Functional Parallel Factor Analysis for Functions of One- and Two-dimensional Arguments.

PubMed

Choi, Ji Yeh; Hwang, Heungsun; Timmerman, Marieke E

2018-03-01

Parallel factor analysis (PARAFAC) is a useful multivariate method for decomposing three-way data that consist of three different types of entities simultaneously. This method estimates trilinear components, each of which is a low-dimensional representation of a set of entities, often called a mode, to explain the maximum variance of the data. Functional PARAFAC permits the entities in different modes to be smooth functions or curves, varying over a continuum, rather than a collection of unconnected responses. The existing functional PARAFAC methods handle functions of a one-dimensional argument (e.g., time) only. In this paper, we propose a new extension of functional PARAFAC for handling three-way data whose responses are sequenced along both a two-dimensional domain (e.g., a plane with x- and y-axis coordinates) and a one-dimensional argument. Technically, the proposed method combines PARAFAC with basis function expansion approximations, using a set of piecewise quadratic finite element basis functions for estimating two-dimensional smooth functions and a set of one-dimensional basis functions for estimating one-dimensional smooth functions. In a simulation study, the proposed method appeared to outperform the conventional PARAFAC. We apply the method to EEG data to demonstrate its empirical usefulness.

Differential use of fresh water environments by wintering waterfowl of coastal Texas

USGS Publications Warehouse

White, D.H.; James, D.

1978-01-01

A comparative study of the environmental relationships among 14 species of wintering waterfowl was conducted at the Welder Wildlife Foundation, San Patricia County, near Sinton, Texas during the fall and early winter of 1973. Measurements of 20 environmental factors (social, vegetational, physical, and chemical) were subjected to multivariate statistical methods to determine certain niche characteristics and environmental relationships of waterfowl wintering in the aquatic community.....Each waterfowl species occupied a unique realized niche by responding to distinct combinations of environmental factors identified by principal component analysis. One percent confidence ellipses circumscribing the mean scores plotted for the first and second principal components gave an indication of relative niche width for each species. The waterfowl environments were significantly different interspecifically and water depth at feeding site and % emergent vegetation were most important in the separation. This was shown by subjecting the transformed data to multivariate analysis of variance with an associated step-down procedure. The species were distributed along a community cline extending from shallow water with abundant emergent vegetation to open deep water with little emergent vegetation of any kind. Four waterfowl subgroups were significantly separated along the cline, as indicated by one-way analysis of variance with Duncan?s multiple range test. Clumping of the bird species toward the middle of the available habitat hyperspace was shown in a plot of the principal component scores for the random samples and individual species.....Naturally occurring relationships among waterfowl were clarified using principal comcomponent analysis and related multivariate procedures. These techniques may prove useful in wetland management for particular groups of waterfowl based on habitat preferences.

Identification and quantification of ciprofloxacin in urine through excitation-emission fluorescence and three-way PARAFAC calibration.

PubMed

Ortiz, M C; Sarabia, L A; Sánchez, M S; Giménez, D

2009-05-29

Due to the second-order advantage, calibration models based on parallel factor analysis (PARAFAC) decomposition of three-way data are becoming important in routine analysis. This work studies the possibility of fitting PARAFAC models with excitation-emission fluorescence data for the determination of ciprofloxacin in human urine. The finally chosen PARAFAC decomposition is built with calibration samples spiked with ciprofloxacin, and with other series of urine samples that were also spiked. One of the series of samples has also another drug because the patient was taking mesalazine. The mesalazine is a fluorescent substance that interferes with the ciprofloxacin. Finally, the procedure is applied to samples of a patient who was being treated with ciprofloxacin. The trueness has been established by the regression "predicted concentration versus added concentration". The recovery factor is 88.3% for ciprofloxacin in urine, and the mean of the absolute value of the relative errors is 4.2% for 46 test samples. The multivariate sensitivity of the fit calibration model is evaluated by a regression between the loadings of PARAFAC linked to ciprofloxacin versus the true concentration in spiked samples. The multivariate capability of discrimination is near 8 microg L(-1) when the probabilities of false non-compliance and false compliance are fixed at 5%.

Mapping the Strategic Thinking of Public Relations Managers in a Crisis Situation: An Illustrative Example Using Conjoint Analysis.

ERIC Educational Resources Information Center

Bronn, Peggy Simcic; Olson, Erik L.

1999-01-01

Illustrates the operationalization of the conjoint analysis multivariate technique for the study of the public relations function within strategic decision making in a crisis situation. Finds that what the theory describes as the strategic way of handling a crisis is also the way each of the managers who were evaluated would prefer to conduct…

Multivariate Tensor-based Morphometry on Surfaces: Application to Mapping Ventricular Abnormalities in HIV/AIDS

PubMed Central

Wang, Yalin; Zhang, Jie; Gutman, Boris; Chan, Tony F.; Becker, James T.; Aizenstein, Howard J.; Lopez, Oscar L.; Tamburo, Robert J.; Toga, Arthur W.; Thompson, Paul M.

2010-01-01

Here we developed a new method, called multivariate tensor-based surface morphometry (TBM), and applied it to study lateral ventricular surface differences associated with HIV/AIDS. Using concepts from differential geometry and the theory of differential forms, we created mathematical structures known as holomorphic one-forms, to obtain an efficient and accurate conformal parameterization of the lateral ventricular surfaces in the brain. The new meshing approach also provides a natural way to register anatomical surfaces across subjects, and improves on prior methods as it handles surfaces that branch and join at complex 3D junctions. To analyze anatomical differences, we computed new statistics from the Riemannian surface metrics - these retain multivariate information on local surface geometry. We applied this framework to analyze lateral ventricular surface morphometry in 3D MRI data from 11 subjects with HIV/AIDS and 8 healthy controls. Our method detected a 3D profile of surface abnormalities even in this small sample. Multivariate statistics on the local tensors gave better effect sizes for detecting group differences, relative to other TBM-based methods including analysis of the Jacobian determinant, the largest and smallest eigenvalues of the surface metric, and the pair of eigenvalues of the Jacobian matrix. The resulting analysis pipeline may improve the power of surface-based morphometry studies of the brain. PMID:19900560

MANCOVA for one way classification with homogeneity of regression coefficient vectors

NASA Astrophysics Data System (ADS)

Mokesh Rayalu, G.; Ravisankar, J.; Mythili, G. Y.

2017-11-01

The MANOVA and MANCOVA are the extensions of the univariate ANOVA and ANCOVA techniques to multidimensional or vector valued observations. The assumption of a Gaussian distribution has been replaced with the Multivariate Gaussian distribution for the vectors data and residual term variables in the statistical models of these techniques. The objective of MANCOVA is to determine if there are statistically reliable mean differences that can be demonstrated between groups later modifying the newly created variable. When randomization assignment of samples or subjects to groups is not possible, multivariate analysis of covariance (MANCOVA) provides statistical matching of groups by adjusting dependent variables as if all subjects scored the same on the covariates. In this research article, an extension has been made to the MANCOVA technique with more number of covariates and homogeneity of regression coefficient vectors is also tested.

Exploring the effects of tape-recording on personality assessment.

PubMed

Lichton, A I; Waehler, C A

1999-06-01

This study examined the possible influence of audio and video recording of personality assessment measures on anxiety. Undergraduate students in psychology were randomly assigned to Audiotape, Videotape, or Control conditions and given the State-Trait Anxiety Inventory and Rorschach Inkblot Method. A one-way multivariate analysis of variance indicated no significant differences among these conditions on the Spielberger, et al. State-Trait Anxiety Inventory, A-State scale, and five Rorschach measures of situational anxiety. Tape-recording itself did not seem to affect the anxiety indices of these frequently used personality assessments.

Four factors underlying mouse behavior in an open field

PubMed Central

Tanaka, Shoji; Young, Jared W.; Halberstadt, Adam L.; Masten, Virginia L.; Geyer, Mark A.

2012-01-01

The observation of the locomotor and exploratory behaviors of rodents in an open field is one of the most fundamental methods used in the field of behavioral pharmacology. A variety of behaviors can be recorded automatically and can readily generate a multivariate pattern of pharmacological effects. Nevertheless, the optimal ways to characterize observed behaviors and concomitant drug effects are still under development. The aim of this study was to extract meaningful behavioral factors that could explain variations in the observed variables from mouse exploration. Behavioral data were recorded from male C57BL/6J mice (n = 268) using the Behavioral Pattern Monitor (BPM). The BPM data were subjected to the exploratory factor analysis. The factor analysis extracted four factors: activity, sequential organization, diversive exploration, and inspective exploration. The activity factor and the two types of exploration factors correlated positively with one another, while the sequential organization factor negatively correlated with the remaining factors. The extracted factor structure constitutes a behavioral model of mouse exploration. This model will provide a platform on which one can assess the effects of psychoactive drugs and genetic manipulations on mouse exploratory behavior. Further studies are currently underway to examine the factor structure of similar multivariate data sets from humans tested in a human BPM. PMID:22569582

Four factors underlying mouse behavior in an open field.

PubMed

Tanaka, Shoji; Young, Jared W; Halberstadt, Adam L; Masten, Virginia L; Geyer, Mark A

2012-07-15

The observation of the locomotor and exploratory behaviors of rodents in an open field is one of the most fundamental methods used in the field of behavioral pharmacology. A variety of behaviors can be recorded automatically and can readily generate a multivariate pattern of pharmacological effects. Nevertheless, the optimal ways to characterize observed behaviors and concomitant drug effects are still under development. The aim of this study was to extract meaningful behavioral factors that could explain variations in the observed variables from mouse exploration. Behavioral data were recorded from male C57BL/6J mice (n=268) using the Behavioral Pattern Monitor (BPM). The BPM data were subjected to the exploratory factor analysis. The factor analysis extracted four factors: activity, sequential organization, diversive exploration, and inspective exploration. The activity factor and the two types of exploration factors correlated positively with one another, while the sequential organization factor negatively correlated with the remaining factors. The extracted factor structure constitutes a behavioral model of mouse exploration. This model will provide a platform on which one can assess the effects of psychoactive drugs and genetic manipulations on mouse exploratory behavior. Further studies are currently underway to examine the factor structure of similar multivariate data sets from humans tested in a human BPM. Copyright © 2012 Elsevier B.V. All rights reserved.

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.

Physical activity in Black breast cancer survivors: implications for quality of life and mood at baseline and 6-month follow-up.

PubMed

Diggins, Allyson D; Hearn, Lauren E; Lechner, Suzanne C; Annane, Debra; Antoni, Michael H; Whitehead, Nicole Ennis

2017-06-01

The present study sought to examine the influence of physical activity on quality of life and negative mood in a sample of Black breast cancer survivors to determine if physical activity (dichotomized) predicted mean differences in negative mood and quality of life in this population. Study participants include 114 women diagnosed with breast cancer (any stage of disease, any type of breast cancer) recruited to participate in an adaptive cognitive-behavioral stress management intervention. The mean body mass index of the sample at baseline was 31.39 (standard deviation = 7.17). A multivariate analysis of covariance (MANCOVA) was conducted to determine if baseline physical activity predicted mean differences in negative mood and quality of life at baseline and at follow ups while controlling for relevant covariates. A one-way MANCOVA revealed a significant multivariate effect by physical activity group for the combined dependent variables at Time 2 (post 10-week intervention), p = .039. The second one-way MANCOVA revealed a significant multivariate effect at Time 3 (6 months after Time 2), p = .034. Specifically, Black breast cancer survivors who engaged in physical activity experienced significantly lower negative mood and higher social/family well-being at Time 2 and higher spiritual and functional well-being at Times 2 and 3. Results show that baseline physical activity served protective functions for breast cancer survivors over time. Developing culturally relevant physical activity interventions specifically for Black breast cancer survivors may prove vital to improving quality of life and mood in this population. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

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…

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

Back pain prevalence and associated factors in children and adolescents: an epidemiological population study

PubMed Central

Noll, Matias; Candotti, Cláudia Tarragô; da Rosa, Bruna Nichele; Loss, Jefferson Fagundes

2016-01-01

ABSTRACT OBJECTIVE To identify the prevalence of back pain among Brazilian school children and the factors associated with this pain. METHODS All 1,720 schoolchildren from the fifth to the eight grade attending schools from the city of Teutonia, RS, Southern Brazil, were invited to participate in the study. From these, 1,597 children participated. We applied the Back Pain and Body Posture Evaluation Instrument. The dependent variable was back pain, while the independent one were demographic, socioeconomic, behavior and heredity data. The prevalence ratio was estimated by multivariate analysis using the Poisson regression model (α = 0.05). RESULTS The prevalence of back pain in the last three months was 55.7% (n = 802). The multivariate analysis showed that back pain is associated with the variables: sex, parents with back pain, weekly frequency of physical activity, daily time spent watching television, studying in bed, sitting posture to write and use the computer, and way of carrying the backpack. CONCLUSIONS The prevalence of back pain in schoolchildren is high and it is associated with demographic, behavior and heredity aspects. PMID:27305406

Multivariate analysis of infant death in England and Wales in 2005-06, with focus on socio-economic status and deprivation.

PubMed

Oakley, Laura; Maconochie, Noreen; Doyle, Pat; Dattani, Nirupa; Moser, Kath

2009-01-01

Current health inequality targets include the goal of reducing the differential in infant mortality between social groups. This article reports on a multivariate analysis of risk factors for infant mortality, with specific focus on deprivation and socio-economic status. Data on all singleton live births in England and Wales in 2005-06 were used, and deprivation quintile (Carstairs index) was assigned to each birth using postcode at birth registration. Deprivation had a strong independent effect on infant mortality, risk of death tending to increase with increasing levels of deprivation. The strength of this relationship depended, however, on whether the babies were low birthweight, preterm or small-for-gestational-age. Trends of increasing mortality risk with increasing deprivation were strongest in the postneonatal period. Uniquely, this article reports the number and proportion of all infant deaths which would potentially be avoided if all levels of deprivation were reduced to that of the least deprived group. It estimates that one quarter of all infant deaths would potentially be avoided if deprivation levels were reduced in this way.

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.

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.

Arsenic health risk assessment in drinking water and source apportionment using multivariate statistical techniques in Kohistan region, northern Pakistan.

PubMed

Muhammad, Said; Tahir Shah, M; Khan, Sardar

2010-10-01

The present study was conducted in Kohistan region, where mafic and ultramafic rocks (Kohistan island arc and Indus suture zone) and metasedimentary rocks (Indian plate) are exposed. Water samples were collected from the springs, streams and Indus river and analyzed for physical parameters, anions, cations and arsenic (As(3+), As(5+) and arsenic total). The water quality in Kohistan region was evaluated by comparing the physio-chemical parameters with permissible limits set by Pakistan environmental protection agency and world health organization. Most of the studied parameters were found within their respective permissible limits. However in some samples, the iron and arsenic concentrations exceeded their permissible limits. For health risk assessment of arsenic, the average daily dose, hazards quotient (HQ) and cancer risk were calculated by using statistical formulas. The values of HQ were found >1 in the samples collected from Jabba, Dubair, while HQ values were <1 in rest of the samples. This level of contamination should have low chronic risk and medium cancer risk when compared with US EPA guidelines. Furthermore, the inter-dependence of physio-chemical parameters and pollution load was also calculated by using multivariate statistical techniques like one-way ANOVA, correlation analysis, regression analysis, cluster analysis and principle component analysis. Copyright © 2010 Elsevier Ltd. All rights reserved.

Better Crunching: Recommendations for Multivariate Data Analysis Approaches for Program Impact Evaluations

ERIC Educational Resources Information Center

Braverman, Marc T.

2016-01-01

Extension program evaluations often present opportunities to analyze data in multiple ways. This article suggests that program evaluations can involve more sophisticated data analysis approaches than are often used. On the basis of a hypothetical program scenario and corresponding data set, two approaches to testing for evidence of program impact…

Multivariate Analysis of Fruit Antioxidant Activities of Blackberry Treated with 1-Methylcyclopropene or Vacuum Precooling

PubMed Central

Li, Jian; Ma, Guowei; Ma, Lin; Bao, Xiaolin; Li, Liping; Zhao, Qian

2018-01-01

Effects of 1-methylcyclopropene (1-MCP) and vacuum precooling on quality and antioxidant properties of blackberries (Rubus spp.) were evaluated using one-way analysis of variance, principal component analysis (PCA), partial least squares (PLS), and path analysis. Results showed that the activities of antioxidant enzymes were enhanced by both 1-MCP treatment and vacuum precooling. PCA could discriminate 1-MCP treated fruit and the vacuum precooled fruit and showed that the radical-scavenging activities in vacuum precooled fruit were higher than those in 1-MCP treated fruit. The scores of PCA showed that H2O2 content was the most important variables of blackberry fruit. PLSR results showed that peroxidase (POD) activity negatively correlated with H2O2 content. The results of path coefficient analysis indicated that glutathione (GSH) also had an indirect effect on H2O2 content. PMID:29487622

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.

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

NASA Astrophysics Data System (ADS)

Azami, Hamed; Escudero, Javier

2017-01-01

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

The effect of single and repeated UVB radiation on rabbit cornea.

PubMed

Fris, Miroslav; Tessem, May-Britt; Cejková, Jitka; Midelfart, Anna

2006-12-01

Cumulative effect of ultraviolet radiation (UVR) is an important aspect of UV corneal damage. The purpose of this study was to apply high resolution magic angle spinning proton nuclear magnetic resonance (HR-MAS 1H NMR) spectroscopy to evaluate the effect of single and repeated UV radiation exposure of the same overall dose on the rabbit cornea. Corneal surfaces of 24 normal rabbit eyes were examined for the effects of UVB exposure (312 nm). In the first group (UVB1), animals were irradiated with a single dose (3.12 J/cm2; 21 min) of UVB radiation. The animals in the second group (UVB2) were irradiated three times for 7 min every other day (dose of 1.04 J/cm2; days 1, 3, 5) to give the same overall dose (3.12 J/cm2). The third group served as an untreated control group. One day after the last irradiation, the animals were sacrificed, and the corneas were removed and frozen. HR-MAS 1H NMR spectra from intact corneas were obtained. Special grouping patterns among the tissue samples and the relative percentage changes in particular metabolite concentrations were evaluated using modern statistical methods (multivariate analysis, one-way ANOVA). The metabolic profile of both groups of UVB-irradiated samples was significantly different from the control corneas. Substantial decreases in taurine, hypo-taurine and choline-derivatives concentrations and substantial elevation in glucose and betaine levels were observed following the UVR exposure. There was no significant difference between the effect of a single and repeated UVB irradiation of the same overall dose. For the first time, the effects of single and repeated UVR doses on the metabolic profile of the rabbit cornea were analysed and compared. The combination of HR-MAS 1H NMR spectroscopy and modern statistical methods (multivariate analysis, one-way ANOVA) proved suitable to assess the overall view of the metabolic alterations in the rabbit corneal tissue following UVB radiation exposure.

Multivariate research in areas of phosphorus cast-iron brake shoes manufacturing using the statistical analysis and the multiple regression equations

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.

2017-05-01

The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for maximal response. For the calculation of the regression coefficients, dispersion and correlation coefficients, the software Matlab was used.

Global spectral graph wavelet signature for surface analysis of carpal bones

NASA Astrophysics Data System (ADS)

Masoumi, Majid; Rezaei, Mahsa; Ben Hamza, A.

2018-02-01

Quantitative shape comparison is a fundamental problem in computer vision, geometry processing and medical imaging. In this paper, we present a spectral graph wavelet approach for shape analysis of carpal bones of the human wrist. We employ spectral graph wavelets to represent the cortical surface of a carpal bone via the spectral geometric analysis of the Laplace-Beltrami operator in the discrete domain. We propose global spectral graph wavelet (GSGW) descriptor that is isometric invariant, efficient to compute, and combines the advantages of both low-pass and band-pass filters. We perform experiments on shapes of the carpal bones of ten women and ten men from a publicly-available database of wrist bones. Using one-way multivariate analysis of variance (MANOVA) and permutation testing, we show through extensive experiments that the proposed GSGW framework gives a much better performance compared to the global point signature embedding approach for comparing shapes of the carpal bones across populations.

Global spectral graph wavelet signature for surface analysis of carpal bones.

PubMed

Masoumi, Majid; Rezaei, Mahsa; Ben Hamza, A

2018-02-05

Quantitative shape comparison is a fundamental problem in computer vision, geometry processing and medical imaging. In this paper, we present a spectral graph wavelet approach for shape analysis of carpal bones of the human wrist. We employ spectral graph wavelets to represent the cortical surface of a carpal bone via the spectral geometric analysis of the Laplace-Beltrami operator in the discrete domain. We propose global spectral graph wavelet (GSGW) descriptor that is isometric invariant, efficient to compute, and combines the advantages of both low-pass and band-pass filters. We perform experiments on shapes of the carpal bones of ten women and ten men from a publicly-available database of wrist bones. Using one-way multivariate analysis of variance (MANOVA) and permutation testing, we show through extensive experiments that the proposed GSGW framework gives a much better performance compared to the global point signature embedding approach for comparing shapes of the carpal bones across populations.

Multivariate singular spectrum analysis and the road to phase synchronization

NASA Astrophysics Data System (ADS)

Groth, Andreas; Ghil, Michael

2010-05-01

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

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.

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

Estuarial fingerprinting through multidimensional fluorescence and multivariate analysis.

PubMed

Hall, Gregory J; Clow, Kerin E; Kenny, Jonathan E

2005-10-01

As part of a strategy for preventing the introduction of aquatic nuisance species (ANS) to U.S. estuaries, ballast water exchange (BWE) regulations have been imposed. Enforcing these regulations requires a reliable method for determining the port of origin of water in the ballast tanks of ships entering U.S. waters. This study shows that a three-dimensional fluorescence fingerprinting technique, excitation emission matrix (EEM) spectroscopy, holds great promise as a ballast water analysis tool. In our technique, EEMs are analyzed by multivariate classification and curve resolution methods, such as N-way partial least squares Regression-discriminant analysis (NPLS-DA) and parallel factor analysis (PARAFAC). We demonstrate that classification techniques can be used to discriminate among sampling sites less than 10 miles apart, encompassing Boston Harbor and two tributaries in the Mystic River Watershed. To our knowledge, this work is the first to use multivariate analysis to classify water as to location of origin. Furthermore, it is shown that curve resolution can show seasonal features within the multidimensional fluorescence data sets, which correlate with difficulty in classification.

Influence of shifting cultivation practices on soil-plant-beetle interactions.

PubMed

Ibrahim, Kalibulla Syed; Momin, Marcy D; Lalrotluanga, R; Rosangliana, David; Ghatak, Souvik; Zothansanga, R; Kumar, Nachimuthu Senthil; Gurusubramanian, Guruswami

2016-08-01

Shifting cultivation (jhum) is a major land use practice in Mizoram. It was considered as an eco-friendly and efficient method when the cycle duration was long (15-30 years), but it poses the problem of land degradation and threat to ecology when shortened (4-5 years) due to increased intensification of farming systems. Studying beetle community structure is very helpful in understanding how shifting cultivation affects the biodiversity features compared to natural forest system. The present study examines the beetle species diversity and estimates the effects of shifting cultivation practices on the beetle assemblages in relation to change in tree species composition and soil nutrients. Scarabaeidae and Carabidae were observed to be the dominant families in the land use systems studied. Shifting cultivation practice significantly (P < 0.05) affected the beetle and tree species diversity as well as the soil nutrients as shown by univariate (one-way analysis of variance (ANOVA), correlation and regression, diversity indices) and multivariate (cluster analysis, principal component analysis (PCA), detrended correspondence analysis (DCA), canonical variate analysis (CVA), permutational multivariate analysis of variance (PERMANOVA), permutational multivariate analysis of dispersion (PERMDISP)) statistical analyses. Besides changing the tree species composition and affecting the soil fertility, shifting cultivation provides less suitable habitat conditions for the beetle species. Bioindicator analysis categorized the beetle species into forest specialists, anthropogenic specialists (shifting cultivation habitat specialist), and habitat generalists. Molecular analysis of bioindicator beetle species was done using mitochondrial cytochrome oxidase subunit I (COI) marker to validate the beetle species and describe genetic variation among them in relation to heterogeneity, transition/transversion bias, codon usage bias, evolutionary distance, and substitution pattern. The present study revealed the fact that shifting cultivation practice significantly affects the beetle species in terms of biodiversity pattern as well as evolutionary features. Spatiotemporal assessment of soil-plant-beetle interactions in shifting cultivation system and their influence in land degradation and ecology will be helpful in making biodiversity conservation decisions in the near future.

Multivariate methods on the excitation emission matrix fluorescence spectroscopic data of diesel-kerosene mixtures: a comparative study.

PubMed

Divya, O; Mishra, Ashok K

2007-05-29

Quantitative determination of kerosene fraction present in diesel has been carried out based on excitation emission matrix fluorescence (EEMF) along with parallel factor analysis (PARAFAC) and N-way partial least squares regression (N-PLS). EEMF is a simple, sensitive and nondestructive method suitable for the analysis of multifluorophoric mixtures. Calibration models consisting of varying compositions of diesel and kerosene were constructed and their validation was carried out using leave-one-out cross validation method. The accuracy of the model was evaluated through the root mean square error of prediction (RMSEP) for the PARAFAC, N-PLS and unfold PLS methods. N-PLS was found to be a better method compared to PARAFAC and unfold PLS method because of its low RMSEP values.

Why you cannot transform your way out of trouble for small counts.

PubMed

Warton, David I

2018-03-01

While data transformation is a common strategy to satisfy linear modeling assumptions, a theoretical result is used to show that transformation cannot reasonably be expected to stabilize variances for small counts. Under broad assumptions, as counts get smaller, it is shown that the variance becomes proportional to the mean under monotonic transformations g(·) that satisfy g(0)=0, excepting a few pathological cases. A suggested rule-of-thumb is that if many predicted counts are less than one then data transformation cannot reasonably be expected to stabilize variances, even for a well-chosen transformation. This result has clear implications for the analysis of counts as often implemented in the applied sciences, but particularly for multivariate analysis in ecology. Multivariate discrete data are often collected in ecology, typically with a large proportion of zeros, and it is currently widespread to use methods of analysis that do not account for differences in variance across observations nor across responses. Simulations demonstrate that failure to account for the mean-variance relationship can have particularly severe consequences in this context, and also in the univariate context if the sampling design is unbalanced. © 2017 The Authors. Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.

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

Gap Shape Classification using Landscape Indices and Multivariate Statistics

PubMed Central

Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung

2016-01-01

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks’ lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap. PMID:27901127

Gap Shape Classification using Landscape Indices and Multivariate Statistics.

PubMed

Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung

2016-11-30

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks' lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap.

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.

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.

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.

Racial/ethnic variation in mental health correlates of substance use among college students.

PubMed

Sumstine, Stephanie; Cruz, Sheena; Schroeder, Cassandra; Takeda, Summer; Bavarian, Niloofar

2018-01-01

This study investigated mental health indicators, substance use, and their relationships, by race/ethnicity. A probability sample of 1,053 students at two California universities self-reported their frequency of substance use and rated their experience with indicators of mental health. One-way analysis of variance (ANOVA), chi-square tests, and multivariate censored regression models were estimated to examine which indicators of mental health were associated with each substance use form by race/ethnicity. Results from the one-way ANOVA and chi-square tests showed differences in substance use prevalence and mental health by race/ethnicity. For example, students who identified as White demonstrate a higher prevalence for every form of substance use in comparison to the Asian, Latino, and "All other" categories. Results from the regression showed, among Whites, inattention was associated with prescription stimulant misuse, and psychological distress was associated with marijuana use. Among Latinos, inattention was associated with cocaine and prescription stimulant use. Among Asians, psychological distress was associated with tobacco use and the misuse of prescription painkillers. Findings highlight the need to ensure subpopulations receive needed services.

Principal component analysis and analysis of variance on the effects of Entellan New on the Raman spectra of fibers.

PubMed

Yu, Marcia M L; Sandercock, P Mark L

2012-01-01

During the forensic examination of textile fibers, fibers are usually mounted on glass slides for visual inspection and identification under the microscope. One method that has the capability to accurately identify single textile fibers without subsequent demounting is Raman microspectroscopy. The effect of the mountant Entellan New on the Raman spectra of fibers was investigated to determine if it is suitable for fiber analysis. Raman spectra of synthetic fibers mounted in three different ways were collected and subjected to multivariate analysis. Principal component analysis score plots revealed that while spectra from different fiber classes formed distinct groups, fibers of the same class formed a single group regardless of the mounting method. The spectra of bare fibers and those mounted in Entellan New were found to be statistically indistinguishable by analysis of variance calculations. These results demonstrate that fibers mounted in Entellan New may be identified directly by Raman microspectroscopy without further sample preparation. © 2011 American Academy of Forensic Sciences.

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.

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

Analysis of algae growth mechanism and water bloom prediction under the effect of multi-affecting factor.

PubMed

Wang, Li; Wang, Xiaoyi; Jin, Xuebo; Xu, Jiping; Zhang, Huiyan; Yu, Jiabin; Sun, Qian; Gao, Chong; Wang, Lingbin

2017-03-01

The formation process of algae is described inaccurately and water blooms are predicted with a low precision by current methods. In this paper, chemical mechanism of algae growth is analyzed, and a correlation analysis of chlorophyll-a and algal density is conducted by chemical measurement. Taking into account the influence of multi-factors on algae growth and water blooms, the comprehensive prediction method combined with multivariate time series and intelligent model is put forward in this paper. Firstly, through the process of photosynthesis, the main factors that affect the reproduction of the algae are analyzed. A compensation prediction method of multivariate time series analysis based on neural network and Support Vector Machine has been put forward which is combined with Kernel Principal Component Analysis to deal with dimension reduction of the influence factors of blooms. Then, Genetic Algorithm is applied to improve the generalization ability of the BP network and Least Squares Support Vector Machine. Experimental results show that this method could better compensate the prediction model of multivariate time series analysis which is an effective way to improve the description accuracy of algae growth and prediction precision of water blooms.

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

PubMed

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

2015-01-01

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

Associations of age, aerobic fitness, and body mass index with injury in an operational Army brigade.

PubMed

Rappole, Catherine; Grier, Tyson; Anderson, Morgan K; Hauschild, Veronique; Jones, Bruce H

2017-11-01

To investigate the effects of age, aerobic fitness, and body mass index (BMI) on injury risk in operational Army soldiers. Retrospective cohort study. Male soldiers from an operational Army brigade were administered electronic surveys regarding personal characteristics, physical fitness, and injuries occurring over the last 12 months. Injury risks were stratified by age, 2-mile run time, and BMI. Analyses included descriptive incidence, a Mantel-Haenszel χ 2 test to determine trends, a multivariable logistic regression to determine factors associated with injury, and a one-way analysis of variance (ANOVA). Forty-seventy percent of 1099 respondents reported at least one injury. A linear trend showed that as age, 2-mile run time, and BMI increased, so did injury risk (p<0.01). When controlling for BMI, the most significant independent injury risk factors were older age (odd ratio (OR) 30years-35years/≤24years=1.25, 95%CI: 1.08-2.32), (OR≥36years/≤24years=2.05, 95%CI: 1.36-3.10), and slow run times (OR≥15.9min/≤13.9min=1.91, 95%CI: 1.28-2.85). An ANOVA showed that both run times and BMI increased with age. The stratified analysis and the multivariable logistic regression suggested that older age and poor aerobic fitness are stronger predictors of injury than BMI. Copyright © 2017 Sports Medicine Australia. All rights reserved.

A new technique for spectrophotometric determination of pseudoephedrine and guaifenesin in syrup and synthetic mixture.

PubMed

Riahi, Siavash; Hadiloo, Farshad; Milani, Seyed Mohammad R; Davarkhah, Nazila; Ganjali, Mohammad R; Norouzi, Parviz; Seyfi, Payam

2011-05-01

The accuracy in predicting different chemometric methods was compared when applied on ordinary UV spectra and first order derivative spectra. Principal component regression (PCR) and partial least squares with one dependent variable (PLS1) and two dependent variables (PLS2) were applied on spectral data of pharmaceutical formula containing pseudoephedrine (PDP) and guaifenesin (GFN). The ability to derivative in resolved overlapping spectra chloropheniramine maleate was evaluated when multivariate methods are adopted for analysis of two component mixtures without using any chemical pretreatment. The chemometrics models were tested on an external validation dataset and finally applied to the analysis of pharmaceuticals. Significant advantages were found in analysis of the real samples when the calibration models from derivative spectra were used. It should also be mentioned that the proposed method is a simple and rapid way requiring no preliminary separation steps and can be used easily for the analysis of these compounds, especially in quality control laboratories. Copyright © 2011 John Wiley & Sons, Ltd.

Statistical Modeling of the Individual: Rationale and Application of Multivariate Stationary Time Series Analysis

ERIC Educational Resources Information Center

Hamaker, Ellen L.; Dolan, Conor V.; Molenaar, Peter C. M.

2005-01-01

Results obtained with interindividual techniques in a representative sample of a population are not necessarily generalizable to the individual members of this population. In this article the specific condition is presented that must be satisfied to generalize from the interindividual level to the intraindividual level. A way to investigate…

A Cross Age Study of Elementary Students' Motivation towards Science Learning

ERIC Educational Resources Information Center

Guvercin, Ozge; Tekkaya, Ceren; Sungur, Semra

2010-01-01

The purpose of this study was to investigate the effect of grade level and gender on elementary school students' motivation towards science learning. A total of 2231 sixth and eight grade students participated in the study. Data were collected through Students' Motivation towards Science Learning Questionnaire. Two-way Multivariate Analysis of…

An evaluation of the use of near infrared (NIR) spectroscopy to identify water and oil-borne preservatives

Treesearch

Chi-Leung So; Stan T. Lebow; Leslie H. Groom; Todd F. Shupe

2003-01-01

In this research we experimented with a new and rapid way of analyzing wood. Near Infrared (NIR)spectroscopy together with multivariate analysis is becoming a widely used technique in the field of forest products especially for property determination and is already firmly established in the pulp and paper industry. This method is ideal for the chemical analysis of wood...

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.

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

Using multivariate techniques to assess the effects of urbanization on surface water quality: a case study in the Liangjiang New Area, China.

PubMed

Luo, Kun; Hu, Xuebin; He, Qiang; Wu, Zhengsong; Cheng, Hao; Hu, Zhenlong; Mazumder, Asit

2017-04-01

Rapid urbanization in China has been causing dramatic deterioration in the water quality of rivers and threatening aquatic ecosystem health. In this paper, multivariate techniques, such as factor analysis (FA) and cluster analysis (CA), were applied to analyze the water quality datasets for 19 rivers in Liangjiang New Area (LJNA), China, collected in April (dry season) and September (wet season) of 2014 and 2015. In most sampling rivers, total phosphorus, total nitrogen, and fecal coliform exceeded the Class V guideline (GB3838-2002), which could thereby threaten the water quality in Yangtze and Jialing Rivers. FA clearly identified the five groups of water quality variables, which explain majority of the experimental data. Nutritious pollution, seasonal changes, and construction activities were three key factors influencing rivers' water quality in LJNA. CA grouped 19 sampling sites into two clusters, which located at sub-catchments with high- and low-level urbanization, respectively. One-way ANOVA showed the nutrients (total phosphorus, soluble reactive phosphorus, total nitrogen, ammonium nitrogen, and nitrite), fecal coliform, and conductivity in cluster 1 were significantly greater than in cluster 2. Thus, catchment urbanization degraded rivers' water quality in Liangjiang New Area. Identifying effective buffer zones at riparian scale to weaken the negative impacts of catchment urbanization was recommended.

Authentication of whisky due to its botanical origin and way of production by instrumental analysis and multivariate classification methods

NASA Astrophysics Data System (ADS)

Wiśniewska, Paulina; Boqué, Ricard; Borràs, Eva; Busto, Olga; Wardencki, Waldemar; Namieśnik, Jacek; Dymerski, Tomasz

2017-02-01

Headspace mass-spectrometry (HS-MS), mid infrared (MIR) and UV-vis spectroscopy were used to authenticate whisky samples from different origins and ways of production ((Irish, Spanish, Bourbon, Tennessee Whisky and Scotch). The collected spectra were processed with partial least-squares discriminant analysis (PLS-DA) to build the classification models. In all cases the five groups of whiskies were distinguished, but the best results were obtained by HS-MS, which indicates that the biggest differences between different types of whisky are due to their aroma. Differences were also found inside groups, showing that not only raw material is important to discriminate samples but also the way of their production. The methodology is quick, easy and does not require sample preparation.

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

Exploring High-D Spaces with Multiform Matrices and Small Multiples

PubMed Central

MacEachren, Alan; Dai, Xiping; Hardisty, Frank; Guo, Diansheng; Lengerich, Gene

2011-01-01

We introduce an approach to visual analysis of multivariate data that integrates several methods from information visualization, exploratory data analysis (EDA), and geovisualization. The approach leverages the component-based architecture implemented in GeoVISTA Studio to construct a flexible, multiview, tightly (but generically) coordinated, EDA toolkit. This toolkit builds upon traditional ideas behind both small multiples and scatterplot matrices in three fundamental ways. First, we develop a general, MultiForm, Bivariate Matrix and a complementary MultiForm, Bivariate Small Multiple plot in which different bivariate representation forms can be used in combination. We demonstrate the flexibility of this approach with matrices and small multiples that depict multivariate data through combinations of: scatterplots, bivariate maps, and space-filling displays. Second, we apply a measure of conditional entropy to (a) identify variables from a high-dimensional data set that are likely to display interesting relationships and (b) generate a default order of these variables in the matrix or small multiple display. Third, we add conditioning, a kind of dynamic query/filtering in which supplementary (undisplayed) variables are used to constrain the view onto variables that are displayed. Conditioning allows the effects of one or more well understood variables to be removed from the analysis, making relationships among remaining variables easier to explore. We illustrate the individual and combined functionality enabled by this approach through application to analysis of cancer diagnosis and mortality data and their associated covariates and risk factors. PMID:21947129

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.

Multivariate approximation methods and applications to geophysics and geodesy

NASA Technical Reports Server (NTRS)

Munteanu, M. J.

1979-01-01

The first report in a series is presented which is intended to be written by the author with the purpose of treating a class of approximation methods of functions in one and several variables and ways of applying them to geophysics and geodesy. The first report is divided in three parts and is devoted to the presentation of the mathematical theory and formulas. Various optimal ways of representing functions in one and several variables and the associated error when information is had about the function such as satellite data of different kinds are discussed. The framework chosen is Hilbert spaces. Experiments were performed on satellite altimeter data and on satellite to satellite tracking data.

Patient preferences and treatment safety for uncomplicated vulvovaginal candidiasis in primary health care

PubMed Central

2011-01-01

Background Vaginitis is a common complaint in primary care. In uncomplicated candidal vaginitis, there are no differences in effectiveness between oral or vaginal treatment. Some studies describe that the preferred treatment is the oral one, but a Cochrane's review points out inconsistencies associated with the report of the preferred way that limit the use of such data. Risk factors associated with recurrent vulvovaginal candidiasis still remain controversial. Methods/Design This work describes a protocol of a multicentric prospective observational study with one year follow up, to describe the women's reasons and preferences to choose the way of administration (oral vs topical) in the treatment of not complicated candidal vaginitis. The number of women required is 765, they are chosen by consecutive sampling. All of whom are aged 16 and over with vaginal discharge and/or vaginal pruritus, diagnosed with not complicated vulvovaginitis in Primary Care in Madrid. The main outcome variable is the preferences of the patients in treatment choice; secondary outcome variables are time to symptoms relief and adverse reactions and the frequency of recurrent vulvovaginitis and the risk factors. In the statistical analysis, for the main objective will be descriptive for each of the variables, bivariant analysis and multivariate analysis (logistic regression).. The dependent variable being the type of treatment chosen (oral or topical) and the independent, the variables that after bivariant analysis, have been associated to the treatment preference. Discussion Clinical decisions, recommendations, and practice guidelines must not only attend to the best available evidence, but also to the values and preferences of the informed patient. PMID:21281464

Modular theory of inverse systems

NASA Technical Reports Server (NTRS)

1979-01-01

The relationship between multivariable zeros and inverse systems was explored. A definition of zero module is given in such a way that it is basis independent. The existence of essential right and left inverses were established. The way in which the abstract zero module captured previous definitions of multivariable zeros is explained and examples are presented.

Generation of vertical angular momentum in single, double, and triple-turn pirouette en dehors in ballet.

PubMed

Kim, Jemin; Wilson, Margaret A; Singhal, Kunal; Gamblin, Sarah; Suh, Cha-Young; Kwon, Young-Hoo

2014-09-01

The purpose of this study was to investigate the vertical angular momentum generation strategies used by skilled ballet dancers in pirouette en dehors. Select kinematic parameters of the pirouette preparation (stance depth, vertical center-of-mass motion range, initial shoulder line position, shoulder line angular displacement, and maximum trunk twist angle) along with vertical angular momentum parameters during the turn (maximum momentums of the whole body and body parts, and duration and rate of generation) were obtained from nine skilled collegiate ballet dancers through a three-dimensional motion analysis and compared among three turn conditions (single, double, and triple). A one-way ('turn') multivariate analysis of variance of the kinematic parameters and angular momentum parameters of the whole body and a two-way analysis of variance ('turn' × 'body') of the maximum angular momentums of the body parts were conducted. Significant 'turn' effects were observed in the kinematic/angular momentum parameters (both the preparation and the turn) (p <  0.05). As the number of turns increased, skilled dancers generated larger vertical angular momentums by predominantly increasing the rate of momentum generation using rotation of the upper trunk and arms. The trail (closing) arm showed the largest contribution to whole-body angular momentum followed by the lead arm.

Influence factors and forecast of carbon emission in China: structure adjustment for emission peak

NASA Astrophysics Data System (ADS)

Wang, B.; Cui, C. Q.; Li, Z. P.

2018-02-01

This paper introduced Principal Component Analysis and Multivariate Linear Regression Model to verify long-term balance relationships between Carbon Emissions and the impact factors. The integrated model of improved PCA and multivariate regression analysis model is attainable to figure out the pattern of carbon emission sources. Main empirical results indicate that among all selected variables, the role of energy consumption scale was largest. GDP and Population follow and also have significant impacts on carbon emission. Industrialization rate and fossil fuel proportion, which is the indicator of reflecting the economic structure and energy structure, have a higher importance than the factor of urbanization rate and the dweller consumption level of urban areas. In this way, some suggestions are put forward for government to achieve the peak of carbon emissions.

A FORTRAN program for multivariate survival analysis on the personal computer.

PubMed

Mulder, P G

1988-01-01

In this paper a FORTRAN program is presented for multivariate survival or life table regression analysis in a competing risks' situation. The relevant failure rate (for example, a particular disease or mortality rate) is modelled as a log-linear function of a vector of (possibly time-dependent) explanatory variables. The explanatory variables may also include the variable time itself, which is useful for parameterizing piecewise exponential time-to-failure distributions in a Gompertz-like or Weibull-like way as a more efficient alternative to Cox's proportional hazards model. Maximum likelihood estimates of the coefficients of the log-linear relationship are obtained from the iterative Newton-Raphson method. The program runs on a personal computer under DOS; running time is quite acceptable, even for large samples.

Brain volume reduction after whole-brain radiotherapy: quantification and prognostic relevance.

PubMed

Hoffmann, Christian; Distel, Luitpold; Knippen, Stefan; Gryc, Thomas; Schmidt, Manuel Alexander; Fietkau, Rainer; Putz, Florian

2018-01-22

Recent studies have questioned the value of adding whole-brain radiotherapy (WBRT) to stereotactic radiosurgery (SRS) for brain metastasis treatment. Neurotoxicity, including radiation-induced brain volume reduction, could be one reason why not all patients benefit from the addition of WBRT. In this study, we quantified brain volume reduction after WBRT and assessed its prognostic significance. Brain volumes of 91 patients with cerebral metastases were measured during a 150-day period after commencing WBRT and were compared with their pretreatment volumes. The average daily relative change in brain volume of each patient, referred to as the "brain volume reduction rate," was calculated. Univariate and multivariate Cox regression analyses were performed to assess the prognostic significance of the brain volume reduction rate, as well as of 3 treatment-related and 9 pretreatment factors. A one-way analysis of variance was used to compare the brain volume reduction rate across recursive partitioning analysis (RPA) classes. On multivariate Cox regression analysis, the brain volume reduction rate was a significant predictor of overall survival after WBRT (P < 0.001), as well as the number of brain metastases (P = 0.002) and age (P = 0.008). Patients with a relatively favorable prognosis (RPA classes 1 and 2) experienced significantly less brain volume decrease after WBRT than patients with a poor prognosis (RPA class 3) (P = 0.001). There was no significant correlation between delivered radiation dose and brain volume reduction rate (P = 0.147). In this retrospective study, a smaller decrease in brain volume after WBRT was an independent predictor of longer overall survival. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

A Quality by Design approach to investigate tablet dissolution shift upon accelerated stability by multivariate methods.

PubMed

Huang, Jun; Goolcharran, Chimanlall; Ghosh, Krishnendu

2011-05-01

This paper presents the use of experimental design, optimization and multivariate techniques to investigate root-cause of tablet dissolution shift (slow-down) upon stability and develop control strategies for a drug product during formulation and process development. The effectiveness and usefulness of these methodologies were demonstrated through two application examples. In both applications, dissolution slow-down was observed during a 4-week accelerated stability test under 51°C/75%RH storage condition. In Application I, an experimental design was carried out to evaluate the interactions and effects of the design factors on critical quality attribute (CQA) of dissolution upon stability. The design space was studied by design of experiment (DOE) and multivariate analysis to ensure desired dissolution profile and minimal dissolution shift upon stability. Multivariate techniques, such as multi-way principal component analysis (MPCA) of the entire dissolution profiles upon stability, were performed to reveal batch relationships and to evaluate the impact of design factors on dissolution. In Application II, an experiment was conducted to study the impact of varying tablet breaking force on dissolution upon stability utilizing MPCA. It was demonstrated that the use of multivariate methods, defined as Quality by Design (QbD) principles and tools in ICH-Q8 guidance, provides an effective means to achieve a greater understanding of tablet dissolution upon stability. Copyright © 2010 Elsevier B.V. All rights reserved.

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.

Authentication of whisky due to its botanical origin and way of production by instrumental analysis and multivariate classification methods.

PubMed

Wiśniewska, Paulina; Boqué, Ricard; Borràs, Eva; Busto, Olga; Wardencki, Waldemar; Namieśnik, Jacek; Dymerski, Tomasz

2017-02-15

Headspace mass-spectrometry (HS-MS), mid infrared (MIR) and UV-vis spectroscopy were used to authenticate whisky samples from different origins and ways of production ((Irish, Spanish, Bourbon, Tennessee Whisky and Scotch). The collected spectra were processed with partial least-squares discriminant analysis (PLS-DA) to build the classification models. In all cases the five groups of whiskies were distinguished, but the best results were obtained by HS-MS, which indicates that the biggest differences between different types of whisky are due to their aroma. Differences were also found inside groups, showing that not only raw material is important to discriminate samples but also the way of their production. The methodology is quick, easy and does not require sample preparation. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

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

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

Prevalence and associated variables of postdialysis fatigue: results of a prospective multicenter study.

PubMed

Bossola, Maurizio; Marzetti, Emanuele; Di Stasio, Enrico; Monteburini, Tania; Cenerelli, Stefano; Mazzoli, Katia; Parodi, Emanuele; Sirolli, Vittorio; Santarelli, Stefano; Ippoliti, Fabio; Nebiolo, Pier Eugenio; Bonomini, Mario; Melatti, Roberta; Vulpio, Carlo

2017-04-17

Little is known about postdialysis fatigue (PDF), a debilitating symptom of hemodialysis patients. In 5 hemodialysis units of northern-centre Italy, patients were regarded to suffer from PDF if they spontaneously offered this complaint when asked the open-ended question: Do you feel better or worse after dialysis? If worse, please specify in which way. A complaint of fatigue would be probed further with questions directed at its duration, frequency and intensity, allowing creation of a fatigue index of severity (one third of the sum of these three parameters, each rated from 1 to 5). Patients were stratified into three groups according the severity of PDF: 1) score = 0; 2) score = 1-3; 3) score > 3. We studied 271 patients: 164 had PDF and 107 had not. PDF patients had significantly longer time of recovery after dialysis (TIRD). TIRD was significantly associated with PDF duration, intensity, and frequency. Patients with PDF were older and had a lower ADL score. At multivariate analysis, PDF was significantly associated with TIRD. In multivariate model that did not include TIRD, PDF was independently associated with age and ADL. Sixty patients had moderate PDF and 104 severe PDF. In patients with severe PDF, age and dialytic age were higher, ADL and IADL scores were lower, TIRD was longer and the ultrafiltration rate was lower. At multivariate analysis, PDF severity was independently associated with TIRD. In the model without TIRD, PDF severity was associated with ADL only. PDF is frequent and associated with age and ADL. Dialytic variables seem unrelated to PDF. This article is protected by copyright. All rights reserved.

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.

Analyzing developmental processes on an individual level using nonstationary time series modeling.

PubMed

Molenaar, Peter C M; Sinclair, Katerina O; Rovine, Michael J; Ram, Nilam; Corneal, Sherry E

2009-01-01

Individuals change over time, often in complex ways. Generally, studies of change over time have combined individuals into groups for analysis, which is inappropriate in most, if not all, studies of development. The authors explain how to identify appropriate levels of analysis (individual vs. group) and demonstrate how to estimate changes in developmental processes over time using a multivariate nonstationary time series model. They apply this model to describe the changing relationships between a biological son and father and a stepson and stepfather at the individual level. The authors also explain how to use an extended Kalman filter with iteration and smoothing estimator to capture how dynamics change over time. Finally, they suggest further applications of the multivariate nonstationary time series model and detail the next steps in the development of statistical models used to analyze individual-level data.

Validation of the concentration profiles obtained from the near infrared/multivariate curve resolution monitoring of reactions of epoxy resins using high performance liquid chromatography as a reference method.

PubMed

Garrido, M; Larrechi, M S; Rius, F X

2007-03-07

This paper reports the validation of the results obtained by combining near infrared spectroscopy and multivariate curve resolution-alternating least squares (MCR-ALS) and using high performance liquid chromatography as a reference method, for the model reaction of phenylglycidylether (PGE) and aniline. The results are obtained as concentration profiles over the reaction time. The trueness of the proposed method has been evaluated in terms of lack of bias. The joint test for the intercept and the slope showed that there were no significant differences between the profiles calculated spectroscopically and the ones obtained experimentally by means of the chromatographic reference method at an overall level of confidence of 5%. The uncertainty of the results was estimated by using information derived from the process of assessment of trueness. Such operational aspects as the cost and availability of instrumentation and the length and cost of the analysis were evaluated. The method proposed is a good way of monitoring the reactions of epoxy resins, and it adequately shows how the species concentration varies over time.

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.

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

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.

Leuconostoc strains isolated from dairy products: Response against food stress conditions.

PubMed

D'Angelo, Luisa; Cicotello, Joaquín; Zago, Miriam; Guglielmotti, Daniela; Quiberoni, Andrea; Suárez, Viviana

2017-09-01

A systematic study about the intrinsic resistance of 29 strains (26 autochthonous and 3 commercial ones), belonging to Leuconostoc genus, against diverse stress factors (thermal, acidic, alkaline, osmotic and oxidative) commonly present at industrial or conservation processes were evaluated. Exhaustive result processing was made by applying one-way ANOVA, Student's test (t), multivariate analysis by Principal Component Analysis (PCA) and Matrix Hierarchical Cluster Analysis. In addition, heat adaptation on 4 strains carefully selected based on previous data analysis was assayed. The strains revealed wide diversity of resistance to stress factors and, in general, a clear relationship between resistance and Leuconostoc species was established. In this sense, the highest resistance was shown by Leuconostoc lactis followed by Leuconostoc mesenteroides strains, while Leuconostoc pseudomesenteroides and Leuconostoc citreum strains revealed the lowest resistance to the stress factors applied. Heat adaptation improved thermal cell survival and resulted in a cross-resistance against the acidic factor. However, all adapted cells showed diminished their oxidative resistance. According to our knowledge, this is the first study regarding response of Leuconostoc strains against technological stress factors and could establish the basis for the selection of "more robust" strains and propose the possibility of improving their performance during industrial processes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Protein differences between human trapezius and vastus lateralis muscles determined with a proteomic approach.

PubMed

Hadrévi, Jenny; Hellström, Fredrik; Kieselbach, Thomas; Malm, Christer; Pedrosa-Domellöf, Fatima

2011-08-10

The trapezius muscle is a neck muscle that is susceptible to chronic pain conditions associated with repetitive tasks, commonly referred to as chronic work-related myalgia, hence making the trapezius a muscle of clinical interest. To provide a basis for further investigations of the proteomic traits of the trapezius muscle in disease, two-dimensional difference gel electrophoresis (2D-DIGE) was performed on the healthy trapezius using vastus lateralis as a reference. To obtain as much information as possible from the vast proteomic data set, both one-way ANOVA, with and without false discovery rate (FDR) correlation, and partial least square projection to latent structures with discriminant analysis (PLS-DA) were combined to compare the outcome of the analysis. The trapezius and vastus lateralis showed significant differences in metabolic, contractile and regulatory proteins, with different results depending on choice of statistical approach and pre-processing technique. Using the standard method, FDR correlated one-way ANOVA, 42 protein spots differed significantly in abundance between the two muscles. Complementary analysis using immunohistochemistry and western blot confirmed the results from the 2D-DIGE analysis. The proteomic approach used in the present study combining 2D-DIGE and multivariate modelling provided a more comprehensive comparison of the protein profiles of the human trapezius and vastus lateralis muscle, than previously possible to obtain with immunohistochemistry or SDS-PAGE alone. Although 2D-DIGE has inherent limitations it is particularly useful to comprehensively screen for important structural and metabolic proteins, and appears to be a promising tool for future studies of patients suffering from chronic work related myalgia or other muscle diseases.

Using Gaussian windows to explore a multivariate data set

NASA Technical Reports Server (NTRS)

Jaeckel, Louis A.

1991-01-01

In an earlier paper, I recounted an exploratory analysis, using Gaussian windows, of a data set derived from the Infrared Astronomical Satellite. Here, my goals are to develop strategies for finding structural features in a data set in a many-dimensional space, and to find ways to describe the shape of such a data set. After a brief review of Gaussian windows, I describe the current implementation of the method. I give some ways of describing features that we might find in the data, such as clusters and saddle points, and also extended structures such as a 'bar', which is an essentially one-dimensional concentration of data points. I then define a distance function, which I use to determine which data points are 'associated' with a feature. Data points not associated with any feature are called 'outliers'. I then explore the data set, giving the strategies that I used and quantitative descriptions of the features that I found, including clusters, bars, and a saddle point. I tried to use strategies and procedures that could, in principle, be used in any number of dimensions.

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

Multivariate Complexity Analysis of Swap Bribery

NASA Astrophysics Data System (ADS)

Dorn, Britta; Schlotter, Ildikó

We consider the computational complexity of a problem modeling bribery in the context of voting systems. In the scenario of Swap Bribery, each voter assigns a certain price for swapping the positions of two consecutive candidates in his preference ranking. The question is whether it is possible, without exceeding a given budget, to bribe the voters in a way that the preferred candidate wins in the election.

Bibliography on Cold Regions Science and Technology. Volume 41. Part 2

DTIC Science & Technology

1987-12-01

Aletschgletscher [1984, p.9-25, eng, 41-622 Aleksandrov, B.M. Multivariate regression analysis of the process of frozen peat dehydration [1986. p.15-19...freezing of high- way bridge decks [1977. 5p., eng] 41-4604 Britton, K.B. Low temperature effects on sorption. hydrolysis ...snowy season in 1986 at Sapporo [1986. p.17-23. jpn) 41-3503 Ishikawa, S. Experimental decomposition of

Kidney transplantation from deceased donors with elevated serum creatinine.

PubMed

Gallinat, Anja; Leerhoff, Sabine; Paul, Andreas; Molmenti, Ernesto P; Schulze, Maren; Witzke, Oliver; Sotiropoulos, Georgios C

2016-12-01

Elevated donor serum creatinine has been associated with inferior graft survival in kidney transplantation (KT). The aim of this study was to evaluate the impact of elevated donor serum creatinine on short and long-term outcomes and to determine possible ways to optimize the use of these organs. All kidney transplants from 01-2000 to 12-2012 with donor creatinine ≥ 2 mg/dl were considered. Risk factors for delayed graft function (DGF) were explored with uni- and multivariate regression analyses. Donor and recipient data were analyzed with uni- and multivariate cox proportional hazard analyses. Graft and patient survival were calculated using the Kaplan-Meier method. Seventy-eight patients were considered. Median recipient age and waiting time on dialysis were 53 years and 5.1 years, respectively. After a median follow-up of 6.2 years, 63 patients are alive. 1, 3, and 5-year graft and patient survival rates were 92, 89, and 89 % and 96, 93, and 89 %, respectively. Serum creatinine level at procurement and recipient's dialysis time prior to KT were predictors of DGF in multivariate analysis (p = 0.0164 and p = 0.0101, respectively). Charlson comorbidity score retained statistical significance by multivariate regression analysis for graft survival (p = 0.0321). Recipient age (p = 0.0035) was predictive of patient survival by multivariate analysis. Satisfactory long-term kidney transplant outcomes in the setting of elevated donor serum creatinine ≥2 mg/dl can be achieved when donor creatinine is <3.5 mg/dl, and the recipient has low comorbidities, is under 56 years of age, and remains in dialysis prior to KT for <6.8 years.

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.

Nearest neighbors by neighborhood counting.

PubMed

Wang, Hui

2006-06-01

Finding nearest neighbors is a general idea that underlies many artificial intelligence tasks, including machine learning, data mining, natural language understanding, and information retrieval. This idea is explicitly used in the k-nearest neighbors algorithm (kNN), a popular classification method. In this paper, this idea is adopted in the development of a general methodology, neighborhood counting, for devising similarity functions. We turn our focus from neighbors to neighborhoods, a region in the data space covering the data point in question. To measure the similarity between two data points, we consider all neighborhoods that cover both data points. We propose to use the number of such neighborhoods as a measure of similarity. Neighborhood can be defined for different types of data in different ways. Here, we consider one definition of neighborhood for multivariate data and derive a formula for such similarity, called neighborhood counting measure or NCM. NCM was tested experimentally in the framework of kNN. Experiments show that NCM is generally comparable to VDM and its variants, the state-of-the-art distance functions for multivariate data, and, at the same time, is consistently better for relatively large k values. Additionally, NCM consistently outperforms HEOM (a mixture of Euclidean and Hamming distances), the "standard" and most widely used distance function for multivariate data. NCM has a computational complexity in the same order as the standard Euclidean distance function and NCM is task independent and works for numerical and categorical data in a conceptually uniform way. The neighborhood counting methodology is proven sound for multivariate data experimentally. We hope it will work for other types of data.

A new approach in space-time analysis of multivariate hydrological data: Application to Brazil's Nordeste region rainfall

NASA Astrophysics Data System (ADS)

Sicard, Emeline; Sabatier, Robert; Niel, HéLèNe; Cadier, Eric

2002-12-01

The objective of this paper is to implement an original method for spatial and multivariate data, combining a method of three-way array analysis (STATIS) with geostatistical tools. The variables of interest are the monthly amounts of rainfall in the Nordeste region of Brazil, recorded from 1937 to 1975. The principle of the technique is the calculation of a linear combination of the initial variables, containing a large part of the initial variability and taking into account the spatial dependencies. It is a promising method that is able to analyze triple variability: spatial, seasonal, and interannual. In our case, the first component obtained discriminates a group of rain gauges, corresponding approximately to the Agreste, from all the others. The monthly variables of July and August strongly influence this separation. Furthermore, an annual study brings out the stability of the spatial structure of components calculated for each year.

A simple prognostic model for overall survival in metastatic renal cell carcinoma.

PubMed

Assi, Hazem I; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis.

A simple prognostic model for overall survival in metastatic renal cell carcinoma

PubMed Central

Assi, Hazem I.; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

Introduction: The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. Methods: We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. Results: There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. Conclusions: In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis. PMID:27217858

Morbidity and Mortality Associated with Geriatric Ankle Fractures: A Medicare Part A Claims Database Analysis.

PubMed

Hsu, Raymond Y; Lee, Yoojin; Hayda, Roman; DiGiovanni, Christopher W; Mor, Vincent; Bariteau, Jason T

2015-11-04

The purpose of this study was to examine the incidence of adverse events in elderly patients who required inpatient admission after sustaining an ankle fracture and to consider these data in relation to geriatric hip fracture and other geriatric patient admissions. A retrospective cohort study of patients admitted with an ankle fracture, a hip fracture, or any other diagnosis was performed with the Medicare Part A database for 2008. The primary outcome measure was the one-year mortality rate, examined with multivariate analysis factoring for both patient age and preexisting comorbidity. Secondary outcome measures analyzed additional morbidity as reflected by length of stay, discharge disposition, readmissions, and medical complications. There were 19,648 patients with ankle fractures, 193,980 patients with hip fractures, and 5,801,831 patients with other admitting diagnoses. Significant differences (p < 0.001) were noted in both age and comorbidity status between the group with ankle fractures and the group with hip fractures. The one-year mortality after admission was 11.9% for patients with ankle fracture, 28.2% for patients with hip fracture, and 21.5% for patients with any other admission. Upon using multivariate analysis to account for both age and comorbidity, the hazard ratio for one-year mortality associated with fracture was 1.088 for patients with hip fracture and 0.557 for patients with ankle fracture. Even after selecting for admitted patients and accounting for both age and comorbidity, geriatric patients with ankle fractures were found to have a lower one-year morbidity compared with geriatric patients who had sustained a hip fracture or alternative admitting diagnoses. Geriatric patients with ankle fractures are likely healthier and more active in ways that are not captured by simply accounting for age and comorbidity. These findings may support more aggressive definitive management of such injuries in this population. Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence. Copyright © 2015 by The Journal of Bone and Joint Surgery, Incorporated.

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.

Effects of induced social roles on the High School Personality Questionnaire.

PubMed

Merydith, S P; Wallbrown, F H

1995-08-01

A one-way multivariate analysis of variance design with a control group (regular directions) and three treatment groups using induced social roles (Faking Good, Teacher, and Ideal Teacher) as independent variables and the High School Personality Questionnaire primary scores as dependent variables was used. Subjects were 384 male high school students from Grades 9 through 12. Within each classroom, students were randomly assigned to the four groups noted above. A broad pattern of differences in scores on primary and secondary personality dimensions were obtained. Significant differences between the control (standard directions) and the Faking Good, Teacher, and Ideal Teacher roles were obtained on three secondary and most of the primary personality dimensions. In several cases the ideal social role and neutral social role showed distinct differences from the more pervasive favorable impression role.

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).

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 Multivariate Genetic Analysis of Specific Phobia, Separation Anxiety and Social Phobia in Early Childhood

ERIC Educational Resources Information Center

Eley, Thalia C.; Rijsdijk, Fruhling V.; Perrin, Sean; O'Connor, Thomas G.; Bolton, Derek

2008-01-01

Background: Comorbidity amongst anxiety disorders is very common in children as in adults and leads to considerable distress and impairment, yet is poorly understood. Multivariate genetic analyses can shed light on the origins of this comorbidity by revealing whether genetic or environmental risks for one disorder also influence another. We…

Fourier Transform Infrared Imaging analysis of dental pulp inflammatory diseases.

PubMed

Giorgini, E; Sabbatini, S; Conti, C; Rubini, C; Rocchetti, R; Fioroni, M; Memè, L; Orilisi, G

2017-05-01

Fourier Transform Infrared microspectroscopy let characterize the macromolecular composition and distribution of tissues and cells, by studying the interaction between infrared radiation and matter. Therefore, we hypothesize to exploit this analytical tool in the analysis of inflamed pulps, to detect the different biochemical features related to various degrees of inflammation. IR maps of 13 irreversible and 12 hyperplastic pulpitis, together with 10 normal pulps, were acquired, compared with histological findings and submitted to multivariate (HCA, PCA, SIMCA) and statistical (one-way ANOVA) analysis. The fit of convoluted bands let calculate meaningful band area ratios (means ± s.d., P < 0.05). The infrared imaging analysis pin-pointed higher amounts of water and lower quantities of type I collagen in all inflamed pulps. Specific vibrational markers were defined for irreversible pulpitis (Lipids/Total Biomass, PhII/Total Biomass, CH 2 /CH 3 , and Ty/AII) and hyperplastic ones (OH/Total Biomass, Collagen/Total Biomass, and CH 3 Collagen/Total Biomass). The study confirmed that FTIR microspectroscopy let discriminate tissues' biological features. The infrared imaging analysis evidenced, in inflamed pulps, alterations in tissues' structure and composition. Changes in lipid metabolism, increasing amounts of tyrosine, and the occurrence of phosphorylative processes were highlighted in irreversible pulpitis, while high amounts of water and low quantities of type I collagen were detected in hyperplastic samples. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Health-Promoting Behaviors and Their Predictors in Iranian Women of Reproductive Age: A Cross-Sectional Study.

PubMed

Bakouei, Sareh; Bakouei, Fatemeh; Omidvar, Shabnam; Bakhtiari, Afsaneh

2017-10-01

Health-promoting behaviors are one of the main determinative components of health in the prevention of many diseases. This cross-sectional study aimed to determine health-promoting behaviors and their relationship to sociodemographic variables on 330 women of reproductive age admitted to the selected primary health-care centers in Qom city (one of the biggest cities in Iran) from March 2016 to July 2016. Data were collected using the health-promoting lifestyle profile II (HPLP-II) questionnaire and the sociodemographic characteristics and also were analyzed by one-way analysis of variance, with post hoc (if necessary), and multivariable linear regression. The total HPLP-II mean score of women was 136.64 ± 22.37. The highest score in the HPLP-II subscales of women was found for interpersonal relations subscale (26.43 ± 4.21) and the lowest score for physical activity subscale (14.66 ± 4.62). The educational level and income had significant association with some subscales ( p < .05).

Three-Way Analysis of Spectrospatial Electromyography Data: Classification and Interpretation

PubMed Central

Kauppi, Jukka-Pekka; Hahne, Janne; Müller, Klaus-Robert; Hyvärinen, Aapo

2015-01-01

Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypothesize that multi-way machine learning methods can efficiently utilize this structure in classification as well as reveal interesting patterns in it. To this end, we investigate the suitability of existing three-way classification methods to EMG-based hand movement classification in spectrospatial domain, as well as extend these methods by sparsification and regularization. We propose to use Fourier-domain independent component analysis as preprocessing to improve classification and interpretability of the results. In high-density EMG experiments on hand movements across 10 subjects, three-way classification yielded higher average performance compared with state-of-the art classification based on temporal features, suggesting that the three-way analysis approach can efficiently utilize detailed spectrospatial information of high-density EMG. Phase and amplitude patterns of features selected by the classifier in finger-movement data were found to be consistent with known physiology. Thus, our approach can accurately resolve hand and finger movements on the basis of detailed spectrospatial information, and at the same time allows for physiological interpretation of the results. PMID:26039100

Is social interaction associated with alcohol consumption in Uganda?

PubMed

Tumwesigye, Nazarius Mbona; Kasirye, Rogers; Nansubuga, Elizabeth

2009-07-01

Little is documented about the association of alcohol consumption and social interaction in Uganda, a country with one of the highest per capita alcohol consumptions in the world. This paper describes the pattern of social interaction by sex and establishes the relationship between social interaction and alcohol consumption with and without the consideration of confounders. The data used had 1479 records and were collected in a survey in 2003. The study was part of a multinational study on Gender, Alcohol, and Culture International Study (GENACIS). Each question on social interaction had been pre-coded in a way that quantified the extent of social interaction. The sum of responses on interaction questions gave a summative score which was used to compute summary indices on social interaction. Principal component analysis (PCA) was used to identify the best combination of variables for a social interaction index. The index was computed by a prediction using a PCA model developed from the selected variables. The index was categorised into quintiles and used in bivariate and multivariate logistic regression analysis of alcohol consumption and social interaction. The stronger the social interaction the more the likelihood of taking alcohol frequently (chi(trend)(2)=4.72, p<0.001). The strength of the association remains significant even after controlling for sex, age group and education level (p=0.008). The strength of relationship between social interaction and heavy consumption of alcohol gets weak in multivariate analysis. Communication messages meant to improve health, well-being and public order need to incorporate dangers of negative influence of social interaction.

Bayesian Factor Analysis as a Variable Selection Problem: Alternative Priors and Consequences

PubMed Central

Lu, Zhao-Hua; Chow, Sy-Miin; Loken, Eric

2016-01-01

Factor analysis is a popular statistical technique for multivariate data analysis. Developments in the structural equation modeling framework have enabled the use of hybrid confirmatory/exploratory approaches in which factor loading structures can be explored relatively flexibly within a confirmatory factor analysis (CFA) framework. Recently, a Bayesian structural equation modeling (BSEM) approach (Muthén & Asparouhov, 2012) has been proposed as a way to explore the presence of cross-loadings in CFA models. We show that the issue of determining factor loading patterns may be formulated as a Bayesian variable selection problem in which Muthén and Asparouhov’s approach can be regarded as a BSEM approach with ridge regression prior (BSEM-RP). We propose another Bayesian approach, denoted herein as the Bayesian structural equation modeling with spike and slab prior (BSEM-SSP), which serves as a one-stage alternative to the BSEM-RP. We review the theoretical advantages and disadvantages of both approaches and compare their empirical performance relative to two modification indices-based approaches and exploratory factor analysis with target rotation. A teacher stress scale data set (Byrne, 2012; Pettegrew & Wolf, 1982) is used to demonstrate our approach. PMID:27314566

Characterization of monofloral honeys with multivariate analysis of their chemical profile and antioxidant activity.

PubMed

Sant'Ana, Luiza D'O; Sousa, Juliana P L M; Salgueiro, Fernanda B; Lorenzon, Maria Cristina Affonso; Castro, Rosane N

2012-01-01

Various bioactive chemical constituents were quantified for 21 honey samples obtained at Rio de Janeiro and Minas Gerais, Brazil. To evaluate their antioxidant activity, 3 different methods were used: the ferric reducing antioxidant power, the 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical-scavenging activity, and the 2,2'-azinobis (3-ethylbenzothiazolin)-6-sulfonate (ABTS) assays. Correlations between the parameters were statistically significant (-0.6684 ≤ r ≤-0.8410, P < 0.05). Principal component analysis showed that honey samples from the same floral origins had more similar profiles, which made it possible to group the eucalyptus, morrão de candeia, and cambara honey samples in 3 distinct areas, while cluster analysis could separate the artificial honey from the floral honeys. This research might aid in the discrimination of honey floral origin, by using simple analytical methods in association with multivariate analysis, which could also show a great difference among floral honeys and artificial honey, indicating a possible way to help with the identification of artificial honeys. © 2011 Institute of Food Technologists®

Gravitational Wave Detection of Compact Binaries Through Multivariate Analysis

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

PubMed

Levine, Matthew E; Albers, David J; Hripcsak, George

2016-01-01

Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

Multivariate analysis of fears in dental phobic patients according to a reduced FSS-II scale.

PubMed

Hakeberg, M; Gustafsson, J E; Berggren, U; Carlsson, S G

1995-10-01

This study analyzed and assessed dimensions of a questionnaire developed to measure general fears and phobias. A previous factor analysis among 109 dental phobics had revealed a five-factor structure with 22 items and an explained total variance of 54%. The present study analyzed the same material using a multivariate statistical procedure (LISREL) to reveal structural latent variables. The LISREL analysis, based on the correlation matrix, yielded a chi-square of 216.6 with 195 degrees of freedom (P = 0.138) and showed a model with seven latent variables. One was a general fear factor correlated to all 22 items. The other six factors concerned "Illness & Death" (5 items), "Failures & Embarrassment" (5 items), "Social situations" (5 items), "Physical injuries" (4 items), "Animals & Natural phenomena" (4 items). One item (opposite sex) was included in both "Failures & Embarrassment" and "Social situations". The last factor, "Social interaction", combined all the items in "Failures & Embarrassment" and "Social situations" (9 items). In conclusion, this multivariate statistical analysis (LISREL) revealed and confirmed a factor structure similar to our previous study, but added two important dimensions not shown with a traditional factor analysis. This reduced FSS-II version measures general fears and phobias and may be used on a routine clinical basis as well as in dental phobia research.

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.

Using Time Series Analysis to Predict Cardiac Arrest in a PICU.

PubMed

Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P

2015-11-01

To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach

NASA Astrophysics Data System (ADS)

Jia, Xiaoliang; An, Haizhong; Sun, Xiaoqi; Huang, Xuan; Gao, Xiangyun

2016-04-01

The globalization and regionalization of crude oil trade inevitably give rise to the difference of crude oil prices. The understanding of the pattern of the crude oil prices' mutual propagation is essential for analyzing the development of global oil trade. Previous research has focused mainly on the fuzzy long- or short-term one-to-one propagation of bivariate oil prices, generally ignoring various patterns of periodical multivariate propagation. This study presents a wavelet-based network approach to help uncover the multipath propagation of multivariable crude oil prices in a joint time-frequency period. The weekly oil spot prices of the OPEC member states from June 1999 to March 2011 are adopted as the sample data. First, we used wavelet analysis to find different subseries based on an optimal decomposing scale to describe the periodical feature of the original oil price time series. Second, a complex network model was constructed based on an optimal threshold selection to describe the structural feature of multivariable oil prices. Third, Bayesian network analysis (BNA) was conducted to find the probability causal relationship based on periodical structural features to describe the various patterns of periodical multivariable propagation. Finally, the significance of the leading and intermediary oil prices is discussed. These findings are beneficial for the implementation of periodical target-oriented pricing policies and investment strategies.

Prevalence of overactive bladder and its impact on quality of life in 1025 patients with type 2 diabetes in mainland China.

PubMed

Xu, Dongjuan; Gao, Jie; Wang, Xiaojuan; Huang, Liqun; Wang, Kefang

2017-08-01

This study examined the prevalence of overactive bladder (OAB) and investigated the impact of OAB on quality of life (QOL) in patients with type 2 diabetes in Mainland China. A total of 1025 patients with type 2 diabetes were surveyed. Patients were grouped into no OAB, dry OAB, and wet OAB groups according to the presence of OAB and urge incontinence. Descriptive analyses, one-way analysis of variance (ANOVA) and multivariable regression models were conducted to assess the prevalence of OAB and the effect of OAB on QOL. The prevalence of OAB among patients with type 2 diabetes was 13.9% (with dry OAB, 6.1%; with wet OAB, 7.8%). Multivariable regression models showed that OAB symptoms caused significant deterioration of the physical and mental aspects of QOL. Compared with dry OAB, wet OAB further decreased the mental aspect of QOL. Moreover, the effect sizes of the impacts of dry and wet OAB on QOL were larger than those of diabetic neuropathy or retinopathy, diabetes duration, or urinary tract infection history. OAB is more common in patients with type 2 diabetes than in the general population and substantially decreases patient QOL. Copyright © 2017 Elsevier Inc. All rights reserved.

FT-Raman and NIR spectroscopy data fusion strategy for multivariate qualitative analysis of food fraud.

PubMed

Márquez, Cristina; López, M Isabel; Ruisánchez, Itziar; Callao, M Pilar

2016-12-01

Two data fusion strategies (high- and mid-level) combined with a multivariate classification approach (Soft Independent Modelling of Class Analogy, SIMCA) have been applied to take advantage of the synergistic effect of the information obtained from two spectroscopic techniques: FT-Raman and NIR. Mid-level data fusion consists of merging some of the previous selected variables from the spectra obtained from each spectroscopic technique and then applying the classification technique. High-level data fusion combines the SIMCA classification results obtained individually from each spectroscopic technique. Of the possible ways to make the necessary combinations, we decided to use fuzzy aggregation connective operators. As a case study, we considered the possible adulteration of hazelnut paste with almond. Using the two-class SIMCA approach, class 1 consisted of unadulterated hazelnut samples and class 2 of samples adulterated with almond. Models performance was also studied with samples adulterated with chickpea. The results show that data fusion is an effective strategy since the performance parameters are better than the individual ones: sensitivity and specificity values between 75% and 100% for the individual techniques and between 96-100% and 88-100% for the mid- and high-level data fusion strategies, respectively. Copyright © 2016 Elsevier B.V. All rights reserved.

Multivariate Analysis for the Choice and Evasion of the Student in a Higher Educational Institution from Southern of Santa Catarina, in Brazil

ERIC Educational Resources Information Center

Queiroz, Fernanda Cristina Barbosa Pereira; Samohyl, Robert Wayne; Queiroz, Jamerson Viegas; Lima, Nilton Cesar; de Souza, Gustavo Henrique Silva

2014-01-01

This paper aims to develop and implement a method to identify the causes of the choice of a course and the reasons for evasion in higher education. This way, we sought to identify the factors that influence student choice to opt for Higher Education Institution parsed, as well as the factors influencing its evasion. The methodology employed was…

Multivariate statistical analysis of the polyphenolic constituents in kiwifruit juices to trace fruit varieties and geographical origins.

PubMed

Guo, Jing; Yuan, Yahong; Dou, Pei; Yue, Tianli

2017-10-01

Fifty-one kiwifruit juice samples of seven kiwifruit varieties from five regions in China were analyzed to determine their polyphenols contents and to trace fruit varieties and geographical origins by multivariate statistical analysis. Twenty-one polyphenols belonging to four compound classes were determined by ultra-high-performance liquid chromatography coupled with ultra-high-resolution TOF mass spectrometry. (-)-Epicatechin, (+)-catechin, procyanidin B1 and caffeic acid derivatives were the predominant phenolic compounds in the juices. Principal component analysis (PCA) allowed a clear separation of the juices according to kiwifruit varieties. Stepwise linear discriminant analysis (SLDA) yielded satisfactory categorization of samples, provided 100% success rate according to kiwifruit varieties and 92.2% success rate according to geographical origins. The result showed that polyphenolic profiles of kiwifruit juices contain enough information to trace fruit varieties and geographical origins. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Effects of Active Videogame Feedback and Practicing Experience on Children's Physical Activity Intensity and Enjoyment.

PubMed

Chen, Han; Sun, Haichun

2017-08-01

The study aims to explore the effects of receiving active videogame (AVG) feedback and playing experience on individuals' moderate-to-vigorous physical activity (MVPA) and perceived enjoyment. This was a within-subject design study. The participants included 36 (n = 15 and 21 for boys and girls, respectively) fourth graders enrolled in a rural elementary school in southern Georgia area. The experiment lasted for 6 weeks with each week including three sessions. The participants were assigned in either front row (sensor feedback) or back row (no sensor feedback) during practice, which was alternated in different sessions. Two different dance games were played during the study with each game implemented for 3 weeks. The MVPA was measured with GT3X+ accelerometers. Physical activity (PA) enjoyment was assessed after the completion of the first two and last two sessions of each game. A repeated one-way ANOVA (analysis of variance) was used to examine the effects of AVG feedback and game on MVPA. A repeated one-way MANOVA (multivariate analysis of variance) was conducted for each game to examine the effects of experience and AVG feedback on enjoyment and MVPA. No effects of AVG feedback were found for MVPA or enjoyment (P > 0.05). The effects of experience on MVPA were found for Just Dance Kids 2014 with experience decreased MVPA (P < 0.05). Students who practiced dance AVG without receiving feedback still demonstrated positive affection and accumulated similar MVPA than when practicing while receiving feedback. Experience for certain dance games tends to decrease PA intensity.

Multi-variants synthesis of Petri nets for FPGA devices

NASA Astrophysics Data System (ADS)

Bukowiec, Arkadiusz; Doligalski, Michał

2015-09-01

There is presented new method of synthesis of application specific logic controllers for FPGA devices. The specification of control algorithm is made with use of control interpreted Petri net (PT type). It allows specifying parallel processes in easy way. The Petri net is decomposed into state-machine type subnets. In this case, each subnet represents one parallel process. For this purpose there are applied algorithms of coloring of Petri nets. There are presented two approaches of such decomposition: with doublers of macroplaces or with one global wait place. Next, subnets are implemented into two-level logic circuit of the controller. The levels of logic circuit are obtained as a result of its architectural decomposition. The first level combinational circuit is responsible for generation of next places and second level decoder is responsible for generation output symbols. There are worked out two variants of such circuits: with one shared operational memory or with many flexible distributed memories as a decoder. Variants of Petri net decomposition and structures of logic circuits can be combined together without any restrictions. It leads to existence of four variants of multi-variants synthesis.

Dissecting the space-time structure of tree-ring datasets using the partial triadic analysis.

PubMed

Rossi, Jean-Pierre; Nardin, Maxime; Godefroid, Martin; Ruiz-Diaz, Manuela; Sergent, Anne-Sophie; Martinez-Meier, Alejandro; Pâques, Luc; Rozenberg, Philippe

2014-01-01

Tree-ring datasets are used in a variety of circumstances, including archeology, climatology, forest ecology, and wood technology. These data are based on microdensity profiles and consist of a set of tree-ring descriptors, such as ring width or early/latewood density, measured for a set of individual trees. Because successive rings correspond to successive years, the resulting dataset is a ring variables × trees × time datacube. Multivariate statistical analyses, such as principal component analysis, have been widely used for extracting worthwhile information from ring datasets, but they typically address two-way matrices, such as ring variables × trees or ring variables × time. Here, we explore the potential of the partial triadic analysis (PTA), a multivariate method dedicated to the analysis of three-way datasets, to apprehend the space-time structure of tree-ring datasets. We analyzed a set of 11 tree-ring descriptors measured in 149 georeferenced individuals of European larch (Larix decidua Miller) during the period of 1967-2007. The processing of densitometry profiles led to a set of ring descriptors for each tree and for each year from 1967-2007. The resulting three-way data table was subjected to two distinct analyses in order to explore i) the temporal evolution of spatial structures and ii) the spatial structure of temporal dynamics. We report the presence of a spatial structure common to the different years, highlighting the inter-individual variability of the ring descriptors at the stand scale. We found a temporal trajectory common to the trees that could be separated into a high and low frequency signal, corresponding to inter-annual variations possibly related to defoliation events and a long-term trend possibly related to climate change. We conclude that PTA is a powerful tool to unravel and hierarchize the different sources of variation within tree-ring datasets.

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.

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.

Inversion of ground-motion data from a seismometer array for rotation using a modification of Jaeger's method

USGS Publications Warehouse

Chi, Wu-Cheng; Lee, W.H.K.; Aston, J.A.D.; Lin, C.J.; Liu, C.-C.

2011-01-01

We develop a new way to invert 2D translational waveforms using Jaeger's (1969) formula to derive rotational ground motions about one axis and estimate the errors in them using techniques from statistical multivariate analysis. This procedure can be used to derive rotational ground motions and strains using arrayed translational data, thus providing an efficient way to calibrate the performance of rotational sensors. This approach does not require a priori information about the noise level of the translational data and elastic properties of the media. This new procedure also provides estimates of the standard deviations of the derived rotations and strains. In this study, we validated this code using synthetic translational waveforms from a seismic array. The results after the inversion of the synthetics for rotations were almost identical with the results derived using a well-tested inversion procedure by Spudich and Fletcher (2009). This new 2D procedure can be applied three times to obtain the full, three-component rotations. Additional modifications can be implemented to the code in the future to study different features of the rotational ground motions and strains induced by the passage of seismic waves.

Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria

NASA Astrophysics Data System (ADS)

Prochazka, D.; Mazura, M.; Samek, O.; Rebrošová, K.; Pořízka, P.; Klus, J.; Prochazková, P.; Novotný, J.; Novotný, K.; Kaiser, J.

2018-01-01

In this work, we investigate the impact of data provided by complementary laser-based spectroscopic methods on multivariate classification accuracy. Discrimination and classification of five Staphylococcus bacterial strains and one strain of Escherichia coli is presented. The technique that we used for measurements is a combination of Raman spectroscopy and Laser-Induced Breakdown Spectroscopy (LIBS). Obtained spectroscopic data were then processed using Multivariate Data Analysis algorithms. Principal Components Analysis (PCA) was selected as the most suitable technique for visualization of bacterial strains data. To classify the bacterial strains, we used Neural Networks, namely a supervised version of Kohonen's self-organizing maps (SOM). We were processing results in three different ways - separately from LIBS measurements, from Raman measurements, and we also merged data from both mentioned methods. The three types of results were then compared. By applying the PCA to Raman spectroscopy data, we observed that two bacterial strains were fully distinguished from the rest of the data set. In the case of LIBS data, three bacterial strains were fully discriminated. Using a combination of data from both methods, we achieved the complete discrimination of all bacterial strains. All the data were classified with a high success rate using SOM algorithm. The most accurate classification was obtained using a combination of data from both techniques. The classification accuracy varied, depending on specific samples and techniques. As for LIBS, the classification accuracy ranged from 45% to 100%, as for Raman Spectroscopy from 50% to 100% and in case of merged data, all samples were classified correctly. Based on the results of the experiments presented in this work, we can assume that the combination of Raman spectroscopy and LIBS significantly enhances discrimination and classification accuracy of bacterial species and strains. The reason is the complementarity in obtained chemical information while using these two methods.

Obesity, metabolic syndrome, impaired fasting glucose, and microvascular dysfunction: a principal component analysis approach.

PubMed

Panazzolo, Diogo G; Sicuro, Fernando L; Clapauch, Ruth; Maranhão, Priscila A; Bouskela, Eliete; Kraemer-Aguiar, Luiz G

2012-11-13

We aimed to evaluate the multivariate association between functional microvascular variables and clinical-laboratorial-anthropometrical measurements. Data from 189 female subjects (34.0 ± 15.5 years, 30.5 ± 7.1 kg/m2), who were non-smokers, non-regular drug users, without a history of diabetes and/or hypertension, were analyzed by principal component analysis (PCA). PCA is a classical multivariate exploratory tool because it highlights common variation between variables allowing inferences about possible biological meaning of associations between them, without pre-establishing cause-effect relationships. In total, 15 variables were used for PCA: body mass index (BMI), waist circumference, systolic and diastolic blood pressure (BP), fasting plasma glucose, levels of total cholesterol, high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), triglycerides (TG), insulin, C-reactive protein (CRP), and functional microvascular variables measured by nailfold videocapillaroscopy. Nailfold videocapillaroscopy was used for direct visualization of nutritive capillaries, assessing functional capillary density, red blood cell velocity (RBCV) at rest and peak after 1 min of arterial occlusion (RBCV(max)), and the time taken to reach RBCV(max) (TRBCV(max)). A total of 35% of subjects had metabolic syndrome, 77% were overweight/obese, and 9.5% had impaired fasting glucose. PCA was able to recognize that functional microvascular variables and clinical-laboratorial-anthropometrical measurements had a similar variation. The first five principal components explained most of the intrinsic variation of the data. For example, principal component 1 was associated with BMI, waist circumference, systolic BP, diastolic BP, insulin, TG, CRP, and TRBCV(max) varying in the same way. Principal component 1 also showed a strong association among HDL-c, RBCV, and RBCV(max), but in the opposite way. Principal component 3 was associated only with microvascular variables in the same way (functional capillary density, RBCV and RBCV(max)). Fasting plasma glucose appeared to be related to principal component 4 and did not show any association with microvascular reactivity. In non-diabetic female subjects, a multivariate scenario of associations between classic clinical variables strictly related to obesity and metabolic syndrome suggests a significant relationship between these diseases and microvascular reactivity.

Multivariate analysis of PRISMA optimized TLC image for predicting antioxidant activity and identification of contributing compounds from Pereskia bleo.

PubMed

Sharif, K M; Rahman, M M; Azmir, J; Khatib, A; Sabina, E; Shamsudin, S H; Zaidul, I S M

2015-12-01

Multivariate analysis of thin-layer chromatography (TLC) images was modeled to predict antioxidant activity of Pereskia bleo leaves and to identify the contributing compounds of the activity. TLC was developed in optimized mobile phase using the 'PRISMA' optimization method and the image was then converted to wavelet signals and imported for multivariate analysis. An orthogonal partial least square (OPLS) model was developed consisting of a wavelet-converted TLC image and 2,2-diphynyl-picrylhydrazyl free radical scavenging activity of 24 different preparations of P. bleo as the x- and y-variables, respectively. The quality of the constructed OPLS model (1 + 1 + 0) with one predictive and one orthogonal component was evaluated by internal and external validity tests. The validated model was then used to identify the contributing spot from the TLC plate that was then analyzed by GC-MS after trimethylsilyl derivatization. Glycerol and amine compounds were mainly found to contribute to the antioxidant activity of the sample. An alternative method to predict the antioxidant activity of a new sample of P. bleo leaves has been developed. Copyright © 2015 John Wiley & Sons, Ltd.

[A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition].

PubMed

Wang, Jinjia; Liu, Yuan

2015-04-01

This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competition III and competition IV reached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.

Vector space methods of photometric analysis - Applications to O stars and interstellar reddening

NASA Technical Reports Server (NTRS)

Massa, D.; Lillie, C. F.

1978-01-01

A multivariate vector-space formulation of photometry is developed which accounts for error propagation. An analysis of uvby and H-beta photometry of O stars is presented, with attention given to observational errors, reddening, general uvby photometry, early stars, and models of O stars. The number of observable parameters in O-star continua is investigated, the way these quantities compare with model-atmosphere predictions is considered, and an interstellar reddening law is derived. It is suggested that photospheric expansion affects the formation of the continuum in at least some O stars.

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.

Efficacy and safety of surgical decompression in patients with cervical spondylotic myelopathy: results of the AOSpine North America prospective multi-center study.

PubMed

Fehlings, Michael G; Wilson, Jefferson R; Kopjar, Branko; Yoon, Sangwook Tim; Arnold, Paul M; Massicotte, Eric M; Vaccaro, Alexander R; Brodke, Darrel S; Shaffrey, Christopher I; Smith, Justin S; Woodard, Eric J; Banco, Robert J; Chapman, Jens R; Janssen, Michael E; Bono, Christopher M; Sasso, Rick C; Dekutoski, Mark B; Gokaslan, Ziya L

2013-09-18

Cervical spondylotic myelopathy is the leading cause of spinal cord dysfunction worldwide. The objective of this study was to evaluate the impact of surgical decompression on functional, quality-of-life, and disability outcomes at one year after surgery in a large cohort of patients with this condition. Adult patients with symptomatic cervical spondylotic myelopathy and magnetic resonance imaging evidence of spinal cord compression were enrolled at twelve North American centers from 2005 to 2007. At enrollment, the myelopathy was categorized as mild (modified Japanese Orthopaedic Association [mJOA] score ≥ 15), moderate (mJOA = 12 to 14), or severe (mJOA < 12). Patients were followed prospectively for one year, at which point the outcomes of interest included the mJOA score, Nurick grade, Neck Disability Index (NDI), and Short Form-36 version 2 (SF-36v2). All outcomes at one year were compared with the preoperative values with use of univariate paired statistics. Outcomes were also compared among the severity classes with use of one-way analysis of variance. Finally, a multivariate analysis that adjusted for baseline differences among the severity groups was performed. Treatment-related complication data were collected and the overall complication rate was calculated. Eighty-five (30.6%) of the 278 enrolled patients had mild cervical spondylotic myelopathy, 110 (39.6%) had moderate disease, and 83 (29.9%) had severe disease preoperatively. One-year follow-up data were available for 222 (85.4%) of 260 patients. There was a significant improvement from baseline to one year postoperatively (p < 0.05) in the mJOA score, Nurick grade, NDI score, and all SF-36v2 health dimensions (including the mental and physical health composite scores) except general health. With the exception of the change in the mJOA, the degree of improvement did not depend on the severity of the preoperative symptoms. These results remained unchanged after adjusting for relevant confounders in the multivariate analysis. Fifty-two patients experienced complications (prevalence, 18.7%), with no significant differences among the severity groups. Surgical decompression for the treatment of cervical spondylotic myelopathy was associated with improvement in functional, disability-related, and quality-of-life outcomes at one year of follow-up for all disease severity categories. Furthermore, complication rates observed in the study were commensurate with those in previously reported cervical spondylotic myelopathy series.

A systematic uncertainty analysis for liner impedance eduction technology

NASA Astrophysics Data System (ADS)

Zhou, Lin; Bodén, Hans

2015-11-01

The so-called impedance eduction technology is widely used for obtaining acoustic properties of liners used in aircraft engines. The measurement uncertainties for this technology are still not well understood though it is essential for data quality assessment and model validation. A systematic framework based on multivariate analysis is presented in this paper to provide 95 percent confidence interval uncertainty estimates in the process of impedance eduction. The analysis is made using a single mode straightforward method based on transmission coefficients involving the classic Ingard-Myers boundary condition. The multivariate technique makes it possible to obtain an uncertainty analysis for the possibly correlated real and imaginary parts of the complex quantities. The results show that the errors in impedance results at low frequency mainly depend on the variability of transmission coefficients, while the mean Mach number accuracy is the most important source of error at high frequencies. The effect of Mach numbers used in the wave dispersion equation and in the Ingard-Myers boundary condition has been separated for comparison of the outcome of impedance eduction. A local Mach number based on friction velocity is suggested as a way to reduce the inconsistencies found when estimating impedance using upstream and downstream acoustic excitation.

Revealing representational content with pattern-information fMRI--an introductory guide.

PubMed

Mur, Marieke; Bandettini, Peter A; Kriegeskorte, Nikolaus

2009-03-01

Conventional statistical analysis methods for functional magnetic resonance imaging (fMRI) data are very successful at detecting brain regions that are activated as a whole during specific mental activities. The overall activation of a region is usually taken to indicate involvement of the region in the task. However, such activation analysis does not consider the multivoxel patterns of activity within a brain region. These patterns of activity, which are thought to reflect neuronal population codes, can be investigated by pattern-information analysis. In this framework, a region's multivariate pattern information is taken to indicate representational content. This tutorial introduction motivates pattern-information analysis, explains its underlying assumptions, introduces the most widespread methods in an intuitive way, and outlines the basic sequence of analysis steps.

Effects of UGT1A9 genetic polymorphisms on monohydroxylated derivative of oxcarbazepine concentrations and oxcarbazepine monotherapeutic efficacy in Chinese patients with epilepsy.

PubMed

Lu, Yao; Fang, Youxin; Wu, Xunyi; Ma, Chunlai; Wang, Yue; Xu, Lan

2017-03-01

The human UDP-glucuronosyltransferase which is genetically polymorphic catalyzes glucuronidations of various drugs. The interactions among UGT1A4, UGT1A6, UGT1A9, and UGT2B15 genetic polymorphisms, monohydroxylated derivative (MHD) of oxcarbazepine (OXC) plasma concentrations, and OXC monotherapeutic efficacy were explored in 124 Chinese patients with epilepsy receiving OXC monotherapy. MHD is the major active metabolite of OXC, and its plasma concentration was measured using high-performance liquid chromatography when patients reached their maintenance dose of OXC. Genomic DNA was extracted from whole blood and SNP genotyping performed using PCR followed by dideoxy chain termination sequencing. We followed the patients for at least 1 year to evaluate the OXC monotherapy efficacy. Patients were divided into two groups according to their therapeutic outcome: group 1, seizure free; group 2, not seizure free. The data were analyzed using T test, one-way analysis of variance (ANOVA), Kruskal-Wallis test, chi-square test, Fisher's exact test, correlation analysis, and multivariate regression analysis. T test analysis showed that MHD plasma concentrations were significantly different between the two groups (p = 0.002). One-way ANOVA followed by Bonferroni post hoc testing of four candidate SNPs revealed that carriers of the UGT1A9 variant allele I399 C > T (TT 13.28 ± 7.44 mg/L, TC 16.41 ± 6.53 mg/L) had significantly lower MHD plasma concentrations and poorer seizure control than noncarriers (CC 22.24 ± 8.49 mg/L, p < 0.05). In our study, we have demonstrated the effects of UGT1A9 genetic polymorphisms on MHD plasma concentrations and OXC therapeutic efficacy. Through MHD monitoring, we can predict OXC therapeutic efficacy, which may be useful for the personalization of OXC therapy in epileptic patients.

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.

Influence of PAHs among other coastal environmental variables on total and PAH-degrading bacterial communities.

PubMed

Sauret, Caroline; Tedetti, Marc; Guigue, Catherine; Dumas, Chloé; Lami, Raphaël; Pujo-Pay, Mireille; Conan, Pascal; Goutx, Madeleine; Ghiglione, Jean-François

2016-03-01

We evaluated the relative impact of anthropogenic polycyclic aromatic hydrocarbons (PAHs) among biogeochemical variables on total, metabolically active, and PAH bacterial communities in summer and winter in surface microlayer (SML) and subsurface seawaters (SSW) across short transects along the NW Mediterranean coast from three harbors, one wastewater effluent, and one nearshore observatory reference site. At both seasons, significant correlations were found between dissolved total PAH concentrations and PAH-degrading bacteria that formed a gradient from the shore to nearshore waters. Accumulation of PAH degraders was particularly high in the SML, where PAHs accumulated. Harbors and wastewater outfalls influenced drastically and in a different way the total and active bacterial community structure, but they only impacted the communities from the nearshore zone (<2 km from the shore). By using direct multivariate statistical analysis, we confirmed the significant effect of PAH concentrations on the spatial and temporal dynamic of total and active communities in this area, but this effect was putted in perspective by the importance of other biogeochemical variables.

Effects of soil and topographic factors on vegetation restoration in opencast coal mine dumps located in a loess area

NASA Astrophysics Data System (ADS)

Wang, Jinman; Wang, Hongdan; Cao, Yingui; Bai, Zhongke; Qin, Qian

2016-02-01

Vegetation plays an important role in improving and restoring fragile ecological environments. In the Antaibao opencast coal mine, located in a loess area, the eco-environment has been substantially disturbed by mining activities, and the relationship between the vegetation and environmental factors is not very clear. Therefore, it is crucial to understand the effects of soil and topographic factors on vegetation restoration to improve the fragile ecosystems of damaged land. An investigation of the soil, topography and vegetation in 50 reclamation sample plots in Shanxi Pingshuo Antaibao opencast coal mine dumps was performed. Statistical analyses in this study included one-way ANOVA and significance testing using SPSS 20.0, and multivariate techniques of detrended correspondence analysis (DCA) and redundancy analysis (RDA) using CANOCO 4.5. The RDA revealed the environmental factors that affected vegetation restoration. Various vegetation and soil variables were significantly correlated. The available K and rock content were good explanatory variables, and they were positively correlated with tree volume. The effects of the soil factors on vegetation restoration were higher than those of the topographic factors.

Effects of soil and topographic factors on vegetation restoration in opencast coal mine dumps located in a loess area

PubMed Central

Wang, Jinman; Wang, Hongdan; Cao, Yingui; Bai, Zhongke; Qin, Qian

2016-01-01

Vegetation plays an important role in improving and restoring fragile ecological environments. In the Antaibao opencast coal mine, located in a loess area, the eco-environment has been substantially disturbed by mining activities, and the relationship between the vegetation and environmental factors is not very clear. Therefore, it is crucial to understand the effects of soil and topographic factors on vegetation restoration to improve the fragile ecosystems of damaged land. An investigation of the soil, topography and vegetation in 50 reclamation sample plots in Shanxi Pingshuo Antaibao opencast coal mine dumps was performed. Statistical analyses in this study included one-way ANOVA and significance testing using SPSS 20.0, and multivariate techniques of detrended correspondence analysis (DCA) and redundancy analysis (RDA) using CANOCO 4.5. The RDA revealed the environmental factors that affected vegetation restoration. Various vegetation and soil variables were significantly correlated. The available K and rock content were good explanatory variables, and they were positively correlated with tree volume. The effects of the soil factors on vegetation restoration were higher than those of the topographic factors. PMID:26916152

Effects of soil and topographic factors on vegetation restoration in opencast coal mine dumps located in a loess area.

PubMed

Wang, Jinman; Wang, Hongdan; Cao, Yingui; Bai, Zhongke; Qin, Qian

2016-02-26

Vegetation plays an important role in improving and restoring fragile ecological environments. In the Antaibao opencast coal mine, located in a loess area, the eco-environment has been substantially disturbed by mining activities, and the relationship between the vegetation and environmental factors is not very clear. Therefore, it is crucial to understand the effects of soil and topographic factors on vegetation restoration to improve the fragile ecosystems of damaged land. An investigation of the soil, topography and vegetation in 50 reclamation sample plots in Shanxi Pingshuo Antaibao opencast coal mine dumps was performed. Statistical analyses in this study included one-way ANOVA and significance testing using SPSS 20.0, and multivariate techniques of detrended correspondence analysis (DCA) and redundancy analysis (RDA) using CANOCO 4.5. The RDA revealed the environmental factors that affected vegetation restoration. Various vegetation and soil variables were significantly correlated. The available K and rock content were good explanatory variables, and they were positively correlated with tree volume. The effects of the soil factors on vegetation restoration were higher than those of the topographic factors.

Application of a Novel S3 Nanowire Gas Sensor Device in Parallel with GC-MS for the Identification of Rind Percentage of Grated Parmigiano Reggiano.

PubMed

Abbatangelo, Marco; Núñez-Carmona, Estefanía; Sberveglieri, Veronica; Zappa, Dario; Comini, Elisabetta; Sberveglieri, Giorgio

2018-05-18

Parmigiano Reggiano cheese is one of the most appreciated and consumed foods worldwide, especially in Italy, for its high content of nutrients and taste. However, these characteristics make this product subject to counterfeiting in different forms. In this study, a novel method based on an electronic nose has been developed to investigate the potentiality of this tool to distinguish rind percentages in grated Parmigiano Reggiano packages that should be lower than 18%. Different samples, in terms of percentage, seasoning and rind working process, were considered to tackle the problem at 360°. In parallel, GC-MS technique was used to give a name to the compounds that characterize Parmigiano and to relate them to sensors responses. Data analysis consisted of two stages: Multivariate analysis (PLS) and classification made in a hierarchical way with PLS-DA ad ANNs. Results were promising, in terms of correct classification of the samples. The correct classification rate (%) was higher for ANNs than PLS-DA, with correct identification approaching 100 percent.

Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods.

PubMed

Jiménez-Carvelo, Ana M; González-Casado, Antonio; Pérez-Castaño, Estefanía; Cuadros-Rodríguez, Luis

2017-03-01

A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phase LC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis took only 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil were used: one input-class, two input-class, and pseudo two input-class.

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.

High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies.

PubMed

Goudey, Benjamin; Abedini, Mani; Hopper, John L; Inouye, Michael; Makalic, Enes; Schmidt, Daniel F; Wagner, John; Zhou, Zeyu; Zobel, Justin; Reumann, Matthias

2015-01-01

Genome-wide association studies (GWAS) are a common approach for systematic discovery of single nucleotide polymorphisms (SNPs) which are associated with a given disease. Univariate analysis approaches commonly employed may miss important SNP associations that only appear through multivariate analysis in complex diseases. However, multivariate SNP analysis is currently limited by its inherent computational complexity. In this work, we present a computational framework that harnesses supercomputers. Based on our results, we estimate a three-way interaction analysis on 1.1 million SNP GWAS data requiring over 5.8 years on the full "Avoca" IBM Blue Gene/Q installation at the Victorian Life Sciences Computation Initiative. This is hundreds of times faster than estimates for other CPU based methods and four times faster than runtimes estimated for GPU methods, indicating how the improvement in the level of hardware applied to interaction analysis may alter the types of analysis that can be performed. Furthermore, the same analysis would take under 3 months on the currently largest IBM Blue Gene/Q supercomputer "Sequoia" at the Lawrence Livermore National Laboratory assuming linear scaling is maintained as our results suggest. Given that the implementation used in this study can be further optimised, this runtime means it is becoming feasible to carry out exhaustive analysis of higher order interaction studies on large modern GWAS.

Recent advances in scalable non-Gaussian geostatistics: The generalized sub-Gaussian model

NASA Astrophysics Data System (ADS)

Guadagnini, Alberto; Riva, Monica; Neuman, Shlomo P.

2018-07-01

Geostatistical analysis has been introduced over half a century ago to allow quantifying seemingly random spatial variations in earth quantities such as rock mineral content or permeability. The traditional approach has been to view such quantities as multivariate Gaussian random functions characterized by one or a few well-defined spatial correlation scales. There is, however, mounting evidence that many spatially varying quantities exhibit non-Gaussian behavior over a multiplicity of scales. The purpose of this minireview is not to paint a broad picture of the subject and its treatment in the literature. Instead, we focus on very recent advances in the recognition and analysis of this ubiquitous phenomenon, which transcends hydrology and the Earth sciences, brought about largely by our own work. In particular, we use porosity data from a deep borehole to illustrate typical aspects of such scalable non-Gaussian behavior, describe a very recent theoretical model that (for the first time) captures all these behavioral aspects in a comprehensive manner, show how this allows generating random realizations of the quantity conditional on sampled values, point toward ways of incorporating scalable non-Gaussian behavior in hydrologic analysis, highlight the significance of doing so, and list open questions requiring further research.

Differences in game-related statistics of basketball performance by game location for men's winning and losing teams.

PubMed

Gómez, Miguel A; Lorenzo, Alberto; Barakat, Rubén; Ortega, Enrique; Palao, José M

2008-02-01

The aim of the present study was to identify game-related statistics that differentiate winning and losing teams according to game location. The sample included 306 games of the 2004-2005 regular season of the Spanish professional men's league (ACB League). The independent variables were game location (home or away) and game result (win or loss). The game-related statistics registered were free throws (successful and unsuccessful), 2- and 3-point field goals (successful and unsuccessful), offensive and defensive rebounds, blocks, assists, fouls, steals, and turnovers. Descriptive and inferential analyses were done (one-way analysis of variance and discriminate analysis). The multivariate analysis showed that winning teams differ from losing teams in defensive rebounds (SC = .42) and in assists (SC = .38). Similarly, winning teams differ from losing teams when they play at home in defensive rebounds (SC = .40) and in assists (SC = .41). On the other hand, winning teams differ from losing teams when they play away in defensive rebounds (SC = .44), assists (SC = .30), successful 2-point field goals (SC = .31), and unsuccessful 3-point field goals (SC = -.35). Defensive rebounds and assists were the only game-related statistics common to all three analyses.

Plasma apelin levels, blood pressure and cardiovascular risk factors in a coastal Chinese population.

PubMed

Zhu, Pengli; Huang, Feng; Lin, Fan; Yuan, Yin; Chen, Falin; Li, Qiaowei

2013-11-01

To describe the relationship of plasma apelin levels with blood pressure in a coastal Chinese population. This cross-sectional study included a total of 1031 subjects from the coastal areas of China. One-way analysis of variance (ANOVA) and linear trend test, Pearson's correlation analysis, as well as multivariate linear regression analysis were used to evaluate the association between plasma apelin levels and blood pressure. Plasma apelin levels dropped with increasing quartiles of systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial blood pressure (MABP) (all P<0.001). SBP, DBP, and MABP values decreased as the apelin levels increased within the quartiles. After adjusting for age and gender, the significant differences in SBP, DBP, and MABP between the groups within the apelin quartiles remained (all P<0.05). A significant negative correlation between SBP, DBP, as well as MABP and apelin levels was observed (all P<0.01); even after adjusting for cardiovascular confounding factors, this negative correlation remained (all P<0.001). A negative correlation between plasma apelin levels and blood pressure was found in this 1000-population-based epidemiological study. Apelin may become a potential therapeutic target of anti-hypertensive treatment.

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Phytate (myo-inositol hexaphosphate) and risk factors for osteoporosis.

PubMed

López-González, A A; Grases, F; Roca, P; Mari, B; Vicente-Herrero, M T; Costa-Bauzá, A

2008-12-01

Several risk factors seem to play a role in the development of osteoporosis. Phytate is a naturally occurring compound that is ingested in significant amounts by those with diets rich in whole grains. The aim of this study was to evaluate phytate consumption as a risk factor in osteoporosis. In a first group of 1,473 volunteer subjects, bone mineral density was determined by means of dual radiological absorptiometry in the calcaneus. In a second group of 433 subjects (used for validation of results obtained for the first group), bone mineral density was determined in the lumbar column and the neck of the femur. Subjects were individually interviewed about selected osteoporosis risk factors. Dietary information related to phytate consumption was acquired by questionnaires conducted on two different occasions, the second between 2 and 3 months after performing the first one. One-way analysis of variance or Student's t test was used to determine statistical differences between groups. Bone mineral density increased with increasing phytate consumption. Multivariate linear regression analysis indicated that body weight and low phytate consumption were the risk factors with greatest influence on bone mineral density. Phytate consumption had a protective effect against osteoporosis, suggesting that low phytate consumption should be considered an osteoporosis risk factor.

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.

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

NASA Astrophysics Data System (ADS)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

Voxelwise multivariate analysis of multimodality magnetic resonance imaging

PubMed Central

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

2015-01-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remains a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. PMID:23408378

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

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.

Gaussianization for fast and accurate inference from cosmological data

NASA Astrophysics Data System (ADS)

Schuhmann, Robert L.; Joachimi, Benjamin; Peiris, Hiranya V.

2016-06-01

We present a method to transform multivariate unimodal non-Gaussian posterior probability densities into approximately Gaussian ones via non-linear mappings, such as Box-Cox transformations and generalizations thereof. This permits an analytical reconstruction of the posterior from a point sample, like a Markov chain, and simplifies the subsequent joint analysis with other experiments. This way, a multivariate posterior density can be reported efficiently, by compressing the information contained in Markov Chain Monte Carlo samples. Further, the model evidence integral (I.e. the marginal likelihood) can be computed analytically. This method is analogous to the search for normal parameters in the cosmic microwave background, but is more general. The search for the optimally Gaussianizing transformation is performed computationally through a maximum-likelihood formalism; its quality can be judged by how well the credible regions of the posterior are reproduced. We demonstrate that our method outperforms kernel density estimates in this objective. Further, we select marginal posterior samples from Planck data with several distinct strongly non-Gaussian features, and verify the reproduction of the marginal contours. To demonstrate evidence computation, we Gaussianize the joint distribution of data from weak lensing and baryon acoustic oscillations, for different cosmological models, and find a preference for flat Λcold dark matter. Comparing to values computed with the Savage-Dickey density ratio, and Population Monte Carlo, we find good agreement of our method within the spread of the other two.

Features predictive of brain arteriovenous malformation hemorrhage: extrapolation to a physiologic model.

PubMed

Sahlein, Daniel H; Mora, Paloma; Becske, Tibor; Huang, Paul; Jafar, Jafar J; Connolly, E Sander; Nelson, Peter K

2014-07-01

Although there is generally thought to be a 2% to 4% per annum rupture risk for brain arteriovenous malformations (bAVMs), there is no way to estimate risk for an individual patient. In this retrospective study, patients were eligible who had nidiform bAVMs and underwent detailed pretreatment diagnostic cerebral angiography at our medical center from 1996 to 2006. All patients had superselective microcatheter angiography, and films were reviewed for the purpose of this project. Patient demographics, clinical presentation, and angioarchitectural characteristics were analyzed. A univariate analysis was performed, and angioarchitectural features with potential physiological significance that showed at least a trend toward significance were added to a multivariate logistic regression model. One hundred twenty-two bAVMs met criteria for study entry. bAVMs with single venous drainage anatomy were more likely to present with hemorrhage. In addition, patients with multiple draining veins and a venous stenosis reverted to a risk similar to those with 1 draining vein, whereas those with multiple draining veins and without stenosis had diminished association with hemorrhage presentation. Those bAVMs with associated aneurysms were more likely to present with hemorrhage. These findings were robust in both univariate and multivariate models. The results of this article lead to the first physiological, internally consistent model of individual bAVM hemorrhage risk, where 1 draining vein, venous stenosis, and associated aneurysms increase risk. © 2014 American Heart Association, Inc.

Comparative analysis of the performance of One-Way and Two-Way urban road networks

NASA Astrophysics Data System (ADS)

Gheorghe, Carmen

2017-10-01

The fact that the number of vehicles is increasing year after year represents a challenge in road traffic management because it is necessary to adjust the road traffic, in order to prevent any incidents, using mostly the same road infrastructure. At this moment one-way road network provides efficient traffic flow for vehicles but it is not ideal for pedestrians. Therefore, a proper solution must be found and applied when and where it is necessary. Replacing one-way road network with two-way road network may be a viable solution especially if in the area is high pedestrian traffic. The paper aims to highlight the influence of both, one-way and two-way urban road networks through an experimental research which was performed by using traffic data collected in the field. Each of the two scenarios analyzed were based on the same traffic data, the same geometrical conditions of the road (lane width, total road segment width, road slopes, total length of the road network) and also the same signaling conditions (signalised intersection or roundabout). The analysis which involves two-way scenario reveals changes in the performance parameters like delay average, stops average, delay stop average and vehicle speed average. Based on the values obtained, it was possible to perform a comparative analysis between the real, one-way, scenario and the theoretical, two-way, scenario.

Scientific Elitism and the Information System of Science

ERIC Educational Resources Information Center

Amick, Daniel James

1973-01-01

Scientific elitism must be viewed as a multidimensional phenomenon. Ten variables of elitism are considered and a principal components factor analysis is used to scale this multivariate domain. Two significant dimensions of elitism were found; one in basic and one in applied science. (20 references) (Author)

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

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

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

NASA Astrophysics Data System (ADS)

Braga, Jez Willian Batista; Trevizan, Lilian Cristina; Nunes, Lidiane Cristina; Rufini, Iolanda Aparecida; Santos, Dário, Jr.; Krug, Francisco José

2010-01-01

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

Multivariate analysis of prognostic factors in synovial sarcoma.

PubMed

Koh, Kyoung Hwan; Cho, Eun Yoon; Kim, Dong Wook; Seo, Sung Wook

2009-11-01

Many studies have described the diversity of synovial sarcoma in terms of its biological characteristics and clinical features. Moreover, much effort has been expended on the identification of prognostic factors because of unpredictable behaviors of synovial sarcomas. However, with the exception of tumor size, published results have been inconsistent. We attempted to identify independent risk factors using survival analysis. Forty-one consecutive patients with synovial sarcoma were prospectively followed from January 1997 to March 2008. Overall and progression-free survival for age, sex, tumor size, tumor location, metastasis at presentation, histologic subtype, chemotherapy, radiation therapy, and resection margin were analyzed, and standard multivariate Cox proportional hazard regression analysis was used to evaluate potential prognostic factors. Tumor size (>5 cm), nonlimb-based tumors, metastasis at presentation, and a monophasic subtype were associated with poorer overall survival. Multivariate analysis showed metastasis at presentation and monophasic tumor subtype affected overall survival. For the progression-free survival, monophasic subtype was found to be only 1 prognostic factor. The study confirmed that histologic subtype is the single most important independent prognostic factors of synovial sarcoma regardless of tumor stage.

What affects the quality of economic analysis for life-saving investments?

PubMed

Hahn, Robert W; Kosec, Katrina; Neumann, Peter J; Wallsten, Scott

2006-06-01

Economic analysis of life-saving investments in both the public and private sectors has the potential to dramatically improve longevity and the quality of life, but only if the analyses on which decisions are based are done well. In this article, we analyze a data set that provides information on the content and quality of journal articles that measure the cost-effectiveness of life-saving investments. Our study is the first to provide a detailed multivariate analysis of factors affecting objective measures of quality. We also explore whether a series of recommendations by an expert panel convened by the U.S. Public Health Service affect the way analyses of specific life-saving investments are done. Our results suggest that four factors are positively correlated with an index we construct to measure analytical quality: (1) having at least one author affiliated with a university, (2) publication in a journal that has experience in publishing these analyses, (3) if the life-saving investment is located in the United States, and (4) if the analysis considers a measure of social costs or benefits. Somewhat surprisingly, a study's funding source and whether it is affiliated with industry are not significantly correlated with the quality index. Finally, neither time nor the panel guidelines had an impact on the index.

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.

An Integrated Analysis of the Physiological Effects of Space Flight: Executive Summary

NASA Technical Reports Server (NTRS)

Leonard, J. I.

1985-01-01

A large array of models were applied in a unified manner to solve problems in space flight physiology. Mathematical simulation was used as an alternative way of looking at physiological systems and maximizing the yield from previous space flight experiments. A medical data analysis system was created which consist of an automated data base, a computerized biostatistical and data analysis system, and a set of simulation models of physiological systems. Five basic models were employed: (1) a pulsatile cardiovascular model; (2) a respiratory model; (3) a thermoregulatory model; (4) a circulatory, fluid, and electrolyte balance model; and (5) an erythropoiesis regulatory model. Algorithms were provided to perform routine statistical tests, multivariate analysis, nonlinear regression analysis, and autocorrelation analysis. Special purpose programs were prepared for rank correlation, factor analysis, and the integration of the metabolic balance data.

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.

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.

Long-term outcome of pronation-external rotation ankle fractures treated with syndesmotic screws only.

PubMed

Lambers, Kaj T A; van den Bekerom, Michel P J; Doornberg, Job N; Stufkens, Sjoerd A S; van Dijk, C Niek; Kloen, Peter

2013-09-04

There is sparse information in the literature on the outcome of Maisonneuve-type pronation-external rotation ankle fractures treated with syndesmotic screws. The primary aim of this study was to determine the long-term results of such treatment of these fractures as indicated by standardized patient-based and physician-based outcome measures. The secondary aim was to identify predictors of the outcome with use of bivariate and multivariate statistical analysis. Fifty patients with pronation-external rotation (predominantly Maisonneuve) fractures were treated with open reduction and internal fixation of the syndesmosis utilizing only one or two screws. The results were evaluated at a mean of twenty-one years after the fracture utilizing three standardized outcomes instruments: (1) the Foot and Ankle Ability Measure (FAAM), (2) the American Orthopaedic Foot & Ankle Society (AOFAS) ankle-hindfoot scale, and (3) the Center for Epidemiologic Studies-Depression (CES-D) Scale. Osteoarthritis was graded according to the van Dijk and revised Takakura radiographic scoring systems. Bivariate and multivariate analyses were performed to identify predictors of long-term outcome. Forty-four (92%) of forty-eighty patients had good or excellent AOFAS scores, and forty-four (90%) of forty-nine had good or excellent FAAM scores. Arthrodesis for severe osteoarthritis was performed in two patients. Radiographic evidence of osteoarthritis was observed in twenty-four (49%) of forty-nine patients. Multivariate analysis identified pain as the most important independent predictor of long-term ankle function as indicated by the AOFAS and FAAM scores, explaining 91% and 53% of the variation in scores, respectively. Analysis of pain as the dependent variable in bivariate analyses revealed that depression, ankle range of motion, and a subsequent surgery were significantly correlated with higher pain scores. No firm conclusions could be drawn after multivariate analysis of predictors of pain. Long-term functional outcomes at a mean of twenty-one years after pronation-external rotation ankle fractures treated with one or two syndesmotic screws were good to excellent in the great majority of patients despite substantial radiographic evidence of osteoarthritis in one-half of the patients. The most important predictor of long-term functional outcome was patient-reported pain rather than physician-reported function or posttraumatic osteoarthritis. There was no significant association between radiographic signs of posttraumatic osteoarthritis and perceived pain in the present series.

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.

Some historical relationships between science and technology with implications for behavior analysis

PubMed Central

Moxley, Roy A.

1989-01-01

The relationship between science and technology is examined in terms of some implications for behavior analysis. Problems result when this relationship is seen as one in which science generally begets technology in a one-way, or hierarchical, relationship. These problems are not found when the relationship between science and technology is seen as two-way, or symmetrical, within a larger context of relationships. Some historical examples are presented. Collectively, these and other examples in the references weaken the case for a prevailing one-way, hierarchical relationship and strengthen the case for a two-way, symmetrical relationship. In addition to being more accurate historically, the symmetrical relationship is also more consistent with the principles of behavior analysis. PMID:22478016

Compliance with the vaccination schedule in children hospitalized with pneumonia and associated factors

PubMed Central

da Silva, Amanda Tabosa Pereira; Lima, Eduardo Jorge da Fonseca; Caminha, Maria de Fátima Costa; da Silva, Andresa Tabosa Pereira; Rodrigues, Edil de Albuquerque; dos Santos, Carmina Silva

2018-01-01

ABSTRACT OBJECTIVE: To verify the adequacy and factors associated with compliance with the immunization schedule (BCG, DTP-Hib, MMR, PCV-10) in children hospitalized with pneumonia at a pediatric referral hospital in Northeast Brazil. METHODS: This is a cross-sectional, descriptive study with an analytical component, with a sample of 452 children hospitalized with pneumonia at the Instituto de Medicina Integral Prof. Fernando Figueira, between 2010 and 2013. The inclusion criterion was children aged from one month to less than five years of age with proof in the immunization record. The exclusion criterion was the presence of hospital-acquired pneumonia or concomitant disease. We have evaluated the adequacy of the immunization schedule for the BCG, tetravalent, MMR, and 10-valent pneumococcal conjugate (PCV-10) vaccines. We used the chi-square test and Fisher's exact test followed by multivariate Poisson regression, estimating the crude and adjusted prevalence ratios and respective 95% confidence intervals. The variables with p < 0.20 in the univariate analysis were included in the multivariate analysis. RESULTS: There was good adequacy in the immunization schedule, except for PCV-10, which presented a percentage lower than 85%. We have observed an association between adequate compliance with the immunization schedule and education level of the mother (89.9% complete high school), sex of the child (87.2% female), age of the child (94.2% younger than six months), and breastfeeding (84.3% breastfed). CONCLUSIONS: Given the high rate of education level of the mother and the high percentage of breastfeeding, we can understand that there is a better understanding of the health of the child by the mothers studied in this study, showing the effectiveness of public policies for infant feeding. However, children did not have good adequacy of the immunization schedule of PCV-10, one of the main vaccines against pneumonia, which can be one of the main factors in the causes of hospitalization, with no influence on the classification of the severity of the disease. In this way, we emphasize that the causes of pneumonia morbidity are not associated with a single factor. PMID:29668816

Variety identification of brown sugar using short-wave near infrared spectroscopy and multivariate calibration

NASA Astrophysics Data System (ADS)

Yang, Haiqing; Wu, Di; He, Yong

2007-11-01

Near-infrared spectroscopy (NIRS) with the characteristics of high speed, non-destructiveness, high precision and reliable detection data, etc. is a pollution-free, rapid, quantitative and qualitative analysis method. A new approach for variety discrimination of brown sugars using short-wave NIR spectroscopy (800-1050nm) was developed in this work. The relationship between the absorbance spectra and brown sugar varieties was established. The spectral data were compressed by the principal component analysis (PCA). The resulting features can be visualized in principal component (PC) space, which can lead to discovery of structures correlative with the different class of spectral samples. It appears to provide a reasonable variety clustering of brown sugars. The 2-D PCs plot obtained using the first two PCs can be used for the pattern recognition. Least-squares support vector machines (LS-SVM) was applied to solve the multivariate calibration problems in a relatively fast way. The work has shown that short-wave NIR spectroscopy technique is available for the brand identification of brown sugar, and LS-SVM has the better identification ability than PLS when the calibration set is small.

Extracting galactic structure parameters from multivariated density estimation

NASA Technical Reports Server (NTRS)

Chen, B.; Creze, M.; Robin, A.; Bienayme, O.

1992-01-01

Multivariate statistical analysis, including includes cluster analysis (unsupervised classification), discriminant analysis (supervised classification) and principle component analysis (dimensionlity reduction method), and nonparameter density estimation have been successfully used to search for meaningful associations in the 5-dimensional space of observables between observed points and the sets of simulated points generated from a synthetic approach of galaxy modelling. These methodologies can be applied as the new tools to obtain information about hidden structure otherwise unrecognizable, and place important constraints on the space distribution of various stellar populations in the Milky Way. In this paper, we concentrate on illustrating how to use nonparameter density estimation to substitute for the true densities in both of the simulating sample and real sample in the five-dimensional space. In order to fit model predicted densities to reality, we derive a set of equations which include n lines (where n is the total number of observed points) and m (where m: the numbers of predefined groups) unknown parameters. A least-square estimation will allow us to determine the density law of different groups and components in the Galaxy. The output from our software, which can be used in many research fields, will also give out the systematic error between the model and the observation by a Bayes rule.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging.

PubMed

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

2014-03-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remain a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. Copyright © 2013 Wiley Periodicals, Inc.

Clinical Trials With Large Numbers of Variables: Important Advantages of Canonical Analysis.

PubMed

Cleophas, Ton J

2016-01-01

Canonical analysis assesses the combined effects of a set of predictor variables on a set of outcome variables, but it is little used in clinical trials despite the omnipresence of multiple variables. The aim of this study was to assess the performance of canonical analysis as compared with traditional multivariate methods using multivariate analysis of covariance (MANCOVA). As an example, a simulated data file with 12 gene expression levels and 4 drug efficacy scores was used. The correlation coefficient between the 12 predictor and 4 outcome variables was 0.87 (P = 0.0001) meaning that 76% of the variability in the outcome variables was explained by the 12 covariates. Repeated testing after the removal of 5 unimportant predictor and 1 outcome variable produced virtually the same overall result. The MANCOVA identified identical unimportant variables, but it was unable to provide overall statistics. (1) Canonical analysis is remarkable, because it can handle many more variables than traditional multivariate methods such as MANCOVA can. (2) At the same time, it accounts for the relative importance of the separate variables, their interactions and differences in units. (3) Canonical analysis provides overall statistics of the effects of sets of variables, whereas traditional multivariate methods only provide the statistics of the separate variables. (4) Unlike other methods for combining the effects of multiple variables such as factor analysis/partial least squares, canonical analysis is scientifically entirely rigorous. (5) Limitations include that it is less flexible than factor analysis/partial least squares, because only 2 sets of variables are used and because multiple solutions instead of one is offered. We do hope that this article will stimulate clinical investigators to start using this remarkable method.

[A novel method of multi-channel feature extraction combining multivariate autoregression and multiple-linear principal component analysis].

PubMed

Wang, Jinjia; Zhang, Yanna

2015-02-01

Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups of IV-III and IV - I. The experimental results proved that the method proposed in this paper was feasible.

Optimal moment determination in POME-copula based hydrometeorological dependence modelling

NASA Astrophysics Data System (ADS)

Liu, Dengfeng; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi

2017-07-01

Copula has been commonly applied in multivariate modelling in various fields where marginal distribution inference is a key element. To develop a flexible, unbiased mathematical inference framework in hydrometeorological multivariate applications, the principle of maximum entropy (POME) is being increasingly coupled with copula. However, in previous POME-based studies, determination of optimal moment constraints has generally not been considered. The main contribution of this study is the determination of optimal moments for POME for developing a coupled optimal moment-POME-copula framework to model hydrometeorological multivariate events. In this framework, margins (marginals, or marginal distributions) are derived with the use of POME, subject to optimal moment constraints. Then, various candidate copulas are constructed according to the derived margins, and finally the most probable one is determined, based on goodness-of-fit statistics. This optimal moment-POME-copula framework is applied to model the dependence patterns of three types of hydrometeorological events: (i) single-site streamflow-water level; (ii) multi-site streamflow; and (iii) multi-site precipitation, with data collected from Yichang and Hankou in the Yangtze River basin, China. Results indicate that the optimal-moment POME is more accurate in margin fitting and the corresponding copulas reflect a good statistical performance in correlation simulation. Also, the derived copulas, capturing more patterns which traditional correlation coefficients cannot reflect, provide an efficient way in other applied scenarios concerning hydrometeorological multivariate modelling.

A new species of Acanthodactylus Fitzinger 1834 (Sauria: Lacertidae) from southern Iran.

PubMed

Nastaran, Heidari; Nasrullah Rastegar, Pouyani; Eskandar, Rastegar-Pouyani; Mehdi, Rajabizadeh

2013-01-01

A new and distinctive species of lacertid genus Acanthodactylus Fitzinger, 1834 is described from 7 km east of Khamir Port, Hormozgan Province, southern Iran at an elevation of 30-40m above sea level (asl). Analyses of morphological characters and the comparison with other formerly known species of this genus have proven the status of this taxon as a new, distinct species. Combinations of scalation characters and distinct morphology, coloration and habitat peculiarities in calcareous mountains distinguish Acanthodactylus khamirensis sp.nov from all remaining species of the genus in the area. In order to show the validity of the new species, we carried out a comparative statistical analysis using 13 metric and six meristic morphological characters on all of the neighboring congeners of the new species using descriptive (one-way ANOVA) as well as multivariate analyses (PCA and DFA). The results confirm the specific status of the new taxon. Detailed information and an updated identification key for the genus A canthodactylus in Iran are presented.

Sexual risk at first coitus: Does alcohol make a difference?

PubMed

Livingston, Jennifer A; Testa, Maria; Windle, Michael; Bay-Cheng, Laina Y

2015-08-01

This study examines whether use of alcohol at first coitus is associated with increased sexual risk for young women. First coitus is the focus of the investigation because it is a memorable, formative experience that has implications for subsequent sexual health. A community sample of young women ages 18-19 years (N = 227) completed retrospective interviews. Characteristics and perceptions of the first coital event were examined using chi squares and one-way multivariate analysis of variance (MANOVA) to determine if there were differences based on alcohol-involvement. Alcohol-involved first coitus events occurred in social settings with risky partners, were rated less positively, and were non-consensual relative to those that did not involve alcohol. Alcohol use was not related to condom use. Alcohol-involvement was associated with subsequent pairing of alcohol with sex and incapacitated rape. Adolescent alcohol use occurs in contexts that increases young women's sexual risk through exposure to risky partners. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Back to Normal! Gaussianizing posterior distributions for cosmological probes

NASA Astrophysics Data System (ADS)

Schuhmann, Robert L.; Joachimi, Benjamin; Peiris, Hiranya V.

2014-05-01

We present a method to map multivariate non-Gaussian posterior probability densities into Gaussian ones via nonlinear Box-Cox transformations, and generalizations thereof. This is analogous to the search for normal parameters in the CMB, but can in principle be applied to any probability density that is continuous and unimodal. The search for the optimally Gaussianizing transformation amongst the Box-Cox family is performed via a maximum likelihood formalism. We can judge the quality of the found transformation a posteriori: qualitatively via statistical tests of Gaussianity, and more illustratively by how well it reproduces the credible regions. The method permits an analytical reconstruction of the posterior from a sample, e.g. a Markov chain, and simplifies the subsequent joint analysis with other experiments. Furthermore, it permits the characterization of a non-Gaussian posterior in a compact and efficient way. The expression for the non-Gaussian posterior can be employed to find analytic formulae for the Bayesian evidence, and consequently be used for model comparison.

Alcohol Involvement in First Sexual Intercourse Experiences of Adolescent Girls

PubMed Central

Livingston, Jennifer A.; Testa, Maria; Windle, Michael; Bay-Cheng, Laina Y.

2015-01-01

This study examines whether use of alcohol at first coitus is associated with increased sexual risk for young women. First coitus is the focus of the investigation because it is a memorable, formative experience that has implications for subsequent sexual health. A community sample of young women ages 18 – 19 years (N = 227) completed retrospective interviews. Characteristics and perceptions of the first coital event were examined using chi squares and one-way multivariate analysis of variance (MANOVA) to determine if there were differences based on alcohol-involvement. Alcohol-involved first coitus events occurred in social settings with risky partners, were rated less positively, and were non-consensual relative to those that did not involve alcohol. Alcohol use was not related to condom use. Alcohol-involvement was associated with subsequent pairing of alcohol with sex and incapacitated rape. Adolescent alcohol use occurs in contexts that increases young women’s sexual risk through exposure to risky partners. PMID:26121927

Statistical Learning Analysis in Neuroscience: Aiming for Transparency

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Haxby, James V.; Pollmann, Stefan

2009-01-01

Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods, neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires “neuroscience-aware” technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here, we review its features and applicability to various neural data modalities. PMID:20582270

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.

Visualization of diversity in large multivariate data sets.

PubMed

Pham, Tuan; Hess, Rob; Ju, Crystal; Zhang, Eugene; Metoyer, Ronald

2010-01-01

Understanding the diversity of a set of multivariate objects is an important problem in many domains, including ecology, college admissions, investing, machine learning, and others. However, to date, very little work has been done to help users achieve this kind of understanding. Visual representation is especially appealing for this task because it offers the potential to allow users to efficiently observe the objects of interest in a direct and holistic way. Thus, in this paper, we attempt to formalize the problem of visualizing the diversity of a large (more than 1000 objects), multivariate (more than 5 attributes) data set as one worth deeper investigation by the information visualization community. In doing so, we contribute a precise definition of diversity, a set of requirements for diversity visualizations based on this definition, and a formal user study design intended to evaluate the capacity of a visual representation for communicating diversity information. Our primary contribution, however, is a visual representation, called the Diversity Map, for visualizing diversity. An evaluation of the Diversity Map using our study design shows that users can judge elements of diversity consistently and as or more accurately than when using the only other representation specifically designed to visualize diversity.

Structure-seeking multilinear methods for the analysis of fMRI data.

PubMed

Andersen, Anders H; Rayens, William S

2004-06-01

In comprehensive fMRI studies of brain function, the data structures often contain higher-order ways such as trial, task condition, subject, and group in addition to the intrinsic dimensions of time and space. While multivariate bilinear methods such as principal component analysis (PCA) have been used successfully for extracting information about spatial and temporal features in data from a single fMRI run, the need to unfold higher-order data sets into bilinear arrays has led to decompositions that are nonunique and to the loss of multiway linkages and interactions present in the data. These additional dimensions or ways can be retained in multilinear models to produce structures that are unique and which admit interpretations that are neurophysiologically meaningful. Multiway analysis of fMRI data from multiple runs of a bilateral finger-tapping paradigm was performed using the parallel factor (PARAFAC) model. A trilinear model was fitted to a data cube of dimensions voxels by time by run. Similarly, a quadrilinear model was fitted to a higher-way structure of dimensions voxels by time by trial by run. The spatial and temporal response components were extracted and validated by comparison to results from traditional SVD/PCA analyses based on scenarios of unfolding into lower-order bilinear structures.

External Ventricular Drain and Hemorrhage in Aneurysmal Subarachnoid Hemorrhage Patients on Dual Antiplatelet Therapy: A Retrospective Cohort Study.

PubMed

Hudson, Joseph S; Prout, Benjamin S; Nagahama, Yasunori; Nakagawa, Daichi; Guerrero, Waldo R; Zanaty, Mario; Chalouhi, Nohra; Jabbour, Pascal; Dandapat, Sudeepta; Allan, Lauren; Ortega-Gutierrez, Santiago; Samaniego, Edgar A; Hasan, David

2018-04-11

Stenting and flow diversion for aneurysmal sub arachnoid hemorrhage (aSAH) require the use of dual antiplatelet therapy (DAPT). To investigate whether DAPT is associated with hemorrhagic complication following placement of external ventricular drains (EVD) in patients with aSAH. Rates of radiographically identified hemorrhage associated with EVD placement were compared between patients who received DAPT for stenting or flow diversion, and patients who underwent microsurgical clipping or coiling and did not receive DAPT by way of a backward stepwise multivariate analysis. Four hundred forty-three patients were admitted for aSAH management. Two hundred ninety-eight patients required placement of an EVD. One hundred twenty patients (40%) were treated with stent-assisted coiling or flow diversion and required DAPT, while 178 patients (60%) were treated with coiling without stents or microsurgical clipping and did not receive DAPT. Forty-two (14%) cases of new hemorrhage along the EVD catheter were identified radiographically. Thirty-two of these hemorrhages occurred in patients on DAPT, while 10 occurred in patients without DAPT. After multivariate analysis, DAPT was significantly associated with radiographic hemorrhage [odds ratio: 4.92, 95% confidence interval: 2.45-9.91, P = .0001]. We did not observe an increased proportion of symptomatic hemorrhage in patients receiving DAPT (10 of 32 [31%]) vs those without (5 of 10 [50%]; P = .4508). Patients with aSAH who receive stent-assisted coiling or flow diversion are at higher risk for radiographic hemorrhage associated with EVD placement. The timing between EVD placement and DAPT initiation does not appear to be of clinical significance. Stenting and flow diversion remain viable options for aSAH patients.

Risk factors for tibial implant malpositioning in total knee arthrosplasty-consecutive series of one thousand, four hundred and seventeen cases.

PubMed

Gaillard, Romain; Cerciello, Simone; Lustig, Sebastien; Servien, Elvire; Neyret, Philippe

2017-04-01

Total knee arthroplasty (TKA) malalignment may result in pain and limited range of motion. The present study assessed the influence of different surgeon's and patient's related factors on the post-operative tibial tray coronal alignment. The charts and the x-rays of a continuous prospective series of 1417 TKAs operated upon between 1987 and 2015 were retrospectively reviewed. The long-leg AP views were performed at two months post-op and the tibial mechanical angle of the tibial tray was measured. Three groups were defined: varus (≤87° n = 167), valgus (≥93° n = 55) and well alignment (88° to 92° n = 1195). The influence of several pre-operative and peri-operative factors was investigated: surgeon handedness and experience (junior or senior), previous tibial osteotomies, Ahlbäck stage of osteoarthritits, pre-operative alignment, height and weight, age at surgery, approach (medial, lateral or tibial tubercle osteotomy), generation of implants, tray fixation, size of the tray and stem lenght. Univariate then multivariate analysis were performed to find out any correlation. Multivariate analysis showed a strong correlation between varus alignment of the tibial tray and pre-operative varus of the lower limb (p = 0.037), increased BMI (p = 0.016) and operated side opposite to the dominant surgeon's arm (p = 0.006). In a similar way a strong correlation was found between valgus alignment and pre-operative valgus of the limb (p = 0.026). Poor alignment of the tibial tray after TKA was associated with pre-operative malalignment of the lower limb, increased BMI and an index knee which was opposite to surgeon's dominant arm.

An Evaluation of h-Index as a Measure of Research Productivity Among Canadian Academic Plastic Surgeons.

PubMed

Hu, Jiayi; Gholami, Arian; Stone, Nicholas; Bartoszko, Justyna; Thoma, Achilleas

2018-02-01

Evaluation of research productivity among plastic surgeons can be complex. The Hirsch index (h-index) was recently introduced to evaluate both the quality and quantity of one's research activity. It has been proposed to be valuable in assessing promotions and grant funding within academic medicine, including plastic surgery. Our objective is to evaluate research productivity among Canadian academic plastic surgeons using the h-index. A list of Canadian academic plastic surgeons was obtained from websites of academic training programs. The h-index was retrieved using the Scopus database. Relevant demographic and academic factors were collected and their effects on the h-index were analyzed using the t test and Wilcoxon Mann-Whitney U test. Nominal and categorical variables were analyzed using χ 2 test and 1-way analysis of variance. Univariate and multivariate models were built a priori. All P values were 2 sided, and P < .05 was considered to be significant. Our study on Canadian plastic surgeons involved 175 surgeons with an average h-index of 7.6. Over 80% of the surgeons were male. Both univariable and multivariable analysis showed that graduate degree ( P < .0001), academic rank ( P = .03), and years in practice ( P < .0001) were positively correlated with h-index. Limitations of the study include that the Scopus database and the websites of training programs were not always up-to-date. The h-index is a novel tool for evaluating research productivity in academic medicine, and this study shows that the h-index can also serve as a useful metric for measuring research productivity in the Canadian plastic surgery community. Plastic surgeons would be wise to familiarize themselves with the h-index concept and should consider using it as an adjunct to existing metrics such as total publication number.

A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model

DOE PAGES

Gan, Yanjun; Duan, Qingyun; Gong, Wei; ...

2014-01-01

Sensitivity analysis (SA) is a commonly used approach for identifying important parameters that dominate model behaviors. We use a newly developed software package, a Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE), to evaluate the effectiveness and efficiency of ten widely used SA methods, including seven qualitative and three quantitative ones. All SA methods are tested using a variety of sampling techniques to screen out the most sensitive (i.e., important) parameters from the insensitive ones. The Sacramento Soil Moisture Accounting (SAC-SMA) model, which has thirteen tunable parameters, is used for illustration. The South Branch Potomac River basin nearmore » Springfield, West Virginia in the U.S. is chosen as the study area. The key findings from this study are: (1) For qualitative SA methods, Correlation Analysis (CA), Regression Analysis (RA), and Gaussian Process (GP) screening methods are shown to be not effective in this example. Morris One-At-a-Time (MOAT) screening is the most efficient, needing only 280 samples to identify the most important parameters, but it is the least robust method. Multivariate Adaptive Regression Splines (MARS), Delta Test (DT) and Sum-Of-Trees (SOT) screening methods need about 400–600 samples for the same purpose. Monte Carlo (MC), Orthogonal Array (OA) and Orthogonal Array based Latin Hypercube (OALH) are appropriate sampling techniques for them; (2) For quantitative SA methods, at least 2777 samples are needed for Fourier Amplitude Sensitivity Test (FAST) to identity parameter main effect. McKay method needs about 360 samples to evaluate the main effect, more than 1000 samples to assess the two-way interaction effect. OALH and LPτ (LPTAU) sampling techniques are more appropriate for McKay method. For the Sobol' method, the minimum samples needed are 1050 to compute the first-order and total sensitivity indices correctly. These comparisons show that qualitative SA methods are more efficient but less accurate and robust than quantitative ones.« less

Entry characteristics and performance in a Masters module in Tropical Medicine: a 5-year analysis.

PubMed

Weigel, R; Robinson, D; Stewart, M; Assinder, S

2016-06-01

Postgraduate courses can contribute to better-qualified personnel in resource-limited settings. We aimed to identify how entry characteristics of applicants predict performance in order to provide support measures early. We describe demographic data and end-of-module examination marks of medical doctors who enrolled in a first semester module of two one-year MSc programmes between 2010 and 2014. We used t-tests and one-way anova to compare, and post hoc tests to locate differences of mean marks between categories of entry characteristics in univariate analysis. After exclusion of collinear variables, multiple regression examined the effect of several characteristics in multivariable analysis. Eighty-nine students (47% male) with a mean age of 32 (SD 6.4) years who received their medical degree in the UK (19%), other European (22%), African (35%) or other countries (24%) attended the 3-months module. Their mean mark was 69.1% (SD 10.9). Medical graduates from UK universities achieved significantly higher mean marks than graduates from other countries. Students' age was significantly negatively correlated with the module mark. In multiple linear regression, place of medical degree (β = -0.44, P < 0.001) and time since graduation (β = -0.28, P = 0.007) were strongest predictors of performance, explaining 32% of the variation of mean marks. Students' performance substantially differs based on their entry criteria in this 1st semester module. Non-UK graduates and mature students might benefit from early support. © 2016 John Wiley & Sons Ltd.

Modelling lecturer performance index of private university in Tulungagung by using survival analysis with multivariate adaptive regression spline

NASA Astrophysics Data System (ADS)

Hasyim, M.; Prastyo, D. D.

2018-03-01

Survival analysis performs relationship between independent variables and survival time as dependent variable. In fact, not all survival data can be recorded completely by any reasons. In such situation, the data is called censored data. Moreover, several model for survival analysis requires assumptions. One of the approaches in survival analysis is nonparametric that gives more relax assumption. In this research, the nonparametric approach that is employed is Multivariate Regression Adaptive Spline (MARS). This study is aimed to measure the performance of private university’s lecturer. The survival time in this study is duration needed by lecturer to obtain their professional certificate. The results show that research activities is a significant factor along with developing courses material, good publication in international or national journal, and activities in research collaboration.

Quantum random oracle model for quantum digital signature

NASA Astrophysics Data System (ADS)

Shang, Tao; Lei, Qi; Liu, Jianwei

2016-10-01

The goal of this work is to provide a general security analysis tool, namely, the quantum random oracle (QRO), for facilitating the security analysis of quantum cryptographic protocols, especially protocols based on quantum one-way function. QRO is used to model quantum one-way function and different queries to QRO are used to model quantum attacks. A typical application of quantum one-way function is the quantum digital signature, whose progress has been hampered by the slow pace of the experimental realization. Alternatively, we use the QRO model to analyze the provable security of a quantum digital signature scheme and elaborate the analysis procedure. The QRO model differs from the prior quantum-accessible random oracle in that it can output quantum states as public keys and give responses to different queries. This tool can be a test bed for the cryptanalysis of more quantum cryptographic protocols based on the quantum one-way function.

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.

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…

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

PubMed

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

2016-09-01

Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows. © 2016 WILEY PERIODICALS, INC.

Understanding handpump sustainability: Determinants of rural water source functionality in the Greater Afram Plains region of Ghana.

PubMed

Fisher, Michael B; Shields, Katherine F; Chan, Terence U; Christenson, Elizabeth; Cronk, Ryan D; Leker, Hannah; Samani, Destina; Apoya, Patrick; Lutz, Alexandra; Bartram, Jamie

2015-10-01

Safe drinking water is critical to human health and development. In rural sub-Saharan Africa, most improved water sources are boreholes with handpumps; studies suggest that up to one third of these handpumps are nonfunctional at any given time. This work presents findings from a secondary analysis of cross-sectional data from 1509 water sources in 570 communities in the rural Greater Afram Plains (GAP) region of Ghana; one of the largest studies of its kind. 79.4% of enumerated water sources were functional when visited; in multivariable regressions, functionality depended on source age, management, tariff collection, the number of other sources in the community, and the district. A Bayesian network (BN) model developed using the same data set found strong dependencies of functionality on implementer, pump type, management, and the availability of tools, with synergistic effects from management determinants on functionality, increasing the likelihood of a source being functional from a baseline of 72% to more than 97% with optimal management and available tools. We suggest that functionality may be a dynamic equilibrium between regular breakdowns and repairs, with management a key determinant of repair rate. Management variables may interact synergistically in ways better captured by BN analysis than by logistic regressions. These qualitative findings may prove generalizable beyond the study area, and may offer new approaches to understanding and increasing handpump functionality and safe water access.

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.

Cluster and Multiple Correspondence Analyses in Rheumatology: Paths to Uncovering Relationships in a Sea of Data.

PubMed

Han, Lu; Benseler, Susanne M; Tyrrell, Pascal N

2018-05-01

Rheumatic diseases encompass a wide range of conditions caused by inflammation and dysregulation of the immune system resulting in organ damage. Research in these heterogeneous diseases benefits from multivariate methods. The aim of this review was to describe and evaluate current literature in rheumatology regarding cluster analysis and correspondence analysis. A systematic review showed an increase in studies making use of these 2 methods. However, standardization in how these methods are applied and reported is needed. Researcher expertise was determined to be the main barrier to considering these approaches, whereas education and collaborating with a biostatistician were suggested ways forward. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

An effective assessment of valproate sodium-induced hepatotoxicity with UPLC-MS and (1)HNMR-based metabonomics approach.

PubMed

Huo, Taoguang; Chen, Xi; Lu, Xiumei; Qu, Lianyue; Liu, Yang; Cai, Shuang

2014-10-15

Valproate sodium is one of the most prescribed antiepileptic drugs. However, valproate sodium has various side effects, especially its toxicity on liver. Current markers for toxicity reflect mostly the late stages of tissue damage; thus, more efficient methods for toxicity evaluation are desired. To evaluate the toxicity of valproate sodium on liver, we performed both UPLC-MS and (1)HNMR-based metabonomics analysis of serum samples from 34 epileptic patients (age: 42.0±18.6, 18 male/16 female) after valproate sodium treatment. Compared to conventional markers, the serum metabolic profiles provided clear distinction of the valproate sodium induced normal liver function and abnormal liver function in epileptic patients. Through multivariate statistical analysis, we identified marker metabolites associated with the hepatotoxicity induced by valproate sodium, such as glucose, lactate, acetoacetate, VLDL/LDL, lysophosphatidylcholines, phosphatidylcholines, choline, creatine, amino acids, N-acetyl glycoprotein, pyruvate and uric acid. This metabonomics approach may provide effective way to evaluate the valproate sodium-induced toxicity in a manner that can complement current measures. This approach is expected to find broader application in other drug-induced toxicity assessment. Copyright © 2014 Elsevier B.V. All rights reserved.

Metabolic discrimination of sea buckthorn from different Hippophaë species by 1H NMR based metabolomics.

PubMed

Liu, Yue; Fan, Gang; Zhang, Jing; Zhang, Yi; Li, Jingjian; Xiong, Chao; Zhang, Qi; Li, Xiaodong; Lai, Xianrong

2017-05-08

Sea buckthorn (Hippophaë; Elaeagnaceae) berries are widely consumed in traditional folk medicines, nutraceuticals, and as a source of food. The growing demand of sea buckthorn berries and morphological similarity of Hippophaë species leads to confusions, which might cause misidentification of plants used in natural products. Detailed information and comparison of the complete set of metabolites of different Hippophaë species are critical for their objective identification and quality control. Herein, the variation among seven species and seven subspecies of Hippophaë was studied using proton nuclear magnetic resonance ( 1 H NMR) metabolomics combined with multivariate data analysis, and the important metabolites were quantified by quantitative 1 H NMR (qNMR) method. The results showed that different Hippophaë species can be clearly discriminated and the important interspecific discriminators, including organic acids, L-quebrachitol, and carbohydrates were identified. Statistical differences were found among most of the Hippophaë species and subspecies at the content levels of the aforementioned interspecific discriminators via qNMR and one-way analysis of variance (ANOVA) test. These findings demonstrated that 1 H NMR-based metabolomics is an applicable and effective approach for simultaneous metabolic profiling, species differentiation and quality assessment.

Developing the Noncentrality Parameter for Calculating Group Sample Sizes in Heterogeneous Analysis of Variance

ERIC Educational Resources Information Center

Luh, Wei-Ming; Guo, Jiin-Huarng

2011-01-01

Sample size determination is an important issue in planning research. In the context of one-way fixed-effect analysis of variance, the conventional sample size formula cannot be applied for the heterogeneous variance cases. This study discusses the sample size requirement for the Welch test in the one-way fixed-effect analysis of variance with…

Automating Geospatial Visualizations with Smart Default Renderers for Data Exploration Web Applications

NASA Astrophysics Data System (ADS)

Ekenes, K.

2017-12-01

This presentation will outline the process of creating a web application for exploring large amounts of scientific geospatial data using modern automated cartographic techniques. Traditional cartographic methods, including data classification, may inadvertently hide geospatial and statistical patterns in the underlying data. This presentation demonstrates how to use smart web APIs that quickly analyze the data when it loads, and provides suggestions for the most appropriate visualizations based on the statistics of the data. Since there are just a few ways to visualize any given dataset well, it is imperative to provide smart default color schemes tailored to the dataset as opposed to static defaults. Since many users don't go beyond default values, it is imperative that they are provided with smart default visualizations. Multiple functions for automating visualizations are available in the Smart APIs, along with UI elements allowing users to create more than one visualization for a dataset since there isn't a single best way to visualize a given dataset. Since bivariate and multivariate visualizations are particularly difficult to create effectively, this automated approach removes the guesswork out of the process and provides a number of ways to generate multivariate visualizations for the same variables. This allows the user to choose which visualization is most appropriate for their presentation. The methods used in these APIs and the renderers generated by them are not available elsewhere. The presentation will show how statistics can be used as the basis for automating default visualizations of data along continuous ramps, creating more refined visualizations while revealing the spread and outliers of the data. Adding interactive components to instantaneously alter visualizations allows users to unearth spatial patterns previously unknown among one or more variables. These applications may focus on a single dataset that is frequently updated, or configurable for a variety of datasets from multiple sources.

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

NASA Astrophysics Data System (ADS)

Most, Sebastian; Nowak, Wolfgang; Bijeljic, Branko

2015-04-01

Fickian transport in groundwater flow is the exception rather than the rule. Transport in porous media is frequently simulated via particle methods (i.e. particle tracking random walk (PTRW) or continuous time random walk (CTRW)). These methods formulate transport as a stochastic process of particle position increments. At the pore scale, geometry and micro-heterogeneities prohibit the commonly made assumption of independent and normally distributed increments to represent dispersion. Many recent particle methods seek to loosen this assumption. Hence, it is important to get a better understanding of the processes at pore scale. For our analysis we track the positions of 10.000 particles migrating through the pore space over time. The data we use come from micro CT scans of a homogeneous sandstone and encompass about 10 grain sizes. Based on those images we discretize the pore structure and simulate flow at the pore scale based on the Navier-Stokes equation. This flow field realistically describes flow inside the pore space and we do not need to add artificial dispersion during the transport simulation. Next, we use particle tracking random walk and simulate pore-scale transport. Finally, we use the obtained particle trajectories to do a multivariate statistical analysis of the particle motion at the pore scale. Our analysis is based on copulas. Every multivariate joint distribution is a combination of its univariate marginal distributions. The copula represents the dependence structure of those univariate marginals and is therefore useful to observe correlation and non-Gaussian interactions (i.e. non-Fickian transport). The first goal of this analysis is to better understand the validity regions of commonly made assumptions. We are investigating three different transport distances: 1) The distance where the statistical dependence between particle increments can be modelled as an order-one Markov process. This would be the Markovian distance for the process, where the validity of yet-unexplored non-Gaussian-but-Markovian random walks start. 2) The distance where bivariate statistical dependence simplifies to a multi-Gaussian dependence based on simple linear correlation (validity of correlated PTRW/CTRW). 3) The distance of complete statistical independence (validity of classical PTRW/CTRW). The second objective is to reveal characteristic dependencies influencing transport the most. Those dependencies can be very complex. Copulas are highly capable of representing linear dependence as well as non-linear dependence. With that tool we are able to detect persistent characteristics dominating transport even across different scales. The results derived from our experimental data set suggest that there are many more non-Fickian aspects of pore-scale transport than the univariate statistics of longitudinal displacements. Non-Fickianity can also be found in transverse displacements, and in the relations between increments at different time steps. Also, the found dependence is non-linear (i.e. beyond simple correlation) and persists over long distances. Thus, our results strongly support the further refinement of techniques like correlated PTRW or correlated CTRW towards non-linear statistical relations.

Synthesizing plant phenological indicators from multispecies datasets

NASA Astrophysics Data System (ADS)

Rutishauser, This; Peñuelas, Josep; Filella, Iolanda; Gehrig, Regula; Scherrer, Simon C.; Röthlisberger, Christian

2014-05-01

Changes in the seasonality of life cycles of plants from phenological observations are traditionally analysed at the species level. Trends and correlations with main environmental driving variables show a coherent picture across the globe. The question arises whether there is an integrated phenological signal across species that describes common interannual variability. Is there a way to express synthetic phenological indicators from multispecies datasets that serve decision makers as usefull tools? Can these indicators be derived in such a robust way that systematic updates yield necessary information for adaptation measures? We address these questions by analysing multi-species phenological data sets with leaf-unfolding and flowering observations from 30 sites across Europe between 40° and 63°N including data from PEP725, the Swiss Plant Phenological Observation Network and one legacy data set. Starting in 1951 the data sets were synthesized by multivariate analysis (Principal Component Analysis). The representativeness of the site specific indicator was tested against subsets including only leaf-unfolding or flowering phases, and by a comparison with a 50% random sample of the available phenophases for 500 time steps. Results show that a synthetic indicators explains up to 79% of the variance at each site - usually 40-50% or more. Robust linear trends over the common period 1971-2000 indicate an overall change of the indicator of -0.32 days/year with lower uncertainty than previous studies. Advances were more pronounced in southern and northern Europe. The indicator-based analysis provides a promising tool for synthesizing site-based plant phenological records and is a companion to, and validating data for, an increasing number of phenological measurements derived from phenological models and satellite sensors.

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.

Multivariate landscape trajectory analysis: An example using simulation modeling of American marten habitat change under four timber harvest scenarios

Treesearch

Samuel A. Cushman; Kevin McGarigal

2007-01-01

Integrating temporal variabilily into spatial analyses is one of the abiding challenges in landscape ecology. In this chapter we use landscape trajectory analysis to assess changes in landscape patterns over time. Landscape trajectory analysis is an approach to quantify changes in landscape structure over time. There are three key concepts which underlie the...

Positive mental health and well-being among a third level student population.

PubMed

Davoren, Martin P; Fitzgerald, Eimear; Shiely, Frances; Perry, Ivan J

2013-01-01

Much research on the health and well-being of third level students is focused on poor mental health leading to a dearth of information on positive mental health and well-being. Recently, the Warwick Edinburgh Mental Well-being scale (WEMWBS) was developed as a measurement of positive mental health and well-being. The aim of this research is to investigate the distribution and determinants of positive mental health and well-being in a large, broadly representative sample of third level students using WEMWBS. Undergraduate students from one large third level institution were sampled using probability proportional to size sampling. Questionnaires were distributed to students attending lectures in the randomly selected degrees. A total of 2,332 self-completed questionnaires were obtained, yielding a response rate of 51% based on students registered to relevant modules and 84% based on attendance. One-way ANOVAs and multivariate logistic regression were utilised to investigate factors associated with positive mental health and well-being. The sample was predominantly female (62.66%), in first year (46.9%) and living in their parents' house (42.4%) or in a rented house or flat (40.8%). In multivariate analysis adjusted for age and stratified by gender, no significant differences in WEMWBS score were observed by area of study, alcohol, smoking or drug use. WEMWBS scores were higher among male students with low levels of physical activity (p=0.04). Men and women reporting one or more sexual partners (p<0.001) were also more likely to report above average mental health and well-being. This is the first study to examine positive mental health and well-being scores in a third level student sample using WEMWBS. The findings suggest that students with a relatively adverse health and lifestyle profile have higher than average mental health and well-being. To confirm these results, this work needs to be replicated across other third level institutions.

Interpretation of a compositional time series

NASA Astrophysics Data System (ADS)

Tolosana-Delgado, R.; van den Boogaart, K. G.

2012-04-01

Common methods for multivariate time series analysis use linear operations, from the definition of a time-lagged covariance/correlation to the prediction of new outcomes. However, when the time series response is a composition (a vector of positive components showing the relative importance of a set of parts in a total, like percentages and proportions), then linear operations are afflicted of several problems. For instance, it has been long recognised that (auto/cross-)correlations between raw percentages are spurious, more dependent on which other components are being considered than on any natural link between the components of interest. Also, a long-term forecast of a composition in models with a linear trend will ultimately predict negative components. In general terms, compositional data should not be treated in a raw scale, but after a log-ratio transformation (Aitchison, 1986: The statistical analysis of compositional data. Chapman and Hill). This is so because the information conveyed by a compositional data is relative, as stated in their definition. The principle of working in coordinates allows to apply any sort of multivariate analysis to a log-ratio transformed composition, as long as this transformation is invertible. This principle is of full application to time series analysis. We will discuss how results (both auto/cross-correlation functions and predictions) can be back-transformed, viewed and interpreted in a meaningful way. One view is to use the exhaustive set of all possible pairwise log-ratios, which allows to express the results into D(D - 1)/2 separate, interpretable sets of one-dimensional models showing the behaviour of each possible pairwise log-ratios. Another view is the interpretation of estimated coefficients or correlations back-transformed in terms of compositions. These two views are compatible and complementary. These issues are illustrated with time series of seasonal precipitation patterns at different rain gauges of the USA. In this data set, the proportion of annual precipitation falling in winter, spring, summer and autumn is considered a 4-component time series. Three invertible log-ratios are defined for calculations, balancing rainfall in autumn vs. winter, in summer vs. spring, and in autumn-winter vs. spring-summer. Results suggest a 2-year correlation range, and certain oscillatory behaviour in the last balance, which does not occur in the other two.

Arm structure in normal spiral galaxies, 1: Multivariate data for 492 galaxies

NASA Technical Reports Server (NTRS)

Magri, Christopher

1994-01-01

Multivariate data have been collected as part of an effort to develop a new classification system for spiral galaxies, one which is not necessarily based on subjective morphological properties. A sample of 492 moderately bright northern Sa and Sc spirals was chosen for future statistical analysis. New observations were made at 20 and 21 cm; the latter data are described in detail here. Infrared Astronomy Satellite (IRAS) fluxes were obtained from archival data. Finally, new estimates of arm pattern radomness and of local environmental harshness were compiled for most sample objects.

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

PubMed

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

2009-01-01

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

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

PubMed Central

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

2009-01-01

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

Characterisation of fatigue and its substantial impact on health status in a large cohort of patients with chronic pulmonary aspergillosis (CPA).

PubMed

Al-Shair, Khaled; Muldoon, Eavan G; Morris, Julie; Atherton, Graham T; Kosmidis, Chris; Denning, David W

2016-05-01

Fatigue is a prominent disabling symptom in several pulmonary diseases. Its impact on health status in patients with chronic pulmonary aspergillosis (CPA) has not been investigated. A total of 151 CPA patients attending the National Aspergillosis Centre completed Manchester COPD Fatigue Scale (MCFS), St. George's Respiratory Questionnaire (SGRQ) and Medical Research Council (MRC) dyspnoea score. Lung function and BMI were measured. Univariate, multivariate linear and binary analyses, and principal component analysis (PCA) were used. Female patients accounted for 44%. The mean (range) of age was 59.6 (31-83) years, FEV1% was 64 (14-140), BMI was 23.6 (16.3-43.4), SGRQ total score was 56 (4-96.2) and MCFS total score was 30.6 (0-54). PCA showed that 27 items of MCFS loaded on three components; physical, psychosocial and cognitive fatigue, explaining 78.4% of fatigue variance. MCFS score correlated strongly with total SGRQ score (r = 0.83, p < 0.001). Using linear multivariate analysis, fatigue was the strongest factor (beta = 0.7 p < 0.0001) associated with impaired health status, after adjusting for age, BMI, FEV1%, and MRC dyspnoea score. Using patients' 5 self-assessment grades of their health, one-way ANOVA showed that those with "very poor" health status had the highest fatigue scores (45 (±6) (p < 0.001)). Logistic regression analysis showed that fatigue score (OR = 0.9, 95% CI 0.84-0.97; p = 0.005) and FEV1% (OR = 1.03, 95% CI 1.01-1.07, p = 0.02) are significantly associated with self-assessed impaired health status after correcting for age, gender and DLCO%. Fatigue is a major component of impaired health status of CPA patients. Copyright © 2016 Elsevier Ltd. All rights reserved.

Relationship between coping, self-esteem, individual factors and mental health among Chinese nursing students: a matched case-control study.

PubMed

Ni, Chunping; Liu, Xiwen; Hua, Qianzhen; Lv, Aili; Wang, Bo; Yan, Yongping

2010-05-01

To investigate the relationship between ways of coping, self-esteem, individual factors and mental health among Chinese nursing students. A sample of 515 nursing students was selected from four public institutes and colleges in Xi'an of China by a random sampling method. They were surveyed by a self-evaluation questionnaire including the Symptom-Checklist 90 (SCL-90), the Simplified Coping Style Questionnaire, the Self-Esteem Scale and the Personal Data Form. On the basis of the total score of SCL-90 obtained in the survey, high and low score groups were formed, each consisting of 100 nursing students. Then a matched case-control design was carried out to explore the relationship between ways of coping, self-esteem, individual factors and mental health. Besides descriptive statistics, the Chi-square analysis, t-test and Multivariate Logistic Regression Analysis were also employed. The active coping and self-esteem scores of the high score group were found to be much lower than those of the low score group (P<0.05), while it was the opposite for passive coping scores (P<0.01). Multivariate Logistic Regression Analysis suggested that study stress (OR=10.017, 95%CI: 3.273-30.654) and physical health problems in the past year (OR=4.384, 95%CI: 1.492-12.877) were independent risk factors of mental health among nursing students, whereas self-fulfillment satisfaction (OR=0.037, 95%CI: 0.014-0.097) and a higher level of self-esteem (OR=0.357, 95%CI: 0.152-0.838) were preventive factors. The mental health of Chinese nursing students was related to the ways of coping, self-esteem, study stress and physical health problems in the past year. In order to improve the mental health of nursing students, aside from reducing the study stress and avoiding passive coping, it is very necessary for them to be supported to ensure that academic stress is minimized, autonomy is promoted, and self-esteem is developed. Copyright 2009 Elsevier Ltd. All rights reserved.

Risk assessment and stock market volatility in the Eurozone: 1986-2014

NASA Astrophysics Data System (ADS)

Menezes, Rui; Oliveira, Álvaro

2015-04-01

This paper studies the stock market return's volatility in the Eurozone as an input for evaluating the market risk. Stock market returns are endogenously determined by long-term interest rate changes and so is the return's conditional variance. The conditional variance is the time-dependent variance of the underlying variable. In other words, it is the variance of the returns measured at each moment t, so it changes through time depending on the specific market structure at each time observation. Thus, a multivariate EGARCH model is proposed to capture the complex nature of this network. By network, in this context, we mean the chain of stock exchanges that co-move and interact in such a way that a shock in one of them propagates up to the other ones (contagion). Previous studies provide evidence that the Eurozone stock exchanges are deeply integrated. The results indicate that asymmetry and leverage effects exist along with fat tails and endogeneity. In-sample and out-of-sample forecasting tests provide clear evidence that the multivariate EGARCH model performs better than the univariate counterpart to predict the behavior of returns both before and after the 2008 crisis.

Data Mining: The Art of Automated Knowledge Extraction

NASA Astrophysics Data System (ADS)

Karimabadi, H.; Sipes, T.

2012-12-01

Data mining algorithms are used routinely in a wide variety of fields and they are gaining adoption in sciences. The realities of real world data analysis are that (a) data has flaws, and (b) the models and assumptions that we bring to the data are inevitably flawed, and/or biased and misspecified in some way. Data mining can improve data analysis by detecting anomalies in the data, check for consistency of the user model assumptions, and decipher complex patterns and relationships that would not be possible otherwise. The common form of data collected from in situ spacecraft measurements is multi-variate time series which represents one of the most challenging problems in data mining. We have successfully developed algorithms to deal with such data and have extended the algorithms to handle streaming data. In this talk, we illustrate the utility of our algorithms through several examples including automated detection of reconnection exhausts in the solar wind and flux ropes in the magnetotail. We also show examples from successful applications of our technique to analysis of 3D kinetic simulations. With an eye to the future, we provide an overview of our upcoming plans that include collaborative data mining, expert outsourcing data mining, computer vision for image analysis, among others. Finally, we discuss the integration of data mining algorithms with web-based services such as VxOs and other Heliophysics data centers and the resulting capabilities that it would enable.

Inhibition of Orthopaedic Implant Infections by Immunomodulatory Effects of Host Defense Peptides

DTIC Science & Technology

2014-12-01

significance was determined by t- tests or by one-way analysis of variance (ANOVA) followed by Bonferroni post hoc tests in experiments with multiple...groups. Non- parametric Mann-Whitney tests , Kruskal-Wallis ANOVA followed by Newman-Kuels post hoc tests , or van Elteren’s two-way tests were applied to...in D, and black symbols in A), statistical analysis was by one-way ANOVA followed by Bonferroni versus control, post hoc tests . Otherwise, statistical

Benthic algae of benchmark streams in agricultural areas of eastern Wisconsin

USGS Publications Warehouse

Scudder, Barbara C.; Stewart, Jana S.

2001-01-01

Multivariate analyses indicated multiple scales of environmental factors affect algae. Although two-way indicator species analysis (TWINSPAN), detrended correspondence analysis (DCA), and canonical correspondence analysis (CCA) generally separated sites according to RHU, only DCA ordination indicated a separation of sites according to ecoregion. Environmental variables con-elated with DCA axes 1 and 2 and therefore indicated as important explanatory factors for algal distribution and abundance were factors related to stream size, basin land use/cover, geomorphology, hydrogeology, and riparian disturbance. CCA analyses with a more limited set of environmental variables indicated that pH, average width of natural riparian vegetation (segment scale), basin land use/cover and Q/Q2 were the most important variables affecting the distribution and relative abundance of benthic algae at the 20 benchmark streams,

Red cell distribution width does not predict stroke severity or functional outcome.

PubMed

Ntaios, George; Gurer, Ozgur; Faouzi, Mohamed; Aubert, Carole; Michel, Patrik

2012-01-01

Red cell distribution width was recently identified as a predictor of cardiovascular and all-cause mortality in patients with previous stroke. Red cell distribution width is also higher in patients with stroke compared with those without. However, there are no data on the association of red cell distribution width, assessed during the acute phase of ischemic stroke, with stroke severity and functional outcome. In the present study, we sought to investigate this relationship and ascertain the main determinants of red cell distribution width in this population. We used data from the Acute Stroke Registry and Analysis of Lausanne for patients between January 2003 and December 2008. Red cell distribution width was generated at admission by the Sysmex XE-2100 automated cell counter from ethylene diamine tetraacetic acid blood samples stored at room temperature until measurement. An χ(2) -test was performed to compare frequencies of categorical variables between different red cell distribution width quartiles, and one-way analysis of variance for continuous variables. The effect of red cell distribution width on severity and functional outcome was investigated in univariate and multivariate robust regression analysis. Level of significance was set at 95%. There were 1504 patients (72±15·76 years, 43·9% females) included in the analysis. Red cell distribution width was significantly associated to NIHSS (β-value=0·24, P=0·01) and functional outcome (odds ratio=10·73 for poor outcome, P<0·001) at univariate analysis but not multivariate. Prehospital Rankin score (β=0·19, P<0·001), serum creatinine (β=0·008, P<0·001), hemoglobin (β=-0·009, P<0·001), mean platelet volume (β=0·09, P<0·05), age (β=0·02, P<0·001), low ejection fraction (β=0·66, P<0·001) and antihypertensive treatment (β=0·32, P<0·001) were independent determinants of red cell distribution width. Red cell distribution width, assessed during the early phase of acute ischemic stroke, does not predict severity or functional outcome. © 2011 The Authors. International Journal of Stroke © 2011 World Stroke Organization.

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…

Analysis of Volatile Compounds by Advanced Analytical Techniques and Multivariate Chemometrics.

PubMed

Lubes, Giuseppe; Goodarzi, Mohammad

2017-05-10

Smelling is one of the five senses, which plays an important role in our everyday lives. Volatile compounds are, for example, characteristics of food where some of them can be perceivable by humans because of their aroma. They have a great influence on the decision making of consumers when they choose to use a product or not. In the case where a product has an offensive and strong aroma, many consumers might not appreciate it. On the contrary, soft and fresh natural aromas definitely increase the acceptance of a given product. These properties can drastically influence the economy; thus, it has been of great importance to manufacturers that the aroma of their food product is characterized by analytical means to provide a basis for further optimization processes. A lot of research has been devoted to this domain in order to link the quality of, e.g., a food to its aroma. By knowing the aromatic profile of a food, one can understand the nature of a given product leading to developing new products, which are more acceptable by consumers. There are two ways to analyze volatiles: one is to use human senses and/or sensory instruments, and the other is based on advanced analytical techniques. This work focuses on the latter. Although requirements are simple, low-cost technology is an attractive research target in this domain; most of the data are generated with very high-resolution analytical instruments. Such data gathered based on different analytical instruments normally have broad, overlapping sensitivity profiles and require substantial data analysis. In this review, we have addressed not only the question of the application of chemometrics for aroma analysis but also of the use of different analytical instruments in this field, highlighting the research needed for future focus.

Statistical analysis of Skylab 3. [endocrine/metabolic studies of astronauts

NASA Technical Reports Server (NTRS)

Johnston, D. A.

1974-01-01

The results of endocrine/metabolic studies of astronauts on Skylab 3 are reported. One-way analysis of variance, contrasts, two-way unbalanced analysis of variance, and analysis of periodic changes in flight are included. Results for blood tests, and urine tests are presented.

On the Experimental Determination of the One-Way Speed of Light

ERIC Educational Resources Information Center

Perez, Israel

2011-01-01

In this paper the question of the isotropy of the one-way speed of light is addressed from an experimental perspective. In particular, we analyse two experimental methods commonly used in its determination. The analysis is aimed at clarifying the view that the one-way speed of light cannot be determined by techniques in which physical entities…

Ways of problem solving as predictors of relapse in alcohol dependent male inpatients.

PubMed

Demirbas, Hatice; Ilhan, Inci Ozgur; Dogan, Yildirim Beyatli

2012-01-01

The purpose of this study was to identify how remitters and relapsers view their everyday problem solving strategies. A total of 128 male alcohol dependent male inpatients who were hospitalized at the Ankara University Psychiatry Clinic, Alcohol and Substance Abuse Treatment Unit were recruited for the study. Subjects demographic status and alcohol use histories were assessed by a self-report questionnaire. Also, patients were evaluated with The Coopersmith Self-esteem Inventory (CSI), The Spielberger State-Trait Anxiety Scale (STAI-I-II), and The Problem Solving Inventory (PSI). Patients were followed for six months with monthly intervals after hospital discharge. Drinking status was assessed in terms of abstinence and relapse. Data were assessed with Student t-test, and univariate and multivariate analyses. In the logistic regression analysis, age, marital status, employment status and PSI subscores were taken as the independent variables and drinking state at the end of six months as the dependent variable. There were significant differences in reflective and avoidant styles, and monitoring style of problem solving between abstainers and relapses. It was found that subjects who perceived their problem solving style as less avoidant and less reflective were at greater risk to relapse. The findings demonstrated that active engagement in problem solving like utilizing avoidant and reflective styles of problem solving enhances abstinence. In treatment, expanding the behavior repertoire and increasing the variety of ways of problem solving ways that can be utilized in daily life should be one of the major goals of the treatment program. Copyright © 2011 Elsevier Ltd. All rights reserved.

1H NMR Metabolomics Study of Spleen from C57BL/6 Mice Exposed to Gamma Radiation

PubMed Central

Xiao, X; Hu, M; Liu, M; Hu, JZ

2016-01-01

Due to the potential risk of accidental exposure to gamma radiation, it’s critical to identify the biomarkers of radiation exposed creatures. In the present study, NMR based metabolomics combined with multivariate data analysis to evaluate the metabolites changed in the C57BL/6 mouse spleen after 4 days whole body exposure to 3.0 Gy and 7.8 Gy gamma radiations. Principal component analysis (PCA) and orthogonal projection to latent structures analysis (OPLS) are employed for classification and identification potential biomarkers associated with gamma irradiation. Two different strategies for NMR spectral data reduction (i.e., spectral binning and spectral deconvolution) are combined with normalize to constant sum and unit weight before multivariate data analysis, respectively. The combination of spectral deconvolution and normalization to unit weight is the best way for identifying discriminatory metabolites between the irradiation and control groups. Normalized to the constant sum may achieve some pseudo biomarkers. PCA and OPLS results shown that the exposed groups can be well separated from the control group. Leucine, 2-aminobutyrate, valine, lactate, arginine, glutathione, 2-oxoglutarate, creatine, tyrosine, phenylalanine, π-methylhistidine, taurine, myoinositol, glycerol and uracil are significantly elevated while ADP is decreased significantly. These significantly changed metabolites are associated with multiple metabolic pathways and may be potential biomarkers in the spleen exposed to gamma irradiation. PMID:27019763

1H NMR metabolomics study of spleen from C57BL/6 mice exposed to gamma radiation

DOE PAGES

Xiao, Xiongjie; Hu, M.; Liu, M.; ...

2016-01-27

Due to the potential risk of accidental exposure to gamma radiation, it’s critical to identify the biomarkers of radiation exposed creatures. In the present study, NMR based metabolomics combined with multivariate data analysis to evaluate the metabolites changed in the C57BL/6 mouse spleen after 4 days whole body exposure to 3.0 Gy and 7.8 Gy gamma radiations. Principal component analysis (PCA) and orthogonal projection to latent structures analysis (OPLS) are employed for classification and identification potential biomarkers associated with gamma irradiation. Two different strategies for NMR spectral data reduction (i.e., spectral binning and spectral deconvolution) are combined with normalize tomore » constant sum and unit weight before multivariate data analysis, respectively. The combination of spectral deconvolution and normalization to unit weight is the best way for identifying discriminatory metabolites between the irradiation and control groups. Normalized to the constant sum may achieve some pseudo biomarkers. PCA and OPLS results shown that the exposed groups can be well separated from the control group. Leucine, 2-aminobutyrate, valine, lactate, arginine, glutathione, 2-oxoglutarate, creatine, tyrosine, phenylalanine, π-methylhistidine, taurine, myoinositol, glycerol and uracil are significantly elevated while ADP is decreased significantly. As a result, these significantly changed metabolites are associated with multiple metabolic pathways and may be potential biomarkers in the spleen exposed to gamma irradiation.« less

Equicontrollability and the model following problem

NASA Technical Reports Server (NTRS)

Curran, R. T.

1971-01-01

Equicontrollability and its application to the linear time-invariant model-following problem are discussed. The problem is presented in the form of two systems, the plant and the model. The requirement is to find a controller to apply to the plant so that the resultant compensated plant behaves, in an input-output sense, the same as the model. All systems are assumed to be linear and time-invariant. The basic approach is to find suitable equicontrollable realizations of the plant and model and to utilize feedback so as to produce a controller of minimal state dimension. The concept of equicontrollability is a generalization of control canonical (phase variable) form applied to multivariable systems. It allows one to visualize clearly the effects of feedback and to pinpoint the parameters of a multivariable system which are invariant under feedback. The basic contributions are the development of equicontrollable form; solution of the model-following problem in an entirely algorithmic way, suitable for computer programming; and resolution of questions on system decoupling.

Multivariable polynomial fitting of controlled single-phase nonlinear load of input current total harmonic distortion

NASA Astrophysics Data System (ADS)

Sikora, Roman; Markiewicz, Przemysław; Pabjańczyk, Wiesława

2018-04-01

The power systems usually include a number of nonlinear receivers. Nonlinear receivers are the source of disturbances generated to the power system in the form of higher harmonics. The level of these disturbances describes the total harmonic distortion coefficient THD. Its value depends on many factors. One of them are the deformation and change in RMS value of supply voltage. A modern LED luminaire is a nonlinear receiver as well. The paper presents the results of the analysis of the influence of change in RMS value of supply voltage and the level of dimming of the tested luminaire on the value of the current THD. The analysis was made using a mathematical model based on multivariable polynomial fitting.

Mathematical models for exploring different aspects of genotoxicity and carcinogenicity databases.

PubMed

Benigni, R; Giuliani, A

1991-12-01

One great obstacle to understanding and using the information contained in the genotoxicity and carcinogenicity databases is the very size of such databases. Their vastness makes them difficult to read; this leads to inadequate exploitation of the information, which becomes costly in terms of time, labor, and money. In its search for adequate approaches to the problem, the scientific community has, curiously, almost entirely neglected an existent series of very powerful methods of data analysis: the multivariate data analysis techniques. These methods were specifically designed for exploring large data sets. This paper presents the multivariate techniques and reports a number of applications to genotoxicity problems. These studies show how biology and mathematical modeling can be combined and how successful this combination is.

Nest-site selection analysis of hooded crane (Grus monacha) in Northeastern China based on a multivariate ensemble model.

PubMed

Jiao, Shengwu; Guo, Yumin; Huettmann, Falk; Lei, Guangchun

2014-07-01

Avian nest-site selection is an important research and management subject. The hooded crane (Grus monacha) is a vulnerable (VU) species according to the IUCN Red List. Here, we present the first long-term Chinese legacy nest data for this species (1993-2010) with publicly available metadata. Further, we provide the first study that reports findings on multivariate nest habitat preference using such long-term field data for this species. Our work was carried out in Northeastern China, where we found and measured 24 nests and 81 randomly selected control plots and their environmental parameters in a vast landscape. We used machine learning (stochastic boosted regression trees) to quantify nest selection. Our analysis further included varclust (R Hmisc) and (TreenNet) to address statistical correlations and two-way interactions. We found that from an initial list of 14 measured field variables, water area (+), water depth (+) and shrub coverage (-) were the main explanatory variables that contributed to hooded crane nest-site selection. Agricultural sites played a smaller role in the selection of these nests. Our results are important for the conservation management of cranes all over East Asia and constitute a defensible and quantitative basis for predictive models.

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

USGS Publications Warehouse

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

2010-01-01

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

A standards-based method for compositional analysis by energy dispersive X-ray spectrometry using multivariate statistical analysis: application to multicomponent alloys.

PubMed

Rathi, Monika; Ahrenkiel, S P; Carapella, J J; Wanlass, M W

2013-02-01

Given an unknown multicomponent alloy, and a set of standard compounds or alloys of known composition, can one improve upon popular standards-based methods for energy dispersive X-ray (EDX) spectrometry to quantify the elemental composition of the unknown specimen? A method is presented here for determining elemental composition of alloys using transmission electron microscopy-based EDX with appropriate standards. The method begins with a discrete set of related reference standards of known composition, applies multivariate statistical analysis to those spectra, and evaluates the compositions with a linear matrix algebra method to relate the spectra to elemental composition. By using associated standards, only limited assumptions about the physical origins of the EDX spectra are needed. Spectral absorption corrections can be performed by providing an estimate of the foil thickness of one or more reference standards. The technique was applied to III-V multicomponent alloy thin films: composition and foil thickness were determined for various III-V alloys. The results were then validated by comparing with X-ray diffraction and photoluminescence analysis, demonstrating accuracy of approximately 1% in atomic fraction.

Heteroscedastic Tests Statistics for One-Way Analysis of Variance: The Trimmed Means and Hall's Transformation Conjunction

ERIC Educational Resources Information Center

Luh, Wei-Ming; Guo, Jiin-Huarng

2005-01-01

To deal with nonnormal and heterogeneous data for the one-way fixed effect analysis of variance model, the authors adopted a trimmed means method in conjunction with Hall's invertible transformation into a heteroscedastic test statistic (Alexander-Govern test or Welch test). The results of simulation experiments showed that the proposed technique…

Teaching Principles of One-Way Analysis of Variance Using M&M's Candy

ERIC Educational Resources Information Center

Schwartz, Todd A.

2013-01-01

I present an active learning classroom exercise illustrating essential principles of one-way analysis of variance (ANOVA) methods. The exercise is easily conducted by the instructor and is instructive (as well as enjoyable) for the students. This is conducive for demonstrating many theoretical and practical issues related to ANOVA and lends itself…

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

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.

Pediatric anemia in rural Ghana: a cross-sectional study of prevalence and risk factors.

PubMed

VanBuskirk, Kelley M; Ofosu, Anthony; Kennedy, Amy; Denno, Donna M

2014-08-01

To assess anemia prevalence and identify associated parameters in children <3 years of age in a rural area of Ghana. Univariate and multivariate logistic regression of cross-sectional survey results from 861 children aged <3 years attending routine immunization services in Berekum district. Anemia prevalence was 73.1%; most were either mildly (31.2%) or moderately (38.7%) affected. Risk factors for anemia (hemoglobin < 11.0 g/dl) in multivariate analysis were malaria parasitemia and male sex; these factors and younger age were associated with anemia severity. A partial defect in glucose-6-phosphate dehydrogenase was associated with decreased severity. Height-for-age, but not weight-for-age, was associated with anemia and its severity. Malaria parasitemia was strongly associated with anemia and its severity, suggesting that malaria control may be the most effective way to reduce the burden of anemia in rural Ghanaian children. © The Author [2014]. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Multi-variate joint PDF for non-Gaussianities: exact formulation and generic approximations

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

Verde, Licia; Jimenez, Raul; Alvarez-Gaume, Luis

2013-06-01

We provide an exact expression for the multi-variate joint probability distribution function of non-Gaussian fields primordially arising from local transformations of a Gaussian field. This kind of non-Gaussianity is generated in many models of inflation. We apply our expression to the non-Gaussianity estimation from Cosmic Microwave Background maps and the halo mass function where we obtain analytical expressions. We also provide analytic approximations and their range of validity. For the Cosmic Microwave Background we give a fast way to compute the PDF which is valid up to more than 7σ for f{sub NL} values (both true and sampled) not ruledmore » out by current observations, which consists of expressing the PDF as a combination of bispectrum and trispectrum of the temperature maps. The resulting expression is valid for any kind of non-Gaussianity and is not limited to the local type. The above results may serve as the basis for a fully Bayesian analysis of the non-Gaussianity parameter.« less

Operational and customer relationship management considerations of electronic prescribing among pharmacists.

PubMed

Smith, Alan D; Motley, Darlene

2009-01-01

Technology in healthcare environments has increasingly become a vital way to communicate vital information in a safe, reliable, precise and secure manner. Healthcare is an arena that is constantly changing and very fast paced, but adoption of electronic prescribing (e-prescribing) has been comparatively slow and painful in the USA. Medical professionals need a system to communicate medications and diagnosis, with patients' safety as the major consideration, especially with the many complexities associated with drug-interactions and allergies. Via multivariate analysis and linear regression analysis, it was found that degree of e-prescribing acceptance is highly predictable by constructs of Technological Sophistication, Operational Factors and Maturity Factors, which are very stable ease-of-use variables derived from the TAM Model by Davis (1989).

Blood lead levels and risk factors in pregnant women from Durango, Mexico.

PubMed

La-Llave-León, Osmel; Estrada-Martínez, Sergio; Manuel Salas-Pacheco, José; Peña-Elósegui, Rocío; Duarte-Sustaita, Jaime; Candelas Rangel, Jorge-Luís; García Vargas, Gonzalo

2011-01-01

In this cross-sectional study the authors determined blood lead levels (BLLs) and some risk factors for lead exposure in pregnant women. Two hundred ninety-nine pregnant women receiving medical attention by the Secretary of Health, State of Durango, Mexico, participated in this study between 2007 and 2008. BLLs were evaluated with graphite furnace atomic absorption spectrometry. The authors used Student t test, 1-way analysis of variance (ANOVA), and linear regression as statistical treatments. BLLs ranged from 0.36 to 23.6 μg/dL (mean = 2.79 μg/dL, standard deviation = 2.14). Multivariate analysis showed that the main predictors of BLLs were working in a place where lead is used, using lead glazed pottery, and eating soil.

Metabolomics of Ulva lactuca Linnaeus (Chlorophyta) exposed to oil fuels: Fourier transform infrared spectroscopy and multivariate analysis as tools for metabolic fingerprint.

PubMed

Pilatti, Fernanda Kokowicz; Ramlov, Fernanda; Schmidt, Eder Carlos; Costa, Christopher; Oliveira, Eva Regina de; Bauer, Claudia M; Rocha, Miguel; Bouzon, Zenilda Laurita; Maraschin, Marcelo

2017-01-30

Fossil fuels, e.g. gasoline and diesel oil, account for substantial share of the pollution that affects marine ecosystems. Environmental metabolomics is an emerging field that may help unravel the effect of these xenobiotics on seaweeds and provide methodologies for biomonitoring coastal ecosystems. In the present study, FTIR and multivariate analysis were used to discriminate metabolic profiles of Ulva lactuca after in vitro exposure to diesel oil and gasoline, in combinations of concentrations (0.001%, 0.01%, 0.1%, and 1.0% - v/v) and times of exposure (30min, 1h, 12h, and 24h). PCA and HCA performed on entire mid-infrared spectral window were able to discriminate diesel oil-exposed thalli from the gasoline-exposed ones. HCA performed on spectral window related to the protein absorbance (1700-1500cm -1 ) enabled the best discrimination between gasoline-exposed samples regarding the time of exposure, and between diesel oil-exposed samples according to the concentration. The results indicate that the combination of FTIR with multivariate analysis is a simple and efficient methodology for metabolic profiling with potential use for biomonitoring strategies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Natural history definition and a suggested clinical approach to Buerger's disease: a case-control study with survival analysis.

PubMed

Fazeli, Bahare; Ravari, Hassan; Assadi, Reza

2012-08-01

The aim of this study was first to describe the natural history of Buerger's disease (BD) and then to discuss a clinical approach to this disease based on multivariate analysis. One hundred eight patients who corresponded with Shionoya's criteria were selected from 2000 to 2007 for this study. Major amputation was considered the ultimate adverse event. Survival analyses were performed by Kaplan-Meier curves. Independent variables including gender, duration of smoking, number of cigarettes smoked per day, minor amputation events and type of treatments, were determined by multivariate Cox regression analysis. The recorded data demonstrated that BD may present in four forms, including relapsing-remitting (75%), secondary progressive (4.6%), primary progressive (14.2%) and benign BD (6.2%). Most of the amputations occurred due to relapses within the six years after diagnosis of BD. In multivariate analysis, duration of smoking of more than 20 years had a significant relationship with further major amputation among patients with BD. Smoking cessation programs with experienced psychotherapists are strongly recommended for those areas in which Buerger's disease is common. Patients who have smoked for more than 20 years should be encouraged to quit smoking, but should also be recommended for more advanced treatment for limb salvage.

Random Initialisation of the Spectral Variables: an Alternate Approach for Initiating Multivariate Curve Resolution Alternating Least Square (MCR-ALS) Analysis.

PubMed

Kumar, Keshav

2017-11-01

Multivariate curve resolution alternating least square (MCR-ALS) analysis is the most commonly used curve resolution technique. The MCR-ALS model is fitted using the alternate least square (ALS) algorithm that needs initialisation of either contribution profiles or spectral profiles of each of the factor. The contribution profiles can be initialised using the evolve factor analysis; however, in principle, this approach requires that data must belong to the sequential process. The initialisation of the spectral profiles are usually carried out using the pure variable approach such as SIMPLISMA algorithm, this approach demands that each factor must have the pure variables in the data sets. Despite these limitations, the existing approaches have been quite a successful for initiating the MCR-ALS analysis. However, the present work proposes an alternate approach for the initialisation of the spectral variables by generating the random variables in the limits spanned by the maxima and minima of each spectral variable of the data set. The proposed approach does not require that there must be pure variables for each component of the multicomponent system or the concentration direction must follow the sequential process. The proposed approach is successfully validated using the excitation-emission matrix fluorescence data sets acquired for certain fluorophores with significant spectral overlap. The calculated contribution and spectral profiles of these fluorophores are found to correlate well with the experimental results. In summary, the present work proposes an alternate way to initiate the MCR-ALS analysis.

Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains.

PubMed

Allefeld, Carsten; Bialonski, Stephan

2007-12-01

Synchronization cluster analysis is an approach to the detection of underlying structures in data sets of multivariate time series, starting from a matrix R of bivariate synchronization indices. A previous method utilized the eigenvectors of R for cluster identification, analogous to several recent attempts at group identification using eigenvectors of the correlation matrix. All of these approaches assumed a one-to-one correspondence of dominant eigenvectors and clusters, which has however been shown to be wrong in important cases. We clarify the usefulness of eigenvalue decomposition for synchronization cluster analysis by translating the problem into the language of stochastic processes, and derive an enhanced clustering method harnessing recent insights from the coarse-graining of finite-state Markov processes. We illustrate the operation of our method using a simulated system of coupled Lorenz oscillators, and we demonstrate its superior performance over the previous approach. Finally we investigate the question of robustness of the algorithm against small sample size, which is important with regard to field applications.

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

Systematic wavelength selection for improved multivariate spectral analysis

DOEpatents

Thomas, Edward V.; Robinson, Mark R.; Haaland, David M.

1995-01-01

Methods and apparatus for determining in a biological material one or more unknown values of at least one known characteristic (e.g. the concentration of an analyte such as glucose in blood or the concentration of one or more blood gas parameters) with a model based on a set of samples with known values of the known characteristics and a multivariate algorithm using several wavelength subsets. The method includes selecting multiple wavelength subsets, from the electromagnetic spectral region appropriate for determining the known characteristic, for use by an algorithm wherein the selection of wavelength subsets improves the model's fitness of the determination for the unknown values of the known characteristic. The selection process utilizes multivariate search methods that select both predictive and synergistic wavelengths within the range of wavelengths utilized. The fitness of the wavelength subsets is determined by the fitness function F=.function.(cost, performance). The method includes the steps of: (1) using one or more applications of a genetic algorithm to produce one or more count spectra, with multiple count spectra then combined to produce a combined count spectrum; (2) smoothing the count spectrum; (3) selecting a threshold count from a count spectrum to select these wavelength subsets which optimize the fitness function; and (4) eliminating a portion of the selected wavelength subsets. The determination of the unknown values can be made: (1) noninvasively and in vivo; (2) invasively and in vivo; or (3) in vitro.

Abstracting Attribute Space for Transfer Function Exploration and Design.

PubMed

Maciejewski, Ross; Jang, Yun; Woo, Insoo; Jänicke, Heike; Gaither, Kelly P; Ebert, David S

2013-01-01

Currently, user centered transfer function design begins with the user interacting with a one or two-dimensional histogram of the volumetric attribute space. The attribute space is visualized as a function of the number of voxels, allowing the user to explore the data in terms of the attribute size/magnitude. However, such visualizations provide the user with no information on the relationship between various attribute spaces (e.g., density, temperature, pressure, x, y, z) within the multivariate data. In this work, we propose a modification to the attribute space visualization in which the user is no longer presented with the magnitude of the attribute; instead, the user is presented with an information metric detailing the relationship between attributes of the multivariate volumetric data. In this way, the user can guide their exploration based on the relationship between the attribute magnitude and user selected attribute information as opposed to being constrained by only visualizing the magnitude of the attribute. We refer to this modification to the traditional histogram widget as an abstract attribute space representation. Our system utilizes common one and two-dimensional histogram widgets where the bins of the abstract attribute space now correspond to an attribute relationship in terms of the mean, standard deviation, entropy, or skewness. In this manner, we exploit the relationships and correlations present in the underlying data with respect to the dimension(s) under examination. These relationships are often times key to insight and allow us to guide attribute discovery as opposed to automatic extraction schemes which try to calculate and extract distinct attributes a priori. In this way, our system aids in the knowledge discovery of the interaction of properties within volumetric data.

Educational Module Intervention for Radiographers to Reduce Repetition Rate of Routine Digital Chest Radiography in Makkah Region of Saudi Arabia Tertiary Hospitals: Protocol of a Quasi-Experimental Study.

PubMed

Almalki, Abdullah A; Abdul Manaf, Rosliza; Hanafiah Juni, Muhamad; Kadir Shahar, Hayati; Noor, Noramaliza Mohd; Gabbad, Abdelsafi

2017-09-26

Repetition of an image is a critical event in any radiology department. When the repetition rate of routine digital chest radiographs is high, radiation exposure of staff and patients is increased. In addition, repetition consumes the equipment's life span, thus affecting the annual budget of the department. The aim of this study is to determine the impact of a printed educational module on reducing the repetition rate of routine digital chest radiography among radiographers in Makkah Region tertiary hospitals. A quasi-experimental time series with a control group will be conducted in Makkah Region tertiary hospitals for 8 months starting in the second quarter of 2017. Four hospitals out of 5 in the region will be selected; 2 of them will be selected as the control group and the other 2 as the intervention group. Stratification and a simple random sampling technique will be used to sample 56 radiographers in each group. Pre- and postintervention assessments will be conducted to determine the radiographer knowledge, motivation, and skills and repetition rate of chest radiographs. Radiographs of the chest performed by sampled radiographers in the selected hospitals will be collected for 2 weeks before and after the intervention. A piloted questionnaire will be distributed and collected by a researcher in both groups. One-way multivariate analysis of variance and 2-way repeated multivariate analysis of variance will be used to analyze the data. It is expected that the repetition rate in the intervention group will decline after implementing the intervention and the change will be statistically significant (P<.05). Furthermore, it is expected that the knowledge, motivation, and skill levels in the intervention group will increase significantly among radiographers after implementation of the intervention (P<.05). Meanwhile, knowledge, motivation, and skills in the control group will not change. A quasi-experimental time series with a control will be conducted to investigate the effect of printed educational material in reducing the repetition rate of routine digital chest radiographs among radiographers in tertiary hospitals in the Makkah Region of Saudi Arabia. ©Abdullah A. Almalki, Rosliza Abdul Manaf, Muhamad Hanafiah Juni, Hayati Kadir Shahar, Noramaliza Mohd Noor, Abdelsafi Gabbad. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 26.09.2017.

The source identification of ambient aerosols in Beijing, China by multivariate analysis coupled with {sup 14}C tracer

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

Xiaoyan Tang; Min Shao; Yuanhang Zhang

1996-12-31

Ambient aerosol is one of most important pollutants in China. This paper showed the results of aerosol sources of Beijing area revealed by combination of multivariate analysis models and 14C tracer measured on Accelerator Mass Spectrometry (AMS). The results indicated that the mass concentration of particulate (<100 (M)) didn`t increase rapidly, compared with economic development in Beijing city. The multivariate analysis showed that the predominant source was soil dust which contributed more than 50% to atmospheric particles. However, it would be a risk to conclude that the aerosol pollution from anthropogenic sources was less important in Beijing city based onmore » above phenomenon. Due to lack of reliable tracers, it was very hard to distinguish coal burning from soil source. Thus, it was suspected that the soil source above might be the mixture of soil dust and coal burning. The 14C measurement showed that carbonaceous species of aerosol had quite different emission sources. For carbonaceous aerosols in Beijing, the contribution from fossil fuel to ambient particles was nearly 2/3, as the man-made activities ( coal-burning, etc.) increased, the fossil part would contribute more to atmospheric carbonaceous particles. For example, in downtown Beijing at space-heating seasons, the fossil fuel even contributed more than 95% to carbonaceous particles, which would be potential harmful to population. By using multivariate analysis together with 14C data, two important sources of aerosols in Beijing (soil and coal) combustion were more reliably distinguished, which was critical important for the assessment of aerosol problem in China.« less

Multivariate assessment of event-related potentials with the t-CWT method.

PubMed

Bostanov, Vladimir

2015-11-05

Event-related brain potentials (ERPs) are usually assessed with univariate statistical tests although they are essentially multivariate objects. Brain-computer interface applications are a notable exception to this practice, because they are based on multivariate classification of single-trial ERPs. Multivariate ERP assessment can be facilitated by feature extraction methods. One such method is t-CWT, a mathematical-statistical algorithm based on the continuous wavelet transform (CWT) and Student's t-test. This article begins with a geometric primer on some basic concepts of multivariate statistics as applied to ERP assessment in general and to the t-CWT method in particular. Further, it presents for the first time a detailed, step-by-step, formal mathematical description of the t-CWT algorithm. A new multivariate outlier rejection procedure based on principal component analysis in the frequency domain is presented as an important pre-processing step. The MATLAB and GNU Octave implementation of t-CWT is also made publicly available for the first time as free and open source code. The method is demonstrated on some example ERP data obtained in a passive oddball paradigm. Finally, some conceptually novel applications of the multivariate approach in general and of the t-CWT method in particular are suggested and discussed. Hopefully, the publication of both the t-CWT source code and its underlying mathematical algorithm along with a didactic geometric introduction to some basic concepts of multivariate statistics would make t-CWT more accessible to both users and developers in the field of neuroscience research.

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

PubMed

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

2017-02-01

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

Linkage Analysis of a Model Quantitative Trait in Humans: Finger Ridge Count Shows Significant Multivariate Linkage to 5q14.1

PubMed Central

Medland, Sarah E; Loesch, Danuta Z; Mdzewski, Bogdan; Zhu, Gu; Montgomery, Grant W; Martin, Nicholas G

2007-01-01

The finger ridge count (a measure of pattern size) is one of the most heritable complex traits studied in humans and has been considered a model human polygenic trait in quantitative genetic analysis. Here, we report the results of the first genome-wide linkage scan for finger ridge count in a sample of 2,114 offspring from 922 nuclear families. Both univariate linkage to the absolute ridge count (a sum of all the ridge counts on all ten fingers), and multivariate linkage analyses of the counts on individual fingers, were conducted. The multivariate analyses yielded significant linkage to 5q14.1 (Logarithm of odds [LOD] = 3.34, pointwise-empirical p-value = 0.00025) that was predominantly driven by linkage to the ring, index, and middle fingers. The strongest univariate linkage was to 1q42.2 (LOD = 2.04, point-wise p-value = 0.002, genome-wide p-value = 0.29). In summary, the combination of univariate and multivariate results was more informative than simple univariate analyses alone. Patterns of quantitative trait loci factor loadings consistent with developmental fields were observed, and the simple pleiotropic model underlying the absolute ridge count was not sufficient to characterize the interrelationships between the ridge counts of individual fingers. PMID:17907812

FIA data and species diversity—successes and failures using multivariate analysis techniques, spatial lag and error models and hot-spot analysis

Treesearch

Andrew J. Hartsell

2015-01-01

This study will investigate how global and local predictors differ with varying spatial scale in relation to species evenness and richness in the gulf coastal plain. Particularly, all-live trees >= one-inch d.b.h. Forest Inventory and Analysis (FIA) data was used as the basis for the study. Watersheds are defined by the USGS 12 digit hydrologic units. The...

Near-infrared spectroscopy (NIRS) as a tool to monitor exhaust air from poultry operations.

PubMed

Druckenmüller, Katharina; Günther, Klaus; Elbers, Gereon

2018-07-15

Intensive poultry operation systems emit a considerable volume of inorganic and organic matter in the surrounding environment. Monitoring cleaning properties of exhaust air cleaning systems and to detect small but significant changes in emission characteristics during a fattening cycle is important for both emission and fattening process control. In the present study, we evaluated the potential of near-infrared spectroscopy (NIRS) combined with chemometric techniques as a monitoring tool of exhaust air from poultry operation systems. To generate a high-quality data set for evaluation, the exhaust air of two poultry houses was sampled by applying state-of-the-art filter sampling protocols. The two stables were identical except for one crucial difference, the presence or absence of an exhaust air cleaning system. In total, twenty-one exhaust air samples were collected at the two sites to monitor spectral differences caused by the cleaning device, and to follow changes in exhaust air characteristics during a fattening period. The total dust load was analyzed by gravimetric determination and included as a response variable in multivariate data analysis. The filter samples were directly measured with NIR spectroscopy. Principal component analysis (PCA), linear discriminant analysis (LDA), and factor analysis (FA) were effective in classifying the NIR exhaust air spectra according to fattening day and origin. The results indicate that the dust load and the composition of exhaust air (inorganic or organic matter) substantially influence the NIR spectral patterns. In conclusion, NIR spectroscopy as a tool is a promising and very rapid way to detect differences between exhaust air samples based on still not clearly defined circumstances triggered during a fattening period and the availability of an exhaust air cleaning system. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

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

Consequences of Assumption Violations Revisited: A Quantitative Review of Alternatives to the One-Way Analysis of Variance "F" Test.

ERIC Educational Resources Information Center

Lix, Lisa M.; And Others

1996-01-01

Meta-analytic techniques were used to summarize the statistical robustness literature on Type I error properties of alternatives to the one-way analysis of variance "F" test. The James (1951) and Welch (1951) tests performed best under violations of the variance homogeneity assumption, although their use is not always appropriate. (SLD)

Population Analysis: Communicating in Context

NASA Technical Reports Server (NTRS)

Rajulu, Sudhakar; Thaxton, Sherry

2008-01-01

Providing accommodation to a widely varying user population presents a challenge to engineers and designers. It is often even difficult to quantify who is accommodated and who is not accommodated by designs, especially for equipment with multiple critical anthropometric dimensions. An approach to communicating levels of accommodation referred to as population analysis applies existing human factors techniques in novel ways. This paper discusses the definition of population analysis as well as major applications and case studies. The major applications of population analysis consist of providing accommodation information for multivariate problems and enhancing the value of feedback from human-in-the-loop testing. The results of these analyses range from the provision of specific accommodation percentages of the user population to recommendations of design specifications based on quantitative data. Such feedback is invaluable to designers and results in the design of products that accommodate the intended user population.

Partial Least Squares and Neural Networks for Quantitative Calibration of Laser-induced Breakdown Spectroscopy (LIBs) of Geologic Samples

NASA Technical Reports Server (NTRS)

Anderson, R. B.; Morris, Richard V.; Clegg, S. M.; Humphries, S. D.; Wiens, R. C.; Bell, J. F., III; Mertzman, S. A.

2010-01-01

The ChemCam instrument [1] on the Mars Science Laboratory (MSL) rover will be used to obtain the chemical composition of surface targets within 7 m of the rover using Laser Induced Breakdown Spectroscopy (LIBS). ChemCam analyzes atomic emission spectra (240-800 nm) from a plasma created by a pulsed Nd:KGW 1067 nm laser. The LIBS spectra can be used in a semiquantitative way to rapidly classify targets (e.g., basalt, andesite, carbonate, sulfate, etc.) and in a quantitative way to estimate their major and minor element chemical compositions. Quantitative chemical analysis from LIBS spectra is complicated by a number of factors, including chemical matrix effects [2]. Recent work has shown promising results using multivariate techniques such as partial least squares (PLS) regression and artificial neural networks (ANN) to predict elemental abundances in samples [e.g. 2-6]. To develop, refine, and evaluate analysis schemes for LIBS spectra of geologic materials, we collected spectra of a diverse set of well-characterized natural geologic samples and are comparing the predictive abilities of PLS, cascade correlation ANN (CC-ANN) and multilayer perceptron ANN (MLP-ANN) analysis procedures.

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

USGS Publications Warehouse

Weber, L.; Davis, J.C.

1990-01-01

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

Drop coating deposition Raman spectroscopy of blood plasma for the detection of colorectal cancer

NASA Astrophysics Data System (ADS)

Li, Pengpeng; Chen, Changshui; Deng, Xiaoyuan; Mao, Hua; Jin, Shaoqin

2015-03-01

We have recently applied the technique of drop coating deposition Raman (DCDR) spectroscopy for colorectal cancer (CRC) detection using blood plasma. The aim of this study was to develop a more convenient and stable method based on blood plasma for noninvasive CRC detection. Significant differences are observed in DCDR spectra between healthy (n=105) and cancer (n=75) plasma from 15 CRC patients and 21 volunteers, particularly in the spectra that are related to proteins, nucleic acids, and β-carotene. The multivariate analysis principal components analysis and the linear discriminate analysis, together with leave-one-out, cross validation were used on DCDR spectra and yielded a sensitivity of 100% (75/75) and specificity of 98.1% (103/105) for detection of CRC. This study demonstrates that DCDR spectroscopy of blood plasma associated with multivariate statistical algorithms has the potential for the noninvasive detection of CRC.

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.

EVENT-LEVEL ANALYSIS OF ANAL SEX ROLES AND SEX DRUG USE AMONG GAY AND BISEXUAL MEN IN VANCOUVER, BRITISH COLUMBIA, CANADA

PubMed Central

Rich, Ashleigh J; Lachowsky, Nathan J; Cui, Zishan; Sereda, Paul; Lal, Allan; Moore, David M; Hogg, Robert S; Roth, Eric A

2015-01-01

This study analyzed event-level partnership data from a computer-assisted survey of 719 gay and bisexual men (GBM) enrolled in the Momentum Health Study to delineate potential linkages between anal sex roles and so-called “sex drugs”, i.e. erectile dysfunction drugs (EDD), poppers and crystal methamphetamine. Univariable and multivariable analyses using generalized linear mixed models with logit link function with sexual encounters (n=2,514) as the unit of analysis tested four hypotheses: 1) EDD are significantly associated with insertive anal sex roles, 2) poppers are significantly associated with receptive anal sex, 3) both poppers and EDD are significantly associated with anal sexual versatility and, 4) crystal methamphetamine is significantly associated with all anal sex roles. Data for survey respondents and their sexual partners allowed testing these hypotheses for both anal sex partners in the same encounter. Multivariable results supported the first three hypotheses. Crystal methamphetamine was significantly associated with all anal sex roles in the univariable models, but not significant in any multivariable ones. Other multivariable significant variables included attending group sex events, venue where first met, and self-described sexual orientation. Results indicate that GBM sex-drug use behavior features rational decision-making strategies linked to anal sex roles. They also suggest that more research on anal sex roles, particularly versatility, is needed, and that sexual behavior research can benefit from partnership analysis. PMID:26525571

Event-Level Analysis of Anal Sex Roles and Sex Drug Use Among Gay and Bisexual Men in Vancouver, British Columbia, Canada.

PubMed

Rich, Ashleigh J; Lachowsky, Nathan J; Cui, Zishan; Sereda, Paul; Lal, Allan; Moore, David M; Hogg, Robert S; Roth, Eric A

2016-08-01

This study analyzed event-level partnership data from a computer-assisted survey of 719 gay and bisexual men (GBM) enrolled in the Momentum Health Study to delineate potential linkages between anal sex roles and the so-called "sex drugs," i.e., erectile dysfunction drugs (EDD), poppers, and crystal methamphetamine. Univariable and multivariable analyses using generalized linear mixed models with logit link function with sexual encounters (n = 2514) as the unit of analysis tested four hypotheses: (1) EDD are significantly associated with insertive anal sex roles, (2) poppers are significantly associated with receptive anal sex, (3) both poppers and EDD are significantly associated with anal sexual versatility, and (4) crystal methamphetamine is significantly associated with all anal sex roles. Data for survey respondents and their sexual partners allowed testing these hypotheses for both anal sex partners in the same encounter. Multivariable results supported the first three hypotheses. Crystal methamphetamine was significantly associated with all anal sex roles in the univariable models, but not significant in any multivariable ones. Other multivariable significant variables included attending group sex events, venue where first met, and self-described sexual orientation. Results indicate that GBM sex-drug use behavior features rational decision-making strategies linked to anal sex roles. They also suggest that more research on anal sex roles, particularly versatility, is needed, and that sexual behavior research can benefit from partnership analysis.

Experts' Understanding of Partial Derivatives Using the Partial Derivative Machine

ERIC Educational Resources Information Center

Roundy, David; Weber, Eric; Dray, Tevian; Bajracharya, Rabindra R.; Dorko, Allison; Smith, Emily M.; Manogue, Corinne A.

2015-01-01

Partial derivatives are used in a variety of different ways within physics. Thermodynamics, in particular, uses partial derivatives in ways that students often find especially confusing. We are at the beginning of a study of the teaching of partial derivatives, with a goal of better aligning the teaching of multivariable calculus with the needs of…

The effect of Think Pair Share (TPS) using scientific approach on students’ self-confidence and mathematical problem-solving

NASA Astrophysics Data System (ADS)

Rifa’i, A.; Lestari, H. P.

2018-03-01

This study was designed to know the effects of Think Pair Share using Scientific Approach on students' self-confidence and mathematical problem-solving. Quasi-experimental with pre-test post-test non-equivalent group method was used as a basis for design this study. Self-confidence questionnaire and problem-solving test have been used for measurement of the two variables. Two classes of the first grade in religious senior high school (MAN) in Indonesia were randomly selected for this study. Teaching sequence and series from mathematics book at control group in the traditional way and at experiment group has been in TPS using scientific approach learning method. For data analysis regarding students’ problem-solving skill and self-confidence, One-Sample t-Test, Independent Sample t-Test, and Multivariate of Variance (MANOVA) were used. The results showed that (1) TPS using a scientific approach and traditional learning had positive effects (2) TPS using scientific approach learning in comparative with traditional learning had a more significant effect on students’ self-confidence and problem-solving skill.

Students' Conceptions of the Nature of Science: Perspectives from Canadian and Korean Middle School Students

NASA Astrophysics Data System (ADS)

Park, Hyeran; Nielsen, Wendy; Woodruff, Earl

2014-05-01

This study examined and compared students' understanding of nature of science (NOS) with 521 Grade 8 Canadian and Korean students using a mixed methods approach. The concepts of NOS were measured using a survey that had both quantitative and qualitative elements. Descriptive statistics and one-way multivariate analysis of variances examined the quantitative data while a conceptually clustered matrix classified the open-ended responses. The country effect could explain 3-12 % of the variances of subjectivity, empirical testability and diverse methods, but it was not significant for the concepts of tentativeness and socio-cultural embeddedness of science. The open-ended responses showed that students believed scientific theories change due to errors or discoveries. Students regarded empirical evidence as undeniable and objective although they acknowledged experiments depend on theories or scientists' knowledge. The open responses revealed that national situations and curriculum content affected their views. For our future democratic citizens to gain scientific literacy, science curricula should include currently acknowledged NOS concepts and should be situated within societal and cultural perspectives.

Direct rapid analysis of trace bioavailable soil macronutrients by chemometrics-assisted energy dispersive X-ray fluorescence and scattering spectrometry.

PubMed

Kaniu, M I; Angeyo, K H; Mwala, A K; Mangala, M J

2012-06-04

Precision agriculture depends on the knowledge and management of soil quality (SQ), which calls for affordable, simple and rapid but accurate analysis of bioavailable soil nutrients. Conventional SQ analysis methods are tedious and expensive. We demonstrate the utility of a new chemometrics-assisted energy dispersive X-ray fluorescence and scattering (EDXRFS) spectroscopy method we have developed for direct rapid analysis of trace 'bioavailable' macronutrients (i.e. C, N, Na, Mg, P) in soils. The method exploits, in addition to X-ray fluorescence, the scatter peaks detected from soil pellets to develop a model for SQ analysis. Spectra were acquired from soil samples held in a Teflon holder analyzed using (109)Cd isotope source EDXRF spectrometer for 200 s. Chemometric techniques namely principal component analysis (PCA), partial least squares (PLS) and artificial neural networks (ANNs) were utilized for pattern recognition based on fluorescence and Compton scatter peaks regions, and to develop multivariate quantitative calibration models based on Compton scatter peak respectively. SQ analyses were realized with high CMD (R(2)>0.9) and low SEP (0.01% for N and Na, 0.05% for C, 0.08% for Mg and 1.98 μg g(-1) for P). Comparison of predicted macronutrients with reference standards using a one-way ANOVA test showed no statistical difference at 95% confidence level. To the best of the authors' knowledge, this is the first time that an XRF method has demonstrated utility in trace analysis of macronutrients in soil or related matrices. Copyright © 2012 Elsevier B.V. All rights reserved.

Factors associated with postpartum weight retention in a Brazilian cohort.

PubMed

Zanotti, Joana; Capp, Edison; Wender, Maria Celeste Osório

2015-04-01

To identify the factors associated with weight retention after pregnancy. A cohort study was performed with 145 women receiving maternity care at a hospital in Caxias do Sul, Rio Grande do Sul, Brazil, aged 19 to 45 years, between weeks 38 and 42 of pregnancy. The patients were evaluated at one month, three months, and six months after delivery. Student's t-test or one-way analysis of variance (ANOVA) was used to compare groups, as indicated; correlations were assessed with Pearson's and Spearman's tests, as indicated; to identify and evaluate confounders independently associated with total weight loss, a multivariate linear regression analysis was performed and statistical significance was set at p ≤ 0.05. There was a significant positive association between total weight gain - and a negative association with physical exercise during pregnancy - with total weight loss. Higher parity, inter-pregnancy interval, calorie intake, pre-pregnancy body mass index (BMI), weight gain related to pre-pregnancy BMI, presence and severity of depression, and lack of exclusive breastfeeding were directly associated with lower weight loss. Among nominal variables, level of education and marital status were significantly associated with total weight loss. In the present study, lower weight retention in the postpartum period was associated with higher educational attainment and with being married. Normal or below-normal pre-pregnancy BMI, physical activity and adequate weight gain during pregnancy, lower parity, exclusive breastfeeding for a longer period, appropriate or low calorie intake, and absence of depression were also determinants of reduced weight retention.

Epidemiological analysis of a cluster within the outbreak of Shiga toxin-producing Escherichia coli serotype O104:H4 in Northern Germany, 2011.

PubMed

Scharlach, Martina; Diercke, Michaela; Dreesman, Johannes; Jahn, Nicola; Krieck, Manuela; Beyrer, Konrad; Claußen, Katja; Pulz, Matthias; Floride, Regina

2013-06-01

In May 2011 one of the worldwide largest outbreaks of haemolytic uraemic syndrome (HUS) and bloody diarrhoea caused by Shiga toxin-producing Escherichia coli (STEC) serotype O104:H4 occurred in Germany. One of the most affected federal states was Lower Saxony. We present the investigation of a cluster of STEC and HUS cases within this outbreak by means of a retrospective cohort study. After a 70th birthday celebration which took place on 7th of May 2011 among 72 attendants seven confirmed cases and four probable cases were identified, two of them developed HUS. Median incubation period was 10 days. Only 35 persons (48.6%) definitely answered the question whether they had eaten the sprouts that were used for garnishing the salad. Univariable analysis revealed different food items, depending on the case definition, with Odds Ratio (OR)>1 indicating an association with STEC infection, but multivariable logistic regression showed no increased risk for STEC infection for any food item and any case definition. Sprouts as the source for the infection had to be assumed based on the results of a tracing back of the delivery ways from the catering company to the sprouts producer who was finally identified as the source of the entire German outbreak. In this large outbreak several case-control studies failed to identify the source of infection. Copyright © 2012 Elsevier GmbH. All rights reserved.

[Evolution of burnout and associated factors in primary care physicians].

PubMed

Matía Cubillo, Angel Carlos; Cordero Guevara, José; Mediavilla Bravo, José Javier; Pereda Riguera, Maria José; González Castro, Maria Luisa; González Sanz, Ana

2012-09-01

To analyse the course of burnout and develop an explanatory model. Prospective cohort dynamics. SITE: All primary health care centres in Burgos. All physicians except medical emergencies, paediatrics and residents. Anonymous self-report questionnaire: Maslach Burnout Inventory (MBI) and related variables. An analysis was performed using the Student-t, X(2) test and logistic regression. The response rate was 47.76% in 2007, which was lower than that of 2005. There were significant differences between 2005 and 2007, for increases in the percentage of physicians who smoked, postgraduate training, residency, and those who believe that coordination with nursing and specialist care and institutional communication is appropriate. There was an increase in the prevalence of burnout by almost one point compared with 2005, a decrease in maximum burnout and emotional exhaustion (EC), and an increase in depersonalisation (DP) and personal accomplishment (RP). The incidence density of burnout was 1/113. 5 primary care physicians per year. The existence of burnout is associated with the use of chronic medication and inadequate coordination between nursing and EC, and also with the high workload. The increase in the prevalence found is consistent with the idea of burnout as a dynamic development and the theoretical model described. Stable and quality employment is one way to indirectly mitigate (by encouraging internal communication) professional burnout. In the multivariate analysis, the most critical variable in the onset of burnout is the inadequate coordination with nursing. Copyright © 2008 Elsevier España, S.L. All rights reserved.

Understanding handpump sustainability: Determinants of rural water source functionality in the Greater Afram Plains region of Ghana†

PubMed Central

Shields, Katherine F.; Chan, Terence U.; Christenson, Elizabeth; Cronk, Ryan D.; Leker, Hannah; Samani, Destina; Apoya, Patrick; Lutz, Alexandra

2015-01-01

Abstract Safe drinking water is critical to human health and development. In rural sub‐Saharan Africa, most improved water sources are boreholes with handpumps; studies suggest that up to one third of these handpumps are nonfunctional at any given time. This work presents findings from a secondary analysis of cross‐sectional data from 1509 water sources in 570 communities in the rural Greater Afram Plains (GAP) region of Ghana; one of the largest studies of its kind. 79.4% of enumerated water sources were functional when visited; in multivariable regressions, functionality depended on source age, management, tariff collection, the number of other sources in the community, and the district. A Bayesian network (BN) model developed using the same data set found strong dependencies of functionality on implementer, pump type, management, and the availability of tools, with synergistic effects from management determinants on functionality, increasing the likelihood of a source being functional from a baseline of 72% to more than 97% with optimal management and available tools. We suggest that functionality may be a dynamic equilibrium between regular breakdowns and repairs, with management a key determinant of repair rate. Management variables may interact synergistically in ways better captured by BN analysis than by logistic regressions. These qualitative findings may prove generalizable beyond the study area, and may offer new approaches to understanding and increasing handpump functionality and safe water access. PMID:27667863

Understanding handpump sustainability: Determinants of rural water source functionality in the Greater Afram Plains region of Ghana

NASA Astrophysics Data System (ADS)

Fisher, Michael B.; Shields, Katherine F.; Chan, Terence U.; Christenson, Elizabeth; Cronk, Ryan D.; Leker, Hannah; Samani, Destina; Apoya, Patrick; Lutz, Alexandra; Bartram, Jamie

2015-10-01

Safe drinking water is critical to human health and development. In rural sub-Saharan Africa, most improved water sources are boreholes with handpumps; studies suggest that up to one third of these handpumps are nonfunctional at any given time. This work presents findings from a secondary analysis of cross-sectional data from 1509 water sources in 570 communities in the rural Greater Afram Plains (GAP) region of Ghana; one of the largest studies of its kind. 79.4% of enumerated water sources were functional when visited; in multivariable regressions, functionality depended on source age, management, tariff collection, the number of other sources in the community, and the district. A Bayesian network (BN) model developed using the same data set found strong dependencies of functionality on implementer, pump type, management, and the availability of tools, with synergistic effects from management determinants on functionality, increasing the likelihood of a source being functional from a baseline of 72% to more than 97% with optimal management and available tools. We suggest that functionality may be a dynamic equilibrium between regular breakdowns and repairs, with management a key determinant of repair rate. Management variables may interact synergistically in ways better captured by BN analysis than by logistic regressions. These qualitative findings may prove generalizable beyond the study area, and may offer new approaches to understanding and increasing handpump functionality and safe water access. This article was corrected on 11 Nov 2015. See the end of the full text for details.

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

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

Wang, Feng, E-mail: fwang@unu.edu; Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft; Huisman, Jaco

2013-11-15

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

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.

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 calibration on NIR data: development of a model for the rapid evaluation of ethanol content in bakery products.

PubMed

Bello, Alessandra; Bianchi, Federica; Careri, Maria; Giannetto, Marco; Mori, Giovanni; Musci, Marilena

2007-11-05

A new NIR method based on multivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new "leave one out" method, so that the number of original variables resulted further reduced.

A hybrid PCA-CART-MARS-based prognostic approach of the remaining useful life for aircraft engines.

PubMed

Sánchez Lasheras, Fernando; García Nieto, Paulino José; de Cos Juez, Francisco Javier; Mayo Bayón, Ricardo; González Suárez, Victor Manuel

2015-03-23

Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines.

A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines

PubMed Central

Lasheras, Fernando Sánchez; Nieto, Paulino José García; de Cos Juez, Francisco Javier; Bayón, Ricardo Mayo; Suárez, Victor Manuel González

2015-01-01

Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines. PMID:25806876

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

PubMed

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

2015-11-01

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

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

PubMed Central

Collins, Tony J.; Ylanko, Jarkko; Geng, Fei

2015-01-01

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

Discrimination of cherry wines based on their sensory properties and aromatic fingerprinting using HS-SPME-GC-MS and multivariate analysis.

PubMed

Xiao, Zuobing; Liu, Shengjiang; Gu, Yongbo; Xu, Na; Shang, Yi; Zhu, Jiancai

2014-03-01

Volatiles of cherry wines were extracted by headspace solid phase microextraction (HS-SPME) and analyzed by gas chromatography mass spectrometry (GC-MS), multivariate statistical techniques (such as principal component analysis (PCA) and cluster analysis (CA) and correlation analysis) to differentiate sensory attributes of 3 groups of the wines through characterization of volatiles of cherry wine. Seventy-five volatiles were identified in 9 samples, including 29 esters, 22 alcohols, 8 acids, 3 ketones, 5 aldehydes, and 8 miscellaneous compounds. The PCA results showed that the cherry wines were mainly differentiated by 8 sensory attributes. The samples W2, W4, and W7 were grouped around sweet aromatic and the samples W1, W5, and W9 were highly associated with the sweet, esters, green, bitter, and fermented. Nevertheless, the samples W3, W6, and W8 were located close to the sour, alcoholic, and fruity. The final result of correlation analysis was in conformity with the conclusion of PCA. The CA results showed that the group of W2, W4, and W7, and the group of W1, W5, and W9 had less difference than the group of W3, W6, and W8. The reason should be that esterification reactions and fermentation process during the ageing period was more extended. The results of analyzing revealed that HS-SPME-GC-MS coupled with chemometrics could give an appropriate way of characterizing and classifying the cherry wines. Attributes that represent and discriminate among cherry wines might be made use of a better comprehending of the wines and for being utilized in future work. In addition, several chemometrics were used to classify the type of wines and try to install the relationship between volatiles and sensory property. Especially, PCA clearly revealed that the most contributing compounds for sensory attributes of cherry wines, CA was a more applicable way to distinguish types of cherry wines. Therefore, a feasible method that would be helpful to promote the quality of the wines by improving the winemaking process and analyzing aromatic characteristics of wines. © 2014 Institute of Food Technologists®

Time-dependent changes in protein expression in rainbow trout muscle following hypoxia.

PubMed

Wulff, Tune; Jokumsen, Alfred; Højrup, Peter; Jessen, Flemming

2012-04-18

Adaptation to hypoxia is a complex process, and individual proteins will be up- or down-regulated in order to address the main challenges at any given time. To investigate the dynamics of the adaptation, rainbow trout (Oncorhynchus mykiss) was exposed to 30% of normal oxygen tension for 1, 2, 5 and 24 h respectively, after which muscle samples were taken. The successful investigation of numerous proteins in a single study was achieved by selectively separating the sarcoplasmic proteins using 2-DE. In total 46 protein spots were identified as changing in abundance in response to hypoxia using one-way ANOVA and multivariate data analysis. Proteins of interest were subsequently identified by MS/MS following tryptic digestion. The observed regulation following hypoxia in skeletal muscle was determined to be time specific, as only a limited number of proteins were regulated in response to more than one time point. The cellular response to hypoxia included regulation of proteins involved in maintaining iron homeostasis, energy levels and muscle structure. In conclusion, this proteome-based study presents a comprehensive investigation of the expression profiles of numerous proteins at four different time points. This increases our understanding of timed changes in protein expression in rainbow trout muscle following hypoxia. Copyright © 2012 Elsevier B.V. All rights reserved.

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…

Tailored multivariate analysis for modulated enhanced diffraction

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

Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni

2015-10-21

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

Framing research for state policymakers who place a priority on cancer.

PubMed

Brownson, Ross C; Dodson, Elizabeth A; Kerner, Jon F; Moreland-Russell, Sarah

2016-08-01

Despite the potential for reducing the cancer burden via state policy change, few data exist on how best to disseminate research information to influence state legislators' policy choices. We explored: (1) the relative importance of core framing issues (source, presentation, timeliness) among policymakers who prioritize cancer and those who do not prioritize cancer and (2) the predictors of use of research in policymaking. Cross-sectional data were collected from US state policymakers (i.e., legislators elected to state houses or senates) from January through October 2012 (n = 862). One-way analysis of variance was performed to investigate the association of the priority of cancer variable with outcome variables. Multivariate logistic regression models examined predictors of the influence of research information. Legislators who prioritized cancer tended to rate characteristics that make research information useful higher than those who did not prioritize cancer. Among differences that were statistically significant were three items in the "source" domain (relevance, delivered by someone respected, supports one's own position), one item in the "presentation" domain (telling a story related to constituents) and two items in the "timeliness" domain (high current state priority, feasible when information is received). Participants who prioritized cancer risk factors were 80 % more likely to rate research information as one of their top reasons for choosing an issue on which to work. Our results suggest the importance of narrative forms of communication and that research information needs to be relevant to the policymakers' constituents in a brief, concise format.

Multisite-multivariable sensitivity analysis of distributed watershed models: enhancing the perceptions from computationally frugal methods

USDA-ARS?s Scientific Manuscript database

This paper assesses the impact of different likelihood functions in identifying sensitive parameters of the highly parameterized, spatially distributed Soil and Water Assessment Tool (SWAT) watershed model for multiple variables at multiple sites. The global one-factor-at-a-time (OAT) method of Morr...

Cultural Correlates of Parent-Nonparent Stereotypes: A Multivariate Analysis.

ERIC Educational Resources Information Center

Bigner, Jerry J.; And Others

1981-01-01

This study determines cultural meanings of parent v nonparent roles using the following variables: one's generational group, place of residence, and family size. Generational differences appear to be the overwhelming influence on parental roles. The decision not to be a parent is an emerging role in contemporary culture. (CT)

A nonparametric clustering technique which estimates the number of clusters

NASA Technical Reports Server (NTRS)

Ramey, D. B.

1983-01-01

In applications of cluster analysis, one usually needs to determine the number of clusters, K, and the assignment of observations to each cluster. A clustering technique based on recursive application of a multivariate test of bimodality which automatically estimates both K and the cluster assignments is presented.

Multivariate Longitudinal Methods for Studying Developmental Relationships between Depression and Academic Achievement

ERIC Educational Resources Information Center

Grimm, Kevin J.

2007-01-01

Recent advances in methods and computer software for longitudinal data analysis have pushed researchers to more critically examine developmental theories. In turn, researchers have also begun to push longitudinal methods by asking more complex developmental questions. One such question involves the relationships between two developmental…

FINGERPRINT ANALYSIS OF CONTAMINANT DATA: A FORENSIC TOOL FOR EVALUATING ENVIRONMENTAL CONTAMINATION

EPA Science Inventory

Several studies have been conducted on behalf of the U .S. Environmental Protection Agency (EPA) to identify detection monitoring parameters for specific industries.1,2,3,4,5 One outcome of these studies was the evolution of an empirical multi-variant contaminant fingerprinting p...

Comparative Analysis of Households Solid Waste Management in Rural and Urban Ghana

PubMed Central

Appiah, Divine Odame; Poku, Adjoa Afriyie; Garsonu, Emmanuel Kofi

2016-01-01

The comparative analysis of solid waste management between rural and urban Ghana is largely lacking. This study investigated the solid waste situation and the organisation of solid waste management in both urban and rural settings from the perspective of households. The study employed cross-sectional survey covering both rural and urban districts in the Ashanti and Greater Accra Regions of Ghana. The study systematically sampled houses from which 400 households and respondents were randomly selected. Pearson's Chi square test was used to compare demographic and socioeconomic variables in rural and urban areas. Multivariate Test, Tests of Between-Subjects Effects, and Pair-Wise Comparisons were performed through one-way MANOVA to determine whether or not solid waste situations in rural and urban areas are significantly different. The results revealed that location significantly affects solid waste management in Ghana. Urban communities had lower mean scores than rural communities for poor solid waste situation in homes. However, urban communities had higher mean scores than rural communities for poor solid waste situation in principal streets and dumping sites. The study recommends that the local government authorities implement very comprehensive policies (sanitary inspection, infrastructure development, and community participation) that will take into consideration the specific solid waste management needs of both urban and rural areas. PMID:27807453

Are older people more vulnerable to long-term impacts of disasters?

PubMed

Rafiey, Hassan; Momtaz, Yadollah Abolfathi; Alipour, Fardin; Khankeh, Hamidreza; Ahmadi, Shokoufeh; Sabzi Khoshnami, Mohammad; Haron, Sharifah Azizah

2016-01-01

Despite the growing interest in the study of disasters, there is limited research addressing the elderly population that lead to prejudiced beliefs that older adults are more vulnerable to disasters than younger adults. This study aimed to compare positive mental health between elderly and young earthquake survivors. Data for this study, consisting of 324 earthquake survivors, were obtained from a population-based cross-sectional survey conducted in Iran, 2015. The long-term effect of earthquake was assessed using the Mental Health Continuum-Short Form questionnaire. A one-way multivariate analysis of covariance (MANCOVA) using SPSS (version 22) was used in data analysis. Older adults scored significantly a higher level of overall positive mental health (mean [M]=34.31, standard deviation [SD]=10.52) than younger age group (M=27.48, SD=10.56, t =-4.41; P <0.001). Results of MANCOVA revealed a statistically significant difference between older and young adults on the combined positive mental health subscales ( F (3,317) =6.95; P <0.001), after controlling for marital status, sex, and employment status. The present findings showing a higher level of positive mental health among elderly earthquake survivors compared with their younger counterparts in the wake of natural disasters suggest that advancing age per se does not contribute to increasing vulnerability.

Comparative Analysis of Households Solid Waste Management in Rural and Urban Ghana.

PubMed

Boateng, Simon; Amoako, Prince; Appiah, Divine Odame; Poku, Adjoa Afriyie; Garsonu, Emmanuel Kofi

2016-01-01

The comparative analysis of solid waste management between rural and urban Ghana is largely lacking. This study investigated the solid waste situation and the organisation of solid waste management in both urban and rural settings from the perspective of households. The study employed cross-sectional survey covering both rural and urban districts in the Ashanti and Greater Accra Regions of Ghana. The study systematically sampled houses from which 400 households and respondents were randomly selected. Pearson's Chi square test was used to compare demographic and socioeconomic variables in rural and urban areas. Multivariate Test, Tests of Between-Subjects Effects, and Pair-Wise Comparisons were performed through one-way MANOVA to determine whether or not solid waste situations in rural and urban areas are significantly different. The results revealed that location significantly affects solid waste management in Ghana. Urban communities had lower mean scores than rural communities for poor solid waste situation in homes. However, urban communities had higher mean scores than rural communities for poor solid waste situation in principal streets and dumping sites. The study recommends that the local government authorities implement very comprehensive policies (sanitary inspection, infrastructure development, and community participation) that will take into consideration the specific solid waste management needs of both urban and rural areas.

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

PubMed

Schnabel, Thomas; Musso, Maurizio; Tondi, Gianluca

2014-01-01

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

Stone loaches of Choman River system, Kurdistan, Iran (Teleostei: Cypriniformes: Nemacheilidae).

PubMed

Kamangar, Barzan Bahrami; Prokofiev, Artem M; Ghaderi, Edris; Nalbant, Theodore T

2014-01-20

For the first time, we present data on species composition and distributions of nemacheilid loaches in the Choman River basin of Kurdistan province, Iran. Two genera and four species are recorded from the area, of which three species are new for science: Oxynoemacheilus kurdistanicus, O. zagrosensis, O. chomanicus spp. nov., and Turcinoemacheilus kosswigi Băn. et Nalb. Detailed and illustrated morphological descriptions and univariate and multivariate analysis of morphometric and meristic features are for each of these species. Forty morphometric and eleven meristic characters were used in multivariate analysis to select characters that could discriminate between the four loach species. Discriminant Function Analysis revealed that sixteen morphometric measures and five meristic characters have the most variability between the loach species. The dendrograms based on cluster analysis of Mahalanobis distances of morphometrics and a combination of both characters confirmed two distinct groups: Oxynoemacheilus spp. and T. kosswigi. Within Oxynoemacheilus, O. zagrosensis and O. chomanicus are more similar to one other rather to either is to O. kurdistanicus.

The impact of multiple endpoint dependency on Q and I(2) in meta-analysis.

PubMed

Thompson, Christopher Glen; Becker, Betsy Jane

2014-09-01

A common assumption in meta-analysis is that effect sizes are independent. When correlated effect sizes are analyzed using traditional univariate techniques, this assumption is violated. This research assesses the impact of dependence arising from treatment-control studies with multiple endpoints on homogeneity measures Q and I(2) in scenarios using the unbiased standardized-mean-difference effect size. Univariate and multivariate meta-analysis methods are examined. Conditions included different overall outcome effects, study sample sizes, numbers of studies, between-outcomes correlations, dependency structures, and ways of computing the correlation. The univariate approach used typical fixed-effects analyses whereas the multivariate approach used generalized least-squares (GLS) estimates of a fixed-effects model, weighted by the inverse variance-covariance matrix. Increased dependence among effect sizes led to increased Type I error rates from univariate models. When effect sizes were strongly dependent, error rates were drastically higher than nominal levels regardless of study sample size and number of studies. In contrast, using GLS estimation to account for multiple-endpoint dependency maintained error rates within nominal levels. Conversely, mean I(2) values were not greatly affected by increased amounts of dependency. Last, we point out that the between-outcomes correlation should be estimated as a pooled within-groups correlation rather than using a full-sample estimator that does not consider treatment/control group membership. Copyright © 2014 John Wiley & Sons, Ltd.

Cerebrovascular risk factors for patients with cerebral watershed infarction: A case-control study based on computed tomography angiography in a population from Southwest China.

PubMed

Dong, Mei-Xue; Hu, Ling; Huang, Yuan-Jun; Xu, Xiao-Min; Liu, Yang; Wei, You-Dong

2017-07-01

To determine cerebrovascular risk factors for patients with cerebral watershed infarction (CWI) from Southwest China.Patients suffering from acute ischemic stroke were categorized into internal CWI (I-CWI), external CWI (E-CWI), or non-CWI (patients without CWI) groups. Clinical data were collected and degrees of steno-occlusion of all cerebral arteries were scored. Arteries associated with the circle of Willis were also assessed. Data were compared using Pearson chi-squared tests for categorical data and 1-way analysis of variance with Bonferroni post hoc tests for continuous data, as appropriate. Multivariate binary logistic regression analysis was performed to determine independent cerebrovascular risk factors for CWI.Compared with non-CWI, I-CWI had higher degrees of steno-occlusion of the ipsilateral middle cerebral artery, ipsilateral carotid artery, and contralateral middle cerebral artery. E-CWI showed no significant differences. All the 3 arteries were independent cerebrovascular risk factors for I-CWI confirmed by multivariate binary logistic regression analysis. I-CWI had higher degrees of steno-occlusion of the ipsilateral middle cerebral artery compared with E-CWI. No significant differences were found among arteries associated with the circle of Willis.The ipsilateral middle cerebral artery, carotid artery, and contralateral middle cerebral artery were independent cerebrovascular risk factors for I-CWI. No cerebrovascular risk factor was identified for E-CWI.

[Incidence and risk factors for acute gastroenteritis among pilgrims following the French way to Santiago de Compostela (Spain) in summer 2008].

PubMed

Duran, Jaume Giménez; Fernández, Miguel Angel Luque; Urrego, Johana Rodríguez; Gil, Cristina Linares; Vargas, Luis Alberto Bonilla; Savulescu, Camelia; Clerger, Guerrier; Martínez-Lamas, Lucia; Pousa, Anxela; Donado, Juan de Mata; Herrera, Dionisio; Martínez, María Victoria

2010-01-01

To determine the incidence of acute gastroenteritis in pilgrims on St. James' Way, as well as associated risk factors and microbiological characteristics. Two studies were designed simultaneously: a cross-sectional study through self-completed questionnaires among pilgrims reaching Santiago, and a case-control study of pilgrims traveling along the Way. Multivariate analysis was performed using logistic regression. In the cross-sectional study, the incidence rate was 23.5 episodes of acute gastroenteritis/10³ pilgrims-day (95% CI: 18.9-2.4/10³. In the case-control study, the major risk factors were age <20 years (OR=4.72; 95% CI: 2.16-10.28), traveling in groups (three or more) (OR=1.49; 95% CI: 0.98-2.28), and drinking unbottled water (OR=2.09; 95% CI: 0.91-4.82). The most frequent etiologic agent was norovirus (56%). Age less than 20 years, traveling in groups and drinking unbottled water were important risk factors for acute gastroenteritis. Copyright © 2010 SESPAS. Published by Elsevier Espana. All rights reserved.

Mental health and academic attitudes and expectations in university populations: results from the healthy minds study.

PubMed

Lipson, Sarah Ketchen; Eisenberg, Daniel

2018-06-01

Mental health problems are highly prevalent in university populations and have been shown to impair academic performance. Yet little is known about the ways in which mental health influences academic outcomes in higher education. This study seeks to offer new insight into the relationship between mental health and academic performance, focusing on students' academic experience and expectations as interrelated mechanisms. Data come from 3556 students at four campuses that participated in the Healthy Minds Study. We explore unadjusted and multivariable relationships between mental health and academic experiences, expectations and impairment. We find significant differences by mental health status, including that one-in-four students with symptoms are dissatisfied with their academic experience, relative to one-in-ten without (p < 0.001). Approximately 30% with symptoms doubt whether higher education is worth their time, money and effort, compared to 15% without (p < 0.001). In multivariable models, mental health problems were a significant predictor of academic dissatisfaction and drop out intentions, while positive mental health was a significant predictor of satisfaction and persistence. This descriptive study offers further evidence of the importance of mental health for university success, identifying pathways related to students' experiences and expectations that may help to explain this relationship.

Support vector regression and artificial neural network models for stability indicating analysis of mebeverine hydrochloride and sulpiride mixtures in pharmaceutical preparation: A comparative study

NASA Astrophysics Data System (ADS)

Naguib, Ibrahim A.; Darwish, Hany W.

2012-02-01

A comparison between support vector regression (SVR) and Artificial Neural Networks (ANNs) multivariate regression methods is established showing the underlying algorithm for each and making a comparison between them to indicate the inherent advantages and limitations. In this paper we compare SVR to ANN with and without variable selection procedure (genetic algorithm (GA)). To project the comparison in a sensible way, the methods are used for the stability indicating quantitative analysis of mixtures of mebeverine hydrochloride and sulpiride in binary mixtures as a case study in presence of their reported impurities and degradation products (summing up to 6 components) in raw materials and pharmaceutical dosage form via handling the UV spectral data. For proper analysis, a 6 factor 5 level experimental design was established resulting in a training set of 25 mixtures containing different ratios of the interfering species. An independent test set consisting of 5 mixtures was used to validate the prediction ability of the suggested models. The proposed methods (linear SVR (without GA) and linear GA-ANN) were successfully applied to the analysis of pharmaceutical tablets containing mebeverine hydrochloride and sulpiride mixtures. The results manifest the problem of nonlinearity and how models like the SVR and ANN can handle it. The methods indicate the ability of the mentioned multivariate calibration models to deconvolute the highly overlapped UV spectra of the 6 components' mixtures, yet using cheap and easy to handle instruments like the UV spectrophotometer.

Exploring effects of therapeutic massage and patient teaching in the practice of diaphragmatic breathing on blood pressure, stress, and anxiety in hypertensive African-American women: an intervention study.

PubMed

Jefferson, Lenetra L

2010-07-01

The problem of hypertension among African-Americans is one of the major areas of health disparities. The American Heart Association (2009) noted that the prevalence of hypertension among African-Americans is perhaps among the highest in the world and this is particularly so among African-American women (44.0%). The purpose of this study was to determine how therapeutic chair massage and patient teaching in diaphragmatic breathing affected African-American women's blood pressure, stress, and anxiety levels over one week or six weeks time periods. A Modified Stress, Coping, and Adaptation Model (Roy, 1976; Lazarus, 1966), Descriptives, T-tests, Pearson Product Moment Correlations, Multivariate analysis of variance (MANOVA), and Multivariate analysis of variance with covariate (MANCOVA) were used. Descriptive statistics indicated a significance for decreased systolic blood pressure levels for the one week post massage intervention measurement with p = .01, diastolic blood pressure level significance for the same group p = .02, significance for this group's State Trait Anxiety Inventory (STAI) Y2 Scale score p = .01, and Roy's Largest Root p = .03.

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.

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

Chemical Structure and Molecular Dimension As Controls on the Inherent Stability of Charcoal in Boreal Forest Soil

NASA Astrophysics Data System (ADS)

Hockaday, W. C.; Kane, E. S.; Ohlson, M.; Huang, R.; Von Bargen, J.; Davis, R.

2014-12-01

Efforts have been made by various scientific disciplines to study hyporheic zones and characterize their associated processes. One way to approach the study of the hyporheic zone is to define facies, which are elements of a (hydrobio) geologic classification scheme that groups components of a complex system with high variability into a manageable set of discrete classes. In this study, we try to classify the hyporheic zone based on the geology, geochemistry, microbiology, and understand their interactive influences on the integrated biogeochemical distributions and processes. A number of measurements have been taken for 21 freeze core samples along the Columbia River bank in the Hanford 300 Area, and unique datasets have been obtained on biomass, pH, number of microbial taxa, percentage of N/C/H/S, microbial activity parameters, as well as microbial community attributes/modules. In order to gain a complete understanding of the geological control on these variables and processes, the explanatory variables are set to include quantitative gravel/sand/mud/silt/clay percentages, statistical moments of grain size distributions, as well as geological (e.g., Folk-Wentworth) and statistical (e.g., hierarchical) clusters. The dominant factors for major microbial and geochemical variables are identified and summarized using exploratory data analysis approaches (e.g., principal component analysis, hierarchical clustering, factor analysis, multivariate analysis of variance). The feasibility of extending the facies definition and its control of microbial and geochemical properties to larger scales is discussed.

Microbial facies distribution and its geological and geochemical controls at the Hanford 300 area

NASA Astrophysics Data System (ADS)

Hou, Z.; Nelson, W.; Stegen, J.; Murray, C. J.; Arntzen, E.

2015-12-01

Efforts have been made by various scientific disciplines to study hyporheic zones and characterize their associated processes. One way to approach the study of the hyporheic zone is to define facies, which are elements of a (hydrobio) geologic classification scheme that groups components of a complex system with high variability into a manageable set of discrete classes. In this study, we try to classify the hyporheic zone based on the geology, geochemistry, microbiology, and understand their interactive influences on the integrated biogeochemical distributions and processes. A number of measurements have been taken for 21 freeze core samples along the Columbia River bank in the Hanford 300 Area, and unique datasets have been obtained on biomass, pH, number of microbial taxa, percentage of N/C/H/S, microbial activity parameters, as well as microbial community attributes/modules. In order to gain a complete understanding of the geological control on these variables and processes, the explanatory variables are set to include quantitative gravel/sand/mud/silt/clay percentages, statistical moments of grain size distributions, as well as geological (e.g., Folk-Wentworth) and statistical (e.g., hierarchical) clusters. The dominant factors for major microbial and geochemical variables are identified and summarized using exploratory data analysis approaches (e.g., principal component analysis, hierarchical clustering, factor analysis, multivariate analysis of variance). The feasibility of extending the facies definition and its control of microbial and geochemical properties to larger scales is discussed.

The NRL relocatable ocean/acoustic ensemble forecast system

NASA Astrophysics Data System (ADS)

Rowley, C.; Martin, P.; Cummings, J.; Jacobs, G.; Coelho, E.; Bishop, C.; Hong, X.; Peggion, G.; Fabre, J.

2009-04-01

A globally relocatable regional ocean nowcast/forecast system has been developed to support rapid implementation of new regional forecast domains. The system is in operational use at the Naval Oceanographic Office for a growing number of regional and coastal implementations. The new system is the basis for an ocean acoustic ensemble forecast and adaptive sampling capability. We present an overview of the forecast system and the ocean ensemble and adaptive sampling methods. The forecast system consists of core ocean data analysis and forecast modules, software for domain configuration, surface and boundary condition forcing processing, and job control, and global databases for ocean climatology, bathymetry, tides, and river locations and transports. The analysis component is the Navy Coupled Ocean Data Assimilation (NCODA) system, a 3D multivariate optimum interpolation system that produces simultaneous analyses of temperature, salinity, geopotential, and vector velocity using remotely-sensed SST, SSH, and sea ice concentration, plus in situ observations of temperature, salinity, and currents from ships, buoys, XBTs, CTDs, profiling floats, and autonomous gliders. The forecast component is the Navy Coastal Ocean Model (NCOM). The system supports one-way nesting and multiple assimilation methods. The ensemble system uses the ensemble transform technique with error variance estimates from the NCODA analysis to represent initial condition error. Perturbed surface forcing or an atmospheric ensemble is used to represent errors in surface forcing. The ensemble transform Kalman filter is used to assess the impact of adaptive observations on future analysis and forecast uncertainty for both ocean and acoustic properties.

Evaluation and simplification of the occupational slip, trip and fall risk-assessment test

PubMed Central

NAKAMURA, Takehiro; OYAMA, Ichiro; FUJINO, Yoshihisa; KUBO, Tatsuhiko; KADOWAKI, Koji; KUNIMOTO, Masamizu; ODOI, Haruka; TABATA, Hidetoshi; MATSUDA, Shinya

2016-01-01

Objective: The purpose of this investigation is to evaluate the efficacy of the occupational slip, trip and fall (STF) risk assessment test developed by the Japan Industrial Safety and Health Association (JISHA). We further intended to simplify the test to improve efficiency. Methods: A previous cohort study was performed using 540 employees aged ≥50 years who took the JISHA’s STF risk assessment test. We conducted multivariate analysis using these previous results as baseline values and answers to questionnaire items or score on physical fitness tests as variables. The screening efficiency of each model was evaluated based on the obtained receiver operating characteristic (ROC) curve. Results: The area under the ROC obtained in multivariate analysis was 0.79 when using all items. Six of the 25 questionnaire items were selected for stepwise analysis, giving an area under the ROC curve of 0.77. Conclusion: Based on the results of follow-up performed one year after the initial examination, we successfully determined the usefulness of the STF risk assessment test. Administering a questionnaire alone is sufficient for screening subjects at risk of STF during the subsequent one-year period. PMID:27021057

Tools based on multivariate statistical analysis for classification of soil and groundwater in Apulian agricultural sites.

PubMed

Ielpo, Pierina; Leardi, Riccardo; Pappagallo, Giuseppe; Uricchio, Vito Felice

2017-06-01

In this paper, the results obtained from multivariate statistical techniques such as PCA (Principal component analysis) and LDA (Linear discriminant analysis) applied to a wide soil data set are presented. The results have been compared with those obtained on a groundwater data set, whose samples were collected together with soil ones, within the project "Improvement of the Regional Agro-meteorological Monitoring Network (2004-2007)". LDA, applied to soil data, has allowed to distinguish the geographical origin of the sample from either one of the two macroaeras: Bari and Foggia provinces vs Brindisi, Lecce e Taranto provinces, with a percentage of correct prediction in cross validation of 87%. In the case of the groundwater data set, the best classification was obtained when the samples were grouped into three macroareas: Foggia province, Bari province and Brindisi, Lecce and Taranto provinces, by reaching a percentage of correct predictions in cross validation of 84%. The obtained information can be very useful in supporting soil and water resource management, such as the reduction of water consumption and the reduction of energy and chemical (nutrients and pesticides) inputs in agriculture.

Second-order data obtained by beta-cyclodextrin complexes: a novel approach for multicomponent analysis with three-way multivariate calibration methods.

PubMed

Khani, Rouhollah; Ghasemi, Jahan B; Shemirani, Farzaneh

2014-10-01

This research reports the first application of β-cyclodextrin (β-CD) complexes as a new method for generation of three way data, combined with second-order calibration methods for quantification of a binary mixture of caffeic (CA) and vanillic (VA) acids, as model compounds in fruit juices samples. At first, the basic experimental parameters affecting the formation of inclusion complexes between target analytes and β-CD were investigated and optimized. Then under the optimum conditions, parallel factor analysis (PARAFAC) and bilinear least squares/residual bilinearization (BLLS/RBL) were applied for deconvolution of trilinear data to get spectral and concentration profiles of CA and VA as a function of β-CD concentrations. Due to severe concentration profile overlapping between CA and VA in β-CD concentration dimension, PARAFAC could not be successfully applied to the studied samples. So, BLLS/RBL performed better than PARAFAC. The resolution of the model compounds was possible due to differences in the spectral absorbance changes of the β-CD complexes signals of the investigated analytes, opening a new approach for second-order data generation. The proposed method was validated by comparison with a reference method based on high-performance liquid chromatography photodiode array detection (HPLC-PDA), and no significant differences were found between the reference values and the ones obtained with the proposed method. Such a chemometrics-based protocol may be a very promising tool for more analytical applications in real samples monitoring, due to its advantages of simplicity, rapidity, accuracy, sufficient spectral resolution and concentration prediction even in the presence of unknown interferents. Copyright © 2014 Elsevier B.V. All rights reserved.

Utilisation of strategic communication to create willingness to change work practices among primary care staff: a long-term follow-up study.

PubMed

Morténius, Helena; Fridlund, Bengt; Marklund, Bertil; Palm, Lars; Baigi, Amir

2012-04-01

To evaluate the long-term utilisation of strategic communication as a factor of importance when changing work practices among primary care staff. In many health care organisations, there is a gap between theory and practice. This gap hinders the provision of optimal evidence-based practice and, in the long term, is unfavourable for patient care. One way of overcoming this barrier is systematically structured communication between the scientific theoretical platform and clinical practice. This longitudinal evaluative study was conducted among a primary care staff cohort. Strategic communication was considered to be the intervention platform and included a network of ambassadors who acted as a component of the implementation. Measurements occurred 7 and 12 years after formation of the cohort. A questionnaire was used to obtain information from participants. In total, 846 employees (70%) agreed to take part in the study. After 12 years, the 352 individuals (60%) who had remained in the organisation were identified and followed up. Descriptive statistics and multivariate analysis were used to analyse the data. Continuous information contributed to significant improvements over time with respect to new ideas and the intention to change work practices. There was a statistically significant synergistic effect on the new way of thinking, that is, willingness to change work practices. During the final two years, the network of ambassadors had created a distinctive image for itself in the sense that primary care staff members were aware of it and its activities. This awareness was associated with a positive change with regard to new ways of thinking. More years of practice was inversely associated with willingness to change work practices. Strategic communication may lead to a scientific platform that promotes high-quality patient care by means of new methods and research findings.

Methods for spectral image analysis by exploiting spatial simplicity

DOEpatents

Keenan, Michael R.

2010-05-25

Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are "simple" in the sense that they consist of relatively discrete chemical phases. That is, at any given location, only one or a few of the chemical species comprising the entire sample have non-zero concentrations. The methods of spectral image analysis of the present invention exploit this simplicity in the spatial domain to make the resulting factor models more realistic. Therefore, more physically accurate and interpretable spectral and abundance components can be extracted from spectral images that have spatially simple structure.

Methods for spectral image analysis by exploiting spatial simplicity

DOEpatents

Keenan, Michael R.

2010-11-23

Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are "simple" in the sense that they consist of relatively discrete chemical phases. That is, at any given location, only one or a few of the chemical species comprising the entire sample have non-zero concentrations. The methods of spectral image analysis of the present invention exploit this simplicity in the spatial domain to make the resulting factor models more realistic. Therefore, more physically accurate and interpretable spectral and abundance components can be extracted from spectral images that have spatially simple structure.

Cell phones, clothing, and sex: first impressions of power using older African Americans as stimuli.

PubMed

Ross, Allison; Barker, Kathleen

2003-12-01

Sex, material possessions, and race have long been associated with prestige or status in American society, yet little research has examined this idea. Little is known about the effect of cell phones on first impressions. In a 2 (cell phone: present, absent) x 2 (clothing: jacket, no jacket) x 2 (sex) between-subjects design, 160 women from a predominantly Black college rated stimuli of older, African Americans on 15 items measuring perceived power on three power subscales: expert, legitimate, and coercive. Multivariate analysis of variance showed a 3-way interaction for clothing, cell phone, and sex of stimulus person.

Multi-country health surveys: are the analyses misleading?

PubMed

Masood, Mohd; Reidpath, Daniel D

2014-05-01

The aim of this paper was to review the types of approaches currently utilized in the analysis of multi-country survey data, specifically focusing on design and modeling issues with a focus on analyses of significant multi-country surveys published in 2010. A systematic search strategy was used to identify the 10 multi-country surveys and the articles published from them in 2010. The surveys were selected to reflect diverse topics and foci; and provide an insight into analytic approaches across research themes. The search identified 159 articles appropriate for full text review and data extraction. The analyses adopted in the multi-country surveys can be broadly classified as: univariate/bivariate analyses, and multivariate/multivariable analyses. Multivariate/multivariable analyses may be further divided into design- and model-based analyses. Of the 159 articles reviewed, 129 articles used model-based analysis, 30 articles used design-based analyses. Similar patterns could be seen in all the individual surveys. While there is general agreement among survey statisticians that complex surveys are most appropriately analyzed using design-based analyses, most researchers continued to use the more common model-based approaches. Recent developments in design-based multi-level analysis may be one approach to include all the survey design characteristics. This is a relatively new area, however, and there remains statistical, as well as applied analytic research required. An important limitation of this study relates to the selection of the surveys used and the choice of year for the analysis, i.e., year 2010 only. There is, however, no strong reason to believe that analytic strategies have changed radically in the past few years, and 2010 provides a credible snapshot of current practice.

Prognostic implications of adhesion molecule expression in colorectal cancer.

PubMed

Seo, Kyung-Jin; Kim, Maru; Kim, Jeana

2015-01-01

Research on the expression of adhesion molecules, E-cadherin (ECAD), CD24, CD44 and osteopontin (OPN) in colorectal cancer (CRC) has been limited, even though CRC is one of the leading causes of cancer-related deaths. This study was conducted to evaluate the expression of adhesion molecules in CRC and to determine their relationships with clinicopathologic variables, and the prognostic significance. The expression of ECAD, CD24, CD44 and OPN was examined in 174 stage II and III CRC specimens by immunohistochemistry of TMA. Negative ECAD expression was significantly correlated with advanced nodal stage and poor tumor differentiation. Multivariate analysis showed that both negative expression of ECAD and positive expression of CD24 were independent prognostic factors for disease-free survival (DFS) in CRC patients (P<0.001, relative risk [RR] = 5.596, 95% CI = 2.712-11.549; P = 0.038, RR = 3.768, 95% CI = 1.077-13.185, respectively). However, for overall survival (OS), only ECAD negativity showed statistically significant results in multivariate analysis (P<0.001, RR = 4.819, 95% CI = 2.515-9.234). Positive expression of CD24 was associated with poor OS in univariate analysis but was of no prognostic value in multivariate analysis. In conclusion, our study suggests that among these four adhesion molecules, ECAD and CD24 expression can be considered independent prognostic factors. The role of CD44 and OPN may need further evaluation.

Prognostic implications of adhesion molecule expression in colorectal cancer

PubMed Central

Seo, Kyung-Jin; Kim, Maru; Kim, Jeana

2015-01-01

Research on the expression of adhesion molecules, E-cadherin (ECAD), CD24, CD44 and osteopontin (OPN) in colorectal cancer (CRC) has been limited, even though CRC is one of the leading causes of cancer-related deaths. This study was conducted to evaluate the expression of adhesion molecules in CRC and to determine their relationships with clinicopathologic variables, and the prognostic significance. The expression of ECAD, CD24, CD44 and OPN was examined in 174 stage II and III CRC specimens by immunohistochemistry of TMA. Negative ECAD expression was significantly correlated with advanced nodal stage and poor tumor differentiation. Multivariate analysis showed that both negative expression of ECAD and positive expression of CD24 were independent prognostic factors for disease-free survival (DFS) in CRC patients (P<0.001, relative risk [RR] = 5.596, 95% CI = 2.712-11.549; P = 0.038, RR = 3.768, 95% CI = 1.077-13.185, respectively). However, for overall survival (OS), only ECAD negativity showed statistically significant results in multivariate analysis (P<0.001, RR = 4.819, 95% CI = 2.515-9.234). Positive expression of CD24 was associated with poor OS in univariate analysis but was of no prognostic value in multivariate analysis. In conclusion, our study suggests that among these four adhesion molecules, ECAD and CD24 expression can be considered independent prognostic factors. The role of CD44 and OPN may need further evaluation. PMID:26097606

Clustering analysis for muon tomography data elaboration in the Muon Portal project

NASA Astrophysics Data System (ADS)

Bandieramonte, M.; Antonuccio-Delogu, V.; Becciani, U.; Costa, A.; La Rocca, P.; Massimino, P.; Petta, C.; Pistagna, C.; Riggi, F.; Riggi, S.; Sciacca, E.; Vitello, F.

2015-05-01

Clustering analysis is one of multivariate data analysis techniques which allows to gather statistical data units into groups, in order to minimize the logical distance within each group and to maximize the one between different groups. In these proceedings, the authors present a novel approach to the muontomography data analysis based on clustering algorithms. As a case study we present the Muon Portal project that aims to build and operate a dedicated particle detector for the inspection of harbor containers to hinder the smuggling of nuclear materials. Clustering techniques, working directly on scattering points, help to detect the presence of suspicious items inside the container, acting, as it will be shown, as a filter for a preliminary analysis of the data.

Single-photon quantum key distribution in the presence of loss

NASA Astrophysics Data System (ADS)

Curty, Marcos; Moroder, Tobias

2007-05-01

We investigate two-way and one-way single-photon quantum key distribution (QKD) protocols in the presence of loss introduced by the quantum channel. Our analysis is based on a simple precondition for secure QKD in each case. In particular, the legitimate users need to prove that there exists no separable state (in the case of two-way QKD), or that there exists no quantum state having a symmetric extension (one-way QKD), that is compatible with the available measurements results. We show that both criteria can be formulated as a convex optimization problem known as a semidefinite program, which can be efficiently solved. Moreover, we prove that the solution to the dual optimization corresponds to the evaluation of an optimal witness operator that belongs to the minimal verification set of them for the given two-way (or one-way) QKD protocol. A positive expectation value of this optimal witness operator states that no secret key can be distilled from the available measurements results. We apply such analysis to several well-known single-photon QKD protocols under losses.

Challenging a dogma: five-year survival does not equal cure in all colorectal cancer patients.

PubMed

Abdel-Rahman, Omar

2018-02-01

The current study tried to evaluate the factors affecting 10- to 20- years' survival among long term survivors (>5 years) of colorectal cancer (CRC). Surveillance, Epidemiology and End Results (SEER) database (1988-2008) was queried through SEER*Stat program.Univariate probability of overall and cancer-specific survival was determined and the difference between groups was examined. Multivariate analysis for factors affecting overall and cancer-specific survival was also conducted. Among node positive patients (Dukes C), 34% of the deaths beyond 5 years can be attributed to CRC; while among M1 patients, 63% of the deaths beyond 5 years can be attributed to CRC. The following factors were predictors of better overall survival in multivariate analysis: younger age, white race (versus black race), female gender, Right colon location (versus rectal location), earlier stage and surgery (P <0.0001 for all parameters). Similarly, the following factors were predictors of better cancer-specific survival in multivariate analysis: younger age, white race (versus black race), female gender, Right colon location (versus left colon and rectal locations), earlier stage and surgery (P <0.0001 for all parameters). Among node positive long-term CRC survivors, more than one third of all deaths can be attributed to CRC.

Multivariate analysis of matrix-assisted laser desorption/ionization mass spectrometric data related to glycoxidation products of human globins in nephropathic patients.

PubMed

Lapolla, Annunziata; Ragazzi, Eugenio; Andretta, Barbara; Fedele, Domenico; Tubaro, Michela; Seraglia, Roberta; Molin, Laura; Traldi, Pietro

2007-06-01

To clarify the possible pathogenetic role of oxidation products originated from the glycation of proteins, human globins from nephropathic patients have been studied by matrix-assisted laser desorption/ionization mass spectrometry (MALDI), revealing not only unglycated and monoglycated globins, but also a series of different species. For the last ones, structural assignments were tentatively done on the basis of observed masses and expectations for the Maillard reaction pattern. Consequently, they must be considered only propositive, and the discussion which will follow must be considered in this view. In our opinion this approach does not seem to compromise the intended diagnostic use of the data because distinctions are valid even if the assignments are uncertain. We studied nine healthy subjects and 19 nephropathic patients and processed the data obtained from the MALDI spectra using a multivariate analysis. Our results showed that multivariate analytical techniques enable differential aspects of the profile of molecular species to be identified in the blood of end stage nephropathic patients. A correct grouping can be achieved by principal component analysis (PCA) and the results suggest that several products involved in carbonyl stress exist in nephropathic patients. These compounds may have a relevant role as specific markers of the pathological state.

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.

Risk factors for superficial surgical site infection after elective rectal cancer resection: a multivariate analysis of 8880 patients from the American College of Surgeons National Surgical Quality Improvement Program database.

PubMed

Sutton, Elie; Miyagaki, Hiromichi; Bellini, Geoffrey; Shantha Kumara, H M C; Yan, Xiaohong; Howe, Brett; Feigel, Amanda; Whelan, Richard L

2017-01-01

Superficial surgical site infection (sSSI) is one of the most common complications after colorectal resection. The goal of this study was to determine the comorbidities and operative characteristics that place patients at risk for sSSI in patients who underwent rectal cancer resection. The American College of Surgeons National Surgical Quality Improvement Program database was queried (via diagnosis and Current Procedural Terminology codes) for patients with rectal cancer who underwent elective resection between 2005 and 2012. Patients for whom data concerning 27 demographic factors, comorbidities, and operative characteristics were available were eligible. A univariate and multivariate analysis was performed to identify possible risk factors for sSSI. A total of 8880 patients met the entry criteria and were included. sSSIs were diagnosed in 861 (9.7%) patients. Univariate analysis found 14 patients statistically significant risk factors for sSSI. Multivariate analysis revealed the following risk factors: male gender, body mass index (BMI) >30, current smoking, history of chronic obstructive pulmonary disease (COPD), American Society of Anesthesiologists III/IV, abdominoperineal resection (APR), stoma formation, open surgery (versus laparoscopic), and operative time >217 min. The greatest difference in sSSI rates was noted in patients with COPD (18.9 versus 9.5%). Of note, 54.2% of sSSIs was noted after hospital discharge. With regard to the timing of presentation, univariate analysis revealed a statistically significant delay in sSSI presentation in patients with the following factors and/or characteristics: BMI <30, previous radiation therapy (RT), APR, minimally invasive surgery, and stoma formation. Multivariate analysis suggested that only laparoscopic surgery (versus open) and preoperative RT were risk factors for delay. Rectal cancer resections are associated with a high incidence of sSSIs, over half of which are noted after discharge. Nine patient and operative characteristics, including smoking, BMI, COPD, APR, and open surgery were found to be significant risk factors for SSI on multivariate analysis. Furthermore, sSSI presentation in patients who had laparoscopic surgery and those who had preoperative RT is significantly delayed for unclear reasons. Copyright © 2016 Elsevier Inc. All rights reserved.

Salicylic acid deposition from wash-off products: comparison of in vivo and porcine deposition models.

PubMed

Davies, M A

2015-10-01

Salicylic acid (SA) is a widely used active in anti-acne face wash products. Only about 1-2% of the total dose is actually deposited on skin during washing, and more efficient deposition systems are sought. The objective of this work was to develop an improved method, including data analysis, to measure deposition of SA from wash-off formulae. Full fluorescence excitation-emission matrices (EEMs) were acquired for non-invasive measurement of deposition of SA from wash-off products. Multivariate data analysis methods - parallel factor analysis and N-way partial least-squares regression - were used to develop and compare deposition models on human volunteers and porcine skin. Although both models are useful, there are differences between them. First, the range of linear response to dosages of SA was 60 μg cm(-2) in vivo compared to 25 μg cm(-2) on porcine skin. Second, the actual shape of the SA band was different between substrates. The methods employed in this work highlight the utility of the use of EEMs, in conjunction with multivariate analysis tools such as parallel factor analysis and multiway partial least-squares calibration, in determining sources of spectral variability in skin and quantification of exogenous species deposited on skin. The human model exhibited the widest range of linearity, but porcine model is still useful up to deposition levels of 25 μg cm(-2) or used with nonlinear calibration models. © 2015 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example

PubMed Central

Winzer, Klaus-Jürgen; Buchholz, Anika; Schumacher, Martin; Sauerbrei, Willi

2016-01-01

Background Prognostic factors and prognostic models play a key role in medical research and patient management. The Nottingham Prognostic Index (NPI) is a well-established prognostic classification scheme for patients with breast cancer. In a very simple way, it combines the information from tumor size, lymph node stage and tumor grade. For the resulting index cutpoints are proposed to classify it into three to six groups with different prognosis. As not all prognostic information from the three and other standard factors is used, we will consider improvement of the prognostic ability using suitable analysis approaches. Methods and Findings Reanalyzing overall survival data of 1560 patients from a clinical database by using multivariable fractional polynomials and further modern statistical methods we illustrate suitable multivariable modelling and methods to derive and assess the prognostic ability of an index. Using a REMARK type profile we summarize relevant steps of the analysis. Adding the information from hormonal receptor status and using the full information from the three NPI components, specifically concerning the number of positive lymph nodes, an extended NPI with improved prognostic ability is derived. Conclusions The prognostic ability of even one of the best established prognostic index in medicine can be improved by using suitable statistical methodology to extract the full information from standard clinical data. This extended version of the NPI can serve as a benchmark to assess the added value of new information, ranging from a new single clinical marker to a derived index from omics data. An established benchmark would also help to harmonize the statistical analyses of such studies and protect against the propagation of many false promises concerning the prognostic value of new measurements. Statistical methods used are generally available and can be used for similar analyses in other diseases. PMID:26938061

Copula-based analysis of rhythm

NASA Astrophysics Data System (ADS)

García, J. E.; González-López, V. A.; Viola, M. L. Lanfredi

2016-06-01

In this paper we establish stochastic profiles of the rhythm for three languages: English, Japanese and Spanish. We model the increase or decrease of the acoustical energy, collected into three bands coming from the acoustic signal. The number of parameters needed to specify a discrete multivariate Markov chain grows exponentially with the order and dimension of the chain. In this case the size of the database is not large enough for a consistent estimation of the model. We apply a strategy to estimate a multivariate process with an order greater than the order achieved using standard procedures. The new strategy consist on obtaining a partition of the state space which is constructed from a combination of the partitions corresponding to the three marginal processes, one for each band of energy, and the partition coming from to the multivariate Markov chain. Then, all the partitions are linked using a copula, in order to estimate the transition probabilities.

Multivariate survivorship analysis using two cross-sectional samples.

PubMed

Hill, M E

1999-11-01

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

Pressure ulcer incidence and Braden subscales: Retrospective cohort analysis in general wards of a Portuguese hospital.

PubMed

Sardo, Pedro Miguel Garcez; Guedes, Jenifer Adriana Domingues; Alvarelhão, José Joaquim Marques; Machado, Paulo Alexandre Puga; Melo, Elsa Maria Oliveira Pinheiro

2018-05-01

To study the influence of Braden subscales scores (at the first pressure ulcer risk assessment) on pressure ulcer incidence using a univariate and a multivariate time to event analysis. Retrospective cohort analysis of electronic health record database from adult patients admitted without pressure ulcer(s) to medical and surgical wards of a Portuguese hospital during 2012. The hazard ratio of developing a pressure ulcer during the length of inpatient stay was calculated by univariate Cox regression for each variable of interest and by multivariate Cox regression for the Braden subscales that were statistically significant. This study included a sample of 6552 participants. During the length of stay, 153 participants developed (at least) one pressure ulcer, giving a pressure ulcer incidence of 2.3%. The univariate time to event analysis showed that all Braden subscales, except "nutrition", were associated with the development of pressure ulcer. By multivariate analysis the scores for "mobility" and "activity" were independently predictive of the development of pressure ulcer(s) for all participants. (Im)"mobility" (the lack of ability to change and control body position) and (in)"activity" (the limited degree of physical activity) were the major risk factors assessed by Braden Scale for pressure ulcer development during the length of inpatient stay. Thus, the greatest efforts in managing pressure ulcer risk should be on "mobility" and "activity", independently of the total Braden Scale score. Copyright © 2018 Tissue Viability Society. Published by Elsevier Ltd. All rights reserved.

Multivariate constrained shape optimization: Application to extrusion bell shape for pasta production

NASA Astrophysics Data System (ADS)

Sarghini, Fabrizio; De Vivo, Angela; Marra, Francesco

2017-10-01

Computational science and engineering methods have allowed a major change in the way products and processes are designed, as validated virtual models - capable to simulate physical, chemical and bio changes occurring during production processes - can be realized and used in place of real prototypes and performing experiments, often time and money consuming. Among such techniques, Optimal Shape Design (OSD) (Mohammadi & Pironneau, 2004) represents an interesting approach. While most classical numerical simulations consider fixed geometrical configurations, in OSD a certain number of geometrical degrees of freedom is considered as a part of the unknowns: this implies that the geometry is not completely defined, but part of it is allowed to move dynamically in order to minimize or maximize the objective function. The applications of optimal shape design (OSD) are uncountable. For systems governed by partial differential equations, they range from structure mechanics to electromagnetism and fluid mechanics or to a combination of the three. This paper presents one of possible applications of OSD, particularly how extrusion bell shape, for past production, can be designed by applying a multivariate constrained shape optimization.

Temporal abstraction for the analysis of intensive care information

NASA Astrophysics Data System (ADS)

Hadad, Alejandro J.; Evin, Diego A.; Drozdowicz, Bartolomé; Chiotti, Omar

2007-11-01

This paper proposes a scheme for the analysis of time-stamped series data from multiple monitoring devices of intensive care units, using Temporal Abstraction concepts. This scheme is oriented to obtain a description of the patient state evolution in an unsupervised way. The case of study is based on a dataset clinically classified with Pulmonary Edema. For this dataset a trends based Temporal Abstraction mechanism is proposed, by means of a Behaviours Base of time-stamped series and then used in a classification step. Combining this approach with the introduction of expert knowledge, using Fuzzy Logic, and multivariate analysis by means of Self-Organizing Maps, a states characterization model is obtained. This model is feasible of being extended to different patients groups and states. The proposed scheme allows to obtain intermediate states descriptions through which it is passing the patient and that could be used to anticipate alert situations.

A survey of variable selection methods in two Chinese epidemiology journals

PubMed Central

2010-01-01

Background Although much has been written on developing better procedures for variable selection, there is little research on how it is practiced in actual studies. This review surveys the variable selection methods reported in two high-ranking Chinese epidemiology journals. Methods Articles published in 2004, 2006, and 2008 in the Chinese Journal of Epidemiology and the Chinese Journal of Preventive Medicine were reviewed. Five categories of methods were identified whereby variables were selected using: A - bivariate analyses; B - multivariable analysis; e.g. stepwise or individual significance testing of model coefficients; C - first bivariate analyses, followed by multivariable analysis; D - bivariate analyses or multivariable analysis; and E - other criteria like prior knowledge or personal judgment. Results Among the 287 articles that reported using variable selection methods, 6%, 26%, 30%, 21%, and 17% were in categories A through E, respectively. One hundred sixty-three studies selected variables using bivariate analyses, 80% (130/163) via multiple significance testing at the 5% alpha-level. Of the 219 multivariable analyses, 97 (44%) used stepwise procedures, 89 (41%) tested individual regression coefficients, but 33 (15%) did not mention how variables were selected. Sixty percent (58/97) of the stepwise routines also did not specify the algorithm and/or significance levels. Conclusions The variable selection methods reported in the two journals were limited in variety, and details were often missing. Many studies still relied on problematic techniques like stepwise procedures and/or multiple testing of bivariate associations at the 0.05 alpha-level. These deficiencies should be rectified to safeguard the scientific validity of articles published in Chinese epidemiology journals. PMID:20920252

Cost-effectiveness of environmental-structural communication interventions for HIV prevention in the female sex industry in the Dominican Republic.

PubMed

Sweat, Michael; Kerrigan, Deanna; Moreno, Luis; Rosario, Santo; Gomez, Bayardo; Jerez, Hector; Weiss, Ellen; Barrington, Clare

2006-01-01

Behavior change communication often focuses on individual-level variables such as knowledge, perceived risk, self-efficacy, and behavior. A growing body of evidence suggests, however, that structural interventions to change the policy environment and environmental interventions designed to modify the physical and social environment further bolster impact. Little is known about the cost-effectiveness of such comprehensive intervention programs. In this study we use standard cost analysis methods to examine the incremental cost-effectiveness of two such interventions conducted in the Dominican Republic in sex establishments. In Santo Domingo the intervention was environmental; in Puerto Plata it was both environmental and structural (levying financial sanctions on sex establishment owners who failed to follow the intervention). The interventions in both sites included elements found in more conventional behavior change communication (BCC) programs (e.g., community mobilization, peer education, educational materials, promotional stickers). One key aim was to examine whether the addition of policy regulation was cost-effective. Data for the analysis were gleaned from structured behavioral questionnaires administered to female sex workers and their male regular paying partners in 41 sex establishments conducted pre- and post-intervention (1 year follow-up); data from HIV sentinel surveillance, STI screening results conducted for the intervention; and detailed cost data we collected. We estimated the number of HIV infections averted from each of the two intervention models and converted these estimates to the number of disability life years saved as compared with no intervention. One-way, two-way, three-way, and multivariate sensitivity analysis were conducted on model parameters. We examine a discount rate of 0%, 3% (base case), and 6% for future costs and benefits. The intervention conducted in Santo Domingo (community mobilization, promotional media, and interpersonal communication) was estimated to avert 64 HIV infections per 10,000 clients reached, and resulted in a cost per disability-adjusted life year (DALY) saved of $1,186. In Puerto Plata a policy/regulatory intervention was added, which resulted in 162 HIV infections averted per 10,000 clients reached, and yielded a cost per DALY saved of $457. Cost-effectiveness estimates were most correlated to the discount rate used and base rates of sexually transmitted infection (which affects the HIV transmission rate). Both intervention models resulted in cost-effective outcomes; however, the intervention that included policy regulation resulted in a substantially more cost-effective outcome.

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.

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.

Classroom Management Self-Efficacy and Burnout: A Multivariate Meta-Analysis

ERIC Educational Resources Information Center

Aloe, Ariel M.; Amo, Laura C.; Shanahan, Michele E.

2014-01-01

Like many in the human services professions, teachers are susceptible to the feelings of burnout due to their job demands, as well as interactions with students, colleagues, administrators, and parents. Many studies have identified teacher burnout as one of the crucial components influencing teacher attrition. It has been suggested that…

Spatial Statistical Model and Optimal Survey Design for Rapid Geophysical Characterization of UXO Sites

DTIC Science & Technology

2003-07-01

4, Gnanadesikan , 1977). An entity whose measured features fall into one of the regions is classified accordingly. For the approaches we discuss here... Gnanadesikan , R. 1977. Methods for Statistical Data Analysis of Multivariate Observations. John Wiley & Sons, New York. Hassig, N. L., O’Brien, R. F

The Effect of Shared versus Individual Reflection on Team Outcomes

ERIC Educational Resources Information Center

Domke-Damonte, Darla J.; Keels, J. Kay

2015-01-01

In this study, teams in a strategic management classroom were given one of two versions of an assignment related to the development of a team contract: independent individual reflections on desired team behaviors versus team-level reflections on desired behavioral norms. Results of a multivariate analysis of covariance, controlling for gender and…

[Referral to internal medicine for alcoholism: influence on follow-up care].

PubMed

Avila, P; Marcos, M; Avila, J J; Laso, F J

2008-11-01

The problem of high rates of patient drop-out in alcohol treatment programs is frequently reported in the literature. Our aim was to investigate if internal medicine referral could improve abstinence and retention rates in a cohort of alcoholic patients. A retrospective observational study was conducted comparing 200 alcoholic patients attending a psychiatric unit (group 1) with 100 patients attending both this unit and an internal medicine unit (group 2). We collected sociodemographic and clinical variables and analysed differences regarding abstinence and retention rates by means of univariate and multivariate analysis. At 3 and 12 months follow-up, group 2 patients had higher retention and abstinence rates than group 1 patients. Multivariate analysis including potential confounding variables showed that independent predictors of one-year retention were internal medicine referral and being married. Independent predictors of one-year abstinence were being married, age > 44 years and receipt of drug treatment. The higher retention rate found among patients referred to Internal Medicine specialists, a result that has not been previously reported to the best of our knowledge, emphasizes the importance of a multidisciplinary team approach in the treatment of alcoholism.

Factors Associated with Routine Dental Attendance among Aboriginal Australians.

PubMed

Amarasena, Najith; Kapellas, Kostas; Skilton, Michael R; Maple-Brown, Louise J; Brown, Alex; Bartold, Mark; O'Dea, Kerin; Celermajer, David; Jamieson, Lisa M

2016-01-01

To determine factors associated with routine dental attendance in Aboriginal Australians. Data of 271 Aboriginal adults residing in Australia's Northern Territory were used. Routine dental attendance was defined as last visiting a dentist less than one year ago or visiting a dentist for a check-up. Both bivariate and multivariable analytical techniques were used. While 27% visited a dentist in the past year, 29% of these visited for a check-up. In bivariate analysis, being female, low psychological distress, and low clinical attachment loss (CAL) were associated with visiting a dentist within last year. Being aged younger than 39 years, male, no oral health impairment, being caries-free, low CAL, and low apolipoprotein B were associated with visiting for a check-up. Clinical attachment loss remained associated with visiting a dentist less than one year ago while being younger than 39 years and having no oral health impairment remained associated with usually visiting for a check-up in multivariable analysis. Younger age, no oral health impairment, and low CAL were associated with routine dental attendance among Indigenous Australians.

Factors Associated with Routine Dental Attendance among Aboriginal Australians.

PubMed

Amarasena, Najith; Kapellas, Kostas; Skilton, Michael R; Maple-Brown, Louise J; Brown, Alex; Bartold, Mark; O'Dea, Kerin; Celermajer, David; Jamieson, Lisa M

2016-02-01

To determine factors associated with routine dental attendance in Aboriginal Australians. Data of 271 Aboriginal adults residing in Australia's Northern Territory were used. Routine dental attendance was defined as last visiting a dentist less than one year ago or visiting a dentist for a check-up. Both bivariate and multivariable analytical techniques were used. While 27% visited a dentist in the past year, 29% of these visited for a check-up. In bivariate analysis, being female, low psychological distress, and low clinical attachment loss (CAL) were associated with visiting a dentist within last year. Being aged younger than 39 years, male, no oral health impairment, being caries-free, low CAL, and low apolipoprotein B were associated with visiting for a check-up. Clinical attachment loss remained associated with visiting a dentist less than one year ago while being younger than 39 years and having no oral health impairment remained associated with usually visiting for a check-up in multivariable analysis. Younger age, no oral health impairment, and low CAL were associated with routine dental attendance among Indigenous Australians.

Deep Space Navigation with Noncoherent Tracking Data

NASA Technical Reports Server (NTRS)

Ellis, J.

1983-01-01

Navigation capabilities of noncoherent tracking data are evaluated for interplanetary cruise phase and planetary (Venus) flyby orbit determination. Results of a formal covariance analysis are presented which show that a combination of one-way Doppler and delta DOR yields orbit accuracies comparable to conventional two-way Doppler tracking. For the interplanetary cruise phase, a tracking cycle consisting of a 3-hour Doppler pass and delta DOR (differential one-way range) from two baselines (one observation per overlap) acquired 3 times a month results in 100-km orbit determination accuracy. For reconstruction of a Venus flyby orbit, 10 days tracking at encounter consisting of continuous one-way Doppler and delta DOR sampled at one observation per overlap is sufficient to satisfy the accuracy requirements.

Insights to Galaxy Evolution Utilizing a Multivariate Comparison of Circumgalactic OVI and MgII

NASA Astrophysics Data System (ADS)

Lewis, James; Churchill, Christopher; Nielsen, Nikole; Kacprzak, Glenn; Muzahid, Sowgat; Charlton, Jane

2018-01-01

We present a promising multivariate method to categorize inter-related astronomical data in meaningful ways. We use data from the MAGIICAT and "Multiphase Galaxy Halos" surveys and limit our sample to those galaxies which are imaged with the Hubble Space Telescope and for which the Circumgalactic Medium (CGM) is measured using high-resolution quasar spectra (HIRES/COS). Utilizing the method to categorize data about the CGM and its host galaxy yields distinct categories of CGM-galaxy pairs that imply a common fate for the outflows of MgII and OVI in redder galaxies. The analysis reveals a lack of circumgalactic OVI in lower mass, bluer (younger) galaxies, and that as the blue galaxies gain mass and age along the green valley strong OVI appears in the CGM predominately along the minor axes. But as the galaxies continue to gain mass and age into the red sequence strong OVI gas is found primarily along the major axes. Furthermore, we find a population of low mass red galaxies in which only weak, uniform, circumgalactic OVI is found. Incorporating our multivariate results for circumgalactic MgII, including evidence for quenching of star formation via weak circumgalactic MgII preferentially found along the minor axes of redder galaxies, and invoking the similarity of OVI column densities and kinematic spreads along the major and minor axes, we infer that OVI is ancient gas in the CGM.

[Factors predicting lack of adherence to highly active antiretroviral treatment].

PubMed

Martín-Sánchez, Vicente; Ortega-Valín, Luis; Pérez-Simón, María del Rosario; Mostaza-Fernández, José Luis; Ortiz de Urbina-González, Juan José; Rodríguez-María, Miriam; Carro-Fernández, José Antonio; Cuevas-González, María José; Alcoba-Leza, Manuel

2002-12-01

Knowledge of adherence to highly active antiretroviral treatment (HAART) and the variables associated with poor compliance is useful for the follow-up of HIV infected patients. Patients were consecutively recruited from the HIV outpatient clinics of the Hospitals of Leon and El Bierzo from January to June 2000. Patients were considered non-adherent to treatment if they failed to take 10% or more of their prescribed total dose of at least one drug during the 4 days before the interview, or if they had accumulated a delay of more than 9 days over the previous 3 months in picking up their prescribed drugs from the hospital pharmacy. Logistic regression analysis was performed with variables found to be associated with adherence in the univariate analysis. The methods used to determine adherence had a Kappa index of 12.6%. Among the 206 patients interviewed, 108 were considered non-adherent (52.4%; CI 95% 5 45.6-59.2). Multivariate analysis showed that the following factors were associated with poor treatment adherence: cocaine consumption in the previous six months (adjusted OR 5 5.1); patients unsure about the proper way to take prescribed treatment; (adjusted OR 5 2.5); and patients not prescribed the zidovudine-lamivudine combination (adjusted OR 5 1.9). Over one-third of patients with no variable associated with treatment adherence were considered non-compliant. Measurement of medication adherence and its predictive factors involved methodological difficulties. With the criteria used in the present study more than half the patients were considered non-compliant. The variables found to be related to poor adherence can be modified by social, psychological or health care interventions.

On the importance of local dynamics in statokinesigram: A multivariate approach for postural control evaluation in elderly.

PubMed

Bargiotas, Ioannis; Audiffren, Julien; Vayatis, Nicolas; Vidal, Pierre-Paul; Buffat, Stephane; Yelnik, Alain P; Ricard, Damien

2018-01-01

The fact that almost one third of population >65 years-old has at least one fall per year, makes the risk-of-fall assessment through easy-to-use measurements an important issue in current clinical practice. A common way to evaluate posture is through the recording of the center-of-pressure (CoP) displacement (statokinesigram) with force platforms. Most of the previous studies, assuming homogeneous statokinesigrams in quiet standing, used global parameters in order to characterize the statokinesigrams. However the latter analysis provides little information about local characteristics of statokinesigrams. In this study, we propose a multidimensional scoring approach which locally characterizes statokinesigrams on small time-periods, or blocks, while highlighting those which are more indicative to the general individual's class (faller/non-faller). Moreover, this information can be used to provide a global score in order to evaluate the postural control and classify fallers/non-fallers. We evaluate our approach using the statokinesigram of 126 community-dwelling elderly (78.5 ± 7.7 years). Participants were recorded with eyes open and eyes closed (25 seconds each acquisition) and information about previous falls was collected. The performance of our findings are assessed using the receiver operating characteristics (ROC) analysis and the area under the curve (AUC). The results show that global scores provided by splitting statokinesigrams in smaller blocks and analyzing them locally, classify fallers/non-fallers more effectively (AUC = 0.77 ± 0.09 instead of AUC = 0.63 ± 0.12 for global analysis when splitting is not used). These promising results indicate that such methodology might provide supplementary information about the risk of fall of an individual and be of major usefulness in assessment of balance-related diseases such as Parkinson's disease.

On the importance of local dynamics in statokinesigram: A multivariate approach for postural control evaluation in elderly

PubMed Central

Audiffren, Julien; Vayatis, Nicolas; Vidal, Pierre-Paul; Buffat, Stephane; Yelnik, Alain P.; Ricard, Damien

2018-01-01

The fact that almost one third of population >65 years-old has at least one fall per year, makes the risk-of-fall assessment through easy-to-use measurements an important issue in current clinical practice. A common way to evaluate posture is through the recording of the center-of-pressure (CoP) displacement (statokinesigram) with force platforms. Most of the previous studies, assuming homogeneous statokinesigrams in quiet standing, used global parameters in order to characterize the statokinesigrams. However the latter analysis provides little information about local characteristics of statokinesigrams. In this study, we propose a multidimensional scoring approach which locally characterizes statokinesigrams on small time-periods, or blocks, while highlighting those which are more indicative to the general individual’s class (faller/non-faller). Moreover, this information can be used to provide a global score in order to evaluate the postural control and classify fallers/non-fallers. We evaluate our approach using the statokinesigram of 126 community-dwelling elderly (78.5 ± 7.7 years). Participants were recorded with eyes open and eyes closed (25 seconds each acquisition) and information about previous falls was collected. The performance of our findings are assessed using the receiver operating characteristics (ROC) analysis and the area under the curve (AUC). The results show that global scores provided by splitting statokinesigrams in smaller blocks and analyzing them locally, classify fallers/non-fallers more effectively (AUC = 0.77 ± 0.09 instead of AUC = 0.63 ± 0.12 for global analysis when splitting is not used). These promising results indicate that such methodology might provide supplementary information about the risk of fall of an individual and be of major usefulness in assessment of balance-related diseases such as Parkinson’s disease. PMID:29474402

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.

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…

Comparative Analysis.

DTIC Science & Technology

1987-11-01

differential qualita- tive (DQ) analysis, which solves the task, providing explanations suitable for use by design systems, automated diagnosis, intelligent...solves the task, providing explanations suitable for use by design systems, automated diagnosis, intelligent tutoring systems, and explanation based...comparative analysis as an important component; the explanation is used in many different ways. * One way method of automated design is the principlvd

Dyes assay for measuring physicochemical parameters.

PubMed

Moczko, Ewa; Meglinski, Igor V; Bessant, Conrad; Piletsky, Sergey A

2009-03-15

A combination of selective fluorescent dyes has been developed for simultaneous quantitative measurements of several physicochemical parameters. The operating principle of the assay is similar to electronic nose and tongue systems, which combine nonspecific or semispecific elements for the determination of diverse analytes and chemometric techniques for multivariate data analysis. The analytical capability of the proposed mixture is engendered by changes in fluorescence signal in response to changes in environment such as pH, temperature, ionic strength, and presence of oxygen. The signal is detected by a three-dimensional spectrofluorimeter, and the acquired data are processed using an artificial neural network (ANN) for multivariate calibration. The fluorescence spectrum of a solution of selected dyes allows discreet reading of emission maxima of all dyes composing the mixture. The variations in peaks intensities caused by environmental changes provide distinctive fluorescence patterns which can be handled in the same way as the signals collected from nose/tongue electrochemical or piezoelectric devices. This optical system opens possibilities for rapid, inexpensive, real-time detection of a multitude of physicochemical parameters and analytes of complex samples.

Multivariate statistical data analysis methods for detecting baroclinic wave interactions in the thermally driven rotating annulus

NASA Astrophysics Data System (ADS)

von Larcher, Thomas; Harlander, Uwe; Alexandrov, Kiril; Wang, Yongtai

2010-05-01

Experiments on baroclinic wave instabilities in a rotating cylindrical gap have been long performed, e.g., to unhide regular waves of different zonal wave number, to better understand the transition to the quasi-chaotic regime, and to reveal the underlying dynamical processes of complex wave flows. We present the application of appropriate multivariate data analysis methods on time series data sets acquired by the use of non-intrusive measurement techniques of a quite different nature. While the high accurate Laser-Doppler-Velocimetry (LDV ) is used for measurements of the radial velocity component at equidistant azimuthal positions, a high sensitive thermographic camera measures the surface temperature field. The measurements are performed at particular parameter points, where our former studies show that kinds of complex wave patterns occur [1, 2]. Obviously, the temperature data set has much more information content as the velocity data set due to the particular measurement techniques. Both sets of time series data are analyzed by using multivariate statistical techniques. While the LDV data sets are studied by applying the Multi-Channel Singular Spectrum Analysis (M - SSA), the temperature data sets are analyzed by applying the Empirical Orthogonal Functions (EOF ). Our goal is (a) to verify the results yielded with the analysis of the velocity data and (b) to compare the data analysis methods. Therefor, the temperature data are processed in a way to become comparable to the LDV data, i.e. reducing the size of the data set in such a manner that the temperature measurements would imaginary be performed at equidistant azimuthal positions only. This approach initially results in a great loss of information. But applying the M - SSA to the reduced temperature data sets enable us to compare the methods. [1] Th. von Larcher and C. Egbers, Experiments on transitions of baroclinic waves in a differentially heated rotating annulus, Nonlinear Processes in Geophysics, 2005, 12, 1033-1041, NPG Print: ISSN 1023-5809, NPG Online: ISSN 1607-7946 [2] U. Harlander, Th. von Larcher, Y. Wang and C. Egbers, PIV- and LDV-measurements of baroclinic wave interactions in a thermally driven rotating annulus, Experiments in Fluids, 2009, DOI: 10.1007/s00348-009-0792-5

Tailored multivariate analysis for modulated enhanced diffraction

DOE PAGES

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

2015-10-21

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

Viewpoints: Interactive Exploration of Large Multivariate Earth and Space Science Data Sets

NASA Astrophysics Data System (ADS)

Levit, C.; Gazis, P. R.

2006-05-01

Analysis and visualization of extremely large and complex data sets may be one of the most significant challenges facing earth and space science investigators in the forthcoming decades. While advances in hardware speed and storage technology have roughly kept up with (indeed, have driven) increases in database size, the same is not of our abilities to manage the complexity of these data. Current missions, instruments, and simulations produce so much data of such high dimensionality that they outstrip the capabilities of traditional visualization and analysis software. This problem can only be expected to get worse as data volumes increase by orders of magnitude in future missions and in ever-larger supercomputer simulations. For large multivariate data (more than 105 samples or records with more than 5 variables per sample) the interactive graphics response of most existing statistical analysis, machine learning, exploratory data analysis, and/or visualization tools such as Torch, MLC++, Matlab, S++/R, and IDL stutters, stalls, or stops working altogether. Fortunately, the graphics processing units (GPUs) built in to all professional desktop and laptop computers currently on the market are capable of transforming, filtering, and rendering hundreds of millions of points per second. We present a prototype open-source cross-platform application which leverages much of the power latent in the GPU to enable smooth interactive exploration and analysis of large high- dimensional data using a variety of classical and recent techniques. The targeted application is the interactive analysis of large, complex, multivariate data sets, with dimensionalities that may surpass 100 and sample sizes that may exceed 106-108.

Ten problems and solutions when predicting individual outcome from lesion site after stroke.

PubMed

Price, Cathy J; Hope, Thomas M; Seghier, Mohamed L

2017-01-15

In this paper, we consider solutions to ten of the challenges faced when trying to predict an individual's functional outcome after stroke on the basis of lesion site. A primary goal is to find lesion-outcome associations that are consistently observed in large populations of stroke patients because consistent associations maximise confidence in future individualised predictions. To understand and control multiple sources of inter-patient variability, we need to systematically investigate each contributing factor and how each factor depends on other factors. This requires very large cohorts of patients, who differ from one another in typical and measurable ways, including lesion site, lesion size, functional outcome and time post stroke (weeks to decades). These multivariate investigations are complex, particularly when the contributions of different variables interact with one another. Machine learning algorithms can help to identify the most influential variables and indicate dependencies between different factors. Multivariate lesion analyses are needed to understand how the effect of damage to one brain region depends on damage or preservation in other brain regions. Such data-led investigations can reveal predictive relationships between lesion site and outcome. However, to understand and improve the predictions we need explanatory models of the neural networks and degenerate pathways that support functions of interest. This will entail integrating the results of lesion analyses with those from functional imaging (fMRI, MEG), transcranial magnetic stimulation (TMS) and diffusor tensor imaging (DTI) studies of healthy participants and patients. Copyright © 2016 Elsevier Inc. All rights reserved.

Ten problems and solutions when predicting individual outcome from lesion site after stroke

PubMed Central

Price, Cathy J.; Hope, Thomas M.; Seghier, Mohamed L.

2016-01-01

In this paper, we consider solutions to ten of the challenges faced when trying to predict an individual’s functional outcome after stroke on the basis of lesion site. A primary goal is to find lesion-outcome associations that are consistently observed in large populations of stroke patients because consistent associations maximise confidence in future individualised predictions. To understand and control multiple sources of inter-patient variability, we need to systematically investigate each contributing factor and how each factor depends on other factors. This requires very large cohorts of patients, who differ from one another in typical and measurable ways, including lesion site, lesion size, functional outcome and time post stroke (weeks to decades). These multivariate investigations are complex, particularly when the contributions of different variables interact with one another. Machine learning algorithms can help to identify the most influential variables and indicate dependencies between different factors. Multivariate lesion analyses are needed to understand how the effect of damage to one brain region depends on damage or preservation in other brain regions. Such data-led investigations can reveal predictive relationships between lesion site and outcome. However, to understand and improve predictions we need explanatory models of the neural networks and degenerate pathways that support functions of interest. This will entail integrating the results of lesion analyses with those from functional imaging (fMRI, MEG), transcranial magnetic stimulation (TMS) and diffusor tensor imaging (DTI) studies of healthy participants and patients. PMID:27502048

Factors influencing knowledge on completion of treatment among TB patients under directly observed treatment strategy, in selected health facilities in Embu County, Kenya.

PubMed

Ndwiga, Joshua Muriuki; Kikuvi, Gideon; Omolo, Jared Odhiambo

2016-01-01

The World Health Organization (WHO) promotes the Directly Observed Treatment (DOT) strategy as the standard to increase adherence to Tuberculosis (TB) medication. However, cases of retreatment and Multi Drug Resistant continue to be reported in many parts of Kenya. This study sought to determine the factors influencing the completion of tuberculosis medication among TB patients in Embu County, Kenya. A descriptive cross-sectional study was conducted on a population of tuberculosis patients under DOT attending selected TB treatment clinics in Embu County, in Kenya. One hundred and forty TB patients interviewed within a period of 3 months. Data were analyzed using SPSS version 17.0 and included Bivariate and Multivariate Analysis. The level of significance was p≤ 0.05. The male and female participants were 61.4% and 38.6% respectively. The mean age of the respondents was 35±31.34-39.3 years. For the majority (52%) of the participants, the highest level of education was primary education. The unemployed participants formed the highest number of the respondent in the study (73%). The majorities (91.4%0) of the respondents were under the home-based DOT strategy (91.4%, 95% C.I: 85.5-95.5). Bivariate analysis using Chi-square showed that the level of education (p=0.003), patients feeling uncomfortable during supervision (p=0.01), and knowledge regarding the frequency of taking medication (p=0.004) were all significantly associated with knowledge regarding the importance of completion of medication. However, none of these factors was significant after multivariate analysis. Most participants did not know the importance of completion of medication. TB programs should come up with better ways to educate TB patients on the importance of supervision and treatment completion during the treatment of TB. The education programs should focus on influencing the attitudes of patients and creating awareness about the importance of treatment completion. The TB programs should be designed towards eliminating the factors influencing the completion of TB medication.

How patients perceive the therapeutic communications skills of their general practitioners, and how that perception affects adherence: use of the TCom-skill GP scale in a specific geographical area.

PubMed

Baumann, Michèle; Baumann, Cédric; Le Bihan, Etienne; Chau, Nearkasen

2008-12-01

To study: (1) the structure and test-retest reliability of a measure of how patients perceive the therapeutic communications skills of their general practitioners (TCom-skill GP), and (2) the associations of that scale with socio-demographic and health-related characteristics, and adherence. A total of 393 people who lived in the same geographic area and invited to attend a preventive medical centre for a check up were asked to complete a self-administered questionnaire concerning TCom-skill GP (15 items), socio-demographic and health-related characteristics, and to answer two questions on perceived adherence. The average age of respondents was 46.8 years (SD 14), and 50.4% were men. The TCom-skill GP score was one-dimensional, had high internal coherence (Cronbach alpha 0.92), and good test-retest reliability (intra-class correlation coefficient 0.74). The overall score was positively related to increasing age. Respondents aged 60+ were more likely to be adherent. The higher the score, the higher the probability of adherence. Multivariate analysis showed that the TCom-skill score was associated with advancing age and the number of consultations with the GP during the previous 3 months, but not with gender, living alone, being employed, job category or educational level. Multivariate analysis also showed that adherence was associated with TCom-skill GP score which concealed the association between adherence and advancing age observed in univariate analysis. The TCom-skill GP scale probably has value in assessing the quality of doctor-patient relationships and therapeutic communications. The psychometric properties of the TCom-skill GP scale were appropriate for its use in this context. Adherence related to the TCom-skill GP and the latter related to the age of patients and the number of their previous consultations. The TCom-skill GP scale may be a useful way to assess, in a specific geographical location, the impact of medical professional training on therapeutic communication.

Folate Deficiency, Atopy, and Severe Asthma Exacerbations in Puerto Rican Children.

PubMed

Blatter, Joshua; Brehm, John M; Sordillo, Joanne; Forno, Erick; Boutaoui, Nadia; Acosta-Pérez, Edna; Alvarez, María; Colón-Semidey, Angel; Weiss, Scott T; Litonjua, Augusto A; Canino, Glorisa; Celedón, Juan C

2016-02-01

Little is known about folate and atopy or severe asthma exacerbations. We examined whether folate deficiency is associated with number of positive skin tests to allergens or severe asthma exacerbations in a high-risk population and further assessed whether such association is explained or modified by vitamin D status. Cross-sectional study of 582 children aged 6 to 14 years with (n = 304) and without (n = 278) asthma in San Juan, Puerto Rico. Folate deficiency was defined as plasma folate less than or equal to 20 ng/ml. Our outcomes were the number of positive skin tests to allergens (range, 0-15) in all children and (in children with asthma) one or more severe exacerbations in the previous year. Logistic and negative binomial regression models were used for the multivariate analysis. All multivariate models were adjusted for age, sex, household income, residential proximity to a major road, and (for atopy) case/control status; those for severe exacerbations were also adjusted for use of inhaled corticosteroids and vitamin D insufficiency (a plasma 25[OH]D < 30 ng/ml). In a multivariate analysis, folate deficiency was significantly associated with an increased degree of atopy and 2.2 times increased odds of at least one severe asthma exacerbation (95% confidence interval for odds ratio, 1.1-4.6). Compared with children who had normal levels of both folate and vitamin D, those with both folate deficiency and vitamin D insufficiency had nearly eightfold increased odds of one or more severe asthma exacerbation (95% confidence interval for adjusted odds ratio, 2.7-21.6). Folate deficiency is associated with increased degree of atopy and severe asthma exacerbations in school-aged Puerto Ricans. Vitamin D insufficiency may further increase detrimental effects of folate deficiency on severe asthma exacerbations.

Metabolomic analysis based on 1H-nuclear magnetic resonance spectroscopy metabolic profiles in tuberculous, malignant and transudative pleural effusion

PubMed Central

Wang, Cheng; Peng, Jingjin; Kuang, Yanling; Zhang, Jiaqiang; Dai, Luming

2017-01-01

Pleural effusion is a common clinical manifestation with various causes. Current diagnostic and therapeutic methods have exhibited numerous limitations. By involving the analysis of dynamic changes in low molecular weight catabolites, metabolomics has been widely applied in various types of disease and have provided platforms to distinguish many novel biomarkers. However, to the best of our knowledge, there are few studies regarding the metabolic profiling for pleural effusion. In the current study, 58 pleural effusion samples were collected, among which 20 were malignant pleural effusions, 20 were tuberculous pleural effusions and 18 were transudative pleural effusions. The small molecule metabolite spectrums were obtained by adopting 1H nuclear magnetic resonance technology, and pattern-recognition multi-variable statistical analysis was used to screen out different metabolites. One-way analysis of variance, and Student-Newman-Keuls and the Kruskal-Wallis test were adopted for statistical analysis. Over 400 metabolites were identified in the untargeted metabolomic analysis and 26 metabolites were identified as significantly different among tuberculous, malignant and transudative pleural effusions. These metabolites were predominantly involved in the metabolic pathways of amino acids metabolism, glycometabolism and lipid metabolism. Statistical analysis revealed that eight metabolites contributed to the distinction between the three groups: Tuberculous, malignant and transudative pleural effusion. In the current study, the feasibility of identifying small molecule biochemical profiles in different types of pleural effusion were investigated reveal novel biological insights into the underlying mechanisms. The results provide specific insights into the biology of tubercular, malignant and transudative pleural effusion and may offer novel strategies for the diagnosis and therapy of associated diseases, including tuberculosis, advanced lung cancer and congestive heart failure. PMID:28627685

ROOT: A C++ framework for petabyte data storage, statistical analysis and visualization

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

Antcheva, I.; /CERN; Ballintijn, M.

2009-01-01

ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, designed for storing and analyzing petabytes of data in an efficient way. Any instance of a C++ class can be stored into a ROOT file in a machine-independent compressed binary format. In ROOT the TTree object container is optimized for statistical data analysis over very large data sets by using vertical data storage techniques. These containers can span a large number of files on local disks, the web or a number of different shared file systems. In order to analyze this data, the user can chose outmore » of a wide set of mathematical and statistical functions, including linear algebra classes, numerical algorithms such as integration and minimization, and various methods for performing regression analysis (fitting). In particular, the RooFit package allows the user to perform complex data modeling and fitting while the RooStats library provides abstractions and implementations for advanced statistical tools. Multivariate classification methods based on machine learning techniques are available via the TMVA package. A central piece in these analysis tools are the histogram classes which provide binning of one- and multi-dimensional data. Results can be saved in high-quality graphical formats like Postscript and PDF or in bitmap formats like JPG or GIF. The result can also be stored into ROOT macros that allow a full recreation and rework of the graphics. Users typically create their analysis macros step by step, making use of the interactive C++ interpreter CINT, while running over small data samples. Once the development is finished, they can run these macros at full compiled speed over large data sets, using on-the-fly compilation, or by creating a stand-alone batch program. Finally, if processing farms are available, the user can reduce the execution time of intrinsically parallel tasks - e.g. data mining in HEP - by using PROOF, which will take care of optimally distributing the work over the available resources in a transparent way.« less

An investigation of gender and grade-level differences in middle school students' attitudes about science, in science process skills ability, and in parental expectations of their children's science performance

NASA Astrophysics Data System (ADS)

White, Terri Renee'

The primary purpose of the study was to examine different variables (i.e. science process skill ability, science attitudes, and parents' levels of expectation for their children in science, which may impinge on science education differently for males and females in grades five, seven, and nine. The research question addressed by the study was: What are the differences between science process skill ability, science attitudes, and parents' levels of expectation in science on the academic success of fifth, seventh, and ninth graders in science and do effects differ according to gender and grade level? The subjects included fifth, seven, and ninth grade students ( n = 543) and their parents (n = 474) from six rural, public elementary schools and two rural, public middle schools in Southern Mississippi. A two-way (grade x gender) multivariate analysis of variance (MANOVA) was used to determine the differences in science process skill abilities of females and males in grade five, seven, and nine. An additional separate two-way multivariate analysis of variance (grade x gender) was also used to determine the differences in science attitudes of males and females in grade five, seven, and nine. A separate analysis of variance (PPSEX [parent's gender]) with the effects being parents' gender was used to determine differences in parents' levels of expectation for their childrens' performance in science. An additional separate analysis of variance (SSEX [student's gender]) with the effects being the gender of the student was also used to determine differences in parents' levels of expectation for their childrens' performance in science. Results of the analyses indicated significant main effects for grade level (p < .001) and gender (p < .001) on the TIPS II. There was no significant grade by gender interaction on the TIPS II. Results for the TOSRA also indicated a significant main effect for grade (p < .001) and the interaction of grade by sex ( p < .001). On variable ATT 5 (enjoyment of science lessons), males' attitudes toward science decreased across the grade levels; whereas, females decreased from grade five to seven, but showed a significant increase from grade seven to nine. Results from the analysis of variance with the parent's gender as the main effect showed no significant difference. The analysis of variance with student's gender as the main effect showed no significant difference.

Handwriting Examination: Moving from Art to Science

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

Jarman, K.H.; Hanlen, R.C.; Manzolillo, P.A.

In this document, we present a method for validating the premises and methodology of forensic handwriting examination. This method is intuitively appealing because it relies on quantitative measurements currently used qualitatively by FDE's in making comparisons, and it is scientifically rigorous because it exploits the power of multivariate statistical analysis. This approach uses measures of both central tendency and variation to construct a profile for a given individual. (Central tendency and variation are important for characterizing an individual's writing and both are currently used by FDE's in comparative analyses). Once constructed, different profiles are then compared for individuality using clustermore » analysis; they are grouped so that profiles within a group cannot be differentiated from one another based on the measured characteristics, whereas profiles between groups can. The cluster analysis procedure used here exploits the power of multivariate hypothesis testing. The result is not only a profile grouping but also an indication of statistical significance of the groups generated.« less

Identification of Enterococcus, Streptococcus, and Staphylococcus by Multivariate Analysis of Proton Magnetic Resonance Spectroscopic Data from Plate Cultures

PubMed Central

Bourne, Roger; Himmelreich, Uwe; Sharma, Ansuiya; Mountford, Carolyn; Sorrell, Tania

2001-01-01

A new fingerprinting technique with the potential for rapid identification of bacteria was developed by combining proton magnetic resonance spectroscopy (1H MRS) with multivariate statistical analysis. This resulted in an objective identification strategy for common clinical isolates belonging to the bacterial species Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecalis, Streptococcus pneumoniae, Streptococcus pyogenes, Streptococcus agalactiae, and the Streptococcus milleri group. Duplicate cultures of 104 different isolates were examined one or more times using 1H MRS. A total of 312 cultures were examined. An optimized classifier was developed using a bootstrapping process and a seven-group linear discriminant analysis to provide objective classification of the spectra. Identification of isolates was based on consistent high-probability classification of spectra from duplicate cultures and achieved 92% agreement with conventional methods of identification. Fewer than 1% of isolates were identified incorrectly. Identification of the remaining 7% of isolates was defined as indeterminate. PMID:11474013

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

High-throughput quantitative biochemical characterization of algal biomass by NIR spectroscopy; multiple linear regression and multivariate linear regression analysis.

PubMed

Laurens, L M L; Wolfrum, E J

2013-12-18

One of the challenges associated with microalgal biomass characterization and the comparison of microalgal strains and conversion processes is the rapid determination of the composition of algae. We have developed and applied a high-throughput screening technology based on near-infrared (NIR) spectroscopy for the rapid and accurate determination of algal biomass composition. We show that NIR spectroscopy can accurately predict the full composition using multivariate linear regression analysis of varying lipid, protein, and carbohydrate content of algal biomass samples from three strains. We also demonstrate a high quality of predictions of an independent validation set. A high-throughput 96-well configuration for spectroscopy gives equally good prediction relative to a ring-cup configuration, and thus, spectra can be obtained from as little as 10-20 mg of material. We found that lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum that allows for the use of single and multiple linear regression of respective wavelengths for the prediction of the biomass lipid content. This is not the case for carbohydrate and protein content, and thus, the use of multivariate statistical modeling approaches remains necessary.

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

PubMed

Gerhardt, H Carl; Brooks, Robert

2009-10-01

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

A Design Selection Procedure.

ERIC Educational Resources Information Center

Kroeker, Leonard P.

The problem of blocking on a status variable was investigated. The one-way fixed-effects analysis of variance, analysis of covariance, and generalized randomized block designs each treat the blocking problem in a different way. In order to compare these designs, it is necessary to restrict attention to experimental situations in which observations…

Statistical inferences for data from studies conducted with an aggregated multivariate outcome-dependent sample design

PubMed Central

Lu, Tsui-Shan; Longnecker, Matthew P.; Zhou, Haibo

2016-01-01

Outcome-dependent sampling (ODS) scheme is a cost-effective sampling scheme where one observes the exposure with a probability that depends on the outcome. The well-known such design is the case-control design for binary response, the case-cohort design for the failure time data and the general ODS design for a continuous response. While substantial work has been done for the univariate response case, statistical inference and design for the ODS with multivariate cases remain under-developed. Motivated by the need in biological studies for taking the advantage of the available responses for subjects in a cluster, we propose a multivariate outcome dependent sampling (Multivariate-ODS) design that is based on a general selection of the continuous responses within a cluster. The proposed inference procedure for the Multivariate-ODS design is semiparametric where all the underlying distributions of covariates are modeled nonparametrically using the empirical likelihood methods. We show that the proposed estimator is consistent and developed the asymptotically normality properties. Simulation studies show that the proposed estimator is more efficient than the estimator obtained using only the simple-random-sample portion of the Multivariate-ODS or the estimator from a simple random sample with the same sample size. The Multivariate-ODS design together with the proposed estimator provides an approach to further improve study efficiency for a given fixed study budget. We illustrate the proposed design and estimator with an analysis of association of PCB exposure to hearing loss in children born to the Collaborative Perinatal Study. PMID:27966260

Economic evaluation of mobile phone text message interventions to improve adherence to HIV therapy in Kenya.

PubMed

Patel, Anik R; Kessler, Jason; Braithwaite, R Scott; Nucifora, Kimberly A; Thirumurthy, Harsha; Zhou, Qinlian; Lester, Richard T; Marra, Carlo A

2017-02-01

A surge in mobile phone availability has fueled low cost short messaging service (SMS) adherence interventions. Multiple systematic reviews have concluded that some SMS-based interventions are effective at improving antiretroviral therapy (ART) adherence, and they are hypothesized to improve retention in care. The objective of this study was to evaluate the cost-effectiveness of SMS-based adherence interventions and explore the added value of retention benefits. We evaluated the cost-effectiveness of weekly SMS interventions compared to standard care among HIV+ individuals initiating ART for the first time in Kenya. We used an individual level micro-simulation model populated with data from two SMS-intervention trials, an East-African HIV+ cohort and published literature. We estimated average quality adjusted life years (QALY) and lifetime HIV-related costs from a healthcare perspective. We explored a wide range of scenarios and assumptions in one-way and multivariate sensitivity analyses. We found that SMS-based adherence interventions were cost-effective by WHO standards, with an incremental cost-effectiveness ratio (ICER) of $1,037/QALY. In the secondary analysis, potential retention benefits improved the cost-effectiveness of SMS intervention (ICER = $864/QALY). In multivariate sensitivity analyses, the interventions remained cost-effective in most analyses, but the ICER was highly sensitive to intervention costs, effectiveness and average cohort CD4 count at ART initiation. SMS interventions remained cost-effective in a test and treat scenario where individuals were assumed to initiate ART upon HIV detection. Effective SMS interventions would likely increase the efficiency of ART programs by improving HIV treatment outcomes at relatively low costs, and they could facilitate achievement of the UNAIDS goal of 90% viral suppression among those on ART by 2020.

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.

Ward mortality after ICU discharge: a multicenter validation of the Sabadell score.

PubMed

Fernandez, Rafael; Serrano, Jose Manuel; Umaran, Isabel; Abizanda, Ricard; Carrillo, Andres; Lopez-Pueyo, Maria Jesus; Rascado, Pedro; Balerdi, Begoña; Suberviola, Borja; Hernandez, Gonzalo

2010-07-01

Tools for predicting post-ICU patients' outcomes are scarce. A single-center study showed that the Sabadell score classified patients into four groups with clear-cut differences in ward mortality. To validate the Sabadell score using a prospective multicenter approach. Thirty-one ICUs in Spain. All patients admitted in the 3-month study period. We recorded variables at ICU admission (age, sex, severity of illness, and do-not-resuscitate orders), during the ICU stay (ICU-specific treatments, ICU-acquired infection, and acute renal failure), and at ICU discharge (Sabadell score). Statistical analyses included one-way ANOVA and multiple regression analysis with ward mortality as the dependent variable. We admitted 4,132 patients (mean age 61.5 +/- 16.7 years) with mean predicted mortality of 23.8 +/- 22.7%; 545 patients (13%) died in the ICU and 3,587 (87%) were discharged to the ward. Overall ward mortality was 6.7%; ward mortality was 1.5% (36/2,422) in patients with score 0 (good prognosis), 9% (64/725) in patients with score 1 (long-term poor prognosis), 23% (79/341) in patients with score 2 (short-term poor prognosis), and 64% (63/99) in patients with score 3 (expected hospital death). Variables associated with ward mortality in the multivariate analysis were predicted risk of death (OR 1.016), ICU readmission (OR 5.9), Sabadell score 1 (OR 4.7), Sabadell score 2 (OR 15.7), and Sabadell score 3 (OR 107.2). We confirm the ability of the Sabadell score at ICU discharge to define four groups of patients with very different likelihoods of hospital survival.

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

High Ki-67 Immunohistochemical Reactivity Correlates With Poor Prognosis in Bladder Carcinoma

PubMed Central

Luo, Yihuan; Zhang, Xin; Mo, Meile; Tan, Zhong; Huang, Lanshan; Zhou, Hong; Wang, Chunqin; Wei, Fanglin; Qiu, Xiaohui; He, Rongquan; Chen, Gang

2016-01-01

Abstract Ki-67 is considered as one of prime biomarkers to reflect cell proliferation and immunohistochemical Ki-67 staining has been widely applied in clinical pathology. To solve the widespread controversy whether Ki-67 reactivity significantly predicts clinical prognosis of bladder carcinoma (BC), we performed a comprehensive meta-analysis by combining results from different literature. A comprehensive search was conducted in the Chinese databases of WanFang, China National Knowledge Infrastructure and Chinese VIP as well as English databases of PubMed, ISI web of science, EMBASE, Science Direct, and Wiley online library. Independent studies linking Ki-67 to cancer-specific survival (CSS), disease-free survival (DFS), overall survival (OS), progression-free survival (PFS), and recurrence-free survival (RFS) were included in our meta-analysis. With the cut-off values literature provided, hazard ratio (HR) values between the survival distributions were extracted and later combined with STATA 12.0. In total, 76 studies (n = 13,053 patients) were eligible for the meta-analysis. It was indicated in either univariate or multivariate analysis for survival that high Ki-67 reactivity significantly predicted poor prognosis. In the univariate analysis, the combined HR for CSS, DFS, OS, PFS, and RFS were 2.588 (95% confidence interval [CI]: 1.623–4.127, P < 0.001), 2.697 (95%CI: 1.874–3.883, P < 0.001), 2.649 (95%CI: 1.632–4.300, P < 0.001), 3.506 (95%CI: 2.231–5.508, P < 0.001), and 1.792 (95%CI: 1.409–2.279, P < 0.001), respectively. The pooled HR of multivariate analysis for CSS, DFS, OS, PFS, and RFS were 1.868 (95%CI: 1.343–2.597, P < 0.001), 2.626 (95%CI: 2.089–3.301, P < 0.001), 1.104 (95%CI: 1.008–1.209, P = 0.032), 1.518 (95%CI: 1.299–1.773, P < 0.001), and 1.294 (95%CI: 1.203–1.392, P < 0.001), respectively. Subgroup analysis of univariate analysis by origin showed that Ki-67 reactivity significantly correlated with all 5 clinical outcome in Asian and European-American patients (P < 0.05). For multivariate analysis, however, the pooled results were only significant for DFS, OS, and RFS in Asian patients, for CSS, DFS, PFS, and RFS in European-American patients (P < 0.05). In the subgroup with low cut-off value (<20%), our meta-analysis indicated that high Ki-67 reactivity was significantly correlated with worsened CSS, DFS, OS, PFS, and RFS on univariate analysis (P < 0.05). For multivariate analysis, the meta-analysis of literature with low cut-off value (<20%) demonstrated that high Ki-67 reactivity predicted shorter DFS, PFS, and RFS in BC patients (P < 0.05). In the subgroup analysis of high cut-off value (≥20%), our meta-analysis indicated that high Ki-67 reactivity, in either univariate or multivariate analysis, significantly correlated with all five clinical outcomes in BC patients (P < 0.05). The meta-analysis indicates that high Ki-67 reactivity significantly correlates with deteriorated clinical outcomes in BC patients and that Ki-67 can be considered as an independent indicator for the prognosis by the meta-analyses of multivariate analysis. PMID:27082587

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

NASA Astrophysics Data System (ADS)

zhang, L.

2011-12-01

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

Tumor invasiveness defined by IASLC/ATS/ERS classification of ground-glass nodules can be predicted by quantitative CT parameters.

PubMed

Zhou, Qian-Jun; Zheng, Zhi-Chun; Zhu, Yong-Qiao; Lu, Pei-Ji; Huang, Jia; Ye, Jian-Ding; Zhang, Jie; Lu, Shun; Luo, Qing-Quan

2017-05-01

To investigate the potential value of CT parameters to differentiate ground-glass nodules between noninvasive adenocarcinoma and invasive pulmonary adenocarcinoma (IPA) as defined by IASLC/ATS/ERS classification. We retrospectively reviewed 211 patients with pathologically proved stage 0-IA lung adenocarcinoma which appeared as subsolid nodules, from January 2012 to January 2013 including 137 pure ground glass nodules (pGGNs) and 74 part-solid nodules (PSNs). Pathological data was classified under the 2011 IASLC/ATS/ERS classification. Both quantitative and qualitative CT parameters were used to determine the tumor invasiveness between noninvasive adenocarcinomas and IPAs. There were 154 noninvasive adenocarcinomas and 57 IPAs. In pGGNs, CT size and area, one-dimensional mean CT value and bubble lucency were significantly different between noninvasive adenocarcinomas and IPAs on univariate analysis. Multivariate regression and ROC analysis revealed that CT size and one-dimensional mean CT value were predictive of noninvasive adenocarcinomas compared to IPAs. Optimal cutoff value was 13.60 mm (sensitivity, 75.0%; specificity, 99.6%), and -583.60 HU (sensitivity, 68.8%; specificity, 66.9%). In PSNs, there were significant differences in CT size and area, solid component area, solid proportion, one-dimensional mean and maximum CT value, three-dimensional (3D) mean CT value between noninvasive adenocarcinomas and IPAs on univariate analysis. Multivariate and ROC analysis showed that CT size and 3D mean CT value were significantly differentiators. Optimal cutoff value was 19.64 mm (sensitivity, 53.7%; specificity, 93.9%), -571.63 HU (sensitivity, 85.4%; specificity, 75.8%). For pGGNs, CT size and one-dimensional mean CT value are determinants for tumor invasiveness. For PSNs, tumor invasiveness can be predicted by CT size and 3D mean CT value.

Sample presentation, sources of error and future perspectives on the application of vibrational spectroscopy in the wine industry.

PubMed

Cozzolino, Daniel

2015-03-30

Vibrational spectroscopy encompasses a number of techniques and methods including ultra-violet, visible, Fourier transform infrared or mid infrared, near infrared and Raman spectroscopy. The use and application of spectroscopy generates spectra containing hundreds of variables (absorbances at each wavenumbers or wavelengths), resulting in the production of large data sets representing the chemical and biochemical wine fingerprint. Multivariate data analysis techniques are then required to handle the large amount of data generated in order to interpret the spectra in a meaningful way in order to develop a specific application. This paper focuses on the developments of sample presentation and main sources of error when vibrational spectroscopy methods are applied in wine analysis. Recent and novel applications will be discussed as examples of these developments. © 2014 Society of Chemical Industry.

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.

Quasi-biennial (QBO), annual (AO), and semi-annual oscillation (SAO) in stratospheric SCIAMACHY O3, NO2, and BrO limb data using a multivariate least squares approach

NASA Astrophysics Data System (ADS)

Dikty, Sebastian; von Savigny, Christian; Sinnhuber, Bjoern-Martin; Rozanov, Alexej; Weber, Mark; Burrows, John P.

We use SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartog-raphY) ozone, nitrogen dioxide and bromine oxide profiles (20-50 km altitude, 2003-2008) to quantify the amplitudes of QBO, AO, and SAO signals with the help of a simple multivariate regression model. The analysis is being carried out with SCIAMACHY data covering all lat-itudes with the exception of polar nights, when measurements are not available. The overall global yield is approximately 10,000 profiles per month, which are binned into 10-steps with one zonal mean profile being calculated per day and per latitude bin.

Analysis of human tissues by total reflection X-ray fluorescence. Application of chemometrics for diagnostic cancer recognition

NASA Astrophysics Data System (ADS)

Benninghoff, L.; von Czarnowski, D.; Denkhaus, E.; Lemke, K.

1997-07-01

For the determination of trace element distributions of more than 20 elements in malignant and normal tissues of the human colon, tissue samples (approx. 400 mg wet weight) were digested with 3 ml of nitric acid (sub-boiled quality) by use of an autoclave system. The accuracy of measurements has been investigated by using certified materials. The analytical results were evaluated by using a spreadsheet program to give an overview of the element distribution in cancerous samples and in normal colon tissues. A further application, cluster analysis of the analytical results, was introduced to demonstrate the possibility of classification for cancer diagnosis. To confirm the results of cluster analysis, multivariate three-way principal component analysis was performed. Additionally, microtome frozen sections (10 μm) were prepared from the same tissue samples to compare the analytical results, i.e. the mass fractions of elements, according to the preparation method and to exclude systematic errors depending on the inhomogeneity of the tissues.

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.

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.

A Multivariate Analysis of Termination Status in a Rural Community Mental Health Center.

ERIC Educational Resources Information Center

Tutin, Judith; Kessler, Marc

It has been estimated that the most pressing problem in community mental health care clinics is dropout, defined as unilateral termination by the client without therapist approval. To clarify the nature of dropout patients, 133 outpatient records at a rural community mental health center were examined over a one year period. Variables expected to…

Ageism and Intervention: What Social Work Students Believe about Treating People Differently Because of Age

ERIC Educational Resources Information Center

Kane, Michael

2004-01-01

BSW and MSW students randomly completed one of two vignettes that were identical with the exception of the age of the vignette's subject. Following the vignette, respondents responded to 16 bio-psycho-social assessment and intervention items relating to health, illness, aging, and death. The multivariate analysis of variance was significant…

Ageism and Intervention: What Social Work Students Believe about Treating People Differently because of Age

ERIC Educational Resources Information Center

Kane, Michael N.

2004-01-01

BSW and MSW students randomly completed one of two vignettes that were identical with the exception of the age of the vignette's subject. Following the vignette, respondents responded to 16 bio-psycho-social assessment and intervention items relating to health, illness, aging, and death. The multivariate analysis of variance was significant…

Using Performance Data Gathered at Several Stages of Achievement in Predicting Subsequent Performance.

ERIC Educational Resources Information Center

Owen, Steven V.; Feldhusen, John F.

This study compares the effectiveness of three models of multivariate prediction for academic success in identifying the criterion variance of achievement in nursing education. The first model involves the use of an optimum set of predictors and one equation derived from a regression analysis on first semester grade average in predicting the…

A cutoff value based on analysis of a reference population decreases overestimation of the prevalence of nocturnal polyuria.

PubMed

van Haarst, Ernst P; Bosch, J L H Ruud

2012-09-01

We sought criteria for nocturnal polyuria in asymptomatic, nonurological adults of all ages by reporting reference values of the ratio of daytime and nighttime urine volumes, and finding nocturia predictors. Data from a database of frequency-volume charts from a reference population of 894 nonurological, asymptomatic volunteers of all age groups were analyzed. The nocturnal polyuria index and the nocturia index were calculated and factors influencing these values were determined by multivariate analysis. The nocturnal polyuria index had wide variation but a normal distribution with a mean ± SD of 30% ± 12%. The 95th percentile of the values was 53%. Above this cutoff a patient had nocturnal polyuria. This value contrasts with the International Continence Society definition of 33% but agrees with several other reports. On multivariate regression analysis with the nocturnal polyuria index as the dependent variable sleeping time, maximum voided volume and age were the covariates. However, the increase in the nocturnal polyuria index by age was small. Excluding polyuria and nocturia from analysis did not alter the results in a relevant way. The nocturnal voiding frequency depended on sleeping time and maximum voided volume but most of all on the nocturia index. The prevalence of nocturnal polyuria is overestimated. We suggest a new cutoff value for the nocturnal polyuria index, that is nocturnal polyuria exists when the nocturnal polyuria index exceeds 53%. The nocturia index is the best predictor of nocturia. Copyright © 2012 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

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…

Body height as risk factor for emphysema in COPD

PubMed Central

Miniati, Massimo; Bottai, Matteo; Pavlickova, Ivana; Monti, Simonetta

2016-01-01

Pulmonary emphysema is a phenotypic component of chronic obstructive pulmonary disease (COPD) which carries substantial morbidity and mortality. We explored the association between emphysema and body height in 726 patients with COPD using computed tomography as the reference diagnostic standard for emphysema. We applied univariate analysis to look for differences between patients with emphysema and those without, and multivariate logistic regression to identify significant predictors of the risk of emphysema. As covariates we included age, sex, body height, body mass index, pack-years of smoking, and forced expiratory volume in one second (FEV1) as percent predicted. The overall prevalence of emphysema was 52%. Emphysemic patients were significantly taller and thinner than non-emphysemic ones, and featured significantly higher pack-years of smoking and lower FEV1 (P < 0.001). The prevalence of emphysema rose linearly by 10-cm increase in body height (r2 = 0.96). In multivariate analysis, the odds of emphysema increased by 5% (95% confidence interval, 3 to 7%) along with one-centimeter increase in body height, and remained unchanged after adjusting for all the potential confounders considered (P < 0.001). The odds of emphysema were not statistically different between males and females. In conclusion, body height is a strong, independent risk factor for emphysema in COPD. PMID:27874046

Behavioral phenomenology in Alzheimer's disease, frontotemporal dementia, and late-life depression: a retrospective analysis.

PubMed

Swartz, J R; Miller, B L; Lesser, I M; Booth, R; Darby, A; Wohl, M; Benson, D F

1997-04-01

Often patients in the early stages of Alzheimer's disease (AD), frontotemporal dementia (FTD), and late-life depression can be difficult to differentiate clinically. Although subtle cognitive distinctions exist between these disorders, noncognitive behavioral phenomenology may provide additional discriminating power. In 19 subjects with AD, 19 with FTD, 16 with late-life psychotic depression (LLPD), and 19 with late-life nonpsychotic depression (LLNPD), noncognitive behavioral symptoms were quantified retrospectively using the Schedules for Clinical Assessment in Neuropsychiatry (SCAN) and compared using both a one-way ANOVA and a multivariate stepwise discriminant analysis, which utilized a jackknife procedure. The FTD group showed the highest mean total SCAN score, while the AD group showed the lowest. ANOVA showed significant differences in the mean total SCAN scores between the four diagnostic groups (P < .0001). With the discriminant analysis, the four disorders demonstrated different clusters of behavioral abnormalities and were differentiated by these symptoms (P < .0001). A subset of 14 SCAN item group symptoms was identified that collectively classified the following percentages of subjects in each diagnostic category: AD 94.7%, FTD 100%, LLPD 87.5%, and LLNPD 100%. These results indicate that AD, FTD, LLPD, and LLNPD were distinguished retrospectively by the SCAN without using cognitive data. Better definition of the longitudinal course of noncognitive behavioral symptoms in different dementias and psychiatric disorders will be valuable both for diagnosis and to help define behavioral syndromes that are associated with selective neuroanatomic and neurochemical brain pathology.

Spatial distribution and source apportionment of water pollution in different administrative zones of Wen-Rui-Tang (WRT) river watershed, China.

PubMed

Yang, Liping; Mei, Kun; Liu, Xingmei; Wu, Laosheng; Zhang, Minghua; Xu, Jianming; Wang, Fan

2013-08-01

Water quality degradation in river systems has caused great concerns all over the world. Identifying the spatial distribution and sources of water pollutants is the very first step for efficient water quality management. A set of water samples collected bimonthly at 12 monitoring sites in 2009 and 2010 were analyzed to determine the spatial distribution of critical parameters and to apportion the sources of pollutants in Wen-Rui-Tang (WRT) river watershed, near the East China Sea. The 12 monitoring sites were divided into three administrative zones of urban, suburban, and rural zones considering differences in land use and population density. Multivariate statistical methods [one-way analysis of variance, principal component analysis (PCA), and absolute principal component score-multiple linear regression (APCS-MLR) methods] were used to investigate the spatial distribution of water quality and to apportion the pollution sources. Results showed that most water quality parameters had no significant difference between the urban and suburban zones, whereas these two zones showed worse water quality than the rural zone. Based on PCA and APCS-MLR analysis, urban domestic sewage and commercial/service pollution, suburban domestic sewage along with fluorine point source pollution, and agricultural nonpoint source pollution with rural domestic sewage pollution were identified to the main pollution sources in urban, suburban, and rural zones, respectively. Understanding the water pollution characteristics of different administrative zones could put insights into effective water management policy-making especially in the area across various administrative zones.

The receiver operational characteristic for binary classification with multiple indices and its application to the neuroimaging study of Alzheimer's disease.

PubMed

Wu, Xia; Li, Juan; Ayutyanont, Napatkamon; Protas, Hillary; Jagust, William; Fleisher, Adam; Reiman, Eric; Yao, Li; Chen, Kewei

2013-01-01

Given a single index, the receiver operational characteristic (ROC) curve analysis is routinely utilized for characterizing performances in distinguishing two conditions/groups in terms of sensitivity and specificity. Given the availability of multiple data sources (referred to as multi-indices), such as multimodal neuroimaging data sets, cognitive tests, and clinical ratings and genomic data in Alzheimer’s disease (AD) studies, the single-index-based ROC underutilizes all available information. For a long time, a number of algorithmic/analytic approaches combining multiple indices have been widely used to simultaneously incorporate multiple sources. In this study, we propose an alternative for combining multiple indices using logical operations, such as “AND,” “OR,” and “at least n” (where n is an integer), to construct multivariate ROC (multiV-ROC) and characterize the sensitivity and specificity statistically associated with the use of multiple indices. With and without the “leave-one-out” cross-validation, we used two data sets from AD studies to showcase the potentially increased sensitivity/specificity of the multiV-ROC in comparison to the single-index ROC and linear discriminant analysis (an analytic way of combining multi-indices). We conclude that, for the data sets we investigated, the proposed multiV-ROC approach is capable of providing a natural and practical alternative with improved classification accuracy as compared to univariate ROC and linear discriminant analysis.

The Receiver Operational Characteristic for Binary Classification with Multiple Indices and Its Application to the Neuroimaging Study of Alzheimer’s Disease

PubMed Central

Wu, Xia; Li, Juan; Ayutyanont, Napatkamon; Protas, Hillary; Jagust, William; Fleisher, Adam; Reiman, Eric; Yao, Li; Chen, Kewei

2014-01-01

Given a single index, the receiver operational characteristic (ROC) curve analysis is routinely utilized for characterizing performances in distinguishing two conditions/groups in terms of sensitivity and specificity. Given the availability of multiple data sources (referred to as multi-indices), such as multimodal neuroimaging data sets, cognitive tests, and clinical ratings and genomic data in Alzheimer’s disease (AD) studies, the single-index-based ROC underutilizes all available information. For a long time, a number of algorithmic/analytic approaches combining multiple indices have been widely used to simultaneously incorporate multiple sources. In this study, we propose an alternative for combining multiple indices using logical operations, such as “AND,” “OR,” and “at least n” (where n is an integer), to construct multivariate ROC (multiV-ROC) and characterize the sensitivity and specificity statistically associated with the use of multiple indices. With and without the “leave-one-out” cross-validation, we used two data sets from AD studies to showcase the potentially increased sensitivity/specificity of the multiV-ROC in comparison to the single-index ROC and linear discriminant analysis (an analytic way of combining multi-indices). We conclude that, for the data sets we investigated, the proposed multiV-ROC approach is capable of providing a natural and practical alternative with improved classification accuracy as compared to univariate ROC and linear discriminant analysis. PMID:23702553

Fast and simultaneously determination of light and heavy rare earth elements in monazite using combination of ultraviolet-visible spectrophotometry and multivariate analysis

NASA Astrophysics Data System (ADS)

Anggraeni, Anni; Arianto, Fernando; Mutalib, Abdul; Pratomo, Uji; Bahti, Husein H.

2017-05-01

Rare Earth Elements (REE) are elements that a lot of function for life, such as metallurgy, optical devices, and manufacture of electronic devices. Sources of REE is present in the mineral, in which each element has similar properties. Currently, to determining the content of REE is used instruments such as ICP-OES, ICP-MS, XRF, and HPLC. But in each instruments, there are still have some weaknesses. Therefore we need an alternative analytical method for the determination of rare earth metal content, one of them is by a combination of UV-Visible spectrophotometry and multivariate analysis, including Principal Component Analysis (PCA), Principal Component Regression (PCR), and Partial Least Square Regression (PLS). The purpose of this experiment is to determine the content of light and medium rare earth elements in the mineral monazite without chemical separation by using a combination of multivariate analysis and UV-Visible spectrophotometric methods. Training set created 22 variations of concentration and absorbance was measured using a UV-Vis spectrophotometer, then the data is processed by PCA, PCR, and PLSR. The results were compared and validated to obtain the mathematical equation with the smallest percent error. From this experiment, mathematical equation used PLS methods was better than PCR after validated, which has RMSE value for La, Ce, Pr, Nd, Gd, Sm, Eu, and Tb respectively 0.095; 0.573; 0.538; 0.440; 3.387; 1.240; 1.870; and 0.639.

Elemental analysis of soils using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and laser-induced breakdown spectroscopy (LIBS) with multivariate discrimination: tape mounting as an alternative to pellets for small forensic transfer specimens.

PubMed

Jantzi, Sarah C; Almirall, José R

2014-01-01

Elemental analysis of soil is a useful application of both laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and laser-induced breakdown spectroscopy (LIBS) in geological, agricultural, environmental, archeological, planetary, and forensic sciences. In forensic science, the question to be answered is often whether soil specimens found on objects (e.g., shoes, tires, or tools) originated from the crime scene or other location of interest. Elemental analysis of the soil from the object and the locations of interest results in a characteristic elemental profile of each specimen, consisting of the amount of each element present. Because multiple elements are measured, multivariate statistics can be used to compare the elemental profiles in order to determine whether the specimen from the object is similar to one of the locations of interest. Previous work involved milling and pressing 0.5 g of soil into pellets before analysis using LA-ICP-MS and LIBS. However, forensic examiners prefer techniques that require smaller samples, are less time consuming, and are less destructive, allowing for future analysis by other techniques. An alternative sample introduction method was developed to meet these needs while still providing quantitative results suitable for multivariate comparisons. The tape-mounting method involved deposition of a thin layer of soil onto double-sided adhesive tape. A comparison of tape-mounting and pellet method performance is reported for both LA-ICP-MS and LIBS. Calibration standards and reference materials, prepared using the tape method, were analyzed by LA-ICP-MS and LIBS. As with the pellet method, linear calibration curves were achieved with the tape method, as well as good precision and low bias. Soil specimens from Miami-Dade County were prepared by both the pellet and tape methods and analyzed by LA-ICP-MS and LIBS. Principal components analysis and linear discriminant analysis were applied to the multivariate data. Results from both the tape method and the pellet method were nearly identical, with clear groupings and correct classification rates of >94%.

Factors associated with sealant outcome in 2 pediatric dental clinics: a multivariate hierarchical analysis.

PubMed

West, Nathan G; Ilief-Ala, Melina A; Douglass, Joanna M; Hagadorn, James I

2011-01-01

This study's purpose was to determine whether one-time sealants placed by pediatric dental residents vs dental students have different outcomes. The effect of isolation technique, behavior, duration of follow-up, and caries history was also examined. Records from 2 inner-city pediatric dental clinics were audited for 6- to 10-year-old patients with a permanent first molar sealant with at least 2 years of follow-up. A successful sealant was a one-time sealant that received no further treatment and was sealed or unsealed but not carious or restored at the final audit. Charts from 203 children with 481 sealants were audited. Of these, 281 sealants were failures. Univariate analysis revealed longer follow-up and younger age were associated with sealant failure. Operator type, child behavior, and isolation technique were not associated with sealant failure. After adjusting for follow-up duration, increased age at treatment reduced the odds of sealant failure while a history of caries reduced the protective effect of increased age. After adjusting for these factors, practitioner type, behavior, and type of isolation were not associated with sealant outcome in multivariate analysis. Age at sealant placement, history of caries prior to placement, and longer duration of follow-up are associated with sealant failure.

Evaluating associations between sources of information, knowledge of the human papillomavirus, and human papillomavirus vaccine uptake for adult women in California.

PubMed

Almeida, Cristina M; Tiro, Jasmin A; Rodriguez, Michael A; Diamant, Allison L

2012-04-19

Vaccines have the potential to reduce morbidity from HPV infections if age-eligible patients receive and parents know about them. Content analyses have demonstrated significant range in the quality of HPV information obtained from different sources. The purpose of this study was to determine the pattern of associations between information source and level of knowledge about HPV and vaccine receipt/intention. We analyzed the 2007 California Health Interview Survey, a population-based, statewide random digit dial survey, using data on adult females ages 18-65 who had heard about HPV (n=16,806). One-way ANOVA and multivariate logistic regression assessed the associations between source of information (advertisement only, advertisement plus other sources, and non-advertisement sources) and knowledge of HPV (3 or greater correct on a 4-point scale). Multivariate logistic regressions were conducted on a subsample of vaccine-eligible women and parents to assess vaccine uptake or intention. Less than half of respondents (43%) correctly answered 3 or more of the HPV knowledge questions. Mean knowledge scores were significantly different when comparing women who reported advertisement only, non-advertisement, and advertisement plus other sources of information (p<0.001). In multivariate analysis, women who reported non-advertisement sources (OR 2.44, 95% CI 2.07-2.87) and advertisements plus other sources (OR 3.03, 95% CI 2.57-3.58) were more likely to have knowledge scores above the 75% level than women who relied on advertisements alone. In the subsample of vaccine-eligible women and parents, those who reported advertisements plus other sources (OR 1.85, 95% CI 1.30-2.62) were more likely to have received or intend to receive the vaccine than those who reported advertisements as their sole information source. Advertisements are the most commonly reported source of information about HPV, and while they inform women of the existence of the vaccine, they do not contribute to accurate knowledge about the virus, nor do they appear to influence vaccine uptake. Other sources may play a larger role in refining knowledge and/or improving uptake. Copyright © 2012 Elsevier Ltd. All rights reserved.

Framing research for state policymakers who place a priority on cancer

PubMed Central

Brownson, Ross C.; Dodson, Elizabeth A.; Kerner, Jon F.; Moreland-Russell, Sarah

2016-01-01

Purpose Despite the potential for reducing the cancer burden via state policy change, few data exist on how best to disseminate research information to influence state legislators' policy choices. We explored: 1) the relative importance of core framing issues (source, presentation, timeliness) among policymakers who prioritize cancer and those who do not prioritize cancer and 2) the predictors of use of research in policymaking. Methods Cross-sectional data were collected from US state policymakers (i.e., legislators elected to state Houses or Senates) from January through October 2012 (n=862). One-way analysis of variance was performed to investigate the association of the priority of cancer variable with outcome variables. Multivariate logistic regression models examined predictors of the influence of research information. Results Legislators who prioritized cancer tended to rate characteristics that make research information useful higher than those who did not prioritize cancer. Among differences that were statistically significant were three items in the “source” domain (relevance, delivered by someone respected, supports one's own position), one item in the “presentation” domain (telling a story related to constituents), and two items in the “timeliness” domain (high current state priority, feasible when information is received). Participants who prioritized cancer risk factors were 80% more likely to rate research information as one of their top reasons for choosing an issue on which to work. Conclusions Our results suggest the importance of narrative forms of communication and that research information needs to be relevant to the policymakers' constituents in a brief, concise format. PMID:27299656

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

Influence of packaging information on consumer liking of chocolate milk.

PubMed

Kim, M K; Lopetcharat, K; Drake, M A

2013-08-01

Chocolate milk varies widely in flavor, color, and viscosity, and liking is influenced by these properties. Additionally, package labels (declared fat content) and brand are some of the extrinsic factors that may influence consumer perception. The objective of this study was to evaluate the effects of packaging labels and brand name on consumer liking and purchase intent of chocolate milk. A consumer acceptance test, conjoint analysis survey, and Kano analysis were conducted. One hundred eight consumers evaluated 7 chocolate milks with and without brand or package information in a 2-d crossover design. A conjoint analysis survey and Kano analysis were conducted after the consumer acceptance test. Results were evaluated by 2-way ANOVA and multivariate analyses. Declared fat content and brand influenced overall liking and purchase intent for chocolate milks to differing degrees. A subsequent conjoint analysis (n=250) revealed that fat content was a driver of choice for purchasing chocolate milk followed by sugar content and brand. Brand name was less important for purchase intent of chocolate milk than fat or sugar content. Among fat content of chocolate milk, 2 and 1% fat level were most appealing to consumers, and reduced sugar and regular sugar were equally important for purchase intent. Kano analysis confirmed that fat content (whole milk, 1, or 2% fat chocolate milk) was an attractive attribute for consumer satisfaction, more so than brand. Organic labeling did not affect the purchase decision of chocolate milk; however, Kano results revealed that having an organic label on a package positively influenced consumer satisfaction. Findings from this study can help chocolate milk producers as well as food marketers better target their product labels with attributes that drive consumer choice of chocolate milk. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Role of Adenosine Receptor A2A in Traumatic Optic Neuropathies (Addendum)

DTIC Science & Technology

2016-03-01

inflammation was evaluated using Western blot, Real-Time PCR and immuno-staining analyses. Role of A2AAR signaling in the anti-inflammation effect of ABT...Neuroimmunology 277 (2014) 96–104were evaluated by analysis of variance (one-way ANOVA), and the significance of differences between groups was assessed by the...Ahmad et al. / Journal of Neuroimmunology 277 (2014) 96–104were evaluated by analysis of variance (one-way ANOVA), and the significance of differences

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

PubMed

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

2017-01-01

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

Genetic association between milk yield, stayability, and mastitis in Holstein cows under tropical conditions.

PubMed

Irano, Natalia; Bignardi, Annaiza Braga; El Faro, Lenira; Santana, Mário Luiz; Cardoso, Vera Lúcia; Albuquerque, Lucia Galvão

2014-03-01

The objective of this study was to estimate genetic parameters for milk yield, stayability, and the occurrence of clinical mastitis in Holstein cows, as well as studying the genetic relationship between them, in order to provide subsidies for the genetic evaluation of these traits. Records from 5,090 Holstein cows with calving varying from 1991 to 2010, were used in the analysis. Two standard multivariate analyses were carried out, one containing the trait of accumulated 305-day milk yields in the first lactation (MY1), stayability (STAY) until the third lactation, and clinical mastitis (CM), as well as the other traits, considering accumulated 305-day milk yields (Y305), STAY, and CM, including the first three lactations as repeated measures for Y305 and CM. The covariance components were obtained by a Bayesian approach. The heritability estimates obtained by multivariate analysis with MY1 were 0.19, 0.28, and 0.13 for MY1, STAY, and CM, respectively, whereas using the multivariate analysis with the Y305, the estimates were 0.19, 0.31, and 0.14, respectively. The genetic correlations between MY1 and STAY, MY1 and CM, and STAY and CM, respectively, were 0.38, 0.12, and -0.49. The genetic correlations between Y305 and STAY, Y305 and CM, and STAY and CM, respectively, were 0.66, -0.25, and -0.52.

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…

Comparative evaluation of spectroscopic models using different multivariate statistical tools in a multicancer scenario

NASA Astrophysics Data System (ADS)

Ghanate, A. D.; Kothiwale, S.; Singh, S. P.; Bertrand, Dominique; Krishna, C. Murali

2011-02-01

Cancer is now recognized as one of the major causes of morbidity and mortality. Histopathological diagnosis, the gold standard, is shown to be subjective, time consuming, prone to interobserver disagreement, and often fails to predict prognosis. Optical spectroscopic methods are being contemplated as adjuncts or alternatives to conventional cancer diagnostics. The most important aspect of these approaches is their objectivity, and multivariate statistical tools play a major role in realizing it. However, rigorous evaluation of the robustness of spectral models is a prerequisite. The utility of Raman spectroscopy in the diagnosis of cancers has been well established. Until now, the specificity and applicability of spectral models have been evaluated for specific cancer types. In this study, we have evaluated the utility of spectroscopic models representing normal and malignant tissues of the breast, cervix, colon, larynx, and oral cavity in a broader perspective, using different multivariate tests. The limit test, which was used in our earlier study, gave high sensitivity but suffered from poor specificity. The performance of other methods such as factorial discriminant analysis and partial least square discriminant analysis are at par with more complex nonlinear methods such as decision trees, but they provide very little information about the classification model. This comparative study thus demonstrates not just the efficacy of Raman spectroscopic models but also the applicability and limitations of different multivariate tools for discrimination under complex conditions such as the multicancer scenario.

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.

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.

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.

Atmospheric bulk deposition to the Lagoon of Venice Part II. Source apportionment analysis near the industrial zone of Porto Marghera, Italy.

PubMed

Guerzoni, S; Rampazzo, G; Molinaroli, E; Rossini, P

2005-09-01

Multivariate statistical analyses were applied to measurements of atmospheric deposition of total particulate (TSP), inorganic elements (Al, Ca, Na, K, Mg, Si, Mn, Fe, Zn, Ni, Cr, Cu, Pb, Cd, As, Hg, V and S) and organic compounds (PAH, PCB, HCB and PCDD/F) collected in four stations, all located in the Lagoon of Venice. Aerosols at the scale of the basin (i.e., within a distance of 20 km) were mainly characterised by two end-members, one natural (composed of mineral particulate and marine spray) and one anthropogenic (with at least two different source components), affecting the sites in various ways. Variability at the two distant (>20 km) sites (Valle Dogà, Valle Figheri) was mainly due to natural components, whereas the other two stations (city of Venice, Dogaletto, approximately 5 km) were mainly impacted by industrial (and urban) sources. Total annual inputs were compared with the limits recently set by law (maximum allowed discharge=MAD). In the year of study, MAD values were exceeded for total As, Cd, Hg, Pb, dissolved Zn, PAH and PCDD/F. These results indicate that industrial sources gave rise to a quasi-permanent compositional (background) effect near the industrial area. The risk associated with atmospheric deposition should be quantified within the DSPIR framework to avoid future negative consequences in populations living in the vicinity of Porto Marghera.

Probabilistic modelling of flood events using the entropy copula

NASA Astrophysics Data System (ADS)

Li, Fan; Zheng, Qian

2016-11-01

The estimation of flood frequency is vital for the flood control strategies and hydraulic structure design. Generating synthetic flood events according to statistical properties of observations is one of plausible methods to analyze the flood frequency. Due to the statistical dependence among the flood event variables (i.e. the flood peak, volume and duration), a multidimensional joint probability estimation is required. Recently, the copula method is widely used for multivariable dependent structure construction, however, the copula family should be chosen before application and the choice process is sometimes rather subjective. The entropy copula, a new copula family, employed in this research proposed a way to avoid the relatively subjective process by combining the theories of copula and entropy. The analysis shows the effectiveness of the entropy copula for probabilistic modelling the flood events of two hydrological gauges, and a comparison of accuracy with the popular copulas was made. The Gibbs sampling technique was applied for trivariate flood events simulation in order to mitigate the calculation difficulties of extending to three dimension directly. The simulation results indicate that the entropy copula is a simple and effective copula family for trivariate flood simulation.

Reporting medical information: effects of press releases and newsworthiness on medical journal articles' visibility in the news media.

PubMed

Stryker, Jo Ellen

2002-11-01

Characteristics defining newsworthiness of journal articles appearing in JAMA and NEJM were examined to determine if they affect visibility in the news media. It was also hypothesized that press releases affected the amount of news coverage of a journal article due to the fact that the most newsworthy journal articles are selected for press releases. Journal articles (N = 95) were coded for characteristics believed to describe the "newsworthiness" of journal articles. Quantity of news coverage of the journal articles was estimated using the LEXIS-NEXIS database. Bivariate associations were examined using one-way analysis of variance, and multivariate analyses utilized OLS regression. Characteristics of the newsworthiness of medical journal articles predicted their visibility in newspapers. The issuing of press releases also predicted newspaper coverage. However, press releases predicted newspaper coverage largely because more newsworthy journal articles had accompanying press releases rather than because the press release itself was influential. Journalists report on medical information that is topical, stratifies risk based on demographic and lifestyle variables, and has lifestyle rather than medical implications. Medical journals issue press releases for articles that possess the characteristics journalists are looking for, thereby further highlighting their importance.

ToF-SIMS and principal component analysis of lipids and amino acids from inflamed and dysplastic human colonic mucosa.

PubMed

Urbini, Marco; Petito, Valentina; de Notaristefani, Francesco; Scaldaferri, Franco; Gasbarrini, Antonio; Tortora, Luca

2017-10-01

Here, time of flight secondary ion mass spectrometry (ToF-SIMS) and multivariate analysis were combined to study the role of ulcerative colitis (UC), a type of inflammatory bowel disease (IBD), in the colon cancer progression. ToF-SIMS was used to obtain mass spectra and chemical maps from the mucosal surface of human normal (NC), inflamed (IC), and dysplastic (DC) colon tissues. Chemical mapping with a lateral resolution of ≈ 1 μm allowed to evaluate zonation of fatty acids and amino acids as well as the morphological condition of the intestinal glands. High mass resolution ToF-SIMS spectra showed chemical differences in lipid and amino acid composition as a function of pathological state. In positive ion mode, mono- (MAG), di- (DAG), and triacylglycerol (TAG) signals were detected in NC tissues, while in IC and DC tissues, the only cholesterol was present as lipid class representative. Signals from fatty acids, collected in negative ion mode, were subjected to principal component analysis (PCA). PCA showed a strict correlation between IC and DC samples, due to an increase of stearic, arachidonic, and linoleic acid. In the same way, differences in the amino acid composition were highlighted through multivariate analysis. PCA revealed that glutamic acid, leucine/isoleucine, and valine fragments are related to IC tissues. On the other hand, tyrosine, methionine, and tryptophan peaks contributed highly to the separation of DC tissues. Finally, a classification of NC, IC, and DC patients was also achieved through hierarchical cluster analysis of amino acid fragments. In this case, human colonic inflammation showed a stronger relationship with normal than dysplastic condition. Graphical Abstract ᅟ.

TU-FG-201-05: Varian MPC as a Statistical Process Control Tool

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

Carver, A; Rowbottom, C

Purpose: Quality assurance in radiotherapy requires the measurement of various machine parameters to ensure they remain within permitted values over time. In Truebeam release 2.0 the Machine Performance Check (MPC) was released allowing beam output and machine axis movements to be assessed in a single test. We aim to evaluate the Varian Machine Performance Check (MPC) as a tool for Statistical Process Control (SPC). Methods: Varian’s MPC tool was used on three Truebeam and one EDGE linac for a period of approximately one year. MPC was commissioned against independent systems. After this period the data were reviewed to determine whethermore » or not the MPC was useful as a process control tool. Analyses on individual tests were analysed using Shewhart control plots, using Matlab for analysis. Principal component analysis was used to determine if a multivariate model was of any benefit in analysing the data. Results: Control charts were found to be useful to detect beam output changes, worn T-nuts and jaw calibration issues. Upper and lower control limits were defined at the 95% level. Multivariate SPC was performed using Principal Component Analysis. We found little evidence of clustering beyond that which might be naively expected such as beam uniformity and beam output. Whilst this makes multivariate analysis of little use it suggests that each test is giving independent information. Conclusion: The variety of independent parameters tested in MPC makes it a sensitive tool for routine machine QA. We have determined that using control charts in our QA programme would rapidly detect changes in machine performance. The use of control charts allows large quantities of tests to be performed on all linacs without visual inspection of all results. The use of control limits alerts users when data are inconsistent with previous measurements before they become out of specification. A. Carver has received a speaker’s honorarium from Varian.« less

Centralization of symptoms and lumbar range of motion in patients with low back pain.

PubMed

Bybee, Ronald F; Olsen, Denise L; Cantu-Boncser, Gloria; Allen, Heather Condie; Byars, Allyn

2009-05-01

This quasi-experimental repeated measures study examined the relationship between centralization of symptoms and lumbar flexion and extension range of motion (ROM) in patients with low back pain. Rapid and lasting changes in lumbar ROM have been noted with centralization of symptoms. However, no study has objectively measured the changes in lumbar ROM occurring with centralization. Forty-two adult subjects (mean age, 45.68 years; SD=15.76 years) with low back pain and associated lower extremity symptoms were followed by McKenzie trained physical therapists. Subjects' lumbar ROM was measured at the beginning and end of each patient visit by using double inclinometers, and pain location was documented. Subjects were grouped as 1) centralized, 2) centralizing, or 3) noncentralized for comparisons of symptom and ROM changes. Data were analyzed by using multivariate analysis of variance and one-way analysis of variance. Significance was set at 0.05. A significant difference was found between initial and final mean extension ROM in the centralized and centralizing groups (p=0.003). No significant difference was found in the noncentralized group (p<0.05). Subjects (n=23) who demonstrated a change in pain location during the initial visit also showed a significant (p<0.001) change in extension ROM, whereas patients with no change in pain location (n=19) did not (p=0.848). Lumbar extension ROM increased as centralization occurred.

Are older people more vulnerable to long-term impacts of disasters?

PubMed Central

Rafiey, Hassan; Momtaz, Yadollah Abolfathi; Alipour, Fardin; Khankeh, Hamidreza; Ahmadi, Shokoufeh; Sabzi Khoshnami, Mohammad; Haron, Sharifah Azizah

2016-01-01

Background Despite the growing interest in the study of disasters, there is limited research addressing the elderly population that lead to prejudiced beliefs that older adults are more vulnerable to disasters than younger adults. This study aimed to compare positive mental health between elderly and young earthquake survivors. Method Data for this study, consisting of 324 earthquake survivors, were obtained from a population-based cross-sectional survey conducted in Iran, 2015. The long-term effect of earthquake was assessed using the Mental Health Continuum-Short Form questionnaire. A one-way multivariate analysis of covariance (MANCOVA) using SPSS (version 22) was used in data analysis. Results Older adults scored significantly a higher level of overall positive mental health (mean [M]=34.31, standard deviation [SD]=10.52) than younger age group (M=27.48, SD=10.56, t=−4.41; P<0.001). Results of MANCOVA revealed a statistically significant difference between older and young adults on the combined positive mental health subscales (F(3,317)=6.95; P<0.001), after controlling for marital status, sex, and employment status. Conclusion The present findings showing a higher level of positive mental health among elderly earthquake survivors compared with their younger counterparts in the wake of natural disasters suggest that advancing age per se does not contribute to increasing vulnerability. PMID:27994445

Effects of Prone Position and Positive End-Expiratory Pressure on Noninvasive Estimators of ICP: A Pilot Study.

PubMed

Robba, Chiara; Bragazzi, Nicola Luigi; Bertuccio, Alessandro; Cardim, Danilo; Donnelly, Joseph; Sekhon, Mypinder; Lavinio, Andrea; Duane, Derek; Burnstein, Rowan; Matta, Basil; Bacigaluppi, Susanna; Lattuada, Marco; Czosnyka, Marek

2017-07-01

Prone positioning and positive end-expiratory pressure can improve pulmonary gas exchange and respiratory mechanics. However, they may be associated with the development of intracranial hypertension. Intracranial pressure (ICP) can be noninvasively estimated from the sonographic measurement of the optic nerve sheath diameter (ONSD) and from the transcranial Doppler analysis of the pulsatility (ICPPI) and the diastolic component (ICPFVd) of the velocity waveform. The effect of the prone positioning and positive end-expiratory pressure on ONSD, ICPFVd, and ICPPI was assessed in a prospective study of 30 patients undergoing spine surgery. One-way repeated measures analysis of variance, fixed-effect multivariate regression models, and receiver operating characteristic analyses were used to analyze numerical data. The mean values of ONSD, ICPFVd, and ICPPI significantly increased after change from supine to prone position. Receiver operating characteristic analyses demonstrated that, among the noninvasive methods, the mean ONSD measure had the greatest area under the curve signifying it is the most effective in distinguishing a hypothetical change in ICP between supine and prone positioning (0.86±0.034 [0.79 to 0.92]). A cutoff of 0.43 cm was found to be a best separator of ONSD value between supine and prone with a specificity of 75.0 and a sensitivity of 86.7. Noninvasive ICP estimation may be useful in patients at risk of developing intracranial hypertension who require prone positioning.

Structural equation modeling for observational studies

USGS Publications Warehouse

Grace, J.B.

2008-01-01

Structural equation modeling (SEM) represents a framework for developing and evaluating complex hypotheses about systems. This method of data analysis differs from conventional univariate and multivariate approaches familiar to most biologists in several ways. First, SEMs are multiequational and capable of representing a wide array of complex hypotheses about how system components interrelate. Second, models are typically developed based on theoretical knowledge and designed to represent competing hypotheses about the processes responsible for data structure. Third, SEM is conceptually based on the analysis of covariance relations. Most commonly, solutions are obtained using maximum-likelihood solution procedures, although a variety of solution procedures are used, including Bayesian estimation. Numerous extensions give SEM a very high degree of flexibility in dealing with nonnormal data, categorical responses, latent variables, hierarchical structure, multigroup comparisons, nonlinearities, and other complicating factors. Structural equation modeling allows researchers to address a variety of questions about systems, such as how different processes work in concert, how the influences of perturbations cascade through systems, and about the relative importance of different influences. I present 2 example applications of SEM, one involving interactions among lynx (Lynx pardinus), mongooses (Herpestes ichneumon), and rabbits (Oryctolagus cuniculus), and the second involving anuran species richness. Many wildlife ecologists may find SEM useful for understanding how populations function within their environments. Along with the capability of the methodology comes a need for care in the proper application of SEM.

Heterogeneous recurrence monitoring and control of nonlinear stochastic processes

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

Yang, Hui, E-mail: huiyang@usf.edu; Chen, Yun

Recurrence is one of the most common phenomena in natural and engineering systems. Process monitoring of dynamic transitions in nonlinear and nonstationary systems is more concerned with aperiodic recurrences and recurrence variations. However, little has been done to investigate the heterogeneous recurrence variations and link with the objectives of process monitoring and anomaly detection. Notably, nonlinear recurrence methodologies are based on homogeneous recurrences, which treat all recurrence states in the same way as black dots, and non-recurrence is white in recurrence plots. Heterogeneous recurrences are more concerned about the variations of recurrence states in terms of state properties (e.g., valuesmore » and relative locations) and the evolving dynamics (e.g., sequential state transitions). This paper presents a novel approach of heterogeneous recurrence analysis that utilizes a new fractal representation to delineate heterogeneous recurrence states in multiple scales, including the recurrences of both single states and multi-state sequences. Further, we developed a new set of heterogeneous recurrence quantifiers that are extracted from fractal representation in the transformed space. To that end, we integrated multivariate statistical control charts with heterogeneous recurrence analysis to simultaneously monitor two or more related quantifiers. Experimental results on nonlinear stochastic processes show that the proposed approach not only captures heterogeneous recurrence patterns in the fractal representation but also effectively monitors the changes in the dynamics of a complex system.« less

Analysis of rainfall and temperature time series to detect long-term climatic trends and variability over semi-arid Botswana

NASA Astrophysics Data System (ADS)

Byakatonda, Jimmy; Parida, B. P.; Kenabatho, Piet K.; Moalafhi, D. B.

2018-03-01

Arid and semi-arid environments have been identified with locations prone to impacts of climate variability and change. Investigating long-term trends is one way of tracing climate change impacts. This study investigates variability through annual and seasonal meteorological time series. Possible inhomogeneities and years of intervention are analysed using four absolute homogeneity tests. Trends in the climatic variables were determined using Mann-Kendall and Sen's Slope estimator statistics. Association of El Niño Southern Oscillation (ENSO) with local climate is also investigated through multivariate analysis. Results from the study show that rainfall time series are fully homogeneous with 78.6 and 50% of the stations for maximum and minimum temperature, respectively, showing homogeneity. Trends also indicate a general decrease of 5.8, 7.4 and 18.1% in annual, summer and winter rainfall, respectively. Warming trends are observed in annual and winter temperature at 0.3 and 1.5% for maximum temperature and 1.7 and 6.5% for minimum temperature, respectively. Rainfall reported a positive correlation with Southern Oscillation Index (SOI) and at the same time negative association with Sea Surface Temperatures (SSTs). Strong relationships between SSTs and maximum temperature are observed during the El Niño and La Niña years. These study findings could facilitate planning and management of agricultural and water resources in Botswana.

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

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.

Outcomes and risk factors for cancer patients undergoing endoscopic intervention of malignant biliary obstruction.

PubMed

Haag, Georg-Martin; Herrmann, Thomas; Jaeger, Dirk; Stremmel, Wolfgang; Schemmer, Peter; Sauer, Peter; Gotthardt, Daniel Nils

2015-12-04

Malignant bile duct obstruction is a common problem among cancer patients with hepatic or lymphatic metastases. Endoscopic retrograde cholangiography (ERC) with the placement of a stent is the method of choice to improve biliary flow. Only little data exist concerning the outcome of patients with malignant biliary obstruction in relationship to microbial isolates from bile. Bile samples were taken during the ERC procedure in tumor patients with biliary obstruction. Clinical data including laboratory values, tumor-specific treatment and outcome data were prospectively collected. 206 ERC interventions in 163 patients were recorded. In 43 % of the patients, systemic treatment was (re-) initiated after successful biliary drainage. A variety of bacteria and fungi was detected in the bile samples. One-year survival was significantly worse in patients from whom multiresistant pathogens were isolated than in patients, in whom other species were detected. Increased levels of inflammatory markers were associated with a poor one-year survival. The negative impact of these two factors was confirmed in multivariate analysis. In patients with pancreatic cancer, univariate analysis showed a negative impact on one-year survival in case of detection of Candida species in the bile. Multivariate analysis confirmed the negative prognostic impact of Candida in the bile in pancreatic cancer patients. Outcome in tumor patients with malignant bile obstruction is associated with the type of microbial biliary colonization. The proof of multiresistant pathogens or Candida, as well as the level of inflammation markers, have an impact on the prognosis of the underlying tumor disease.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

NASA Astrophysics Data System (ADS)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

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

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

PubMed Central

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-01-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states. PMID:26996254

Changes in the proportion of facility-based deliveries and related maternal health services among the poor in rural Jhang, Pakistan: results from a demand-side financing intervention.

PubMed

Agha, Sohail

2011-11-30

Demand-side financing projects are now being implemented in many developing countries, yet evidence showing that they reach the poor is scanty. A maternal health voucher scheme provided voucher-paid services in Jhang, a predominantly rural district of Pakistan, during 2010. A pre-test/post-test quasi-experimental design was used to assess the changes in the proportion of facility-based deliveries and related maternal health services among the poor. Household interviews were conducted with randomly selected women in the intervention and control union councils, before and after the intervention.A strong outreach model was used. Voucher promoters were given basic training in identification of poor women using the Poverty Scorecard for Pakistan, in the types of problems women could face during delivery, and in the promotion of antenatal care (ANC), institutional delivery and postnatal care (PNC). Voucher booklets valued at Rs. 4,000 ($48), including three ANC visits, a PNC visit, an institutional delivery, and a postnatal family planning visit, were sold for Rs. 100 ($1.2) to low-income women targeted by project outreach workers. Women suffering from complications were referred to emergency obstetric care services.Analysis was conducted at the bivariate and the multivariate levels. At the multivariate level, logistic regression analysis was conducted to determine whether the increase in institutional delivery was greater among poor women (defined for this study as women in the fourth or fifth quintiles) relative to non-poor women (defined for this study as women in the first quintile) in the intervention union councils compared to the control union councils. Bivariate analysis showed significant increases in the institutional delivery rate among women in the fourth or fifth wealth quintiles in the intervention union councils but no significant changes in this indicator among women in the same wealth quintiles in the control union councils. Multivariate analysis showed that the increase in institutional delivery among poor women relative to non-poor women was significantly greater in the intervention compared to the control union councils. Demand-side financing projects using vouchers can be an effective way of reducing inequities in institutional delivery.

Pleiotropy Analysis of Quantitative Traits at Gene Level by Multivariate Functional Linear Models

PubMed Central

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

2015-01-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks’s Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. PMID:25809955

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

PubMed

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

2015-05-01

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

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…

Achieving optimal SERS through enhanced experimental design

PubMed Central

Fisk, Heidi; Westley, Chloe; Turner, Nicholas J.

2016-01-01

One of the current limitations surrounding surface‐enhanced Raman scattering (SERS) is the perceived lack of reproducibility. SERS is indeed challenging, and for analyte detection, it is vital that the analyte interacts with the metal surface. However, as this is analyte dependent, there is not a single set of SERS conditions that are universal. This means that experimental optimisation for optimum SERS response is vital. Most researchers optimise one factor at a time, where a single parameter is altered first before going onto optimise the next. This is a very inefficient way of searching the experimental landscape. In this review, we explore the use of more powerful multivariate approaches to SERS experimental optimisation based on design of experiments and evolutionary computational methods. We particularly focus on colloidal‐based SERS rather than thin film preparations as a result of their popularity. © 2015 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons, Ltd. PMID:27587905

Achieving optimal SERS through enhanced experimental design.

PubMed

Fisk, Heidi; Westley, Chloe; Turner, Nicholas J; Goodacre, Royston

2016-01-01

One of the current limitations surrounding surface-enhanced Raman scattering (SERS) is the perceived lack of reproducibility. SERS is indeed challenging, and for analyte detection, it is vital that the analyte interacts with the metal surface. However, as this is analyte dependent, there is not a single set of SERS conditions that are universal. This means that experimental optimisation for optimum SERS response is vital. Most researchers optimise one factor at a time, where a single parameter is altered first before going onto optimise the next. This is a very inefficient way of searching the experimental landscape. In this review, we explore the use of more powerful multivariate approaches to SERS experimental optimisation based on design of experiments and evolutionary computational methods. We particularly focus on colloidal-based SERS rather than thin film preparations as a result of their popularity. © 2015 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons, Ltd.

Chemometrics-assisted chromatographic fingerprinting: An illicit methamphetamine case study.

PubMed

Shekari, Nafiseh; Vosough, Maryam; Tabar Heidar, Kourosh

2017-03-01

The volatile chemical constituents in complex mixtures can be analyzed using gas chromatography with mass spectrometry. This analysis allows the tentative identification of diverse impurities of an illicit methamphetamine sample. The acquired two-dimensional data of liquid-liquid extraction was resolved by multivariate curve resolution alternating curve resolution to elucidate the embedded peaks effectively. This is the first report on the application of a curve resolution approach for chromatogram fingerprinting to identify particularly the embedded impurities of a drug of abuse. Indeed, the strong and broad peak of methamphetamine makes identifying the underlying peaks problematic and even impossible. Mathematical separation instead of conventional chromatographic approaches was performed in a way that trace components embedded in methamphetamine peak were successfully resolved. Comprehensive analysis of the chromatogram, using multivariate curve resolution, resulted in elution profiles and mass spectra for each pure compound. Impurities such as benzaldehyde, benzyl alcohol, benzene, propenyl methyl ketone, benzyl methyl ketone, amphetamine, N-benzyl-2-methylaziridine, phenethylamine, N,N,α-trimethylamine, phenethylamine, N,α,α-trimethylmethamphetamine, N-acetylmethamphetamine, N-formylmethamphetamine, and other chemicals were identified. A route-specific impurity, N-benzyl-2-methylaziridine, indicating a synthesis route based on ephedrine/pseudoephedrine was identified. Moreover, this is the first report on the detection of impurities such as phenethylamine, N,α,α-trimethylamine (a structurally related impurity), and clonitazene (as an adulterant) in an illicit methamphetamine sample. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Forensic Analysis of Window’s(Registered) Virtual Memory Incorporating the System’s Page-File

DTIC Science & Technology

2008-12-01

Management and Budget, Paperwork Reduction Project (0704-0188) Washington DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE December...data in a meaningful way. One reason for this is how memory is managed by the operating system. Data belonging to one process can be distributed...way. One reason for this is how memory is managed by the operating system. Data belonging to one process can be distributed arbitrarily across

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.

Statistical inferences for data from studies conducted with an aggregated multivariate outcome-dependent sample design.

PubMed

Lu, Tsui-Shan; Longnecker, Matthew P; Zhou, Haibo

2017-03-15

Outcome-dependent sampling (ODS) scheme is a cost-effective sampling scheme where one observes the exposure with a probability that depends on the outcome. The well-known such design is the case-control design for binary response, the case-cohort design for the failure time data, and the general ODS design for a continuous response. While substantial work has been carried out for the univariate response case, statistical inference and design for the ODS with multivariate cases remain under-developed. Motivated by the need in biological studies for taking the advantage of the available responses for subjects in a cluster, we propose a multivariate outcome-dependent sampling (multivariate-ODS) design that is based on a general selection of the continuous responses within a cluster. The proposed inference procedure for the multivariate-ODS design is semiparametric where all the underlying distributions of covariates are modeled nonparametrically using the empirical likelihood methods. We show that the proposed estimator is consistent and developed the asymptotically normality properties. Simulation studies show that the proposed estimator is more efficient than the estimator obtained using only the simple-random-sample portion of the multivariate-ODS or the estimator from a simple random sample with the same sample size. The multivariate-ODS design together with the proposed estimator provides an approach to further improve study efficiency for a given fixed study budget. We illustrate the proposed design and estimator with an analysis of association of polychlorinated biphenyl exposure to hearing loss in children born to the Collaborative Perinatal Study. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

TATES: Efficient Multivariate Genotype-Phenotype Analysis for Genome-Wide Association Studies

PubMed Central

van der Sluis, Sophie; Posthuma, Danielle; Dolan, Conor V.

2013-01-01

To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype–phenotype models, show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests based on composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype–phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor. PMID:23359524

Does Learning to Read Improve Intelligence? A Longitudinal Multivariate Analysis in Identical Twins from Age 7 to 16

ERIC Educational Resources Information Center

Ritchie, Stuart J.; Bates, Timothy C.; Plomin, Robert

2015-01-01

Evidence from twin studies points to substantial environmental influences on intelligence, but the specifics of this influence are unclear. This study examined one developmental process that potentially causes intelligence differences: learning to read. In 1,890 twin pairs tested at 7, 9, 10, 12, and 16 years, a cross-lagged…

A Multi-Variate Analysis of Teacher-Student Interpretations of Non-Verbal Cues: The Measurement of Visuo-Gestural Channel Expression.

ERIC Educational Resources Information Center

Teresa, Joseph G.; Francis, John B.

This study sought to ascertain how teachers and students interpret non-verbal cues in the form of visuo-gestural channel expressions by having them assign affective meaning to such expressions depicted photographically. Subjects were 377 students and 19 teachers from two elementary schools: one, urban and characterized as low socioeconomic status;…

Multivariate Analysis of Student Loan Defaulters at Texas A&M University

ERIC Educational Resources Information Center

Steiner, Matt; Teszler, Natali

2005-01-01

In an effort to better understand student loan default behavior at Texas A&M University (TAMU), the research staff at TG, at the request of TAMU, conducted a study of the relationship between loan default, on the one hand, and many student and borrower characteristics, on the other hand. The study examines the default behavior of 12,776…

Significance of serum CA125 and TPS antigen levels for determination of overall survival after three chemotherapy courses in ovarian cancer patients during long-term follow-up.

PubMed

van Dalen, A; Favier, J; Hallensleben, E; Burges, A; Stieber, P; de Bruijn, H W A; Fink, D; Ferrero, A; McGing, P; Harlozinska, A; Kainz, Ch; Markowska, J; Molina, R; Sturgeon, C; Bowman, A; Einarsson, R; Goike, H

2009-01-01

To evaluate the prognostic significance for overall survival rate for the marker combination TPS and CA125 in ovarian cancer patients after three chemotherapy courses during long-term clinical follow-up. The overall survival of 212 (out of 213) ovarian cancer patients (FIGO Stages I-IV) was analyzed in a prospective multicenter study during a 10-year clinical follow-up by univariate and multivariate analysis. In patients with ovarian cancer FIGO Stage I (34 patients) or FIGO Stage II (30 patients) disease, the univariate and multivariate analysis of the 10-year overall survival data showed that CA125 and TPS serum levels were not independent prognostic factors. In the FIGO Stage III group (112 patients), the 10-year overall survival was 15.2%; while in the FIGO Stage IV group (36 patients) a 10-year overall survival of 5.6% was seen. Here, the tumor markers CA125 and TPS levels were significant prognostic factors in both univariate and multivariate analysis (p < 0.0001). In a combined FIGO Stage III + FIGO Stage IV group (60 patients with optimal debulking surgery), multivariate analysis demonstrated that CA125 and TPS levels were independent prognostic factors. For patients in this combined FIGO Stage III + IV group having both markers below respective discrimination level, 35.3% survived for more than ten years, as opposed to patients having one marker above the discrimination level where the 10-year survival was reduced to 10% of the patients. For patients showing both markers above the respective discrimination level, none of the patients survived for the 10-year follow-up time. In FIGO III and IV ovarian cancer patients, only patients with CA 125 and TPS markers below the discrimination level after three chemotherapy courses indicated a favorable prognosis. Patients with an elevated level of CA 125 or TPS or both markers after three chemotherapy courses showed unfavorable prognosis.

Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data

PubMed Central

Havlicek, Martin; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D.

2015-01-01

Increasing interest in understanding dynamic interactions of brain neural networks leads to formulation of sophisticated connectivity analysis methods. Recent studies have applied Granger causality based on standard multivariate autoregressive (MAR) modeling to assess the brain connectivity. Nevertheless, one important flaw of this commonly proposed method is that it requires the analyzed time series to be stationary, whereas such assumption is mostly violated due to the weakly nonstationary nature of functional magnetic resonance imaging (fMRI) time series. Therefore, we propose an approach to dynamic Granger causality in the frequency domain for evaluating functional network connectivity in fMRI data. The effectiveness and robustness of the dynamic approach was significantly improved by combining a forward and backward Kalman filter that improved estimates compared to the standard time-invariant MAR modeling. In our method, the functional networks were first detected by independent component analysis (ICA), a computational method for separating a multivariate signal into maximally independent components. Then the measure of Granger causality was evaluated using generalized partial directed coherence that is suitable for bivariate as well as multivariate data. Moreover, this metric provides identification of causal relation in frequency domain, which allows one to distinguish the frequency components related to the experimental paradigm. The procedure of evaluating Granger causality via dynamic MAR was demonstrated on simulated time series as well as on two sets of group fMRI data collected during an auditory sensorimotor (SM) or auditory oddball discrimination (AOD) tasks. Finally, a comparison with the results obtained from a standard time-invariant MAR model was provided. PMID:20561919

Human Adenocarcinoma Cell Line Sensitivity to Essential Oil Phytocomplexes from Pistacia Species: a Multivariate Approach.

PubMed

Buriani, Alessandro; Fortinguerra, Stefano; Sorrenti, Vincenzo; Dall'Acqua, Stefano; Innocenti, Gabbriella; Montopoli, Monica; Gabbia, Daniela; Carrara, Maria

2017-08-11

Principal component analysis (PCA) multivariate analysis was applied to study the cytotoxic activity of essential oils from various species of the Pistacia genus on human tumor cell lines. In particular, the cytotoxic activity of essential oils obtained from P. lentiscus , P. lentiscus var. chia (mastic gum), P. terebinthus , P. vera , and P. integerrima , was screened on three human adenocarcinoma cell lines: MCF-7 (breast), 2008 (ovarian), and LoVo (colon). The results indicate that all the Pistacia phytocomplexes, with the exception of mastic gum oil, induce cytotoxic effects on one or more of the three cell lines. PCA highlighted the presence of different cooperating clusters of bioactive molecules. Cluster variability among species, and even within the same species, could explain some of the differences seen among samples suggesting the presence of both common and species-specific mechanisms. Single molecules from one of the most significant clusters were tested, but only bornyl-acetate presented cytotoxic activity, although at much higher concentrations (IC 50 = 138.5 µg/mL) than those present in the essential oils, indicating that understanding of the full biological effect requires a holistic vision of the phytocomplexes with all its constituents.

Multiscale entropy analysis of biological signals: a fundamental bi-scaling law

PubMed Central

Gao, Jianbo; Hu, Jing; Liu, Feiyan; Cao, Yinhe

2015-01-01

Since introduced in early 2000, multiscale entropy (MSE) has found many applications in biosignal analysis, and been extended to multivariate MSE. So far, however, no analytic results for MSE or multivariate MSE have been reported. This has severely limited our basic understanding of MSE. For example, it has not been studied whether MSE estimated using default parameter values and short data set is meaningful or not. Nor is it known whether MSE has any relation with other complexity measures, such as the Hurst parameter, which characterizes the correlation structure of the data. To overcome this limitation, and more importantly, to guide more fruitful applications of MSE in various areas of life sciences, we derive a fundamental bi-scaling law for fractal time series, one for the scale in phase space, the other for the block size used for smoothing. We illustrate the usefulness of the approach by examining two types of physiological data. One is heart rate variability (HRV) data, for the purpose of distinguishing healthy subjects from patients with congestive heart failure, a life-threatening condition. The other is electroencephalogram (EEG) data, for the purpose of distinguishing epileptic seizure EEG from normal healthy EEG. PMID:26082711

[Predictive factors of complications during CT-guided transthoracic biopsy].

PubMed

Fontaine-Delaruelle, C; Souquet, P-J; Gamondes, D; Pradat, E; de Leusse, A; Ferretti, G R; Couraud, S

2017-04-01

CT-guided transthoracic core-needle biopsy (TTNB) is frequently used for the diagnosis of lung nodules. The aim of this study is to describe TTNBs' complications and to investigate predictive factors of complications. All consecutive TTNBs performed in three centers between 2006 and 2012 were included. Binary logistic regression was used for multivariate analysis. Overall, 970 TTNBs were performed in 929 patients. The complication rate was 34% (life-threatening complication in 6%). The most frequent complications were pneumothorax (29% included 4% which required chest-tube) and hemoptysis (5%). The mortality rate was 0.1% (n=1). In multivariate analysis, predictive factor for a complication was small target size (AOR=0.984; 95% CI [0.976-0.992]; P<0.001). This predictive factor was also found for occurrence of life-threatening complication (AOR=0.982; [0.965-0.999]; P=0.037), of pneumothorax (AOR=0.987; [0.978-0.995]; P=0.002) and of hemoptysis (AOR=0.973; [0.951-0.997]; P=0.024). One complication occurred in one-third of TTNBs. The proportion of life-threatening complication was 6%. A small lesion size was predictive of complication occurrence. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Prevalence of gestational diabetes mellitus in Europe: A meta-analysis.

PubMed

Eades, Claire E; Cameron, Dawn M; Evans, Josie M M

2017-07-01

Estimates of the prevalence of gestational diabetes vary widely. It is important to have a clear understanding of the prevalence of this condition to be able to plan interventions and health care provision. This paper describes a meta-analysis of primary research data reporting the prevalence of gestational diabetes mellitus in the general pregnant population of developed countries in Europe. Four electronic databases were systematically searched in May 2016. English language articles reporting gestational diabetes mellitus prevalence using universal screening in general pregnant population samples from developed countries in Europe were included. All papers identified by the search were screened by one author, and then half screened independently by a second author and half by a third author. Data were extracted by one author. Values for the measures of interest were combined using a random effects model and analysis of the effects of moderator variables was carried out. A total of 3258 abstracts were screened, with 40 studies included in the review. Overall prevalence of gestational diabetes mellitus was 5.4% (3.8-7.8). Maternal age, year of data collection, country, area of Europe, week of gestation at testing, and diagnostic criteria were found to have a significant univariate effect on GDM prevalence, and area, week of gestation at testing and year of data collection remained statistically significant in multivariate analysis. Quality category was significant in multivariate but not univariate analysis. This meta-analysis shows prevalence of GDM that is at the upper end of previous estimates in Europe. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Confidence limits for contribution plots in multivariate statistical process control using bootstrap estimates.

PubMed

Babamoradi, Hamid; van den Berg, Frans; Rinnan, Åsmund

2016-02-18

In Multivariate Statistical Process Control, when a fault is expected or detected in the process, contribution plots are essential for operators and optimization engineers in identifying those process variables that were affected by or might be the cause of the fault. The traditional way of interpreting a contribution plot is to examine the largest contributing process variables as the most probable faulty ones. This might result in false readings purely due to the differences in natural variation, measurement uncertainties, etc. It is more reasonable to compare variable contributions for new process runs with historical results achieved under Normal Operating Conditions, where confidence limits for contribution plots estimated from training data are used to judge new production runs. Asymptotic methods cannot provide confidence limits for contribution plots, leaving re-sampling methods as the only option. We suggest bootstrap re-sampling to build confidence limits for all contribution plots in online PCA-based MSPC. The new strategy to estimate CLs is compared to the previously reported CLs for contribution plots. An industrial batch process dataset was used to illustrate the concepts. Copyright © 2016 Elsevier B.V. All rights reserved.

Which kind of psychometrics is adequate for patient satisfaction questionnaires?

PubMed

Konerding, Uwe

2016-01-01

The construction and psychometric analysis of patient satisfaction questionnaires are discussed. The discussion is based upon the classification of multi-item questionnaires into scales or indices. Scales consist of items that describe the effects of the latent psychological variable to be measured, and indices consist of items that describe the causes of this variable. Whether patient satisfaction questionnaires should be constructed and analyzed as scales or as indices depends upon the purpose for which these questionnaires are required. If the final aim is improving care with regard to patients' preferences, then these questionnaires should be constructed and analyzed as indices. This implies two requirements: 1) items for patient satisfaction questionnaires should be selected in such a way that the universe of possible causes of patient satisfaction is covered optimally and 2) Cronbach's alpha, principal component analysis, exploratory factor analysis, confirmatory factor analysis, and analyses with models from item response theory, such as the Rasch Model, should not be applied for psychometric analyses. Instead, multivariate regression analyses with a direct rating of patient satisfaction as the dependent variable and the individual questionnaire items as independent variables should be performed. The coefficients produced by such an analysis can be applied for selecting the best items and for weighting the selected items when a sum score is determined. The lower boundaries of the validity of the unweighted and the weighted sum scores can be estimated by their correlations with the direct satisfaction rating. While the first requirement is fulfilled in the majority of the previous patient satisfaction questionnaires, the second one deviates from previous practice. Hence, if patient satisfaction is actually measured with the final aim of improving care with regard to patients' preferences, then future practice should be changed so that the second requirement is also fulfilled.

Energy dispersive X-ray fluorescence and scattering assessment of soil quality via partial least squares and artificial neural networks analytical modeling approaches.

PubMed

Kaniu, M I; Angeyo, K H; Mwala, A K; Mwangi, F K

2012-08-30

Soil quality assessment (SQA) calls for rapid, simple and affordable but accurate analysis of soil quality indicators (SQIs). Routine methods of soil analysis are tedious and expensive. Energy dispersive X-ray fluorescence and scattering (EDXRFS) spectrometry in conjunction with chemometrics is a potentially powerful method for rapid SQA. In this study, a 25 m Ci (109)Cd isotope source XRF spectrometer was used to realize EDXRFS spectrometry of soils. Glycerol (a simulate of "organic" soil solution) and kaolin (a model clay soil) doped with soil micro (Fe, Cu, Zn) and macro (NO(3)(-), SO(4)(2-), H(2)PO(4)(-)) nutrients were used to train multivariate chemometric calibration models for direct (non-invasive) analysis of SQIs based on partial least squares (PLS) and artificial neural networks (ANN). The techniques were compared for each SQI with respect to speed, robustness, correction ability for matrix effects, and resolution of spectral overlap. The method was then applied to perform direct rapid analysis of SQIs in field soils. A one-way ANOVA test showed no statistical difference at 95% confidence interval between PLS and ANN results compared to reference soil nutrients. PLS was more accurate analyzing C, N, Na, P and Zn (R(2)>0.9) and low SEP of (0.05%, 0.01%, 0.01%, and 1.98 μg g(-1)respectively), while ANN was better suited for analysis of Mg, Cu and Fe (R(2)>0.9 and SEP of 0.08%, 4.02 μg g(-1), and 0.88 μg g(-1) respectively). Copyright © 2012 Elsevier B.V. All rights reserved.

Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis.

PubMed

Kim, Sunmee; Choi, Ji Yeh; Hwang, Heungsun

2017-01-01

Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.

The pLISA project in ASTERICS

NASA Astrophysics Data System (ADS)

De Bonis, Giulia; Bozza, Cristiano

2017-03-01

In the framework of Horizon 2020, the European Commission approved the ASTERICS initiative (ASTronomy ESFRI and Research Infrastructure CluSter) to collect knowledge and experiences from astronomy, astrophysics and particle physics and foster synergies among existing research infrastructures and scientific communities, hence paving the way for future ones. ASTERICS aims at producing a common set of tools and strategies to be applied in Astronomy ESFRI facilities. In particular, it will target the so-called multi-messenger approach to combine information from optical and radio telescopes, photon counters and neutrino telescopes. pLISA is a software tool under development in ASTERICS to help and promote machine learning as a unified approach to multivariate analysis of astrophysical data and signals. The library will offer a collection of classification parameters, estimators, classes and methods to be linked and used in reconstruction programs (and possibly also extended), to characterize events in terms of particle identification and energy. The pLISA library aims at offering the software infras tructure for applications developed inside different experiments and has been designed with an effort to extrapolate general, physics-related estimators from the specific features of the data model related to each particular experiment. pLISA is oriented towards parallel computing architectures, with awareness of the opportunity of using GPUs as accelerators demanding specifically optimized algorithms and to reduce the costs of pro cessing hardware requested for the reconstruction tasks. Indeed, a fast (ideally, real-time) reconstruction can open the way for the development or improvement of alert systems, typically required by multi-messenger search programmes among the different experi mental facilities involved in ASTERICS.

Somatic comorbidity in anorexia nervosa: First results of a 21-year follow-up study on female inpatients

PubMed Central

2012-01-01

Background Anorexia nervosa is a severe psychosomatic disease with somatic complications in the long-term course and a high mortality rate. Somatic comorbidities independent of anorexia nervosa have rarely been studied, but pose a challenge to clinical practitioners. We investigated somatic comorbidities in an inpatient cohort and compared somatically ill anorexic patients and patients without a somatic comorbidity. In order to evaluate the impact of somatic comorbidity for the long-term course of anorexia nervosa, we monitored survival in a long-term follow-up. Method One hundred and sixty-nine female inpatients with anorexia nervosa were treated at the Charité University Medical Centre, Campus Benjamin Franklin, Berlin, between 1979 and 2011. We conducted retrospective analyses using patient's medical and psychological records. Information on survival and mortality were required through the local registration office and was available for one hundred patients. The mean follow-up interval for this subgroup was m = 20.9 years (sd = 4.7, min = 13.3, max = 31.6, range = 18.3). We conducted survival analysis using cox regression and included somatic comorbidity in a multivariate model. Results N = 41 patients (24.3%) showed a somatic comorbidity, n = 13 patients (7.7%) showed somatic comorbidities related to anorexia nervosa and n = 26 patients (15.4%) showed somatic comorbidities independent of anorexia nervosa, n = 2 patients showed somatic complications related to other psychiatric disorders. Patients with a somatic comorbidity were significantly older (m = 29.5, sd = 10.3 vs m = 25.0, sd = 8.7; p = .006), showed a later anorexia nervosa onset (m = 24.8, sd = 9.9 vs. m = 18.6, sd = 5.1; p < .000) and a longer duration of treatment in our clinic (m = 66.6, sd = 50.3 vs. m = 50.0, sd = 47; p = .05) than inpatients without somatic comorbidity. Out of 100 patients, 9 patients (9%) had died, on average at age of m = 37 years (sd = 9.5). Mortality was more common among inpatients with somatic comorbidity (n = 6, 66.7%) than among inpatients without a somatic disease (n = 3, 33.3%; p = .03). Somatic comorbidity was a significant coefficient in a multivariate survival model (B = 2.32, p = .04). Conclusion Somatic comorbidity seems to be an important factor for anorexia nervosa outcome and should be included in multivariate analyses on the long-term course of anorexia nervosa as an independent variable. Further investigations are needed in order to understand in which way anorexia nervosa and a somatic disease can interact. PMID:22300749

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.

Application of two tests of multivariate discordancy to fisheries data sets

USGS Publications Warehouse

Stapanian, M.A.; Kocovsky, P.M.; Garner, F.C.

2008-01-01

The generalized (Mahalanobis) distance and multivariate kurtosis are two powerful tests of multivariate discordancies (outliers). Unlike the generalized distance test, the multivariate kurtosis test has not been applied as a test of discordancy to fisheries data heretofore. We applied both tests, along with published algorithms for identifying suspected causal variable(s) of discordant observations, to two fisheries data sets from Lake Erie: total length, mass, and age from 1,234 burbot, Lota lota; and 22 combinations of unique subsets of 10 morphometrics taken from 119 yellow perch, Perca flavescens. For the burbot data set, the generalized distance test identified six discordant observations and the multivariate kurtosis test identified 24 discordant observations. In contrast with the multivariate tests, the univariate generalized distance test identified no discordancies when applied separately to each variable. Removing discordancies had a substantial effect on length-versus-mass regression equations. For 500-mm burbot, the percent difference in estimated mass after removing discordancies in our study was greater than the percent difference in masses estimated for burbot of the same length in lakes that differed substantially in productivity. The number of discordant yellow perch detected ranged from 0 to 2 with the multivariate generalized distance test and from 6 to 11 with the multivariate kurtosis test. With the kurtosis test, 108 yellow perch (90.7%) were identified as discordant in zero to two combinations, and five (4.2%) were identified as discordant in either all or 21 of the 22 combinations. The relationship among the variables included in each combination determined which variables were identified as causal. The generalized distance test identified between zero and six discordancies when applied separately to each variable. Removing the discordancies found in at least one-half of the combinations (k=5) had a marked effect on a principal components analysis. In particular, the percent of the total variation explained by second and third principal components, which explain shape, increased by 52 and 44% respectively when the discordancies were removed. Multivariate applications of the tests have numerous ecological advantages over univariate applications, including improved management of fish stocks and interpretation of multivariate morphometric data. ?? 2007 Springer Science+Business Media B.V.

Place of origin and violent disagreement among Asian American families: analysis across five States.

PubMed

Wang, Jong-Yi; Probst, Janice C; Moore, Charity G; Martin, Amy B; Bennett, Kevin J

2011-08-01

We examined the prevalence of and factors associated with violent and heated disagreements in the Asian American families, with an emphasis on place of birth differences between parent and child. Data were obtained from the 2003 National Survey of Children's Health, limited to five states with the highest concentration of Asian-Americans (n = 793). Multivariable analysis used generalized logistic regression models with a three-level outcome, violent and heated disagreement versus calm discussion. Violent disagreements were reported in 13.7% of Asian-American homes and 9.9% of white homes. Differential parent-child place of birth was associated with increased odds for heated disagreement in Asian-American families. Parenting stress increased the likelihood of violent disagreements in both Asian-American and white families. Asian-American families are not immune to potential family violence. Reducing parenting stress and intervening in culturally appropriate ways to reduce generation differences should be violence prevention priorities.

An empirical study of innovation-performance linkage in the paper industry

NASA Astrophysics Data System (ADS)

Farooquie, Parveen; Gani, Abdul; Zuberi, Arsalanullah K.; Hashmi, Imran

2012-10-01

To enter new markets and remain competitive in the existing markets, companies need to shift their focus from traditional means and ways to some innovative approaches. Though the paper industry in India has improved remarkably on its technological and environmental issues, yet it shows a low rate of innovation. The present paper attempts to review the industry in the perspective of technological innovations and investigates empirically the role of innovations in performance improvement and pollution control. Multivariate analysis of variance and discriminant function analysis are applied for data processing. The findings reveal that the mean scores on the factors, such as sales, quality, and flexibility, are higher for the good innovators than those for the poor innovators. Conversely, the factors which are likely to be reduced as a result of innovations, such as time, cost, emissions, and disposal of waste, have shown higher means for the poor innovators.

Sarcopenia predicts 1-year mortality in elderly patients undergoing curative gastrectomy for gastric cancer: a prospective study.

PubMed

Huang, Dong-Dong; Chen, Xiao-Xi; Chen, Xi-Yi; Wang, Su-Lin; Shen, Xian; Chen, Xiao-Lei; Yu, Zhen; Zhuang, Cheng-Le

2016-11-01

One-year mortality is vital for elderly oncologic patients undergoing surgery. Recent studies have demonstrated that sarcopenia can predict outcomes after major abdominal surgeries, but the association of sarcopenia and 1-year mortality has never been investigated in a prospective study. We conducted a prospective study of elderly patients (≥65 years) who underwent curative gastrectomy for gastric cancer from July 2014 to July 2015. Sarcopenia was determined by the measurements of muscle mass, handgrip strength, and gait speed. Univariate and multivariate analyses were used to identify the risk factors associated with 1-year mortality. A total of 173 patients were included, in which 52 (30.1 %) patients were identified as having sarcopenia. Twenty-four (13.9 %) patients died within 1 year of surgery. Multivariate analysis showed that sarcopenia was an independent risk factor for 1-year mortality. Area under the receiver operating characteristic curve demonstrated an increased predictive power for 1-year mortality with the inclusion of sarcopenia, from 0.835 to 0.868. Solely low muscle mass was not predictive of 1-year mortality in the multivariate analysis. Sarcopenia is predictive of 1-year mortality in elderly patients undergoing gastric cancer surgery. The measurement of muscle function is important for sarcopenia as a preoperative assessment tool.

Simulating Multivariate Nonnormal Data Using an Iterative Algorithm

ERIC Educational Resources Information Center

Ruscio, John; Kaczetow, Walter

2008-01-01

Simulating multivariate nonnormal data with specified correlation matrices is difficult. One especially popular method is Vale and Maurelli's (1983) extension of Fleishman's (1978) polynomial transformation technique to multivariate applications. This requires the specification of distributional moments and the calculation of an intermediate…

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.

Sexual Partnership Types as Determinant of HIV Risk in South African MSM: An Event-Level Cluster Analysis

PubMed Central

Sandfort, Theo; Yi, Huso; Knox, Justin; Reddy, Vasu

2012-01-01

While individual determinants of HIV risk among MSM have been widely studied, there is limited understanding of how relational characteristics determine sexual risk. Based on data collected among 300 South African men who have sex with men (MSM) and using cluster analysis, this study developed a typology of four partnership types: the “Race-Economic Similar,” “Age-Race-Economic Discordant,” “Non-regular Neighbourhood,” and “Familiar” partnership types. Support for the meaningfulness of these types was found through associations of these partnership types with participant characteristics and characteristics of the last anal sex event. Furthermore, in a multivariate analysis, only partnership type independently predicted whether the last anal sex event was unprotected. Findings of the study illustrate the importance of taking into account the relational context in understanding unprotected sexual practices and present ways to target intervention efforts as well as identify relationship specific determinants of unprotected sex. PMID:22956229

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

What counts as knowing: Constructing a communicative repertoire for student demonstration of knowledge in science

NASA Astrophysics Data System (ADS)

Crawford, Teresa

2005-02-01

The purpose of this study was twofold. One purpose was to identify the locally negotiated literate practices that defined ways of communicating information and knowledge across the curriculum in a fourth/fifth grade classroom. Through an ethnographic and sociolinguistic set of analyses, this investigation illustrated how the teacher worked to construct a learning environment that valued the use of multiple discourses as a way of communicating competence as a learner. Another purpose was specifically to address the ways that these practices contributed to student demonstration of knowledge in science. This was accomplished by an analysis of one student's presentation of science knowledge after a study of simple machines. A comparative analysis between his use of two discourses, written and oral, showed that the opportunity to choose between multiple discourses led to his success in demonstrating competence in ways that may have otherwise remained questionable.

Nitrate source identification in groundwater of multiple land-use areas by combining isotopes and multivariate statistical analysis: A case study of Asopos basin (Central Greece).

PubMed

Matiatos, Ioannis

2016-01-15

Nitrate (NO3) is one of the most common contaminants in aquatic environments and groundwater. Nitrate concentrations and environmental isotope data (δ(15)N-NO3 and δ(18)O-NO3) from groundwater of Asopos basin, which has different land-use types, i.e., a large number of industries (e.g., textile, metal processing, food, fertilizers, paint), urban and agricultural areas and livestock breeding facilities, were analyzed to identify the nitrate sources of water contamination and N-biogeochemical transformations. A Bayesian isotope mixing model (SIAR) and multivariate statistical analysis of hydrochemical data were used to estimate the proportional contribution of different NO3 sources and to identify the dominant factors controlling the nitrate content of the groundwater in the region. The comparison of SIAR and Principal Component Analysis showed that wastes originating from urban and industrial zones of the basin are mainly responsible for nitrate contamination of groundwater in these areas. Agricultural fertilizers and manure likely contribute to groundwater contamination away from urban fabric and industrial land-use areas. Soil contribution to nitrate contamination due to organic matter is higher in the south-western part of the area far from the industries and the urban settlements. The present study aims to highlight the use of environmental isotopes combined with multivariate statistical analysis in locating sources of nitrate contamination in groundwater leading to a more effective planning of environmental measures and remediation strategies in river basins and water bodies as defined by the European Water Frame Directive (Directive 2000/60/EC).

Strategies to optimize monitoring schemes of recreational waters from Salta, Argentina: a multivariate approach

PubMed Central

Gutiérrez-Cacciabue, Dolores; Teich, Ingrid; Poma, Hugo Ramiro; Cruz, Mercedes Cecilia; Balzarini, Mónica; Rajal, Verónica Beatriz

2014-01-01

Several recreational surface waters in Salta, Argentina, were selected to assess their quality. Seventy percent of the measurements exceeded at least one of the limits established by international legislation becoming unsuitable for their use. To interpret results of complex data, multivariate techniques were applied. Arenales River, due to the variability observed in the data, was divided in two: upstream and downstream representing low and high pollution sites, respectively; and Cluster Analysis supported that differentiation. Arenales River downstream and Campo Alegre Reservoir were the most different environments and Vaqueros and La Caldera Rivers were the most similar. Canonical Correlation Analysis allowed exploration of correlations between physicochemical and microbiological variables except in both parts of Arenales River, and Principal Component Analysis allowed finding relationships among the 9 measured variables in all aquatic environments. Variable’s loadings showed that Arenales River downstream was impacted by industrial and domestic activities, Arenales River upstream was affected by agricultural activities, Campo Alegre Reservoir was disturbed by anthropogenic and ecological effects, and La Caldera and Vaqueros Rivers were influenced by recreational activities. Discriminant Analysis allowed identification of subgroup of variables responsible for seasonal and spatial variations. Enterococcus, dissolved oxygen, conductivity, E. coli, pH, and fecal coliforms are sufficient to spatially describe the quality of the aquatic environments. Regarding seasonal variations, dissolved oxygen, conductivity, fecal coliforms, and pH can be used to describe water quality during dry season, while dissolved oxygen, conductivity, total coliforms, E. coli, and Enterococcus during wet season. Thus, the use of multivariate techniques allowed optimizing monitoring tasks and minimizing costs involved. PMID:25190636

Metabolomic Fingerprinting of Romaneschi Globe Artichokes by NMR Spectroscopy and Multivariate Data Analysis.

PubMed

de Falco, Bruna; Incerti, Guido; Pepe, Rosa; Amato, Mariana; Lanzotti, Virginia

2016-09-01

Globe artichoke (Cynara cardunculus L. var. scolymus L. Fiori) and cardoon (Cynara cardunculus L. var. altilis DC) are sources of nutraceuticals and bioactive compounds. To apply a NMR metabolomic fingerprinting approach to Cynara cardunculus heads to obtain simultaneous identification and quantitation of the major classes of organic compounds. The edible part of 14 Globe artichoke populations, belonging to the Romaneschi varietal group, were extracted to obtain apolar and polar organic extracts. The analysis was also extended to one species of cultivated cardoon for comparison. The (1) H-NMR of the extracts allowed simultaneous identification of the bioactive metabolites whose quantitation have been obtained by spectral integration followed by principal component analysis (PCA). Apolar organic extracts were mainly based on highly unsaturated long chain lipids. Polar organic extracts contained organic acids, amino acids, sugars (mainly inulin), caffeoyl derivatives (mainly cynarin), flavonoids, and terpenes. The level of nutraceuticals was found to be highest in the Italian landraces Bianco di Pertosa zia E and Natalina while cardoon showed the lowest content of all metabolites thus confirming the genetic distance between artichokes and cardoon. Metabolomic approach coupling NMR spectroscopy with multivariate data analysis allowed for a detailed metabolite profile of artichoke and cardoon varieties to be obtained. Relevant differences in the relative content of the metabolites were observed for the species analysed. This work is the first application of (1) H-NMR with multivariate statistics to provide a metabolomic fingerprinting of Cynara scolymus. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Multivariate Analysis for Quantification of Plutonium(IV) in Nitric Acid Based on Absorption Spectra

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

Lines, Amanda M.; Adami, Susan R.; Sinkov, Sergey I.

Development of more effective, reliable, and fast methods for monitoring process streams is a growing opportunity for analytical applications. Many fields can benefit from on-line monitoring, including the nuclear fuel cycle where improved methods for monitoring radioactive materials will facilitate maintenance of proper safeguards and ensure safe and efficient processing of materials. On-line process monitoring with a focus on optical spectroscopy can provide a fast, non-destructive method for monitoring chemical species. However, identification and quantification of species can be hindered by the complexity of the solutions if bands overlap or show condition-dependent spectral features. Plutonium (IV) is one example ofmore » a species which displays significant spectral variation with changing nitric acid concentration. Single variate analysis (i.e. Beer’s Law) is difficult to apply to the quantification of Pu(IV) unless the nitric acid concentration is known and separate calibration curves have been made for all possible acid strengths. Multivariate, or chemometric, analysis is an approach that allows for the accurate quantification of Pu(IV) without a priori knowledge of nitric acid concentration.« less

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

PubMed

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

2017-03-17

An ultimate goal of investigations of rooibos plant material subjected to different stages of fermentation is to identify the chemical changes taking place in the phenolic composition, using an untargeted approach and chromatographic fingerprints. Realization of this goal requires, among others, identification of the main components of the plant material involved in chemical reactions during the fermentation process. Quantitative chromatographic data for the compounds for extracts of green, semi-fermented and fermented rooibos form the basis of preliminary study following a targeted approach. The aim is to estimate whether treatment has a significant effect based on all quantified compounds and to identify the compounds, which contribute significantly to it. Analysis of variance is performed using modern multivariate methods such as ANOVA-Simultaneous Component Analysis, ANOVA - Target Projection and regularized MANOVA. This study is the first one in which all three approaches are compared and evaluated. For the data studied, all tree methods reveal the same significance of the fermentation effect on the extract compositions, but they lead to its different interpretation. Copyright © 2017 Elsevier B.V. All rights reserved.

Applications of High Resolution Laser Induced Breakdown Spectroscopy for Environmental and Biological Samples

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

Martin, Madhavi Z; Labbe, Nicole; Wagner, Rebekah J.

2013-01-01

This chapter details the application of LIBS in a number of environmental areas of research such as carbon sequestration and climate change. LIBS has also been shown to be useful in other high resolution environmental applications for example, elemental mapping and detection of metals in plant materials. LIBS has also been used in phytoremediation applications. Other biological research involves a detailed understanding of wood chemistry response to precipitation variations and also to forest fires. A cross-section of Mountain pine (pinceae Pinus pungen Lamb.) was scanned using a translational stage to determine the differences in the chemical features both before andmore » after a fire event. Consequently, by monitoring the elemental composition pattern of a tree and by looking for abrupt changes, one can reconstruct the disturbance history of a tree and a forest. Lastly we have shown that multivariate analysis of the LIBS data is necessary to standardize the analysis and correlate to other standard laboratory techniques. LIBS along with multivariate statistical analysis makes it a very powerful technology that can be transferred from laboratory to field applications with ease.« less

Improved Quantitative Analysis of Ion Mobility Spectrometry by Chemometric Multivariate Calibration

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

Fraga, Carlos G.; Kerr, Dayle; Atkinson, David A.

2009-09-01

Traditional peak-area calibration and the multivariate calibration methods of principle component regression (PCR) and partial least squares (PLS), including unfolded PLS (U-PLS) and multi-way PLS (N-PLS), were evaluated for the quantification of 2,4,6-trinitrotoluene (TNT) and cyclo-1,3,5-trimethylene-2,4,6-trinitramine (RDX) in Composition B samples analyzed by temperature step desorption ion mobility spectrometry (TSD-IMS). The true TNT and RDX concentrations of eight Composition B samples were determined by high performance liquid chromatography with UV absorbance detection. Most of the Composition B samples were found to have distinct TNT and RDX concentrations. Applying PCR and PLS on the exact same IMS spectra used for themore » peak-area study improved quantitative accuracy and precision approximately 3 to 5 fold and 2 to 4 fold, respectively. This in turn improved the probability of correctly identifying Composition B samples based upon the estimated RDX and TNT concentrations from 11% with peak area to 44% and 89% with PLS. This improvement increases the potential of obtaining forensic information from IMS analyzers by providing some ability to differentiate or match Composition B samples based on their TNT and RDX concentrations.« less

Perceived Devaluation and STI Testing Uptake among a Cohort of Street-Involved Youth in a Canadian Setting.

PubMed

Karamouzian, Mohammad; Shoveller, Jean; Dong, Huiru; Gilbert, Mark; Kerr, Thomas; DeBeck, Kora

2017-10-01

Perceived devaluation has been shown to have adverse effects on the mental and physical health outcomes of people who use drugs. However, the impact of perceived devaluation on sexually transmitted infections (STI) testing uptake among street-involved youth, who face multiple and intersecting stigmas due to their association with drug use and risky sexual practices, has not been fully characterized. Data were obtained between December 2013 and November 2014 from a cohort of street-involved youth who use illicit drugs aged 14-26 in Vancouver, British Columbia. Multivariable generalized estimating equations were constructed to assess the independent relationship between perceived devaluation and STI testing uptake. Among 300 street-involved youth, 87.0% reported a high perceived devaluation score at baseline. In the multivariable analysis, high perceived devaluation was negatively associated with STI testing uptake after adjustment for potential confounders (Adjusted Odds Ratio = 0.38, 95% Confidence Interval 0.15-0.98). Perceived devaluation was high among street-involved youth in our sample and appears to have adverse effects on STI testing uptake. HIV prevention and care programs should be examined and improved to better meet the special needs of street-involved youth in non-stigmatizing ways.

The same analysis approach: Practical protection against the pitfalls of novel neuroimaging analysis methods.

PubMed

Görgen, Kai; Hebart, Martin N; Allefeld, Carsten; Haynes, John-Dylan

2017-12-27

Standard neuroimaging data analysis based on traditional principles of experimental design, modelling, and statistical inference is increasingly complemented by novel analysis methods, driven e.g. by machine learning methods. While these novel approaches provide new insights into neuroimaging data, they often have unexpected properties, generating a growing literature on possible pitfalls. We propose to meet this challenge by adopting a habit of systematic testing of experimental design, analysis procedures, and statistical inference. Specifically, we suggest to apply the analysis method used for experimental data also to aspects of the experimental design, simulated confounds, simulated null data, and control data. We stress the importance of keeping the analysis method the same in main and test analyses, because only this way possible confounds and unexpected properties can be reliably detected and avoided. We describe and discuss this Same Analysis Approach in detail, and demonstrate it in two worked examples using multivariate decoding. With these examples, we reveal two sources of error: A mismatch between counterbalancing (crossover designs) and cross-validation which leads to systematic below-chance accuracies, and linear decoding of a nonlinear effect, a difference in variance. Copyright © 2017 Elsevier Inc. All rights reserved.

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

Detection technology research on the one-way clutch of automatic brake adjuster

NASA Astrophysics Data System (ADS)

Jiang, Wensong; Luo, Zai; Lu, Yi

2013-10-01

In this article, we provide a new testing method to evaluate the acceptable quality of the one-way clutch of automatic brake adjuster. To analysis the suitable adjusting brake moment which keeps the automatic brake adjuster out of failure, we build a mechanical model of one-way clutch according to the structure and the working principle of one-way clutch. The ranges of adjusting brake moment both clockwise and anti-clockwise can be calculated through the mechanical model of one-way clutch. Its critical moment, as well, are picked up as the ideal values of adjusting brake moment to evaluate the acceptable quality of one-way clutch of automatic brake adjuster. we calculate the ideal values of critical moment depending on the different structure of one-way clutch based on its mechanical model before the adjusting brake moment test begin. In addition, an experimental apparatus, which the uncertainty of measurement is ±0.1Nm, is specially designed to test the adjusting brake moment both clockwise and anti-clockwise. Than we can judge the acceptable quality of one-way clutch of automatic brake adjuster by comparing the test results and the ideal values instead of the EXP. In fact, the evaluation standard of adjusting brake moment applied on the project are still using the EXP provided by manufacturer currently in China, but it would be unavailable when the material of one-way clutch changed. Five kinds of automatic brake adjusters are used in the verification experiment to verify the accuracy of the test method. The experimental results show that the experimental values of adjusting brake moment both clockwise and anti-clockwise are within the ranges of theoretical results. The testing method provided by this article vividly meet the requirements of manufacturer's standard.

Home Language and Literacy Practices of Parents at One Spanish-English Two-Way Immersion Charter School

ERIC Educational Resources Information Center

Feinauer, Erika; Whiting, Erin Feinauer

2014-01-01

This study looks at how parents in one Spanish-English two-way immersion (TWI) charter school report their participation in various home language and literacy practices. Parent groups, based on a cluster analysis, highlight the heterogeneity of Latino parents at the school while acknowledging the commonalities as well. Four parent groups emerged…

MAOA, MTHFR, and TNF-β genes polymorphisms and personality traits in the pathogenesis of migraine.

PubMed

Ishii, Masakazu; Shimizu, Shunichi; Sakairi, Yuki; Nagamine, Ayumu; Naito, Yuika; Hosaka, Yukiko; Naito, Yuko; Kurihara, Tatsuya; Onaya, Tomomi; Oyamada, Hideto; Imagawa, Atsuko; Shida, Kenji; Takahashi, Johji; Oguchi, Katsuji; Masuda, Yutaka; Hara, Hajime; Usami, Shino; Kiuchi, Yuji

2012-04-01

Migraine is a multifactorial disease with various factors, such as genetic polymorphisms and personality traits, but the contribution of those factors is not clear. To clarify the pathogenesis of migraine, the contributions of genetic polymorphisms and personality traits were simultaneously investigated using multivariate analysis. Ninety-one migraine patients and 119 non-headache healthy volunteers were enrolled. The 12 gene polymorphisms analysis and NEO-FFI personality test were performed. At first, the univariate analysis was performed to extract the contributing factors to pathogenesis of migraine. We then extracted the factors that independently contributed to the pathogenesis of migraine using multivariate stepwise logistic regression analysis. Using the multivariate analysis, three gene polymorphisms including monoamine oxidase A (MAOA) T941G, methylenetetrahydrofolate reductase (MTHFR) C677T, and tumor necrosis factor beta (TNF-β) G252Α, and the neuroticism and conscientiousness scores in NEO-FFI were selected as significant factors that independently contributed to the pathogenesis of migraine. Their odds ratios were 1.099 (per point of neuroticism score), 1.080 (per point of conscientiousness score), 2.272 (T and T/T or T/G vs G and G/G genotype of MAOA), 1.939 (C/T or T/T vs C/C genotype of MTHFR), and 2.748 (G/A or A/A vs G/G genotype of TNF-β), respectively. We suggested that multiple factors, such as gene polymorphisms and personality traits, contribute to the pathogenesis of migraine. The contribution of polymorphisms, such as MAOA T941G, MTHFR C677T, and TNF-β G252A, were more important than personality traits in the pathogenesis of migraine, a multifactorial disorder.

Multivariate analysis to determine the factors affecting the attitudes toward organ donation of healthcare assistants in Spanish and Mexican healthcare centers.

PubMed

Ríos, A; López-Navas, A; Ayala-García, M A; Sebastián, M; Febrero, B; Ramírez, E J; Muñoz, G; Palacios, G; Rodríguez, J S; Martínez, M A; Nieto, A; Martínez-Alarcón, L; Ramis, G; Ramírez, P; Parrilla, P

2012-01-01

Healthcare assistants are an important group of workers who can influence public opinion. Their attitudes toward organ donation may influence public awareness of healthcare matters; negative attitudes toward donation and transplantation could have a negative impact on public attitudes. Our objective was analyze the attitudes of healthcare assistants, in Spanish and Mexican healthcare centers toward organ donation and determine factors affecting them using a multivariate analysis. As part of the "International Collaborative Donor Project," 32 primary care centers and 4 hospitals were selected in Spain and 5 hospitals in Mexico. A randomized sample of healthcare assistants was stratified according to healthcare services. Attitudes were evaluated using a validated questionnaire of the psychosocial aspects of donation, which was self-completed anonymously by the respondent. Statistical analysis used the chi-square test, Student t test, and logistic regression analysis. Of 532 respondents, 66% in favored donation and 34% were against it or undecided. Upon multivariate analysis, the following variables had the most weight: 1) country of origin (Mexicans were more in favor than Spanish; odds ratio [OR]) = 1.964; P = .014); 2) a partner with a favorable attitude (OR = 2.597; P = .013); 3) not being concerned about possible bodily mutilation after donation (OR = 2.631; P = .006); 4) preference for options apart from burial for handling the body after death (OR = 4.694; P < .001) and 5) accepting an autopsy if one was needed (OR = 3.584; P < .001). The attitudes of healthcare assistants toward organ donation varied considerably according to the respondent's country of origin. The psycho-social profile of a person with a positive attitude to donation was similar to that described within the general public. Copyright © 2012 Elsevier Inc. All rights reserved.

Multivariate analysis of the immune response to a vaccine as an alternative to the repetition of animal challenge studies for vaccines with demonstrated efficacy.

PubMed

Chapat, Ludivine; Hilaire, Florence; Bouvet, Jérome; Pialot, Daniel; Philippe-Reversat, Corinne; Guiot, Anne-Laure; Remolue, Lydie; Lechenet, Jacques; Andreoni, Christine; Poulet, Hervé; Day, Michael J; De Luca, Karelle; Cariou, Carine; Cupillard, Lionel

2017-07-01

The assessment of vaccine combinations, or the evaluation of the impact of minor modifications of one component in well-established vaccines, requires animal challenges in the absence of previously validated correlates of protection. As an alternative, we propose conducting a multivariate analysis of the specific immune response to the vaccine. This approach is consistent with the principles of the 3Rs (Refinement, Reduction and Replacement) and avoids repeating efficacy studies based on infectious challenges in vivo. To validate this approach, a set of nine immunological parameters was selected in order to characterize B and T lymphocyte responses against canine rabies virus and to evaluate the compatibility between two canine vaccines, an inactivated rabies vaccine (RABISIN ® ) and a combined vaccine (EURICAN ® DAPPi-Lmulti) injected at two different sites in the same animals. The analysis was focused on the magnitude and quality of the immune response. The multi-dimensional picture given by this 'immune fingerprint' was used to assess the impact of the concomitant injection of the combined vaccine on the immunogenicity of the rabies vaccine. A principal component analysis fully discriminated the control group from the groups vaccinated with RABISIN ® alone or RABISIN ® +EURICAN ® DAPPi-Lmulti and confirmed the compatibility between the rabies vaccines. This study suggests that determining the immune fingerprint, combined with a multivariate statistical analysis, is a promising approach to characterizing the immunogenicity of a vaccine with an established record of efficacy. It may also avoid the need to repeat efficacy studies involving challenge infection in case of minor modifications of the vaccine or for compatibility studies. Copyright © 2017 Elsevier B.V. All rights reserved.