-01

Multivariate curve resolution-alternating least squares (MCR-ALS) analysis is proposed to solve chromatographic challenges during two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) analysis of complex samples, such as crude oil extract. In view of the fact that the MCR-ALS method is based on the fulfillment of the bilinear model assumption, three-way and four-way GC × GC-TOFMS data are preferably arranged in a column-wise superaugmented data matrix in which mass-to-charge ratios (m/z) are in its columns and the elution times in the second and first chromatographic columns are in its rows. Since m/z values are common for all measured spectra in all second-column modulations, unavoidable chromatographic challenges such as retention time shifts within and between GC × GC-TOFMS experiments are properly handled. In addition, baseline/background contributions can be modeled by adding extra components to the MCR-ALS model. Another outstanding aspect of MCR-ALS analysis is its extreme flexibility to consider all samples (standards, unknowns, and replicates) in a single superaugmented data matrix, allowing joint analysis. In this way, resolution, identification, and quantification results can be simultaneously obtained in a very fast and reliable way. The potential of MCR-ALS analysis is demonstrated in GC × GC-TOFMS analysis of a North Sea crude oil extract sample with relative errors in estimated concentrations of target compounds below 6.0 % and relative standard deviations lower than 7.0 %. The results obtained, along with reasonable values for the lack of fit of the MCR-ALS model and high values of the reversed match factor in mass spectra similarity searches, confirm the reliability of the proposed strategy for GC × GC-TOFMS data analysis.
Comparison of Penalty Functions for Sparse Canonical Correlation Analysis

PubMed Central

Chalise, Prabhakar; Fridley, Brooke L.

2011-01-01

Canonical correlation analysis (CCA) is a widely used multivariate method for assessing the association between two sets of variables. However, when the number of variables far exceeds the number of subjects, such in the case of large-scale genomic studies, the traditional CCA method is not appropriate. In addition, when the variables are highly correlated the sample covariance matrices become unstable or undefined. To overcome these two issues, sparse canonical correlation analysis (SCCA) for multiple data sets has been proposed using a Lasso type of penalty. However, these methods do not have direct control over sparsity of solution. An additional step that uses Bayesian Information Criterion (BIC) has also been suggested to further filter out unimportant features. In this paper, a comparison of four penalty functions (Lasso, Elastic-net, SCAD and Hard-threshold) for SCCA with and without the BIC filtering step have been carried out using both real and simulated genotypic and mRNA expression data. This study indicates that the SCAD penalty with BIC filter would be a preferable penalty function for application of SCCA to genomic data. PMID:21984855
A Framework for Establishing Standard Reference Scale of Texture by Multivariate Statistical Analysis Based on Instrumental Measurement and Sensory Evaluation.

PubMed

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

2016-01-13

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

PubMed

Ghosh, Sudipta; Dosaev, Tasbulat; Prakash, Jai; Livshits, Gregory

2017-04-01

The major aim of this study was to conduct comparative quantitative-genetic analysis of the body composition (BCP) and somatotype (STP) variation, as well as their correlations with blood pressure (BP) in two ethnically, culturally and geographically different populations: Santhal, indigenous ethnic group from India and Chuvash, indigenous population from Russia. Correspondently two pedigree-based samples were collected from 1,262 Santhal and1,558 Chuvash individuals, respectively. At the first stage of the study, descriptive statistics and a series of univariate regression analyses were calculated. Finally, multiple and multivariate regression (MMR) analyses, with BP measurements as dependent variables and age, sex, BCP and STP as independent variables were carried out in each sample separately. The significant and independent covariates of BP were identified and used for re-examination in pedigree-based variance decomposition analysis. Despite clear and significant differences between the populations in BCP/STP, both Santhal and Chuvash were found to be predominantly mesomorphic irrespective of their sex. According to MMR analyses variation of BP significantly depended on age and mesomorphic component in both samples, and in addition on sex, ectomorphy and fat mass index in Santhal and on fat free mass index in Chuvash samples, respectively. Additive genetic component contributes to a substantial proportion of blood pressure and body composition variance. Variance component analysis in addition to above mentioned results suggests that additive genetic factors influence BP and BCP/STP associations significantly. © 2017 Wiley Periodicals, Inc.
Big-data reflection high energy electron diffraction analysis for understanding epitaxial film growth processes.

PubMed

Vasudevan, Rama K; Tselev, Alexander; Baddorf, Arthur P; Kalinin, Sergei V

2014-10-28

Reflection high energy electron diffraction (RHEED) has by now become a standard tool for in situ monitoring of film growth by pulsed laser deposition and molecular beam epitaxy. Yet despite the widespread adoption and wealth of information in RHEED images, most applications are limited to observing intensity oscillations of the specular spot, and much additional information on growth is discarded. With ease of data acquisition and increased computation speeds, statistical methods to rapidly mine the data set are now feasible. Here, we develop such an approach to the analysis of the fundamental growth processes through multivariate statistical analysis of a RHEED image sequence. This approach is illustrated for growth of La(x)Ca(1-x)MnO(3) films grown on etched (001) SrTiO(3) substrates, but is universal. The multivariate methods including principal component analysis and k-means clustering provide insight into the relevant behaviors, the timing and nature of a disordered to ordered growth change, and highlight statistically significant patterns. Fourier analysis yields the harmonic components of the signal and allows separation of the relevant components and baselines, isolating the asymmetric nature of the step density function and the transmission spots from the imperfect layer-by-layer (LBL) growth. These studies show the promise of big data approaches to obtaining more insight into film properties during and after epitaxial film growth. Furthermore, these studies open the pathway to use forward prediction methods to potentially allow significantly more control over growth process and hence final film quality.
Rapid determination of chemical composition and classification of bamboo fractions using visible-near infrared spectroscopy coupled with multivariate data analysis.

PubMed

Yang, Zhong; Li, Kang; Zhang, Maomao; Xin, Donglin; Zhang, Junhua

2016-01-01

During conversion of bamboo into biofuels and chemicals, it is necessary to efficiently predict the chemical composition and digestibility of biomass. However, traditional methods for determination of lignocellulosic biomass composition are expensive and time consuming. In this work, a novel and fast method for quantitative and qualitative analysis of chemical composition and enzymatic digestibilities of juvenile bamboo and mature bamboo fractions (bamboo green, bamboo timber, bamboo yellow, bamboo node, and bamboo branch) using visible-near infrared spectra was evaluated. The developed partial least squares models yielded coefficients of determination in calibration of 0.88, 0.94, and 0.96, for cellulose, xylan, and lignin of bamboo fractions in raw spectra, respectively. After visible-near infrared spectra being pretreated, the corresponding coefficients of determination in calibration yielded by the developed partial least squares models are 0.994, 0.990, and 0.996, respectively. The score plots of principal component analysis of mature bamboo, juvenile bamboo, and different fractions of mature bamboo were obviously distinguished in raw spectra. Based on partial least squares discriminant analysis, the classification accuracies of mature bamboo, juvenile bamboo, and different fractions of bamboo (bamboo green, bamboo timber, bamboo yellow, and bamboo branch) all reached 100 %. In addition, high accuracies of evaluation of the enzymatic digestibilities of bamboo fractions after pretreatment with aqueous ammonia were also observed. The results showed the potential of visible-near infrared spectroscopy in combination with multivariate analysis in efficiently analyzing the chemical composition and hydrolysabilities of lignocellulosic biomass, such as bamboo fractions.

A Course in... Multivariable Control Methods.

ERIC Educational Resources Information Center

Deshpande, Pradeep B.

1988-01-01

Describes an engineering course for graduate study in process control. Lists four major topics: interaction analysis, multiloop controller design, decoupling, and multivariable control strategies. Suggests a course outline and gives information about each topic. (MVL)
Independent Predictors of Prognosis Based on Oral Cavity Squamous Cell Carcinoma Surgical Margins.

PubMed

Buchakjian, Marisa R; Ginader, Timothy; Tasche, Kendall K; Pagedar, Nitin A; Smith, Brian J; Sperry, Steven M

2018-05-01

Objective To conduct a multivariate analysis of a large cohort of oral cavity squamous cell carcinoma (OCSCC) cases for independent predictors of local recurrence (LR) and overall survival (OS), with emphasis on the relationship between (1) prognosis and (2) main specimen permanent margins and intraoperative tumor bed frozen margins. Study Design Retrospective cohort study. Setting Tertiary academic head and neck cancer program. Subjects and Methods This study included 426 patients treated with OCSCC resection between 2005 and 2014 at University of Iowa Hospitals and Clinics. Patients underwent excision of OCSCC with intraoperative tumor bed frozen margin sampling and main specimen permanent margin assessment. Multivariate analysis of the data set to predict LR and OS was performed. Results Independent predictors of LR included nodal involvement, histologic grade, and main specimen permanent margin status. Specifically, the presence of a positive margin (odds ratio, 6.21; 95% CI, 3.3-11.9) or <1-mm/carcinoma in situ margin (odds ratio, 2.41; 95% CI, 1.19-4.87) on the main specimen was an independent predictor of LR, whereas intraoperative tumor bed margins were not predictive of LR on multivariate analysis. Similarly, independent predictors of OS on multivariate analysis included nodal involvement, extracapsular extension, and a positive main specimen margin. Tumor bed margins did not independently predict OS. Conclusion The main specimen margin is a strong independent predictor of LR and OS on multivariate analysis. Intraoperative tumor bed frozen margins do not independently predict prognosis. We conclude that emphasis should be placed on evaluating the main specimen margins when estimating prognosis after OCSCC resection.
Craniofacial morphometric analysis of mandibular prognathism.

PubMed

Chang, H P; Liu, P H; Yang, Y H; Lin, H C; Chang, C H

2006-03-01

The purpose of this study was to provide more information about the morphological characteristics of the craniofacial complex in mandibular prognathism. Forty young adult males having mandibular prognathism were compared with 40 having normal occlusion. This was conducted to carry out geometric morphometric assessments to localize alterations, using Procrustes analysis and thin-plate spline analysis, in addition to conventional cephalometric techniques. Procrustes analysis indicated that the mean craniofacial, midfacial and mandibular morphology was significantly different in prognathic subjects compared with normal controls. This finding was corroborated by the multivariate Hotelling T(2)-test of cephalometric variables. Mandibular prognathism demonstrated a shorter and slightly retropositioned maxilla, a greater total length and anterior positioning of the mandible. Thin-plate spline analysis revealed a developmental diminution of the palatomaxillary region anteroposteriorly and a developmental elongation of the mandible anteroposteriorly, leading to the appearance of a prognathic mandibular profile. In conclusion, thin-plate spline analysis seems to provide a valuable supplement for conventional cephalometric analysis because the complex patterns of craniofacial shape change are visualized suggestive by means of grid deformations.
Copula Multivariate analysis of Gross primary production and its hydro-environmental driver; A BIOME-BGC model applied to the Antisana páramos

NASA Astrophysics Data System (ADS)

Minaya, Veronica; Corzo, Gerald; van der Kwast, Johannes; Galarraga, Remigio; Mynett, Arthur

2014-05-01

Simulations of carbon cycling are prone to uncertainties from different sources, which in general are related to input data, parameters and the model representation capacities itself. The gross carbon uptake in the cycle is represented by the gross primary production (GPP), which deals with the spatio-temporal variability of the precipitation and the soil moisture dynamics. This variability associated with uncertainty of the parameters can be modelled by multivariate probabilistic distributions. Our study presents a novel methodology that uses multivariate Copulas analysis to assess the GPP. Multi-species and elevations variables are included in a first scenario of the analysis. Hydro-meteorological conditions that might generate a change in the next 50 or more years are included in a second scenario of this analysis. The biogeochemical model BIOME-BGC was applied in the Ecuadorian Andean region in elevations greater than 4000 masl with the presence of typical vegetation of páramo. The change of GPP over time is crucial for climate scenarios of the carbon cycling in this type of ecosystem. The results help to improve our understanding of the ecosystem function and clarify the dynamics and the relationship with the change of climate variables. Keywords: multivariate analysis, Copula, BIOME-BGC, NPP, páramos
Multivariate analysis of cytokine profiles in pregnancy complications.

PubMed

Azizieh, Fawaz; Dingle, Kamaludin; Raghupathy, Raj; Johnson, Kjell; VanderPlas, Jacob; Ansari, Ali

2018-03-01

The immunoregulation to tolerate the semiallogeneic fetus during pregnancy includes a harmonious dynamic balance between anti- and pro-inflammatory cytokines. Several earlier studies reported significantly different levels and/or ratios of several cytokines in complicated pregnancy as compared to normal pregnancy. However, as cytokines operate in networks with potentially complex interactions, it is also interesting to compare groups with multi-cytokine data sets, with multivariate analysis. Such analysis will further examine how great the differences are, and which cytokines are more different than others. Various multivariate statistical tools, such as Cramer test, classification and regression trees, partial least squares regression figures, 2-dimensional Kolmogorov-Smirmov test, principal component analysis and gap statistic, were used to compare cytokine data of normal vs anomalous groups of different pregnancy complications. Multivariate analysis assisted in examining if the groups were different, how strongly they differed, in what ways they differed and further reported evidence for subgroups in 1 group (pregnancy-induced hypertension), possibly indicating multiple causes for the complication. This work contributes to a better understanding of cytokines interaction and may have important implications on targeting cytokine balance modulation or design of future medications or interventions that best direct management or prevention from an immunological approach. © 2018 The Authors. American Journal of Reproductive Immunology Published by John Wiley & Sons Ltd.
Characterization of Interfacial Chemistry of Adhesive/Dentin Bond Using FTIR Chemical Imaging With Univariate and Multivariate Data Processing

PubMed Central

Wang, Yong; Yao, Xiaomei; Parthasarathy, Ranganathan

2008-01-01

Fourier transform infrared (FTIR) chemical imaging can be used to investigate molecular chemical features of the adhesive/dentin interfaces. However, the information is not straightforward, and is not easily extracted. The objective of this study was to use multivariate analysis methods, principal component analysis and fuzzy c-means clustering, to analyze spectral data in comparison with univariate analysis. The spectral imaging data collected from both the adhesive/healthy dentin and adhesive/caries-affected dentin specimens were used and compared. The univariate statistical methods such as mapping of intensities of specific functional group do not always accurately identify functional group locations and concentrations due to more or less band overlapping in adhesive and dentin. Apart from the ease with which information can be extracted, multivariate methods highlight subtle and often important changes in the spectra that are difficult to observe using univariate methods. The results showed that the multivariate methods gave more satisfactory, interpretable results than univariate methods and were conclusive in showing that they can discriminate and classify differences between healthy dentin and caries-affected dentin within the interfacial regions. It is demonstrated that the multivariate FTIR imaging approaches can be used in the rapid characterization of heterogeneous, complex structure. PMID:18980198
Multivariate Analysis of Longitudinal Rates of Change

PubMed Central

Bryan, Matthew; Heagerty, Patrick J.

2016-01-01

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed by Roy and Lin [1]; Proust-Lima, Letenneur and Jacqmin-Gadda [2]; and Gray and Brookmeyer [3] among others. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, Gray and Brookmeyer [3] introduce an “accelerated time” method which assumes that covariates rescale time in longitudinal models for disease progression. In this manuscript we detail an alternative multivariate model formulation that directly structures longitudinal rates of change, and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. PMID:27417129
Exploring the Structure of Library and Information Science Web Space Based on Multivariate Analysis of Social Tags

ERIC Educational Resources Information Center

Joo, Soohyung; Kipp, Margaret E. I.

2015-01-01

Introduction: This study examines the structure of Web space in the field of library and information science using multivariate analysis of social tags from the Website, Delicious.com. A few studies have examined mathematical modelling of tags, mainly examining tagging in terms of tripartite graphs, pattern tracing and descriptive statistics. This…
Multivariate Analysis of High Through-Put Adhesively Bonded Single Lap Joints: Experimental and Workflow Protocols

DTIC Science & Technology

2016-06-01

unlimited. v List of Tables Table 1 Single-lap-joint experimental parameters ..............................................7 Table 2 Survey ...Joints: Experimental and Workflow Protocols by Robert E Jensen, Daniel C DeSchepper, and David P Flanagan Approved for...TR-7696 ● JUNE 2016 US Army Research Laboratory Multivariate Analysis of High Through-Put Adhesively Bonded Single Lap Joints: Experimental
A Multivariate Model for the Meta-Analysis of Study Level Survival Data at Multiple Times

ERIC Educational Resources Information Center

Jackson, Dan; Rollins, Katie; Coughlin, Patrick

2014-01-01

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

PubMed

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

2011-06-01

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

PubMed

Bastidas, Camila Y; von Plessing, Carlos; Troncoso, José; Del P Castillo, Rosario

2018-04-15

Fourier Transform infrared imaging and multivariate analysis were used to identify, at the microscopic level, the presence of florfenicol (FF), a heavily-used antibiotic in the salmon industry, supplied to fishes in feed pellets for the treatment of salmonid rickettsial septicemia (SRS). The FF distribution was evaluated using Principal Component Analysis (PCA) and Augmented Multivariate Curve Resolution with Alternating Least Squares (augmented MCR-ALS) on the spectra obtained from images with pixel sizes of 6.25 μm × 6.25 μm and 1.56 μm × 1.56 μm, in different zones of feed pellets. Since the concentration of the drug was 3.44 mg FF/g pellet, this is the first report showing the powerful ability of the used of spectroscopic techniques and multivariate analysis, especially the augmented MCR-ALS, to describe the FF distribution in both the surface and inner parts of feed pellets at low concentration, in a complex matrix and at the microscopic level. The results allow monitoring the incorporation of the drug into the feed pellets. Copyright © 2018 Elsevier B.V. All rights reserved.
Risk factors for incidental durotomy during lumbar surgery: a retrospective study by multivariate analysis.

PubMed

Chen, Zhixiang; Shao, Peng; Sun, Qizhao; Zhao, Dong

2015-03-01

The purpose of the present study was to use a prospectively collected data to evaluate the rate of incidental durotomy (ID) during lumbar surgery and determine the associated risk factors by using univariate and multivariate analysis. We retrospectively reviewed 2184 patients who underwent lumbar surgery from January 1, 2009 to December 31, 2011 at a single hospital. Patients with ID (n=97) were compared with the patients without ID (n=2019). The influences of several potential risk factors that might affect the occurrence of ID were assessed using univariate and multivariate analyses. The overall incidence of ID was 4.62%. Univariate analysis demonstrated that older age, diabetes, lumbar central stenosis, posterior approach, revision surgery, prior lumber surgery and minimal invasive surgery are risk factors for ID during lumbar surgery. However, multivariate analysis identified older age, prior lumber surgery, revision surgery, and minimally invasive surgery as independent risk factors. Older age, prior lumber surgery, revision surgery, and minimal invasive surgery were independent risk factors for ID during lumbar surgery. These findings may guide clinicians making future surgical decisions regarding ID and aid in the patient counseling process to alleviate risks and complications. Copyright © 2015 Elsevier B.V. All rights reserved.
Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.
Multivariate reference technique for quantitative analysis of fiber-optic tissue Raman spectroscopy.

PubMed

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

2013-12-03

We report a novel method making use of multivariate reference signals of fused silica and sapphire Raman signals generated from a ball-lens fiber-optic Raman probe for quantitative analysis of in vivo tissue Raman measurements in real time. Partial least-squares (PLS) regression modeling is applied to extract the characteristic internal reference Raman signals (e.g., shoulder of the prominent fused silica boson peak (~130 cm(-1)); distinct sapphire ball-lens peaks (380, 417, 646, and 751 cm(-1))) from the ball-lens fiber-optic Raman probe for quantitative analysis of fiber-optic Raman spectroscopy. To evaluate the analytical value of this novel multivariate reference technique, a rapid Raman spectroscopy system coupled with a ball-lens fiber-optic Raman probe is used for in vivo oral tissue Raman measurements (n = 25 subjects) under 785 nm laser excitation powers ranging from 5 to 65 mW. An accurate linear relationship (R(2) = 0.981) with a root-mean-square error of cross validation (RMSECV) of 2.5 mW can be obtained for predicting the laser excitation power changes based on a leave-one-subject-out cross-validation, which is superior to the normal univariate reference method (RMSE = 6.2 mW). A root-mean-square error of prediction (RMSEP) of 2.4 mW (R(2) = 0.985) can also be achieved for laser power prediction in real time when we applied the multivariate method independently on the five new subjects (n = 166 spectra). We further apply the multivariate reference technique for quantitative analysis of gelatin tissue phantoms that gives rise to an RMSEP of ~2.0% (R(2) = 0.998) independent of laser excitation power variations. This work demonstrates that multivariate reference technique can be advantageously used to monitor and correct the variations of laser excitation power and fiber coupling efficiency in situ for standardizing the tissue Raman intensity to realize quantitative analysis of tissue Raman measurements in vivo, which is particularly appealing in challenging Raman endoscopic applications.
A comparative analysis of heat waves and associated mortality in St. Louis, Missouri--1980 and 1995.

PubMed

Smoyer, K E

1998-08-01

This research investigates heat-related mortality during the 1980 and 1995 heat waves in St. Louis, Missouri. St. Louis has a long history of extreme summer weather, and heat-related mortality is a public health concern. Heat waves are defined as days with apparent temperatures exceeding 40.6 degrees C (105 degrees F). The study uses a multivariate analysis to investigate the relationship between mortality and heat wave intensity, duration, and timing within the summer season. The heat wave of 1980 was more severe and had higher associated mortality than that of 1995. To learn if changing population characteristics, in addition to weather conditions, contributed to this difference, changes in population vulnerability between 1980 and 1995 are evaluated under simulated heat wave conditions. The findings show that St. Louis remains at risk of heat wave mortality. In addition, there is evidence that vulnerability has increased despite increased air-conditioning penetration and public health interventions.
Causal diagrams and multivariate analysis II: precision work.

PubMed

Jupiter, Daniel C

2014-01-01

In this Investigators' Corner, I continue my discussion of when and why we researchers should include variables in multivariate regression. My examination focuses on studies comparing treatment groups and situations for which we can either exclude variables from multivariate analyses or include them for reasons of precision. Copyright © 2014 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.
Multiscale Characterization of PM2.5 in Southern Taiwan based on Noise-assisted Multivariate Empirical Mode Decomposition and Time-dependent Intrinsic Correlation

NASA Astrophysics Data System (ADS)

Hsiao, Y. R.; Tsai, C.

2017-12-01

As the WHO Air Quality Guideline indicates, ambient air pollution exposes world populations under threat of fatal symptoms (e.g. heart disease, lung cancer, asthma etc.), raising concerns of air pollution sources and relative factors. This study presents a novel approach to investigating the multiscale variations of PM2.5 in southern Taiwan over the past decade, with four meteorological influencing factors (Temperature, relative humidity, precipitation and wind speed),based on Noise-assisted Multivariate Empirical Mode Decomposition(NAMEMD) algorithm, Hilbert Spectral Analysis(HSA) and Time-dependent Intrinsic Correlation(TDIC) method. NAMEMD algorithm is a fully data-driven approach designed for nonlinear and nonstationary multivariate signals, and is performed to decompose multivariate signals into a collection of channels of Intrinsic Mode Functions (IMFs). TDIC method is an EMD-based method using a set of sliding window sizes to quantify localized correlation coefficients for multiscale signals. With the alignment property and quasi-dyadic filter bank of NAMEMD algorithm, one is able to produce same number of IMFs for all variables and estimates the cross correlation in a more accurate way. The performance of spectral representation of NAMEMD-HSA method is compared with Complementary Empirical Mode Decomposition/ Hilbert Spectral Analysis (CEEMD-HSA) and Wavelet Analysis. The nature of NAMAMD-based TDICC analysis is then compared with CEEMD-based TDIC analysis and the traditional correlation analysis.
The McMillan and Newton polygons of a feedback system and the construction of root loci

NASA Technical Reports Server (NTRS)

Byrnes, C. I.; Stevens, P. K.

1982-01-01

The local behaviour of root loci around zeros and poles is investigated. This is done by relating the Newton diagrams which arise in the local analysis to the McMillan structure of the open-loop system, by means of what we shall call the McMillan polygon. This geometric construct serves to clarify the precise relationship between the McMillan structure, the principal structure, and the branching patterns of the root loci. In addition, several rules are obtained which are useful in the construction of the root loci of multivariable control systems.
ENSO related variability in the Southern Hemisphere, 1948-2000

NASA Astrophysics Data System (ADS)

Ribera, Pedro; Mann, Michael E.

2003-01-01

The spatiotemporal evolution of Southern Hemisphere climate variability is diagnosed based on the use of the NCEP reanalysis (1948-2000) dataset. Using the MTM-SVD analysis method, significant narrowband variability is isolated from the multi-variate dataset. It is found that the ENSO signal exhibits statistically significant behavior at quasiquadrennial (3-6 yr) timescales for the full time-period. A significant quasibiennial (2-3 yr) timescales emerges only for the latter half of period. Analyses of the spatial evolution of the two reconstructed signals shed additional light on linkages between low and high-latitude Southern Hemisphere climate anomalies.

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

NASA Technical Reports Server (NTRS)

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

1985-01-01

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

PubMed

Naşcu-Briciu, Rodica D; Sârbu, Costel

2013-04-01

A representative series of natural toxins belonging to alkaloids and mycotoxins classes was investigated by TLC on classical chemically bonded plates and also on oils- and fats-impregnated plates. Their lipophilicity indices are employed in the characterization and comparison of oils and fats. The retention results allowed an accurate indirect estimation of oils and fats lipophilicity. The investigated fats and oils near classical chemically bonded phases are classified and compared by means of multivariate exploratory techniques, such as cluster analysis, principal component analysis, or fuzzy-principal component analysis. Additionally, a concrete hierarchy of oils and fats derived from the observed lipophilic character is suggested. Human fat seems to be very similar to animal fats, but also possess RP-18, RP-18W, and RP-8. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing.

PubMed

Stamate, Mirela Cristina; Todor, Nicolae; Cosgarea, Marcel

2015-01-01

The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies.
Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing

PubMed Central

STAMATE, MIRELA CRISTINA; TODOR, NICOLAE; COSGAREA, MARCEL

2015-01-01

Background and aim The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. Methods The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. Results We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Conclusion Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies. PMID:26733749
Application of infrared microspectroscopy and multivariate analysis for monitoring the effect of adjunct cultures during Swiss cheese ripening.

PubMed

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

2009-08-01

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

PubMed

Pion, Johan A; Fransen, Job; Deprez, Dieter N; Segers, Veerle I; Vaeyens, Roel; Philippaerts, Renaat M; Lenoir, Matthieu

2015-06-01

It was hypothesized that differences in anthropometry, physical performance, and motor coordination would be found between Belgian elite and sub-elite level female volleyball players using a retrospective analysis of test results gathered over a 5-year period. The test sample in this study consisted of 21 young female volleyball players (15.3 ± 1.5 years) who were selected to train at the Flemish Top Sports Academy for Volleyball in 2008. All players (elite, n = 13; sub-elite, n = 8) were included in the same talent development program, and the elite-level athletes were of a high to very high performance levels according to European competition level in 2013. Five multivariate analyses of variance were used. There was no significant effect of playing level on measures of anthropometry (F = 0.455, p = 0.718, (Equation is included in full-text article.)= 0.07), flexibility (F = 1.861, p = 0.188, (Equation is included in full-text article.)= 0.19), strength (F = 1.218, p = 0.355, (Equation is included in full-text article.)= 0.32); and speed and agility (F = 1.176, p = 0.350, (Equation is included in full-text article.)= 0.18). Multivariate analyses of variance revealed significant multivariate effects between playing levels for motor coordination (F = 3.470, p = 0.036, (Equation is included in full-text article.)= 0.59). A Mann-Whitney U test and a sequential discriminant analysis confirmed these results. Previous research revealed that stature and jump height are prerequisites for talent identification in female volleyball. In addition, the results show that motor coordination is an important factor in determining inclusion into the elite level in female volleyball.
Factors associated with secondhand smoke exposure in different settings: Results from the German Health Update (GEDA) 2012.

PubMed

Fischer, Florian; Kraemer, Alexander

2016-04-14

The ubiquity of secondhand smoke (SHS) exposure at home or in private establishments, workplaces and public areas poses several challenges for the reduction of SHS exposure. This study aimed to describe the prevalence of SHS exposure in Germany and key factors associated with exposure. Results were also differentiated by place of exposure. A secondary data analysis based on the public use file of the German Health Update 2012 was conducted (n = 13,933). Only non-smokers were included in the analysis. In a multivariable logistic regression model the factors associated with SHS exposure were calculated. In addition, a further set of multivariable logistic regressions were calculated for factors associated with the place of SHS exposure (workplace, at home, bars/discotheques, restaurants, at the house of a friend). More than a quarter of non-smoking study participants were exposed to SHS. The main area of exposure was the workplace (40.9 %). The multivariable logistic regression indicated young age as the most important factor associated with SHS exposure. The odds for SHS exposure was higher in men than in women. The likelihood of SHS exposure decreased with higher education. SHS exposure and the associated factors varied between different places of exposure. Despite several actions to protect non-smokers which were implemented in Germany during the past years, SHS exposure still remains a relevant risk factor at a population level. According to the results of this study, particularly the workplace and other public places such as bars and discotheques have to be taken into account for the development of strategies to reduce SHS exposure.
Multivariate Bias Correction Procedures for Improving Water Quality Predictions from the SWAT Model

NASA Astrophysics Data System (ADS)

Arumugam, S.; Libera, D.

2017-12-01

Water quality observations are usually not available on a continuous basis for longer than 1-2 years at a time over a decadal period given the labor requirements making calibrating and validating mechanistic models difficult. Further, any physical model predictions inherently have bias (i.e., under/over estimation) and require post-simulation techniques to preserve the long-term mean monthly attributes. This study suggests a multivariate bias-correction technique and compares to a common technique in improving the performance of the SWAT model in predicting daily streamflow and TN loads across the southeast based on split-sample validation. The approach is a dimension reduction technique, canonical correlation analysis (CCA) that regresses the observed multivariate attributes with the SWAT model simulated values. The common approach is a regression based technique that uses an ordinary least squares regression to adjust model values. The observed cross-correlation between loadings and streamflow is better preserved when using canonical correlation while simultaneously reducing individual biases. Additionally, canonical correlation analysis does a better job in preserving the observed joint likelihood of observed streamflow and loadings. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically, watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are compared for the observed period and over a multi-decadal period using loading estimates from the USGS LOADEST model. Lastly, the CCA technique is applied in a forecasting sense by using 1-month ahead forecasts of P & T from ECHAM4.5 as forcings in the SWAT model. Skill in using the SWAT model for forecasting loadings and streamflow at the monthly and seasonal timescale is also discussed.
Multivariate random regression analysis for body weight and main morphological traits in genetically improved farmed tilapia (Oreochromis niloticus).

PubMed

He, Jie; Zhao, Yunfeng; Zhao, Jingli; Gao, Jin; Han, Dandan; Xu, Pao; Yang, Runqing

2017-11-02

Because of their high economic importance, growth traits in fish are under continuous improvement. For growth traits that are recorded at multiple time-points in life, the use of univariate and multivariate animal models is limited because of the variable and irregular timing of these measures. Thus, the univariate random regression model (RRM) was introduced for the genetic analysis of dynamic growth traits in fish breeding. We used a multivariate random regression model (MRRM) to analyze genetic changes in growth traits recorded at multiple time-point of genetically-improved farmed tilapia. Legendre polynomials of different orders were applied to characterize the influences of fixed and random effects on growth trajectories. The final MRRM was determined by optimizing the univariate RRM for the analyzed traits separately via penalizing adaptively the likelihood statistical criterion, which is superior to both the Akaike information criterion and the Bayesian information criterion. In the selected MRRM, the additive genetic effects were modeled by Legendre polynomials of three orders for body weight (BWE) and body length (BL) and of two orders for body depth (BD). By using the covariance functions of the MRRM, estimated heritabilities were between 0.086 and 0.628 for BWE, 0.155 and 0.556 for BL, and 0.056 and 0.607 for BD. Only heritabilities for BD measured from 60 to 140 days of age were consistently higher than those estimated by the univariate RRM. All genetic correlations between growth time-points exceeded 0.5 for either single or pairwise time-points. Moreover, correlations between early and late growth time-points were lower. Thus, for phenotypes that are measured repeatedly in aquaculture, an MRRM can enhance the efficiency of the comprehensive selection for BWE and the main morphological traits.
Estimating irradiated nuclear fuel characteristics by nonlinear multivariate regression of simulated gamma-ray emissions

NASA Astrophysics Data System (ADS)

Åberg Lindell, M.; Andersson, P.; Grape, S.; Håkansson, A.; Thulin, M.

2018-07-01

In addition to verifying operator declared parameters of spent nuclear fuel, the ability to experimentally infer such parameters with a minimum of intrusiveness is of great interest and has been long-sought after in the nuclear safeguards community. It can also be anticipated that such ability would be of interest for quality assurance in e.g. recycling facilities in future Generation IV nuclear fuel cycles. One way to obtain information regarding spent nuclear fuel is to measure various gamma-ray intensities using high-resolution gamma-ray spectroscopy. While intensities from a few isotopes obtained from such measurements have traditionally been used pairwise, the approach in this work is to simultaneously analyze correlations between all available isotopes, using multivariate analysis techniques. Based on this approach, a methodology for inferring burnup, cooling time, and initial fissile content of PWR fuels using passive gamma-ray spectroscopy data has been investigated. PWR nuclear fuels, of UOX and MOX type, and their gamma-ray emissions, were simulated using the Monte Carlo code Serpent. Data comprising relative isotope activities was analyzed with decision trees and support vector machines, for predicting fuel parameters and their associated uncertainties. From this work it may be concluded that up to a cooling time of twenty years, the 95% prediction intervals of burnup, cooling time and initial fissile content could be inferred to within approximately 7 MWd/kgHM, 8 months, and 1.4 percentage points, respectively. An attempt aiming to estimate the plutonium content in spent UOX fuel, using the developed multivariate analysis model, is also presented. The results for Pu mass estimation are promising and call for further studies.
Predicting need for additional CT scan in children with a non-diagnostic ultrasound for appendicitis in the emergency department.

PubMed

Nishizawa, Takuya; Maeda, Shigenobu; Goldman, Ran D; Hayashi, Hiroyuki

2018-01-01

This study aimed to determine which children with suspected appendicitis should be considered for a computerized tomography (CT) scan after a non-diagnostic ultrasound (US) in the Emergency Department (ED). We retrospectively reviewed patients 0-18year old, who presented to the ED with complaints of abdominal pain, during 2011-2015 and while in the hospital had both US and CT. We recorded demographic and clinical data and outcomes, and used univariate and multivariate methods for comparing patients who did and didn't have appendicitis on CT after non-diagnostic US. Multivariate analysis was performed using logistic regression to determine what variables were independently associated with appendicitis. A total of 328 patients were enrolled, 257 with non-diagnostic US (CT: 82 had appendicitis, 175 no-appendicitis). Younger children and those who reported vomiting or had right lower abdominal quadrant (RLQ) tenderness, peritoneal signs or White Blood Cell (WBC) count >10,000 in mm 3 were more likely to have appendicitis on CT. RLQ tenderness (Odds Ratio: 2.84, 95%CI: 1.07-7.53), peritoneal signs (Odds Ratio: 11.37, 95%CI: 5.08-25.47) and WBC count >10,000 in mm 3 (Odds Ratio: 21.88, 95%CI: 7.95-60.21) remained significant after multivariate analysis. Considering CT with 2 or 3 of these predictors would have resulted in sensitivity of 94%, specificity of 67%, positive predictive value of 57% and negative predictive value of 96% for appendicitis. Ordering CT should be considered after non-diagnostic US for appendicitis only when children meet at least 2 predictors of RLQ tenderness, peritoneal signs and WBC>10,000 in mm 3 . Copyright © 2017 Elsevier Inc. All rights reserved.
Leptospira Exposure and Gardeners: A Case-Control Seroprevalence Study

PubMed Central

Alvarado-Esquivel, Cosme; Hernandez-Tinoco, Jesus; Sanchez-Anguiano, Luis Francisco; Ramos-Nevarez, Agar; Cerrillo-Soto, Sandra Margarita; Guido-Arreola, Carlos Alberto

2016-01-01

Background Leptospira can be found in soil. However, it is unclear whether occupational exposure to soil may represent a risk for Leptospira infection in humans. Therefore, we sought to determine the association of Leptospira IgG seroprevalence with the occupation of gardener, and to determine the epidemiological characteristics of gardeners associated with Leptospira exposure. Methods We performed a case-control study in 168 gardeners and 168 age- and gender-matched control subjects without gardening occupation in Durango City, Mexico. The seroprevalence of anti-Leptospira IgG antibodies in cases and controls was determined using an enzyme immunoassay. Bivariate and multivariate analyses were used to assess the association of Leptospira exposure and the characteristics of the gardeners. Results Anti-Leptospira IgG antibodies were found in 10 (6%) of 168 gardeners and in 15 (8.9%) of 168 control subjects (odds ratio (OR): 0.64; 95% confidence interval (CI): 0.28 - 1.48; P = 0.40). Multivariate analysis showed that Leptospira seropositivity was positively associated with female gender (OR: 5.82; 95% CI: 1.11 - 30.46; P = 0.03), and negatively associated with eating while working (OR: 0.21; 95% CI: 0.05 - 0.87; P = 0.03). In addition, multivariate analysis showed that high anti-Leptospira levels were associated with consumption of boar meat (OR: 28.00; 95% CI: 1.20 - 648.80; P = 0.03). Conclusions This is the first case-control study of Leptospira exposure in gardeners. Results do not support an association of Leptospira exposure with the occupation of gardener. However, further studies to confirm the lack of this association are needed. The potential role of consumption of boar meat in Leptospira infection deserves further investigation. PMID:26668679
Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment.

PubMed

Rosswog, Carolina; Schmidt, Rene; Oberthuer, André; Juraeva, Dilafruz; Brors, Benedikt; Engesser, Anne; Kahlert, Yvonne; Volland, Ruth; Bartenhagen, Christoph; Simon, Thorsten; Berthold, Frank; Hero, Barbara; Faldum, Andreas; Fischer, Matthias

2017-12-01

Current risk stratification systems for neuroblastoma patients consider clinical, histopathological, and genetic variables, and additional prognostic markers have been proposed in recent years. We here sought to select highly informative covariates in a multistep strategy based on consecutive Cox regression models, resulting in a risk score that integrates hazard ratios of prognostic variables. A cohort of 695 neuroblastoma patients was divided into a discovery set (n=75) for multigene predictor generation, a training set (n=411) for risk score development, and a validation set (n=209). Relevant prognostic variables were identified by stepwise multivariable L1-penalized least absolute shrinkage and selection operator (LASSO) Cox regression, followed by backward selection in multivariable Cox regression, and then integrated into a novel risk score. The variables stage, age, MYCN status, and two multigene predictors, NB-th24 and NB-th44, were selected as independent prognostic markers by LASSO Cox regression analysis. Following backward selection, only the multigene predictors were retained in the final model. Integration of these classifiers in a risk scoring system distinguished three patient subgroups that differed substantially in their outcome. The scoring system discriminated patients with diverging outcome in the validation cohort (5-year event-free survival, 84.9±3.4 vs 63.6±14.5 vs 31.0±5.4; P<.001), and its prognostic value was validated by multivariable analysis. We here propose a translational strategy for developing risk assessment systems based on hazard ratios of relevant prognostic variables. Our final neuroblastoma risk score comprised two multigene predictors only, supporting the notion that molecular properties of the tumor cells strongly impact clinical courses of neuroblastoma patients. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
Newly Graduated Nurses' Competence and Individual and Organizational Factors: A Multivariate Analysis.

PubMed

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 the significantly associated work-related factors. Nursing professional bodies, managers, and supervisors can use the findings in planning orientation programs and other occupational interventions for NGNs. © 2015 Sigma Theta Tau International.
Multivariable Regression Analysis in Schistosoma mansoni-Infected Individuals in the Sudan Reveals Unique Immunoepidemiological Profiles in Uninfected, egg+ and Non-egg+ Infected Individuals.

PubMed

Elfaki, Tayseer Elamin Mohamed; Arndts, Kathrin; Wiszniewsky, Anna; Ritter, Manuel; Goreish, Ibtisam A; Atti El Mekki, Misk El Yemen A; Arriens, Sandra; Pfarr, Kenneth; Fimmers, Rolf; Doenhoff, Mike; Hoerauf, Achim; Layland, Laura E

2016-05-01

In the Sudan, Schistosoma mansoni infections are a major cause of morbidity in school-aged children and infection rates are associated with available clean water sources. During infection, immune responses pass through a Th1 followed by Th2 and Treg phases and patterns can relate to different stages of infection or immunity. This retrospective study evaluated immunoepidemiological aspects in 234 individuals (range 4-85 years old) from Kassala and Khartoum states in 2011. Systemic immune profiles (cytokines and immunoglobulins) and epidemiological parameters were surveyed in n = 110 persons presenting patent S. mansoni infections (egg+), n = 63 individuals positive for S. mansoni via PCR in sera but egg negative (SmPCR+) and n = 61 people who were infection-free (Sm uninf). Immunoepidemiological findings were further investigated using two binary multivariable regression analysis. Nearly all egg+ individuals had no access to latrines and over 90% obtained water via the canal stemming from the Atbara River. With regards to age, infection and an egg+ status was linked to young and adolescent groups. In terms of immunology, S. mansoni infection per se was strongly associated with increased SEA-specific IgG4 but not IgE levels. IL-6, IL-13 and IL-10 were significantly elevated in patently-infected individuals and positively correlated with egg load. In contrast, IL-2 and IL-1β were significantly lower in SmPCR+ individuals when compared to Sm uninf and egg+ groups which was further confirmed during multivariate regression analysis. Schistosomiasis remains an important public health problem in the Sudan with a high number of patent individuals. In addition, SmPCR diagnostics revealed another cohort of infected individuals with a unique immunological profile and provides an avenue for future studies on non-patent infection states. Future studies should investigate the downstream signalling pathways/mechanisms of IL-2 and IL-1β as potential diagnostic markers in order to distinguish patent from non-patent individuals.
Risk Factors for Urinary Tract Infections in Cardiac Surgical Patients

PubMed Central

Gillen, Jacob R.; Isbell, James M.; Michaels, Alex D.; Lau, Christine L.

2015-01-01

Abstract Background: Risk factors for catheter-associated urinary tract infections (CAUTIs) in patients undergoing non-cardiac surgical procedures have been well documented. However, the variables associated with CAUTIs in the cardiac surgical population have not been clearly defined. Therefore, the purpose of this study was to investigate risk factors associated with CAUTIs in patients undergoing cardiac procedures. Methods: All patients undergoing cardiac surgery at a single institution from 2006 through 2012 (4,883 patients) were reviewed. Patients with U.S. Centers for Disease Control (CDC) criteria for CAUTI were identified from the hospital's Quality Assessment database. Pre-operative, operative, and post-operative patient factors were evaluated. Univariate and multivariable analyses were used to identify significant correlations between perioperative characteristics and CAUTIs. Results: There were 55 (1.1%) documented CAUTIs in the study population. On univariate analysis, older age, female gender, diabetes mellitus, cardiogenic shock, urgent or emergent operation, packed red blood cell (PRBC) units transfused, and intensive care unit length of stay (ICU LOS) were all significantly associated with CAUTI [p<0.05]. On multivariable logistic regression, older age, female gender, diabetes mellitus, and ICU LOS remained significantly associated with CAUTI. Additionally, there was a significant association between CAUTI and 30-d mortality on univariate analysis. However, when controlling for common predictors of operative mortality on multivariable analysis, CAUTI was no longer associated with mortality. Conclusions: There are several identifiable risk factors for CAUTI in patients undergoing cardiac procedures. CAUTI is not independently associated with increased mortality, but it does serve as a marker of sicker patients more likely to die from other comorbidities or complications. Therefore, awareness of the high-risk nature of these patients should lead to increased diligence and may help to improve peri-operative outcomes. Recognizing patients at high risk for CAUTI may lead to improved measures to decrease CAUTI rates within this population. PMID:26115336
Risk Factors for Urinary Tract Infections in Cardiac Surgical Patients.

PubMed

Gillen, Jacob R; Isbell, James M; Michaels, Alex D; Lau, Christine L; Sawyer, Robert G

2015-10-01

Risk factors for catheter-associated urinary tract infections (CAUTIs) in patients undergoing non-cardiac surgical procedures have been well documented. However, the variables associated with CAUTIs in the cardiac surgical population have not been clearly defined. Therefore, the purpose of this study was to investigate risk factors associated with CAUTIs in patients undergoing cardiac procedures. All patients undergoing cardiac surgery at a single institution from 2006 through 2012 (4,883 patients) were reviewed. Patients with U.S. Centers for Disease Control (CDC) criteria for CAUTI were identified from the hospital's Quality Assessment database. Pre-operative, operative, and post-operative patient factors were evaluated. Univariate and multivariable analyses were used to identify significant correlations between perioperative characteristics and CAUTIs. There were 55 (1.1%) documented CAUTIs in the study population. On univariate analysis, older age, female gender, diabetes mellitus, cardiogenic shock, urgent or emergent operation, packed red blood cell (PRBC) units transfused, and intensive care unit length of stay (ICU LOS) were all significantly associated with CAUTI [p<0.05]. On multivariable logistic regression, older age, female gender, diabetes mellitus, and ICU LOS remained significantly associated with CAUTI. Additionally, there was a significant association between CAUTI and 30-d mortality on univariate analysis. However, when controlling for common predictors of operative mortality on multivariable analysis, CAUTI was no longer associated with mortality. There are several identifiable risk factors for CAUTI in patients undergoing cardiac procedures. CAUTI is not independently associated with increased mortality, but it does serve as a marker of sicker patients more likely to die from other comorbidities or complications. Therefore, awareness of the high-risk nature of these patients should lead to increased diligence and may help to improve peri-operative outcomes. Recognizing patients at high risk for CAUTI may lead to improved measures to decrease CAUTI rates within this population.
Abandonment of antiretroviral therapy among HIV-positive patients attended at the reference center for HIV/AIDS in Vitória, Brazil.

PubMed

Zago, Adriana Marchon; Morelato, Paola; Endringer, Emmanuele de Angeli; Dan, Germano de Freitas; Ribeiro, Evanira Mendes; Miranda, Angelica Espinosa

2012-01-01

This study evaluates the risk factors for the abandonment of antiretroviral therapy (ART) among patients receiving care in an AIDS clinic in Vitória, Brazil. We conducted a case-control study of patients with AIDS attending a reference center for sexually transmitted disease (STD)/AIDS. A total of 62 patients, who abandoned therapy in 2008, and 188 HIV-infected patients answered an interview including demographic, social, and clinical characteristics. Risk factors associated with abandon in univariate analysis were entered into logistic regression models. A total of 250 patients were included in the study. Groups were similar regarding age, gender, and monthly income. In the final multivariate model, illicit drug use (adjusted odds ratio [AOR], 2.3; 95% confidence interval [CI], 1.03-5.07), previous abandon of medication (AOR 38.6; 95% CI 10.49-142.25), last CD4 count <200 cells/mm(3) (AOR 1.5; 95% CI 1.03-2.10), and viral load higher than 1000 copies/mL (AOR 2.0 (95% CI 1.34-3.09) were independent predictors of abandonment of ART. In addition to the clinical indicators, behavioral factors remained important throughout the multivariate analysis in our study.
Rural-urban disparities in child abuse management resources in the emergency department.

PubMed

Choo, Esther K; Spiro, David M; Lowe, Robert A; Newgard, Craig D; Hall, Michael Kennedy; McConnell, Kenneth John

2010-01-01

To characterize differences in child abuse management resources between urban and rural emergency departments (EDs). We surveyed ED directors and nurse managers at hospitals in Oregon to gain information about available abuse-related resources. Chi-square analysis was used to test differences between urban and rural EDs. Multivariate analysis was performed to examine the association between a variety of hospital characteristics, in addition to rural location, and presence of child abuse resources. Fifty-five Oregon hospitals were surveyed. A smaller proportion of rural EDs had written abuse policies (62% vs 95%, P= .006) or on-site child abuse advocates (35% vs 71%, P= .009). Thirty-two percent of rural EDs had none of the examined abuse resources (vs 0% of urban EDs, P= .01). Of hospital characteristics studied in the multivariate model, only rural location was associated with decreased availability of child abuse resources (OR 0.19 [95% CI, 0.05-0.70]). Rural EDs have fewer resources than urban EDs for the management of child abuse. Other studied hospital characteristics were not associated with availability of abuse resources. Further work is needed to identify barriers to resource utilization and to create resources that can be made accessible to all ED settings. © 2010 National Rural Health Association.
Laser-induced breakdown spectroscopy-based investigation and classification of pharmaceutical tablets using multivariate chemometric analysis

PubMed Central

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

2012-01-01

We report the effectiveness of laser-induced breakdown spectroscopy (LIBS) in probing the content of pharmaceutical tablets and also investigate its feasibility for routine classification. This method is particularly beneficial in applications where its exquisite chemical specificity and suitability for remote and on site characterization significantly improves the speed and accuracy of quality control and assurance process. Our experiments reveal that in addition to the presence of carbon, hydrogen, nitrogen and oxygen, which can be primarily attributed to the active pharmaceutical ingredients, specific inorganic atoms were also present in all the tablets. Initial attempts at classification by a ratiometric approach using oxygen to nitrogen compositional values yielded an optimal value (at 746.83 nm) with the least relative standard deviation but nevertheless failed to provide an acceptable classification. To overcome this bottleneck in the detection process, two chemometric algorithms, i.e. principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA), were implemented to exploit the multivariate nature of the LIBS data demonstrating that LIBS has the potential to differentiate and discriminate among pharmaceutical tablets. We report excellent prospective classification accuracy using supervised classification via the SIMCA algorithm, demonstrating its potential for future applications in process analytical technology, especially for fast on-line process control monitoring applications in the pharmaceutical industry. PMID:22099648

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

PubMed

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

2009-02-15

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

PubMed

Tang, Tze-Chun; Yang, Pinchen; Yen, Cheng-Fang; Liu, Tai-Ling

2015-07-01

In this case-control study, we aimed to assess the intervention effects of four-session eye movement desensitization and reprocessing (EMDR) on reducing the severity of disaster-related anxiety, general anxiety, and depressive symptoms in Taiwanese adolescents who experienced Typhoon Morakot. A total of 83 adolescents with posttraumatic stress disorder related to Typhoon Morakot, major depressive disorder, or current moderate or high suicide risk after experiencing Typhoon Morakot were allocated to a four-session course of EMDR (N = 41) or to treatment as usual (TAU; N = 42). A multivariate analysis of covariance was performed to examine the effects of EMDR in reducing the severity of disaster-related anxiety, general anxiety, and depressive symptoms in adolescents by using preintervention severity values as covariates. The multivariate analysis of covariance results indicated that the EMDR group exhibited significantly lower preintervention severity values of general anxiety and depression than did the TAU group. In addition, the preintervention severity value of disaster-related anxiety in the EMDR group was lower than that in the TAU group (p = 0.05). The results of this study support that EMDR could alleviate general anxiety and depressive symptoms and reduce disaster-related anxiety in adolescents experiencing major traumatic disasters. Copyright © 2015. Published by Elsevier Taiwan.
Saturnius minutus n. sp. and S. dimitrovi n. sp. (Digenea: Hemiuridae) from Mugil cephalus L. (Teleostei: Mugilidae), with a multivariate morphological analysis of the Mediterranean species of Saturnius Manter, 1969.

PubMed

Blasco-Costa, I; Pankov, P; Gibson, D I; Balbuena, J A; Raga, J A; Sarabeev, V L; Kostadinova, A

2006-09-01

Three species of the bunocotyline genus Saturnius Manter, 1969 are described from the stomach lining of mugilid fishes of the Mediterranean and Black Seas. Two of the species are new: S. minutus n. sp. occurs in Mugil cephalus off the Mediterranean coast of Spain; and S. dimitrovi n. sp., a parasite of M. cephalus off the Bulgarian Black Sea coast and the Spanish Mediterranean coast, was originally described as S. papernai by Dimitrov et al. (1998). In addition, S. papernai Overstreet, 1977 is redescribed from M. cephalus off the Spanish Mediterranean coast and from Liza aurata and L. saliens off the Bulgarian Black Sea coast. The three species are distinguished morphometrically using univariate and multivariate analyses. These results were verified using Linear Discriminant Analysis which correctly allocated all specimens to their species designations based on morphology (i.e. 100% successful classification rate) and assigned almost all specimens to the correct population (locality). The following variables were selected for optimal separation between samples: the length of the forebody, ventral sucker and posterior testis, the length and width of the posteriormost pseudosegment, and the width of the muscular flange at ventral sucker level.
Genetic Structure of Bluefin Tuna in the Mediterranean Sea Correlates with Environmental Variables

PubMed Central

Riccioni, Giulia; Stagioni, Marco; Landi, Monica; Ferrara, Giorgia; Barbujani, Guido; Tinti, Fausto

2013-01-01

Background Atlantic Bluefin Tuna (ABFT) shows complex demography and ecological variation in the Mediterranean Sea. Genetic surveys have detected significant, although weak, signals of population structuring; catch series analyses and tagging programs identified complex ABFT spatial dynamics and migration patterns. Here, we tested the hypothesis that the genetic structure of the ABFT in the Mediterranean is correlated with mean surface temperature and salinity. Methodology We used six samples collected from Western and Central Mediterranean integrated with a new sample collected from the recently identified easternmost reproductive area of Levantine Sea. To assess population structure in the Mediterranean we used a multidisciplinary framework combining classical population genetics, spatial and Bayesian clustering methods and a multivariate approach based on factor analysis. Conclusions FST analysis and Bayesian clustering methods detected several subpopulations in the Mediterranean, a result also supported by multivariate analyses. In addition, we identified significant correlations of genetic diversity with mean salinity and surface temperature values revealing that ABFT is genetically structured along two environmental gradients. These results suggest that a preference for some spawning habitat conditions could contribute to shape ABFT genetic structuring in the Mediterranean. However, further studies should be performed to assess to what extent ABFT spawning behaviour in the Mediterranean Sea can be affected by environmental variation. PMID:24260341
Spectral compression algorithms for the analysis of very large multivariate images

DOEpatents

Keenan, Michael R.

2007-10-16

A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.
New evidence for involvement of ESR1 gene in susceptibility to Chinese migraine.

PubMed

An, Xingkai; Fang, Jie; Lin, Qing; Lu, Congxia; Ma, Qilin; Qu, Hongli

2017-01-01

Migraine is a common and disabling nervous system disease with a significant genetic predisposition. The sex hormones play an important role in the pathogenesis of migraine. However, the conclusions of the previous genetic relation studies are conflicting. The aim of this study is to determine whether variants in genes involved in estrogen receptor and estrogen hormone metabolism are related to Chinese migraine. By employing a case-control approach, 8 SNPs in the ESR1, ESR2, and CYP19A1 genes are studied in a cohort of 494 migraine cases and 533 controls. In addition, genotyping is performed using Sequenom MALDI-TOF mass spectrometry iPLEX platform. Univariate and multivariate analyses are carried out by logistic regression. The corresponding haplotypes are studied with the Haploview software and gene-gene interaction is assessed using the Generalized Multifactor Dimensionality Reduction (GMDR) analysis. There are significant differences in allelic distributions for rs2234693 and rs9340799 in ESR1 gene between patients with migraine and control subjects. Univariate logistic analysis shows that rs2234693 and rs9340799 are risk factors for migraine, but multivariate analysis reveals that only rs2234693 is significant associated with migraine. In the subgroup analysis, rs2234693 in ESR1 gene is found associated with menstrually related migraine. Further haplotypic analysis shows that rs2234693-rs9340799 TA haplotype serves as risk haplotype for migraine. The GMDR analysis identifies rs2234693 in ESR1 alone to be a crucial candidate in migraine susceptibility. This study is in agreement with the previous studies that variants in the ESR1 gene are associated with migraine suggesting that it plays a role in the migraine process.
Intelligent peak deconvolution through in-depth study of the data matrix from liquid chromatography coupled with a photo-diode array detector applied to pharmaceutical analysis.

PubMed

Arase, Shuntaro; Horie, Kanta; Kato, Takashi; Noda, Akira; Mito, Yasuhiro; Takahashi, Masatoshi; Yanagisawa, Toshinobu

2016-10-21

Multivariate curve resolution-alternating least squares (MCR-ALS) method was investigated for its potential to accelerate pharmaceutical research and development. The fast and efficient separation of complex mixtures consisting of multiple components, including impurities as well as major drug substances, remains a challenging application for liquid chromatography in the field of pharmaceutical analysis. In this paper we suggest an integrated analysis algorithm functioning on a matrix of data generated from HPLC coupled with photo-diode array detector (HPLC-PDA) and consisting of the mathematical program for the developed multivariate curve resolution method using an expectation maximization (EM) algorithm with a bidirectional exponentially modified Gaussian (BEMG) model function as a constraint for chromatograms and numerous PDA spectra aligned with time axis. The algorithm provided less than ±1.0% error between true and separated peak area values at resolution (R s ) of 0.6 using simulation data for a three-component mixture with an elution order of a/b/c with similarity (a/b)=0.8410, (b/c)=0.9123 and (a/c)=0.9809 of spectra at peak apex. This software concept provides fast and robust separation analysis even when method development efforts fail to achieve complete separation of the target peaks. Additionally, this approach is potentially applicable to peak deconvolution, allowing quantitative analysis of co-eluted compounds having exactly the same molecular weight. This is complementary to the use of LC-MS to perform quantitative analysis on co-eluted compounds using selected ions to differentiate the proportion of response attributable to each compound. Copyright © 2016 Elsevier B.V. All rights reserved.
Interstitial Features at Chest CT Enhance the Deleterious Effects of Emphysema in the COPDGene Cohort.

PubMed

Ash, Samuel Y; Harmouche, Rola; Ross, James C; Diaz, Alejandro A; Rahaghi, Farbod N; Sanchez-Ferrero, Gonzalo Vegas; Putman, Rachel K; Hunninghake, Gary M; Onieva, Jorge Onieva; Martinez, Fernando J; Choi, Augustine M; Bowler, Russell P; Lynch, David A; Hatabu, Hiroto; Bhatt, Surya P; Dransfield, Mark T; Wells, J Michael; Rosas, Ivan O; San Jose Estepar, Raul; Washko, George R

2018-06-05

Purpose To determine if interstitial features at chest CT enhance the effect of emphysema on clinical disease severity in smokers without clinical pulmonary fibrosis. Materials and Methods In this retrospective cohort study, an objective CT analysis tool was used to measure interstitial features (reticular changes, honeycombing, centrilobular nodules, linear scar, nodular changes, subpleural lines, and ground-glass opacities) and emphysema in 8266 participants in a study of chronic obstructive pulmonary disease (COPD) called COPDGene (recruited between October 2006 and January 2011). Additive differences in patients with emphysema with interstitial features and in those without interstitial features were analyzed by using t tests, multivariable linear regression, and Kaplan-Meier analysis. Multivariable linear and Cox regression were used to determine if interstitial features modified the effect of continuously measured emphysema on clinical measures of disease severity and mortality. Results Compared with individuals with emphysema alone, those with emphysema and interstitial features had a higher percentage predicted forced expiratory volume in 1 second (absolute difference, 6.4%; P < .001), a lower percentage predicted diffusing capacity of lung for carbon monoxide (DLCO) (absolute difference, 7.4%; P = .034), a 0.019 higher right ventricular-to-left ventricular (RVLV) volume ratio (P = .029), a 43.2-m shorter 6-minute walk distance (6MWD) (P < .001), a 5.9-point higher St George's Respiratory Questionnaire (SGRQ) score (P < .001), and 82% higher mortality (P < .001). In addition, interstitial features modified the effect of emphysema on percentage predicted DLCO, RVLV volume ratio, 6WMD, SGRQ score, and mortality (P for interaction < .05 for all). Conclusion In smokers, the combined presence of interstitial features and emphysema was associated with worse clinical disease severity and higher mortality than was emphysema alone. In addition, interstitial features enhanced the deleterious effects of emphysema on clinical disease severity and mortality. © RSNA, 2018 Online supplemental material is available for this article.
Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis

NASA Astrophysics Data System (ADS)

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

2013-01-01

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

NASA Technical Reports Server (NTRS)

Bauer, Frank H.; Downing, John P.; Thorpe, Christopher J.

1989-01-01

The INteractive Controls Analysis (INCA) program was developed at NASA's Goddard Space Flight Center to provide a user friendly, efficient environment for the design and analysis of control systems, specifically spacecraft control systems. Since its inception, INCA has found extensive use in the design, development, and analysis of control systems for spacecraft, instruments, robotics, and pointing systems. The (INCA) program was initially developed as a comprehensive classical design analysis tool for small and large order control systems. The latest version of INCA, expected to be released in February of 1990, was expanded to include the capability to perform multivariable controls analysis and design.
Testing Mean Differences among Groups: Multivariate and Repeated Measures Analysis with Minimal Assumptions

PubMed Central

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

2018-01-01

ABSTRACT 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. PMID:29565679
Development of multivariate exposure and fatal accident involvement rates for 1977

DOT National Transportation Integrated Search

1985-10-01

The need for multivariate accident involvement rates is often encounted in : accident analysis. The FARS (Fatal Accident Reporting System) files contain : records of fatal involvements characterized by many variables while NPTS : (National Personal T...
Bayesian multivariate hierarchical transformation models for ROC analysis.

PubMed

O'Malley, A James; Zou, Kelly H

2006-02-15

A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box-Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial.
Bayesian multivariate hierarchical transformation models for ROC analysis

PubMed Central

O'Malley, A. James; Zou, Kelly H.

2006-01-01

SUMMARY A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box–Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial. PMID:16217836
A Novel Approach to Detect Accelerated Aged and Surface-Mediated Degradation in Explosives by UPLC-ESI-MS.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beppler, Christina L

2015-12-01

A new approach was created for studying energetic material degradation. This approach involved detecting and tentatively identifying non-volatile chemical species by liquid chromatography-mass spectrometry (LC-MS) with multivariate statistical data analysis that form as the CL-20 energetic material thermally degraded. Multivariate data analysis showed clear separation and clustering of samples based on sample group: either pristine or aged material. Further analysis showed counter-clockwise trends in the principal components analysis (PCA), a type of multivariate data analysis, Scores plots. These trends may indicate that there was a discrete shift in the chemical markers as the went from pristine to aged material, andmore » then again when the aged CL-20 mixed with a potentially incompatible material was thermally aged for 4, 6, or 9 months. This new approach to studying energetic material degradation should provide greater knowledge of potential degradation markers in these materials.« less
Complex numbers in chemometrics: examples from multivariate impedance measurements on lipid monolayers.

PubMed

Geladi, Paul; Nelson, Andrew; Lindholm-Sethson, Britta

2007-07-09

Electrical impedance gives multivariate complex number data as results. Two examples of multivariate electrical impedance data measured on lipid monolayers in different solutions give rise to matrices (16x50 and 38x50) of complex numbers. Multivariate data analysis by principal component analysis (PCA) or singular value decomposition (SVD) can be used for complex data and the necessary equations are given. The scores and loadings obtained are vectors of complex numbers. It is shown that the complex number PCA and SVD are better at concentrating information in a few components than the naïve juxtaposition method and that Argand diagrams can replace score and loading plots. Different concentrations of Magainin and Gramicidin A give different responses and also the role of the electrolyte medium can be studied. An interaction of Gramicidin A in the solution with the monolayer over time can be observed.
A Multivariate Methodological Workflow for the Analysis of FTIR Chemical Mapping Applied on Historic Paint Stratigraphies

PubMed Central

Sciutto, Giorgia; Oliveri, Paolo; Catelli, Emilio; Bonacini, Irene

2017-01-01

In the field of applied researches in heritage science, the use of multivariate approach is still quite limited and often chemometric results obtained are often underinterpreted. Within this scenario, the present paper is aimed at disseminating the use of suitable multivariate methodologies and proposes a procedural workflow applied on a representative group of case studies, of considerable importance for conservation purposes, as a sort of guideline on the processing and on the interpretation of this FTIR data. Initially, principal component analysis (PCA) is performed and the score values are converted into chemical maps. Successively, the brushing approach is applied, demonstrating its usefulness for a deep understanding of the relationships between the multivariate map and PC score space, as well as for the identification of the spectral bands mainly involved in the definition of each area localised within the score maps. PMID:29333162
Risk Factors for Central Serous Chorioretinopathy: Multivariate Approach in a Case-Control Study.

PubMed

Chatziralli, Irini; Kabanarou, Stamatina A; Parikakis, Efstratios; Chatzirallis, Alexandros; Xirou, Tina; Mitropoulos, Panagiotis

2017-07-01

The purpose of this prospective study was to investigate the potential risk factors associated independently with central serous retinopathy (CSR) in a Greek population, using multivariate approach. Participants in the study were 183 consecutive patients diagnosed with CSR and 183 controls, matched for age. All participants underwent complete ophthalmological examination and information regarding their sociodemographic, clinical, medical and ophthalmological history were recorded, so as to assess potential risk factors for CSR. Univariate and multivariate analysis was performed. Univariate analysis showed that male sex, high educational status, high income, alcohol consumption, smoking, hypertension, coronary heart disease, obstructive sleep apnea, autoimmune disorders, H. pylori infection, type A personality and stress, steroid use, pregnancy and hyperopia were associated with CSR, while myopia was found to protect from CSR. In multivariate analysis, alcohol consumption, hypertension, coronary heart disease and autoimmune disorders lost their significance, while the remaining factors were all independently associated with CSR. It is important to take into account the various risk factors for CSR, so as to define vulnerable groups and to shed light into the pathogenesis of the disease.
Independent Prognostic Value of Serum Markers in Diffuse Large B-Cell Lymphoma in the Era of the NCCN-IPI.

PubMed

Melchardt, Thomas; Troppan, Katharina; Weiss, Lukas; Hufnagl, Clemens; Neureiter, Daniel; Tränkenschuh, Wolfgang; Schlick, Konstantin; Huemer, Florian; Deutsch, Alexander; Neumeister, Peter; Greil, Richard; Pichler, Martin; Egle, Alexander

2015-12-01

Several serum parameters have been evaluated for adding prognostic value to clinical scoring systems in diffuse large B-cell lymphoma (DLBCL), but none of the reports used multivariate testing of more than one parameter at a time. The goal of this study was to validate widely available serum parameters for their independent prognostic impact in the era of the National Comprehensive Cancer Network-International Prognostic Index (NCCN-IPI) score to determine which were the most useful. This retrospective bicenter analysis includes 515 unselected patients with DLBCL who were treated with rituximab and anthracycline-based chemoimmunotherapy between 2004 and January 2014. Anemia, high C-reactive protein, and high bilirubin levels had an independent prognostic value for survival in multivariate analyses in addition to the NCCN-IPI, whereas neutrophil-to-lymphocyte ratio, high gamma-glutamyl transferase levels, and platelets-to-lymphocyte ratio did not. In our cohort, we describe the most promising markers to improve the NCCN-IPI. Anemia and high C-reactive protein levels retain their power in multivariate testing even in the era of the NCCN-IPI. The negative role of high bilirubin levels may be associated as a marker of liver function. Further studies are warranted to incorporate these markers into prognostic models and define their role opposite novel molecular markers. Copyright © 2015 by the National Comprehensive Cancer Network.
Differences in passenger car and large truck involved crash frequencies at urban signalized intersections: an exploratory analysis.

PubMed

Dong, Chunjiao; Clarke, David B; Richards, Stephen H; Huang, Baoshan

2014-01-01

The influence of intersection features on safety has been examined extensively because intersections experience a relatively large proportion of motor vehicle conflicts and crashes. Although there are distinct differences between passenger cars and large trucks-size, operating characteristics, dimensions, and weight-modeling crash counts across vehicle types is rarely addressed. This paper develops and presents a multivariate regression model of crash frequencies by collision vehicle type using crash data for urban signalized intersections in Tennessee. In addition, the performance of univariate Poisson-lognormal (UVPLN), multivariate Poisson (MVP), and multivariate Poisson-lognormal (MVPLN) regression models in establishing the relationship between crashes, traffic factors, and geometric design of roadway intersections is investigated. Bayesian methods are used to estimate the unknown parameters of these models. The evaluation results suggest that the MVPLN model possesses most of the desirable statistical properties in developing the relationships. Compared to the UVPLN and MVP models, the MVPLN model better identifies significant factors and predicts crash frequencies. The findings suggest that traffic volume, truck percentage, lighting condition, and intersection angle significantly affect intersection safety. Important differences in car, car-truck, and truck crash frequencies with respect to various risk factors were found to exist between models. The paper provides some new or more comprehensive observations that have not been covered in previous studies. Copyright © 2013 Elsevier Ltd. All rights reserved.

18F-FET PET prior to recurrent high-grade glioma re-irradiation-additional prognostic value of dynamic time-to-peak analysis and early static summation images?

PubMed

Fleischmann, Daniel F; Unterrainer, Marcus; Bartenstein, Peter; Belka, Claus; Albert, Nathalie L; Niyazi, Maximilian

2017-04-01

Most high-grade gliomas (HGG) recur after initial multimodal therapy and re-irradiation (Re-RT) has been shown to be a valuable re-treatment option in selected patients. We evaluated the prognostic value of dynamic time-to-peak analysis and early static summation images in O-(2- 18 F-fluoroethyl)-l-tyrosine ( 18 F-FET) PET for patients treated with Re-RT ± concomitant bevacizumab. We retrospectively analyzed 72 patients suffering from recurrent HGG with 18 F-FET PET prior to Re-RT. PET analysis revealed the maximal tumor-to-background-ratio (TBR max ), the biological tumor volume, the number of PET-foci and pattern of time-activity-curves (TACs; increasing vs. decreasing). Furthermore, the novel PET parameters early TBR max (at 5-15 min post-injection) and minimal time-to-peak (TTP min ) were evaluated. Additional analysis was performed for gender, age, KPS, O6-methylguanine-DNA methyltransferase methylation status, isocitrate dehydrogenase 1 mutational status, WHO grade and concomitant bevacizumab therapy. The influence of PET and clinical parameters on post-recurrence survival (PRS) was investigated. Shorter TTP min was related to shorter PRS after Re-RT with 6 months for TTP min < 12.5 min, 7 months for TTP min 12.5-25 min and 11 months for TTP min >25 min (p = 0.027). TTP min had a significant impact on PRS both on univariate (p = 0.027; continuous) and multivariate analysis (p = 0.011; continuous). Other factors significantly related to PRS on multivariate analysis were increasing vs. decreasing TACs (p = 0.008) and Karnofsky Performance Score (p = 0.015; <70 vs. ≥70). Early TBR max as well as the other conventional PET parameters were not significantly related to PRS on univariate analysis. Dynamic 18 F-FET PET with TTP min provides a high prognostic value for recurrent HGG prior to Re-RT, whereas early TBR max does not. Dynamic 18 F-FET PET using TTP min might help to personalize Re-RT treatment regimens in future through voxelwise TTP min analysis for dose painting purposes and PET-guided dose escalation.
Social Cognitive and Planned Behavior Variables Associated with Stages of Change for Physical Activity in Spinal Cord Injury: A Multivariate Analysis

ERIC Educational Resources Information Center

Keegan, John; Ditchman, Nicole; Dutta, Alo; Chiu, Chung-Yi; Muller, Veronica; Chan, Fong; Kundu, Madan

2016-01-01

Purpose: To apply the constructs of social cognitive theory (SCT) and the theory of planned behavior (TPB) to understand the stages of change (SOC) for physical activities among individuals with a spinal cord injury (SCI). Method: Ex post facto design using multivariate analysis of variance (MANOVA). The participants were 144 individuals with SCI…
To See the World in a Grain of Sand: Recognizing the Origin of Sand Specimens by Diffuse Reflectance Infrared Fourier Transform Spectroscopy and Multivariate Exploratory Data Analysis

ERIC Educational Resources Information Center

Pezzolo, Alessandra De Lorenzi

2011-01-01

The diffuse reflectance infrared Fourier transform (DRIFT) spectra of sand samples exhibit features reflecting their composition. Basic multivariate analysis (MVA) can be used to effectively sort subsets of homogeneous specimens collected from nearby locations, as well as pointing out similarities in composition among sands of different origins.…
Testing key predictions of the associative account of mirror neurons in humans using multivariate pattern analysis.

PubMed

Oosterhof, Nikolaas N; Wiggett, Alison J; Cross, Emily S

2014-04-01

Cook et al. overstate the evidence supporting their associative account of mirror neurons in humans: most studies do not address a key property, action-specificity that generalizes across the visual and motor domains. Multivariate pattern analysis (MVPA) of neuroimaging data can address this concern, and we illustrate how MVPA can be used to test key predictions of their account.
Extended-release niacin therapy and risk of ischemic stroke in patients with cardiovascular disease: the Atherothrombosis Intervention in Metabolic Syndrome with low HDL/High Triglycerides: Impact on Global Health Outcome (AIM-HIGH) trial.

PubMed

Teo, Koon K; Goldstein, Larry B; Chaitman, Bernard R; Grant, Shannon; Weintraub, William S; Anderson, David C; Sila, Cathy A; Cruz-Flores, Salvador; Padley, Robert J; Kostuk, William J; Boden, William E

2013-10-01

In Atherothrombosis Intervention in Metabolic Syndrome with low HDL/High Triglycerides: Impact on Global Health Outcomes (AIM-HIGH) trial, addition of extended-release niacin (ERN) to simvastatin in participants with established cardiovascular disease, low high-density lipoprotein cholesterol, and high triglycerides had no incremental benefit, despite increases in high-density lipoprotein cholesterol. Preliminary analysis based on incomplete end point adjudication suggested increased ischemic stroke risk among participants randomized to ERN. This final analysis was conducted after complete AIM-HIGH event ascertainment to further explore potential relationship between niacin therapy and ischemic stroke risk. There was no group difference in trial primary composite end point at a mean 36-month follow-up among 3414 patients (85% men; mean age, 64±9 years) randomized to simvastatin plus ERN (1500-2000 mg/d) versus simvastatin plus matching placebo. In the intention-to-treat analysis, there were 50 fatal or nonfatal ischemic strokes: 18 (1.06%) in placebo arm versus 32 (1.86%) in ERN arm (hazard ratio [HR], 1.78 [95% confidence interval {CI}, 1.00-3.17; P=0.050). Multivariate analysis showed independent associations between ischemic stroke risk and >65 years of age (HR, 3.58; 95% CI, 1.82-7.05; P=0.0002), history of stroke/transient ischemic attack/carotid disease (HR, 2.18; 95% CI, 1.23-3.88; P=0.0079), elevated baseline Lp(a) (HR, 2.80; 95% CI, 1.25-6.27 comparing the middle with the lowest tertile; HR, 2.31; 95% CI, 1.002-5.30 comparing the highest with the lowest tertile; overall P=0.042) but a nonsignificant association with ERN (HR, 1.74; 95% CI, 0.97-3.11; P=0.063). Although there were numerically more ischemic strokes with addition of ERN to simvastatin that reached nominal significance, the number was small, and multivariable analysis accounting for known risk factors did not support a significant association between niacin and ischemic stroke risk. http://www.clinicaltrials.gov. Unique identifier: NCT00120289.
Multivariate statistical analysis of low-voltage EDS spectrum images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson, I.M.

1998-03-01

Whereas energy-dispersive X-ray spectrometry (EDS) has been used for compositional analysis in the scanning electron microscope for 30 years, the benefits of using low operating voltages for such analyses have been explored only during the last few years. This paper couples low-voltage EDS with two other emerging areas of characterization: spectrum imaging and multivariate statistical analysis. The specimen analyzed for this study was a finished Intel Pentium processor, with the polyimide protective coating stripped off to expose the final active layers.
Multivariate two-part statistics for analysis of correlated mass spectrometry data from multiple biological specimens.

PubMed

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.
Supporting inquiry learning by promoting normative understanding of multivariable causality

NASA Astrophysics Data System (ADS)

Keselman, Alla

2003-11-01

Early adolescents may lack the cognitive and metacognitive skills necessary for effective inquiry learning. In particular, they are likely to have a nonnormative mental model of multivariable causality in which effects of individual variables are neither additive nor consistent. Described here is a software-based intervention designed to facilitate students' metalevel and performance-level inquiry skills by enhancing their understanding of multivariable causality. Relative to an exploration-only group, sixth graders who practiced predicting an outcome (earthquake risk) based on multiple factors demonstrated increased attention to evidence, improved metalevel appreciation of effective strategies, and a trend toward consistent use of a controlled comparison strategy. Sixth graders who also received explicit instruction in making predictions based on multiple factors showed additional improvement in their ability to compare multiple instances as a basis for inferences and constructed the most accurate knowledge of the system. Gains were maintained in transfer tasks. The cognitive skills and metalevel understanding examined here are essential to inquiry learning.
The application of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the stratum corneum.

PubMed

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

2017-02-01

Drug permeation through the intercellular lipids, which pack around and between corneocytes, may be enhanced by increasing the thermodynamic activity of the active in a formulation. However, this may also result in unwanted drug crystallisation on and in the skin. In this work, we explore the combination of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the skin. Ex vivo permeation studies of saturated solutions of diclofenac sodium (DF Na) in two vehicles, propylene glycol (PG) and dimethyl sulphoxide (DMSO), were carried out in porcine ear skin. Tape stripping and ATR-FTIR spectroscopy were conducted simultaneously to collect spectral data as a function of skin depth. Multivariate data analysis was applied to visualise and categorise the spectral data in the region of interest (1700-1500cm -1 ) containing the carboxylate (COO - ) asymmetric stretching vibrations of DF Na. Spectral data showed the redshifts of the COO - asymmetric stretching vibrations for DF Na in the solution compared with solid drug. Similar shifts were evident following application of saturated solutions of DF Na to porcine skin samples. Multivariate data analysis categorised the spectral data based on the spectral differences and drug crystallisation was found to be confined to the upper layers of the skin. This proof-of-concept study highlights the utility of ATR-FTIR spectroscopy in combination with multivariate data analysis as a simple and rapid approach in the investigation of drug deposition in the skin. The approach described here will be extended to the study of other actives for topical application to the skin. Copyright © 2016 Elsevier B.V. All rights reserved.
Multielement geochemistry identifies the spatial pattern of soil and sediment contamination in an urban parkland, Western Australia.

PubMed

Rate, Andrew W

2018-06-15

Urban environments are dynamic and highly heterogeneous, and multiple additions of potential contaminants are likely on timescales which are short relative to natural processes. The likely sources and location of soil or sediment contamination in urban environment should therefore be detectable using multielement geochemical composition combined with rigorously applied multivariate statistical techniques. Soil, wetland sediment, and street dust was sampled along intersecting transects in Robertson Park in metropolitan Perth, Western Australia. Samples were analysed for near-total concentrations of multiple elements (including Cd, Ce, Co, Cr, Cu, Fe, Gd, La, Mn, Nd, Ni, Pb, Y, and Zn), as well as pH, and electrical conductivity. Samples at some locations within Robertson Park had high concentrations of potentially toxic elements (Pb above Health Investigation Limits; As, Ba, Cu, Mn, Ni, Pb, V, and Zn above Ecological Investigation Limits). However, these concentrations carry low risk due to the main land use as recreational open space, the low proportion of samples exceeding guideline values, and a tendency for the highest concentrations to be located within the less accessible wetland basin. The different spatial distributions of different groups of contaminants was consistent with different inputs of contaminants related to changes in land use and technology over the history of the site. Multivariate statistical analyses reinforced the spatial information, with principal component analysis identifying geochemical associations of elements which were also spatially related. A multivariate linear discriminant model was able to discriminate samples into a-priori types, and could predict sample type with 84% accuracy based on multielement composition. The findings suggest substantial advantages of characterising a site using multielement and multivariate analyses, an approach which could benefit investigations of other sites of concern. Copyright © 2018 Elsevier B.V. All rights reserved.
Quantitative analysis of essential oils in perfume using multivariate curve resolution combined with comprehensive two-dimensional gas chromatography.

PubMed

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

2011-08-05

The use of multivariate curve resolution (MCR) to build multivariate quantitative models using data obtained from comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) is presented and evaluated. The MCR algorithm presents some important features, such as second order advantage and the recovery of the instrumental response for each pure component after optimization by an alternating least squares (ALS) procedure. A model to quantify the essential oil of rosemary was built using a calibration set containing only known concentrations of the essential oil and cereal alcohol as solvent. A calibration curve correlating the concentration of the essential oil of rosemary and the instrumental response obtained from the MCR-ALS algorithm was obtained, and this calibration model was applied to predict the concentration of the oil in complex samples (mixtures of the essential oil, pineapple essence and commercial perfume). The values of the root mean square error of prediction (RMSEP) and of the root mean square error of the percentage deviation (RMSPD) obtained were 0.4% (v/v) and 7.2%, respectively. Additionally, a second model was built and used to evaluate the accuracy of the method. A model to quantify the essential oil of lemon grass was built and its concentration was predicted in the validation set and real perfume samples. The RMSEP and RMSPD obtained were 0.5% (v/v) and 6.9%, respectively, and the concentration of the essential oil of lemon grass in perfume agreed to the value informed by the manufacturer. The result indicates that the MCR algorithm is adequate to resolve the target chromatogram from the complex sample and to build multivariate models of GC×GC-FID data. Copyright © 2011 Elsevier B.V. All rights reserved.
Describing the Elephant: Structure and Function in Multivariate Data.

ERIC Educational Resources Information Center

McDonald, Roderick P.

1986-01-01

There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family of models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain. (Author)
Spot sign on 90-second delayed computed tomography angiography improves sensitivity for hematoma expansion and mortality: prospective study.

PubMed

Ciura, Viesha A; Brouwers, H Bart; Pizzolato, Raffaella; Ortiz, Claudia J; Rosand, Jonathan; Goldstein, Joshua N; Greenberg, Steven M; Pomerantz, Stuart R; Gonzalez, R Gilberto; Romero, Javier M

2014-11-01

The computed tomography angiography (CTA) spot sign is a validated biomarker for poor outcome and hematoma expansion in intracerebral hemorrhage. The spot sign has proven to be a dynamic entity, with multimodal imaging proving to be of additional value. We investigated whether the addition of a 90-second delayed CTA acquisition would capture additional intracerebral hemorrhage patients with the spot sign and increase the sensitivity of the spot sign. We prospectively enrolled consecutive intracerebral hemorrhage patients undergoing first pass and 90-second delayed CTA for 18 months at a single academic center. Univariate and multivariate logistic regression were performed to assess clinical and neuroimaging covariates for relationship with hematoma expansion and mortality. Sensitivity of the spot sign for hematoma expansion on first pass CTA was 55%, which increased to 64% if the spot sign was present on either CTA acquisition. In multivariate analysis the spot sign presence was associated with significant hematoma expansion: odds ratio, 17.7 (95% confidence interval, 3.7-84.2; P=0.0004), 8.3 (95% confidence interval, 2.0-33.4; P=0.004), and 12.0 (95% confidence interval, 2.9-50.5; P=0.0008) if present on first pass, delayed, or either CTA acquisition, respectively. Spot sign presence on either acquisitions was also significant for mortality. We demonstrate improved sensitivity for predicting hematoma expansion and poor outcome by adding a 90-second delayed CTA, which may enhance selection of patients who may benefit from hemostatic therapy. © 2014 American Heart Association, Inc.
Evaluating the role of admixture in cancer therapy via in vitro drug response and multivariate genome-wide associations

PubMed Central

Jack, John; Havener, Tammy M; McLeod, Howard L; Motsinger-Reif, Alison A; Foster, Matthew

2015-01-01

Aim: We investigate the role of ethnicity and admixture in drug response across a broad group of chemotherapeutic drugs. Also, we generate hypotheses on the genetic variants driving differential drug response through multivariate genome-wide association studies. Methods: Immortalized lymphoblastoid cell lines from 589 individuals (Hispanic or non-Hispanic/Caucasian) were used to investigate dose-response for 28 chemotherapeutic compounds. Univariate and multivariate statistical models were used to elucidate associations between genetic variants and differential drug response as well as the role of ethnicity in drug potency and efficacy. Results & Conclusion: For many drugs, the variability in drug response appears to correlate with self-reported race and estimates of genetic ancestry. Additionally, multivariate genome-wide association analyses offered interesting hypotheses governing these differential responses. PMID:26314407
Multivariate Statistical Analysis of MSL APXS Bulk Geochemical Data

NASA Astrophysics Data System (ADS)

Hamilton, V. E.; Edwards, C. S.; Thompson, L. M.; Schmidt, M. E.

2014-12-01

We apply cluster and factor analyses to bulk chemical data of 130 soil and rock samples measured by the Alpha Particle X-ray Spectrometer (APXS) on the Mars Science Laboratory (MSL) rover Curiosity through sol 650. Multivariate approaches such as principal components analysis (PCA), cluster analysis, and factor analysis compliment more traditional approaches (e.g., Harker diagrams), with the advantage of simultaneously examining the relationships between multiple variables for large numbers of samples. Principal components analysis has been applied with success to APXS, Pancam, and Mössbauer data from the Mars Exploration Rovers. Factor analysis and cluster analysis have been applied with success to thermal infrared (TIR) spectral data of Mars. Cluster analyses group the input data by similarity, where there are a number of different methods for defining similarity (hierarchical, density, distribution, etc.). For example, without any assumptions about the chemical contributions of surface dust, preliminary hierarchical and K-means cluster analyses clearly distinguish the physically adjacent rock targets Windjana and Stephen as being distinctly different than lithologies observed prior to Curiosity's arrival at The Kimberley. In addition, they are separated from each other, consistent with chemical trends observed in variation diagrams but without requiring assumptions about chemical relationships. We will discuss the variation in cluster analysis results as a function of clustering method and pre-processing (e.g., log transformation, correction for dust cover) and implications for interpreting chemical data. Factor analysis shares some similarities with PCA, and examines the variability among observed components of a dataset so as to reveal variations attributable to unobserved components. Factor analysis has been used to extract the TIR spectra of components that are typically observed in mixtures and only rarely in isolation; there is the potential for similar results with data from APXS. These techniques offer new ways to understand the chemical relationships between the materials interrogated by Curiosity, and potentially their relation to materials observed by APXS instruments on other landed missions.
Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization.

PubMed

Brier, Matthew R; Mitra, Anish; McCarthy, John E; Ances, Beau M; Snyder, Abraham Z

2015-11-01

Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity. Copyright © 2015 Elsevier Inc. All rights reserved.
Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization

PubMed Central

Brier, Matthew R.; Mitra, Anish; McCarthy, John E.; Ances, Beau M.; Snyder, Abraham Z.

2015-01-01

Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity. PMID:26208872
Multivariate outcome prediction in traumatic brain injury with focus on laboratory values.

PubMed

Nelson, David W; Rudehill, Anders; MacCallum, Robert M; Holst, Anders; Wanecek, Michael; Weitzberg, Eddie; Bellander, Bo-Michael

2012-11-20

Traumatic brain injury (TBI) is a major cause of morbidity and mortality. Identifying factors relevant to outcome can provide a better understanding of TBI pathophysiology, in addition to aiding prognostication. Many common laboratory variables have been related to outcome but may not be independent predictors in a multivariate setting. In this study, 757 patients were identified in the Karolinska TBI database who had retrievable early laboratory variables. These were analyzed towards a dichotomized Glasgow Outcome Scale (GOS) with logistic regression and relevance vector machines, a non-linear machine learning method, univariately and controlled for the known important predictors in TBI outcome: age, Glasgow Coma Score (GCS), pupil response, and computed tomography (CT) score. Accuracy was assessed with Nagelkerke's pseudo R². Of the 18 investigated laboratory variables, 15 were found significant (p<0.05) towards outcome in univariate analyses. In contrast, when adjusting for other predictors, few remained significant. Creatinine was found an independent predictor of TBI outcome. Glucose, albumin, and osmolarity levels were also identified as predictors, depending on analysis method. A worse outcome related to increasing osmolarity may warrant further study. Importantly, hemoglobin was not found significant when adjusted for post-resuscitation GCS as opposed to an admission GCS, and timing of GCS can thus have a major impact on conclusions. In total, laboratory variables added an additional 1.3-4.4% to pseudo R².
A multivariate study for characterizing particulate matter (PM(10), PM(2.5), and PM(1)) in Seoul metropolitan subway stations, Korea.

PubMed

Kwon, Soon-Bark; Jeong, Wootae; Park, Duckshin; Kim, Ki-Tae; Cho, Kyung Hwa

2015-10-30

Given that around eight million commuters use the Seoul Metropolitan Subway (SMS) each day, the indoor air quality (IAQ) of its stations has attracted much public attention. We have monitored the concentration of particulate matters (PMx) (i.e., PM10, PM2.5, and PM1) in six major transfer stations per minute for three weeks during the summer, autumn, and winter in 2014 and 2015. The data were analyzed to investigate the relationship between PMx concentration and multivariate environmental factors using statistical methods. The average PM concentration observed was approximately two or three times higher than outdoor PM10 concentration, showing similar temporal patterns at concourses and platforms. This implies that outdoor PM10 is the most significant factor in controlling indoor PM concentration. In addition, the station depth and number of trains passing through stations were found to be additional influences on PMx. Principal component analysis (PCA) and self-organizing map (SOM) were employed, through which we found that the number of trains influences PM concentration in the vicinity of platforms only, and PMx hotspots were determined. This study identifies the external and internal factors affecting PMx characteristics in six SMS stations, which can assist in the development of effective IAQ management plans to improve public health. Copyright © 2015 Elsevier B.V. All rights reserved.
Prognostic factors and relative risk for survival in N1-3 oral squamous cell carcinoma: a multivariate analysis using Cox's hazard model.

PubMed

Noguchi, M; Kido, Y; Kubota, H; Kinjo, H; Kohama, G

1999-12-01

The records of 136 patients with N1-3 oral squamous cell carcinoma treated by surgery were investigated retrospectively, with the aim of finding out which factors were predictive of survival on multivariate analysis. Four independent factors significantly influenced survival in the following order: pN stage; T stage; histological grade; and N stage. The most significant was pN stage, the five-year survival for patients with pN0 being 91% and for patients with pN1-3 41%. A further study was carried out on the 80 patients with pN1-3 to find out their prognostic factors for survival and the independent factors identified by multivariate analysis were T stage and presence or absence of extracapsular spread to metastatic lymph nodes.

Calypso: a user-friendly web-server for mining and visualizing microbiome-environment interactions.

PubMed

Zakrzewski, Martha; Proietti, Carla; Ellis, Jonathan J; Hasan, Shihab; Brion, Marie-Jo; Berger, Bernard; Krause, Lutz

2017-03-01

Calypso is an easy-to-use online software suite that allows non-expert users to mine, interpret and compare taxonomic information from metagenomic or 16S rDNA datasets. Calypso has a focus on multivariate statistical approaches that can identify complex environment-microbiome associations. The software enables quantitative visualizations, statistical testing, multivariate analysis, supervised learning, factor analysis, multivariable regression, network analysis and diversity estimates. Comprehensive help pages, tutorials and videos are provided via a wiki page. The web-interface is accessible via http://cgenome.net/calypso/ . The software is programmed in Java, PERL and R and the source code is available from Zenodo ( https://zenodo.org/record/50931 ). The software is freely available for non-commercial users. l.krause@uq.edu.au. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.
DigOut: viewing differential expression genes as outliers.

PubMed

Yu, Hui; Tu, Kang; Xie, Lu; Li, Yuan-Yuan

2010-12-01

With regards to well-replicated two-conditional microarray datasets, the selection of differentially expressed (DE) genes is a well-studied computational topic, but for multi-conditional microarray datasets with limited or no replication, the same task is not properly addressed by previous studies. This paper adopts multivariate outlier analysis to analyze replication-lacking multi-conditional microarray datasets, finding that it performs significantly better than the widely used limit fold change (LFC) model in a simulated comparative experiment. Compared with the LFC model, the multivariate outlier analysis also demonstrates improved stability against sample variations in a series of manipulated real expression datasets. The reanalysis of a real non-replicated multi-conditional expression dataset series leads to satisfactory results. In conclusion, a multivariate outlier analysis algorithm, like DigOut, is particularly useful for selecting DE genes from non-replicated multi-conditional gene expression dataset.
Is a multivariate consensus representation of genetic relationships among populations always meaningful?

PubMed Central

Moazami-Goudarzi, K; Laloë, D

2002-01-01

To determine the relationships among closely related populations or species, two methods are commonly used in the literature: phylogenetic reconstruction or multivariate analysis. The aim of this article is to assess the reliability of multivariate analysis. We describe a method that is based on principal component analysis and Mantel correlations, using a two-step process: The first step consists of a single-marker analysis and the second step tests if each marker reveals the same typology concerning population differentiation. We conclude that if single markers are not congruent, the compromise structure is not meaningful. Our model is not based on any particular mutation process and it can be applied to most of the commonly used genetic markers. This method is also useful to determine the contribution of each marker to the typology of populations. We test whether our method is efficient with two real data sets based on microsatellite markers. Our analysis suggests that for closely related populations, it is not always possible to accept the hypothesis that an increase in the number of markers will increase the reliability of the typology analysis. PMID:12242255
Application of multivariate analysis to investigate the trace element contamination in top soil of coal mining district in Jorong, South Kalimantan, Indonesia

NASA Astrophysics Data System (ADS)

Pujiwati, Arie; Nakamura, K.; Watanabe, N.; Komai, T.

2018-02-01

Multivariate analysis is applied to investigate geochemistry of several trace elements in top soils and their relation with the contamination source as the influence of coal mines in Jorong, South Kalimantan. Total concentration of Cd, V, Co, Ni, Cr, Zn, As, Pb, Sb, Cu and Ba was determined in 20 soil samples by the bulk analysis. Pearson correlation is applied to specify the linear correlation among the elements. Principal Component Analysis (PCA) and Cluster Analysis (CA) were applied to observe the classification of trace elements and contamination sources. The results suggest that contamination loading is contributed by Cr, Cu, Ni, Zn, As, and Pb. The elemental loading mostly affects the non-coal mining area, for instances the area near settlement and agricultural land use. Moreover, the contamination source is classified into the areas that are influenced by the coal mining activity, the agricultural types, and the river mixing zone. Multivariate analysis could elucidate the elemental loading and the contamination sources of trace elements in the vicinity of coal mine area.
Habitual betel quid chewing and risk for hepatocellular carcinoma complicating cirrhosis.

PubMed

Tsai, Jung-Fa; Jeng, Jen-Eing; Chuang, Lee-Yea; Ho, Mei-Shang; Ko, Ying-Chin; Lin, Zu-Yau; Hsieh, Min-Yuh; Chen, Shin-Chern; Chuang, Wan-Lung; Wang, Liang-Yen; Yu, Ming-Lung; Dai, Chia-Yen

2004-05-01

This case-control study aimed to assess the independent and interactive role of habitual betel quid chewing and known risk factors for hepatocellular carcinoma (HCC). Subjects enrolled included 210 pairs of sex- and age-matched cirrhotic patients with HCC, patients with cirrhosis alone, and healthy controls. Information on risk factors was obtained through serologic examination of hepatitis B surface antigen (HBsAg) and antibodies to hepatitis C virus (anti-HCV), and a standardized personal interview with a structured questionnaire. Multivariate analysis indicated that betel quid chewing (odds ratio [OR], 5.81; 95% confidence interval [CI], 2.26-14.94); HBsAg (OR, 37.98; 95% CI, 19.65-73.42); and anti-HCV (OR, 47.23; 95% CI, 18.86-118.25) were independent risk factors for HCC when HCC patients were compared with healthy controls. Using patients with cirrhosis alone as a reference group, multivariate analysis indicated that only betel quid chewing (OR, 1.69; 95% CI, 1.04-2.76) and HBsAg (OR, 1.54; 95% CI, l.01-2.37) were independent risk factors for HCC. There was an additive interaction between betel quid chewing and the presence of either HBsAg (synergy index, 5.22) or anti-HCV (synergy index, 1.35). Moreover, a higher risk of HCC was associated with a longer duration of betel quid chewing and a larger amount of betel quid consumed (each p(for trend) < 0.0001). In conclusion, betel quid chewing is an independent risk factor for cirrhotic HCC. There is an additive interaction between betel quid chewing and chronic hepatitis B and/or hepatitis C virus infection.
Lymph Node Micrometastases are Associated with Worse Survival in Patients with Otherwise Node-Negative Hilar Cholangiocarcinoma.

PubMed

Mantel, Hendrik T J; Wiggers, Jim K; Verheij, Joanne; Doff, Jan J; Sieders, Egbert; van Gulik, Thomas M; Gouw, Annette S H; Porte, Robert J

2015-12-01

Lymph node metastases on routine histology are a strong negative predictor for survival after resection of hilar cholangiocarcinoma. Additional immunohistochemistry can detect lymph node micrometastases in patients who are otherwise node negative, but the prognostic value is unsure. The objective of this study was to assess the effect on survival of immunohistochemically detected lymph node micrometastases in patients with node-negative (pN0) hilar cholangiocarcinoma on routine histology. Between 1990 and 2010, a total of 146 patients underwent curative-intent resection of hilar cholangiocarcinoma with regional lymphadenectomy at two university medical centers in the Netherlands. Ninety-one patients (62 %) without lymph node metastases at routine histology were included. Micrometastases were identified by multiple sectioning of all lymph nodes and additional immunostaining with an antibody against cytokeratin 19 (K19). The association with overall survival was assessed in univariable and multivariable analysis. Median follow-up was 48 months. Micrometastases were identified in 16 (5 %) of 324 lymph nodes, corresponding to 11 (12 %) of 91 patients. There were no differences in clinical variables between K19 lymph node-positive and -negative patients. Five-year survival rates in patients with lymph node micrometastases were significantly lower compared to patients without micrometastases (27 vs. 54 %, P = 0.01). Multivariable analysis confirmed micrometastases as an independent prognostic factor for survival (adjusted Hazard ratio 2.4, P = 0.02). Lymph node micrometastases are associated with worse survival after resection of hilar cholangiocarcinoma. Immunohistochemical detection of lymph node micrometastases leads to better staging of patients who were initially diagnosed with node-negative (pN0) hilar cholangiocarcinoma on routine histology.
Pretreatment Nomogram to Predict the Risk of Acute Urinary Retention After I-125 Prostate Brachytherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roeloffzen, Ellen M., E-mail: e.m.a.roeloffzen@umcutrecht.nl; Vulpen, Marco van; Battermann, Jan J.

Purpose: Acute urinary retention (AUR) after iodine-125 (I-125) prostate brachytherapy negatively influences long-term quality of life and therefore should be prevented. We aimed to develop a nomogram to preoperatively predict the risk of AUR. Methods: Using the preoperative data of 714 consecutive patients who underwent I-125 prostate brachytherapy between 2005 and 2008 at our department, we modeled the probability of AUR. Multivariate logistic regression analysis was used to assess the predictive ability of a set of pretreatment predictors and the additional value of a new risk factor (the extent of prostate protrusion into the bladder). The performance of the finalmore » model was assessed with calibration and discrimination measures. Results: Of the 714 patients, 57 patients (8.0%) developed AUR after implantation. Multivariate analysis showed that the combination of prostate volume, IPSS score, neoadjuvant hormonal treatment and the extent of prostate protrusion contribute to the prediction of AUR. The discriminative value (receiver operator characteristic area, ROC) of the basic model (including prostate volume, International Prostate Symptom Score, and neoadjuvant hormonal treatment) to predict the development of AUR was 0.70. The addition of prostate protrusion significantly increased the discriminative power of the model (ROC 0.82). Calibration of this final model was good. The nomogram showed that among patients with a low sum score (<18 points), the risk of AUR was only 0%-5%. However, in patients with a high sum score (>35 points), the risk of AUR was more than 20%. Conclusion: This nomogram is a useful tool for physicians to predict the risk of AUR after I-125 prostate brachytherapy. The nomogram can aid in individualized treatment decision-making and patient counseling.« less
The association between insulin resistance, metabolic variables, and depressive symptoms in Mexican-American elderly: A population-based study.

PubMed

Diniz, Breno S; Fisher-Hoch, Susan; McCormick, Joseph

2018-02-01

Depressive symptoms are common among older adults with obesity and diabetes. Nonetheless, the mechanisms for this association are not clear but may involve changes in the insulin cascade signaling. We aimed to investigate the association, and potential mediators, between obesity, insulin resistance, and depressive symptoms among older adults from a homogenous cohort of Mexican-Americans. We included a total of 500 Mexican-American older adults assessed in the Cameron County Health Study. We evaluated depressive symptoms using the Center for Epidemiologic Survey Depression Scale (CES-D). Central obesity was defined by waist circumference. Insulin resistance was evaluated by the HOMA-IR index. We estimated the association between obesity, insulin resistance, and depressive symptoms by carrying out univariate and multivariate regression analyses. In unadjusted regression analysis, HOMA-IR (unstandardized β = 0.31 ± 0.12, P = 0.007), waist circumference (unstandardized β = 0.066 ± 0.0.028, P = 0.017), and Hb1Ac levels (unstandardized β = 0.52 ± 0.24, P = 0.03) were significantly associated with CES-D scores. The association of HOMA-IR and CES-D remained statistically significant after controlling for socio-demographic and clinical variables in multivariate analysis (unstandardized β = 0.28 ± 0.11, P = 0.01). Our results suggest that depressive symptoms are associated with insulin resistance in older Mexican-American adults. In addition, poorer glucose control and obesity are important mediators of this relationship. Additional studies are needed to evaluate whether interventions that increase insulin sensitivity can also reduce depressive symptoms in this population. Copyright © 2017 John Wiley & Sons, Ltd.
Chemical and biological comparison of raw and vinegar-baked Radix Bupleuri.

PubMed

Li, Zhen-Yu; Sun, Hui-Min; Xing, Jie; Qin, Xue-Mei; Du, Guan-Hua

2015-05-13

Radix Bupleuri (RB) is a commonly used herbal drug in Traditional Chinese Medicine (TCM), and it can be baked with vinegar to afford vinegar-baked Radix Bupleuri (VBRB), which is used in TCM for liver diseases treatment. In this study, the chemical compositions and biological effects between raw and two processed RBs by different vinegars were systematically compared. The chemical compositions of raw and two processed RBs were analyzed by (1)H NMR spectroscopy coupled with multivariate analysis. Two different extraction procedures were used, including direct extraction and liquid-liquid partition. Then HPLC was applied to determine the changes of saikosaponin contents. In addition, their liver protective effects against CCl4 induced liver injury were also investigated, and the biochemical parameters and histopathology were measured after treatment of mice with raw RB and two processed RBs (5 g/kg/day) for 14 days. Multivariate analysis showed clear differences between the raw and the two processed RBs, and the vinegar-baking process induced elevated contents of ssb1, ssb2, acetic acid, malic acid, citric acid, 5-HMF, and ligustrazine, as well as the decreased contents of ssa, ssd, sucrose, glycine, succinic acid etc. In addition, both raw and processed RBs showed liver protective effects against CCl4 induced liver injury, and the vinegar-baked RBs showed better effects than that of raw RB. The raw and vinegar-baked RBs differed not only in the chemical compositions but also in the pharmacological effects. And two processed RBs also showed chemical differences, suggesting that the type of vinegar had an important effect on vinegar baking. In order to ensure the therapeutic effect and safety of TCM, the effect of different vinegars on processing of herbal drugs should be further studied. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia

PubMed Central

Plis, Sergey M; Sui, Jing; Lane, Terran; Roy, Sushmita; Clark, Vincent P; Potluru, Vamsi K; Huster, Rene J; Michael, Andrew; Sponheim, Scott R; Weisend, Michael P; Calhoun, Vince D

2013-01-01

Identifying the complex activity relationships present in rich, modern neuroimaging data sets remains a key challenge for neuroscience. The problem is hard because (a) the underlying spatial and temporal networks may be nonlinear and multivariate and (b) the observed data may be driven by numerous latent factors. Further, modern experiments often produce data sets containing multiple stimulus contexts or tasks processed by the same subjects. Fusing such multi-session data sets may reveal additional structure, but raises further statistical challenges. We present a novel analysis method for extracting complex activity networks from such multifaceted imaging data sets. Compared to previous methods, we choose a new point in the trade-off space, sacrificing detailed generative probability models and explicit latent variable inference in order to achieve robust estimation of multivariate, nonlinear group factors (“network clusters”). We apply our method to identify relationships of task-specific intrinsic networks in schizophrenia patients and control subjects from a large fMRI study. After identifying network-clusters characterized by within- and between-task interactions, we find significant differences between patient and control groups in interaction strength among networks. Our results are consistent with known findings of brain regions exhibiting deviations in schizophrenic patients. However, we also find high-order, nonlinear interactions that discriminate groups but that are not detected by linear, pair-wise methods. We additionally identify high-order relationships that provide new insights into schizophrenia but that have not been found by traditional univariate or second-order methods. Overall, our approach can identify key relationships that are missed by existing analysis methods, without losing the ability to find relationships that are known to be important. PMID:23876245
Potential host-related risk factors for recurrent urinary tract infection in Saudi women of childbearing age.

PubMed

Ahmed, Abul-Fotouh Abdel-Maguid; Solyman, Awatif Abdel-Karim; Kamal, Sanaa Moharram

2016-08-01

Risk factors for recurrent urinary tract infection (rUTI) in women may differ between individuals, age, and the community. This study aimed to evaluate host related risk factors for rUTI in sexually active Saudi women during the childbearing period. A case-control study was conducted in five healthcare centers and included married, nonpregnant women aged 18-40 years. A total of 217 women had rUTI (cases) and 252 did not (controls). A validated questionnaire, with a face-to-face interview, was applied to assess various demographic, behavioral, medical, and sexual data. Additionally, a thorough physical examination, saliva and blood analyses, uroflowmetry, and genitourinary ultrasonography were performed. Multivariate logistic regression analysis was used to identify the significant host related risk factors associated with rUTI. In multivariate analysis, attributable risks for rUTI were a history of childhood UTI [odds ratio (OR) = 6.8)] back-to-front douching/wiping after bowel movement (OR = 2.6), younger age at first intercourse (OR = 6.3), increased frequency of sexual intercourse (OR = 4.8), obstructed urinary flow (OR = 1.9), and genital prolapse (OR = 3.4). A total of 9.68 % of cases and none of the controls had high postvoid residual urine (positive predictive value for rUTI = 100 %). This is the first reported study to evaluate host related risk factors for rUTI in childbearing-age women in Saudi Arabia. Study findings indicate the association between rUTI and various factors that have been already established, with addition of improper rectal hygiene as a potential risk for recurrence.
Long term outcome and prognostic factors for large hepatocellular carcinoma (10 cm or more) after surgical resection.

PubMed

Pandey, Durgatosh; Lee, Kang-Hoe; Wai, Chun-Tao; Wagholikar, Gajanan; Tan, Kai-Chah

2007-10-01

Surgical resection is the standard treatment for hepatocellular carcinoma (HCC). However, the role of surgery in treatment of large tumors (10 cm or more) is controversial. We have analyzed, in a single centre, the long-term outcome associated with surgical resection in patients with such large tumors. We retrospectively investigated 166 patients who had undergone surgical resection between July 1995 and December 2006 because of large (10 cm or more) HCC. Survival analysis was done using the Kaplan-Meier method. Prognostic factors were evaluated using univariate and multivariate analyses. Of the 166 patients evaluated, 80% were associated with viral hepatitis and 48.2% had cirrhosis. The majority of patients underwent a major hepatectomy (48.2% had four or more segments resected and 9% had additional organ resection). The postoperative mortality was 3%. The median survival in our study was 20 months, with an actuarial 5-year and 10-year overall survival of 28.6% and 25.6%, respectively. Of these patients, 60% had additional treatment in the form of transarterial chemoembolization, radiofrequency ablation or both. On multivariate analysis, vascular invasion (P < 0.001), cirrhosis (P = 0.028), and satellite lesions/multicentricity (P = 0.006) were significant prognostic factors influencing survival. The patients who had none of these three risk factors had 5-year and 10-year overall survivals of 57.7% each, compared with 22.5% and 19.3%, respectively, for those with at least one risk factor (P < 0.001). Surgical resection for those with large HCC can be safely performed with a reasonable long-term survival. For tumors with poor prognostic factors, there is a pressing need for effective adjuvant therapy.
Quantitative methods for analysing cumulative effects on fish migration success: a review.

PubMed

Johnson, J E; Patterson, D A; Martins, E G; Cooke, S J; Hinch, S G

2012-07-01

It is often recognized, but seldom addressed, that a quantitative assessment of the cumulative effects, both additive and non-additive, of multiple stressors on fish survival would provide a more realistic representation of the factors that influence fish migration. This review presents a compilation of analytical methods applied to a well-studied fish migration, a more general review of quantitative multivariable methods, and a synthesis on how to apply new analytical techniques in fish migration studies. A compilation of adult migration papers from Fraser River sockeye salmon Oncorhynchus nerka revealed a limited number of multivariable methods being applied and the sub-optimal reliance on univariable methods for multivariable problems. The literature review of fisheries science, general biology and medicine identified a large number of alternative methods for dealing with cumulative effects, with a limited number of techniques being used in fish migration studies. An evaluation of the different methods revealed that certain classes of multivariable analyses will probably prove useful in future assessments of cumulative effects on fish migration. This overview and evaluation of quantitative methods gathered from the disparate fields should serve as a primer for anyone seeking to quantify cumulative effects on fish migration survival. © 2012 The Authors. Journal of Fish Biology © 2012 The Fisheries Society of the British Isles.
The classification of secondary colorectal liver cancer in human biopsy samples using angular dispersive x-ray diffraction and multivariate analysis

NASA Astrophysics Data System (ADS)

Theodorakou, Chrysoula; Farquharson, Michael J.

2009-08-01

The motivation behind this study is to assess whether angular dispersive x-ray diffraction (ADXRD) data, processed using multivariate analysis techniques, can be used for classifying secondary colorectal liver cancer tissue and normal surrounding liver tissue in human liver biopsy samples. The ADXRD profiles from a total of 60 samples of normal liver tissue and colorectal liver metastases were measured using a synchrotron radiation source. The data were analysed for 56 samples using nonlinear peak-fitting software. Four peaks were fitted to all of the ADXRD profiles, and the amplitude, area, amplitude and area ratios for three of the four peaks were calculated and used for the statistical and multivariate analysis. The statistical analysis showed that there are significant differences between all the peak-fitting parameters and ratios between the normal and the diseased tissue groups. The technique of soft independent modelling of class analogy (SIMCA) was used to classify normal liver tissue and colorectal liver metastases resulting in 67% of the normal tissue samples and 60% of the secondary colorectal liver tissue samples being classified correctly. This study has shown that the ADXRD data of normal and secondary colorectal liver cancer are statistically different and x-ray diffraction data analysed using multivariate analysis have the potential to be used as a method of tissue classification.
Multivariate analysis of risk factors for long-term urethroplasty outcome.

PubMed

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

PubMed

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

2016-01-01

Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.
Defining critical habitats of threatened and endemic reef fishes with a multivariate approach.

PubMed

Purcell, Steven W; Clarke, K Robert; Rushworth, Kelvin; Dalton, Steven J

2014-12-01

Understanding critical habitats of threatened and endemic animals is essential for mitigating extinction risks, developing recovery plans, and siting reserves, but assessment methods are generally lacking. We evaluated critical habitats of 8 threatened or endemic fish species on coral and rocky reefs of subtropical eastern Australia, by measuring physical and substratum-type variables of habitats at fish sightings. We used nonmetric and metric multidimensional scaling (nMDS, mMDS), Analysis of similarities (ANOSIM), similarity percentages analysis (SIMPER), permutational analysis of multivariate dispersions (PERMDISP), and other multivariate tools to distinguish critical habitats. Niche breadth was widest for 2 endemic wrasses, and reef inclination was important for several species, often found in relatively deep microhabitats. Critical habitats of mainland reef species included small caves or habitat-forming hosts such as gorgonian corals and black coral trees. Hard corals appeared important for reef fishes at Lord Howe Island, and red algae for mainland reef fishes. A wide range of habitat variables are required to assess critical habitats owing to varied affinities of species to different habitat features. We advocate assessments of critical habitats matched to the spatial scale used by the animals and a combination of multivariate methods. Our multivariate approach furnishes a general template for assessing the critical habitats of species, understanding how these vary among species, and determining differences in the degree of habitat specificity. © 2014 Society for Conservation Biology.
Parameters Selection for Bivariate Multiscale Entropy Analysis of Postural Fluctuations in Fallers and Non-Fallers Older Adults.

PubMed

Ramdani, Sofiane; Bonnet, Vincent; Tallon, Guillaume; Lagarde, Julien; Bernard, Pierre Louis; Blain, Hubert

2016-08-01

Entropy measures are often used to quantify the regularity of postural sway time series. Recent methodological developments provided both multivariate and multiscale approaches allowing the extraction of complexity features from physiological signals; see "Dynamical complexity of human responses: A multivariate data-adaptive framework," in Bulletin of Polish Academy of Science and Technology, vol. 60, p. 433, 2012. The resulting entropy measures are good candidates for the analysis of bivariate postural sway signals exhibiting nonstationarity and multiscale properties. These methods are dependant on several input parameters such as embedding parameters. Using two data sets collected from institutionalized frail older adults, we numerically investigate the behavior of a recent multivariate and multiscale entropy estimator; see "Multivariate multiscale entropy: A tool for complexity analysis of multichannel data," Physics Review E, vol. 84, p. 061918, 2011. We propose criteria for the selection of the input parameters. Using these optimal parameters, we statistically compare the multivariate and multiscale entropy values of postural sway data of non-faller subjects to those of fallers. These two groups are discriminated by the resulting measures over multiple time scales. We also demonstrate that the typical parameter settings proposed in the literature lead to entropy measures that do not distinguish the two groups. This last result confirms the importance of the selection of appropriate input parameters.
Longitudinal study of factors affecting taste sense decline in old-old individuals.

PubMed

Ogawa, T; Uota, M; Ikebe, K; Arai, Y; Kamide, K; Gondo, Y; Masui, Y; Ishizaki, T; Inomata, C; Takeshita, H; Mihara, Y; Hatta, K; Maeda, Y

2017-01-01

The sense of taste plays a pivotal role for personal assessment of the nutritional value, safety and quality of foods. Although it is commonly recognised that taste sensitivity decreases with age, alterations in that sensitivity over time in an old-old population have not been previously reported. Furthermore, no known studies utilised comprehensive variables regarding taste changes and related factors for assessments. Here, we report novel findings from a 3-year longitudinal study model aimed to elucidate taste sensitivity decline and its related factors in old-old individuals. We utilised 621 subjects aged 79-81 years who participated in the Septuagenarians, Octogenarians, Nonagenarians Investigation with Centenarians Study for baseline assessments performed in 2011 and 2012, and then conducted follow-up assessments 3 years later in 328 of those. Assessment of general health, an oral examination and determination of taste sensitivity were performed for each. We also evaluated cognitive function using Montreal Cognitive Assessment findings, then excluded from analysis those with a score lower than 20 in order to secure the validity and reliability of the subjects' answers. Contributing variables were selected using univariate analysis, then analysed with multivariate logistic regression analysis. We found that males showed significantly greater declines in taste sensitivity for sweet and sour tastes than females. Additionally, subjects with lower cognitive scores showed a significantly greater taste decrease for salty in multivariate analysis. In conclusion, our longitudinal study revealed that gender and cognitive status are major factors affecting taste sensitivity in geriatric individuals. © 2016 John Wiley & Sons Ltd.
Feasibility of Image-Guided Transthoracic Core Needle Biopsy in the BATTLE Lung Trial

PubMed Central

Tam, Alda L.; Kim, Edward S.; Lee, J. Jack; Ensor, Joe E.; Hicks, Marshall E.; Tang, Ximing; Blumenschein, George R.; Alden, Christine M.; Erasmus, Jeremy J.; Tsao, Anne; Lippman, Scott M.; Hong, Waun K.; Wistuba, Ignacio I.; Gupta, Sanjay

2013-01-01

Purpose As therapy for non-small cell lung cancer (NSCLC) patients becomes more personalized, additional tissue in the form of core needle biopsies (CNBs) for biomarker analysis is increasingly required for determining appropriate treatment and for enrollment into clinical trials. We report our experience with small-caliber percutaneous transthoracic (PT) CNBs for the evaluation of multiple molecular biomarkers in BATTLE (Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination), a personalized, targeted therapy NSCLC clinical trial. Methods The medical records of patients who underwent PTCNB for consideration of enrollment in BATTLE, were reviewed for diagnostic yield of 11 predetermined molecular markers, and procedural complications. Univariate and multivariate analyses of factors related to patient and lesion characteristics were performed to determine possible influences on diagnostic yield. Results One hundred and seventy PTCNBs were performed using 20-gauge biopsy needles in 151 NSCLC patients screened for the trial. 82.9% of the biopsy specimens were found to have adequate tumor tissue for analysis of the required biomarkers. On multivariate analysis, metastatic lesions were 5.4 times more likely to yield diagnostic tissue as compared to primary tumors (p = 0.0079). Pneumothorax and chest tube insertion rates were 15.3% and 9.4%, respectively. Conclusions Image-guided 20-gauge PTCNB is safe and provides adequate tissue for analysis of multiple biomarkers in the majority of patients being considered for enrollment into a personalized, targeted therapy NSCLC clinical trial. Metastatic lesions are more likely to yield diagnostic tissue as compared to primary tumors. PMID:23442309

The Effect of Coffee and Quantity of Consumption on Specific Cardiovascular and All-Cause Mortality: Coffee Consumption Does Not Affect Mortality.

PubMed

Loomba, Rohit S; Aggarwal, Saurabh; Arora, Rohit R

2016-01-01

Previous studies have examined whether or not an association exists between the consumption of caffeinated coffee to all-cause and cardiovascular mortality. This study aimed to delineate this association using population representative data from the National Health and Nutrition Examination Survey III. Patients were included in the study if all the following criteria were met: (1) follow-up mortality data were available, (2) age of at least 45 years, and (3) reported amount of average coffee consumption. A total of 8608 patients were included, with patients stratified into the following groups of average daily coffee consumption: (1) no coffee consumption, (2) less than 1 cup, (3) 1 cup a day, (4) 2-3 cups, (5) 4-5 cups, (6) more than 6 cups a day. Odds ratios, 95% confidence intervals, and P values were calculated for univariate analysis to compare the prevalence of all-cause mortality, ischemia-related mortality, congestive heart failure-related mortality, and stroke-related mortality, using the no coffee consumption group as reference. These were then adjusted for confounding factors for a multivariate analysis. P < 0.05 were considered statistically significant. Univariate analysis demonstrated an association between coffee consumption and mortality, although this became insignificant on multivariate analysis. Coffee consumption, thus, does not seem to impact all-cause mortality or specific cardiovascular mortality. These findings do differ from those of recently published studies. Coffee consumption of any quantity seems to be safe without any increased mortality risk. There may be some protective effects but additional data are needed to further delineate this.
Lack of Thy1 (CD90) expression in neuroblastomas is correlated with impaired survival.

PubMed

Fiegel, Henning C; Kaifi, Jussuf T; Quaas, Alexander; Varol, Emine; Krickhahn, Annika; Metzger, Roman; Sauter, Guido; Till, Holger; Izbicki, Jakob R; Erttmann, Rudolf; Kluth, Dietrich

2008-01-01

Neuroblastoma (NBL) is the most common solid tumor in children. Tumors in advanced stage or with positive risk factors still have a poor prognosis. Thy1 (CD90) is a membrane glycoprotein expressed in thymus, retinal ganglionic cells, and several types of stem cells. The aim of this study was to assess Thy1 expression in NBL and analyze the correlation with clinical outcome. Sixty-three specimens of NBL were stained for Thy1 on a tissue microarray by immunohistochemistry. Fresh frozen tumor tissues were used for RNA isolation, and RT-PCR analysis for Thy1-mRNA expression was performed. Patients' survival data were correlated with Thy1 status using a log rank test and a Cox regression multivariate analysis. Thy1 was expressed on 51 (81%) of the tumors. Kaplan-Meier survival analysis showed a significantly impaired survival in patients with NBL missing Thy1 (P < 0.005 by log-rank test). A multivariate Cox regression showed an independent prognostic value of Thy1 status for overall survival (P < 0.05). In addition, the frequency of events and deaths was significantly higher in the group of patients with Thy1 negative tumors, as assessed by ANOVA analysis (P < 0.05 by F-test). The data showed that Thy1-negative NBL patients have a significantly impaired overall survival compared with Thy1-positive NBL patients. Thus, Thy1 seemed to be a marker with a specific prognostic value in NBL patients. Future studies are aiming at the biological role of this marker in the tumor cell differentiation.
Pulmonary metastasectomy in colorectal cancer patients with previously resected liver metastasis: pooled analysis.

PubMed

Salah, Samer; Ardissone, Francesco; Gonzalez, Michel; Gervaz, Pascal; Riquet, Marc; Watanabe, Kazuhiro; Zabaleta, Jon; Al-Rimawi, Dalia; Toubasi, Samar; Massad, Ehab; Lisi, Elena; Hamed, Osama H

2015-01-01

Data addressing the outcomes and patterns of recurrence after pulmonary metastasectomy (PM) in patients with colorectal cancer (CRC) and previously resected liver metastasis are limited. We searched the PubMed database for studies assessing PM in CRC and gathered individual data for patients who had PM and a previous curative liver resection. The influence of potential factors on overall survival (OS) was analyzed through univariate and multivariate analysis. Between 1983 and 2009, 146 patients from five studies underwent PM and had previous liver resection. The median interval from resection of liver metastasis until detection of lung metastasis and the median follow-up from PM were 23 and 48 months, respectively. Five-year OS and recurrence-free survival rates calculated from the date of PM were 54.4 and 29.3 %, respectively. Factors predicting inferior OS in univariate analysis included thoracic lymph node (LN) involvement and size of largest lung nodule ≥2 cm. Adjuvant chemotherapy and whether lung metastasis was detected synchronous or metachronous to liver metastasis had no influence on survival. In multivariate analysis, thoracic LN involvement emerged as the only independent factor (hazard ratio 4.86, 95 % confidence interval 1.56-15.14, p = 0.006). PM offers a chance for long-term survival in selected patients with CRC and previously resected liver metastasis. Thoracic LN involvement predicted poor prognosis; therefore, significant efforts should be undertaken for adequate staging of the mediastinum before PM. In addition, adequate intraoperative LN sampling allows proper prognostic stratification and enrollment in novel adjuvant therapy trials.
Factors Influencing Relapse-Free Survival in Merkel Cell Carcinoma of the Lower Limb-A Review of 60 Cases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Poulsen, Michael, E-mail: michael_poulsen@health.qld.gov.a; Round, Caroline; Keller, Jacqui

2010-02-01

Purpose: Factors affecting relapse-free survival (RFS) in patients with Merkel cell carcinoma (MCC) of the lower limb were reviewed. Methods and Materials: The records of 60 patients from 1986 to 2005 with a diagnosis of MCC of the lower limb or buttock were retrospectively reviewed. The patients were treated with curative intent with surgery, radiation, or chemotherapy. Results: The 5-year overall survival, disease-specific survival, and RFS were 53%, 61%, and 20%, respectively. Factors influencing RFS were analyzed using univariate analysis. It appeared that recurrent disease worsened RFS (p = 0.03) and the addition of any radiotherapy improved RFS (p <0.001),more » as did radiotherapy to the inguinal nodes (p = 0.01) or primary site and inguinal nodes (p = 0.003). Age, surgical margins, and stage were not statistically significant. On multivariate analysis, the only significant factor was the addition of radiotherapy (hazard ratio = 0.51 p = 0.03). Conclusion: The addition of radiotherapy improves RFS compared with surgery alone. Elective treatment should be given to the inguinal nodes to reduce the risk of relapse.« less
Nontargeted, Rapid Screening of Extra Virgin Olive Oil Products for Authenticity Using Near-Infrared Spectroscopy in Combination with Conformity Index and Multivariate Statistical Analyses.

PubMed

Karunathilaka, Sanjeewa R; Kia, Ali-Reza Fardin; Srigley, Cynthia; Chung, Jin Kyu; Mossoba, Magdi M

2016-10-01

A rapid tool for evaluating authenticity was developed and applied to the screening of extra virgin olive oil (EVOO) retail products by using Fourier-transform near infrared (FT-NIR) spectroscopy in combination with univariate and multivariate data analysis methods. Using disposable glass tubes, spectra for 62 reference EVOO, 10 edible oil adulterants, 20 blends consisting of EVOO spiked with adulterants, 88 retail EVOO products and other test samples were rapidly measured in the transmission mode without any sample preparation. The univariate conformity index (CI) and the multivariate supervised soft independent modeling of class analogy (SIMCA) classification tool were used to analyze the various olive oil products which were tested for authenticity against a library of reference EVOO. Better discrimination between the authentic EVOO and some commercial EVOO products was observed with SIMCA than with CI analysis. Approximately 61% of all EVOO commercial products were flagged by SIMCA analysis, suggesting that further analysis be performed to identify quality issues and/or potential adulterants. Due to its simplicity and speed, FT-NIR spectroscopy in combination with multivariate data analysis can be used as a complementary tool to conventional official methods of analysis to rapidly flag EVOO products that may not belong to the class of authentic EVOO. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.
Metric Selection for Evaluation of Human Supervisory Control Systems

DTIC Science & Technology

2009-12-01

finding a significant effect when there is none becomes more likely. The inflation of type I error due to multiple dependent variables can be handled...with multivariate analysis techniques, such as Multivariate Analysis of Variance (MANOVA) (Johnson & Wichern, 2002). However, it should be noted that...the few significant differences among many insignificant ones. The best way to avoid failure to identify significant differences is to design an
A Civilian/Military Trauma Institute: National Trauma Coordinating Center

DTIC Science & Technology

2015-12-01

zip codes was used in “proximity to violence” analysis. Data were analyzed using SPSS (version 20.0, SPSS Inc., Chicago, IL). Multivariable linear...number of adverse events and serious events was not statistically higher in one group, the incidence of deep venous thrombosis (DVT) was statistically ...subjects the lack of statistical difference on multivariate analysis may be related to an underpowered sample size. It was recommended that the
Exploratory Multivariate Analysis. A Graphical Approach.

DTIC Science & Technology

1981-01-01

Gnanadesikan , 1977) but we feel that these should be used with great caution unless one really has good reason to believe that the data came from such a...are referred to Gnanadesikan (1977). The present author hopes that the convenience of a single summary or significance level will not deter his readers...fit of a harmonic model to meteorological data. (In preparation). Gnanadesikan , R. (1977). Methods for Statistical Data Analysis of Multivariate
The Covariance Adjustment Approaches for Combining Incomparable Cox Regressions Caused by Unbalanced Covariates Adjustment: A Multivariate Meta-Analysis Study.

PubMed

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.
The Fourier decomposition method for nonlinear and non-stationary time series analysis.

PubMed

Singh, Pushpendra; Joshi, Shiv Dutt; Patney, Rakesh Kumar; Saha, Kaushik

2017-03-01

for many decades, there has been a general perception in the literature that Fourier methods are not suitable for the analysis of nonlinear and non-stationary data. In this paper, we propose a novel and adaptive Fourier decomposition method (FDM), based on the Fourier theory, and demonstrate its efficacy for the analysis of nonlinear and non-stationary time series. The proposed FDM decomposes any data into a small number of 'Fourier intrinsic band functions' (FIBFs). The FDM presents a generalized Fourier expansion with variable amplitudes and variable frequencies of a time series by the Fourier method itself. We propose an idea of zero-phase filter bank-based multivariate FDM (MFDM), for the analysis of multivariate nonlinear and non-stationary time series, using the FDM. We also present an algorithm to obtain cut-off frequencies for MFDM. The proposed MFDM generates a finite number of band-limited multivariate FIBFs (MFIBFs). The MFDM preserves some intrinsic physical properties of the multivariate data, such as scale alignment, trend and instantaneous frequency. The proposed methods provide a time-frequency-energy (TFE) distribution that reveals the intrinsic structure of a data. Numerical computations and simulations have been carried out and comparison is made with the empirical mode decomposition algorithms.
The Fourier decomposition method for nonlinear and non-stationary time series analysis

PubMed Central

Joshi, Shiv Dutt; Patney, Rakesh Kumar; Saha, Kaushik

2017-01-01

for many decades, there has been a general perception in the literature that Fourier methods are not suitable for the analysis of nonlinear and non-stationary data. In this paper, we propose a novel and adaptive Fourier decomposition method (FDM), based on the Fourier theory, and demonstrate its efficacy for the analysis of nonlinear and non-stationary time series. The proposed FDM decomposes any data into a small number of ‘Fourier intrinsic band functions’ (FIBFs). The FDM presents a generalized Fourier expansion with variable amplitudes and variable frequencies of a time series by the Fourier method itself. We propose an idea of zero-phase filter bank-based multivariate FDM (MFDM), for the analysis of multivariate nonlinear and non-stationary time series, using the FDM. We also present an algorithm to obtain cut-off frequencies for MFDM. The proposed MFDM generates a finite number of band-limited multivariate FIBFs (MFIBFs). The MFDM preserves some intrinsic physical properties of the multivariate data, such as scale alignment, trend and instantaneous frequency. The proposed methods provide a time–frequency–energy (TFE) distribution that reveals the intrinsic structure of a data. Numerical computations and simulations have been carried out and comparison is made with the empirical mode decomposition algorithms. PMID:28413352
New robust bilinear least squares method for the analysis of spectral-pH matrix data.

PubMed

Goicoechea, Héctor C; Olivieri, Alejandro C

2005-07-01

A new second-order multivariate method has been developed for the analysis of spectral-pH matrix data, based on a bilinear least-squares (BLLS) model achieving the second-order advantage and handling multiple calibration standards. A simulated Monte Carlo study of synthetic absorbance-pH data allowed comparison of the newly proposed BLLS methodology with constrained parallel factor analysis (PARAFAC) and with the combination multivariate curve resolution-alternating least-squares (MCR-ALS) technique under different conditions of sample-to-sample pH mismatch and analyte-background ratio. The results indicate an improved prediction ability for the new method. Experimental data generated by measuring absorption spectra of several calibration standards of ascorbic acid and samples of orange juice were subjected to second-order calibration analysis with PARAFAC, MCR-ALS, and the new BLLS method. The results indicate that the latter method provides the best analytical results in regard to analyte recovery in samples of complex composition requiring strict adherence to the second-order advantage. Linear dependencies appear when multivariate data are produced by using the pH or a reaction time as one of the data dimensions, posing a challenge to classical multivariate calibration models. The presently discussed algorithm is useful for these latter systems.
Multivariate evaluation of Thyroid Imaging Reporting and Data System (TI-RADS) in diagnosis malignant thyroid nodule: application to PCA and PLS-DA analysis.

PubMed

Zhang, Tan; Li, Fangxuan; Mu, Jiali; Liu, Juntian; Zhang, Sheng

2017-06-01

To explore the significance of ultrasonic features in differential diagnosis of thyroid nodules via combining the thyroid imaging reporting and data system (TI-RADS) and multivariate statistical analysis. Patients who received surgical treatment and was diagnosed with single thyroid nodule by postoperative pathology and preoperative ultrasound were enrolled in this study. Multivariate analysis was applied to assess the significant ultrasonic features which correlated with identifying benign or malignance and grading the TI-RADS classification of thyroid nodule. There were significant differences in the nodule size, aspect ratio, internal, echogenicity, boundary, presence or absence of calcifications, calcification type and CDFI between benign and malignant thyroid nodules. Multivariate analysis showed clear-cut distinction both between benign and malignance and among different TI-RADS categories of malignancy nodules. The shape and calcification of the nodule were important factors for distinguish the benign and malignance. Height of the nodule, aspect and calcification was important factors for grading TI-RADS categories of malignancy thyroid nodules. Ill-defined boundary, irregular shape and presence of calcification related with highly malignant risk for thyroid nodule. The larger height and aspect and presence of calcification related with higher TI-RADS classification of malignancy thyroid nodule.
Meta-analysis identifies gene-by-environment interactions as demonstrated in a study of 4,965 mice.

PubMed

Kang, Eun Yong; Han, Buhm; Furlotte, Nicholas; Joo, Jong Wha J; Shih, Diana; Davis, Richard C; Lusis, Aldons J; Eskin, Eleazar

2014-01-01

Identifying environmentally-specific genetic effects is a key challenge in understanding the structure of complex traits. Model organisms play a crucial role in the identification of such gene-by-environment interactions, as a result of the unique ability to observe genetically similar individuals across multiple distinct environments. Many model organism studies examine the same traits but under varying environmental conditions. For example, knock-out or diet-controlled studies are often used to examine cholesterol in mice. These studies, when examined in aggregate, provide an opportunity to identify genomic loci exhibiting environmentally-dependent effects. However, the straightforward application of traditional methodologies to aggregate separate studies suffers from several problems. First, environmental conditions are often variable and do not fit the standard univariate model for interactions. Additionally, applying a multivariate model results in increased degrees of freedom and low statistical power. In this paper, we jointly analyze multiple studies with varying environmental conditions using a meta-analytic approach based on a random effects model to identify loci involved in gene-by-environment interactions. Our approach is motivated by the observation that methods for discovering gene-by-environment interactions are closely related to random effects models for meta-analysis. We show that interactions can be interpreted as heterogeneity and can be detected without utilizing the traditional uni- or multi-variate approaches for discovery of gene-by-environment interactions. We apply our new method to combine 17 mouse studies containing in aggregate 4,965 distinct animals. We identify 26 significant loci involved in High-density lipoprotein (HDL) cholesterol, many of which are consistent with previous findings. Several of these loci show significant evidence of involvement in gene-by-environment interactions. An additional advantage of our meta-analysis approach is that our combined study has significantly higher power and improved resolution compared to any single study thus explaining the large number of loci discovered in the combined study.
Meta-Analysis Identifies Gene-by-Environment Interactions as Demonstrated in a Study of 4,965 Mice

PubMed Central

Joo, Jong Wha J.; Shih, Diana; Davis, Richard C.; Lusis, Aldons J.; Eskin, Eleazar

2014-01-01

Identifying environmentally-specific genetic effects is a key challenge in understanding the structure of complex traits. Model organisms play a crucial role in the identification of such gene-by-environment interactions, as a result of the unique ability to observe genetically similar individuals across multiple distinct environments. Many model organism studies examine the same traits but under varying environmental conditions. For example, knock-out or diet-controlled studies are often used to examine cholesterol in mice. These studies, when examined in aggregate, provide an opportunity to identify genomic loci exhibiting environmentally-dependent effects. However, the straightforward application of traditional methodologies to aggregate separate studies suffers from several problems. First, environmental conditions are often variable and do not fit the standard univariate model for interactions. Additionally, applying a multivariate model results in increased degrees of freedom and low statistical power. In this paper, we jointly analyze multiple studies with varying environmental conditions using a meta-analytic approach based on a random effects model to identify loci involved in gene-by-environment interactions. Our approach is motivated by the observation that methods for discovering gene-by-environment interactions are closely related to random effects models for meta-analysis. We show that interactions can be interpreted as heterogeneity and can be detected without utilizing the traditional uni- or multi-variate approaches for discovery of gene-by-environment interactions. We apply our new method to combine 17 mouse studies containing in aggregate 4,965 distinct animals. We identify 26 significant loci involved in High-density lipoprotein (HDL) cholesterol, many of which are consistent with previous findings. Several of these loci show significant evidence of involvement in gene-by-environment interactions. An additional advantage of our meta-analysis approach is that our combined study has significantly higher power and improved resolution compared to any single study thus explaining the large number of loci discovered in the combined study. PMID:24415945
Semiparametric Thurstonian Models for Recurrent Choices: A Bayesian Analysis

ERIC Educational Resources Information Center

Ansari, Asim; Iyengar, Raghuram

2006-01-01

We develop semiparametric Bayesian Thurstonian models for analyzing repeated choice decisions involving multinomial, multivariate binary or multivariate ordinal data. Our modeling framework has multiple components that together yield considerable flexibility in modeling preference utilities, cross-sectional heterogeneity and parameter-driven…
The use of multivariate statistics in studies of wildlife habitat

Treesearch

David E. Capen

1981-01-01

This report contains edited and reviewed versions of papers presented at a workshop held at the University of Vermont in April 1980. Topics include sampling avian habitats, multivariate methods, applications, examples, and new approaches to analysis and interpretation.
Rejection of Multivariate Outliers.

DTIC Science & Technology

1983-05-01

available in Gnanadesikan (1977). 2 The motivation for the present investigation lies in a recent paper of Schvager and Margolin (1982) who derive a... Gnanadesikan , R. (1977). Methods for Statistical Data Analysis of Multivariate Observations. Wiley, New York. [7] Hawkins, D.M. (1980). Identification of
Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis.

PubMed

Xia, Jianguo; Wishart, David S

2016-09-07

MetaboAnalyst (http://www.metaboanalyst.ca) is a comprehensive Web application for metabolomic data analysis and interpretation. MetaboAnalyst handles most of the common metabolomic data types from most kinds of metabolomics platforms (MS and NMR) for most kinds of metabolomics experiments (targeted, untargeted, quantitative). In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst also supports a number of data analysis and data visualization tasks using a range of univariate, multivariate methods such as PCA (principal component analysis), PLS-DA (partial least squares discriminant analysis), heatmap clustering and machine learning methods. MetaboAnalyst also offers a variety of tools for metabolomic data interpretation including MSEA (metabolite set enrichment analysis), MetPA (metabolite pathway analysis), and biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. This unit provides an overview of the main functional modules and the general workflow of the latest version of MetaboAnalyst (MetaboAnalyst 3.0), followed by eight detailed protocols. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.
Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment.

PubMed

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

2013-01-01

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

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