The influence of different classification standards of age groups on prognosis in high-grade hemispheric glioma patients.

PubMed

Chen, Jian-Wu; Zhou, Chang-Fu; Lin, Zhi-Xiong

2015-09-15

Although age is thought to correlate with the prognosis of glioma patients, the most appropriate age-group classification standard to evaluate prognosis had not been fully studied. This study aimed to investigate the influence of age-group classification standards on the prognosis of patients with high-grade hemispheric glioma (HGG). This retrospective study of 125 HGG patients used three different classification standards of age-groups (≤ 50 and >50 years old, ≤ 60 and >60 years old, ≤ 45 and 45-65 and ≥ 65 years old) to evaluate the impact of age on prognosis. The primary end-point was overall survival (OS). The Kaplan-Meier method was applied for univariate analysis and Cox proportional hazards model for multivariate analysis. Univariate analysis showed a significant correlation between OS and all three classification standards of age-groups as well as between OS and pathological grade, gender, location of glioma, and regular chemotherapy and radiotherapy treatment. Multivariate analysis showed that the only independent predictors of OS were classification standard of age-groups ≤ 50 and > 50 years old, pathological grade and regular chemotherapy. In summary, the most appropriate classification standard of age-groups as an independent prognostic factor was ≤ 50 and > 50 years old. Pathological grade and chemotherapy were also independent predictors of OS in post-operative HGG patients. Copyright © 2015. Published by Elsevier B.V.

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

Extending Inferential Group Analysis in Type 2 Diabetic Patients with Multivariate GLM Implemented in SPM8.

PubMed

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

2017-01-01

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

Extending Inferential Group Analysis in Type 2 Diabetic Patients with Multivariate GLM Implemented in SPM8

PubMed Central

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

2017-01-01

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

Statistical Evaluation of Time Series Analysis Techniques

NASA Technical Reports Server (NTRS)

Benignus, V. A.

1973-01-01

The performance of a modified version of NASA's multivariate spectrum analysis program is discussed. A multiple regression model was used to make the revisions. Performance improvements were documented and compared to the standard fast Fourier transform by Monte Carlo techniques.

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

NONPARAMETRIC MANOVA APPROACHES FOR NON-NORMAL MULTIVARIATE OUTCOMES WITH MISSING VALUES

PubMed Central

He, Fanyin; Mazumdar, Sati; Tang, Gong; Bhatia, Triptish; Anderson, Stewart J.; Dew, Mary Amanda; Krafty, Robert; Nimgaonkar, Vishwajit; Deshpande, Smita; Hall, Martica; Reynolds, Charles F.

2017-01-01

Between-group comparisons often entail many correlated response variables. The multivariate linear model, with its assumption of multivariate normality, is the accepted standard tool for these tests. When this assumption is violated, the nonparametric multivariate Kruskal-Wallis (MKW) test is frequently used. However, this test requires complete cases with no missing values in response variables. Deletion of cases with missing values likely leads to inefficient statistical inference. Here we extend the MKW test to retain information from partially-observed cases. Results of simulated studies and analysis of real data show that the proposed method provides adequate coverage and superior power to complete-case analyses. PMID:29416225

Multivariate longitudinal data analysis with censored and intermittent missing responses.

PubMed

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

2018-05-08

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

Multiple imputation for handling missing outcome data when estimating the relative risk.

PubMed

Sullivan, Thomas R; Lee, Katherine J; Ryan, Philip; Salter, Amy B

2017-09-06

Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whether misspecification of the imputation model in this setting could lead to biased parameter estimates. Using simulated data, we evaluated the performance of multiple imputation for handling missing data prior to estimating adjusted relative risks from a correctly specified multivariable log binomial model. We considered an arbitrary pattern of missing data in both outcome and exposure variables, with missing data induced under missing at random mechanisms. Focusing on standard model-based methods of multiple imputation, missing data were imputed using multivariate normal imputation or fully conditional specification with a logistic imputation model for the outcome. Multivariate normal imputation performed poorly in the simulation study, consistently producing estimates of the relative risk that were biased towards the null. Despite outperforming multivariate normal imputation, fully conditional specification also produced somewhat biased estimates, with greater bias observed for higher outcome prevalences and larger relative risks. Deleting imputed outcomes from analysis datasets did not improve the performance of fully conditional specification. Both multivariate normal imputation and fully conditional specification produced biased estimates of the relative risk, presumably since both use a misspecified imputation model. Based on simulation results, we recommend researchers use fully conditional specification rather than multivariate normal imputation and retain imputed outcomes in the analysis when estimating relative risks. However fully conditional specification is not without its shortcomings, and so further research is needed to identify optimal approaches for relative risk estimation within the multiple imputation framework.

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.

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

PubMed

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

2015-11-04

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

Bayesian multivariate hierarchical transformation models for ROC analysis.

PubMed

O'Malley, A James; Zou, Kelly H

2006-02-15

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

Bayesian multivariate hierarchical transformation models for ROC analysis

PubMed Central

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

2006-01-01

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

Assessing signal-to-noise in quantitative proteomics: multivariate statistical analysis in DIGE experiments.

PubMed

Friedman, David B

2012-01-01

All quantitative proteomics experiments measure variation between samples. When performing large-scale experiments that involve multiple conditions or treatments, the experimental design should include the appropriate number of individual biological replicates from each condition to enable the distinction between a relevant biological signal from technical noise. Multivariate statistical analyses, such as principal component analysis (PCA), provide a global perspective on experimental variation, thereby enabling the assessment of whether the variation describes the expected biological signal or the unanticipated technical/biological noise inherent in the system. Examples will be shown from high-resolution multivariable DIGE experiments where PCA was instrumental in demonstrating biologically significant variation as well as sample outliers, fouled samples, and overriding technical variation that would not be readily observed using standard univariate tests.

Performance of Modified Test Statistics in Covariance and Correlation Structure Analysis under Conditions of Multivariate Nonnormality.

ERIC Educational Resources Information Center

Fouladi, Rachel T.

2000-01-01

Provides an overview of standard and modified normal theory and asymptotically distribution-free covariance and correlation structure analysis techniques and details Monte Carlo simulation results on Type I and Type II error control. Demonstrates through the simulation that robustness and nonrobustness of structure analysis techniques vary as a…

MGAS: a powerful tool for multivariate gene-based genome-wide association analysis.

PubMed

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

2015-04-01

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

A Multitaper, Causal Decomposition for Stochastic, Multivariate Time Series: Application to High-Frequency Calcium Imaging Data.

PubMed

Sornborger, Andrew T; Lauderdale, James D

2016-11-01

Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, C ( τ ), as opposed to standard methods that decompose the time series, X ( t ), using only information at zero-lag. In both simulated and neural imaging examples, we demonstrate that methods that neglect the full causal structure may be discarding important dynamical information in a time series.

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.

A Unified Framework for Association Analysis with Multiple Related Phenotypes

PubMed Central

Stephens, Matthew

2013-01-01

We consider the problem of assessing associations between multiple related outcome variables, and a single explanatory variable of interest. This problem arises in many settings, including genetic association studies, where the explanatory variable is genotype at a genetic variant. We outline a framework for conducting this type of analysis, based on Bayesian model comparison and model averaging for multivariate regressions. This framework unifies several common approaches to this problem, and includes both standard univariate and standard multivariate association tests as special cases. The framework also unifies the problems of testing for associations and explaining associations – that is, identifying which outcome variables are associated with genotype. This provides an alternative to the usual, but conceptually unsatisfying, approach of resorting to univariate tests when explaining and interpreting significant multivariate findings. The method is computationally tractable genome-wide for modest numbers of phenotypes (e.g. 5–10), and can be applied to summary data, without access to raw genotype and phenotype data. We illustrate the methods on both simulated examples, and to a genome-wide association study of blood lipid traits where we identify 18 potential novel genetic associations that were not identified by univariate analyses of the same data. PMID:23861737

A General Approach for Estimating Scale Score Reliability for Panel Survey Data

ERIC Educational Resources Information Center

Biemer, Paul P.; Christ, Sharon L.; Wiesen, Christopher A.

2009-01-01

Scale score measures are ubiquitous in the psychological literature and can be used as both dependent and independent variables in data analysis. Poor reliability of scale score measures leads to inflated standard errors and/or biased estimates, particularly in multivariate analysis. Reliability estimation is usually an integral step to assess…

Introducing Undergraduate Students to Metabolomics Using a NMR-Based Analysis of Coffee Beans

ERIC Educational Resources Information Center

Sandusky, Peter Olaf

2017-01-01

Metabolomics applies multivariate statistical analysis to sets of high-resolution spectra taken over a population of biologically derived samples. The objective is to distinguish subpopulations within the overall sample population, and possibly also to identify biomarkers. While metabolomics has become part of the standard analytical toolbox in…

Conventional 4-field box radiotherapy technique for cancer cervix: potential for geographic miss without CECT scan-based planning.

PubMed

Nagar, Y S; Singh, S; Kumar, S; Lal, P

2004-01-01

The advantage of 4-field radiation to the pelvis is that the use of lateral portals spares a portion of the small bowel anteriorly and rectum posteriorly. The standard lateral portals defined in textbooks are not always adequate especially in advanced cancer cervix. An analysis was done to determine adequacy of margins of standard lateral pelvic portals with CECT defined tumor volumes. The study included 40 patients of FIGO stage IIB and IIIB treated definitively for cancer cervix between 1998 and 2000. An inadequate margin was defined if the cervical growth and uterus were not encompassed by the 95% isodose. An inadequate posterior margin was common with bulky disease (P = 0.06) and with retroverted uterus (P = 0.08). Menopausal status, FIGO stage, associated myoma, and age were of no apparent prognostic significance. Bulk retained significant on multivariate analysis. An inadequate anterior margin was common in premenopausal (P = 0.01); anteverted uterus (P = 0.02); associated myoma (P = 0.01); and younger patients (P = 0.03). It was not influenced by bulk or stage. Menopausal status and associated myoma retained significant on multivariate analysis. Without the knowledge of precise tumor volume, the 4-field technique with standard portals is potentially risky as it may under dose the tumor through lateral portals and the standard AP/ PA portals are a safer option.

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing.

PubMed

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

2015-01-01

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

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing

PubMed Central

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

2015-01-01

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

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

PubMed

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

2017-04-01

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

A mixed-effects regression model for longitudinal multivariate ordinal data.

PubMed

Liu, Li C; Hedeker, Donald

2006-03-01

A mixed-effects item response theory model that allows for three-level multivariate ordinal outcomes and accommodates multiple random subject effects is proposed for analysis of multivariate ordinal outcomes in longitudinal studies. This model allows for the estimation of different item factor loadings (item discrimination parameters) for the multiple outcomes. The covariates in the model do not have to follow the proportional odds assumption and can be at any level. Assuming either a probit or logistic response function, maximum marginal likelihood estimation is proposed utilizing multidimensional Gauss-Hermite quadrature for integration of the random effects. An iterative Fisher scoring solution, which provides standard errors for all model parameters, is used. An analysis of a longitudinal substance use data set, where four items of substance use behavior (cigarette use, alcohol use, marijuana use, and getting drunk or high) are repeatedly measured over time, is used to illustrate application of the proposed model.

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.

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

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis.

PubMed

Nespeca, Maurilio Gustavo; Hatanaka, Rafael Rodrigues; Flumignan, Danilo Luiz; de Oliveira, José Eduardo

2018-01-01

Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000-650 cm -1 . The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time.

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis

PubMed Central

Hatanaka, Rafael Rodrigues; Flumignan, Danilo Luiz; de Oliveira, José Eduardo

2018-01-01

Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000–650 cm−1. The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time. PMID:29629209

What matters? Assessing and developing inquiry and multivariable reasoning skills in high school chemistry

NASA Astrophysics Data System (ADS)

Daftedar Abdelhadi, Raghda Mohamed

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

Site 765: Sedimentology

USGS Publications Warehouse

,

1990-01-01

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

Multivariate approaches for stability control of the olive oil reference materials for sensory analysis - part I: framework and fundamentals.

PubMed

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

2018-02-09

Virgin olive oil is the only food product for which sensory analysis is regulated to classify it in different quality categories. To harmonize the results of the sensorial method, the use of standards or reference materials is crucial. The stability of sensory reference materials is required to enable their suitable control, aiming to confirm that their specific target values are maintained on an ongoing basis. Currently, such stability is monitored by means of sensory analysis and the sensory panels are in the paradoxical situation of controlling the standards that are devoted to controlling the panels. In the present study, several approaches based on similarity analysis are exploited. For each approach, the specific methodology to build a proper multivariate control chart to monitor the stability of the sensory properties is explained and discussed. The normalized Euclidean and Mahalanobis distances, the so-called nearness and hardiness indices respectively, have been defined as new similarity indices to range the values from 0 to 1. Also, the squared mean from Hotelling's T 2 -statistic and Q 2 -statistic has been proposed as another similarity index. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

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

Membrane Introduction Mass Spectrometry Combined with an Orthogonal Partial-Least Squares Calibration Model for Mixture Analysis.

PubMed

Li, Min; Zhang, Lu; Yao, Xiaolong; Jiang, Xingyu

2017-01-01

The emerging membrane introduction mass spectrometry technique has been successfully used to detect benzene, toluene, ethyl benzene and xylene (BTEX), while overlapped spectra have unfortunately hindered its further application to the analysis of mixtures. Multivariate calibration, an efficient method to analyze mixtures, has been widely applied. In this paper, we compared univariate and multivariate analyses for quantification of the individual components of mixture samples. The results showed that the univariate analysis creates poor models with regression coefficients of 0.912, 0.867, 0.440 and 0.351 for BTEX, respectively. For multivariate analysis, a comparison to the partial-least squares (PLS) model shows that the orthogonal partial-least squares (OPLS) regression exhibits an optimal performance with regression coefficients of 0.995, 0.999, 0.980 and 0.976, favorable calibration parameters (RMSEC and RMSECV) and a favorable validation parameter (RMSEP). Furthermore, the OPLS exhibits a good recovery of 73.86 - 122.20% and relative standard deviation (RSD) of the repeatability of 1.14 - 4.87%. Thus, MIMS coupled with the OPLS regression provides an optimal approach for a quantitative BTEX mixture analysis in monitoring and predicting water pollution.

Network structure of multivariate time series.

PubMed

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

Improving the quality of pressure ulcer care with prevention: a cost-effectiveness analysis.

PubMed

Padula, William V; Mishra, Manish K; Makic, Mary Beth F; Sullivan, Patrick W

2011-04-01

In October 2008, Centers for Medicare and Medicaid Services discontinued reimbursement for hospital-acquired pressure ulcers (HAPUs), thus placing stress on hospitals to prevent incidence of this costly condition. To evaluate whether prevention methods are cost-effective compared with standard care in the management of HAPUs. A semi-Markov model simulated the admission of patients to an acute care hospital from the time of admission through 1 year using the societal perspective. The model simulated health states that could potentially lead to an HAPU through either the practice of "prevention" or "standard care." Univariate sensitivity analyses, threshold analyses, and Bayesian multivariate probabilistic sensitivity analysis using 10,000 Monte Carlo simulations were conducted. Cost per quality-adjusted life-years (QALYs) gained for the prevention of HAPUs. Prevention was cost saving and resulted in greater expected effectiveness compared with the standard care approach per hospitalization. The expected cost of prevention was $7276.35, and the expected effectiveness was 11.241 QALYs. The expected cost for standard care was $10,053.95, and the expected effectiveness was 9.342 QALYs. The multivariate probabilistic sensitivity analysis showed that prevention resulted in cost savings in 99.99% of the simulations. The threshold cost of prevention was $821.53 per day per person, whereas the cost of prevention was estimated to be $54.66 per day per person. This study suggests that it is more cost effective to pay for prevention of HAPUs compared with standard care. Continuous preventive care of HAPUs in acutely ill patients could potentially reduce incidence and prevalence, as well as lead to lower expenditures.

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

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

A need for a standardization in anaerobic digestion experiments? Let's get some insight from meta-analysis and multivariate analysis.

PubMed

Lavergne, Céline; Jeison, David; Ortega, Valentina; Chamy, Rolando; Donoso-Bravo, Andrés

2018-09-15

An important variability in the experimental results in anaerobic digestion lab test has been reported. This study presents a meta-analysis coupled with multivariate analysis aiming to assess the impact of this experimental variability in batch and continuous operation at mesophilic and thermophilic anaerobic digestion of waste activated sludge. An analysis of variance showed that there was no significant difference between mesophilic and thermophilic conditions in both continuous and batch conditions. Concerning the operation mode, the values of methane yield were significantly higher in batch experiment than in continuous reactors. According to the PCA, for both cases, the methane yield is positive correlated to the temperature rises. Interestingly, in the batch experiments, the higher the volatile solids in the substrate was, the lowest was the methane production, which is correlated to experimental flaws when setting up those tests. In continuous mode, unlike the batch test, the methane yield is strongly (positively) correlated to the organic content of the substrate. Experimental standardization, above all, in batch conditions are urgently necessary or move to continuous experiments for reporting results. The modeling can also be a source of disturbance in batch test. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

Ibrutinib versus previous standard of care: an adjusted comparison in patients with relapsed/refractory chronic lymphocytic leukaemia.

PubMed

Hansson, Lotta; Asklid, Anna; Diels, Joris; Eketorp-Sylvan, Sandra; Repits, Johanna; Søltoft, Frans; Jäger, Ulrich; Österborg, Anders

2017-10-01

This study explored the relative efficacy of ibrutinib versus previous standard-of-care treatments in relapsed/refractory patients with chronic lymphocytic leukaemia (CLL), using multivariate regression modelling to adjust for baseline prognostic factors. Individual patient data were collected from an observational Stockholm cohort of consecutive patients (n = 144) diagnosed with CLL between 2002 and 2013 who had received at least second-line treatment. Data were compared with results of the RESONATE clinical trial. A multivariate Cox proportional hazards regression model was used which estimated the hazard ratio (HR) of ibrutinib versus previous standard of care. The adjusted HR of ibrutinib versus the previous standard-of-care cohort was 0.15 (p < 0.0001) for progression-free survival (PFS) and 0.36 (p < 0.0001) for overall survival (OS). A similar difference was observed also when patients treated late in the period (2012-) were compared separately. Multivariate analysis showed that later line of therapy, male gender, older age and poor performance status were significant independent risk factors for worse PFS and OS. Our results suggest that PFS and OS with ibrutinib in the RESONATE study were significantly longer than with previous standard-of-care regimens used in second or later lines in routine healthcare. The approach used, which must be interpreted with caution, compares patient-level data from a clinical trial with outcomes observed in a daily clinical practice and may complement results from randomised trials or provide preliminary wider comparative information until phase 3 data exist.

Multivariate approaches for stability control of the olive oil reference materials for sensory analysis - part II: applications.

PubMed

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

2018-02-09

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

Gauging Skills of Hospital Security Personnel: a Statistically-driven, Questionnaire-based Approach.

PubMed

Rinkoo, Arvind Vashishta; Mishra, Shubhra; Rahesuddin; Nabi, Tauqeer; Chandra, Vidha; Chandra, Hem

2013-01-01

This study aims to gauge the technical and soft skills of the hospital security personnel so as to enable prioritization of their training needs. A cross sectional questionnaire based study was conducted in December 2011. Two separate predesigned and pretested questionnaires were used for gauging soft skills and technical skills of the security personnel. Extensive statistical analysis, including Multivariate Analysis (Pillai-Bartlett trace along with Multi-factorial ANOVA) and Post-hoc Tests (Bonferroni Test) was applied. The 143 participants performed better on the soft skills front with an average score of 6.43 and standard deviation of 1.40. The average technical skills score was 5.09 with a standard deviation of 1.44. The study avowed a need for formal hands on training with greater emphasis on technical skills. Multivariate analysis of the available data further helped in identifying 20 security personnel who should be prioritized for soft skills training and a group of 36 security personnel who should receive maximum attention during technical skills training. This statistically driven approach can be used as a prototype by healthcare delivery institutions worldwide, after situation specific customizations, to identify the training needs of any category of healthcare staff.

Gauging Skills of Hospital Security Personnel: a Statistically-driven, Questionnaire-based Approach

PubMed Central

Rinkoo, Arvind Vashishta; Mishra, Shubhra; Rahesuddin; Nabi, Tauqeer; Chandra, Vidha; Chandra, Hem

2013-01-01

Objectives This study aims to gauge the technical and soft skills of the hospital security personnel so as to enable prioritization of their training needs. Methodology A cross sectional questionnaire based study was conducted in December 2011. Two separate predesigned and pretested questionnaires were used for gauging soft skills and technical skills of the security personnel. Extensive statistical analysis, including Multivariate Analysis (Pillai-Bartlett trace along with Multi-factorial ANOVA) and Post-hoc Tests (Bonferroni Test) was applied. Results The 143 participants performed better on the soft skills front with an average score of 6.43 and standard deviation of 1.40. The average technical skills score was 5.09 with a standard deviation of 1.44. The study avowed a need for formal hands on training with greater emphasis on technical skills. Multivariate analysis of the available data further helped in identifying 20 security personnel who should be prioritized for soft skills training and a group of 36 security personnel who should receive maximum attention during technical skills training. Conclusion This statistically driven approach can be used as a prototype by healthcare delivery institutions worldwide, after situation specific customizations, to identify the training needs of any category of healthcare staff. PMID:23559904

Cohort comparisons: emotional well-being among adolescents and older adults

PubMed Central

Momtaz, Yadollah Abolfathi; Hamid, Tengku Aizan; Ibrahim, Rahimah

2014-01-01

Background There are several negative stereotypes about older adults that have negatively influenced people’s attitude about aging. The present study compared emotional well-being between older adults and adolescents. Methods Data for this study came from 1,403 community-dwelling elderly persons and 1,190 secondary school students and were obtained from two national cross-sectional surveys. Emotional well-being was measured using the World Health Organization-Five Well-Being Index. Data analysis was conducted using a multivariate analysis of covariance with SPSS software version 20 (IBM Corporation, Armonk, NY, USA). Results Elderly people significantly scored higher levels of emotional well-being (mean, 62.3; standard deviation, 22.55) than younger people (mean, 57.9; standard deviation, 18.46; t, 5.32; P≤0.001). The findings from the multivariate analysis of covariance revealed a significant difference between older adults and younger people in emotional well-being [F(3, 2587)=120.21; P≤0.001; η2=0.122] after controlling for sex. Conclusion Contrary to negative stereotypes about aging, our findings show a higher level of emotional well-being among older adults compared with younger people. PMID:24872683

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

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

Clegg, Samuel M; Barefield, James E; Wiens, Roger C

2008-01-01

Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from whichmore » unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.« less

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.

Assessment Practices of Child Clinicians.

PubMed

Cook, Jonathan R; Hausman, Estee M; Jensen-Doss, Amanda; Hawley, Kristin M

2017-03-01

Assessment is an integral component of treatment. However, prior surveys indicate clinicians may not use standardized assessment strategies. We surveyed 1,510 clinicians and used multivariate analysis of variance to explore group differences in specific measure use. Clinicians used unstandardized measures more frequently than standardized measures, although psychologists used standardized measures more frequently than nonpsychologists. We also used latent profile analysis to classify clinicians based on their overall approach to assessment and examined associations between clinician-level variables and assessment class or profile membership. A four-profile model best fit the data. The largest profile consisted of clinicians who primarily used unstandardized assessments (76.7%), followed by broad-spectrum assessors who regularly use both standardized and unstandardized assessment (11.9%), and two smaller profiles of minimal (6.0%) and selective assessors (5.5%). Compared with broad-spectrum assessors, unstandardized and minimal assessors were less likely to report having adequate standardized measures training. Implications for clinical practice and training are discussed.

Quality assessment of pharmaceutical tablet samples using Fourier transform near infrared spectroscopy and multivariate analysis

NASA Astrophysics Data System (ADS)

Kandpal, Lalit Mohan; Tewari, Jagdish; Gopinathan, Nishanth; Stolee, Jessica; Strong, Rick; Boulas, Pierre; Cho, Byoung-Kwan

2017-09-01

Determination of the content uniformity, assessed by the amount of an active pharmaceutical ingredient (API), and hardness of pharmaceutical materials is important for achieving a high-quality formulation and to ensure the intended therapeutic effects of the end-product. In this work, Fourier transform near infrared (FT-NIR) spectroscopy was used to determine the content uniformity and hardness of a pharmaceutical mini-tablet and standard tablet samples. Tablet samples were scanned using an FT-NIR instrument and tablet spectra were collected at wavelengths of 1000-2500 nm. Furthermore, multivariate analysis was applied to extract the relationship between the FT-NIR spectra and the measured parameters. The results of FT-NIR spectroscopy for API and hardness prediction were as precise as the reference high-performance liquid chromatography and mechanical hardness tests. For the prediction of mini-tablet API content, the highest coefficient of determination for the prediction (R2p) was found to be 0.99 with a standard error of prediction (SEP) of 0.72 mg. Moreover, the standard tablet hardness measurement had a R2p value of 0.91 with an SEP of 0.25 kg. These results suggest that FT-NIR spectroscopy is an alternative and accurate nondestructive measurement tool for the detection of the chemical and physical properties of pharmaceutical samples.

Chemometrics resolution and quantification power evaluation: Application on pharmaceutical quaternary mixture of Paracetamol, Guaifenesin, Phenylephrine and p-aminophenol

NASA Astrophysics Data System (ADS)

Yehia, Ali M.; Mohamed, Heba M.

2016-01-01

Three advanced chemmometric-assisted spectrophotometric methods namely; Concentration Residuals Augmented Classical Least Squares (CRACLS), Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Principal Component Analysis-Artificial Neural Networks (PCA-ANN) were developed, validated and benchmarked to PLS calibration; to resolve the severely overlapped spectra and simultaneously determine; Paracetamol (PAR), Guaifenesin (GUA) and Phenylephrine (PHE) in their ternary mixture and in presence of p-aminophenol (AP) the main degradation product and synthesis impurity of Paracetamol. The analytical performance of the proposed methods was described by percentage recoveries, root mean square error of calibration and standard error of prediction. The four multivariate calibration methods could be directly used without any preliminary separation step and successfully applied for pharmaceutical formulation analysis, showing no excipients' interference.

Ordinary chondrites - Multivariate statistical analysis of trace element contents

NASA Technical Reports Server (NTRS)

Lipschutz, Michael E.; Samuels, Stephen M.

1991-01-01

The contents of mobile trace elements (Co, Au, Sb, Ga, Se, Rb, Cs, Te, Bi, Ag, In, Tl, Zn, and Cd) in Antarctic and non-Antarctic populations of H4-6 and L4-6 chondrites, were compared using standard multivariate discriminant functions borrowed from linear discriminant analysis and logistic regression. A nonstandard randomization-simulation method was developed, making it possible to carry out probability assignments on a distribution-free basis. Compositional differences were found both between the Antarctic and non-Antarctic H4-6 chondrite populations and between two L4-6 chondrite populations. It is shown that, for various types of meteorites (in particular, for the H4-6 chondrites), the Antarctic/non-Antarctic compositional difference is due to preterrestrial differences in the genesis of their parent materials.

Virtual quantification of metabolites by capillary electrophoresis-electrospray ionization-mass spectrometry: predicting ionization efficiency without chemical standards.

PubMed

Chalcraft, Kenneth R; Lee, Richard; Mills, Casandra; Britz-McKibbin, Philip

2009-04-01

A major obstacle in metabolomics remains the identification and quantification of a large fraction of unknown metabolites in complex biological samples when purified standards are unavailable. Herein we introduce a multivariate strategy for de novo quantification of cationic/zwitterionic metabolites using capillary electrophoresis-electrospray ionization-mass spectrometry (CE-ESI-MS) based on fundamental molecular, thermodynamic, and electrokinetic properties of an ion. Multivariate calibration was used to derive a quantitative relationship between the measured relative response factor (RRF) of polar metabolites with respect to four physicochemical properties associated with ion evaporation in ESI-MS, namely, molecular volume (MV), octanol-water distribution coefficient (log D), absolute mobility (mu(o)), and effective charge (z(eff)). Our studies revealed that a limited set of intrinsic solute properties can be used to predict the RRF of various classes of metabolites (e.g., amino acids, amines, peptides, acylcarnitines, nucleosides, etc.) with reasonable accuracy and robustness provided that an appropriate training set is validated and ion responses are normalized to an internal standard(s). The applicability of the multivariate model to quantify micromolar levels of metabolites spiked in red blood cell (RBC) lysates was also examined by CE-ESI-MS without significant matrix effects caused by involatile salts and/or major co-ion interferences. This work demonstrates the feasibility for virtual quantification of low-abundance metabolites and their isomers in real-world samples using physicochemical properties estimated by computer modeling, while providing deeper insight into the wide disparity of solute responses in ESI-MS. New strategies for predicting ionization efficiency in silico allow for rapid and semiquantitative analysis of newly discovered biomarkers and/or drug metabolites in metabolomics research when chemical standards do not exist.

Using multivariate generalizability theory to assess the effect of content stratification on the reliability of a performance assessment.

PubMed

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

2010-12-01

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

Anxiety after completion of treatment for early-stage breast cancer: a systematic review to identify candidate predictors and evaluate multivariable model development.

PubMed

Harris, Jenny; Cornelius, Victoria; Ream, Emma; Cheevers, Katy; Armes, Jo

2017-07-01

The purpose of this review was to identify potential candidate predictors of anxiety in women with early-stage breast cancer (BC) after adjuvant treatments and evaluate methodological development of existing multivariable models to inform the future development of a predictive risk stratification model (PRSM). Databases (MEDLINE, Web of Science, CINAHL, CENTRAL and PsycINFO) were searched from inception to November 2015. Eligible studies were prospective, recruited women with stage 0-3 BC, used a validated anxiety outcome ≥3 months post-treatment completion and used multivariable prediction models. Internationally accepted quality standards were used to assess predictive risk of bias and strength of evidence. Seven studies were identified: five were observational cohorts and two secondary analyses of RCTs. Variability of measurement and selective reporting precluded meta-analysis. Twenty-one candidate predictors were identified in total. Younger age and previous mental health problems were identified as risk factors in ≥3 studies. Clinical variables (e.g. treatment, tumour grade) were not identified as predictors in any studies. No studies adhered to all quality standards. Pre-existing vulnerability to mental health problems and younger age increased the risk of anxiety after completion of treatment for BC survivors, but there was no evidence that chemotherapy was a predictor. Multiple predictors were identified but many lacked reproducibility or were not measured across studies, and inadequate reporting did not allow full evaluation of the multivariable models. The use of quality standards in the development of PRSM within supportive cancer care would improve model quality and performance, thereby allowing professionals to better target support for patients.

Time Series Model Identification by Estimating Information, Memory, and Quantiles.

DTIC Science & Technology

1983-07-01

Standards, Sect. D, 68D, 937-951. Parzen, Emanuel (1969) "Multiple time series modeling" Multivariate Analysis - II, edited by P. Krishnaiah , Academic... Krishnaiah , North Holland: Amsterdam, 283-295. Parzen, Emanuel (1979) "Forecasting and Whitening Filter Estimation" TIMS Studies in the Management...principle. Applications of Statistics, P. R. Krishnaiah , ed. North Holland: Amsterdam, 27-41. Box, G. E. P. and Jenkins, G. M. (1970) Time Series Analysis

FGWAS: Functional genome wide association analysis.

PubMed

Huang, Chao; Thompson, Paul; Wang, Yalin; Yu, Yang; Zhang, Jingwen; Kong, Dehan; Colen, Rivka R; Knickmeyer, Rebecca C; Zhu, Hongtu

2017-10-01

Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs. Copyright © 2017 Elsevier Inc. All rights reserved.

Determining the Number of Component Clusters in the Standard Multivariate Normal Mixture Model Using Model-Selection Criteria.

DTIC Science & Technology

1983-06-16

has been advocated by Gnanadesikan and 𔃾ilk (1969), and others in the literature. This suggests that, if we use the formal signficance test type...American Statistical Asso., 62, 1159-1178. Gnanadesikan , R., and Wilk, M..B. (1969). Data Analytic Methods in Multi- variate Statistical Analysis. In

Composting of cow dung and crop residues using termite mounds as bulking agent.

PubMed

Karak, Tanmoy; Sonar, Indira; Paul, Ranjit K; Das, Sampa; Boruah, R K; Dutta, Amrit K; Das, Dilip K

2014-10-01

The present study reports the suitability of termite mounds as a bulking agent for composting with crop residues and cow dung in pit method. Use of 50 kg termite mound with the crop residues (stover of ground nut: 361.65 kg; soybean: 354.59 kg; potato: 357.67 kg and mustard: 373.19 kg) and cow dung (84.90 kg) formed a good quality compost within 70 days of composting having nitrogen, phosphorus and potassium as 20.19, 3.78 and 32.77 g kg(-1) respectively with a bulk density of 0.85 g cm(-3). Other physico-chemical and germination parameters of the compost were within Indian standard, which had been confirmed by the application of multivariate analysis of variance and multivariate contrast analysis. Principal component analysis was applied in order to gain insight into the characteristic variables. Four composting treatments formed two different groups when hierarchical cluster analysis was applied. Copyright © 2014 Elsevier Ltd. All rights reserved.

SPReM: Sparse Projection Regression Model For High-dimensional Linear Regression *

PubMed Central

Sun, Qiang; Zhu, Hongtu; Liu, Yufeng; Ibrahim, Joseph G.

2014-01-01

The aim of this paper is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low statistical power issue of many standard statistical approaches, such as the Hotelling’s T2 test statistic or a mass univariate analysis, for high-dimensional data. We formulate the estimation problem of SPREM as a novel sparse unit rank projection (SURP) problem and propose a fast optimization algorithm for SURP. Furthermore, we extend SURP to the sparse multi-rank projection (SMURP) by adopting a sequential SURP approximation. Theoretically, we have systematically investigated the convergence properties of SURP and the convergence rate of SURP estimates. Our simulation results and real data analysis have shown that SPReM out-performs other state-of-the-art methods. PMID:26527844

Chemometrics resolution and quantification power evaluation: Application on pharmaceutical quaternary mixture of Paracetamol, Guaifenesin, Phenylephrine and p-aminophenol.

PubMed

Yehia, Ali M; Mohamed, Heba M

2016-01-05

Three advanced chemmometric-assisted spectrophotometric methods namely; Concentration Residuals Augmented Classical Least Squares (CRACLS), Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Principal Component Analysis-Artificial Neural Networks (PCA-ANN) were developed, validated and benchmarked to PLS calibration; to resolve the severely overlapped spectra and simultaneously determine; Paracetamol (PAR), Guaifenesin (GUA) and Phenylephrine (PHE) in their ternary mixture and in presence of p-aminophenol (AP) the main degradation product and synthesis impurity of Paracetamol. The analytical performance of the proposed methods was described by percentage recoveries, root mean square error of calibration and standard error of prediction. The four multivariate calibration methods could be directly used without any preliminary separation step and successfully applied for pharmaceutical formulation analysis, showing no excipients' interference. Copyright © 2015 Elsevier B.V. All rights reserved.

Measures of precision for dissimilarity-based multivariate analysis of ecological communities

PubMed Central

Anderson, Marti J; Santana-Garcon, Julia

2015-01-01

Ecological studies require key decisions regarding the appropriate size and number of sampling units. No methods currently exist to measure precision for multivariate assemblage data when dissimilarity-based analyses are intended to follow. Here, we propose a pseudo multivariate dissimilarity-based standard error (MultSE) as a useful quantity for assessing sample-size adequacy in studies of ecological communities. Based on sums of squared dissimilarities, MultSE measures variability in the position of the centroid in the space of a chosen dissimilarity measure under repeated sampling for a given sample size. We describe a novel double resampling method to quantify uncertainty in MultSE values with increasing sample size. For more complex designs, values of MultSE can be calculated from the pseudo residual mean square of a permanova model, with the double resampling done within appropriate cells in the design. R code functions for implementing these techniques, along with ecological examples, are provided. PMID:25438826

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.

Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach.

PubMed

Lie, Octavian V; van Mierlo, Pieter

2017-01-01

The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (<60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.

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

Modeling Multi-Variate Gaussian Distributions and Analysis of Higgs Boson Couplings with the ATLAS Detector

NASA Astrophysics Data System (ADS)

Krohn, Olivia; Armbruster, Aaron; Gao, Yongsheng; Atlas Collaboration

2017-01-01

Software tools developed for the purpose of modeling CERN LHC pp collision data to aid in its interpretation are presented. Some measurements are not adequately described by a Gaussian distribution; thus an interpretation assuming Gaussian uncertainties will inevitably introduce bias, necessitating analytical tools to recreate and evaluate non-Gaussian features. One example is the measurements of Higgs boson production rates in different decay channels, and the interpretation of these measurements. The ratios of data to Standard Model expectations (μ) for five arbitrary signals were modeled by building five Poisson distributions with mixed signal contributions such that the measured values of μ are correlated. Algorithms were designed to recreate probability distribution functions of μ as multi-variate Gaussians, where the standard deviation (σ) and correlation coefficients (ρ) are parametrized. There was good success with modeling 1-D likelihood contours of μ, and the multi-dimensional distributions were well modeled within 1- σ but the model began to diverge after 2- σ due to unmerited assumptions in developing ρ. Future plans to improve the algorithms and develop a user-friendly analysis package will also be discussed. NSF International Research Experiences for Students

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

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

PubMed Central

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

2016-01-01

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

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

PubMed

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

2016-01-01

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

Cancer Treatment in Patients With HIV Infection and Non-AIDS-Defining Cancers: A Survey of US Oncologists.

PubMed

Suneja, Gita; Boyer, Matthew; Yehia, Baligh R; Shiels, Meredith S; Engels, Eric A; Bekelman, Justin E; Long, Judith A

2015-05-01

HIV-infected individuals with non-AIDS-defining cancers are less likely to receive cancer treatment compared with uninfected individuals. We sought to identify provider-level factors influencing the delivery of oncology care to HIV-infected patients. A survey was mailed to 500 randomly selected US medical and radiation oncologists. The primary outcome was delivery of standard treatment, assessed by responses to three specialty-specific management questions. We used the χ(2) test to evaluate associations between delivery of standard treatment, provider demographics, and perceptions of HIV-infected individuals. Multivariable logistic regression identified associations using factor analysis to combine several correlated survey questions. Our response rate was 60%; 69% of respondents felt that available cancer management guidelines were insufficient for the care of HIV-infected patients with cancer; 45% never or rarely discussed their cancer management plan with an HIV specialist; 20% and 15% of providers were not comfortable discussing cancer treatment adverse effects and prognosis with their HIV-infected patients with cancer, respectively; 79% indicated that they would provide standard cancer treatment to HIV-infected patients. In multivariable analysis, physicians comfortable discussing adverse effects and prognosis were more likely to provide standard cancer treatment (adjusted odds ratio, 1.52; 95% CI, 1.12 to 2.07). Physicians with concerns about toxicity and efficacy of treatment were significantly less likely to provide standard cancer treatment (adjusted odds ratio, 0.67; 95% CI, 0.53 to 0.85). Provider-level factors are associated with delivery of nonstandard cancer treatment to HIV-infected patients. Policy change, provider education, and multidisciplinary collaboration are needed to improve access to cancer treatment. Copyright © 2015 by American Society of Clinical Oncology.

A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series

ERIC Educational Resources Information Center

Zhang, Guangjian; Chow, Sy-Miin; Ong, Anthony D.

2011-01-01

Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a…

Quality of care received and patient-reported regret in prostate cancer: Analysis of a population-based prospective cohort.

PubMed

Holmes, Jordan A; Bensen, Jeannette T; Mohler, James L; Song, Lixin; Mishel, Merle H; Chen, Ronald C

2017-01-01

Meeting quality of care standards in oncology is recognized as important by physicians, professional organizations, and payers. Data from a population-based cohort of patients with prostate cancer were used to examine whether receipt of care was consistent with published consensus metrics and whether receiving high-quality care was associated with less patient-reported treatment decisional regret. Patients with incident prostate cancer were enrolled in collaboration with the North Carolina Central Cancer Registry, with an oversampling of minority patients. Medical record abstraction was used to determine whether participants received high-quality care based on 5 standards: 1) discussion of all treatment options; 2) complete workup (prostate-specific antigen, Gleason grade, and clinical stage); 3) low-risk participants did not undergo a bone scan; 4) high-risk participants treated with radiotherapy (RT) received androgen deprivation therapy; and 5) participants treated with RT received conformal or intensity-modulated RT. Treatment decisional regret was assessed using a validated instrument. A total of 804 participants were analyzed. Overall, 66% of African American and 73% of white participants received care that met all standards (P = .03); this racial difference was confirmed by multivariable analysis. Care that included "discussion of all treatment options" was found to be associated with less patient-reported regret on univariable analysis (P = .03) and multivariable analysis (odds ratio, 0.59; 95% confidence interval, 0.37-0.95). The majority of participants received high-quality care, but racial disparity existed. Participants who discussed all treatment options appeared to have less treatment decisional regret. To the authors' knowledge, this is the first study to demonstrate an association between a quality of care metric and patient-reported outcome. Cancer 2017;138-143. © 2016 American Cancer Society. © 2016 American Cancer Society.

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

PubMed Central

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

2016-01-01

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community. PMID:26848405

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

PubMed

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

2015-11-01

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

A Hybrid Index for Characterizing Drought Based on a Nonparametric Kernel Estimator

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

Huang, Shengzhi; Huang, Qiang; Leng, Guoyong

This study develops a nonparametric multivariate drought index, namely, the Nonparametric Multivariate Standardized Drought Index (NMSDI), by considering the variations of both precipitation and streamflow. Building upon previous efforts in constructing Nonparametric Multivariate Drought Index, we use the nonparametric kernel estimator to derive the joint distribution of precipitation and streamflow, thus providing additional insights in drought index development. The proposed NMSDI are applied in the Wei River Basin (WRB), based on which the drought evolution characteristics are investigated. Results indicate: (1) generally, NMSDI captures the drought onset similar to Standardized Precipitation Index (SPI) and drought termination and persistence similar tomore » Standardized Streamflow Index (SSFI). The drought events identified by NMSDI match well with historical drought records in the WRB. The performances are also consistent with that by an existing Multivariate Standardized Drought Index (MSDI) at various timescales, confirming the validity of the newly constructed NMSDI in drought detections (2) An increasing risk of drought has been detected for the past decades, and will be persistent to a certain extent in future in most areas of the WRB; (3) the identified change points of annual NMSDI are mainly concentrated in the early 1970s and middle 1990s, coincident with extensive water use and soil reservation practices. This study highlights the nonparametric multivariable drought index, which can be used for drought detections and predictions efficiently and comprehensively.« less

High-throughput investigation of single and binary protein adsorption isotherms in anion exchange chromatography employing multivariate analysis.

PubMed

Field, Nicholas; Konstantinidis, Spyridon; Velayudhan, Ajoy

2017-08-11

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

Combination of multivariate curve resolution and multivariate classification techniques for comprehensive high-performance liquid chromatography-diode array absorbance detection fingerprints analysis of Salvia reuterana extracts.

PubMed

Hakimzadeh, Neda; Parastar, Hadi; Fattahi, Mohammad

2014-01-24

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

Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

PubMed

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P; McDonald-Maier, Klaus D

2015-05-08

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.

Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition

PubMed Central

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P.; McDonald-Maier, Klaus D.

2015-01-01

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences. PMID:26007714

Improving the accuracy of hyaluronic acid molecular weight estimation by conventional size exclusion chromatography.

PubMed

Shanmuga Doss, Sreeja; Bhatt, Nirav Pravinbhai; Jayaraman, Guhan

2017-08-15

There is an unreasonably high variation in the literature reports on molecular weight of hyaluronic acid (HA) estimated using conventional size exclusion chromatography (SEC). This variation is most likely due to errors in estimation. Working with commercially available HA molecular weight standards, this work examines the extent of error in molecular weight estimation due to two factors: use of non-HA based calibration and concentration of sample injected into the SEC column. We develop a multivariate regression correlation to correct for concentration effect. Our analysis showed that, SEC calibration based on non-HA standards like polyethylene oxide and pullulan led to approximately 2 and 10 times overestimation, respectively, when compared to HA-based calibration. Further, we found that injected sample concentration has an effect on molecular weight estimation. Even at 1g/l injected sample concentration, HA molecular weight standards of 0.7 and 1.64MDa showed appreciable underestimation of 11-24%. The multivariate correlation developed was found to reduce error in estimations at 1g/l to <4%. The correlation was also successfully applied to accurately estimate the molecular weight of HA produced by a recombinant Lactococcus lactis fermentation. Copyright © 2017 Elsevier B.V. All rights reserved.

Multivariate pattern analysis reveals subtle brain anomalies relevant to the cognitive phenotype in neurofibromatosis type 1.

PubMed

Duarte, João V; Ribeiro, Maria J; Violante, Inês R; Cunha, Gil; Silva, Eduardo; Castelo-Branco, Miguel

2014-01-01

Neurofibromatosis Type 1 (NF1) is a common genetic condition associated with cognitive dysfunction. However, the pathophysiology of the NF1 cognitive deficits is not well understood. Abnormal brain structure, including increased total brain volume, white matter (WM) and grey matter (GM) abnormalities have been reported in the NF1 brain. These previous studies employed univariate model-driven methods preventing detection of subtle and spatially distributed differences in brain anatomy. Multivariate pattern analysis allows the combination of information from multiple spatial locations yielding a discriminative power beyond that of single voxels. Here we investigated for the first time subtle anomalies in the NF1 brain, using a multivariate data-driven classification approach. We used support vector machines (SVM) to classify whole-brain GM and WM segments of structural T1 -weighted MRI scans from 39 participants with NF1 and 60 non-affected individuals, divided in children/adolescents and adults groups. We also employed voxel-based morphometry (VBM) as a univariate gold standard to study brain structural differences. SVM classifiers correctly classified 94% of cases (sensitivity 92%; specificity 96%) revealing the existence of brain structural anomalies that discriminate NF1 individuals from controls. Accordingly, VBM analysis revealed structural differences in agreement with the SVM weight maps representing the most relevant brain regions for group discrimination. These included the hippocampus, basal ganglia, thalamus, and visual cortex. This multivariate data-driven analysis thus identified subtle anomalies in brain structure in the absence of visible pathology. Our results provide further insight into the neuroanatomical correlates of known features of the cognitive phenotype of NF1. Copyright © 2012 Wiley Periodicals, Inc.

Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis.

PubMed

Cohen, Mitchell J; Grossman, Adam D; Morabito, Diane; Knudson, M Margaret; Butte, Atul J; Manley, Geoffrey T

2010-01-01

Advances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome. Multivariate physiologic and ventilator data were collected continuously using a multimodal bioinformatics system in the surgical ICU at San Francisco General Hospital. These data were incorporated with non-continuous data and stored on a server in the ICU. A hierarchical clustering algorithm grouped each minute of data into 1 of 10 clusters. Clusters were correlated with outcome measures including incidence of infection, multiple organ failure (MOF), and mortality. We identified 10 clusters, which we defined as distinct patient states. While patients transitioned between states, they spent significant amounts of time in each. Clusters were enriched for our outcome measures: 2 of the 10 states were enriched for infection, 6 of 10 were enriched for MOF, and 3 of 10 were enriched for death. Further analysis of correlations between pairs of variables within each cluster reveals significant differences in physiology between clusters. Here we show for the first time the feasibility of clustering physiological measurements to identify clinically relevant patient states after trauma. These results demonstrate that hierarchical clustering techniques can be useful for visualizing complex multivariate data and may provide new insights for the care of critically injured patients.

Search for a standard model Higgs boson in WH --> lvbb in pp collisions at square root s = 1.96 TeV.

PubMed

Aaltonen, T; Adelman, J; Akimoto, T; Alvarez González, B; Amerio, S; Amidei, D; Anastassov, A; Annovi, A; Antos, J; Apollinari, G; Apresyan, A; Arisawa, T; Artikov, A; Ashmanskas, W; Attal, A; Aurisano, A; Azfar, F; Azzurri, P; Badgett, W; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Bartsch, V; Bauer, G; Beauchemin, P-H; Bedeschi, F; Beecher, D; Behari, S; Bellettini, G; Bellinger, J; Benjamin, D; Beretvas, A; Beringer, J; Bhatti, A; Binkley, M; Bisello, D; Bizjak, I; Blair, R E; Blocker, C; Blumenfeld, B; Bocci, A; Bodek, A; Boisvert, V; Bolla, G; Bortoletto, D; Boudreau, J; Boveia, A; Brau, B; Bridgeman, A; Brigliadori, L; Bromberg, C; Brubaker, E; Budagov, J; Budd, H S; Budd, S; Burke, S; Burkett, K; Busetto, G; Bussey, P; Buzatu, A; Byrum, K L; Cabrera, S; Calancha, C; Campanelli, M; Campbell, M; Canelli, F; Canepa, A; Carls, B; Carlsmith, D; Carosi, R; Carrillo, S; Carron, S; Casal, B; Casarsa, M; Castro, A; Catastini, P; Cauz, D; Cavaliere, V; Cavalli-Sforza, M; Cerri, A; Cerrito, L; Chang, S H; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Chlebana, F; Cho, K; Chokheli, D; Chou, J P; Choudalakis, G; Chuang, S H; Chung, K; Chung, W H; Chung, Y S; Chwalek, T; Ciobanu, C I; Ciocci, M A; Clark, A; Clark, D; Compostella, G; Convery, M E; Conway, J; Cordelli, M; Cortiana, G; Cox, C A; Cox, D J; Crescioli, F; Cuenca Almenar, C; Cuevas, J; Culbertson, R; Cully, J C; Dagenhart, D; Datta, M; Davies, T; de Barbaro, P; De Cecco, S; Deisher, A; De Lorenzo, G; Dell'orso, M; Deluca, C; Demortier, L; Deng, J; Deninno, M; Derwent, P F; di Giovanni, G P; Dionisi, C; Di Ruzza, B; Dittmann, J R; D'Onofrio, M; Donati, S; Dong, P; Donini, J; Dorigo, T; Dube, S; Efron, J; Elagin, A; Erbacher, R; Errede, D; Errede, S; Eusebi, R; Fang, H C; Farrington, S; Fedorko, W T; Feild, R G; Feindt, M; Fernandez, J P; Ferrazza, C; Field, R; Flanagan, G; Forrest, R; Frank, M J; Franklin, M; Freeman, J C; Furic, I; Gallinaro, M; Galyardt, J; Garberson, F; Garcia, J E; Garfinkel, A F; Genser, K; Gerberich, H; Gerdes, D; Gessler, A; Giagu, S; Giakoumopoulou, V; Giannetti, P; Gibson, K; Gimmell, J L; Ginsburg, C M; Giokaris, N; Giordani, M; Giromini, P; Giunta, M; Giurgiu, G; Glagolev, V; Glenzinski, D; Gold, M; Goldschmidt, N; Golossanov, A; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González, O; Gorelov, I; Goshaw, A T; Goulianos, K; Gresele, A; Grinstein, S; Grosso-Pilcher, C; Grundler, U; Guimaraes da Costa, J; Gunay-Unalan, Z; Haber, C; Hahn, K; Hahn, S R; Halkiadakis, E; Han, B-Y; Han, J Y; Happacher, F; Hara, K; Hare, D; Hare, M; Harper, S; Harr, R F; Harris, R M; Hartz, M; Hatakeyama, K; Hays, C; Heck, M; Heijboer, A; Heinrich, J; Henderson, C; Herndon, M; Heuser, J; Hewamanage, S; Hidas, D; Hill, C S; Hirschbuehl, D; Hocker, A; Hou, S; Houlden, M; Hsu, S-C; Huffman, B T; Hughes, R E; Husemann, U; Hussein, M; Husemann, U; Huston, J; Incandela, J; Introzzi, G; Iori, M; Ivanov, A; James, E; Jayatilaka, B; Jeon, E J; Jha, M K; Jindariani, S; Johnson, W; Jones, M; Joo, K K; Jun, S Y; Jung, J E; Junk, T R; Kamon, T; Kar, D; Karchin, P E; Kato, Y; Kephart, R; Keung, J; Khotilovich, V; Kilminster, B; Kim, D H; Kim, H S; Kim, H W; Kim, J E; Kim, M J; Kim, S B; Kim, S H; Kim, Y K; Kimura, N; Kirsch, L; Klimenko, S; Knuteson, B; Ko, B R; Kondo, K; Kong, D J; Konigsberg, J; Korytov, A; Kotwal, A V; Kreps, M; Kroll, J; Krop, D; Krumnack, N; Kruse, M; Krutelyov, V; Kubo, T; Kuhr, T; Kulkarni, N P; Kurata, M; Kwang, S; Laasanen, A T; Lami, S; Lammel, S; Lancaster, M; Lander, R L; Lannon, K; Lath, A; Latino, G; Lazzizzera, I; Lecompte, T; Lee, E; Lee, H S; Lee, S W; Leone, S; Lewis, J D; Lin, C-S; Linacre, J; Lindgren, M; Lipeles, E; Lister, A; Litvintsev, D O; Liu, C; Liu, T; Lockyer, N S; Loginov, A; Loreti, M; Lovas, L; Lucchesi, D; Luci, C; Lueck, J; Lujan, P; Lukens, P; Lungu, G; Lyons, L; Lys, J; Lysak, R; Macqueen, D; Madrak, R; Maeshima, K; Makhoul, K; Maki, T; Maksimovic, P; Malde, S; Malik, S; Manca, G; Manousakis-Katsikakis, A; Margaroli, F; Marino, C; Marino, C P; Martin, A; Martin, V; Martínez, M; Martínez-Ballarín, R; Maruyama, T; Mastrandrea, P; Masubuchi, T; Mathis, M; Mattson, M E; Mazzanti, P; McFarland, K S; McIntyre, P; McNulty, R; Mehta, A; Mehtala, P; Menzione, A; Merkel, P; Mesropian, C; Miao, T; Miladinovic, N; Miller, R; Mills, C; Milnik, M; Mitra, A; Mitselmakher, G; Miyake, H; Moggi, N; Moon, C S; Moore, R; Morello, M J; Morlock, J; Movilla Fernandez, P; Mülmenstädt, J; Mukherjee, A; Muller, Th; Mumford, R; Murat, P; Mussini, M; Nachtman, J; Nagai, Y; Nagano, A; Naganoma, J; Nakamura, K; Nakano, I; Napier, A; Necula, V; Nett, J; Neu, C; Neubauer, M S; Neubauer, S; Nielsen, J; Nodulman, L; Norman, M; Norniella, O; Nurse, E; Oakes, L; Oh, S H; Oh, Y D; Oksuzian, I; Okusawa, T; Orava, R; Osterberg, K; Pagan Griso, S; Palencia, E; Papadimitriou, V; Papaikonomou, A; Paramonov, A A; Parks, B; Pashapour, S; Patrick, J; Pauletta, G; Paulini, M; Paus, C; Peiffer, T; Pellett, D E; Penzo, A; Phillips, T J; Piacentino, G; Pianori, E; Pinera, L; Pitts, K; Plager, C; Pondrom, L; Poukhov, O; Pounder, N; Prakoshyn, F; Pronko, A; Proudfoot, J; Ptohos, F; Pueschel, E; Punzi, G; Pursley, J; Rademacker, J; Rahaman, A; Ramakrishnan, V; Ranjan, N; Redondo, I; Renton, P; Renz, M; Rescigno, M; Richter, S; Rimondi, F; Ristori, L; Robson, A; Rodrigo, T; Rodriguez, T; Rogers, E; Rolli, S; Roser, R; Rossi, M; Rossin, R; Roy, P; Ruiz, A; Russ, J; Rusu, V; Saarikko, H; Safonov, A; Sakumoto, W K; Saltó, O; Santi, L; Sarkar, S; Sartori, L; Sato, K; Savoy-Navarro, A; Schlabach, P; Schmidt, A; Schmidt, E E; Schmidt, M A; Schmidt, M P; Schmitt, M; Schwarz, T; Scodellaro, L; Scribano, A; Scuri, F; Sedov, A; Seidel, S; Seiya, Y; Semenov, A; Sexton-Kennedy, L; Sforza, F; Sfyrla, A; Shalhout, S Z; Shears, T; Shepard, P F; Shimojima, M; Shiraishi, S; Shochet, M; Shon, Y; Shreyber, I; Sidoti, A; Sinervo, P; Sisakyan, A; Slaughter, A J; Slaunwhite, J; Sliwa, K; Smith, J R; Snider, F D; Snihur, R; Soha, A; Somalwar, S; Sorin, V; Spalding, J; Spreitzer, T; Squillacioti, P; Stanitzki, M; St Denis, R; Stelzer, B; Stelzer-Chilton, O; Stentz, D; Strologas, J; Strycker, G L; Stuart, D; Suh, J S; Sukhanov, A; Suslov, I; Suzuki, T; Taffard, A; Takashima, R; Takeuchi, Y; Tanaka, R; Tecchio, M; Teng, P K; Terashi, K; Thom, J; Thompson, A S; Thompson, G A; Thomson, E; Tipton, P; Ttito-Guzmán, P; Tkaczyk, S; Toback, D; Tokar, S; Tollefson, K; Tomura, T; Tonelli, D; Torre, S; Torretta, D; Totaro, P; Tourneur, S; Trovato, M; Tsai, S-Y; Tu, Y; Turini, N; Ukegawa, F; Vallecorsa, S; van Remortel, N; Varganov, A; Vataga, E; Vázquez, F; Velev, G; Vellidis, C; Vidal, M; Vidal, R; Vila, I; Vilar, R; Vine, T; Vogel, M; Volobouev, I; Volpi, G; Wagner, P; Wagner, R G; Wagner, R L; Wagner, W; Wagner-Kuhr, J; Wakisaka, T; Wallny, R; Wang, S M; Warburton, A; Waters, D; Weinberger, M; Weinelt, J; Wester, W C; Whitehouse, B; Whiteson, D; Wicklund, A B; Wicklund, E; Wilbur, S; Williams, G; Williams, H H; Wilson, P; Winer, B L; Wittich, P; Wolbers, S; Wolfe, C; Wright, T; Wu, X; Würthwein, F; Xie, S; Yagil, A; Yamamoto, K; Yamaoka, J; Yang, U K; Yang, Y C; Yao, W M; Yeh, G P; Yoh, J; Yorita, K; Yoshida, T; Yu, G B; Yu, I; Yu, S S; Yun, J C; Zanello, L; Zanetti, A; Zhang, X; Zheng, Y; Zucchelli, S

2009-09-04

We present a search for a standard model Higgs boson produced in association with a W boson using 2.7 fb(-1) of integrated luminosity of pp collision data taken at square root s = 1.96 TeV. Limits on the Higgs boson production rate are obtained for masses between 100 and 150 GeV/c(2). Through the use of multivariate techniques, the analysis achieves an observed (expected) 95% confidence level upper limit of 5.6 (4.8) times the theoretically expected production cross section for a standard model Higgs boson with a mass of 115 GeV/c(2).

The Impact of Female Schooling on Fertility and Contraceptive Use: A Study of Fourteen Sub-Saharan Countries. Living Standards Measurement Study Working Paper No. 110.

ERIC Educational Resources Information Center

Ainsworth, Martha; And Others

This paper examines the relationship between female schooling and two behaviors--cumulative fertility and contraceptive use--in 14 Sub-Saharan African countries where Demographic and Health Surveys (DHS) have been conducted since the mid-1980s. Using multivariate regression analysis, the paper compares the effect of schooling across countries, in…

Early PET/CT scan is more effective than RECIST in predicting outcome of patients with liver metastases from colorectal cancer treated with preoperative chemotherapy plus bevacizumab.

PubMed

Lastoria, Secondo; Piccirillo, Maria Carmela; Caracò, Corradina; Nasti, Guglielmo; Aloj, Luigi; Arrichiello, Cecilia; de Lutio di Castelguidone, Elisabetta; Tatangelo, Fabiana; Ottaiano, Alessandro; Iaffaioli, Rosario Vincenzo; Izzo, Francesco; Romano, Giovanni; Giordano, Pasqualina; Signoriello, Simona; Gallo, Ciro; Perrone, Francesco

2013-12-01

Markers predictive of treatment effect might be useful to improve the treatment of patients with metastatic solid tumors. Particularly, early changes in tumor metabolism measured by PET/CT with (18)F-FDG could predict the efficacy of treatment better than standard dimensional Response Evaluation Criteria In Solid Tumors (RECIST) response. We performed PET/CT evaluation before and after 1 cycle of treatment in patients with resectable liver metastases from colorectal cancer, within a phase 2 trial of preoperative FOLFIRI plus bevacizumab. For each lesion, the maximum standardized uptake value (SUV) and the total lesion glycolysis (TLG) were determined. On the basis of previous studies, a ≤ -50% change from baseline was used as a threshold for significant metabolic response for maximum SUV and, exploratively, for TLG. Standard RECIST response was assessed with CT after 3 mo of treatment. Pathologic response was assessed in patients undergoing resection. The association between metabolic and CT/RECIST and pathologic response was tested with the McNemar test; the ability to predict progression-free survival (PFS) and overall survival (OS) was tested with the Log-rank test and a multivariable Cox model. Thirty-three patients were analyzed. After treatment, there was a notable decrease of all the parameters measured by PET/CT. Early metabolic PET/CT response (either SUV- or TLG-based) had a stronger, independent and statistically significant predictive value for PFS and OS than both CT/RECIST and pathologic response at multivariate analysis, although with different degrees of statistical significance. The predictive value of CT/RECIST response was not significant at multivariate analysis. PET/CT response was significantly predictive of long-term outcomes during preoperative treatment of patients with liver metastases from colorectal cancer, and its predictive ability was higher than that of CT/RECIST response after 3 mo of treatment. Such findings need to be confirmed by larger prospective trials.

Improving performance of Zambia Defence Force antiretroviral therapy providers: evaluation of a standards-based approach

PubMed Central

Kim, Young Mi; Banda, Joseph; Kanjipite, Webby; Sarkar, Supriya; Bazant, Eva; Hiner, Cyndi; Tholandi, Maya; Reinhardt, Stephanie; Njobvu, Panganani Dalisani; Kols, Adrienne; Benavides, Bruno

2013-01-01

ABSTRACT Background: The Zambia Defence Force (ZDF) has applied the Standards-Based Management and Recognition (SBM-R®) approach, which uses detailed performance standards, at some health facilities to improve HIV-related services offered to military personnel and surrounding civilian communities. This study examines the effectiveness of the SBM-R approach in improving facility readiness and provider performance at ZDF facilities. Methods: We collected data on facility readiness and provider performance before and after the 2010–2012 intervention at 4 intervention sites selected for their relatively poor performance and 4 comparison sites. Assessors observed whether each facility met 16 readiness standards and whether providers met 9 performance standards during consultations with 354 returning antiretroviral therapy (ART) clients. We then calculated the percentages of criteria achieved for each readiness and performance standard and conducted bivariate and multivariate analyses of provider performance data. Results: Facilities' ART readiness scores exceeded 80% before the intervention at both intervention and comparison sites. At endline, scores improved on 4 facility readiness standards in the intervention group but on only 1 standard in the comparison group. Multivariate analysis found that the overall provider performance score increased significantly in the intervention group (from 58% to 84%; P<.01) but not in the comparison group (from 62% to 70%). The before-and-after improvement in scores was significantly greater among intervention sites than among comparison sites for 2 standards—initial assessment of the client's condition and nutrition counseling. Conclusion: The standards-based approach, which involved intensive and mutually reinforcing intervention activities, showed modest improvements in some aspects of providers' performance during ART consultations. Further research is needed to determine whether improvements in provider performance affect client outcomes such as adherence to ART. PMID:25276534

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.

Impact of baseline systolic blood pressure on visit-to-visit blood pressure variability: the Kailuan study.

PubMed

Wang, Anxin; Li, Zhifang; Yang, Yuling; Chen, Guojuan; Wang, Chunxue; Wu, Yuntao; Ruan, Chunyu; Liu, Yan; Wang, Yilong; Wu, Shouling

2016-01-01

To investigate the relationship between baseline systolic blood pressure (SBP) and visit-to-visit blood pressure variability in a general population. This is a prospective longitudinal cohort study on cardiovascular risk factors and cardiovascular or cerebrovascular events. Study participants attended a face-to-face interview every 2 years. Blood pressure variability was defined using the standard deviation and coefficient of variation of all SBP values at baseline and follow-up visits. The coefficient of variation is the ratio of the standard deviation to the mean SBP. We used multivariate linear regression models to test the relationships between SBP and standard deviation, and between SBP and coefficient of variation. Approximately 43,360 participants (mean age: 48.2±11.5 years) were selected. In multivariate analysis, after adjustment for potential confounders, baseline SBPs <120 mmHg were inversely related to standard deviation (P<0.001) and coefficient of variation (P<0.001). In contrast, baseline SBPs ≥140 mmHg were significantly positively associated with standard deviation (P<0.001) and coefficient of variation (P<0.001). Baseline SBPs of 120-140 mmHg were associated with the lowest standard deviation and coefficient of variation. The associations between baseline SBP and standard deviation, and between SBP and coefficient of variation during follow-ups showed a U curve. Both lower and higher baseline SBPs were associated with increased blood pressure variability. To control blood pressure variability, a good target SBP range for a general population might be 120-139 mmHg.

A flexible model for multivariate interval-censored survival times with complex correlation structure.

PubMed

Falcaro, Milena; Pickles, Andrew

2007-02-10

We focus on the analysis of multivariate survival times with highly structured interdependency and subject to interval censoring. Such data are common in developmental genetics and genetic epidemiology. We propose a flexible mixed probit model that deals naturally with complex but uninformative censoring. The recorded ages of onset are treated as possibly censored ordinal outcomes with the interval censoring mechanism seen as arising from a coarsened measurement of a continuous variable observed as falling between subject-specific thresholds. This bypasses the requirement for the failure times to be observed as falling into non-overlapping intervals. The assumption of a normal age-of-onset distribution of the standard probit model is relaxed by embedding within it a multivariate Box-Cox transformation whose parameters are jointly estimated with the other parameters of the model. Complex decompositions of the underlying multivariate normal covariance matrix of the transformed ages of onset become possible. The new methodology is here applied to a multivariate study of the ages of first use of tobacco and first consumption of alcohol without parental permission in twins. The proposed model allows estimation of the genetic and environmental effects that are shared by both of these risk behaviours as well as those that are specific. 2006 John Wiley & Sons, Ltd.

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.

Flood-frequency prediction methods for unregulated streams of Tennessee, 2000

USGS Publications Warehouse

Law, George S.; Tasker, Gary D.

2003-01-01

Up-to-date flood-frequency prediction methods for unregulated, ungaged rivers and streams of Tennessee have been developed. Prediction methods include the regional-regression method and the newer region-of-influence method. The prediction methods were developed using stream-gage records from unregulated streams draining basins having from 1 percent to about 30 percent total impervious area. These methods, however, should not be used in heavily developed or storm-sewered basins with impervious areas greater than 10 percent. The methods can be used to estimate 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence-interval floods of most unregulated rural streams in Tennessee. A computer application was developed that automates the calculation of flood frequency for unregulated, ungaged rivers and streams of Tennessee. Regional-regression equations were derived by using both single-variable and multivariable regional-regression analysis. Contributing drainage area is the explanatory variable used in the single-variable equations. Contributing drainage area, main-channel slope, and a climate factor are the explanatory variables used in the multivariable equations. Deleted-residual standard error for the single-variable equations ranged from 32 to 65 percent. Deleted-residual standard error for the multivariable equations ranged from 31 to 63 percent. These equations are included in the computer application to allow easy comparison of results produced by the different methods. The region-of-influence method calculates multivariable regression equations for each ungaged site and recurrence interval using basin characteristics from 60 similar sites selected from the study area. Explanatory variables that may be used in regression equations computed by the region-of-influence method include contributing drainage area, main-channel slope, a climate factor, and a physiographic-region factor. Deleted-residual standard error for the region-of-influence method tended to be only slightly smaller than those for the regional-regression method and ranged from 27 to 62 percent.

Principal Components Analysis on the spectral Bidirectional Reflectance Distribution Function of ceramic colour standards.

PubMed

Ferrero, A; Campos, J; Rabal, A M; Pons, A; Hernanz, M L; Corróns, A

2011-09-26

The Bidirectional Reflectance Distribution Function (BRDF) is essential to characterize an object's reflectance properties. This function depends both on the various illumination-observation geometries as well as on the wavelength. As a result, the comprehensive interpretation of the data becomes rather complex. In this work we assess the use of the multivariable analysis technique of Principal Components Analysis (PCA) applied to the experimental BRDF data of a ceramic colour standard. It will be shown that the result may be linked to the various reflection processes occurring on the surface, assuming that the incoming spectral distribution is affected by each one of these processes in a specific manner. Moreover, this procedure facilitates the task of interpolating a series of BRDF measurements obtained for a particular sample. © 2011 Optical Society of America

A microcomputer-based whole-body counter for personnel routine monitoring.

PubMed

Chou, H P; Tsai, T M; Lan, C Y

1993-05-01

The paper describes a cost-effective NaI(Tl) whole-body counter developed for routine examinations of worker intakes at an isotope production facility. Signal processing, data analysis and system operation are microcomputer-controlled for minimum human interactions. The pulse height analyzer is developed as an microcomputer add-on card for easy manipulation. The scheme for radionuclide analysis is aimed for fast running according to a knowledge base established from background samples and phantom experiments in conjunction with a multivariate regression analysis. Long-term stability and calibration with standards and in vivo measurements are reported.

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

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.

Improved modeling of in vivo confocal Raman data using multivariate curve resolution (MCR) augmentation of ordinary least squares models.

PubMed

Hancewicz, Thomas M; Xiao, Chunhong; Zhang, Shuliang; Misra, Manoj

2013-12-01

In vivo confocal Raman spectroscopy has become the measurement technique of choice for skin health and skin care related communities as a way of measuring functional chemistry aspects of skin that are key indicators for care and treatment of various skin conditions. Chief among these techniques are stratum corneum water content, a critical health indicator for severe skin condition related to dryness, and natural moisturizing factor components that are associated with skin protection and barrier health. In addition, in vivo Raman spectroscopy has proven to be a rapid and effective method for quantifying component penetration in skin for topically applied skin care formulations. The benefit of such a capability is that noninvasive analytical chemistry can be performed in vivo in a clinical setting, significantly simplifying studies aimed at evaluating product performance. This presumes, however, that the data and analysis methods used are compatible and appropriate for the intended purpose. The standard analysis method used by most researchers for in vivo Raman data is ordinary least squares (OLS) regression. The focus of work described in this paper is the applicability of OLS for in vivo Raman analysis with particular attention given to use for non-ideal data that often violate the inherent limitations and deficiencies associated with proper application of OLS. We then describe a newly developed in vivo Raman spectroscopic analysis methodology called multivariate curve resolution-augmented ordinary least squares (MCR-OLS), a relatively simple route to addressing many of the issues with OLS. The method is compared with the standard OLS method using the same in vivo Raman data set and using both qualitative and quantitative comparisons based on model fit error, adherence to known data constraints, and performance against calibration samples. A clear improvement is shown in each comparison for MCR-OLS over standard OLS, thus supporting the premise that the MCR-OLS method is better suited for general-purpose multicomponent analysis of in vivo Raman spectral data. This suggests that the methodology is more readily adaptable to a wide range of component systems and is thus more generally applicable than standard OLS.

Measures of precision for dissimilarity-based multivariate analysis of ecological communities.

PubMed

Anderson, Marti J; Santana-Garcon, Julia

2015-01-01

Ecological studies require key decisions regarding the appropriate size and number of sampling units. No methods currently exist to measure precision for multivariate assemblage data when dissimilarity-based analyses are intended to follow. Here, we propose a pseudo multivariate dissimilarity-based standard error (MultSE) as a useful quantity for assessing sample-size adequacy in studies of ecological communities. Based on sums of squared dissimilarities, MultSE measures variability in the position of the centroid in the space of a chosen dissimilarity measure under repeated sampling for a given sample size. We describe a novel double resampling method to quantify uncertainty in MultSE values with increasing sample size. For more complex designs, values of MultSE can be calculated from the pseudo residual mean square of a permanova model, with the double resampling done within appropriate cells in the design. R code functions for implementing these techniques, along with ecological examples, are provided. © 2014 The Authors. Ecology Letters published by John Wiley & Sons Ltd and CNRS.

A Comparison of Pseudo-Maximum Likelihood and Asymptotically Distribution-Free Dynamic Factor Analysis Parameter Estimation in Fitting Covariance Structure Models to Block-Toeplitz Matrices Representing Single-Subject Multivariate Time-Series.

ERIC Educational Resources Information Center

Molenaar, Peter C. M.; Nesselroade, John R.

1998-01-01

Pseudo-Maximum Likelihood (p-ML) and Asymptotically Distribution Free (ADF) estimation methods for estimating dynamic factor model parameters within a covariance structure framework were compared through a Monte Carlo simulation. Both methods appear to give consistent model parameter estimates, but only ADF gives standard errors and chi-square…

Fourier-transform infrared spectroscopy as a novel approach to providing effect-based endpoints in duckweed toxicity testing.

PubMed

Hu, Li-Xin; Ying, Guang-Guo; Chen, Xiao-Wen; Huang, Guo-Yong; Liu, You-Sheng; Jiang, Yu-Xia; Pan, Chang-Gui; Tian, Fei; Martin, Francis L

2017-02-01

Traditional duckweed toxicity tests only measure plant growth inhibition as an endpoint, with limited effects-based data. The present study aimed to investigate whether Fourier-transform infrared (FTIR) spectroscopy could enhance the duckweed (Lemna minor L.) toxicity test. Four chemicals (Cu, Cd, atrazine, and acetochlor) and 4 metal-containing industrial wastewater samples were tested. After exposure of duckweed to the chemicals, standard toxicity endpoints (frond number and chlorophyll content) were determined; the fronds were also interrogated using FTIR spectroscopy under optimized test conditions. Biochemical alterations associated with each treatment were assessed and further analyzed by multivariate analysis. The results showed that comparable x% of effective concentration (ECx) values could be achieved based on FTIR spectroscopy in comparison with those based on traditional toxicity endpoints. Biochemical alterations associated with different doses of toxicant were mainly attributed to lipid, protein, nucleic acid, and carbohydrate structural changes, which helped to explain toxic mechanisms. With the help of multivariate analysis, separation of clusters related to different exposure doses could be achieved. The present study is the first to show successful application of FTIR spectroscopy in standard duckweed toxicity tests with biochemical alterations as new endpoints. Environ Toxicol Chem 2017;36:346-353. © 2016 SETAC. © 2016 SETAC.

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.

Once upon Multivariate Analyses: When They Tell Several Stories about Biological Evolution.

PubMed

Renaud, Sabrina; Dufour, Anne-Béatrice; Hardouin, Emilie A; Ledevin, Ronan; Auffray, Jean-Christophe

2015-01-01

Geometric morphometrics aims to characterize of the geometry of complex traits. It is therefore by essence multivariate. The most popular methods to investigate patterns of differentiation in this context are (1) the Principal Component Analysis (PCA), which is an eigenvalue decomposition of the total variance-covariance matrix among all specimens; (2) the Canonical Variate Analysis (CVA, a.k.a. linear discriminant analysis (LDA) for more than two groups), which aims at separating the groups by maximizing the between-group to within-group variance ratio; (3) the between-group PCA (bgPCA) which investigates patterns of between-group variation, without standardizing by the within-group variance. Standardizing within-group variance, as performed in the CVA, distorts the relationships among groups, an effect that is particularly strong if the variance is similarly oriented in a comparable way in all groups. Such shared direction of main morphological variance may occur and have a biological meaning, for instance corresponding to the most frequent standing genetic variation in a population. Here we undertake a case study of the evolution of house mouse molar shape across various islands, based on the real dataset and simulations. We investigated how patterns of main variance influence the depiction of among-group differentiation according to the interpretation of the PCA, bgPCA and CVA. Without arguing about a method performing 'better' than another, it rather emerges that working on the total or between-group variance (PCA and bgPCA) will tend to put the focus on the role of direction of main variance as line of least resistance to evolution. Standardizing by the within-group variance (CVA), by dampening the expression of this line of least resistance, has the potential to reveal other relevant patterns of differentiation that may otherwise be blurred.

Assessing Principal Component Regression Prediction of Neurochemicals Detected with Fast-Scan Cyclic Voltammetry

PubMed Central

2011-01-01

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook’s distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards. PMID:21966586

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.

Assessing principal component regression prediction of neurochemicals detected with fast-scan cyclic voltammetry.

PubMed

Keithley, Richard B; Wightman, R Mark

2011-06-07

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook's distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards.

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

Integrated Data Visualization and Virtual Reality Tool

NASA Technical Reports Server (NTRS)

Dryer, David A.

1998-01-01

The Integrated Data Visualization and Virtual Reality Tool (IDVVRT) Phase II effort was for the design and development of an innovative Data Visualization Environment Tool (DVET) for NASA engineers and scientists, enabling them to visualize complex multidimensional and multivariate data in a virtual environment. The objectives of the project were to: (1) demonstrate the transfer and manipulation of standard engineering data in a virtual world; (2) demonstrate the effects of design and changes using finite element analysis tools; and (3) determine the training and engineering design and analysis effectiveness of the visualization system.

HPLC-TOF-MS and HPLC-MS/MS combined with multivariate analysis for the characterization and discrimination of phenolic profiles in nonfumigated and sulfur-fumigated rhubarb.

PubMed

Yan, Yan; Zhang, Qianqian; Feng, Fang

2016-07-01

Sulfur fumigation has recently been used during the postharvest handling of rhubarb to reduce the drying duration and control pests. However, a few reports question the effect of sulfur fumigation on the bioactive components of rhubarb, which is crucial for the quality evaluation of the herbal medicine. The bottleneck limiting the study comes from the complex compounds that exist in herb samples with diverse structural features, wide concentration range and the difficulty to obtain all the reference standards. In this study, an integrated strategy based on the highly effective separation and analysis by liquid chromatography coupled with diode-array detection and time-of-flight/triple-quadruple tandem mass spectrometry combined with multivariate analysis was established. 68 phenolic compounds that exist in nonfumigated and sulfur-fumigated herb samples of rhubarb were tentatively assigned based on their retention behavior, UV spectra, accurate molecular weight, and mass spectral fragments. Qualitative and semiquantitative comparison revealed a serious reduction of the majority of phenolic compounds in sulfur-fumigated rhubarb. Furthermore, multivariate analysis was applied to holistically discriminate nonfumigated from sulfur-fumigated rhubarb and explore the characteristic chemical markers. The established approach was specific and rapid for characterizing and screening sulfur-fumigated rhubarb among commercial samples and could be applied for the quality assessment of other sulfur-fumigated herbs. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

Effect of Flexible Duty Hour Policies on Length of Stay for Complex Intra-Abdominal Operations: A Flexibility in Duty Hour Requirements for Surgical Trainees (FIRST) Trial Analysis.

PubMed

Stulberg, Jonah J; Pavey, Emily S; Cohen, Mark E; Ko, Clifford Y; Hoyt, David B; Bilimoria, Karl Y

2017-02-01

Changes to resident duty hour policies in the Flexibility in Duty Hour Requirements for Surgical Trainees (FIRST) trial could impact hospitalized patients' length of stay (LOS) by altering care coordination. Length of stay can also serve as a reflection of all complications, particularly those not captured in the FIRST trial (eg pneumothorax from central line). Programs were randomized to either maintaining current ACGME duty hour policies (Standard arm) or more flexible policies waiving rules on maximum shift lengths and time off between shifts (Flexible arm). Our objective was to determine whether flexibility in resident duty hours affected LOS in patients undergoing high-risk surgical operations. Patients were identified who underwent hepatectomy, pancreatectomy, laparoscopic colectomy, open colectomy, or ventral hernia repair (2014-2015 academic year) at 154 hospitals participating in the FIRST trial. Two procedure-stratified evaluations of LOS were undertaken: multivariable negative binomial regression analysis on LOS and a multivariable logistic regression analysis on the likelihood of a prolonged LOS (>75 th percentile). Before any adjustments, there was no statistically significant difference in overall mean LOS between study arms (Flexible Policy: mean [SD] LOS 6.03 [5.78] days vs Standard Policy: mean LOS 6.21 [5.82] days; p = 0.74). In adjusted analyses, there was no statistically significant difference in LOS between study arms overall (incidence rate ratio for Flexible vs Standard: 0.982; 95% CI, 0.939-1.026; p = 0.41) or for any individual procedures. In addition, there was no statistically significant difference in the proportion of patients with prolonged LOS between study arms overall (Flexible vs Standard: odds ratio = 1.028; 95% CI, 0.871-1.212) or for any individual procedures. Duty hour flexibility had no statistically significant effect on LOS in patients undergoing complex intra-abdominal operations. Copyright © 2016 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

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

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.

Cement Leakage in Percutaneous Vertebral Augmentation for Osteoporotic Vertebral Compression Fractures: Analysis of Risk Factors.

PubMed

Xie, Weixing; Jin, Daxiang; Ma, Hui; Ding, Jinyong; Xu, Jixi; Zhang, Shuncong; Liang, De

2016-05-01

The risk factors for cement leakage were retrospectively reviewed in 192 patients who underwent percutaneous vertebral augmentation (PVA). To discuss the factors related to the cement leakage in PVA procedure for the treatment of osteoporotic vertebral compression fractures. PVA is widely applied for the treatment of osteoporotic vertebral fractures. Cement leakage is a major complication of this procedure. The risk factors for cement leakage were controversial. A retrospective review of 192 patients who underwent PVA was conducted. The following data were recorded: age, sex, bone density, number of fractured vertebrae before surgery, number of treated vertebrae, severity of the treated vertebrae, operative approach, volume of injected bone cement, preoperative vertebral compression ratio, preoperative local kyphosis angle, intraosseous clefts, preoperative vertebral cortical bone defect, and ratio and type of cement leakage. To study the correlation between each factor and cement leakage ratio, bivariate regression analysis was employed to perform univariate analysis, whereas multivariate linear regression analysis was employed to perform multivariate analysis. The study included 192 patients (282 treated vertebrae), and cement leakage occurred in 100 vertebrae (35.46%). The vertebrae with preoperative cortical bone defects generally exhibited higher cement leakage ratio, and the leakage is typically type C. Vertebrae with intact cortical bones before the procedure tend to experience type S leakage. Univariate analysis showed that patient age, bone density, number of fractured vertebrae before surgery, and vertebral cortical bone were associated with cement leakage ratio (P<0.05). Multivariate analysis showed that the main factors influencing bone cement leakage are bone density and vertebral cortical bone defect, with standardized partial regression coefficients of -0.085 and 0.144, respectively. High bone density and vertebral cortical bone defect are independent risk factors associated with bone cement leakage.

Coping with matrix effects in headspace solid phase microextraction gas chromatography using multivariate calibration strategies.

PubMed

Ferreira, Vicente; Herrero, Paula; Zapata, Julián; Escudero, Ana

2015-08-14

SPME is extremely sensitive to experimental parameters affecting liquid-gas and gas-solid distribution coefficients. Our aims were to measure the weights of these factors and to design a multivariate strategy based on the addition of a pool of internal standards, to minimize matrix effects. Synthetic but real-like wines containing selected analytes and variable amounts of ethanol, non-volatile constituents and major volatile compounds were prepared following a factorial design. The ANOVA study revealed that even using a strong matrix dilution, matrix effects are important and additive with non-significant interaction effects and that it is the presence of major volatile constituents the most dominant factor. A single internal standard provided a robust calibration for 15 out of 47 analytes. Then, two different multivariate calibration strategies based on Partial Least Square Regression were run in order to build calibration functions based on 13 different internal standards able to cope with matrix effects. The first one is based in the calculation of Multivariate Internal Standards (MIS), linear combinations of the normalized signals of the 13 internal standards, which provide the expected area of a given unit of analyte present in each sample. The second strategy is a direct calibration relating concentration to the 13 relative areas measured in each sample for each analyte. Overall, 47 different compounds can be reliably quantified in a single fully automated method with overall uncertainties better than 15%. Copyright © 2015 Elsevier B.V. All rights reserved.

Multivariate-Statistical Assessment of Heavy Metals for Agricultural Soils in Northern China

PubMed Central

Yang, Pingguo; Yang, Miao; Mao, Renzhao; Shao, Hongbo

2014-01-01

The study evaluated eight heavy metals content and soil pollution from agricultural soils in northern China. Multivariate and geostatistical analysis approaches were used to determine the anthropogenic and natural contribution of soil heavy metal concentrations. Single pollution index and integrated pollution index could be used to evaluate soil heavy metal risk. The results show that the first factor explains 27.3% of the eight soil heavy metals with strong positive loadings on Cu, Zn, and Cd, which indicates that Cu, Zn, and Cd are associated with and controlled by anthropic activities. The average value of heavy metal is lower than the second grade standard values of soil environmental quality standards in China. Single pollution index is lower than 1, and the Nemerow integrated pollution index is 0.305, which means that study area has not been polluted. The semivariograms of soil heavy metal single pollution index fitted spherical and exponential models. The variable ratio of single pollution index showed moderately spatial dependence. Heavy metal contents showed relative safety in the study area. PMID:24892058

Multivariate analysis in thoracic research.

PubMed

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

2015-03-01

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

A data fusion-based drought index

NASA Astrophysics Data System (ADS)

Azmi, Mohammad; Rüdiger, Christoph; Walker, Jeffrey P.

2016-03-01

Drought and water stress monitoring plays an important role in the management of water resources, especially during periods of extreme climate conditions. Here, a data fusion-based drought index (DFDI) has been developed and analyzed for three different locations of varying land use and climate regimes in Australia. The proposed index comprehensively considers all types of drought through a selection of indices and proxies associated with each drought type. In deriving the proposed index, weekly data from three different data sources (OzFlux Network, Asia-Pacific Water Monitor, and MODIS-Terra satellite) were employed to first derive commonly used individual standardized drought indices (SDIs), which were then grouped using an advanced clustering method. Next, three different multivariate methods (principal component analysis, factor analysis, and independent component analysis) were utilized to aggregate the SDIs located within each group. For the two clusters in which the grouped SDIs best reflected the water availability and vegetation conditions, the variables were aggregated based on an averaging between the standardized first principal components of the different multivariate methods. Then, considering those two aggregated indices as well as the classifications of months (dry/wet months and active/non-active months), the proposed DFDI was developed. Finally, the symbolic regression method was used to derive mathematical equations for the proposed DFDI. The results presented here show that the proposed index has revealed new aspects in water stress monitoring which previous indices were not able to, by simultaneously considering both hydrometeorological and ecological concepts to define the real water stress of the study areas.

Milk consumption in relation to incidence of nasopharyngeal carcinoma in 48 countries/regions.

PubMed

Mai, Zhi-Ming; Lo, Ching-Man; Xu, Jun; Chan, King-Pan; Wong, Chit-Ming; Lung, Maria Li; Lam, Tai-Hing

2015-12-21

Decreasing trends of nasopharyngeal carcinoma (NPC) incidence have been consistently reported in endemic populations but the etiology of NPC remains unclear. The objective of our study was to assess the international and local (Hong Kong) correlations of milk and dairy products per capita consumption with NPC incidence. We conducted an ecological study in 48 countries/regions. Age standardized incidence rates of NPC were obtained from the Cancer Incidence in Five Continents. Dairy product consumption and Human Development Index were obtained from the Food and Agriculture Organization of the United Nations and the United Nations Development Programme. Spearman correlation, multivariate analysis and time-lagged analysis were performed. The negative correlations between milk consumption and decreased age standardized incidence rates of NPC were observed in the 48 countries/regions adjusting for Human Development Index in endemic countries/regions. In Hong Kong, multivariate analysis, after adjusting for other potential confounders, including salted fish, cigarette, vegetable consumption and socioeconomic status, showed consistently negative and significant correlations between milk consumption and NPC incidence (The strongest coefficient (β) was observed at 10-year lag in males [β = -0.439; P < 0.01] and in females [β = -0.258; P < 0.01]). Our study showed the correlations on milk consumption per capita and against lower risk of NPC in 48 countries/regions and in Hong Kong. These hypothesis-generating results could support further studies on individual exposures and the disease.

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

NASA Astrophysics Data System (ADS)

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

2016-01-01

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

[Methods of the multivariate statistical analysis of so-called polyetiological diseases using the example of coronary heart disease].

PubMed

Lifshits, A M

1979-01-01

General characteristics of the multivariate statistical analysis (MSA) is given. Methodical premises and criteria for the selection of an adequate MSA method applicable to pathoanatomic investigations of the epidemiology of multicausal diseases are presented. The experience of using MSA with computors and standard computing programs in studies of coronary arteries aterosclerosis on the materials of 2060 autopsies is described. The combined use of 4 MSA methods: sequential, correlational, regressional, and discriminant permitted to quantitate the contribution of each of the 8 examined risk factors in the development of aterosclerosis. The most important factors were found to be the age, arterial hypertension, and heredity. Occupational hypodynamia and increased fatness were more important in men, whereas diabetes melitus--in women. The registration of this combination of risk factors by MSA methods provides for more reliable prognosis of the likelihood of coronary heart disease with a fatal outcome than prognosis of the degree of coronary aterosclerosis.

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%.

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…

Race and Older Mothers’ Differentiation: A Sequential Quantitative and Qualitative Analysis

PubMed Central

Sechrist, Jori; Suitor, J. Jill; Riffin, Catherine; Taylor-Watson, Kadari; Pillemer, Karl

2011-01-01

The goal of this paper is to demonstrate a process by which qualitative and quantitative approaches are combined to reveal patterns in the data that are unlikely to be detected and confirmed by either method alone. Specifically, we take a sequential approach to combining qualitative and quantitative data to explore race differences in how mothers differentiate among their adult children. We began with a standard multivariate analysis examining race differences in mothers’ differentiation among their adult children regarding emotional closeness and confiding. Finding no race differences in this analysis, we conducted an in-depth comparison of the Black and White mothers’ narratives to determine whether there were underlying patterns that we had been unable to detect in our first analysis. Using this method, we found that Black mothers were substantially more likely than White mothers to emphasize interpersonal relationships within the family when describing differences among their children. In our final step, we developed a measure of familism based on the qualitative data and conducted a multivariate analysis to confirm the patterns revealed by the in-depth comparison of the mother’s narratives. We conclude that using such a sequential mixed methods approach to data analysis has the potential to shed new light on complex family relations. PMID:21967639

Combining fibre optic Raman spectroscopy and tactile resonance measurement for tissue characterization

NASA Astrophysics Data System (ADS)

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

2010-12-01

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

Combination of near infrared spectroscopy and chemometrics for authentication of taro flour from wheat and sago flour

NASA Astrophysics Data System (ADS)

Rachmawati; Rohaeti, E.; Rafi, M.

2017-05-01

Taro flour on the market is usually sold at higher price than wheat and sago flour. This situation could be a cause for adulteration of taro flour from wheat and sago flour. For this reason, we will need an identification and authentication. Combination of near infrared (NIR) spectrum with multivariate analysis was used in this study to identify and authenticate taro flour from wheat and sago flour. The authentication model of taro flour was developed by using a mixture of 5%, 25%, and 50% of adulterated taro flour from wheat and sago flour. Before subjected to multivariate analysis, an initial preprocessing signal was used namely normalization and standard normal variate to the NIR spectrum. We used principal component analysis followed by discriminant analysis to make an identification and authentication model of taro flour. From the result obtained, about 90.48% of the taro flour mixed with wheat flour and 85% of taro flour mixed with sago flour were successfully classified into their groups. So the combination of NIR spectrum with chemometrics could be used for identification and authentication of taro flour from wheat and sago flour.

Ultrasound texture analysis: Association with lymph node metastasis of papillary thyroid microcarcinoma.

PubMed

Kim, Soo-Yeon; Lee, Eunjung; Nam, Se Jin; Kim, Eun-Kyung; Moon, Hee Jung; Yoon, Jung Hyun; Han, Kyung Hwa; Kwak, Jin Young

2017-01-01

This retrospective study aimed to evaluate whether ultrasound texture analysis is useful to predict lymph node metastasis in patients with papillary thyroid microcarcinoma (PTMC). This study was approved by the Institutional Review Board, and the need to obtain informed consent was waived. Between May and July 2013, 361 patients (mean age, 43.8 ± 11.3 years; range, 16-72 years) who underwent staging ultrasound (US) and subsequent thyroidectomy for conventional PTMC ≤ 10 mm between May and July 2013 were included. Each PTMC was manually segmented and its histogram parameters (Mean, Standard deviation, Skewness, Kurtosis, and Entropy) were extracted with Matlab software. The mean values of histogram parameters and clinical and US features were compared according to lymph node metastasis using the independent t-test and Chi-square test. Multivariate logistic regression analysis was performed to identify the independent factors associated with lymph node metastasis. Tumors with lymph node metastasis (n = 117) had significantly higher entropy compared to those without lymph node metastasis (n = 244) (mean±standard deviation, 6.268±0.407 vs. 6.171±.0.405; P = .035). No additional histogram parameters showed differences in mean values according to lymph node metastasis. Entropy was not independently associated with lymph node metastasis on multivariate logistic regression analysis (Odds ratio, 0.977 [95% confidence interval (CI), 0.482-1.980]; P = .949). Younger age (Odds ratio, 0.962 [95% CI, 0.940-0.984]; P = .001) and lymph node metastasis on US (Odds ratio, 7.325 [95% CI, 3.573-15.020]; P < .001) were independently associated with lymph node metastasis. Texture analysis was not useful in predicting lymph node metastasis in patients with PTMC.

Comparing interval estimates for small sample ordinal CFA models

PubMed Central

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading. Therefore, editors and policymakers should continue to emphasize the inclusion of interval estimates in research. PMID:26579002

Comparing interval estimates for small sample ordinal CFA models.

PubMed

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading. Therefore, editors and policymakers should continue to emphasize the inclusion of interval estimates in research.

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

PubMed

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

2016-01-01

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

Characterizing psychological dimensions in non-pathological subjects through autonomic nervous system dynamics

PubMed Central

Nardelli, Mimma; Valenza, Gaetano; Cristea, Ioana A.; Gentili, Claudio; Cotet, Carmen; David, Daniel; Lanata, Antonio; Scilingo, Enzo P.

2015-01-01

The objective assessment of psychological traits of healthy subjects and psychiatric patients has been growing interest in clinical and bioengineering research fields during the last decade. Several experimental evidences strongly suggest that a link between Autonomic Nervous System (ANS) dynamics and specific dimensions such as anxiety, social phobia, stress, and emotional regulation might exist. Nevertheless, an extensive investigation on a wide range of psycho-cognitive scales and ANS non-invasive markers gathered from standard and non-linear analysis still needs to be addressed. In this study, we analyzed the discerning and correlation capabilities of a comprehensive set of ANS features and psycho-cognitive scales in 29 non-pathological subjects monitored during resting conditions. In particular, the state of the art of standard and non-linear analysis was performed on Heart Rate Variability, InterBreath Interval series, and InterBeat Respiration series, which were considered as monovariate and multivariate measurements. Experimental results show that each ANS feature is linked to specific psychological traits. Moreover, non-linear analysis outperforms the psychological assessment with respect to standard analysis. Considering that the current clinical practice relies only on subjective scores from interviews and questionnaires, this study provides objective tools for the assessment of psychological dimensions. PMID:25859212

[Quality assurance program for pain management after obstetrical perineal injury].

PubMed

Urion, L; Bayoumeu, F; Jandard, C; Fontaine, B; Bouaziz, H

2004-11-01

A quality insurance program has been set up in order to improve the relief of pain in patients with perineal injury after childbirth. The program has been developed according to the French standards of accreditation. After elaboration of a referential, a first study (103 patients) allowed to evaluate the ongoing practices and to appreciate the pain intensities. After analysis of the results, an action strategy has been elaborated, with a brand new therapeutic standard and a pain-monitoring program for nurses. Six months later, a second study (105 patients) measured the efficiency of the accomplished actions. The statistic analysis used chi2 and Kruskal-Wallis tests and a multivariate analyse (p <0.05). Several indicators led to conclude to the success of this program: analgesics prescribed systematically and earlier, best observance, larger utilisation of the NSAI, decrease of the analgesics requests, improvement of the satisfaction referred to the relief of pain. The multivariate analyse showed a risk twice as little as in the second study to have a 36th hour VAS score superior to four (p =0.03). The apply of this quality insurance program allowed to improve the analgesia after obstetric perineal injuries. A few adaptations are needed, and also more formations of the medical and paramedical staff. The durability of the accomplished actions shall be evaluated in the future.

Risk factors of significant pain syndrome 90 days after minor thoracic injury: trajectory analysis.

PubMed

Daoust, Raoul; Emond, Marcel; Bergeron, Eric; LeSage, Natalie; Camden, Stéphanie; Guimont, Chantal; Vanier, Laurent; Chauny, Jean-Marc

2013-11-01

The objective was to identify the risk factors of clinically significant pain at 90 days in patients with minor thoracic injury (MTI) discharged from the emergency department (ED). A prospective, multicenter, cohort study was conducted in four Canadian EDs from November 2006 to November 2010. All consecutive patients aged 16 years or older with MTI were eligible at discharge from EDs. They underwent standardized clinical and radiologic evaluations at 1 and 2 weeks, followed by standardized telephone interviews at 30 and 90 days. A pain trajectory model characterized groups of patients with different pain evolutions and ascertained specific risk factors in each group through multivariate analysis. In this cohort of 1,132 patients, 734 were eligible for study inclusion. The authors identified a pain trajectory that characterized 18.2% of the study population experiencing clinically significant pain (>3 of 10) at 90 days after a MTI. Multivariate modeling found two or more rib fractures, smoking, and initial oxygen saturation below 95% to be predictors of this group of patients. To the authors' knowledge, this is the first prospective study of trajectory modeling to detect risk factors associated with significant pain at 90 days after MTI. These factors may help in planning specific treatment strategies and should be validated in another prospective cohort. © 2013 by the Society for Academic Emergency Medicine.

A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores

PubMed Central

Neelon, Brian; Gelfand, Alan E.; Miranda, Marie Lynn

2013-01-01

Summary Researchers in the health and social sciences often wish to examine joint spatial patterns for two or more related outcomes. Examples include infant birth weight and gestational length, psychosocial and behavioral indices, and educational test scores from different cognitive domains. We propose a multivariate spatial mixture model for the joint analysis of continuous individual-level outcomes that are referenced to areal units. The responses are modeled as a finite mixture of multivariate normals, which accommodates a wide range of marginal response distributions and allows investigators to examine covariate effects within subpopulations of interest. The model has a hierarchical structure built at the individual level (i.e., individuals are nested within areal units), and thus incorporates both individual- and areal-level predictors as well as spatial random effects for each mixture component. Conditional autoregressive (CAR) priors on the random effects provide spatial smoothing and allow the shape of the multivariate distribution to vary flexibly across geographic regions. We adopt a Bayesian modeling approach and develop an efficient Markov chain Monte Carlo model fitting algorithm that relies primarily on closed-form full conditionals. We use the model to explore geographic patterns in end-of-grade math and reading test scores among school-age children in North Carolina. PMID:26401059

Hybrid approach combining chemometrics and likelihood ratio framework for reporting the evidential value of spectra.

PubMed

Martyna, Agnieszka; Zadora, Grzegorz; Neocleous, Tereza; Michalska, Aleksandra; Dean, Nema

2016-08-10

Many chemometric tools are invaluable and have proven effective in data mining and substantial dimensionality reduction of highly multivariate data. This becomes vital for interpreting various physicochemical data due to rapid development of advanced analytical techniques, delivering much information in a single measurement run. This concerns especially spectra, which are frequently used as the subject of comparative analysis in e.g. forensic sciences. In the presented study the microtraces collected from the scenarios of hit-and-run accidents were analysed. Plastic containers and automotive plastics (e.g. bumpers, headlamp lenses) were subjected to Fourier transform infrared spectrometry and car paints were analysed using Raman spectroscopy. In the forensic context analytical results must be interpreted and reported according to the standards of the interpretation schemes acknowledged in forensic sciences using the likelihood ratio approach. However, for proper construction of LR models for highly multivariate data, such as spectra, chemometric tools must be employed for substantial data compression. Conversion from classical feature representation to distance representation was proposed for revealing hidden data peculiarities and linear discriminant analysis was further applied for minimising the within-sample variability while maximising the between-sample variability. Both techniques enabled substantial reduction of data dimensionality. Univariate and multivariate likelihood ratio models were proposed for such data. It was shown that the combination of chemometric tools and the likelihood ratio approach is capable of solving the comparison problem of highly multivariate and correlated data after proper extraction of the most relevant features and variance information hidden in the data structure. Copyright © 2016 Elsevier B.V. All rights reserved.

Multivariate statistics applied to the reaction of common bean plants to parasitism by Meloidogyne javanica.

PubMed

Santos, L N S; Cabral, P D S; Neves, G A R; Alves, F R; Teixeira, M B; Cunha, F N; Silva, N F

2017-03-16

The availability of common bean cultivars tolerant to Meloidogyne javanica is limited in Brazil. Thus, the present study aimed to evaluate the reactions of 33 common bean genotypes (23 landrace, 8 commercial, 1 susceptible standard and 1 resistant standard) to M. javanica, employing multivariate statistics to discriminate the reaction of the genotypes. The experiment was conducted in a greenhouse using a completely randomized design with seven replicates. The seeds were sown in 1-L pots containing autoclaved soil and sand in a 1:1 ratio (v:v). On day 19, after emergence of the seedlings, the plants were treated with inoculum containing 4000 eggs + second-stage juveniles (J2). At 60 days after inoculation, the seedlings were evaluated based on biometric and parasitism-related traits, such as number of galls, final nematode population per root system, reproduction factor, and percent reduction in the reproduction factor of the nematode (%RRF). The data were subjected to analysis of variance using the F-test. The Mahalanobis generalized distance was used to obtain the dissimilarity matrix, and the average linkage between groups was used for clustering. The use of multivariate statistics allowed groups to be separated according to the resistance levels of genotypes, as observed in the %RRF. The landrace genotypes FORT-09, FORT-17, FORT-31, FORT-32, FORT-34 and FORT-36 presented resistance to M. javanica; thus, these genotypes can be considered potential sources of resistance.

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.

An analysis of fracture trace patterns in areas of flat-lying sedimentary rocks for the detection of buried geologic structure. [Kansas and Texas

NASA Technical Reports Server (NTRS)

Podwysocki, M. H.

1974-01-01

Two study areas in a cratonic platform underlain by flat-lying sedimentary rocks were analyzed to determine if a quantitative relationship exists between fracture trace patterns and their frequency distributions and subsurface structural closures which might contain petroleum. Fracture trace lengths and frequency (number of fracture traces per unit area) were analyzed by trend surface analysis and length frequency distributions also were compared to a standard Gaussian distribution. Composite rose diagrams of fracture traces were analyzed using a multivariate analysis method which grouped or clustered the rose diagrams and their respective areas on the basis of the behavior of the rays of the rose diagram. Analysis indicates that the lengths of fracture traces are log-normally distributed according to the mapping technique used. Fracture trace frequency appeared higher on the flanks of active structures and lower around passive reef structures. Fracture trace log-mean lengths were shorter over several types of structures, perhaps due to increased fracturing and subsequent erosion. Analysis of rose diagrams using a multivariate technique indicated lithology as the primary control for the lower grouping levels. Groupings at higher levels indicated that areas overlying active structures may be isolated from their neighbors by this technique while passive structures showed no differences which could be isolated.

Multivariate pattern dependence

PubMed Central

Saxe, Rebecca

2017-01-01

When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD): a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS) and to the fusiform face area (FFA), using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity. PMID:29155809

A Search for the tt¯H (H → bb) Large Hadron Collider with the atlas detector using a matrix element method

NASA Astrophysics Data System (ADS)

Basye, Austin T.

A matrix element method analysis of the Standard Model Higgs boson, produced in association with two top quarks decaying to the lepton-plus-jets channel is presented. Based on 20.3 fb--1 of s=8 TeV data, produced at the Large Hadron Collider and collected by the ATLAS detector, this analysis utilizes multiple advanced techniques to search for ttH signatures with a 125 GeV Higgs boson decaying to two b -quarks. After categorizing selected events based on their jet and b-tag multiplicities, signal rich regions are analyzed using the matrix element method. Resulting variables are then propagated to two parallel multivariate analyses utilizing Neural Networks and Boosted Decision Trees respectively. As no significant excess is found, an observed (expected) limit of 3.4 (2.2) times the Standard Model cross-section is determined at 95% confidence, using the CLs method, for the Neural Network analysis. For the Boosted Decision Tree analysis, an observed (expected) limit of 5.2 (2.7) times the Standard Model cross-section is determined at 95% confidence, using the CLs method. Corresponding unconstrained fits of the Higgs boson signal strength to the observed data result in the measured signal cross-section to Standard Model cross-section prediction of mu = 1.2 +/- 1.3(total) +/- 0.7(stat.) for the Neural Network analysis, and mu = 2.9 +/- 1.4(total) +/- 0.8(stat.) for the Boosted Decision Tree analysis.

Identification of ginsenoside markers from dry purified extract of Panax ginseng by a dereplication approach and UPLC-QTOF/MS analysis.

PubMed

Yang, Heejung; Lee, Dong Young; Kang, Kyo Bin; Kim, Jeom Yong; Kim, Sun Ok; Yoo, Young Hyo; Sung, Sang Hyun

2015-05-10

A dry purified extract of Panax ginseng (PEG) was prepared using a manufacturing process that includes column chromatography, acid hydrolysis, and an enzyme reaction. During the manufacturing process, the more polar ginsenosides were altered into less polar forms via cleavage of their sugar chains and structural modifications of the aglycones, such as hydroxylation and dehydroxylation. The structural changes of ginsenosides during the intermediate steps from dried ginseng extract (DGE) to PEG were monitored by ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectroscopy (UPLC-QTOF/MS). 22 ginsenosides isolated from PEG were used as the reference standards for determining of unknown ginsenosides and further suggesting of the metabolic markers. The elution order of 22 ginsenosides based on the type of aglycones, and the location and number of sugar chains can be used for the structural elucidation of unknown ginsenosides. This information could be used in a dereplication process for quick and efficient identification of ginsenoside derivatives in ginseng preparations. A dereplication approach helped the identification of the metabolic markers in the UPLC-QTOF/MS chromatograms during the conversion process with multivariate analyses, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) plots. These metabolic markers were identified by comparing with the dereplication information of the reference standards of 22 ginsenosides, or they were assigned using the pattern of the MS/MS fragmented ions. Consequently, the developed metabolic profiling approach using UPLC-QTOF/MS and multivariate analysis represents a new method for providing quality control as well as useful criteria for a similarity evaluation of the manufacturing process of ginseng preparations. Copyright © 2015 Elsevier B.V. All rights reserved.

Deconstructing multivariate decoding for the study of brain function.

PubMed

Hebart, Martin N; Baker, Chris I

2017-08-04

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

Perceived fairness of pay among people with and without disabilities: a propensity score matched analysis of working Australians.

PubMed

Milner, Allison; Aitken, Zoe; Krnjacki, Lauren; Bentley, Rebecca; Blakely, Tony; LaMontagne, Anthony D; Kavanagh, Anne M

2015-09-01

Equity and fairness at work are associated with a range of organizational and health outcomes. Past research suggests that workers with disabilities experience inequity in the workplace. It is difficult to conclude whether the presence of disability is the reason for perceived unfair treatment due to the possible confounding of effect estimates by other demographic or socioeconomic factors. The data source was the Household, Income, and Labor Dynamics in Australia (HILDA) survey (2001-2012). Propensity for disability was calculated from logistic models including gender, age, education, country of birth, and father's occupational skill level as predictors. We then used nearest neighbor (on propensity score) matched analysis to match workers with disabilities to workers without disability. Results suggest that disability is independently associated with lower fairness of pay after controlling for confounding factors in the propensity score matched analysis; although results do suggest less than half a standard deviation difference, indicating small effects. Similar results were apparent in standard multivariable regression models and alternative propensity score analyses (stratification, covariate adjustment using the propensity score, and inverse probability of treatment weighting). Whilst neither multivariable regression nor propensity scores adjust for unmeasured confounding, and there remains the potential for other biases, similar results for the two methodological approaches to confounder adjustment provide some confidence of an independent association of disability with perceived unfairness of pay. Based on this, we suggest that the disparity in the perceived fairness of pay between people with and without disabilities may be explained by worse treatment of people with disabilities in the workplace.

Assessment and clinical factors associated with pain in patients undergoing transrectal prostate biopsy.

PubMed

Gómez-Gómez, E; Ramírez, M; Gómez-Ferrer, A; Rubio-Briones, J; Iborra, I; J Carrasco-Valiente; Campos, J P; Ruiz-García, J; Requena-Tapia, M J; Solsona, E

2015-09-01

To quantify the degree of pain experienced by patients who undergo ultrasound-guided transrectal prostate biopsy in standard clinical practice and assess the clinical factors associated with increased pain. Analysis of a multicenter series of patients with prostate biopsy according to standard clinical practice. The biopsy was performed transrectally with a protocol of local anesthesia on the posterolateral nerve bundle. The pain was assessed at 20minutes into the procedure using the visual analog scale (0-10). The degree of pain was analyzed, and the association was studied using a univariate/multivariate analysis of selected clinical variables and the degree of pain. A total of 1188 patients with a median age of 64 years were analyzed. Thirty percent of the biopsies were diagnosed with a tumor. The median pain score was 2, with 65% of the patients reporting a pain score ≤2. The multivariate analysis showed that the prostate volume (RR, 1.34; 95% CI 1.01-1.77; P=.04), having a previous biopsy (RR, 2.25; 95% CI 1.44-3.52; P<.01), age (RR, .63; 95% CI .47-.85; P<.01) and feel palpation (RR, 1.95; 95% CI 1.28-2.96; P<.01) were factors independently associated with greater pain during the procedure. Transrectal biopsy with local anesthesia is a relatively painless technique. Factors such as age, a previous biopsy, pain on being touched and prostate volume were associated with the presence of greater pain during the procedure. Copyright © 2014 AEU. Publicado por Elsevier España, S.L.U. All rights reserved.

The Statistical Consulting Center for Astronomy (SCCA)

NASA Technical Reports Server (NTRS)

Akritas, Michael

2001-01-01

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

Multivariate analysis of the impacts of the turbine fuel JP-4 in a microcosm toxicity test with implications for the evaluation of ecosystem dynamics and risk assessment.

PubMed

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

1993-12-01

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

Application of Multivariable Analysis and FTIR-ATR Spectroscopy to the Prediction of Properties in Campeche Honey

PubMed Central

Pat, Lucio; Ali, Bassam; Guerrero, Armando; Córdova, Atl V.; Garduza, José P.

2016-01-01

Attenuated total reflectance-Fourier transform infrared spectrometry and chemometrics model was used for determination of physicochemical properties (pH, redox potential, free acidity, electrical conductivity, moisture, total soluble solids (TSS), ash, and HMF) in honey samples. The reference values of 189 honey samples of different botanical origin were determined using Association Official Analytical Chemists, (AOAC), 1990; Codex Alimentarius, 2001, International Honey Commission, 2002, methods. Multivariate calibration models were built using partial least squares (PLS) for the measurands studied. The developed models were validated using cross-validation and external validation; several statistical parameters were obtained to determine the robustness of the calibration models: (PCs) optimum number of components principal, (SECV) standard error of cross-validation, (R 2 cal) coefficient of determination of cross-validation, (SEP) standard error of validation, and (R 2 val) coefficient of determination for external validation and coefficient of variation (CV). The prediction accuracy for pH, redox potential, electrical conductivity, moisture, TSS, and ash was good, while for free acidity and HMF it was poor. The results demonstrate that attenuated total reflectance-Fourier transform infrared spectrometry is a valuable, rapid, and nondestructive tool for the quantification of physicochemical properties of honey. PMID:28070445

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.

On the potential for the Partial Triadic Analysis to grasp the spatio-temporal variability of groundwater hydrochemistry

NASA Astrophysics Data System (ADS)

Gourdol, L.; Hissler, C.; Pfister, L.

2012-04-01

The Luxembourg sandstone aquifer is of major relevance for the national supply of drinking water in Luxembourg. The city of Luxembourg (20% of the country's population) gets almost 2/3 of its drinking water from this aquifer. As a consequence, the study of both the groundwater hydrochemistry, as well as its spatial and temporal variations, are considered as of highest priority. Since 2005, a monitoring network has been implemented by the Water Department of Luxembourg City, with a view to a more sustainable management of this strategic water resource. The data collected to date forms a large and complex dataset, describing spatial and temporal variations of many hydrochemical parameters. The data treatment issue is tightly connected to this kind of water monitoring programs and complex databases. Standard multivariate statistical techniques, such as principal components analysis and hierarchical cluster analysis, have been widely used as unbiased methods for extracting meaningful information from groundwater quality data and are now classically used in many hydrogeological studies, in particular to characterize temporal or spatial hydrochemical variations induced by natural and anthropogenic factors. But these classical multivariate methods deal with two-way matrices, usually parameters/sites or parameters/time, while often the dataset resulting from qualitative water monitoring programs should be seen as a datacube parameters/sites/time. Three-way matrices, such as the one we propose here, are difficult to handle and to analyse by classical multivariate statistical tools and thus should be treated with approaches dealing with three-way data structures. One possible analysis approach consists in the use of partial triadic analysis (PTA). The PTA was previously used with success in many ecological studies but never to date in the domain of hydrogeology. Applied to the dataset of the Luxembourg Sandstone aquifer, the PTA appears as a new promising statistical instrument for hydrogeologists, in particular to characterize temporal and spatial hydrochemical variations induced by natural and anthropogenic factors. This new approach for groundwater management offers potential for 1) identifying a common multivariate spatial structure, 2) untapping the different hydrochemical patterns and explaining their controlling factors and 3) analysing the temporal variability of this structure and grasping hydrochemical changes.

Multivariate statistical techniques for the evaluation of surface water quality of the Himalayan foothills streams, Pakistan

NASA Astrophysics Data System (ADS)

Malik, Riffat Naseem; Hashmi, Muhammad Zaffar

2017-10-01

Himalayan foothills streams, Pakistan play an important role in living water supply and irrigation of farmlands; thus, the water quality is closely related to public health. Multivariate techniques were applied to check spatial and seasonal trends, and metals contamination sources of the Himalayan foothills streams, Pakistan. Grab surface water samples were collected from different sites (5-15 cm water depth) in pre-washed polyethylene containers. Fast Sequential Atomic Absorption Spectrophotometer (Varian FSAA-240) was used to measure the metals concentration. Concentrations of Ni, Cu, and Mn were high in pre-monsoon season than the post-monsoon season. Cluster analysis identified impaired, moderately impaired and least impaired clusters based on water parameters. Discriminant function analysis indicated spatial variability in water was due to temperature, electrical conductivity, nitrates, iron and lead whereas seasonal variations were correlated with 16 physicochemical parameters. Factor analysis identified municipal and poultry waste, automobile activities, surface runoff, and soil weathering as major sources of contamination. Levels of Mn, Cr, Fe, Pb, Cd, Zn and alkalinity were above the WHO and USEPA standards for surface water. The results of present study will help to higher authorities for the management of the Himalayan foothills streams.

Revealing hidden spectral information of chlorine and sulfur in data of a mobile Laser-induced Breakdown Spectroscopy system using chemometrics

NASA Astrophysics Data System (ADS)

Gottlieb, C.; Millar, S.; Günther, T.; Wilsch, G.

2017-06-01

For the damage assessment of reinforced concrete structures the quantified ingress profiles of harmful species like chlorides, sulfates and alkali need to be determined. In order to provide on-site analysis of concrete a fast and reliable method is necessary. Low transition probabilities as well as the high ionization energies for chlorine and sulfur in the near-infrared range makes the detection of Cl I and S I in low concentrations a difficult task. For the on-site analysis a mobile LIBS-system (λ = 1064 nm, Epulse ≤ 3 mJ, τ = 1.5 ns) with an automated scanner has been developed at BAM. Weak chlorine and sulfur signal intensities do not allow classical univariate analysis for process data derived from the mobile system. In order to improve the analytical performance multivariate analysis like PLS-R will be presented in this work. A comparison to standard univariate analysis will be carried out and results covering important parameters like detection and quantification limits (LOD, LOQ) as well as processing variances will be discussed (Allegrini and Olivieri, 2014 [1]; Ostra et al., 2008 [2]). It will be shown that for the first time a low cost mobile system is capable of providing reproducible chlorine and sulfur analysis on concrete by using a low sensitive system in combination with multivariate evaluation.

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.

Observation of the associated production of a single top quark and a W boson in pp collisions at sqrt[s] = 8 TeV.

PubMed

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2014-06-13

The first observation of the associated production of a single top quark and a W boson is presented. The analysis is based on a data set corresponding to an integrated luminosity of 12.2 fb(-1) of proton-proton collisions at sqrt[s] = 8 TeV recorded by the CMS experiment at the LHC. Events with two leptons and a jet originating from a b quark are selected. A multivariate analysis based on kinematic and topological properties is used to separate the signal from the dominant tt background. An excess consistent with the signal hypothesis is observed, with a significance which corresponds to 6.1 standard deviations above a background-only hypothesis. The measured production cross section is 23.4 ± 5.4 pb, in agreement with the standard model prediction.

Observation of the Associated Production of a Single Top Quark -and a W Boson in pp Collisions at √s =8 TeV

NASA Astrophysics Data System (ADS)

Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Bergauer, T.; Dragicevic, M.; Erö, J.; Fabjan, C.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hartl, C.; Hörmann, N.; Hrubec, J.; Jeitler, M.; Kiesenhofer, W.; Knünz, V.; Krammer, M.; Krätschmer, I.; Liko, D.; Mikulec, I.; Rabady, D.; Rahbaran, B.; Rohringer, H.; Schöfbeck, R.; Strauss, J.; Taurok, A.; Treberer-Treberspurg, W.; Waltenberger, W.; Wulz, C.-E.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Alderweireldt, S.; Bansal, M.; Bansal, S.; Cornelis, T.; De Wolf, E. A.; Janssen, X.; Knutsson, A.; Luyckx, S.; Mucibello, L.; Ochesanu, S.; Roland, B.; Rougny, R.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Blekman, F.; Blyweert, S.; D'Hondt, J.; Heracleous, N.; Kalogeropoulos, A.; Keaveney, J.; Kim, T. J.; Lowette, S.; Maes, M.; Olbrechts, A.; Strom, D.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Onsem, G. 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S.; Colaleo, A.; Creanza, D.; De Filippis, N.; De Palma, M.; Fiore, L.; Iaselli, G.; Maggi, G.; Maggi, M.; Marangelli, B.; My, S.; Nuzzo, S.; Pacifico, N.; Pompili, A.; Pugliese, G.; Radogna, R.; Selvaggi, G.; Silvestris, L.; Singh, G.; Venditti, R.; Verwilligen, P.; Zito, G.; Abbiendi, G.; Benvenuti, A. C.; Bonacorsi, D.; Braibant-Giacomelli, S.; Brigliadori, L.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Fasanella, D.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Marcellini, S.; Masetti, G.; Meneghelli, M.; Montanari, A.; Navarria, F. L.; Odorici, F.; Perrotta, A.; Primavera, F.; Rossi, A. M.; Rovelli, T.; Siroli, G. 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M.; Lista, L.; Meola, S.; Merola, M.; Paolucci, P.; Azzi, P.; Bacchetta, N.; Bisello, D.; Branca, A.; Carlin, R.; Checchia, P.; Dorigo, T.; Galanti, M.; Gasparini, F.; Gasparini, U.; Giubilato, P.; Gozzelino, A.; Kanishchev, K.; Lacaprara, S.; Lazzizzera, I.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Pozzobon, N.; Ronchese, P.; Simonetto, F.; Torassa, E.; Tosi, M.; Triossi, A.; Vanini, S.; Ventura, S.; Zotto, P.; Zucchetta, A.; Zumerle, G.; Gabusi, M.; Ratti, S. P.; Riccardi, C.; Vitulo, P.; Biasini, M.; Bilei, G. M.; Fanò, L.; Lariccia, P.; Mantovani, G.; Menichelli, M.; Romeo, F.; Saha, A.; Santocchia, A.; Spiezia, A.; Androsov, K.; Azzurri, P.; Bagliesi, G.; Bernardini, J.; Boccali, T.; Broccolo, G.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Fiori, F.; Foà, L.; Giassi, A.; Grippo, M. T.; Kraan, A.; Ligabue, F.; Lomtadze, T.; Martini, L.; Messineo, A.; Moon, C. S.; Palla, F.; Rizzi, A.; Savoy-Navarro, A.; Serban, A. 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I.; Vlimant, J. R.; Wöhri, H. K.; Zeuner, W. D.; Bertl, W.; Deiters, K.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; König, S.; Kotlinski, D.; Langenegger, U.; Renker, D.; Rohe, T.; Bachmair, F.; Bäni, L.; Bianchini, L.; Bortignon, P.; Buchmann, M. A.; Casal, B.; Chanon, N.; Deisher, A.; Dissertori, G.; Dittmar, M.; Donegà, M.; Dünser, M.; Eller, P.; Grab, C.; Hits, D.; Lustermann, W.; Mangano, B.; Marini, A. C.; Martinez Ruiz del Arbol, P.; Meister, D.; Mohr, N.; Nägeli, C.; Nef, P.; Nessi-Tedaldi, F.; Pandolfi, F.; Pape, L.; Pauss, F.; Peruzzi, M.; Quittnat, M.; Ronga, F. J.; Rossini, M.; Starodumov, A.; Takahashi, M.; Tauscher, L.; Theofilatos, K.; Treille, D.; Wallny, R.; Weber, H. A.; Amsler, C.; Chiochia, V.; De Cosa, A.; Favaro, C.; Hinzmann, A.; Hreus, T.; Ivova Rikova, M.; Kilminster, B.; Millan Mejias, B.; Ngadiuba, J.; Robmann, P.; Snoek, H.; Taroni, S.; Verzetti, M.; Yang, Y.; Cardaci, M.; Chen, K. H.; Ferro, C.; Kuo, C. M.; Li, S. W.; Lin, W.; Lu, Y. J.; Volpe, R.; Yu, S. S.; Bartalini, P.; Chang, P.; Chang, Y. H.; Chang, Y. W.; Chao, Y.; Chen, K. F.; Chen, P. H.; Dietz, C.; Grundler, U.; Hou, W.-S.; Hsiung, Y.; Kao, K. Y.; Lei, Y. J.; Liu, Y. F.; Lu, R.-S.; Majumder, D.; Petrakou, E.; Shi, X.; Shiu, J. G.; Tzeng, Y. M.; Wang, M.; Wilken, R.; Asavapibhop, B.; Suwonjandee, N.; Adiguzel, A.; Bakirci, M. N.; Cerci, S.; Dozen, C.; Dumanoglu, I.; Eskut, E.; Girgis, S.; Gokbulut, G.; Gurpinar, E.; Hos, I.; Kangal, E. E.; Kayis Topaksu, A.; Onengut, G.; Ozdemir, K.; Ozturk, S.; Polatoz, A.; Sogut, K.; Sunar Cerci, D.; Tali, B.; Topakli, H.; Vergili, M.; Akin, I. V.; Aliev, T.; Bilin, B.; Bilmis, S.; Deniz, M.; Gamsizkan, H.; Guler, A. M.; Karapinar, G.; Ocalan, K.; Ozpineci, A.; Serin, M.; Sever, R.; Surat, U. E.; Yalvac, M.; Zeyrek, M.; Gülmez, E.; Isildak, B.; Kaya, M.; Kaya, O.; Ozkorucuklu, S.; Bahtiyar, H.; Barlas, E.; Cankocak, K.; Günaydin, Y. O.; Vardarlı, F. I.; Yücel, M.; Levchuk, L.; Sorokin, P.; Brooke, J. J.; Clement, E.; Cussans, D.; Flacher, H.; Frazier, R.; Goldstein, J.; Grimes, M.; Heath, G. P.; Heath, H. F.; Jacob, J.; Kreczko, L.; Lucas, C.; Meng, Z.; Newbold, D. M.; Paramesvaran, S.; Poll, A.; Senkin, S.; Smith, V. J.; Williams, T.; Bell, K. W.; Belyaev, A.; Brew, C.; Brown, R. M.; Cockerill, D. J. A.; Coughlan, J. A.; Harder, K.; Harper, S.; Ilic, J.; Olaiya, E.; Petyt, D.; Shepherd-Themistocleous, C. H.; Thea, A.; Tomalin, I. R.; Womersley, W. J.; Worm, S. D.; Baber, M.; Bainbridge, R.; Buchmuller, O.; Burton, D.; Colling, D.; Cripps, N.; Cutajar, M.; Dauncey, P.; Davies, G.; Della Negra, M.; Ferguson, W.; Fulcher, J.; Futyan, D.; Gilbert, A.; Guneratne Bryer, A.; Hall, G.; Hatherell, Z.; Hays, J.; Iles, G.; Jarvis, M.; Karapostoli, G.; Kenzie, M.; Lane, R.; Lucas, R.; Lyons, L.; Magnan, A.-M.; Marrouche, J.; Mathias, B.; Nandi, R.; Nash, J.; Nikitenko, A.; Pela, J.; Pesaresi, M.; Petridis, K.; Pioppi, M.; Raymond, D. M.; Rogerson, S.; Rose, A.; Seez, C.; Sharp, P.; Sparrow, A.; Tapper, A.; Vazquez Acosta, M.; Virdee, T.; Wakefield, S.; Wardle, N.; Cole, J. E.; Hobson, P. R.; Khan, A.; Kyberd, P.; Leggat, D.; Leslie, D.; Martin, W.; Reid, I. D.; Symonds, P.; Teodorescu, L.; Turner, M.; Dittmann, J.; Hatakeyama, K.; Kasmi, A.; Liu, H.; Scarborough, T.; Charaf, O.; Cooper, S. I.; Henderson, C.; Rumerio, P.; Avetisyan, A.; Bose, T.; Fantasia, C.; Heister, A.; Lawson, P.; Lazic, D.; Rohlf, J.; Sperka, D.; St. John, J.; Sulak, L.; Alimena, J.; Bhattacharya, S.; Christopher, G.; Cutts, D.; Demiragli, Z.; Ferapontov, A.; Garabedian, A.; Heintz, U.; Jabeen, S.; Kukartsev, G.; Laird, E.; Landsberg, G.; Luk, M.; Narain, M.; Segala, M.; Sinthuprasith, T.; Speer, T.; Swanson, J.; Breedon, R.; Breto, G.; Calderon De La Barca Sanchez, M.; Chauhan, S.; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Erbacher, R.; Gardner, M.; Ko, W.; Kopecky, A.; Lander, R.; Miceli, T.; Pellett, D.; Pilot, J.; Ricci-Tam, F.; Rutherford, B.; Searle, M.; Shalhout, S.; Smith, J.; Squires, M.; Tripathi, M.; Wilbur, S.; Yohay, R.; Andreev, V.; Cline, D.; Cousins, R.; Erhan, S.; Everaerts, P.; Farrell, C.; Felcini, M.; Hauser, J.; Ignatenko, M.; Jarvis, C.; Rakness, G.; Schlein, P.; Takasugi, E.; Valuev, V.; Weber, M.; Babb, J.; Clare, R.; Ellison, J.; Gary, J. W.; Hanson, G.; Heilman, J.; Jandir, P.; Lacroix, F.; Liu, H.; Long, O. R.; Luthra, A.; Malberti, M.; Nguyen, H.; Shrinivas, A.; Sturdy, J.; Sumowidagdo, S.; Wimpenny, S.; Andrews, W.; Branson, J. G.; Cerati, G. B.; Cittolin, S.; D'Agnolo, R. T.; Evans, D.; Holzner, A.; Kelley, R.; Kovalskyi, D.; Lebourgeois, M.; Letts, J.; Macneill, I.; Padhi, S.; Palmer, C.; Pieri, M.; Sani, M.; Sharma, V.; Simon, S.; Sudano, E.; Tadel, M.; Tu, Y.; Vartak, A.; Wasserbaech, S.; Würthwein, F.; Yagil, A.; Yoo, J.; Barge, D.; Campagnari, C.; Danielson, T.; Flowers, K.; Geffert, P.; George, C.; Golf, F.; Incandela, J.; Justus, C.; Magaña Villalba, R.; Mccoll, N.; Pavlunin, V.; Richman, J.; Rossin, R.; Stuart, D.; To, W.; West, C.; Apresyan, A.; Bornheim, A.; Bunn, J.; Chen, Y.; Di Marco, E.; Duarte, J.; Kcira, D.; Mott, A.; Newman, H. B.; Pena, C.; Rogan, C.; Spiropulu, M.; Timciuc, V.; Wilkinson, R.; Xie, S.; Zhu, R. Y.; Azzolini, V.; Calamba, A.; Carroll, R.; Ferguson, T.; Iiyama, Y.; Jang, D. W.; Paulini, M.; Russ, J.; Vogel, H.; Vorobiev, I.; Cumalat, J. P.; Drell, B. R.; Ford, W. T.; Gaz, A.; Luiggi Lopez, E.; Nauenberg, U.; Smith, J. G.; Stenson, K.; Ulmer, K. A.; Wagner, S. R.; Alexander, J.; Chatterjee, A.; Eggert, N.; Gibbons, L. K.; Hopkins, W.; Khukhunaishvili, A.; Kreis, B.; Mirman, N.; Nicolas Kaufman, G.; Patterson, J. R.; Ryd, A.; Salvati, E.; Sun, W.; Teo, W. D.; Thom, J.; Thompson, J.; Tucker, J.; Weng, Y.; Winstrom, L.; Wittich, P.; Winn, D.; Abdullin, S.; Albrow, M.; Anderson, J.; Apollinari, G.; Bauerdick, L. A. T.; Beretvas, A.; Berryhill, J.; Bhat, P. C.; Burkett, K.; Butler, J. N.; Chetluru, V.; Cheung, H. W. K.; Chlebana, F.; Cihangir, S.; Elvira, V. D.; Fisk, I.; Freeman, J.; Gao, Y.; Gottschalk, E.; Gray, L.; Green, D.; Grünendahl, S.; Gutsche, O.; Hare, D.; Harris, R. M.; Hirschauer, J.; Hooberman, B.; Jindariani, S.; Johnson, M.; Joshi, U.; Kaadze, K.; Klima, B.; Kwan, S.; Linacre, J.; Lincoln, D.; Lipton, R.; Lykken, J.; Maeshima, K.; Marraffino, J. M.; Martinez Outschoorn, V. I.; Maruyama, S.; Mason, D.; McBride, P.; Mishra, K.; Mrenna, S.; Musienko, Y.; Nahn, S.; Newman-Holmes, C.; O'Dell, V.; Prokofyev, O.; Ratnikova, N.; Sexton-Kennedy, E.; Sharma, S.; Spalding, W. J.; Spiegel, L.; Taylor, L.; Tkaczyk, S.; Tran, N. V.; Uplegger, L.; Vaandering, E. W.; Vidal, R.; Whitbeck, A.; Whitmore, J.; Wu, W.; Yang, F.; Yun, J. C.; Acosta, D.; Avery, P.; Bourilkov, D.; Cheng, T.; Das, S.; De Gruttola, M.; Di Giovanni, G. P.; Dobur, D.; Field, R. D.; Fisher, M.; Fu, Y.; Furic, I. K.; Hugon, J.; Kim, B.; Konigsberg, J.; Korytov, A.; Kropivnitskaya, A.; Kypreos, T.; Low, J. F.; Matchev, K.; Milenovic, P.; Mitselmakher, G.; Muniz, L.; Rinkevicius, A.; Shchutska, L.; Skhirtladze, N.; Snowball, M.; Yelton, J.; Zakaria, M.; Gaultney, V.; Hewamanage, S.; Linn, S.; Markowitz, P.; Martinez, G.; Rodriguez, J. L.; Adams, T.; Askew, A.; Bochenek, J.; Chen, J.; Diamond, B.; Haas, J.; Hagopian, S.; Hagopian, V.; Johnson, K. F.; Prosper, H.; Veeraraghavan, V.; Weinberg, M.; Baarmand, M. M.; Dorney, B.; Hohlmann, M.; Kalakhety, H.; Yumiceva, F.; Adams, M. R.; Apanasevich, L.; Bazterra, V. E.; Betts, R. R.; Bucinskaite, I.; Cavanaugh, R.; Evdokimov, O.; Gauthier, L.; Gerber, C. E.; Hofman, D. J.; Khalatyan, S.; Kurt, P.; Moon, D. H.; O'Brien, C.; Silkworth, C.; Turner, P.; Varelas, N.; Akgun, U.; Albayrak, E. A.; Bilki, B.; Clarida, W.; Dilsiz, K.; Duru, F.; Haytmyradov, M.; Merlo, J.-P.; Mermerkaya, H.; Mestvirishvili, A.; Moeller, A.; Nachtman, J.; Ogul, H.; Onel, Y.; Ozok, F.; Sen, S.; Tan, P.; Tiras, E.; Wetzel, J.; Yetkin, T.; Yi, K.; Barnett, B. A.; Blumenfeld, B.; Bolognesi, S.; Fehling, D.; Gritsan, A. V.; Maksimovic, P.; Martin, C.; Swartz, M.; Baringer, P.; Bean, A.; Benelli, G.; Kenny, R. P.; Murray, M.; Noonan, D.; Sanders, S.; Sekaric, J.; Stringer, R.; Wang, Q.; Wood, J. S.; Barfuss, A. F.; Chakaberia, I.; Ivanov, A.; Khalil, S.; Makouski, M.; Maravin, Y.; Saini, L. K.; Shrestha, S.; Svintradze, I.; Gronberg, J.; Lange, D.; Rebassoo, F.; Wright, D.; Baden, A.; Calvert, B.; Eno, S. C.; Gomez, J. A.; Hadley, N. J.; Kellogg, R. G.; Kolberg, T.; Lu, Y.; Marionneau, M.; Mignerey, A. C.; Pedro, K.; Skuja, A.; Temple, J.; Tonjes, M. B.; Tonwar, S. C.; Apyan, A.; Barbieri, R.; Bauer, G.; Busza, W.; Cali, I. A.; Chan, M.; Di Matteo, L.; Dutta, V.; Gomez Ceballos, G.; Goncharov, M.; Gulhan, D.; Klute, M.; Lai, Y. S.; Lee, Y.-J.; Levin, A.; Luckey, P. D.; Ma, T.; Paus, C.; Ralph, D.; Roland, C.; Roland, G.; Stephans, G. S. F.; Stöckli, F.; Sumorok, K.; Velicanu, D.; Veverka, J.; Wyslouch, B.; Yang, M.; Yoon, A. S.; Zanetti, M.; Zhukova, V.; Dahmes, B.; De Benedetti, A.; Gude, A.; Kao, S. C.; Klapoetke, K.; Kubota, Y.; Mans, J.; Pastika, N.; Rusack, R.; Singovsky, A.; Tambe, N.; Turkewitz, J.; Acosta, J. G.; Cremaldi, L. M.; Kroeger, R.; Oliveros, S.; Perera, L.; Rahmat, R.; Sanders, D. A.; Summers, D.; Avdeeva, E.; Bloom, K.; Bose, S.; Claes, D. R.; Dominguez, A.; Gonzalez Suarez, R.; Keller, J.; Knowlton, D.; Kravchenko, I.; Lazo-Flores, J.; Malik, S.; Meier, F.; Snow, G. R.; Dolen, J.; Godshalk, A.; Iashvili, I.; Jain, S.; Kharchilava, A.; Kumar, A.; Rappoccio, S.; Wan, Z.; Alverson, G.; Barberis, E.; Baumgartel, D.; Chasco, M.; Haley, J.; Massironi, A.; Nash, D.; Orimoto, T.; Trocino, D.; Wood, D.; Zhang, J.; Anastassov, A.; Hahn, K. A.; Kubik, A.; Lusito, L.; Mucia, N.; Odell, N.; Pollack, B.; Pozdnyakov, A.; Schmitt, M.; Stoynev, S.; Sung, K.; Velasco, M.; Won, S.; Berry, D.; Brinkerhoff, A.; Chan, K. M.; Drozdetskiy, A.; Hildreth, M.; Jessop, C.; Karmgard, D. J.; Kellams, N.; Kolb, J.; Lannon, K.; Luo, W.; Lynch, S.; Marinelli, N.; Morse, D. M.; Pearson, T.; Planer, M.; Ruchti, R.; Slaunwhite, J.; Valls, N.; Wayne, M.; Wolf, M.; Woodard, A.; Antonelli, L.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Hill, C.; Hughes, R.; Kotov, K.; Ling, T. Y.; Puigh, D.; Rodenburg, M.; Smith, G.; Vuosalo, C.; Winer, B. L.; Wolfe, H.; Wulsin, H. W.; Berry, E.; Elmer, P.; Halyo, V.; Hebda, P.; Hegeman, J.; Hunt, A.; Jindal, P.; Koay, S. A.; Lujan, P.; Marlow, D.; Medvedeva, T.; Mooney, M.; Olsen, J.; Piroué, P.; Quan, X.; Raval, A.; Saka, H.; Stickland, D.; Tully, C.; Werner, J. S.; Zenz, S. C.; Zuranski, A.; Brownson, E.; Lopez, A.; Mendez, H.; Ramirez Vargas, J. E.; Alagoz, E.; Benedetti, D.; Bolla, G.; Bortoletto, D.; De Mattia, M.; Everett, A.; Hu, Z.; Jones, M.; Jung, K.; Kress, M.; Leonardo, N.; Lopes Pegna, D.; Maroussov, V.; Merkel, P.; Miller, D. H.; Neumeister, N.; Radburn-Smith, B. C.; Shipsey, I.; Silvers, D.; Svyatkovskiy, A.; Wang, F.; Xie, W.; Xu, L.; Yoo, H. D.; Zablocki, J.; Zheng, Y.; Parashar, N.; Adair, A.; Akgun, B.; Ecklund, K. M.; Geurts, F. J. M.; Li, W.; Michlin, B.; Padley, B. P.; Redjimi, R.; Roberts, J.; Zabel, J.; Betchart, B.; Bodek, A.; Covarelli, R.; de Barbaro, P.; Demina, R.; Eshaq, Y.; Ferbel, T.; Garcia-Bellido, A.; Goldenzweig, P.; Han, J.; Harel, A.; Miner, D. C.; Petrillo, G.; Vishnevskiy, D.; Zielinski, M.; Bhatti, A.; Ciesielski, R.; Demortier, L.; Goulianos, K.; Lungu, G.; Malik, S.; Mesropian, C.; Arora, S.; Barker, A.; Chou, J. P.; Contreras-Campana, C.; Contreras-Campana, E.; Duggan, D.; Ferencek, D.; Gershtein, Y.; Gray, R.; Halkiadakis, E.; Hidas, D.; Lath, A.; Panwalkar, S.; Park, M.; Patel, R.; Rekovic, V.; Robles, J.; Salur, S.; Schnetzer, S.; Seitz, C.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Rose, K.; Spanier, S.; Yang, Z. C.; York, A.; Bouhali, O.; Eusebi, R.; Flanagan, W.; Gilmore, J.; Kamon, T.; Khotilovich, V.; Krutelyov, V.; Montalvo, R.; Osipenkov, I.; Pakhotin, Y.; Perloff, A.; Roe, J.; Safonov, A.; Sakuma, T.; Suarez, I.; Tatarinov, A.; Toback, D.; Akchurin, N.; Cowden, C.; Damgov, J.; Dragoiu, C.; Dudero, P. R.; Kovitanggoon, K.; Kunori, S.; Lee, S. W.; Libeiro, T.; Volobouev, I.; Appelt, E.; Delannoy, A. G.; Greene, S.; Gurrola, A.; Johns, W.; Maguire, C.; Mao, Y.; Melo, A.; Sharma, M.; Sheldon, P.; Snook, B.; Tuo, S.; Velkovska, J.; Arenton, M. W.; Boutle, S.; Cox, B.; Francis, B.; Goodell, J.; Hirosky, R.; Ledovskoy, A.; Lin, C.; Neu, C.; Wood, J.; Gollapinni, S.; Harr, R.; Karchin, P. E.; Kottachchi Kankanamge Don, C.; Lamichhane, P.; Belknap, D. A.; Borrello, L.; Carlsmith, D.; Cepeda, M.; Dasu, S.; Duric, S.; Friis, E.; Grothe, M.; Hall-Wilton, R.; Herndon, M.; Hervé, A.; Klabbers, P.; Klukas, J.; Lanaro, A.; Levine, A.; Loveless, R.; Mohapatra, A.; Ojalvo, I.; Perry, T.; Pierro, G. A.; Polese, G.; Ross, I.; Sakharov, A.; Sarangi, T.; Savin, A.; Smith, W. H.; CMS Collaboration

2014-06-01

The first observation of the associated production of a single top quark and a W boson is presented. The analysis is based on a data set corresponding to an integrated luminosity of 12.2 fb-1 of proton-proton collisions at √s =8 TeV recorded by the CMS experiment at the LHC. Events with two leptons and a jet originating from a b quark are selected. A multivariate analysis based on kinematic and topological properties is used to separate the signal from the dominant tt¯ background. An excess consistent with the signal hypothesis is observed, with a significance which corresponds to 6.1 standard deviations above a background-only hypothesis. The measured production cross section is 23.4±5.4 pb, in agreement with the standard model prediction.

Outcomes after extended pancreatectomy in patients with borderline resectable and locally advanced pancreatic cancer.

PubMed

Hartwig, W; Gluth, A; Hinz, U; Koliogiannis, D; Strobel, O; Hackert, T; Werner, J; Büchler, M W

2016-11-01

In the recent International Study Group of Pancreatic Surgery (ISGPS) consensus on extended pancreatectomy, several issues on perioperative outcome and long-term survival remained unclear. Robust data on outcomes are sparse. The present study aimed to assess the outcome of extended pancreatectomy for borderline resectable and locally advanced pancreatic cancer. A consecutive series of patients with primary pancreatic adenocarcinoma undergoing extended pancreatectomies, as defined by the new ISGPS consensus, were compared with patients who had a standard pancreatectomy. Univariable and multivariable analysis was performed to identify risk factors for perioperative mortality and characteristics associated with survival. Long-term outcome was assessed by means of Kaplan-Meier analysis. The 611 patients who had an extended pancreatectomy had significantly greater surgical morbidity than the 1217 patients who underwent a standard resection (42·7 versus 34·2 per cent respectively), and higher 30-day mortality (4·3 versus 1·8 per cent) and in-hospital mortality (7·5 versus 3·6 per cent) rates. Operating time of 300 min or more, extended total pancreatectomy, and ASA fitness grade of III or IV were associated with increased in-hospital mortality in multivariable analysis, whereas resections involving the colon, portal vein or arteries were not. Median survival and 5-year overall survival rate were reduced in patients having extended pancreatectomy compared with those undergoing a standard resection (16·1 versus 23·6 months, and 11·3 versus 20·6 per cent, respectively). Older age, G3/4 tumours, two or more positive lymph nodes, macroscopic positive resection margins, duration of surgery of 420 min or above, and blood loss of 1000 ml or more were independently associated with decreased overall survival. Extended resections are associated with increased perioperative morbidity and mortality, particularly when extended total pancreatectomy is performed. Favourable long-term outcome is achieved in some patients. © 2016 BJS Society Ltd Published by John Wiley & Sons Ltd.

Heavy metals in soils and sediments from Dongting Lake in China: occurrence, sources, and spatial distribution by multivariate statistical analysis.

PubMed

Zhang, Yaxin; Tian, Ye; Shen, Maocai; Zeng, Guangming

2018-05-01

Heavy metal contamination in soils/sediments and its impact on human health and ecological environment have aroused wide concerns. Our study investigated 30 samples of soils and sediments around Dongting Lake to analyze the concentration of As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn in the samples and to distinguish the natural and anthropogenic sources. Also, the relationship between heavy metals and the physicochemical properties of samples was studied by multivariate statistical analysis. Concentration of Cd at most sampling sites were more than five times that of national environmental quality standard for soil in China (GB 15618-1995), and Pb and Zn levels exceeded one to two times. Moreover, Cr in the soil was higher than the national environmental quality standards for one to two times while in sediment was lower than the national standard. The investigation revealed that the accumulations of As, Cd, Mn, and Pb in the soils, and sediments were affected apparently by anthropogenic activities; however, Cr, Fe, and Ni levels were impacted by parent materials. Human activities around Dongting Lake mainly consisted of industrial activities, mining and smelting, sewage discharges, fossil fuel combustion, and agricultural chemicals. The spatial distribution of heavy metal in soil followed the rule of geographical gradient, whereas in sediments, it was significantly affected by the river basins and human activities. The result of principal component analysis (PCA) demonstrated that heavy metals in soils were associated with pH and total phosphorus (TP), while in sediments, As, Cr, Fe, and Ni were closely associated with cation exchange capacity (CEC) and pH, where Pb, Zn, and Cd were associated with total nitrogen (TN), TP, total carbon (TC), moisture content (MC), soil organic matter (SOM), and ignition lost (IL). Our research provides comprehensive approaches to better understand the potential sources and the fate of contaminants in lakeshore soils and sediments.

Multivariate statistical analysis of heavy metal concentration in soils of Yelagiri Hills, Tamilnadu, India--spectroscopical approach.

PubMed

Chandrasekaran, A; Ravisankar, R; Harikrishnan, N; Satapathy, K K; Prasad, M V R; Kanagasabapathy, K V

2015-02-25

Anthropogenic activities increase the accumulation of heavy metals in the soil environment. Soil pollution significantly reduces environmental quality and affects the human health. In the present study soil samples were collected at different locations of Yelagiri Hills, Tamilnadu, India for heavy metal analysis. The samples were analyzed for twelve selected heavy metals (Mg, Al, K, Ca, Ti, Fe, V, Cr, Mn, Co, Ni and Zn) using energy dispersive X-ray fluorescence (EDXRF) spectroscopy. Heavy metals concentration in soil were investigated using enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (CF) and pollution load index (PLI) to determine metal accumulation, distribution and its pollution status. Heavy metal toxicity risk was assessed using soil quality guidelines (SQGs) given by target and intervention values of Dutch soil standards. The concentration of Ni, Co, Zn, Cr, Mn, Fe, Ti, K, Al, Mg were mainly controlled by natural sources. Multivariate statistical methods such as correlation matrix, principal component analysis and cluster analysis were applied for the identification of heavy metal sources (anthropogenic/natural origin). Geo-statistical methods such as kirging identified hot spots of metal contamination in road areas influenced mainly by presence of natural rocks. Copyright © 2014 Elsevier B.V. All rights reserved.

Positron emission tomography–computed tomography predictors of progression after DA-R-EPOCH for PMBCL

PubMed Central

Ng, Andrea K.; Dabaja, Bouthaina S.; Milgrom, Sarah A.; Gunther, Jillian R.; Fuller, C. David; Smith, Grace L.; Abou Yehia, Zeinab; Qiao, Wei; Wogan, Christine F.; Akhtari, Mani; Mawlawi, Osama; Medeiros, L. Jeffrey; Chuang, Hubert H.; Martin-Doyle, William; Armand, Philippe; LaCasce, Ann S.; Oki, Yasuhiro; Fanale, Michelle; Westin, Jason; Neelapu, Sattva; Nastoupil, Loretta

2018-01-01

Dose-adjusted rituximab plus etoposide, prednisone, vincristine, cyclophosphamide, and doxorubicin (DA-R-EPOCH) has produced good outcomes in primary mediastinal B-cell lymphoma (PMBCL), but predictors of resistance to this treatment are unclear. We investigated whether [18F]fluorodeoxyglucose positron emission tomography–computed tomography (PET-CT) findings could identify patients with PMBCL who would not respond completely to DA-R-EPOCH. We performed a retrospective analysis of 65 patients with newly diagnosed stage I to IV PMBCL treated at 2 tertiary cancer centers who had PET-CT scans available before and after frontline therapy with DA-R-EPOCH. Pretreatment variables assessed included metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Optimal cutoff points for progression-free survival (PFS) were determined by a machine learning approach. Univariate and multivariable models were constructed to assess associations between radiographic variables and PFS. At a median follow-up of 36.6 months (95% confidence interval, 28.1-45.1), 2-year PFS and overall survival rates for the 65 patients were 81.4% and 98.4%, respectively. Machine learning–derived thresholds for baseline MTV and TLG were associated with inferior PFS (elevated MTV: hazard ratio [HR], 11.5; P = .019; elevated TLG: HR, 8.99; P = .005); other pretreatment clinical factors, including International Prognostic Index and bulky (>10 cm) disease, were not. On multivariable analysis, only TLG retained statistical significance (P = .049). Univariate analysis of posttreatment variables revealed that residual CT tumor volume, maximum standardized uptake value, and Deauville score were associated with PFS; a Deauville score of 5 remained significant on multivariable analysis (P = .006). A model combining baseline TLG and end-of-therapy Deauville score identified patients at increased risk of progression. PMID:29895624

Positron emission tomography-computed tomography predictors of progression after DA-R-EPOCH for PMBCL.

PubMed

Pinnix, Chelsea C; Ng, Andrea K; Dabaja, Bouthaina S; Milgrom, Sarah A; Gunther, Jillian R; Fuller, C David; Smith, Grace L; Abou Yehia, Zeinab; Qiao, Wei; Wogan, Christine F; Akhtari, Mani; Mawlawi, Osama; Medeiros, L Jeffrey; Chuang, Hubert H; Martin-Doyle, William; Armand, Philippe; LaCasce, Ann S; Oki, Yasuhiro; Fanale, Michelle; Westin, Jason; Neelapu, Sattva; Nastoupil, Loretta

2018-06-12

Dose-adjusted rituximab plus etoposide, prednisone, vincristine, cyclophosphamide, and doxorubicin (DA-R-EPOCH) has produced good outcomes in primary mediastinal B-cell lymphoma (PMBCL), but predictors of resistance to this treatment are unclear. We investigated whether [ 18 F]fluorodeoxyglucose positron emission tomography-computed tomography (PET-CT) findings could identify patients with PMBCL who would not respond completely to DA-R-EPOCH. We performed a retrospective analysis of 65 patients with newly diagnosed stage I to IV PMBCL treated at 2 tertiary cancer centers who had PET-CT scans available before and after frontline therapy with DA-R-EPOCH. Pretreatment variables assessed included metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Optimal cutoff points for progression-free survival (PFS) were determined by a machine learning approach. Univariate and multivariable models were constructed to assess associations between radiographic variables and PFS. At a median follow-up of 36.6 months (95% confidence interval, 28.1-45.1), 2-year PFS and overall survival rates for the 65 patients were 81.4% and 98.4%, respectively. Machine learning-derived thresholds for baseline MTV and TLG were associated with inferior PFS (elevated MTV: hazard ratio [HR], 11.5; P = .019; elevated TLG: HR, 8.99; P = .005); other pretreatment clinical factors, including International Prognostic Index and bulky (>10 cm) disease, were not. On multivariable analysis, only TLG retained statistical significance ( P = .049). Univariate analysis of posttreatment variables revealed that residual CT tumor volume, maximum standardized uptake value, and Deauville score were associated with PFS; a Deauville score of 5 remained significant on multivariable analysis ( P = .006). A model combining baseline TLG and end-of-therapy Deauville score identified patients at increased risk of progression. © 2018 by The American Society of Hematology.

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, whereas algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons. © The Author(s) 2017. Published by Oxford University Press on behalf of the Systematic Biology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Using Boosting Decision Trees in Gravitational Wave Searches triggered by Gamma-ray Bursts

NASA Astrophysics Data System (ADS)

Zuraw, Sarah; LIGO Collaboration

2015-04-01

The search for gravitational wave bursts requires the ability to distinguish weak signals from background detector noise. Gravitational wave bursts are characterized by their transient nature, making them particularly difficult to detect as they are similar to non-Gaussian noise fluctuations in the detector. The Boosted Decision Tree method is a powerful machine learning algorithm which uses Multivariate Analysis techniques to explore high-dimensional data sets in order to distinguish between gravitational wave signal and background detector noise. It does so by training with known noise events and simulated gravitational wave events. The method is tested using waveform models and compared with the performance of the standard gravitational wave burst search pipeline for Gamma-ray Bursts. It is shown that the method is able to effectively distinguish between signal and background events under a variety of conditions and over multiple Gamma-ray Burst events. This example demonstrates the usefulness and robustness of the Boosted Decision Tree and Multivariate Analysis techniques as a detection method for gravitational wave bursts. LIGO, UMass, PREP, NEGAP.

Universal Pressure Ulcer Prevention Bundle With WOC Nurse Support.

PubMed

Anderson, Megan; Finch Guthrie, Patricia; Kraft, Wendy; Reicks, Patty; Skay, Carol; Beal, Alan L

2015-01-01

This study examined the effectiveness of a universal pressure ulcer prevention bundle (UPUPB) applied to intensive care unit (ICU) patients combined with proactive, semiweekly WOC nurse rounds. The UPUBP was compared to a standard guideline with referral-based WOC nurse involvement measuring adherence to 5 evidence-based prevention interventions and incidence of pressure ulcers. The study used a quasi-experimental, pre-, and postintervention design in which each phase included different subjects. Descriptive methods assisted in exploring the content of WOC nurse rounds. One hundred eighty-one pre- and 146 postintervention subjects who met inclusion criteria and were admitted to ICU for more than 24 hours participated in the study. The research setting was 3 ICUs located at North Memorial Medical Center in Minneapolis, Minnesota. Data collection included admission/discharge skin assessments, chart reviews for 5 evidence-based interventions and patient characteristics, and WOC nurse rounding logs. Study subjects with intact skin on admission identified with an initial skin assessment were enrolled in which prephase subjects received standard care and postphase subjects received the UPUPB. Skin assessments on ICU discharge and chart reviews throughout the stay determined the presence of unit-acquired pressure ulcers and skin care received. Analysis included description of WOC nurse rounds, t-tests for guideline adherence, and multivariate analysis for intervention effect on pressure ulcer incidence. Unit assignment, Braden Scale score, and ICU length of stay were covariates for a multivariate model based on bivariate logistic regression screening. The incidence of unit-acquired pressure ulcers decreased from 15.5% to 2.1%. WOC nurses logged 204 rounds over 6 months, focusing primarily on early detection of pressure sources. Data analysis revealed significantly increased adherence to heel elevation (t = -3.905, df = 325, P < .001) and repositioning (t = -2.441, df = 325, P < .015). Multivariate logistic regression modeling showed a significant reduction in unit-acquired pressure ulcers (P < .001). The intervention increased the Nagelkerke R-Square value by 0.099 (P < .001) more than 0.297 (P < .001) when including only covariates, for a final model value of 0.396 (P < .001). The UPUPB with WOC nurse rounds resulted in a statistically significant and clinically relevant reduction in the incidence of pressure ulcers.

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

Changes of visual-field global indices after cataract surgery in primary open-angle glaucoma patients.

PubMed

Seol, Bo Ram; Jeoung, Jin Wook; Park, Ki Ho

2016-11-01

To determine changes of visual-field (VF) global indices after cataract surgery and the factors associated with the effect of cataracts on those indices in primary open-angle glaucoma (POAG) patients. A retrospective chart review of 60 POAG patients who had undergone phacoemulsification and intraocular lens insertion was conducted. All of the patients were evaluated with standard automated perimetry (SAP; 30-2 Swedish interactive threshold algorithm; Carl Zeiss Meditec Inc.) before and after surgery. VF global indices before surgery were compared with those after surgery. The best-corrected visual acuity, intraocular pressure (IOP), number of glaucoma medications before surgery, mean total deviation (TD) values, mean pattern deviation (PD) value, and mean TD-PD value were also compared with the corresponding postoperative values. Additionally, postoperative peak IOP and mean IOP were evaluated. Univariate and multivariate logistic regression analyses were performed to identify the factors associated with the effect of cataract on global indices. Mean deviation (MD) after cataract surgery was significantly improved compared with the preoperative MD. Pattern standard deviation (PSD) and visual-field index (VFI) after surgery were similar to those before surgery. Also, mean TD and mean TD-PD were significantly improved after surgery. The posterior subcapsular cataract (PSC) type showed greater MD changes than did the non-PSC type in both the univariate and multivariate logistic regression analyses. In the univariate logistic regression analysis, the preoperative TD-PD value and type of cataract were associated with MD change. However, in the multivariate logistic regression analysis, type of cataract was the only associated factor. None of the other factors was associated with MD change. MD was significantly affected by cataracts, whereas PSD and VFI were not. Most notably, the PSC type showed better MD improvement compared with the non-PSC type after cataract surgery. Clinicians therefore should carefully analyze VF examination results for POAG patients with the PSC type.

Variation in Risk-Standardized Mortality of Stroke among Hospitals in Japan.

PubMed

Matsui, Hiroki; Fushimi, Kiyohide; Yasunaga, Hideo

2015-01-01

Despite recent advances in care, stroke remains a life-threatening disease. Little is known about current hospital mortality with stroke and how it varies by hospital in a national clinical setting in Japan. Using the Diagnosis Procedure Combination database (a national inpatient database in Japan), we identified patients aged ≥ 20 years who were admitted to the hospital with a primary diagnosis of stroke within 3 days of stroke onset from April 2012 to March 2013. We constructed a multivariable logistic regression model to predict in-hospital death for each patient with patient-level factors, including age, sex, type of stroke, Japan Coma Scale, and modified Rankin Scale. We defined risk-standardized mortality ratio as the ratio of the actual number of in-hospital deaths to the expected number of such deaths for each hospital. A hospital-level multivariable linear regression was modeled to analyze the association between risk-standardized mortality ratio and hospital-level factors. We performed a patient-level Cox regression analysis to examine the association of in-hospital death with both patient-level and hospital-level factors. Of 176,753 eligible patients from 894 hospitals, overall in-hospital mortality was 10.8%. The risk-standardized mortality ratio for stroke varied widely among the hospitals; the proportions of hospitals with risk-standardized mortality ratio categories of ≤ 0.50, 0.51-1.00, 1.01-1.50, 1.51-2.00, and >2.00 were 3.9%, 47.9%, 41.4%, 5.2%, and 1.5%, respectively. Academic status, presence of a stroke care unit, higher hospital volume and availability of endovascular therapy had a significantly lower risk-standardized mortality ratio; distance from the patient's residence to the hospital was not associated with the risk-standardized mortality ratio. Our results suggest that stroke-ready hospitals play an important role in improving stroke mortality in Japan.

Environmental assessment of Al-Hammar Marsh, Southern Iraq.

PubMed

Al-Gburi, Hind Fadhil Abdullah; Al-Tawash, Balsam Salim; Al-Lafta, Hadi Salim

2017-02-01

(a) To determine the spatial distributions and levels of major and minor elements, as well as heavy metals, in water, sediment, and biota (plant and fish) in Al-Hammar Marsh, southern Iraq, and ultimately to supply more comprehensive information for policy-makers to manage the contaminants input into the marsh so that their concentrations do not reach toxic levels. (b) to characterize the seasonal changes in the marsh surface water quality. (c) to address the potential environmental risk of these elements by comparison with the historical levels and global quality guidelines (i.e., World Health Organization (WHO) standard limits). (d) to define the sources of these elements (i.e., natural and/or anthropogenic) using combined multivariate statistical techniques such as Principal Component Analysis (PCA) and Agglomerative Hierarchical Cluster Analysis (AHCA) along with pollution analysis (i.e., enrichment factor analysis). Water, sediment, plant, and fish samples were collected from the marsh, and analyzed for major and minor ions, as well as heavy metals, and then compared to historical levels and global quality guidelines (WHO guidelines). Then, multivariate statistical techniques, such as PCA and AHCA, were used to determine the element sourcing. Water analyses revealed unacceptable values for almost all physio-chemical and biological properties, according to WHO standard limits for drinking water. Almost all major ions and heavy metal concentrations in water showed a distinct decreasing trend at the marsh outlet station compared to other stations. In general, major and minor ions, as well as heavy metals exhibit higher concentrations in winter than in summer. Sediment analyses using multivariate statistical techniques revealed that Mg, Fe, S, P, V, Zn, As, Se, Mo, Co, Ni, Cu, Sr, Br, Cd, Ca, N, Mn, Cr, and Pb were derived from anthropogenic sources, while Al, Si, Ti, K, and Zr were primarily derived from natural sources. Enrichment factor analysis gave results compatible with multivariate statistical techniques findings. Analysis of heavy metals in plant samples revealed that there is no pollution in plants in Al-Hammar Marsh. However, the concentrations of heavy metals in fish samples showed that all samples were contaminated by Pb, Mn, and Ni, while some samples were contaminated by Pb, Mn, and Ni. Decreasing of Tigris and Euphrates discharges during the past decades due to drought conditions and upstream damming, as well as the increasing stress of wastewater effluents from anthropogenic activities, led to degradation of the downstream Al-Hammar Marsh water quality in terms of physical, chemical, and biological properties. As such properties were found to consistently exceed the historical and global quality objectives. However, element concentration decreasing trend at the marsh outlet station compared to other stations indicate that the marsh plays an important role as a natural filtration and bioremediation system. Higher element concentrations in winter were due to runoff from the washing of the surrounding Sabkha during flooding by winter rainstorms. Finally, the high concentrations of heavy metals in fish samples can be attributed to bioaccumulation and biomagnification processes.

Sex estimation standards for medieval and contemporary Croats

PubMed Central

Bašić, Željana; Kružić, Ivana; Jerković, Ivan; Anđelinović, Deny; Anđelinović, Šimun

2017-01-01

Aim To develop discriminant functions for sex estimation on medieval Croatian population and test their application on contemporary Croatian population. Methods From a total of 519 skeletons, we chose 84 adult excellently preserved skeletons free of antemortem and postmortem changes and took all standard measurements. Sex was estimated/determined using standard anthropological procedures and ancient DNA (amelogenin analysis) where pelvis was insufficiently preserved or where sex morphological indicators were not consistent. We explored which measurements showed sexual dimorphism and used them for developing univariate and multivariate discriminant functions for sex estimation. We included only those functions that reached accuracy rate ≥80%. We tested the applicability of developed functions on modern Croatian sample (n = 37). Results From 69 standard skeletal measurements used in this study, 56 of them showed statistically significant sexual dimorphism (74.7%). We developed five univariate discriminant functions with classification rate 80.6%-85.2% and seven multivariate discriminant functions with an accuracy rate of 81.8%-93.0%. When tested on the modern population functions showed classification rates 74.1%-100%, and ten of them reached aimed accuracy rate. Females showed higher classification rates in the medieval populations, whereas males were better classified in the modern populations. Conclusion Developed discriminant functions are sufficiently accurate for reliable sex estimation in both medieval Croatian population and modern Croatian samples and may be used in forensic settings. The methodological issues that emerged regarding the importance of considering external factors in development and application of discriminant functions for sex estimation should be further explored. PMID:28613039

Accurate predictions of iron redox state in silicate glasses: A multivariate approach using X-ray absorption spectroscopy

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

Dyar, M. Darby; McCanta, Molly; Breves, Elly

2016-03-01

Pre-edge features in the K absorption edge of X-ray absorption spectra are commonly used to predict Fe3+ valence state in silicate glasses. However, this study shows that using the entire spectral region from the pre-edge into the extended X-ray absorption fine-structure region provides more accurate results when combined with multivariate analysis techniques. The least absolute shrinkage and selection operator (lasso) regression technique yields %Fe3+ values that are accurate to ±3.6% absolute when the full spectral region is employed. This method can be used across a broad range of glass compositions, is easily automated, and is demonstrated to yield accurate resultsmore » from different synchrotrons. It will enable future studies involving X-ray mapping of redox gradients on standard thin sections at 1 × 1 μm pixel sizes.« less

Accurate predictions of iron redox state in silicate glasses: A multivariate approach using X-ray absorption spectroscopy

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

Dyar, M. Darby; McCanta, Molly; Breves, Elly

2016-03-01

Pre-edge features in the K absorption edge of X-ray absorption spectra are commonly used to predict Fe 3+ valence state in silicate glasses. However, this study shows that using the entire spectral region from the pre-edge into the extended X-ray absorption fine-structure region provides more accurate results when combined with multivariate analysis techniques. The least absolute shrinkage and selection operator (lasso) regression technique yields %Fe 3+ values that are accurate to ±3.6% absolute when the full spectral region is employed. This method can be used across a broad range of glass compositions, is easily automated, and is demonstrated to yieldmore » accurate results from different synchrotrons. It will enable future studies involving X-ray mapping of redox gradients on standard thin sections at 1 × 1 μm pixel sizes.« less

[Multivariate ordinal logistic regression analysis on the association between consumption of fried food and both esophageal cancer and precancerous lesions].

PubMed

Guo, L W; Liu, S Z; Zhang, M; Chen, Q; Zhang, S K; Sun, X B

2017-12-10

Objective: To investigate the effect of fried food intake on the pathogenesis of esophageal cancer and precancerous lesions. Methods: From 2005 to 2013, all the residents aged 40-69 years from 11 counties (cities) where cancer screening of upper gastrointestinal cancer had been conducted in rural areas of Henan province, were recruited as the subjects of study. Information on demography and lifestyle was collected. The residents under study were screened with iodine staining endoscopic examination and biopsy samples were diagnosed pathologically, under standardized criteria. Subjects with high risk were divided into the groups based on their different pathological degrees. Multivariate ordinal logistic regression analysis was used to analyze the relationship between the frequency of fried food intake and esophageal cancer and precancerous lesions. Results: A total number of 8 792 cases with normal esophagus, 3 680 with mild hyperplasia, 972 with moderate hyperplasia, 413 with severe hyperplasia carcinoma in situ, and 336 cases of esophageal cancer were recruited. Results from multivariate logistic regression analysis showed that, when compared with those who did not eat fried food, the intake of fried food (<2 times/week: OR =1.60, 95% CI : 1.40-1.83; ≥2 times/week: OR =2.58, 95% CI : 1.98-3.37) appeared a risk factor for both esophageal cancer or precancerous lesions after adjustment for age, sex, marital status, educational level, body mass index, smoking and alcohol intake. Conclusion: The intake of fried food appeared a risk factor for both esophageal cancer and precancerous lesions.

Population differences in the postcrania of modern South Africans and the implications for ancestry estimation.

PubMed

Liebenberg, Leandi; L'Abbé, Ericka N; Stull, Kyra E

2015-12-01

The cranium is widely recognized as the most important skeletal element to use when evaluating population differences and estimating ancestry. However, the cranium is not always intact or available for analysis, which emphasizes the need for postcranial alternatives. The purpose of this study was to quantify postcraniometric differences among South Africans that can be used to estimate ancestry. Thirty-nine standard measurements from 11 postcranial bones were collected from 360 modern black, white and coloured South Africans; the sex and ancestry distribution were equal. Group differences were explored with analysis of variance (ANOVA) and Tukey's honestly significant difference (HSD) test. Linear and flexible discriminant analysis (LDA and FDA, respectively) were conducted with bone models as well as numerous multivariate subsets to identify the model and method that yielded the highest correct classifications. Leave-one-out (LDA) and k-fold (k=10; FDA) cross-validation with equal priors were used for all models. ANOVA and Tukey's HSD results reveal statistically significant differences between at least two of the three groups for the majority of the variables, with varying degrees of group overlap. Bone models, which consisted of all measurements per bone, resulted in low accuracies that ranged from 46% to 63% (LDA) and 41% to 66% (FDA). In contrast, the multivariate subsets, which consisted of different variable combinations from all elements, achieved accuracies as high as 85% (LDA) and 87% (FDA). Thus, when using a multivariate approach, the postcranial skeleton can distinguish among three modern South African groups with high accuracy. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Groundwater quality in Ghaziabad district, Uttar Pradesh, India: Multivariate and health risk assessment.

PubMed

Chabukdhara, Mayuri; Gupta, Sanjay Kumar; Kotecha, Yatharth; Nema, Arvind K

2017-07-01

This study aimed to assess the quality of groundwater and potential health risk due to ingestion of heavy metals in the peri-urban and urban-industrial clusters of Ghaziabad district, Uttar Pradesh, India. Furthermore, the study aimed to evaluate heavy metals sources and their pollution level using multivariate analysis and fuzzy comprehensive assessment (FCA), respectively. Multivariate analysis using principle component analysis (PCA) showed mixed origin for Pb, Cd, Zn, Fe, and Ni, natural source for Cu and Mn and anthropogenic source for Cr. Among all the metals, Pb, Cd, Fe and Ni were above the safe limits of Bureau of Indian Standards (BIS) and World Health Organization (WHO) except Ni. Health risk in terms of hazard quotient (HQ) showed that the HQ values for children were higher than the safe level (HQ = 1) for Pb (2.4) and Cd (2.1) in pre-monsoon while in post-monsoon the value exceeded only for Pb (HQ = 1.23). The health risks of heavy metals for the adults were well within safe limits. The finding of this study indicates potential health risks to the children due to chronic exposure to contaminated groundwater in the region. Based on FCA, groundwater pollution could be categorized as quite high in the peri-urban region, and absolutely high in the urban region of Ghaziabad district. This study showed that different approaches are required for the integrated assessment of the groundwater pollution, and provides a scientific basis for the strategic future planning and comprehensive management. Copyright © 2017 Elsevier Ltd. All rights reserved.

Failure of Standard Training Sets in the Analysis of Fast-Scan Cyclic Voltammetry Data.

PubMed

Johnson, Justin A; Rodeberg, Nathan T; Wightman, R Mark

2016-03-16

The use of principal component regression, a multivariate calibration method, in the analysis of in vivo fast-scan cyclic voltammetry data allows for separation of overlapping signal contributions, permitting evaluation of the temporal dynamics of multiple neurotransmitters simultaneously. To accomplish this, the technique relies on information about current-concentration relationships across the scan-potential window gained from analysis of training sets. The ability of the constructed models to resolve analytes depends critically on the quality of these data. Recently, the use of standard training sets obtained under conditions other than those of the experimental data collection (e.g., with different electrodes, animals, or equipment) has been reported. This study evaluates the analyte resolution capabilities of models constructed using this approach from both a theoretical and experimental viewpoint. A detailed discussion of the theory of principal component regression is provided to inform this discussion. The findings demonstrate that the use of standard training sets leads to misassignment of the current-concentration relationships across the scan-potential window. This directly results in poor analyte resolution and, consequently, inaccurate quantitation, which may lead to erroneous conclusions being drawn from experimental data. Thus, it is strongly advocated that training sets be obtained under the experimental conditions to allow for accurate data analysis.

Associations Between Physician Empathy, Physician Characteristics, and Standardized Measures of Patient Experience.

PubMed

Chaitoff, Alexander; Sun, Bob; Windover, Amy; Bokar, Daniel; Featherall, Joseph; Rothberg, Michael B; Misra-Hebert, Anita D

2017-10-01

To identify correlates of physician empathy and determine whether physician empathy is related to standardized measures of patient experience. Demographic, professional, and empathy data were collected during 2013-2015 from Cleveland Clinic Health System physicians prior to participation in mandatory communication skills training. Empathy was assessed using the Jefferson Scale of Empathy. Data were also collected for seven measures (six provider communication items and overall provider rating) from the visit-specific and 12-month Consumer Assessment of Healthcare Providers and Systems Clinician and Group (CG-CAHPS) surveys. Associations between empathy and provider characteristics were assessed by linear regression, ANOVA, or a nonparametric equivalent. Significant predictors were included in a multivariable linear regression model. Correlations between empathy and CG-CAHPS scores were assessed using Spearman rank correlation coefficients. In bivariable analysis (n = 847 physicians), female sex (P < .001), specialty (P < .01), outpatient practice setting (P < .05), and DO degree (P < .05) were associated with higher empathy scores. In multivariable analysis, female sex (P < .001) and four specialties (obstetrics-gynecology, pediatrics, psychiatry, and thoracic surgery; all P < .05) were significantly associated with higher empathy scores. Of the seven CG-CAHPS measures, scores on five for the 583 physicians with visit-specific data and on three for the 277 physicians with 12-month data were positively correlated with empathy. Specialty and sex were independently associated with physician empathy. Empathy was correlated with higher scores on multiple CG-CAHPS items, suggesting improving physician empathy might play a role in improving patient experience.

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.

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

Bhattacherjee, Biplob; Mukhopadhyay, Satyanarayan; Nojiri, Mihoko M.

Here, we study the impact of including quark- and gluon-initiated jet discrimination in the search for strongly interacting supersymmetric particles at the LHC. Taking the example of gluino pair production, considerable improvement is observed in the LHC search reach on including the jet substructure observables to the standard kinematic variables within a multivariate analysis. In particular, quark and gluon jet separation has higher impact in the region of intermediate mass-gap between the gluino and the lightest neutralino, as the difference between the signal and the standard model background kinematic distributions is reduced in this region. We also compare the predictionsmore » from different Monte Carlo event generators to estimate the uncertainty originating from the modelling of the parton shower and hadronization processes.« less

Evidence for production of single top quarks and first direct measurement of |Vtb|.

PubMed

Abazov, V M; Abbott, B; Abolins, M; Acharya, B S; Adams, M; Adams, T; Aguilo, E; Ahn, S H; Ahsan, M; Alexeev, G D; Alkhazov, G; Alton, A; Alverson, G; Alves, G A; Anastasoaie, M; Ancu, L S; Andeen, T; Anderson, S; Andrieu, B; Anzelc, M S; Arnoud, Y; Arov, M; Askew, A; Asman, B; Assis Jesus, A C S; Atramentov, O; Autermann, C; Avila, C; Ay, C; Badaud, F; Baden, A; Bagby, L; Baldin, B; Bandurin, D V; Banerjee, P; Banerjee, S; Barberis, E; Bargassa, P; Baringer, P; Barnes, C; Barreto, J; Bartlett, J F; Bassler, U; Bauer, D; Beale, S; Bean, A; Begalli, M; Begel, M; Belanger-Champagne, C; Bellantoni, L; Bellavance, A; Benitez, J A; Beri, S B; Bernardi, G; Bernhard, R; Berntzon, L; Bertram, I; Besançon, M; Beuselinck, R; Bezzubov, V A; Bhat, P C; Bhatnagar, V; Binder, M; Biscarat, C; Blackler, I; Blazey, G; Blekman, F; Blessing, S; Bloch, D; Bloom, K; Boehnlein, A; Boline, D; Bolton, T A; Boos, E E; Borissov, G; Bos, K; Bose, T; Brandt, A; Brock, R; Brooijmans, G; Bross, A; Brown, D; Buchanan, N J; Buchholz, D; Buehler, M; Buescher, V; Bunichev, V; Burdin, S; Burke, S; Burnett, T H; Busato, E; Buszello, C P; Butler, J M; Calfayan, P; Calvet, S; Cammin, J; Caron, S; Carvalho, W; Casey, B C K; Cason, N M; Castilla-Valdez, H; Chakrabarti, S; Chakraborty, D; Chan, K M; Chandra, A; Charles, F; Cheu, E; Chevallier, F; Cho, D K; Choi, S; Choudhary, B; Christofek, L; Claes, D; Clément, B; Clément, C; Coadou, Y; Cooke, M; Cooper, W E; Corcoran, M; Couderc, F; Cousinou, M-C; Cox, B; Crépé-Renaudin, S; Cutts, D; Cwiok, M; da Motta, H; Das, A; Das, M; Davies, B; Davies, G; De, K; de Jong, P; de Jong, S J; De La Cruz-Burelo, E; De Oliveira Martins, C; Degenhardt, J D; Déliot, F; Demarteau, M; Demina, R; Denisov, D; Denisov, S P; Desai, S; Diehl, H T; Diesburg, M; Doidge, M; Dominguez, A; Dong, H; Dudko, L V; Duflot, L; Dugad, S R; Duggan, D; Duperrin, A; Dyer, J; Dyshkant, A; Eads, M; Edmunds, D; Ellison, J; Elvira, V D; Enari, Y; Eno, S; Ermolov, P; Evans, H; Evdokimov, A; Evdokimov, V N; Feligioni, L; Ferapontov, A V; Ferbel, T; Fiedler, F; Filthaut, F; Fisher, W; Fisk, H E; Ford, M; Fortner, M; Fox, H; Fu, S; Fuess, S; Gadfort, T; Galea, C F; Gallas, E; Galyaev, E; Garcia, C; Garcia-Bellido, A; Gavrilov, V; Gay, A; Gay, P; Geist, W; Gelé, D; Gelhaus, R; Gerber, C E; Gershtein, Y; Gillberg, D; Ginther, G; Gollub, N; Gómez, B; Goussiou, A; Grannis, P D; Greenlee, H; Greenwood, Z D; Gregores, E M; Grenier, G; Gris, Ph; Grivaz, J-F; Grohsjean, A; Grünendahl, S; Grünewald, M W; Guo, F; Guo, J; Gutierrez, G; Gutierrez, P; Haas, A; Hadley, N J; Haefner, P; Hagopian, S; Haley, J; Hall, I; Hall, R E; Han, L; Hanagaki, K; Hansson, P; Harder, K; Harel, A; Harrington, R; Hauptman, J M; Hauser, R; Hays, J; Hebbeker, T; Hedin, D; Hegeman, J G; Heinmiller, J M; Heinson, A P; Heintz, U; Hensel, C; Herner, K; Hesketh, G; Hildreth, M D; Hirosky, R; Hobbs, J D; Hoeneisen, B; Hoeth, H; Hohlfeld, M; Hong, S J; Hooper, R; Houben, P; Hu, Y; Hubacek, Z; Hynek, V; Iashvili, I; Illingworth, R; Ito, A S; Jabeen, S; Jaffré, M; Jain, S; Jakobs, K; Jarvis, C; Jenkins, A; Jesik, R; Johns, K; Johnson, C; Johnson, M; Jonckheere, A; Jonsson, P; Juste, A; Käfer, D; Kahn, S; Kajfasz, E; Kalinin, A M; Kalk, J M; Kalk, J R; Kappler, S; Karmanov, D; Kasper, J; Kasper, P; Katsanos, I; Kau, D; Kaur, R; Kehoe, R; Kermiche, S; Khalatyan, N; Khanov, A; Kharchilava, A; Kharzheev, Y M; Khatidze, D; Kim, H; Kim, T J; Kirby, M H; Klima, B; Kohli, J M; Konrath, J-P; Kopal, M; Korablev, V M; Kotcher, J; Kothari, B; Koubarovsky, A; Kozelov, A V; Krop, D; Kryemadhi, A; Kuhl, T; Kumar, A; Kunori, S; Kupco, A; Kurca, T; Kvita, J; Lam, D; Lammers, S; Landsberg, G; Lazoflores, J; Le Bihan, A-C; Lebrun, P; Lee, W M; Leflat, A; Lehner, F; Lesne, V; Leveque, J; Lewis, P; Li, J; Li, L; Li, Q Z; Lietti, S M; Lima, J G R; Lincoln, D; Linnemann, J; Lipaev, V V; Lipton, R; Liu, Z; Lobo, L; Lobodenko, A; Lokajicek, M; Lounis, A; Love, P; Lubatti, H J; Lynker, M; Lyon, A L; Maciel, A K A; Madaras, R J; Mättig, P; Magass, C; Magerkurth, A; Makovec, N; Mal, P K; Malbouisson, H B; Malik, S; Malyshev, V L; Mao, H S; Maravin, Y; McCarthy, R; Melnitchouk, A; Mendes, A; Mendoza, L; Mercadante, P G; Merkin, M; Merritt, K W; Meyer, A; Meyer, J; Michaut, M; Miettinen, H; Millet, T; Mitrevski, J; Molina, J; Mommsen, R K; Mondal, N K; Monk, J; Moore, R W; Moulik, T; Muanza, G S; Mulders, M; Mulhearn, M; Mundal, O; Mundim, L; Nagy, E; Naimuddin, M; Narain, M; Naumann, N A; Neal, H A; Negret, J P; Neustroev, P; Noeding, C; Nomerotski, A; Novaes, S F; Nunnemann, T; O'Dell, V; O'Neil, D C; Obrant, G; Ochando, C; Oguri, V; Oliveira, N; Onoprienko, D; Oshima, N; Osta, J; Otec, R; Otero Y Garzón, G J; Owen, M; Padley, P; Pangilinan, M; Parashar, N; Park, S-J; Park, S K; Parsons, J; Partridge, R; Parua, N; Patwa, A; Pawloski, G; Perea, P M; Perfilov, M; Peters, K; Peters, Y; Pétroff, P; Petteni, M; Piegaia, R; Piper, J; Pleier, M-A; Podesta-Lerma, P L M; Podstavkov, V M; Pogorelov, Y; Pol, M-E; Pompos, A; Pope, B G; Popov, A V; Potter, C; Prado da Silva, W L; Prosper, H B; Protopopescu, S; Qian, J; Quadt, A; Quinn, B; Rangel, M S; Rani, K J; Ranjan, K; Ratoff, P N; Renkel, P; Reucroft, S; Rijssenbeek, M; Ripp-Baudot, I; Rizatdinova, F; Robinson, S; Rodrigues, R F; Royon, C; Rubinov, P; Ruchti, R; Sajot, G; Sánchez-Hernández, A; Sanders, M P; Santoro, A; Savage, G; Sawyer, L; Scanlon, T; Schaile, D; Schamberger, R D; Scheglov, Y; Schellman, H; Schieferdecker, P; Schmitt, C; Schwanenberger, C; Schwartzman, A; Schwienhorst, R; Sekaric, J; Sengupta, S; Severini, H; Shabalina, E; Shamim, M; Shary, V; Shchukin, A A; Shivpuri, R K; Shpakov, D; Siccardi, V; Sidwell, R A; Simak, V; Sirotenko, V; Skubic, P; Slattery, P; Smith, R P; Snow, G R; Snow, J; Snyder, S; Söldner-Rembold, S; Song, X; Sonnenschein, L; Sopczak, A; Sosebee, M; Soustruznik, K; Souza, M; Spurlock, B; Stark, J; Steele, J; Stolin, V; Stone, A; Stoyanova, D A; Strandberg, J; Strandberg, S; Strang, M A; Strauss, M; Ströhmer, R; Strom, D; Strovink, M; Stutte, L; Sumowidagdo, S; Svoisky, P; Sznajder, A; Talby, M; Tamburello, P; Taylor, W; Telford, P; Temple, J; Tiller, B; Titov, M; Tokmenin, V V; Tomoto, M; Toole, T; Torchiani, I; Trefzger, T; Trincaz-Duvoid, S; Tsybychev, D; Tuchming, B; Tully, C; Tuts, P M; Unalan, R; Uvarov, L; Uvarov, S; Uzunyan, S; Vachon, B; van den Berg, P J; van Eijk, B; Van Kooten, R; van Leeuwen, W M; Varelas, N; Varnes, E W; Vartapetian, A; Vasilyev, I A; Vaupel, M; Verdier, P; Vertogradov, L S; Verzocchi, M; Vetterli, M; Villeneuve-Seguier, F; Vint, P; Vlimant, J-R; Von Toerne, E; Voutilainen, M; Vreeswijk, M; Wahl, H D; Wang, L; Wang, M H L S; Warchol, J; Watts, G; Wayne, M; Weber, G; Weber, M; Weerts, H; Wermes, N; Wetstein, M; White, A; Wicke, D; Wilson, G W; Wimpenny, S J; Wobisch, M; Womersley, J; Wood, D R; Wyatt, T R; Xie, Y; Yacoob, S; Yamada, R; Yan, M; Yasuda, T; Yatsunenko, Y A; Yip, K; Yoo, H D; Youn, S W; Yu, C; Yu, J; Yurkewicz, A; Zatserklyaniy, A; Zeitnitz, C; Zhang, D; Zhao, T; Zhou, B; Zhu, J; Zielinski, M; Zieminska, D; Zieminski, A; Zutshi, V; Zverev, E G

2007-05-04

The D0 Collaboration presents first evidence for the production of single top quarks at the Fermilab Tevatron pp[over ] collider. Using a 0.9 fb(-1) dataset, we apply a multivariate analysis to separate signal from background and measure sigma(pp[over ]-->tb+X,tqb+X)=4.9+/-1.4 pb. The probability to measure a cross section at this value or higher in the absence of a signal is 0.035%, corresponding to a 3.4 standard deviation significance. We use the cross section measurement to directly determine the Cabibbo-Kobayashi-Maskawa matrix element that describes the Wtb coupling and find 0.68<|V(tb)|

Multivariate test power approximations for balanced linear mixed models in studies with missing data.

PubMed

Ringham, Brandy M; Kreidler, Sarah M; Muller, Keith E; Glueck, Deborah H

2016-07-30

Multilevel and longitudinal studies are frequently subject to missing data. For example, biomarker studies for oral cancer may involve multiple assays for each participant. Assays may fail, resulting in missing data values that can be assumed to be missing completely at random. Catellier and Muller proposed a data analytic technique to account for data missing at random in multilevel and longitudinal studies. They suggested modifying the degrees of freedom for both the Hotelling-Lawley trace F statistic and its null case reference distribution. We propose parallel adjustments to approximate power for this multivariate test in studies with missing data. The power approximations use a modified non-central F statistic, which is a function of (i) the expected number of complete cases, (ii) the expected number of non-missing pairs of responses, or (iii) the trimmed sample size, which is the planned sample size reduced by the anticipated proportion of missing data. The accuracy of the method is assessed by comparing the theoretical results to the Monte Carlo simulated power for the Catellier and Muller multivariate test. Over all experimental conditions, the closest approximation to the empirical power of the Catellier and Muller multivariate test is obtained by adjusting power calculations with the expected number of complete cases. The utility of the method is demonstrated with a multivariate power analysis for a hypothetical oral cancer biomarkers study. We describe how to implement the method using standard, commercially available software products and give example code. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

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

PubMed

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

2015-03-27

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

Can we discover double Higgs production at the LHC?

NASA Astrophysics Data System (ADS)

Alves, Alexandre; Ghosh, Tathagata; Sinha, Kuver

2017-08-01

We explore double Higgs production via gluon fusion in the b b ¯γ γ channel at the high-luminosity LHC using machine learning tools. We first propose a Bayesian optimization approach to select cuts on kinematic variables, obtaining a 30%-50% increase in the significance compared to current results in the literature. We show that this improvement persists once systematic uncertainties are taken into account. We next use boosted decision trees (BDT) to further discriminate signal and background events. Our analysis shows that a joint optimization of kinematic cuts and BDT hyperparameters results in an appreciable improvement in the significance. Finally, we perform a multivariate analysis of the output scores of the BDT. We find that assuming a very low level of systematics, the techniques proposed here will be able to confirm the production of a pair of standard model Higgs bosons at 5 σ level with 3 ab-1 of data. Assuming a more realistic projection of the level of systematics, around 10%, the optimization of cuts to train BDTs combined with a multivariate analysis delivers a respectable significance of 4.6 σ . Even assuming large systematics of 20%, our analysis predicts a 3.6 σ significance, which represents at least strong evidence in favor of double Higgs production. We carefully incorporate background contributions coming from light flavor jets or c jets being misidentified as b jets and jets being misidentified as photons in our analysis.

Uncertainty Analysis of Instrument Calibration and Application

NASA Technical Reports Server (NTRS)

Tripp, John S.; Tcheng, Ping

1999-01-01

Experimental aerodynamic researchers require estimated precision and bias uncertainties of measured physical quantities, typically at 95 percent confidence levels. Uncertainties of final computed aerodynamic parameters are obtained by propagation of individual measurement uncertainties through the defining functional expressions. In this paper, rigorous mathematical techniques are extended to determine precision and bias uncertainties of any instrument-sensor system. Through this analysis, instrument uncertainties determined through calibration are now expressed as functions of the corresponding measurement for linear and nonlinear univariate and multivariate processes. Treatment of correlated measurement precision error is developed. During laboratory calibration, calibration standard uncertainties are assumed to be an order of magnitude less than those of the instrument being calibrated. Often calibration standards do not satisfy this assumption. This paper applies rigorous statistical methods for inclusion of calibration standard uncertainty and covariance due to the order of their application. The effects of mathematical modeling error on calibration bias uncertainty are quantified. The effects of experimental design on uncertainty are analyzed. The importance of replication is emphasized, techniques for estimation of both bias and precision uncertainties using replication are developed. Statistical tests for stationarity of calibration parameters over time are obtained.

Phenotypes of intermediate forms of Fasciola hepatica and F. gigantica in buffaloes from Central Punjab, Pakistan.

PubMed

Afshan, K; Valero, M A; Qayyum, M; Peixoto, R V; Magraner, A; Mas-Coma, S

2014-12-01

Fascioliasis is an important food-borne parasitic disease caused by the two trematode species, Fasciola hepatica and Fasciola gigantica. The phenotypic features of fasciolid adults and eggs infecting buffaloes inhabiting the Central Punjab area, Pakistan, have been studied to characterize fasciolid populations involved. Morphometric analyses were made with a computer image analysis system (CIAS) applied on the basis of standardized measurements. Since it is the first study of this kind undertaken in Pakistan, the results are compared to pure fasciolid populations: (a) F. hepatica from the European Mediterranean area; and (b) F. gigantica from Burkina Faso; i.e. geographical areas where both species do not co-exist. Only parasites obtained from bovines were used. The multivariate analysis showed that the characteristics, including egg morphometrics, of fasciolids from Central Punjab, Pakistan, are between F. hepatica and F. gigantica standard populations. Similarly, the morphometric measurements of fasciolid eggs from Central Punjab are also between F. hepatica and F. gigantica standard populations. These results demonstrate the existence of fasciolid intermediate forms in endemic areas in Pakistan.

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…

Comparative forensic soil analysis of New Jersey state parks using a combination of simple techniques with multivariate statistics.

PubMed

Bonetti, Jennifer; Quarino, Lawrence

2014-05-01

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

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

PubMed Central

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

2012-01-01

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

Determination of glucose in a biological matrix by multivariate analysis of multiple band-pass-filtered Fourier transform near-infrared interferograms.

PubMed

Mattu, M J; Small, G W; Arnold, M A

1997-11-15

A multivariate calibration method is described in which Fourier transform near-infrared interferogram data are used to determine clinically relevant levels of glucose in an aqueous matrix of bovine serum albumin (BSA) and triacetin. BSA and triacetin are used to model the protein and triglycerides in blood, respectively, and are present in levels spanning the normal human physiological range. A full factorial experimental design is constructed for the data collection, with glucose at 10 levels, BSA at 4 levels, and triacetin at 4 levels. Gaussian-shaped band-pass digital filters are applied to the interferogram data to extract frequencies associated with an absorption band of interest. Separate filters of various widths are positioned on the glucose band at 4400 cm-1, the BSA band at 4606 cm-1, and the triacetin band at 4446 cm-1. Each filter is applied to the raw interferogram, producing one, two, or three filtered interferograms, depending on the number of filters used. Segments of these filtered interferograms are used together in a partial least-squares regression analysis to build glucose calibration models. The optimal calibration model is realized by use of separate segments of interferograms filtered with three filters centered on the glucose, BSA, and triacetin bands. Over the physiological range of 1-20 mM glucose, this 17-term model exhibits values of R2, standard error of calibration, and standard error of prediction of 98.85%, 0.631 mM, and 0.677 mM, respectively. These results are comparable to those obtained in a conventional analysis of spectral data. The interferogram-based method operates without the use of a separate background measurement and employs only a short section of the interferogram.

Stochastic univariate and multivariate time series analysis of PM2.5 and PM10 air pollution: A comparative case study for Plovdiv and Asenovgrad, Bulgaria

NASA Astrophysics Data System (ADS)

Gocheva-Ilieva, S.; Stoimenova, M.; Ivanov, A.; Voynikova, D.; Iliev, I.

2016-10-01

Fine particulate matter PM2.5 and PM10 air pollutants are a serious problem in many urban areas affecting both the health of the population and the environment as a whole. The availability of large data arrays for the levels of these pollutants makes it possible to perform statistical analysis, to obtain relevant information, and to find patterns within the data. Research in this field is particularly topical for a number of Bulgarian cities, European country, where in recent years regulatory air pollution health limits are constantly being exceeded. This paper examines average daily data for air pollution with PM2.5 and PM10, collected by 3 monitoring stations in the cities of Plovdiv and Asenovgrad between 2011 and 2016. The goal is to find and analyze actual relationships in data time series, to build adequate mathematical models, and to develop short-term forecasts. Modeling is carried out by stochastic univariate and multivariate time series analysis, based on Box-Jenkins methodology. The best models are selected following initial transformation of the data and using a set of standard and robust statistical criteria. The Mathematica and SPSS software were used to perform calculations. This examination showed measured concentrations of PM2.5 and PM10 in the region of Plovdiv and Asenovgrad regularly exceed permissible European and national health and safety thresholds. We obtained adequate stochastic models with high statistical fit with the data and good quality forecasting when compared against actual measurements. The mathematical approach applied provides an independent alternative to standard official monitoring and control means for air pollution in urban areas.

Prognostic Value of Pretherapeutic Tumor-to-Blood Standardized Uptake Ratio in Patients with Esophageal Carcinoma.

PubMed

Bütof, Rebecca; Hofheinz, Frank; Zöphel, Klaus; Stadelmann, Tobias; Schmollack, Julia; Jentsch, Christina; Löck, Steffen; Kotzerke, Jörg; Baumann, Michael; van den Hoff, Jörg

2015-08-01

Despite ongoing efforts to develop new treatment options, the prognosis for patients with inoperable esophageal carcinoma is still poor and the reliability of individual therapy outcome prediction based on clinical parameters is not convincing. The aim of this work was to investigate whether PET can provide independent prognostic information in such a patient group and whether the tumor-to-blood standardized uptake ratio (SUR) can improve the prognostic value of tracer uptake values. (18)F-FDG PET/CT was performed in 130 consecutive patients (mean age ± SD, 63 ± 11 y; 113 men, 17 women) with newly diagnosed esophageal cancer before definitive radiochemotherapy. In the PET images, the metabolically active tumor volume (MTV) of the primary tumor was delineated with an adaptive threshold method. The blood standardized uptake value (SUV) was determined by manually delineating the aorta in the low-dose CT. SUR values were computed as the ratio of tumor SUV and blood SUV. Uptake values were scan-time-corrected to 60 min after injection. Univariate Cox regression and Kaplan-Meier analysis with respect to overall survival (OS), distant metastases-free survival (DM), and locoregional tumor control (LRC) was performed. Additionally, a multivariate Cox regression including clinically relevant parameters was performed. In multivariate Cox regression with respect to OS, including T stage, N stage, and smoking state, MTV- and SUR-based parameters were significant prognostic factors for OS with similar effect size. Multivariate analysis with respect to DM revealed smoking state, MTV, and all SUR-based parameters as significant prognostic factors. The highest hazard ratios (HRs) were found for scan-time-corrected maximum SUR (HR = 3.9) and mean SUR (HR = 4.4). None of the PET parameters was associated with LRC. Univariate Cox regression with respect to LRC revealed a significant effect only for N stage greater than 0 (P = 0.048). PET provides independent prognostic information for OS and DM but not for LRC in patients with locally advanced esophageal carcinoma treated with definitive radiochemotherapy in addition to clinical parameters. Among the investigated uptake-based parameters, only SUR was an independent prognostic factor for OS and DM. These results suggest that the prognostic value of tracer uptake can be improved when characterized by SUR instead of SUV. Further investigations are required to confirm these preliminary results. © 2015 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Corrigendum to "Pharmaceutical analysis in solids using front face fluorescence spectroscopy and multivariate calibration with matrix correction by piecewise direct standardization" [Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. 103 (2013) 311-318

NASA Astrophysics Data System (ADS)

Alves, Julio Cesar L.; Poppi, Ronei J.

2014-03-01

The authors regret to inform that the tick labels of the ternary diagram axes in Fig. 1 were shown from 0% to 1.0% instead of 0% to 100%. The correct values of 0% to 100% are shown in the corrected Fig. 1 (see below). The right contents of the active ingredients in the sample sets shown in the diagram are now in agreement with the stated throughout the paper.

Simultaneous quantification of the boar-taint compounds skatole and androstenone by surface-enhanced Raman scattering (SERS) and multivariate data analysis.

PubMed

Sørensen, Klavs M; Westley, Chloe; Goodacre, Royston; Engelsen, Søren Balling

2015-10-01

This study investigates the feasibility of using surface-enhanced Raman scattering (SERS) for the quantification of absolute levels of the boar-taint compounds skatole and androstenone in porcine fat. By investigation of different types of nanoparticles, pH and aggregating agents, an optimized environment that promotes SERS of the analytes was developed and tested with different multivariate spectral pre-processing techniques, and this was combined with variable selection on a series of analytical standards. The resulting method exhibited prediction errors (root mean square error of cross validation, RMSECV) of 2.4 × 10(-6) M skatole and 1.2 × 10(-7) M androstenone, with a limit of detection corresponding to approximately 2.1 × 10(-11) M for skatole and approximately 1.8 × 10(-10) for androstenone. The method was subsequently tested on porcine fat extract, leading to prediction errors (RMSECV) of 0.17 μg/g for skatole and 1.5 μg/g for androstenone. It is clear that this optimized SERS method, when combined with multivariate analysis, shows great potential for optimization into an on-line application, which will be the first of its kind, and opens up possibilities for simultaneous detection of other meat-quality metabolites or pathogen markers. Graphical abstract Artistic rendering of a laser-illuminated gold colloid sphere with skatole and androstenone adsorbed on the surface.

Approximating Multivariate Normal Orthant Probabilities. ONR Technical Report. [Biometric Lab Report No. 90-1.

ERIC Educational Resources Information Center

Gibbons, Robert D.; And Others

The probability integral of the multivariate normal distribution (ND) has received considerable attention since W. F. Sheppard's (1900) and K. Pearson's (1901) seminal work on the bivariate ND. This paper evaluates the formula that represents the "n x n" correlation matrix of the "chi(sub i)" and the standardized multivariate…

Analysis techniques for multivariate root loci. [a tool in linear control systems

NASA Technical Reports Server (NTRS)

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

1980-01-01

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

Methods for presentation and display of multivariate data

NASA Technical Reports Server (NTRS)

Myers, R. H.

1981-01-01

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

A Primer on Multivariate Analysis of Variance (MANOVA) for Behavioral Scientists

ERIC Educational Resources Information Center

Warne, Russell T.

2014-01-01

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

Multivariate Drought Characterization in India for Monitoring and Prediction

NASA Astrophysics Data System (ADS)

Sreekumaran Unnithan, P.; Mondal, A.

2016-12-01

Droughts are one of the most important natural hazards that affect the society significantly in terms of mortality and productivity. The metric that is most widely used by the India Meteorological Department (IMD) to monitor and predict the occurrence, spread, intensification and termination of drought is based on the univariate Standardized Precipitation Index (SPI). However, droughts may be caused by the influence and interaction of many variables (such as precipitation, soil moisture, runoff, etc.), emphasizing the need for a multivariate approach for drought characterization. This study advocates and illustrates use of the recently proposed multivariate standardized drought index (MSDI) in monitoring and prediction of drought and assessing its concerned risk in the Indian region. MSDI combines information from multiple sources: precipitation and soil moisture, and has been deemed to be a more reliable drought index. All-India monthly rainfall and soil moisture data sets are analysed for the period 1980 to 2014 to characterize historical droughts using both the univariate indices, the precipitation-based SPI and the standardized soil moisture index (SSI), as well as the multivariate MSDI using parametric and non-parametric approaches. We confirm that MSDI can capture droughts of 1986 and 1990 that aren't detected by using SPI alone. Moreover, in 1987, MSDI indicated a higher severity of drought when a deficiency in both soil moisture and precipitation was encountered. Further, this study also explores the use of MSDI for drought forecasts and assesses its performance vis-à-vis existing predictions from the IMD. Future research efforts will be directed towards formulating a more robust standardized drought indicator that can take into account socio-economic aspects that also play a key role for water-stressed regions such as India.

Multivariate EMD and full spectrum based condition monitoring for rotating machinery

NASA Astrophysics Data System (ADS)

Zhao, Xiaomin; Patel, Tejas H.; Zuo, Ming J.

2012-02-01

Early assessment of machinery health condition is of paramount importance today. A sensor network with sensors in multiple directions and locations is usually employed for monitoring the condition of rotating machinery. Extraction of health condition information from these sensors for effective fault detection and fault tracking is always challenging. Empirical mode decomposition (EMD) is an advanced signal processing technology that has been widely used for this purpose. Standard EMD has the limitation in that it works only for a single real-valued signal. When dealing with data from multiple sensors and multiple health conditions, standard EMD faces two problems. First, because of the local and self-adaptive nature of standard EMD, the decomposition of signals from different sources may not match in either number or frequency content. Second, it may not be possible to express the joint information between different sensors. The present study proposes a method of extracting fault information by employing multivariate EMD and full spectrum. Multivariate EMD can overcome the limitations of standard EMD when dealing with data from multiple sources. It is used to extract the intrinsic mode functions (IMFs) embedded in raw multivariate signals. A criterion based on mutual information is proposed for selecting a sensitive IMF. A full spectral feature is then extracted from the selected fault-sensitive IMF to capture the joint information between signals measured from two orthogonal directions. The proposed method is first explained using simple simulated data, and then is tested for the condition monitoring of rotating machinery applications. The effectiveness of the proposed method is demonstrated through monitoring damage on the vane trailing edge of an impeller and rotor-stator rub in an experimental rotor rig.

Creation and validation of a novel body condition scoring method for the magellanic penguin (Spheniscus magellanicus) in the zoo setting.

PubMed

Clements, Julie; Sanchez, Jessica N

2015-11-01

This research aims to validate a novel, visual body scoring system created for the Magellanic penguin (Spheniscus magellanicus) suitable for the zoo practitioner. Magellanics go through marked seasonal fluctuations in body mass gains and losses. A standardized multi-variable visual body condition guide may provide a more sensitive and objective assessment tool compared to the previously used single variable method. Accurate body condition scores paired with seasonal weight variation measurements give veterinary and keeper staff a clearer understanding of an individual's nutritional status. San Francisco Zoo staff previously used a nine-point body condition scale based on the classic bird standard of a single point of keel palpation with the bird restrained in hand, with no standard measure of reference assigned to each scoring category. We created a novel, visual body condition scoring system that does not require restraint to assesses subcutaneous fat and muscle at seven body landmarks using illustrations and descriptive terms. The scores range from one, the least robust or under-conditioned, to five, the most robust, or over-conditioned. The ratio of body weight to wing length was used as a "gold standard" index of body condition and compared to both the novel multi-variable and previously used single-variable body condition scores. The novel multi-variable scale showed improved agreement with weight:wing ratio compared to the single-variable scale, demonstrating greater accuracy, and reliability when a trained assessor uses the multi-variable body condition scoring system. Zoo staff may use this tool to manage both the colony and the individual to assist in seasonally appropriate Magellanic penguin nutrition assessment. © 2015 Wiley Periodicals, Inc.

A nonparametric multivariate standardized drought index for characterizing socioeconomic drought: A case study in the Heihe River Basin

NASA Astrophysics Data System (ADS)

Huang, Shengzhi; Huang, Qiang; Leng, Guoyong; Liu, Saiyan

2016-11-01

Among various drought types, socioeconomic drought is the least investigated type of droughts. Most existing drought indicators ignore the role of local reservoirs and water demand in coping with climatic extremes. In this study, a Multivariate Standardized Reliability and Resilience Index (MSRRI) combining inflow-demand reliability index (IDR) and water storage resilience index (WSR) was applied to examine the evolution characteristics of the socioeconomic droughts in the Heihe River Basin, the second largest inland river basin in northwestern China. Furthermore, the cross wavelet analysis was adopted to explore the associations between annual MSRRI series and El Niño Southern Oscillation (ENSO)/Atlantic Oscillation (AO). Results indicated that: (1) the developed MSRRI is more sensitive to the onset and termination of socioeconomic droughts than IDR and WSR, owing to its joint distribution function of IDR and WSR, responding to changes in either or both of the indices; (2) the MSRRI series in the Heihe River Basin shows non-significant trends at both monthly and annual scales; (3) both ENSO and AO contribute to the changes in the socioeconomic droughts in the Heihe River Basin, and the impacts of ENSO on the socioeconomic droughts are stronger than those of AO.

Is math anxiety in the secondary classroom limiting physics mastery? A study of math anxiety and physics performance

NASA Astrophysics Data System (ADS)

Mercer, Gary J.

This quantitative study examined the relationship between secondary students with math anxiety and physics performance in an inquiry-based constructivist classroom. The Revised Math Anxiety Rating Scale was used to evaluate math anxiety levels. The results were then compared to the performance on a physics standardized final examination. A simple correlation was performed, followed by a multivariate regression analysis to examine effects based on gender and prior math background. The correlation showed statistical significance between math anxiety and physics performance. The regression analysis showed statistical significance for math anxiety, physics performance, and prior math background, but did not show statistical significance for math anxiety, physics performance, and gender.

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.

Measuring the Indonesian provinces competitiveness by using PCA technique

NASA Astrophysics Data System (ADS)

Runita, Ditha; Fajriyah, Rohmatul

2017-12-01

Indonesia is a country which has vast teritoty. It has 34 provinces. Building local competitiveness is critical to enhance the long-term national competitiveness especially for a country as diverse as Indonesia. A competitive local government can attract and maintain successful firms and increase living standards for its inhabitants, because investment and skilled workers gravitate from uncompetitive regions to more competitive ones. Altough there are other methods to measuring competitiveness, but here we have demonstrated a simple method using principal component analysis (PCA). It can directly be applied to correlated, multivariate data. The analysis on Indonesian provinces provides 3 clusters based on the competitiveness measurement and the clusters are Bad, Good and Best perform provinces.

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.

Study on rapid valid acidity evaluation of apple by fiber optic diffuse reflectance technique

NASA Astrophysics Data System (ADS)

Liu, Yande; Ying, Yibin; Fu, Xiaping; Jiang, Xuesong

2004-03-01

Some issues related to nondestructive evaluation of valid acidity in intact apples by means of Fourier transform near infrared (FTNIR) (800-2631nm) method were addressed. A relationship was established between the diffuse reflectance spectra recorded with a bifurcated optic fiber and the valid acidity. The data were analyzed by multivariate calibration analysis such as partial least squares (PLS) analysis and principal component regression (PCR) technique. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influence of data preprocessing and different spectra treatments were also investigated. Models based on smoothing spectra were slightly worse than models based on derivative spectra and the best result was obtained when the segment length was 5 and the gap size was 10. Depending on data preprocessing and multivariate calibration technique, the best prediction model had a correlation efficient (0.871), a low RMSEP (0.0677), a low RMSEC (0.056) and a small difference between RMSEP and RMSEC by PLS analysis. The results point out the feasibility of FTNIR spectral analysis to predict the fruit valid acidity non-destructively. The ratio of data standard deviation to the root mean square error of prediction (SDR) is better to be less than 3 in calibration models, however, the results cannot meet the demand of actual application. Therefore, further study is required for better calibration and prediction.

Concomitant Mediastinoscopy Increases the Risk of Postoperative Pneumonia After Pulmonary Lobectomy.

PubMed

Yendamuri, Sai; Battoo, Athar; Attwood, Kris; Dhillon, Samjot Singh; Dy, Grace K; Hennon, Mark; Picone, Anthony; Nwogu, Chukwumere; Demmy, Todd; Dexter, Elisabeth

2018-05-01

Mediastinoscopy is considered the gold standard for preresectional staging of lung cancer. We sought to examine the effect of concomitant mediastinoscopy on postoperative pneumonia (POP) in patients undergoing lobectomy. All patients in our institutional database (2008-2015) undergoing lobectomy who did not receive neoadjuvant therapy were included in our study. The relationship between mediastinoscopy and POP was examined using univariate (Chi square) and multivariate analyses (binary logistic regression). In order to validate our institutional findings, lobectomy data in the National Surgical Quality Improvement Program (NSQIP) from 2005 to 2014 were analyzed for these associations. Of 810 patients who underwent a lobectomy at our institution, 741 (91.5%) surgeries were performed by video-assisted thoracic surgery (VATS) and 487 (60.1%) patients underwent concomitant mediastinoscopy. Univariate analysis demonstrated an association between mediastinoscopy and POP in patients undergoing VATS [odds ratio (OR) 1.80; p = 0.003], but not open lobectomy. Multivariate analysis retained mediastinoscopy as a variable, although the relationship showed only a trend (OR 1.64; p = 0.1). In the NSQIP cohort (N = 12,562), concomitant mediastinoscopy was performed in 9.0% of patients, with 44.5% of all the lobectomies performed by VATS. Mediastinoscopy was associated with POP in patients having both open (OR1.69; p < 0.001) and VATS lobectomy (OR 1.72; p = 0.002). This effect remained in multivariate analysis in both the open and VATS lobectomy groups (OR 1.46, p = 0.003; and 1.53, p = 0.02, respectively). Mediastinoscopy may be associated with an increased risk of POP after pulmonary lobectomy. This observation should be examined in other datasets as it potentially impacts preresectional staging algorithms for patients with lung cancer.

Risk Assessment of Hepatocellular Carcinoma in Patients with Hepatitis C in China and the USA.

PubMed

Parikh, Neehar D; Fu, Sherry; Rao, Huiying; Yang, Ming; Li, Yumeng; Powell, Corey; Wu, Elizabeth; Lin, Andy; Xing, Baocai; Wei, Lai; Lok, Anna S F

2017-11-01

Hepatitis C (HCV) infection is an increasingly common cause of hepatocellular carcinoma (HCC) in China. We aimed to determine differences in demographic and behavioral profiles associated with HCC in HCV+ patients in China and the USA. Consecutive HCV+ patients were recruited from centers in China and the USA. Clinical data and lifestyle profiles were obtained through standardized questionnaires. Multivariable analysis was conducted to determine factors associated with HCC diagnosis within groups. We included 41 HCC patients from China and 71 from the USA, and 931 non-HCC patients in China and 859 in China. Chinese patients with HCC were significantly younger, less likely to be male and to be obese than US patients with HCC (all p < 0.001). Chinese patients with HCC had a significantly lower rate of cirrhosis diagnosis (36.6 vs. 78.9%, p < 0.001); however, they also had a higher rate of hepatitis B core antibody positivity (63.4 vs. 36.8%, p = 0.007). In a multivariable analysis of the entire Chinese cohort, age > 55, male sex, the presence of diabetes, and time from maximum weight were associated with HCC, while tea consumption was associated with a decreased HCC risk (OR 0.37, 95% CI 0.16-0.88). In the US cohort, age > 55, male sex, and cirrhosis were associated with HCC on multivariable analysis. With the aging Chinese population and increasing rates of diabetes, there will likely be continued increase in the incidence of HCV-related HCC in China. The protective effect of tea consumption on HCC development deserves further validation.

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

ERIC Educational Resources Information Center

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

2009-01-01

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

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…

Ethnic identities and lifestyles in a multi-ethnic cancer patient population.

PubMed

Gotay, Carolyn Cook; Holup, Joan

2004-09-01

This report examined ethnic identity in 367 recently diagnosed cancer patients in Hawai'i's primary ethnic groups: Japanese, Hawaiians, Europeans, and Filipinos. The study assessed ethnic self-identify; definitions of and participation in different ethnic lifestyles; and relationships between measures of ethnic self-identity, lifestyle, and other indicators of ethnic and cultural affiliations. Results indicated that medical record-based ethnic indicators were well linked to individual self-reports of family pedigree. Self-descriptors included non-standard terms such as "American" and "Local," and respondents reported following between five and six different ethnically-associated ways of life. Multivariate analysis indicated that ethnic self-identity made a unique contribution that went beyond standard ethnic and acculturative markers in explaining lifestyles. This study provides strong support for multiculturalism in this ethnically heterogeneous population.

Quark-gluon discrimination in the search for gluino pair production at the LHC

DOE PAGES

Bhattacherjee, Biplob; Mukhopadhyay, Satyanarayan; Nojiri, Mihoko M.; ...

2017-01-11

Here, we study the impact of including quark- and gluon-initiated jet discrimination in the search for strongly interacting supersymmetric particles at the LHC. Taking the example of gluino pair production, considerable improvement is observed in the LHC search reach on including the jet substructure observables to the standard kinematic variables within a multivariate analysis. In particular, quark and gluon jet separation has higher impact in the region of intermediate mass-gap between the gluino and the lightest neutralino, as the difference between the signal and the standard model background kinematic distributions is reduced in this region. We also compare the predictionsmore » from different Monte Carlo event generators to estimate the uncertainty originating from the modelling of the parton shower and hadronization processes.« less

Bivariate versus multivariate smart spectrophotometric calibration methods for the simultaneous determination of a quaternary mixture of mosapride, pantoprazole and their degradation products.

PubMed

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

2013-05-01

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

Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs

Treesearch

Daniel A. Yaussy; Robert L. Brisbin

1983-01-01

A multivariate regression model was developed to predict green board-foot yields for the seven common factory lumber grades processed from northern red oak (Quercus rubra L.) factory grade logs. The model uses the standard log measurements of grade, scaling diameter, length, and percent defect. It was validated with an independent data set. The model...

Multivariate regression model for predicting yields of grade lumber from yellow birch sawlogs

Treesearch

Andrew F. Howard; Daniel A. Yaussy

1986-01-01

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and...

Process analysis of recycled thermoplasts from consumer electronics by laser-induced plasma spectroscopy.

PubMed

Fink, Herbert; Panne, Ulrich; Niessner, Reinhard

2002-09-01

An experimental setup for direct elemental analysis of recycled thermoplasts from consumer electronics by laser-induced plasma spectroscopy (LIPS, or laser-induced breakdown spectroscopy, LIBS) was realized. The combination of a echelle spectrograph, featuring a high resolution with a broad spectral coverage, with multivariate methods, such as PLS, PCR, and variable subset selection via a genetic algorithm, resulted in considerable improvements in selectivity and sensitivity for this complex matrix. With a normalization to carbon as internal standard, the limits of detection were in the ppm range. A preliminary pattern recognition study points to the possibility of polymer recognition via the line-rich echelle spectra. Several experiments at an extruder within a recycling plant demonstrated successfully the capability of LIPS for different kinds of routine on-line process analysis.

Monthly income, standard of living and erectile function in late life.

PubMed

Cheng, J Y W; Ng, E M L; Ko, J S N; Chen, R Y L

2007-01-01

This was a cross-sectional study that enrolled 160 men aged 50 and above who were sexually active (sexual intercourse in the preceding 6 months) from a large primary care treatment centre. The subjects of interest were elderly aged 65 and above, and men aged 50-65 were used for comparison. The overall response rate was 66.9%. The men who participated were generally more affluent. Standard of living was measured by the presence of maid and housing type. Erectile function (EF) score was significantly higher in those who hired maids (P=0.02). Housing type was not associated with erectile dysfunction (ED). In Model A (included both monthly income and education), significant non-parametric correlations were found between monthly income and EF, intercourse satisfaction (IS), orgasmic function (OF) and sexual desire (SD) domains. After statistical adjustments, only EF (P<0.01) and IS (P=0.04) remained positively associated with monthly income. OF was negatively associated with age (P<0.01) and diabetes (P=0.04), whereas SD was negatively associated with age (P<0.01) in the multivariate analysis. Overall satisfaction was not significantly associated with any factor. In Model B (excluded monthly income from analysis), education attainment was positively associated with OF (P=0.04), but was not significant after adjustment for multiple testing. In the final multivariate model, only monthly income (P<0.01) and age (P<0.01), but not education (P=0.47), remained significantly associated with EF. This study suggests the influence of social determinants on EF and that this influence can extend into late life.

Rapid Quantification of Melamine in Different Brands/Types of Milk Powders Using Standard Addition Net Analyte Signal and Near-Infrared Spectroscopy

PubMed Central

2016-01-01

Multivariate calibration (MVC) and near-infrared (NIR) spectroscopy have demonstrated potential for rapid analysis of melamine in various dairy products. However, the practical application of ordinary MVC can be largely restricted because the prediction of a new sample from an uncalibrated batch would be subject to a significant bias due to matrix effect. In this study, the feasibility of using NIR spectroscopy and the standard addition (SA) net analyte signal (NAS) method (SANAS) for rapid quantification of melamine in different brands/types of milk powders was investigated. In SANAS, the NAS vector of melamine in an unknown sample as well as in a series of samples added with melamine standards was calculated and then the Euclidean norms of series standards were used to build a straightforward univariate regression model. The analysis results of 10 different brands/types of milk powders with melamine levels 0~0.12% (w/w) indicate that SANAS obtained accurate results with the root mean squared error of prediction (RMSEP) values ranging from 0.0012 to 0.0029. An additional advantage of NAS is to visualize and control the possible unwanted variations during standard addition. The proposed method will provide a practically useful tool for rapid and nondestructive quantification of melamine in different brands/types of milk powders. PMID:27525154

Histologic prognosticators in feline osteosarcoma: a comparison with phenotypically similar canine osteosarcoma.

PubMed

Dimopoulou, Maria; Kirpensteijn, Jolle; Moens, Hester; Kik, Marja

2008-07-01

To investigate the histologic characteristics of feline osteosarcoma (OS) and compare the histologic data with phenotypically comparable canine OS. The effects of histologic and clinical variables on survival statistics were evaluated. Retrospective study. Cats (n=62) and dogs (22). Medical records of 62 cats with OS were reviewed for clinically relevant data. Clinical outcome was obtained by telephone interview. Histologic characteristics of OS were classified using a standardized grading system. Histologic characteristics in 22 feline skeletal OS were compared with 22 canine skeletal OS of identical location and subtype. Prognostic variables for clinical outcome were determined using multivariate analysis. Feline OS was characterized by moderate to abundant cellular pleomorphism, low mitotic index, small to moderate amounts of matrix, high cellularity, and a moderate amount of necrosis. There was no significant difference between histologic variables in feline and canine OS. Histologic grade, surgery, and mitotic index significantly influenced clinical outcome as determined by multivariate analysis. Tumor invasion into vessels was not identified as a significant prognosticator. Feline and canine skeletal OS have similar histologic but different prognostic characteristics. Prognosis for cats with OS is related to histologic grade and mitotic index of the tumor.

Progeny's mental aptitudes in man: relationship with parental age at conception and with some environmental factors.

PubMed

Auroux, Maurice; Volteau, Magali; Ducot, Béatrice; Wack, Thierry; Letierce, Alexia; Meyer, Laurence; Mayaux, Marie-Jeanne

2009-07-01

Psychometric tests obtained from 6564 young men were studied as a function of the parents' ages at conception and of some characteristics of the subject's postnatal environment. Individual scores, from 0 to 20, were divided into two groups: n(1)11 and n(2)<11. In univariate analysis, scores <11 were respectively related to low height, high number of siblings and junior in birth order, subject's and parents' tobacco consumption, parents' alcohol consumption, subject's and parents' low academic standard, parents' youth or ageing at conception. In multivariate analysis, these scores remained related to low height, junior in birth order, subject's and parents' tobacco consumption, parents' low academic standard, parents' youth (both <20). Regarding the respective influences of the environment and of the subject's genome on his cerebral development, one can hypothesize a complementarity between these two factors through the possibility of a genetically determined individual synaptic potential, revealing itself, more or less, according to environmental conditions.

Predictive value of clinical scoring and simplified gait analysis for acetabulum fractures.

PubMed

Braun, Benedikt J; Wrona, Julian; Veith, Nils T; Rollman, Mika; Orth, Marcel; Herath, Steven C; Holstein, Jörg H; Pohlemann, Tim

2016-12-01

Fractures of the acetabulum show a high, long-term complication rate. The aim of the present study was to determine the predictive value of clinical scoring and standardized, simplified gait analysis on the outcome after these fractures. Forty-one patients with acetabular fractures treated between 2008 and 2013 and available, standardized video recorded aftercare were identified from a prospective database. A visual gait score was used to determine the patients walking abilities 6-m postoperatively. Clinical (Merle d'Aubigne and Postel score, visual analogue scale pain, EQ5d) and radiological scoring (Kellgren-Lawrence score, postoperative computed tomography, and Matta classification) were used to perform correlation and multivariate regression analysis. The average patient age was 48 y (range, 15-82 y), six female patients were included in the study. Mean follow-up was 1.6 y (range, 1-2 y). Moderate correlation between the gait score and outcome (versus EQ5d: r s  = 0.477; versus Merle d'Aubigne: r s  = 0.444; versus Kellgren-Lawrence: r s  = -0.533), as well as high correlation between the Merle d'Aubigne score and outcome were seen (versus EQ5d: r s  = 0.575; versus Merle d'Aubigne: r s  = 0.776; versus Kellgren-Lawrence: r s  = -0.419). Using a multivariate regression model, the 6 m gait score (B = -0.299; P < 0.05) and early osteoarthritis development (B = 1.026; P < 0.05) were determined as predictors of final osteoarthritis. A good fit of the regression model was seen (R 2  = 904). Easy and available clinical scoring (gait score/Merle d'Aubigne) can predict short-term radiological and functional outcome after acetabular fractures with sufficient accuracy. Decisions on further treatment and interventions could be based on simplified gait analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

Profiling and classification of French propolis by combined multivariate data analysis of planar chromatograms and scanning direct analysis in real time mass spectra.

PubMed

Chasset, Thibaut; Häbe, Tim T; Ristivojevic, Petar; Morlock, Gertrud E

2016-09-23

Quality control of propolis is challenging, as it is a complex natural mixture of compounds, and thus, very difficult to analyze and standardize. Shown on the example of 30 French propolis samples, a strategy for an improved quality control was demonstrated in which high-performance thin-layer chromatography (HPTLC) fingerprints were evaluated in combination with selected mass signals obtained by desorption-based scanning mass spectrometry (MS). The French propolis sample extracts were separated by a newly developed reversed phase (RP)-HPTLC method. The fingerprints obtained by two different detection modes, i.e. after (1) derivatization and fluorescence detection (FLD) at UV 366nm and (2) scanning direct analysis in real time (DART)-MS, were analyzed by multivariate data analysis. Thus, RP-HPTLC-FLD and RP-HPTLC-DART-MS fingerprints were explored and the best classification was obtained using both methods in combination with pattern recognition techniques, such as principal component analysis. All investigated French propolis samples were divided in two types and characteristic patterns were observed. Phenolic compounds such as caffeic acid, p-coumaric acid, chrysin, pinobanksin, pinobanksin-3-acetate, galangin, kaempferol, tectochrysin and pinocembrin were identified as characteristic marker compounds of French propolis samples. This study expanded the research on the European poplar type of propolis and confirmed the presence of two botanically different types of propolis, known as the blue and orange types. Copyright © 2016 Elsevier B.V. All rights reserved.

Big-Data RHEED analysis for understanding epitaxial film growth processes

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

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

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

Polytopic vector analysis in igneous petrology: Application to lunar petrogenesis

NASA Technical Reports Server (NTRS)

Shervais, John W.; Ehrlich, R.

1993-01-01

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

Assessment of cardio-respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis.

PubMed

Indic, Premananda; Bloch-Salisbury, Elisabeth; Bednarek, Frank; Brown, Emery N; Paydarfar, David; Barbieri, Riccardo

2011-07-01

Cardio-respiratory interactions are weak at the earliest stages of human development, suggesting that assessment of their presence and integrity may be an important indicator of development in infants. Despite the valuable research devoted to infant development, there is still a need for specifically targeted standards and methods to assess cardiopulmonary functions in the early stages of life. We present a new methodological framework for the analysis of cardiovascular variables in preterm infants. Our approach is based on a set of mathematical tools that have been successful in quantifying important cardiovascular control mechanisms in adult humans, here specifically adapted to reflect the physiology of the developing cardiovascular system. We applied our methodology in a study of cardio-respiratory responses for 11 preterm infants. We quantified cardio-respiratory interactions using specifically tailored multivariate autoregressive analysis and calculated the coherence as well as gain using causal approaches. The significance of the interactions in each subject was determined by surrogate data analysis. The method was tested in control conditions as well as in two different experimental conditions; with and without use of mild mechanosensory intervention. Our multivariate analysis revealed a significantly higher coherence, as confirmed by surrogate data analysis, in the frequency range associated with eupneic breathing compared to the other ranges. Our analysis validates the models behind our new approaches, and our results confirm the presence of cardio-respiratory coupling in early stages of development, particularly during periods of mild mechanosensory intervention, thus encouraging further application of our approach. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

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.

No improvement in the reporting of clinical trial subgroup effects in high-impact general medical journals.

PubMed

Gabler, Nicole B; Duan, Naihua; Raneses, Eli; Suttner, Leah; Ciarametaro, Michael; Cooney, Elizabeth; Dubois, Robert W; Halpern, Scott D; Kravitz, Richard L

2016-07-16

When subgroup analyses are not correctly analyzed and reported, incorrect conclusions may be drawn, and inappropriate treatments provided. Despite the increased recognition of the importance of subgroup analysis, little information exists regarding the prevalence, appropriateness, and study characteristics that influence subgroup analysis. The objective of this study is to determine (1) if the use of subgroup analyses and multivariable risk indices has increased, (2) whether statistical methodology has improved over time, and (3) which study characteristics predict subgroup analysis. We randomly selected randomized controlled trials (RCTs) from five high-impact general medical journals during three time periods. Data from these articles were abstracted in duplicate using standard forms and a standard protocol. Subgroup analysis was defined as reporting any subgroup effect. Appropriate methods for subgroup analysis included a formal test for heterogeneity or interaction across treatment-by-covariate groups. We used logistic regression to determine the variables significantly associated with any subgroup analysis or, among RCTs reporting subgroup analyses, using appropriate methodology. The final sample of 416 articles reported 437 RCTs, of which 270 (62 %) reported subgroup analysis. Among these, 185 (69 %) used appropriate methods to conduct such analyses. Subgroup analysis was reported in 62, 55, and 67 % of the articles from 2007, 2010, and 2013, respectively. The percentage using appropriate methods decreased over the three time points from 77 % in 2007 to 63 % in 2013 (p < 0.05). Significant predictors of reporting subgroup analysis included industry funding (OR 1.94 (95 % CI 1.17, 3.21)), sample size (OR 1.98 per quintile (1.64, 2.40), and a significant primary outcome (OR 0.55 (0.33, 0.92)). The use of appropriate methods to conduct subgroup analysis decreased by year (OR 0.88 (0.76, 1.00)) and was less common with industry funding (OR 0.35 (0.18, 0.70)). Only 33 (18 %) of the RCTs examined subgroup effects using a multivariable risk index. While we found no significant increase in the reporting of subgroup analysis over time, our results show a significant decrease in the reporting of subgroup analyses using appropriate methods during recent years. Industry-sponsored trials may more commonly report subgroup analyses, but without utilizing appropriate methods. Suboptimal reporting of subgroup effects may impact optimal physician-patient decision-making.

Multiple imputation for multivariate data with missing and below-threshold measurements: time-series concentrations of pollutants in the Arctic.

PubMed

Hopke, P K; Liu, C; Rubin, D B

2001-03-01

Many chemical and environmental data sets are complicated by the existence of fully missing values or censored values known to lie below detection thresholds. For example, week-long samples of airborne particulate matter were obtained at Alert, NWT, Canada, between 1980 and 1991, where some of the concentrations of 24 particulate constituents were coarsened in the sense of being either fully missing or below detection limits. To facilitate scientific analysis, it is appealing to create complete data by filling in missing values so that standard complete-data methods can be applied. We briefly review commonly used strategies for handling missing values and focus on the multiple-imputation approach, which generally leads to valid inferences when faced with missing data. Three statistical models are developed for multiply imputing the missing values of airborne particulate matter. We expect that these models are useful for creating multiple imputations in a variety of incomplete multivariate time series data sets.

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

Child wasting is a severe public health problem in the predominantly rural population of Ethiopia: A community based cross-sectional study.

PubMed

Tariku, Amare; Bikis, Gashaw Andargie; Woldie, Haile; Wassie, Molla Mesele; Worku, Abebaw Gebeyehu

2017-01-01

In Ethiopia, child wasting has remained a public health problem for a decade's, suggesting the need to further monitoring of the problem. Hence, this study aimed at assessing the prevalence of wasting and associated factors among children aged 6-59 months at Dabat District, northwest Ethiopia. A Community based cross-sectional study was undertaken from May to June, 2015, in Dabat District, northwest Ethiopia. A total of 1184 children aged under five years and their mothers/caretakers were included in the study. An interviewer-administered, pre-tested, and structured questionnaire was used to collect data. Standardized anthropometric body measurements were employed to assess the height and weight of the participants. Anthropometric body measurements were analyzed by the WHO Anthro Plus software version 1.0.4. Wasting was defined as having a weight-for-height of Z-score lower than two standard deviations (WHZ < -2 SD) compared to the WHO reference population of the same age and sex group. In the binary logistic regression, both bivariate and multivariate analyses were done to list out factors associated with wasting. All variables with P-values of < 0.2 in the bivariate analysis were earmarked for the multivariate analysis. Both Crude Odds Ratio (COR) and Adjusted Odds Ratio (AOR) at 95% Confidence Interval (CI) were computed to determine the strength of association. In the multivariate analysis, variables at P -values of < 0.05 were identified as determinants of wasting. The overall prevalence of wasting was 18.2%; 10.3% and 7.9% of the children were moderately and severely wasted, respectively. Poor dietary diversity [AOR = 2.08, 95% CI: 1.53, 4.46], late initiation of breastfeeding [AOR = 1.43, 95% CI: 1.04, 1.95], no postnatal vitamin-A supplementation [AOR = 1.55, 95% CI: 1.04, 2.30], and maternal occupational status [AOR = 2.31, 95% CI: 1.56, 3.42] were independently associated with wasting in the study area. Wasting is a severe public health problem in Dabat District. Therefore, there is a need to strengthen the implementation of optimal breastfeeding practice and dietary diversity. In addition, improving the coverage of mothers ' postnatal vitamin-A supplementation is essential to address the burden of child wasting.

Efficacy of tolvaptan in patients with refractory ascites in a clinical setting

PubMed Central

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

2015-01-01

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

Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students

ERIC Educational Resources Information Center

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

2011-01-01

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

Economic Impact of Increased Utilization of Multivariate Assay Testing to Guide the Treatment of Ovarian Cancer: Implications for Payers.

PubMed

Brodsky, Burton S; Owens, Gary M; Scotti, Dennis J; Needham, Keith A; Cool, Christina L

2017-10-01

Ovarian cancer is the eighth most common cancer among women, but ranks fifth in cancer-related causes of death, the majority of which are detected in late stages, after the cancer has metastasized. The CA125 test is the standard of care for assessing suspicious pelvic masses. However, the primary use of CA125 is to monitor treatment progress rather than to screen for disease, and its sensitivity is exceedingly low, unlike the multivariate assay OVA1. A cost-effective treatment of ovarian cancer requires early and accurate diagnosis of pelvic masses and reduced referrals of patients with benign tumors to a gynecologic oncologist. To analyze the economic impact of increased utilization of a multivariate assay, such as OVA1, to guide the treatment of ovarian cancer. The study population was drawn from Medicare and commercial health plan claims data. A budget impact model was constructed to estimate the economic consequences of substituting the multivariate assay OVA1 to replace the single biomarker assay CA125 to assess the likelihood of pelvic mass malignancy in premenopausal and/or postmenopausal women. All patients selected for the analysis had CA125 testing before surgical intervention. A total of 92,843 health plan members were included for analysis, comprising 48,113 commercially insured members and 44,730 Medicare beneficiaries. Estimates of future health plan expenditures, which were calculated from base-case assumptions, projected overall savings of $0.05 per-member per-month (PMPM) for commercially insured members and $0.01 PMPM for Medicare beneficiaries as a result of increased utilization of OVA1. Sensitivity analysis revealed potential savings of up to $0.17 PMPM for commercially insured patients and up to $0.05 for Medicare beneficiaries. The results of the budget impact model support the use of OVA1 instead of CA125 by indicating that modest cost-savings can be achieved, while reaping the clinical benefits of improved diagnostic accuracy, early disease detection, and reductions in multiple, and possibly unnecessary, referrals to gynecologic oncologists.

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

Guideline-Driven Care Improves Outcomes in Patients with Traumatic Rib Fractures.

PubMed

Flarity, Kathleen; Rhodes, Whitney C; Berson, Andrew J; Leininger, Brian E; Reckard, Paul E; Riley, Keyan D; Shahan, Charles P; Schroeppel, Thomas J

2017-09-01

There is no established national standard for rib fracture management. A clinical practice guideline (CPG) for rib fractures, including monitoring of pulmonary function, early initiation of aggressive loco-regional analgesia, and early identification of deteriorating respiratory function, was implemented in 2013. The objective of the study was to evaluate the effect of the CPG on hospital length of stay. Hospital length of stay (LOS) was compared for adult patients admitted to the hospital with rib fracture(s) two years before and two years after CPG implementation. A separate analysis was done for the patients admitted to the intensive care unit (ICU). Over the 48-month study period, 571 patients met inclusion criteria for the study. Pre-CPG and CPG study groups were well matched with few differences. Multivariable regression did not demonstrate a difference in LOS (B = -0.838; P = 0.095) in the total study cohort. In the ICU cohort (n = 274), patients in the CPG group were older (57 vs 52 years; P = 0.023) and had more rib fractures (4 vs 3; P = 0.003). Multivariable regression identified a significant decrease in LOS for those patients admitted in the CPG period (B = -2.29; P = 0.019). Despite being significantly older with more rib fractures in the ICU cohort, patients admitted after implementation of the CPG had a significantly reduced LOS on multivariable analysis, reducing LOS by over two days. This structured intervention can limit narcotic usage, improve pulmonary function, and decrease LOS in the most injured patients with chest trauma.

Sex hormones and quantitative ultrasound parameters at the heel in men and women from the general population.

PubMed

Pätzug, Konrad; Friedrich, Nele; Kische, Hanna; Hannemann, Anke; Völzke, Henry; Nauck, Matthias; Keevil, Brian G; Haring, Robin

2017-12-01

The present study investigates potential associations between liquid chromatography-mass spectrometry (LC-MS) measured sex hormones, dehydroepiandrosterone sulphate, sex hormone-binding globulin (SHBG) and bone ultrasound parameters at the heel in men and women from the general population. Data from 502 women and 425 men from the population-based Study of Health in Pomerania (SHIP-TREND) were used. Cross-sectional associations of sex hormones including testosterone (TT), calculated free testosterone (FT), dehydroepiandrosterone sulphate (DHEAS), androstenedione (ASD), estrone (E1) and SHBG with quantitative ultrasound (QUS) parameters at the heel, including broadband ultrasound attenuation (BUA), speed of sound (SOS) and stiffness index (SI) were examined by analysis of variance (ANOVA) and multivariable quantile regression models. Multivariable regression analysis showed a sex-specific inverse association of DHEAS with SI in men (Beta per SI unit = - 3.08, standard error (SE) = 0.88), but not in women (Beta = - 0.01, SE = 2.09). Furthermore, FT was positively associated with BUA in men (Beta per BUA unit = 29.0, SE = 10.1). None of the other sex hormones (ASD, E1) or SHBG was associated with QUS parameters after multivariable adjustment. This cross-sectional population-based study revealed independent associations of DHEAS and FT with QUS parameters in men, suggesting a potential influence on male bone metabolism. The predictive role of DHEAS and FT as a marker for osteoporosis in men warrants further investigation in clinical trials and large-scale observational studies.

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

ERIC Educational Resources Information Center

Gaotlhobogwe, Michael; Laugharne, Janet; Durance, Isabelle

2011-01-01

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

Two-sample tests and one-way MANOVA for multivariate biomarker data with nondetects.

PubMed

Thulin, M

2016-09-10

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

A non-iterative extension of the multivariate random effects meta-analysis.

PubMed

Makambi, Kepher H; Seung, Hyunuk

2015-01-01

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

Standardized Reporting System Use During Handoffs Reduces Patient Length of Stay in the Emergency Department.

PubMed

Dahlquist, Robert T; Reyner, Karina; Robinson, Richard D; Farzad, Ali; Laureano-Phillips, Jessica; Garrett, John S; Young, Joseph M; Zenarosa, Nestor R; Wang, Hao

2018-05-01

Emergency department (ED) shift handoffs are potential sources of delay in care. We aimed to determine the impact that using standardized reporting tool and process may have on throughput metrics for patients undergoing a transition of care at shift change. We performed a prospective, pre- and post-intervention quality improvement study from September 1 to November 30, 2015. A handoff procedure intervention, including a mandatory workshop and personnel training on a standard reporting system template, was implemented. The primary endpoint was patient length of stay (LOS). A comparative analysis of differences between patient LOS and various handoff communication methods were assessed pre- and post-intervention. Communication methods were entered a multivariable logistic regression model independently as risk factors for patient LOS. The final analysis included 1,006 patients, with 327 comprising the pre-intervention and 679 comprising the post-intervention populations. Bedside rounding occurred 45% of the time without a standard reporting during pre-intervention and increased to 85% of the time with the use of a standard reporting system in the post-intervention period (P < 0.001). Provider time (provider-initiated care to patient care completed) in the pre-intervention period averaged 297 min, but decreased to 265 min in the post-intervention period (P < 0.001). After adjusting for other communication methods, the use of a standard reporting system during handoff was associated with shortened ED LOS (OR = 0.60, 95% CI 0.40 - 0.90, P < 0.05). Standard reporting system use during emergency physician handoffs at shift change improves ED throughput efficiency and is associated with shorter ED LOS.

Probabilistic, meso-scale flood loss modelling

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Botto, Anna; Schröter, Kai; Merz, Bruno

2016-04-01

Flood risk analyses are an important basis for decisions on flood risk management and adaptation. However, such analyses are associated with significant uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention during the last years, they are still not standard practice for flood risk assessments and even more for flood loss modelling. State of the art in flood loss modelling is still the use of simple, deterministic approaches like stage-damage functions. Novel probabilistic, multi-variate flood loss models have been developed and validated on the micro-scale using a data-mining approach, namely bagging decision trees (Merz et al. 2013). In this presentation we demonstrate and evaluate the upscaling of the approach to the meso-scale, namely on the basis of land-use units. The model is applied in 19 municipalities which were affected during the 2002 flood by the River Mulde in Saxony, Germany (Botto et al. submitted). The application of bagging decision tree based loss models provide a probability distribution of estimated loss per municipality. Validation is undertaken on the one hand via a comparison with eight deterministic loss models including stage-damage functions as well as multi-variate models. On the other hand the results are compared with official loss data provided by the Saxon Relief Bank (SAB). The results show, that uncertainties of loss estimation remain high. Thus, the significant advantage of this probabilistic flood loss estimation approach is that it inherently provides quantitative information about the uncertainty of the prediction. References: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64. Botto A, Kreibich H, Merz B, Schröter K (submitted) Probabilistic, multi-variable flood loss modelling on the meso-scale with BT-FLEMO. Risk Analysis.

Breakthrough seizures—Further analysis of the Standard versus New Antiepileptic Drugs (SANAD) study

PubMed Central

Powell, Graham A.; Tudur Smith, Catrin; Marson, Anthony G.

2017-01-01

Objectives To develop prognostic models for risk of a breakthrough seizure, risk of seizure recurrence after a breakthrough seizure, and likelihood of achieving 12-month remission following a breakthrough seizure. A breakthrough seizure is one that occurs following at least 12 months remission whilst on treatment. Methods We analysed data from the SANAD study. This long-term randomised trial compared treatments for participants with newly diagnosed epilepsy. Multivariable Cox models investigated how clinical factors affect the probability of each outcome. Best fitting multivariable models were produced with variable reduction by Akaike’s Information Criterion. Risks associated with combinations of risk factors were calculated from each multivariable model. Results Significant factors in the multivariable model for risk of a breakthrough seizure following 12-month remission were number of tonic-clonic seizures by achievement of 12-month remission, time taken to achieve 12-month remission, and neurological insult. Significant factors in the model for risk of seizure recurrence following a breakthrough seizure were total number of drugs attempted to achieve 12-month remission, time to achieve 12-month remission prior to breakthrough seizure, and breakthrough seizure treatment decision. Significant factors in the model for likelihood of achieving 12-month remission after a breakthrough seizure were gender, age at breakthrough seizure, time to achieve 12-month remission prior to breakthrough, and breakthrough seizure treatment decision. Conclusions This is the first analysis to consider risk of a breakthrough seizure and subsequent outcomes. The described models can be used to identify people most likely to have a breakthrough seizure, a seizure recurrence following a breakthrough seizure, and to achieve 12-month remission following a breakthrough seizure. The results suggest that focussing on achieving 12-month remission swiftly represents the best therapeutic aim to reduce the risk of a breakthrough seizure and subsequent negative outcomes. This will aid individual patient risk stratification and the design of future epilepsy trials. PMID:29267375

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

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.

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.

Pharmaceutical analysis in solids using front face fluorescence spectroscopy and multivariate calibration with matrix correction by piecewise direct standardization

NASA Astrophysics Data System (ADS)

Alves, Julio Cesar L.; Poppi, Ronei J.

2013-02-01

This paper reports the application of piecewise direct standardization (PDS) for matrix correction in front face fluorescence spectroscopy of solids when different excipients are used in a pharmaceutical preparation based on a mixture of acetylsalicylic acid (ASA), paracetamol (acetaminophen) and caffeine. As verified in earlier studies, the use of different excipients and their ratio can cause a displacement, change in fluorescence intensity or band profile. To overcome this important drawback, a standardization strategy was adopted to convert all the excitation-emission fluorescence spectra into those used for model development. An excitation-emission matrix (EEM) for which excitation and emission wavelengths ranging from 265 to 405 nm and 300 to 480 nm, respectively, was used. Excellent results were obtained using unfolded partial least squares (U-PLS), with RMSEP values of 8.2 mg/g, 10.9 mg/g and 2.7 mg/g for ASA, paracetamol and caffeine, respectively, and with relative errors lesser than 5% for the three analytes.

Multivariate analysis for scanning tunneling spectroscopy data

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Moral distress and burnout syndrome: are there relationships between these phenomena in nursing workers?

PubMed

Dalmolin, Graziele de Lima; Lunardi, Valéria Lerch; Lunardi, Guilherme Lerch; Barlem, Edison Luiz Devos; Silveira, Rosemary Silva da

2014-01-01

to identify relationships between moral distress and Burnout in the professional performance from the perceptions of the experiences of nursing workers. this is a survey type study with 375 nursing workers working in three different hospitals of southern Rio Grande do Sul, with the application of adaptations of the Moral Distress Scale and the Maslach Burnout Inventory, validated and standardized for use in Brazil. Data validation occurred through factor analysis and Cronbach's alpha. For the data analysis bivariate analysis using Pearson's correlation and multivariate analysis using multiple regression were performed. the existence of a weak correlation between moral distress and Burnout was verified. A possible positive correlation between Burnout and therapeutic obstinacy, and a negative correlation between professional fulfillment and moral distress were identified. the need was identified for further studies that include mediating and moderating variables that may explain more clearly the models studied.

Moral distress and Burnout syndrome: are there relationships between these phenomena in nursing workers?1

PubMed Central

Dalmolin, Graziele de Lima; Lunardi, Valéria Lerch; Lunardi, Guilherme Lerch; Barlem, Edison Luiz Devos; da Silveira, Rosemary Silva

2014-01-01

Objective to identify relationships between moral distress and Burnout in the professional performance from the perceptions of the experiences of nursing workers. Methods this is a survey type study with 375 nursing workers working in three different hospitals of southern Rio Grande do Sul, with the application of adaptations of the Moral Distress Scale and the Maslach Burnout Inventory, validated and standardized for use in Brazil. Data validation occurred through factor analysis and Cronbach's alpha. For the data analysis bivariate analysis using Pearson's correlation and multivariate analysis using multiple regression were performed. Results the existence of a weak correlation between moral distress and Burnout was verified. A possible positive correlation between Burnout and therapeutic obstinacy, and a negative correlation between professional fulfillment and moral distress were identified. Conclusion the need was identified for further studies that include mediating and moderating variables that may explain more clearly the models studied. PMID:24553701

Quality characterization and pollution source identification of surface water using multivariate statistical techniques, Nalagarh Valley, Himachal Pradesh, India

NASA Astrophysics Data System (ADS)

Herojeet, Rajkumar; Rishi, Madhuri S.; Lata, Renu; Dolma, Konchok

2017-09-01

Sirsa River flows through the central part of the Nalagarh valley, belongs to the rapid industrial belt of Baddi, Barotiwala and Nalagarh (BBN). The appraisal of surface water quality to ascertain its utility in such ecologically sensitive areas is need of the hour. The present study envisages the application of multivariate analysis, water utility class and conventional graphical representation to reveal the hidden factor responsible for deterioration of water quality and determine the hydrochemical facies and its evolution processes of water types in Nalagarh valley, India. The quality assessment is made by estimating pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness, major ions (Na+, K+, Ca2+, Mg2+, HCO3 -, Cl-, SO4 2-, NO3 - and PO4 3-), dissolved oxygen (DO), biological oxygen demand (BOD) and total coliform (TC) to determine its suitability for drinking and domestic purposes. The parameters like pH, TDS, TH, Ca2+, HCO3 -, Cl-, SO4 2-, NO3 - are within the desirable limit as per Bureau of Indian Standards (Indian Standard Drinking Water Specification (Second Edition) IS:10500. Indian Standard Institute, New Delhi, pp 1-18, 2012). Mg2+, Na+ and K+ ions for pre monsoon and EC during pre and post monsoon at few sites and approx 40% samples of BOD and TC for both seasons exceeds the permissible limits indicate organic contamination from human activities. Water quality classification for designated use indicates that maximum surface water samples are not suitable for drinking water source without conventional treatment. The result of piper trillinear and Chadha's diagram classified majority of surface water samples for both seasons fall in the fields of Ca2+-Mg2+-HCO3 - water type indicating temporary hardness. PCA and CA reveal that the surface water chemistry is influenced by natural factors such as weathering of minerals, ion exchange processes and anthropogenic factors. Thus, the present paper illustrates the importance of multivariate techniques for reliable quality characterization of surface water quality to develop effective pollution reduction strategies and maintain a fine balance between the industrialization and ecological integrity.

Analysis of Observational Studies in the Presence of Treatment Selection Bias: Effects of Invasive Cardiac Management on AMI Survival Using Propensity Score and Instrumental Variable Methods

PubMed Central

Stukel, Thérèse A.; Fisher, Elliott S; Wennberg, David E.; Alter, David A.; Gottlieb, Daniel J.; Vermeulen, Marian J.

2007-01-01

Context Comparisons of outcomes between patients treated and untreated in observational studies may be biased due to differences in patient prognosis between groups, often because of unobserved treatment selection biases. Objective To compare 4 analytic methods for removing the effects of selection bias in observational studies: multivariable model risk adjustment, propensity score risk adjustment, propensity-based matching, and instrumental variable analysis. Design, Setting, and Patients A national cohort of 122 124 patients who were elderly (aged 65–84 years), receiving Medicare, and hospitalized with acute myocardial infarction (AMI) in 1994–1995, and who were eligible for cardiac catheterization. Baseline chart reviews were taken from the Cooperative Cardiovascular Project and linked to Medicare health administrative data to provide a rich set of prognostic variables. Patients were followed up for 7 years through December 31, 2001, to assess the association between long-term survival and cardiac catheterization within 30 days of hospital admission. Main Outcome Measure Risk-adjusted relative mortality rate using each of the analytic methods. Results Patients who received cardiac catheterization (n=73 238) were younger and had lower AMI severity than those who did not. After adjustment for prognostic factors by using standard statistical risk-adjustment methods, cardiac catheterization was associated with a 50% relative decrease in mortality (for multivariable model risk adjustment: adjusted relative risk [RR], 0.51; 95% confidence interval [CI], 0.50–0.52; for propensity score risk adjustment: adjusted RR, 0.54; 95% CI, 0.53–0.55; and for propensity-based matching: adjusted RR, 0.54; 95% CI, 0.52–0.56). Using regional catheterization rate as an instrument, instrumental variable analysis showed a 16% relative decrease in mortality (adjusted RR, 0.84; 95% CI, 0.79–0.90). The survival benefits of routine invasive care from randomized clinical trials are between 8% and 21 %. Conclusions Estimates of the observational association of cardiac catheterization with long-term AMI mortality are highly sensitive to analytic method. All standard risk-adjustment methods have the same limitations regarding removal of unmeasured treatment selection biases. Compared with standard modeling, instrumental variable analysis may produce less biased estimates of treatment effects, but is more suited to answering policy questions than specific clinical questions. PMID:17227979

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…

Robust tumor morphometry in multispectral fluorescence microscopy

NASA Astrophysics Data System (ADS)

Tabesh, Ali; Vengrenyuk, Yevgen; Teverovskiy, Mikhail; Khan, Faisal M.; Sapir, Marina; Powell, Douglas; Mesa-Tejada, Ricardo; Donovan, Michael J.; Fernandez, Gerardo

2009-02-01

Morphological and architectural characteristics of primary tissue compartments, such as epithelial nuclei (EN) and cytoplasm, provide important cues for cancer diagnosis, prognosis, and therapeutic response prediction. We propose two feature sets for the robust quantification of these characteristics in multiplex immunofluorescence (IF) microscopy images of prostate biopsy specimens. To enable feature extraction, EN and cytoplasm regions were first segmented from the IF images. Then, feature sets consisting of the characteristics of the minimum spanning tree (MST) connecting the EN and the fractal dimension (FD) of gland boundaries were obtained from the segmented compartments. We demonstrated the utility of the proposed features in prostate cancer recurrence prediction on a multi-institution cohort of 1027 patients. Univariate analysis revealed that both FD and one of the MST features were highly effective for predicting cancer recurrence (p <= 0.0001). In multivariate analysis, an MST feature was selected for a model incorporating clinical and image features. The model achieved a concordance index (CI) of 0.73 on the validation set, which was significantly higher than the CI of 0.69 for the standard multivariate model based solely on clinical features currently used in clinical practice (p < 0.0001). The contributions of this work are twofold. First, it is the first demonstration of the utility of the proposed features in morphometric analysis of IF images. Second, this is the largest scale study of the efficacy and robustness of the proposed features in prostate cancer prognosis.

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.

[Local recurrence based on size after conservative surgery in breast cancer stage T1-T2. A population-based study].

PubMed

Martínez-Ramos, David; Fortea-Sanchis, Carlos; Escrig-Sos, Javier; Prats-de Puig, Miguel; Queralt-Martín, Raquel; Salvador-Sanchis, José Luís

2014-01-01

Conservative surgery can be regarded as the standard treatment for most early stage breast tumors. However, a minority of patients treated with conservative surgery will present local or locoregional recurrence. Therefore, it is of interest to evaluate the possible factors associated with this recurrence. A population-based retrospective study using data from the Tumor Registry of Castellón (Valencia, Spain) of patients operated on for primary nonmetastatic breast cancer between January 2000 and December 2008 was designed. Kaplan-Meier curves and log-rank test to estimate 5-year local recurrence were used. Two groups of patients were defined, one with conservative surgery and another with nonconservative surgery. Cox multivariate analysis was conducted. The total number of patients was 410. Average local recurrence was 6.8%. In univariate analysis, only tumor size and lymph node involvement showed significant differences. On multivariate analysis, independent prognostic factors were conservative surgery (hazard ratio [HR] 4.62; 95% confidence interval [CI]: 1.12-16.82), number of positive lymph nodes (HR 1.07; 95% CI: 1.01-1.17) and tumor size (in mm) (HR 1.02; 95% CI: 1.01-1.06). Local recurrence after breast-conserving surgery is higher in tumors >2 cm. Although tumor size should not be a contraindication for conservative surgery, it should be a risk factor to be considered.

Clinical validation of robot simulation of toothbrushing - comparative plaque removal efficacy

PubMed Central

2014-01-01

Background Clinical validation of laboratory toothbrushing tests has important advantages. It was, therefore, the aim to demonstrate correlation of tooth cleaning efficiency of a new robot brushing simulation technique with clinical plaque removal. Methods Clinical programme: 27 subjects received dental cleaning prior to 3-day-plaque-regrowth-interval. Plaque was stained, photographically documented and scored using planimetrical index. Subjects brushed teeth 33–47 with three techniques (horizontal, rotating, vertical), each for 20s buccally and for 20s orally in 3 consecutive intervals. The force was calibrated, the brushing technique was video supported. Two different brushes were randomly assigned to the subject. Robot programme: Clinical brushing programmes were transfered to a 6-axis-robot. Artificial teeth 33–47 were covered with plaque-simulating substrate. All brushing techniques were repeated 7 times, results were scored according to clinical planimetry. All data underwent statistical analysis by t-test, U-test and multivariate analysis. Results The individual clinical cleaning patterns are well reproduced by the robot programmes. Differences in plaque removal are statistically significant for the two brushes, reproduced in clinical and robot data. Multivariate analysis confirms the higher cleaning efficiency for anterior teeth and for the buccal sites. Conclusions The robot tooth brushing simulation programme showed good correlation with clinically standardized tooth brushing. This new robot brushing simulation programme can be used for rapid, reproducible laboratory testing of tooth cleaning. PMID:24996973

Clinical validation of robot simulation of toothbrushing--comparative plaque removal efficacy.

PubMed

Lang, Tomas; Staufer, Sebastian; Jennes, Barbara; Gaengler, Peter

2014-07-04

Clinical validation of laboratory toothbrushing tests has important advantages. It was, therefore, the aim to demonstrate correlation of tooth cleaning efficiency of a new robot brushing simulation technique with clinical plaque removal. Clinical programme: 27 subjects received dental cleaning prior to 3-day-plaque-regrowth-interval. Plaque was stained, photographically documented and scored using planimetrical index. Subjects brushed teeth 33-47 with three techniques (horizontal, rotating, vertical), each for 20s buccally and for 20s orally in 3 consecutive intervals. The force was calibrated, the brushing technique was video supported. Two different brushes were randomly assigned to the subject. Robot programme: Clinical brushing programmes were transfered to a 6-axis-robot. Artificial teeth 33-47 were covered with plaque-simulating substrate. All brushing techniques were repeated 7 times, results were scored according to clinical planimetry. All data underwent statistical analysis by t-test, U-test and multivariate analysis. The individual clinical cleaning patterns are well reproduced by the robot programmes. Differences in plaque removal are statistically significant for the two brushes, reproduced in clinical and robot data. Multivariate analysis confirms the higher cleaning efficiency for anterior teeth and for the buccal sites. The robot tooth brushing simulation programme showed good correlation with clinically standardized tooth brushing.This new robot brushing simulation programme can be used for rapid, reproducible laboratory testing of tooth cleaning.

Glycated hemoglobin as a predictor for metabolic syndrome in an Iranian population with normal glucose tolerance.

PubMed

Janghorbani, Mohsen; Amini, Masoud

2012-12-01

The aim of this study was to determine the ability of glycated hemoglobin (GHb) to predict metabolic syndrome in an Iranian population with normal glucose tolerance (NGT). A cross-sectional study of first-degree relatives (FDRs) of patients with type 2 diabetes was conducted from 2003 to 2005. A total of 1386 FDRs of consecutive patients with type 2 diabetes 30-60 years old (355 men and 1031 women) with NGT were examined. All subjects underwent a standard 75-gram 2-h oral glucose tolerance test and GHb measurement. Consensus criteria in 2009 were used to identify metabolic syndrome. Unadjusted and adjusted multivariate logistic regression analysis was performed to assess the risk of metabolic syndrome. The mean [standard deviation (SD)] age of participants was 42.4 (6.3) years. The prevalence of metabolic syndrome was 17.5% in men and 21.5% in women. The multivariate-adjusted odds ratio (95% CI) of metabolic syndrome was 2.01 (1.03, 3.93) for the highest quintile of GHb compared with lowest quintile. These data indicate that GHb was associated with metabolic syndrome, independently of gender among FDRs of patients with type 2 diabetes with NGT. These data indicate that GHb below the level for prediabetes might be a predictive measure of metabolic syndrome in FDRs of patients with type 2 diabetes with NGT.

The impact of maternal body mass index on external cephalic version success.

PubMed

Chaudhary, Shahrukh; Contag, Stephen; Yao, Ruofan

2018-01-21

The purpose of this study is to determine the association between body mass index (BMI) and success of ECV. This is a cross-sectional analysis of singleton live births in the USA from 2010 to 2014 using birth certificate data. Patients were assigned a BMI category according to standard WHO classification. Comparisons of success of ECV between the BMI categories were made using chi-square analysis with normal BMI as the reference group. Cochran-Armitage test was performed to look for a trend of decreasing success of ECV as BMI increased. The odds for successful ECV were estimated using multivariate logistic regression analysis, adjusting for possible confounders. A total of 51,002 patients with documented ECV were available for analysis. There was a decreased success rate for ECV as BMI increased (p < .01). Women with a BMI of 40 kg/m 2 or greater had a 58.5% success rate of ECV; women with a normal BMI had 65.0% success rate of ECV. Multivariate analyses demonstrated significant decrease in success of ECV in women with BMI of 40 kg/m 2 or greater (OR 0.621, CI 0.542-0.712). Among women with BMI of 40 kg/m 2 or greater with successful ECV, 59.5% delivered vaginally. In contrast, 81.0% of women with normal BMI and successful ECV delivered vaginally. Morbidly obese women have decreased success rate of ECV as BMI increases and decreased vaginal delivery rates after successful ECV.

Vitrified-warmed embryo transfer is associated with mean higher singleton birth weight compared to fresh embryo transfer.

PubMed

Beyer, Daniel Alexander; Griesinger, Georg

2016-08-01

To test for differences in birth weight between singletons born after IVF with fresh embryo transfer vs. vitrified-warmed 2PN embryo transfer (vitrification protocol). Retrospective analysis of 464 singleton live births after IVF or ICSI during a 12 year period. University hospital. Fresh embryo transfer, vitrified-warmed 2PN embryo transfer (vitrification protocol). Birth weight standardized as a z-score, adjusting for gestational week at delivery and fetal sex. As a reference, birth weight means from regular deliveries from the same hospital were used. Multivariate regression analysis was used to investigate the relationship between the dependent variable z-score (fetal birth weight) and the independent predictor variables maternal age, weight, height, body mass index, RDS prophylaxis, transfer protocol, number of embryos transferred, indication for IVF treatment and sperm quality. The mean z-score was significantly lower after fresh transfer (-0.11±92) as compared to vitrification transfer (0.72±83) (p<0.001). Multivariate regression analysis indicated that only maternal height and maternal body mass index, but not type of cryopreservation protocol, was a significant predictor of birth weight. In this analysis focusing on 2PN oocytes, vitrified-warmed embryo transfer is associated with mean higher birth weight compared to fresh embryo transfer. Maternal height and body mass index are significant confounders of fetal birth weight and need to be taken into account when studying birth weight differences between ART protocols. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Search for a supersymmetric partner to the top quark using a multivariate analysis technique

NASA Astrophysics Data System (ADS)

Darmora, Smita

Supersymmetry (SUSY) is an extension to the Standard Model (SM) which introduces supersymmetric partners of the known fermions and bosons. Top squark (stop) searches are a natural extension of inclusive SUSY searches at the LHC. If SUSY solves the naturalness problem, the stop should be light enough to cancel the top loop contribution to the Higgs mass parameter. The 3rd generation squarks may be the first SUSY particles to be discovered at the LHC. The stop can decay into a variety of final states, depending, amongst other factors, on the hierarchy of the mass eigenstates formed from the linear superposition of the SUSY partners of the Higgs boson and electroweak gauge bosons. In this study the relevant mass eigenstates are the lightest chargino (chi+/-1) and the neutralino (chi +/-0). A search is presented for a heavy SUSY top partner decaying to a lepton, neutrino and the lightest supersymmetric particle (chi+/-0), via a b-quark and a chargino (chi +/-1) in events with two leptons in the final state. The analysis targets searches for a SUSY top partner by means of Multivariate Analysis Technique, used to discriminate between the stop signal and the background with a learning algorithm based on Monte Carlo generated signal and background samples. The analysis uses data corresponding to 20.3 fb --1 of integrated luminosity at √s = 8 TeV, collected by the ATLAS experiment at the Large Hadron Collider in 2012.

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.

Compound effects of temperature and precipitation in making droughts more frequent in Marathwada, India

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Establishing Benchmarks for Outcome Indicators: A Statistical Approach to Developing Performance Standards.

ERIC Educational Resources Information Center

Henry, Gary T.; And Others

1992-01-01

A statistical technique is presented for developing performance standards based on benchmark groups. The benchmark groups are selected using a multivariate technique that relies on a squared Euclidean distance method. For each observation unit (a school district in the example), a unique comparison group is selected. (SLD)

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.

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.

Belimumab in the treatment of systemic lupus erythematosus: high disease activity predictors of response.

PubMed

van Vollenhoven, Ronald F; Petri, Michelle A; Cervera, Ricard; Roth, David A; Ji, Beulah N; Kleoudis, Christi S; Zhong, Z John; Freimuth, William

2012-08-01

To identify factors that predict response to belimumab treatment in the phase 3 BLISS trials of autoantibody-positive systemic lupus erythematosus (SLE) and further analyse clinical efficacy in various patient subsets. The BLISS trials compared belimumab 1 and 10 mg/kg versus placebo, all plus standard SLE therapy, over 52 or 76 weeks. Pooled subgroup analyses of week 52 SLE responder index rates (the primary endpoint in both trials) were performed based on demographic characteristics and baseline disease activity indicators. Pooled multivariate analysis was performed to determine predictors of response and treatment effect. Pooled univariate and multivariate analyses (N=1684) identified baseline factors associated with an increased benefit of belimumab versus placebo. These factors included the Safety Of Estrogens In Lupus Erythematosus National Assessment-Systemic Lupus Erythematosus Disease Activity Index (SELENA-SLEDAI) ≥10, low complement, anti-dsDNA positivity and corticosteroid use. Efficacy outcomes were assessed in the low complement/anti-dsDNA-positive and SELENA-SLEDAI ≥10 subgroups. Week 52 SLE Responder Index rates in the low complement/anti-dsDNA-positive subgroup were 31.7%, 41.5% (p=0.002) and 51.5% (p<0.001) with placebo and belimumab 1 mg/kg and 10 mg/kg, respectively; corresponding rates in the SELENA-SLEDAI ≥10 subgroup were 44.3%, 58.0% (p<0.001) and 63.2% (p<0.001). Further analysis of secondary endpoints in the low complement/anti-dsDNA-positive subgroup showed that compared with placebo, belimumab produced greater benefits regarding severe flares, corticosteroid use and health-related quality of life. These findings suggest that belimumab has greater therapeutic benefit than standard therapy alone in patients with higher disease activity, anti-dsDNA positivity, low complement or corticosteroid treatment at baseline. CLINICALTRIALS.GOV: identifiers NCT00424476 and NCT00410384.

Contrast medium at the site of the anastomosis is crucial in detecting anastomotic leakage with CT imaging after colorectal surgery.

PubMed

Huiberts, Astrid A M; Dijksman, Lea M; Boer, Simone A; Krul, Eveline J T; Peringa, Jan; Donkervoort, Sandra C

2015-06-01

The use of computed tomography (CT) to detect anastomotic leakage (AL) is becoming the standard of care. Accurate detection of AL is crucial. The aim of this study was to define CT criteria that are most predictive for AL. From January 2006 to December 2012, all consecutive patients who had undergone CT imaging because of clinical suspicion of anastomotic leakage after colorectal surgery were analysed. All CT scans were re-evaluated by two independent abdominal radiologists blinded for clinical outcome. The images were scored with a set of criteria and a conclusion whether or not AL was present was drawn. Each separate criterion was analysed for its value in predicting AL by uni- and multivariable logistic regression Of 668 patients with colorectal surgery, 108 had undergone CT imaging within 16 days postoperatively. According to our standard of reference, 34 (31%) of the patients had AL. Univariable analysis showed that "fluid near anastomosis" (radiologist 1 (rad 1), p < 0.001; radiologist 2 (rad 2), p < 0.001) and "air near anastomosis" (rad 1, p < 0.001; rad 2, p < 0.001), "air intra-abdominally" (rad 1, p = 0.019; rad 2, p = 0.004) and "contrast leakage" (rad 1, p < 0.001; rad 2, p < 0.001) were associated with AL. Contrast leakage was the only independent predictor for AL in multivariable analysis for both radiologists (rad 1, OR 5.43 (95% CI 1.18-25.02); rad 2, OR 8.51 (95% CI 2.21-32.83)). The only independent variable predicting AL is leakage of contrast medium. To improve the accuracy of CT imaging, optimal contrast administration near the anastomosis appears to be crucial.

Results of third-generation epirubicin/cisplatin/xeloda adjuvant chemotherapy in patients with radically resected gastric cancer.

PubMed

Cainap, Calin; Nagy, Viorica; Seicean, Andrada; Gherman, Alexandra; Laszlo, Istvan; Lisencu, Cosmin; Nadim, Al Hajar; Constantin, Anne-Marie; Cainap, Simona

2016-01-01

The purpose of this study was to evaluate the efficacy and toxicity of a third-generation chemotherapy regimen in the adjuvant setting to radically operated patients with gastric cancer. This proposed new adjuvant regimen was also compared with a consecutive retrospective cohort of patients treated with the classic McDonald regimen. Starting in 2006, a non-randomized prospective phase II study was conducted at the Institute of Oncology of Cluj-Napoca on 40 patients with stage IB-IV radically resected gastric adenocarcinoma. These patients were administered a chemotherapy regimen already considered to be standard treatment in the metastatic setting: ECX (epirubicin, cisplatin, xeloda) and were compared to a retrospective control group consisting of 54 patients, treated between 2001 and 2006 according to McDonald's trial. In a previous paper, we reported toxicities and the possible predictive factors for these toxicities; in the present article, we report on the results concerning predictive factors on overall survival (OS) and disease free survival (DFS). The proposed ECX treatment was not less effective than the standard suggested by McDonald's trial. Age was an independent prognostic factor in multivariate analysis. N3 stage was an independent prognostic factor for OS and DFS. N ratio >70% was an independent predictive factor for OS and locoregional disease control. The resection margins were independent prognostic factors for OS and DFS. The proposed treatment is not less effective compared with the McDonald's trial. Age was an independent prognostic factor in multivariate analysis. N3 stage represented an independent prognostic factor and N ratio >70% was a predictive factor for OS and DFS. The resection margins were proven to be independent prognostic factors for OS and DFS.

Multivariate Statistical Analysis of Cigarette Design Feature Influence on ISO TNCO Yields.

PubMed

Agnew-Heard, Kimberly A; Lancaster, Vicki A; Bravo, Roberto; Watson, Clifford; Walters, Matthew J; Holman, Matthew R

2016-06-20

The aim of this study is to explore how differences in cigarette physical design parameters influence tar, nicotine, and carbon monoxide (TNCO) yields in mainstream smoke (MSS) using the International Organization of Standardization (ISO) smoking regimen. Standardized smoking methods were used to evaluate 50 U.S. domestic brand cigarettes and a reference cigarette representing a range of TNCO yields in MSS collected from linear smoking machines using a nonintense smoking regimen. Multivariate statistical methods were used to form clusters of cigarettes based on their ISO TNCO yields and then to explore the relationship between the ISO generated TNCO yields and the nine cigarette physical design parameters between and within each cluster simultaneously. The ISO generated TNCO yields in MSS are 1.1-17.0 mg tar/cigarette, 0.1-2.2 mg nicotine/cigarette, and 1.6-17.3 mg CO/cigarette. Cluster analysis divided the 51 cigarettes into five discrete clusters based on their ISO TNCO yields. No one physical parameter dominated across all clusters. Predicting ISO machine generated TNCO yields based on these nine physical design parameters is complex due to the correlation among and between the nine physical design parameters and TNCO yields. From these analyses, it is estimated that approximately 20% of the variability in the ISO generated TNCO yields comes from other parameters (e.g., filter material, filter type, inclusion of expanded or reconstituted tobacco, and tobacco blend composition, along with differences in tobacco leaf origin and stalk positions and added ingredients). A future article will examine the influence of these physical design parameters on TNCO yields under a Canadian Intense (CI) smoking regimen. Together, these papers will provide a more robust picture of the design features that contribute to TNCO exposure across the range of real world smoking patterns.

Standard operating procedures for antibiotic therapy and the occurrence of acute kidney injury: a prospective, clinical, non-interventional, observational study.

PubMed

Nachtigall, Irit; Tafelski, Sascha; Günzel, Karsten; Uhrig, Alexander; Powollik, Robert; Tamarkin, Andrey; Wernecke, Klaus D; Spies, Claudia

2014-06-12

Acute kidney injury (AKI) occurs in 7% of hospitalized and 66% of Intensive Care Unit (ICU) patients. It increases mortality, hospital length of stay, and costs. The aim of this study was to investigate, whether there is an association between adherence to guidelines (standard operating procedures (SOP)) for potentially nephrotoxic antibiotics and the occurrence of AKI. This study was carried out as a prospective, clinical, non-interventional, observational study. Data collection was performed over a total of 170 days in three ICUs at Charité - Universitaetsmedizin Berlin. A total of 675 patients were included; 163 of these had therapy with vancomycin, gentamicin, or tobramycin; were >18 years; and treated in the ICU for >24 hours. Patients with an adherence to SOP >70% were classified into the high adherence group (HAG) and patients with an adherence of <70% into the low adherence group (LAG). AKI was defined according to RIFLE criteria. Adherence to SOPs was evaluated by retrospective expert audit. Development of AKI was compared between groups with exact Chi2-test and multivariate logistic regression analysis (two-sided P <0.05). LAG consisted of 75 patients (46%) versus 88 HAG patients (54%). AKI occurred significantly more often in LAG with 36% versus 21% in HAG (P = 0.035). Basic characteristics were comparable, except an increased rate of soft tissue infections in LAG. Multivariate analysis revealed an odds ratio of 2.5-fold for LAG to develop AKI compared with HAG (95% confidence interval 1.195 to 5.124, P = 0.039). Low adherence to SOPs for potentially nephrotoxic antibiotics was associated with a higher occurrence of AKI. Current Controlled Trials ISRCTN54598675. Registered 17 August 2007.

Standard operating procedures for antibiotic therapy and the occurrence of acute kidney injury: a prospective, clinical, non-interventional, observational study

PubMed Central

2014-01-01

Introduction Acute kidney injury (AKI) occurs in 7% of hospitalized and 66% of Intensive Care Unit (ICU) patients. It increases mortality, hospital length of stay, and costs. The aim of this study was to investigate, whether there is an association between adherence to guidelines (standard operating procedures (SOP)) for potentially nephrotoxic antibiotics and the occurrence of AKI. Methods This study was carried out as a prospective, clinical, non-interventional, observational study. Data collection was performed over a total of 170 days in three ICUs at Charité – Universitaetsmedizin Berlin. A total of 675 patients were included; 163 of these had therapy with vancomycin, gentamicin, or tobramycin; were >18 years; and treated in the ICU for >24 hours. Patients with an adherence to SOP >70% were classified into the high adherence group (HAG) and patients with an adherence of <70% into the low adherence group (LAG). AKI was defined according to RIFLE criteria. Adherence to SOPs was evaluated by retrospective expert audit. Development of AKI was compared between groups with exact Chi2-test and multivariate logistic regression analysis (two-sided P <0.05). Results LAG consisted of 75 patients (46%) versus 88 HAG patients (54%). AKI occurred significantly more often in LAG with 36% versus 21% in HAG (P = 0.035). Basic characteristics were comparable, except an increased rate of soft tissue infections in LAG. Multivariate analysis revealed an odds ratio of 2.5-fold for LAG to develop AKI compared with HAG (95% confidence interval 1.195 to 5.124, P = 0.039). Conclusion Low adherence to SOPs for potentially nephrotoxic antibiotics was associated with a higher occurrence of AKI. Trial registration Current Controlled Trials ISRCTN54598675. Registered 17 August 2007. PMID:24923469

XRCC1 Polymorphism Associated With Late Toxicity After Radiation Therapy in Breast Cancer Patients

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

Seibold, Petra; Behrens, Sabine; Schmezer, Peter

Purpose: To identify single-nucleotide polymorphisms (SNPs) in oxidative stress–related genes associated with risk of late toxicities in breast cancer patients receiving radiation therapy. Methods and Materials: Using a 2-stage design, 305 SNPs in 59 candidate genes were investigated in the discovery phase in 753 breast cancer patients from 2 prospective cohorts from Germany. The 10 most promising SNPs in 4 genes were evaluated in the replication phase in up to 1883 breast cancer patients from 6 cohorts identified through the Radiogenomics Consortium. Outcomes of interest were late skin toxicity and fibrosis of the breast, as well as an overall toxicity score (Standardized Totalmore » Average Toxicity). Multivariable logistic and linear regression models were used to assess associations between SNPs and late toxicity. A meta-analysis approach was used to summarize evidence. Results: The association of a genetic variant in the base excision repair gene XRCC1, rs2682585, with normal tissue late radiation toxicity was replicated in all tested studies. In the combined analysis of discovery and replication cohorts, carrying the rare allele was associated with a significantly lower risk of skin toxicities (multivariate odds ratio 0.77, 95% confidence interval 0.61-0.96, P=.02) and a decrease in Standardized Total Average Toxicity scores (−0.08, 95% confidence interval −0.15 to −0.02, P=.016). Conclusions: Using a stage design with replication, we identified a variant allele in the base excision repair gene XRCC1 that could be used in combination with additional variants for developing a test to predict late toxicities after radiation therapy in breast cancer patients.« less

Prognostic implications of mutation-specific QTc standard deviation in congenital long QT syndrome.

PubMed

Mathias, Andrew; Moss, Arthur J; Lopes, Coeli M; Barsheshet, Alon; McNitt, Scott; Zareba, Wojciech; Robinson, Jennifer L; Locati, Emanuela H; Ackerman, Michael J; Benhorin, Jesaia; Kaufman, Elizabeth S; Platonov, Pyotr G; Qi, Ming; Shimizu, Wataru; Towbin, Jeffrey A; Michael Vincent, G; Wilde, Arthur A M; Zhang, Li; Goldenberg, Ilan

2013-05-01

Individual corrected QT interval (QTc) may vary widely among carriers of the same long QT syndrome (LQTS) mutation. Currently, neither the mechanism nor the implications of this variable penetrance are well understood. To hypothesize that the assessment of QTc variance in patients with congenital LQTS who carry the same mutation provides incremental prognostic information on the patient-specific QTc. The study population comprised 1206 patients with LQTS with 95 different mutations and ≥ 5 individuals who carry the same mutation. Multivariate Cox proportional hazards regression analysis was used to assess the effect of mutation-specific standard deviation of QTc (QTcSD) on the risk of cardiac events (comprising syncope, aborted cardiac arrest, and sudden cardiac death) from birth through age 40 years in the total population and by genotype. Assessment of mutation-specific QTcSD showed large differences among carriers of the same mutations (median QTcSD 45 ms). Multivariate analysis showed that each 20 ms increment in QTcSD was associated with a significant 33% (P = .002) increase in the risk of cardiac events after adjustment for the patient-specific QTc duration and the family effect on QTc. The risk associated with QTcSD was pronounced among patients with long QT syndrome type 1 (hazard ratio 1.55 per 20 ms increment; P<.001), whereas among patients with long QT syndrome type 2, the risk associated with QTcSD was not statistically significant (hazard ratio 0.99; P = .95; P value for QTcSD-by-genotype interaction = .002). Our findings suggest that mutations with a wider variation in QTc duration are associated with increased risk of cardiac events. These findings appear to be genotype-specific, with a pronounced effect among patients with the long QT syndrome type 1 genotype. Copyright © 2013. Published by Elsevier Inc.

Superior long-term outcome with idarubicin compared with high-dose daunorubicin in patients with acute myeloid leukemia age 50 years and older.

PubMed

Gardin, Claude; Chevret, Sylvie; Pautas, Cécile; Turlure, Pascal; Raffoux, Emmanuel; Thomas, Xavier; Quesnel, Bruno; de Revel, Thierry; de Botton, Stéphane; Gachard, Nathalie; Renneville, Aline; Boissel, Nicolas; Preudhomme, Claude; Terré, Christine; Fenaux, Pierre; Bordessoule, Dominique; Celli-Lebras, Karine; Castaigne, Sylvie; Dombret, Hervé

2013-01-20

Although standard chemotherapy remains associated with a poor outcome in older patients with acute myeloid leukemia (AML), it is unclear which patients can survive long enough to be considered as cured. This study aimed to identify factors influencing the long-term outcome in these patients. The study included 727 older patients with AML (median age, 67 years) treated in two idarubicin (IDA) versus daunorubicin (DNR) Acute Leukemia French Association trials. Prognostic analysis was based on standard univariate and multivariate models and also included a cure fraction model to focus on long-term outcome. Age, WBC count, secondary AML, Eastern Cooperative Oncology Group (ECOG) performance status (PS), and adverse-risk and favorable-risk AML subsets (European LeukemiaNet classification) all influenced complete remission (CR) rate and overall survival (OS). IDA random assignment was associated with higher CR rate, but not with longer OS (P = .13). The overall cure rate was 13.3%. Older age and ECOG-PS more than 1 negatively influenced cure rate, which was higher in patients with favorable-risk AML (39.1% v 8.0% in adverse-risk AML; P < .001) and those treated with IDA (16.6% v 9.8% with DNR; P = .018). The long-term impact of IDA was still observed in patients younger than age 65 years, although all of the younger patients in the DNR control arm received high DNR doses (cure rate, 27.4% for IDA v 15.9% for DNR; P = .049). In multivariate analysis, IDA random assignment remained associated with a higher cure rate (P = .04), together with younger age and favorable-risk AML, despite not influencing OS (P = .11). In older patients with AML, younger age, favorable-risk AML, and IDA treatment predict a better long-term outcome.

Wound complications in rectal cancer patients undergoing primary closure of the perineal wound after abdominoperineal resection.

PubMed

El-Gazzaz, Galal; Kiran, Ravi Pokala; Lavery, Ian

2009-12-01

Perineal wound complications have a significant impact on postoperative morbidity after excision of the rectum and anus. The aim of this study is to evaluate factors affecting perineal wound complications after primary closure of the wound following abdominoperineal resection. Data were reviewed from all patients who underwent abdominoperineal resection for rectal carcinoma between 1982 and 2007. Data pertaining to demographics, tumor characteristics, and use of preoperative neoadjuvant therapy were retrieved. Complications studied included delayed wound healing, wound infection, dehiscence, abscess or sinus, reoperation, and perineal hernias. Patients who developed perineal wound complications (Group A) were compared with the remaining patients (Group B) to evaluate factors associated with the development of perineal wound complications. Six hundred ninety-six patients (59% male) met the inclusion criteria. The mean age was 63 years (standard deviation, 13), and the mean body mass index was 28.9 kg/m2 (standard deviation, 7.8). Two hundred seventy-three patients (39.2%) received neoadjuvant chemoradiation. The overall rate of wound complications was 16.2%, and reoperation was required in 5.2% of patients. Group A and Group B patients were similar with respect to age (P = 0.1), gender (P = 0.7), grade (P = 0.4), and stage of disease (P = 0.5). A greater proportion of Group A patients had associated comorbidity (P = 0.001), obesity (0.04), neoadjuvant chemoradiation (0.02), and intraoperative bleeding (0.04). In multivariate analysis, comorbidity was the only independent factor associated with the development of perineal complications (odds ratio, 1.8 (1.09-2.96)). Most patients have perineal wound healing without complications after abdominoperineal resection. In multivariate analysis, comorbidity was the only significant factor that predicted perineal wound complications.

Biological variation of Vanilla planifolia leaf metabolome.

PubMed

Palama, Tony Lionel; Fock, Isabelle; Choi, Young Hae; Verpoorte, Robert; Kodja, Hippolyte

2010-04-01

The metabolomic analysis of Vanilla planifolia leaves collected at different developmental stages was carried out using (1)H-nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis in order to evaluate their variation. Ontogenic changes of the metabolome were considered since leaves of different ages were collected at two different times of the day and in two different seasons. Principal component analysis (PCA) and partial least square modeling discriminate analysis (PLS-DA) of (1)H NMR data provided a clear separation according to leaf age, time of the day and season of collection. Young leaves were found to have higher levels of glucose, bis[4-(beta-D-glucopyranosyloxy)-benzyl]-2-isopropyltartrate (glucoside A) and bis[4-(beta-D-glucopyranosyloxy)-benzyl]-2-(2-butyl)-tartrate (glucoside B), whereas older leaves had more sucrose, acetic acid, homocitric acid and malic acid. Results obtained from PLS-DA analysis showed that leaves collected in March 2008 had higher levels of glucosides A and B as compared to those collected in August 2007. However, the relative standard deviation (RSD) exhibited by the individual values of glucosides A and B showed that those compounds vary more according to their developmental stage (50%) than to the time of day or the season in which they were collected (19%). Although morphological variations of the V. planifolia accessions were observed, no clear separation of the accessions was determined from the analysis of the NMR spectra. The results obtained in this study, show that this method based on the use of (1)H NMR spectroscopy in combination with multivariate analysis has a great potential for further applications in the study of vanilla leaf metabolome. Copyright 2009 Elsevier Ltd. All rights reserved.

Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

NASA Astrophysics Data System (ADS)

Ahmadalipour, Ali; Rana, Arun; Moradkhani, Hamid; Sharma, Ashish

2017-04-01

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.

Big-data reflection high energy electron diffraction analysis for understanding epitaxial film growth processes.

PubMed

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

2014-10-28

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

Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

NASA Astrophysics Data System (ADS)

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

2004-06-01

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

A Study of Effects of MultiCollinearity in the Multivariable Analysis

PubMed Central

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

2015-01-01

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables. PMID:25664257

A Study of Effects of MultiCollinearity in the Multivariable Analysis.

PubMed

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

2014-10-01

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

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

PubMed

Olswold, Curtis; de Andrade, Mariza

2003-12-31

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

Perceptions of Peer Sexual Behavior: Do Adolescents Believe in a Sexual Double Standard?

PubMed

Young, Michael; Cardenas, Susan; Donnelly, Joseph; J Kittleson, Mark

2016-12-01

The purpose of the study was to (1) examine attitudes of adolescents toward peer models having sex or choosing abstinence, and (2) determine whether a "double standard" in perception existed concerning adolescent abstinence and sexual behavior. Adolescents (N = 173) completed questionnaires that included 1 of 6 randomly assigned vignettes that described male and female peer models 3 ways: (1) no information about model's sexual behavior, (2) model in love but choosing abstinence, and (3) model in love and having sex. Participants read the vignette to which they had been assigned and responded to statements about the peer model. Data were analyzed using multivariate analysis of variance. Results did not show evidence of a sexual double standard among male participants, but did show some evidence of a sexual double standard among female participants. Additionally, both male and female participants evaluated more harshly peer models that were having sex than peer models that chose abstinence. Findings provide insight concerning the lack of a sexual double standard among male participants, the existence, to some degree, of a sexual double standard among female participants, and demonstrate the existence of a social cost to both young men and young women for choosing to have sex. © 2016, American School Health Association.

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

Postoperative Radiotherapy Patterns of Care and Survival Implications for Medulloblastoma in Young Children.

PubMed

Kann, Benjamin H; Park, Henry S; Lester-Coll, Nataniel H; Yeboa, Debra N; Benitez, Viviana; Khan, Atif J; Bindra, Ranjit S; Marks, Asher M; Roberts, Kenneth B

2016-12-01

Postoperative radiotherapy to the craniospinal axis is standard-of-care for pediatric medulloblastoma but is associated with long-term morbidity, particularly in young children. With the advent of modern adjuvant chemotherapy strategies, postoperative radiotherapy deferral has gained acceptance in children younger than 3 years, although it remains controversial in older children. To analyze recent postoperative radiotherapy national treatment patterns and implications for overall survival in patients with medulloblastoma ages 3 to 8 years. Using the National Cancer Data Base, patients ages 3 to 8 years diagnosed as having histologically confirmed medulloblastoma in 2004 to 2012, without distant metastases, who underwent surgery and adjuvant chemotherapy with or without postoperative radiotherapy at facilities nationwide accredited by the Commission on Cancer were identified. Patients were designated as having "postoperative radiotherapy upfront" if they received radiotherapy within 90 days of surgery or "postoperative radiotherapy deferred" otherwise. Factors associated with postoperative radiotherapy deferral were identified using multivariable logistic regression. Overall survival (OS) was compared using Kaplan-Meier analysis with log-rank tests and multivariable Cox regression. Statistical tests were 2-sided. Postoperative radiotherapy utilization and overall survival. Among 816 patients, 123 (15.1%) had postoperative radiotherapy deferred, and 693 (84.9%) had postoperative radiotherapy upfront; 36.8% of 3-year-olds and 4.1% of 8-year-olds had postoperative radiotherapy deferred (P < .001). On multivariable logistic regression, variables associated with postoperative radiotherapy deferral were age (odds ratio [OR], 0.57 per year; 95% CI, 0.49-0.67 per year) and year of diagnosis (OR, 1.18 per year; 95% CI, 1.08-1.29 per year). On survival analysis, with median follow-up of 4.8 years, OS was improved for those receiving postoperative radiotherapy upfront vs postoperative radiotherapy deferred (5-year OS: 82.0% vs 63.4%; P < .001). On multivariable analysis, variables associated with poorer OS were postoperative radiotherapy deferral (hazards ratio [HR], 1.95; 95% CI, 1.15-3.31); stage M1-3 disease (HR, 1.86; 95% CI, 1.10-3.16), and low facility volume (HR, 1.75; 95% CI, 1.04-2.94). Our national database analysis reveals a higher-than-expected and increasing rate of postoperative radiotherapy deferral in children with medulloblastoma ages 3 to 8 years. The analysis suggests that postoperative radiotherapy deferral is associated with worse survival in this age group, even in the modern era of chemotherapy.

Evidence-based provisional clinical classification criteria for autoinflammatory periodic fevers.

PubMed

Federici, Silvia; Sormani, Maria Pia; Ozen, Seza; Lachmann, Helen J; Amaryan, Gayane; Woo, Patricia; Koné-Paut, Isabelle; Dewarrat, Natacha; Cantarini, Luca; Insalaco, Antonella; Uziel, Yosef; Rigante, Donato; Quartier, Pierre; Demirkaya, Erkan; Herlin, Troels; Meini, Antonella; Fabio, Giovanna; Kallinich, Tilmann; Martino, Silvana; Butbul, Aviel Yonatan; Olivieri, Alma; Kuemmerle-Deschner, Jasmin; Neven, Benedicte; Simon, Anna; Ozdogan, Huri; Touitou, Isabelle; Frenkel, Joost; Hofer, Michael; Martini, Alberto; Ruperto, Nicolino; Gattorno, Marco

2015-05-01

The objective of this work was to develop and validate a set of clinical criteria for the classification of patients affected by periodic fevers. Patients with inherited periodic fevers (familial Mediterranean fever (FMF); mevalonate kinase deficiency (MKD); tumour necrosis factor receptor-associated periodic fever syndrome (TRAPS); cryopyrin-associated periodic syndromes (CAPS)) enrolled in the Eurofever Registry up until March 2013 were evaluated. Patients with periodic fever, aphthosis, pharyngitis and adenitis (PFAPA) syndrome were used as negative controls. For each genetic disease, patients were considered to be 'gold standard' on the basis of the presence of a confirmatory genetic analysis. Clinical criteria were formulated on the basis of univariate and multivariate analysis in an initial group of patients (training set) and validated in an independent set of patients (validation set). A total of 1215 consecutive patients with periodic fevers were identified, and 518 gold standard patients (291 FMF, 74 MKD, 86 TRAPS, 67 CAPS) and 199 patients with PFAPA as disease controls were evaluated. The univariate and multivariate analyses identified a number of clinical variables that correlated independently with each disease, and four provisional classification scores were created. Cut-off values of the classification scores were chosen using receiver operating characteristic curve analysis as those giving the highest sensitivity and specificity. The classification scores were then tested in an independent set of patients (validation set) with an area under the curve of 0.98 for FMF, 0.95 for TRAPS, 0.96 for MKD, and 0.99 for CAPS. In conclusion, evidence-based provisional clinical criteria with high sensitivity and specificity for the clinical classification of patients with inherited periodic fevers have been developed. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Review of validation and reporting of non-targeted fingerprinting approaches for food authentication.

PubMed

Riedl, Janet; Esslinger, Susanne; Fauhl-Hassek, Carsten

2015-07-23

Food fingerprinting approaches are expected to become a very potent tool in authentication processes aiming at a comprehensive characterization of complex food matrices. By non-targeted spectrometric or spectroscopic chemical analysis with a subsequent (multivariate) statistical evaluation of acquired data, food matrices can be investigated in terms of their geographical origin, species variety or possible adulterations. Although many successful research projects have already demonstrated the feasibility of non-targeted fingerprinting approaches, their uptake and implementation into routine analysis and food surveillance is still limited. In many proof-of-principle studies, the prediction ability of only one data set was explored, measured within a limited period of time using one instrument within one laboratory. Thorough validation strategies that guarantee reliability of the respective data basis and that allow conclusion on the applicability of the respective approaches for its fit-for-purpose have not yet been proposed. Within this review, critical steps of the fingerprinting workflow were explored to develop a generic scheme for multivariate model validation. As a result, a proposed scheme for "good practice" shall guide users through validation and reporting of non-targeted fingerprinting results. Furthermore, food fingerprinting studies were selected by a systematic search approach and reviewed with regard to (a) transparency of data processing and (b) validity of study results. Subsequently, the studies were inspected for measures of statistical model validation, analytical method validation and quality assurance measures. In this context, issues and recommendations were found that might be considered as an actual starting point for developing validation standards of non-targeted metabolomics approaches for food authentication in the future. Hence, this review intends to contribute to the harmonization and standardization of food fingerprinting, both required as a prior condition for the authentication of food in routine analysis and official control. Copyright © 2015 Elsevier B.V. All rights reserved.

Simultaneous determination of potassium guaiacolsulfonate, guaifenesin, diphenhydramine HCl and carbetapentane citrate in syrups by using HPLC-DAD coupled with partial least squares multivariate calibration.

PubMed

Dönmez, Ozlem Aksu; Aşçi, Bürge; Bozdoğan, Abdürrezzak; Sungur, Sidika

2011-02-15

A simple and rapid analytical procedure was proposed for the determination of chromatographic peaks by means of partial least squares multivariate calibration (PLS) of high-performance liquid chromatography with diode array detection (HPLC-DAD). The method is exemplified with analysis of quaternary mixtures of potassium guaiacolsulfonate (PG), guaifenesin (GU), diphenhydramine HCI (DP) and carbetapentane citrate (CP) in syrup preparations. In this method, the area does not need to be directly measured and predictions are more accurate. Though the chromatographic and spectral peaks of the analytes were heavily overlapped and interferents coeluted with the compounds studied, good recoveries of analytes could be obtained with HPLC-DAD coupled with PLS calibration. This method was tested by analyzing the synthetic mixture of PG, GU, DP and CP. As a comparison method, a classsical HPLC method was used. The proposed methods were applied to syrups samples containing four drugs and the obtained results were statistically compared with each other. Finally, the main advantage of HPLC-PLS method over the classical HPLC method tried to emphasized as the using of simple mobile phase, shorter analysis time and no use of internal standard and gradient elution. Copyright © 2010 Elsevier B.V. All rights reserved.

Laser-induced breakdown spectroscopy-based investigation and classification of pharmaceutical tablets using multivariate chemometric analysis

PubMed Central

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

2012-01-01

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

Postoperative complications in gastrointestinal cancer patients: the joint role of the nutritional status and the nutritional support.

PubMed

Bozzetti, Federico; Gianotti, Luca; Braga, Mario; Di Carlo, Valerio; Mariani, Luigi

2007-12-01

This study investigated the effects of nutritional support on postoperative complications, in relation with demographic and nutritional factors, intraoperative factors, type and routes of nutritional regimens. A series of 1410 subjects underwent major abdominal surgery for gastrointestinal cancer and received various types of nutritional support: standard intravenous fluids (SIF; n=149), total parenteral nutrition (TPN; n=368), enteral nutrition (EN; n=393), and immune-enhancing enteral nutrition (IEEN; n=500). Postoperative complications, considered as major (if lethal or requiring re-operation, or transfer to intensive care unit), or otherwise minor, were recorded. Major and minor complications occurred in 101 (7.2%) and 446 (31.6%) patients, respectively. Factors correlated with postoperative complications at multivariate analysis were pancreatic surgery, (p<0.001), advanced age (p=0.002), weight loss (p=0.019), low serum albumin (p=0.019) and nutritional support (p=0.001). Nutritional support reduced morbidity versus SIF with an increasing protective effect of TPN, EN, and IEEN. This effect remained valid regardless the severity of risk factors identified at the multivariate analysis and it was more evident by considering infectious complications only. Pancreatic surgery, advanced age, weight loss and low serum albumin are independent risk factors for the onset of postoperative complications. Nutritional support, particularly IEEN, significantly reduced postoperative morbidity.

Relationship of breastfeeding self-efficacy with quality of life in Iranian breastfeeding mothers.

PubMed

Mirghafourvand, Mojgan; Kamalifard, Mahin; Ranjbar, Fatemeh; Gordani, Nasrin

2017-07-20

Due to the importance of breastfeeding, we decided to conduct a study to examine the relationship between breastfeeding self-efficacy and quality of life. This study was a cross-sectional study, which was carried out on 547 breastfeeding mothers that had 2-6 months old infants. The participants were selected randomly, and the sociodemographic characteristics questionnaire, Dennis' breastfeeding self-efficacy scale, and WHO's Quality of Life (WHOQOL) questionnaire were completed through interview. The multivariate linear regression model was used for data analysis. The means (standard deviations) of breastfeeding self-efficacy score and quality of life score were 134.5 (13.3) and 67.7 (13.7), respectively. Quality of life and all of its dimensions were directly and significantly related to breastfeeding self-efficacy. According to the results of multivariate linear regression analysis, there was a relationship between breastfeeding self-efficacy and the following variables: environmental dimension of quality of life, education, spouse's age, spouse's job, average duration of previous breastfeeding period and receiving breastfeeding training. Findings showed that there is direct and significant relationship between breastfeeding self-efficacy and quality of life. Moreover, it seems that the development of appropriate training programs is necessary for improving the quality of life of pregnant women, as it consequently enhances breastfeeding self-efficacy.

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

Determinants of graft survival in pediatric and adolescent live donor kidney transplant recipients: a single center experience.

PubMed

El-Husseini, Amr A; Foda, Mohamed A; Shokeir, Ahmed A; Shehab El-Din, Ahmed B; Sobh, Mohamed A; Ghoneim, Mohamed A

2005-12-01

To study the independent determinants of graft survival among pediatric and adolescent live donor kidney transplant recipients. Between March 1976 and March 2004, 1600 live donor kidney transplants were carried out in our center. Of them 284 were 20 yr old or younger (mean age 13.1 yr, ranging from 5 to 20 yr). Evaluation of the possible variables that may affect graft survival were carried out using univariate and multivariate analyses. Studied factors included age, gender, relation between donor and recipient, original kidney disease, ABO blood group, pretransplant blood transfusion, human leukocyte antigen (HLA) matching, pretransplant dialysis, height standard deviation score (SDS), pretransplant hypertension, cold ischemia time, number of renal arteries, ureteral anastomosis, time to diuresis, time of transplantation, occurrence of acute tubular necrosis (ATN), primary and secondary immunosuppression, total dose of steroids in the first 3 months, development of acute rejection and post-transplant hypertension. Using univariate analysis, the significant predictors for graft survival were HLA matching, type of primary urinary recontinuity, time to diuresis, ATN, acute rejection and post-transplant hypertension. The multivariate analysis restricted the significance to acute rejection and post-transplant hypertension. The independent determinants of graft survival in live-donor pediatric and adolescent renal transplant recipients are acute rejection and post-transplant hypertension.

Micro vs. macrodiscectomy: Does use of the microscope reduce complication rates?

PubMed

Murphy, Meghan E; Hakim, Jeffrey S; Kerezoudis, Panagiotis; Alvi, Mohammed Ali; Ubl, Daniel S; Habermann, Elizabeth B; Bydon, Mohamad

2017-01-01

A single level discectomy is one of the most common procedures performed by spine surgeons. While some practitioners utilize the microscope, others do not. We postulate improved visualization with an intraoperative microscope decreases complications and inferior outcomes. A multicenter surgical registry was utilized for this retrospective cohort analysis. Patients with degenerative spinal diagnoses undergoing elective single level discectomies from 2010 to 2014 were included. Univariate analysis was performed comparing demographics, patient characteristics, operative data, and outcomes for discectomies performed with and without a microscope. Multivariable logistic regression analysis was then applied to compare outcomes of micro- and macrodiscectomies. Query of the registry yielded 23,583 patients meeting inclusion criteria. On univariate analysis the microscope was used in a greater proportion of the oldest age group as well as Hispanic white patients. Patients with any functional dependency, history of congestive heart failure, chronic corticosteroid use, or anemia (hematocrit<35%) also had greater proportions of microdiscectomies. Thoracic region discectomies more frequently involved use of the microscope than cervical or lumbar discectomies (25.0% vs. 16.4% and 13.0%, respectively, p<0.001). Median operative time (IQR) was increased in microscope cases [80min (60, 108) vs. 74min (54, 102), p<0.001]. Of the patients that required reoperation within 30days, 2.5% of them had undergone a microdiscectomy compared to 1.9% who had undergone a macrodiscectomy, p=0.044. On multivariable analysis, microdiscectomies were more likely to have an operative time in the top quartile of discectomy operative times, ≥103min (OR 1.256, 95% CI 1.151-1.371, p<0.001). In regards to other multivariable outcome models for any complication, surgical site infection, dural tears, reoperation, and readmission, no significant association with microdiscectomy was found. The use of the microscope was found to significantly increase the odds of longer operative time, but not influence rates of postoperative complications. Thus, without evidence from this study that the microscope decreases complications, the use of the microscope should be at the surgeon's discretion, validating the use of both macro and micro approaches to discectomy as acceptable standards of care. Copyright © 2016 Elsevier B.V. All rights reserved.

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

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

Cognitive models of medical decision-making capacity in patients with mild cognitive impairment.

PubMed

Okonkwo, O C; Griffith, H R; Belue, K; Lanza, S; Zamrini, E Y; Harrell, L E; Brockington, J C; Clark, D; Raman, R; Marson, D C

2008-03-01

This study investigated cognitive predictors of medical decision-making capacity (MDC) in patients with amnestic mild cognitive impairment (MCI). A total of 56 healthy controls, 60 patients with MCI, and 31 patients with mild Alzheimer's disease (AD) were administered the Capacity to Consent to Treatment Instrument (CCTI) and a neuropsychological test battery. The CCTI assesses MDC across four established treatment consent standards--S1 (expressing choice), S3 (appreciation), S4 (reasoning), and S5 (understanding)--and one experimental standard [S2] (reasonable choice). Scores on neuropsychological measures were correlated with scores on each CCTI standard. Significant bivariate correlates were subsequently entered into stepwise regression analyses to identity group-specific multivariable predictors of MDC across CCTI standards. Different multivariable cognitive models emerged across groups and consent standards. For the MCI group, measures of short-term verbal memory were key predictors of MDC for each of the three clinically relevant standards (S3, S4, and S5). Secondary predictors were measures of executive function. In contrast, in the mild AD group, measures tapping executive function and processing speed were primary predictors of S3, S4, and S5. MDC in patients with MCI is supported primarily by short-term verbal memory. The findings demonstrate the impact of amnestic deficits on MDC in patients with MCI.

How to compare cross-lagged associations in a multilevel autoregressive model.

PubMed

Schuurman, Noémi K; Ferrer, Emilio; de Boer-Sonnenschein, Mieke; Hamaker, Ellen L

2016-06-01

By modeling variables over time it is possible to investigate the Granger-causal cross-lagged associations between variables. By comparing the standardized cross-lagged coefficients, the relative strength of these associations can be evaluated in order to determine important driving forces in the dynamic system. The aim of this study was twofold: first, to illustrate the added value of a multilevel multivariate autoregressive modeling approach for investigating these associations over more traditional techniques; and second, to discuss how the coefficients of the multilevel autoregressive model should be standardized for comparing the strength of the cross-lagged associations. The hierarchical structure of multilevel multivariate autoregressive models complicates standardization, because subject-based statistics or group-based statistics can be used to standardize the coefficients, and each method may result in different conclusions. We argue that in order to make a meaningful comparison of the strength of the cross-lagged associations, the coefficients should be standardized within persons. We further illustrate the bivariate multilevel autoregressive model and the standardization of the coefficients, and we show that disregarding individual differences in dynamics can prove misleading, by means of an empirical example on experienced competence and exhaustion in persons diagnosed with burnout. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Standardized Reporting System Use During Handoffs Reduces Patient Length of Stay in the Emergency Department

PubMed Central

Dahlquist, Robert T.; Reyner, Karina; Robinson, Richard D.; Farzad, Ali; Laureano-Phillips, Jessica; Garrett, John S.; Young, Joseph M.; Zenarosa, Nestor R.; Wang, Hao

2018-01-01

Background Emergency department (ED) shift handoffs are potential sources of delay in care. We aimed to determine the impact that using standardized reporting tool and process may have on throughput metrics for patients undergoing a transition of care at shift change. Methods We performed a prospective, pre- and post-intervention quality improvement study from September 1 to November 30, 2015. A handoff procedure intervention, including a mandatory workshop and personnel training on a standard reporting system template, was implemented. The primary endpoint was patient length of stay (LOS). A comparative analysis of differences between patient LOS and various handoff communication methods were assessed pre- and post-intervention. Communication methods were entered a multivariable logistic regression model independently as risk factors for patient LOS. Results The final analysis included 1,006 patients, with 327 comprising the pre-intervention and 679 comprising the post-intervention populations. Bedside rounding occurred 45% of the time without a standard reporting during pre-intervention and increased to 85% of the time with the use of a standard reporting system in the post-intervention period (P < 0.001). Provider time (provider-initiated care to patient care completed) in the pre-intervention period averaged 297 min, but decreased to 265 min in the post-intervention period (P < 0.001). After adjusting for other communication methods, the use of a standard reporting system during handoff was associated with shortened ED LOS (OR = 0.60, 95% CI 0.40 - 0.90, P < 0.05). Conclusions Standard reporting system use during emergency physician handoffs at shift change improves ED throughput efficiency and is associated with shorter ED LOS. PMID:29581808

Simulation Study of Invisible Decays of the Higgs Boson with the Circular Electron Positron Collider

NASA Astrophysics Data System (ADS)

Jyotishmati, Susmita

A Higgs-like boson has been discovered by the experiments ATLAS and CMS at the LHC. We need to verify that it is the Standard Model (SM) Higgs and understand its nature. A Circular Electron Positron Collider (CEPC), has been proposed as a Higgs factory for detailed study of the Higgs boson. In this dissertation we study the feasibility of measuring the H → Invisible decays at the CEPC. Dark Matter (DM) interacts with matter by gravity, thus appears to be invisible in the CEPC experiment. If Higgs boson couples to DM it could be an important "portal" to New Physics. A Monte Carlo analysis of H → Invisible optimized to achieve high signal significance, and low backgrounds in the e +e- → ZH, Z → mu +mu- channel based on an integrated luminosity of 5 ab-1 expected for ten years run of the CEPC, is performed. Precision on the Higgs to invisible branching ratio at the input values of 0.1%(SM) and Beyond Standard Model (BSM) cases 0%, 1%, 5% and 10% is determined. Two approaches have been employed. They are the cut-based analysis and the multivariate analysis. Based on this dissertation study a baseline analysis approach is recommended for future CEPC design and studies.

Rapid analysis of Aurantii Fructus Immaturus (Zhishi) using paper spray ionization mass spectrometry.

PubMed

Liu, Xuemei; Gu, Zhixin; Guo, Yuan; Liu, Jingjing; Ma, Ming; Chen, Bo; Wang, Liping

2017-04-15

Paper spray-mass spectrometry (PS-MS) is a rapid, solvent-efficient, and high-throughput analytical method for analyzing complex samples. In this study, a PS-MS method was developed to obtain MS profiles of Aurantii Fructus Immaturus (aka Zhishi in Chinese) in positive and negative ion modes. In combination with multivariate analyses, including principal component analysis and cluster analysis, the PS-MS profiles of 25 batches of Zhishi were discriminated in 25 batches of Citri Reticulatae Pericarpium Viride (aka Qingpi in Chinese; an adulterant of Zhishi). Moreover, a rapid quantitative analysis of synephrine, a prescriptive quality control component of Zhishi listed in the Chinese Pharmacopoeia, was conducted with PS-MS using synephrine-d2 as an internal standard (IS). The linearity range was 1.68-16.8μg/mL (R 2 =0.9985), the limit of quantitation was 0.5μg/mL. Relative standard deviations in the intra- and inter-day precision of the MS were 4.87 and 4.90%, respectively. Compared with HPLC results, there was no significant difference in the quantitation of synephrine. This study demonstrated that the PS-MS method is useful for the rapid discrimination and quality control of Zhishi samples. Copyright © 2017 Elsevier B.V. All rights reserved.

Multivariate time series analysis of neuroscience data: some challenges and opportunities.

PubMed

Pourahmadi, Mohsen; Noorbaloochi, Siamak

2016-04-01

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

Estimated incidence of erythema migrans in five regions of France and ecological correlations with environmental characteristics.

PubMed

Mariet, Anne-Sophie; Retel, Olivier; Avocat, Hélène; Serre, Anne; Schapman, Lucie; Schmitt, Marielle; Charron, Martine; Monnet, Elisabeth

2013-09-01

While several studies conducted on Lyme borreliosis (LB) risk in the United States showed an association with environmental characteristics, most of European studies considered solely the effect of climate characteristics. The aims of this study were to estimate incidence of erythema migrans (EM) in five regions of France and to analyze associations with several environmental characteristics of the place of residence. LB surveillance networks of general practitioners (GPs) were set up for a period of 2 years in five regions of France. Participating GPs reported all patients with EM during the study period. Data were pooled according to a standardized EM case definition. For each area with a participating GP, age-standardized incidence rates and ratios were estimated. Associations with altitude, indicators of landscape composition, and indicators of landscape configuration were tested with multivariate Poisson regression. Standardized estimated incidence rates of EM per 10(5) person-years were 8.8 [95% confidence interval (CI)=7.9-9.7] in Aquitaine, 40.0 (95% CI 36.4-43.6) in Limousin, 76.0 (95% CI 72.9-79.1) in the three participating départements of Rhône-Alpes, 46.1 (95% CI 43.0-49.2) in Franche-Comté, and 87.7 (95% CI 84.6-90.8) in Alsace. In multivariate analysis, age-adjusted incidence rates increased with the altitude (p<0.0001) and decreased with forest patch density (p<0.0001). The marked variations in EM risk among the five regions were partly related to differences in landscape and environmental characteristics. The latter may point out potential risk areas and provide information for targeting preventive actions.

Prevalence of Diabetes and Associated Factors in the Uyghur and Han Population in Xinjiang, China.

PubMed

Gong, Haiying; Pa, Lize; Wang, Ke; Mu, Hebuli; Dong, Fen; Ya, Shengjiang; Xu, Guodong; Tao, Ning; Pan, Li; Wang, Bin; Shan, Guangliang

2015-10-14

To estimate the prevalence of diabetes and identify risk factors in the Uyghur and Han population in Xinjiang, China. A cross-sectional study in urban and rural areas in Xinjiang, including 2863 members of the Uyghur population and 3060 of the Han population aged 20 to 80 years, was conducted from June 2013 to August 2013. Data on fasting plasma glucose (FPG) and personal history of diabetes were used to estimate the prevalence of diabetes. Data on demographic characteristics, lifestyle risk factors, and lipid profiles were collected to identify risks factors using the multivariate logistic regression model. In urban areas, the age- and gender-standardized prevalence of diabetes was 8.21%, and the age- and gender-standardized prevalence of diabetes was higher in the Uyghur population (10.47%) than in the Han population (7.36%). In rural areas, the age- and gender-standardized prevalence of diabetes was 6.08%, and it did not differ significantly between the Uyghur population (5.71%) and the Han population (6.59%). The results of the multivariate logistic regression analysis showed that older age, obesity, high triglycerides (TG), and hypertension were all associated with an increased risk of diabetes in the Uyghur and Han population. Urban residence and low high-density lipoprotein cholesterol (HDL-C) were associated with an increased risk of diabetes in the Uyghur population. Being an ex-drinker was associated with an increased risk of diabetes and heavy physical activity was associated with a decreased risk of diabetes in the Han population. Our study indicates that diabetes is more prevalent in the Uyghur population compared with the Han population in urban areas. Strategies aimed at the prevention of diabetes require ethnic targeting.

The prognostic value of standardized reference values for speckle-tracking global longitudinal strain in hypertrophic cardiomyopathy.

PubMed

Hartlage, Gregory R; Kim, Jonathan H; Strickland, Patrick T; Cheng, Alan C; Ghasemzadeh, Nima; Pernetz, Maria A; Clements, Stephen D; Williams, B Robinson

2015-03-01

Speckle-tracking left ventricular global longitudinal strain (GLS) assessment may provide substantial prognostic information for hypertrophic cardiomyopathy (HCM) patients. Reference values for GLS have been recently published. We aimed to evaluate the prognostic value of standardized reference values for GLS in HCM patients. An analysis of HCM clinic patients who underwent GLS was performed. GLS was defined as normal (more negative or equal to -16%) and abnormal (less negative than -16%) based on recently published reference values. Patients were followed for a composite of events including heart failure hospitalization, sustained ventricular arrhythmia, and all-cause death. The power of GLS to predict outcomes was assessed relative to traditional clinical and echocardiographic variables present in HCM. 79 HCM patients were followed for a median of 22 months (interquartile range 9-30 months) after imaging. During follow-up, 15 patients (19%) met the primary outcome. Abnormal GLS was the only echocardiographic variable independently predictive of the primary outcome [multivariate Hazard ratio 5.05 (95% confidence interval 1.09-23.4, p = 0.038)]. When combined with traditional clinical variables, abnormal GLS remained independently predictive of the primary outcome [multivariate Hazard ratio 5.31 (95 % confidence interval 1.18-24, p = 0.030)]. In a model including the strongest clinical and echocardiographic predictors of the primary outcome, abnormal GLS demonstrated significant incremental benefit for risk stratification [net reclassification improvement 0.75 (95 % confidence interval 0.21-1.23, p < 0.0001)]. Abnormal GLS is an independent predictor of adverse outcomes in HCM patients. Standardized use of GLS may provide significant incremental value over traditional variables for risk stratification.

New standard measures for clinical voice analysis include high speed films

NASA Astrophysics Data System (ADS)

Pedersen, Mette; Munch, Kasper

2012-02-01

In the clinical work with patients in a medical voice clinic it is important to have a normal updated reference for the data used. Several new parameters have to be correlated to older traditional measures. The older ones are stroboscopy, eventually coordinated with electroglottography (EGG), the Multi- Dimensional-Voice Program and airflow rates. Long Time Averaged Spectrograms (LTAS) and phonetograms (voice profiles) are calculating the range and dynamics of tones of the patients. High-speed films, updated airflow measures as well as area calculations of phonotograms add information to the understanding of the glottis closure in single movements of the vocal cords. A multivariate analysis was made to study the connection between the measures. This information can be used in many connections, also in the otolaryngological clinic.

Enhanced ID Pit Sizing Using Multivariate Regression Algorithm

NASA Astrophysics Data System (ADS)

Krzywosz, Kenji

2007-03-01

EPRI is funding a program to enhance and improve the reliability of inside diameter (ID) pit sizing for balance-of plant heat exchangers, such as condensers and component cooling water heat exchangers. More traditional approaches to ID pit sizing involve the use of frequency-specific amplitude or phase angles. The enhanced multivariate regression algorithm for ID pit depth sizing incorporates three simultaneous input parameters of frequency, amplitude, and phase angle. A set of calibration data sets consisting of machined pits of various rounded and elongated shapes and depths was acquired in the frequency range of 100 kHz to 1 MHz for stainless steel tubing having nominal wall thickness of 0.028 inch. To add noise to the acquired data set, each test sample was rotated and test data acquired at 3, 6, 9, and 12 o'clock positions. The ID pit depths were estimated using a second order and fourth order regression functions by relying on normalized amplitude and phase angle information from multiple frequencies. Due to unique damage morphology associated with the microbiologically-influenced ID pits, it was necessary to modify the elongated calibration standard-based algorithms by relying on the algorithm developed solely from the destructive sectioning results. This paper presents the use of transformed multivariate regression algorithm to estimate ID pit depths and compare the results with the traditional univariate phase angle analysis. Both estimates were then compared with the destructive sectioning results.

Data analysis techniques

NASA Technical Reports Server (NTRS)

Park, Steve

1990-01-01

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

Chemical structure of wood charcoal by infrared spectroscopy and multivariate analysis

Treesearch

Nicole Labbe; David Harper; Timothy Rials; Thomas Elder

2006-01-01

In this work, the effect of temperature on charcoal structure and chemical composition is investigated for four tree species. Wood charcoal carbonized at various temperatures is analyzed by mid infrared spectroscopy coupled with multivariate analysis and by thermogravimetric analysis to characterize the chemical composition during the carbonization process. The...

Root Cause Analysis of Quality Defects Using HPLC-MS Fingerprint Knowledgebase for Batch-to-batch Quality Control of Herbal Drugs.

PubMed

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

2015-01-01

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

Morphometric discrimination of early life stage Lampetra tridentata and L richardsoni (Petromyzonidae) from the Columbia river basin

USGS Publications Warehouse

Meeuwig, M.H.; Bayer, J.M.; Reiche, R.A.

2006-01-01

The effectiveness of morphometric and meristic characteristics for taxonomic discrimination of Lampetra tridentata and L. richardsoni (Petromyzonidae) during embryological, prolarval, and early larval stages (i.e., age class 1) were examined. Mean chorion diameter increased with time from fertilization to hatch and was significantly greater for L. tridentata than for L. richardsoni at 1, 8, and 15 days postfertilization. Lampetra tridentata larvae had significantly more trunk myomeres than L. richardsoni; however, trunk myomere numbers were highly variable within species and deviated from previously published data. Multivariate examinations of prolarval and larval L. tridentata (7.2-11.0 mm; standard length) and L. richardsoni (6.6-10.8 mm) were conducted based on standard length and truss element lengths established from eight homologous landmarks. Principal components analysis indicated allometric relationships among the morphometric characteristics examined. Changes in body shape were indicated by groupings of morphometric characteristics associated with body regions (e.g., oral hood, branchial region, trunk region, and tail region). Discriminant function analysis using morphometric characteristics was successful in classifying a large proportion (>94.7%) of the lampreys sampled. 

Methodology to assess clinical liver safety data.

PubMed

Merz, Michael; Lee, Kwan R; Kullak-Ublick, Gerd A; Brueckner, Andreas; Watkins, Paul B

2014-11-01

Analysis of liver safety data has to be multivariate by nature and needs to take into account time dependency of observations. Current standard tools for liver safety assessment such as summary tables, individual data listings, and narratives address these requirements to a limited extent only. Using graphics in the context of a systematic workflow including predefined graph templates is a valuable addition to standard instruments, helping to ensure completeness of evaluation, and supporting both hypothesis generation and testing. Employing graphical workflows interactively allows analysis in a team-based setting and facilitates identification of the most suitable graphics for publishing and regulatory reporting. Another important tool is statistical outlier detection, accounting for the fact that for assessment of Drug-Induced Liver Injury, identification and thorough evaluation of extreme values has much more relevance than measures of central tendency in the data. Taken together, systematical graphical data exploration and statistical outlier detection may have the potential to significantly improve assessment and interpretation of clinical liver safety data. A workshop was convened to discuss best practices for the assessment of drug-induced liver injury (DILI) in clinical trials.

Neuroimaging-based biomarkers in psychiatry: clinical opportunities of a paradigm shift.

PubMed

Fu, Cynthia H Y; Costafreda, Sergi G

2013-09-01

Neuroimaging research has substantiated the functional and structural abnormalities underlying psychiatric disorders but has, thus far, failed to have a significant impact on clinical practice. Recently, neuroimaging-based diagnoses and clinical predictions derived from machine learning analysis have shown significant potential for clinical translation. This review introduces the key concepts of this approach, including how the multivariate integration of patterns of brain abnormalities is a crucial component. We survey recent findings that have potential application for diagnosis, in particular early and differential diagnoses in Alzheimer disease and schizophrenia, and the prediction of clinical response to treatment in depression. We discuss the specific clinical opportunities and the challenges for developing biomarkers for psychiatry in the absence of a diagnostic gold standard. We propose that longitudinal outcomes, such as early diagnosis and prediction of treatment response, offer definite opportunities for progress. We propose that efforts should be directed toward clinically challenging predictions in which neuroimaging may have added value, compared with the existing standard assessment. We conclude that diagnostic and prognostic biomarkers will be developed through the joint application of expert psychiatric knowledge in addition to advanced methods of analysis.

Novel wound management system reduction of surgical site morbidity after ventral hernia repairs: a critical analysis.

PubMed

Soares, Kevin C; Baltodano, Pablo A; Hicks, Caitlin W; Cooney, Carisa M; Olorundare, Israel O; Cornell, Peter; Burce, Karen; Eckhauser, Frederic E

2015-02-01

Prophylactic incisional negative-pressure wound therapy use after ventral hernia repairs (VHRs) remains controversial. We assessed the impact of a modified negative-pressure wound therapy system (hybrid-VAC or HVAC) on outcomes of open VHR. A 5-year retrospective analysis of all VHRs performed by a single surgeon at a single institution compared outcomes after HVAC versus standard wound dressings. Multivariable logistic regression compared surgical site infections, surgical site occurrences, morbidity, and reoperation rates. We evaluated 199 patients (115 HVAC vs 84 standard wound dressing patients). Mean follow-up was 9 months. The HVAC cohort had lower surgical site infections (9% vs 32%, P < .001) and surgical site occurrences (17% vs 42%, P = .001) rates. Rates of major morbidity (19% vs 31%, P = .04) and 90-day reoperation (5% vs 14%, P = .02) were lower in the HVAC cohort. The HVAC system is associated with optimized outcomes following open VHR. Prospective studies should validate these findings and define the economic implications of this intervention. Copyright © 2015 Elsevier Inc. All rights reserved.

Effect of acute dietary standardization on the urinary, plasma, and salivary metabolomic profiles of healthy humans.

PubMed

Walsh, Marianne C; Brennan, Lorraine; Malthouse, J Paul G; Roche, Helen M; Gibney, Michael J

2006-09-01

Metabolomics in human nutrition research is faced with the challenge that changes in metabolic profiles resulting from diet may be difficult to differentiate from normal physiologic variation. We assessed the extent of intra- and interindividual variation in normal human metabolic profiles and investigated the effect of standardizing diet on reducing variation. Urine, plasma, and saliva were collected from 30 healthy volunteers (23 females, 7 males) on 4 separate mornings. For visits 1 and 2, free food choice was permitted on the day before biofluid collection. Food choice on the day before visit 3 was intended to mimic that for visit 2, and all foods were standardized on the day before visit 4. Samples were analyzed by using 1H nuclear magnetic resonance spectroscopy followed by multivariate data analysis. Intra- and interindividual variations were considerable for each biofluid. Visual inspection of the principal components analysis scores plots indicated a reduction in interindividual variation in urine, but not in plasma or saliva, after the standard diet. Partial least-squares discriminant analysis indicated time-dependent changes in urinary and salivary samples, mainly resulting from creatinine in urine and acetate in saliva. The predictive power of each model to classify the samples as either night or morning was 85% for urine and 75% for saliva. Urine represented a sensitive metabolic profile that reflected acute dietary intake, whereas plasma and saliva did not. Future metabolomics studies should consider recent dietary intake and time of sample collection as a means of reducing normal physiologic variation.

Multivariate analysis: greater insights into complex systems

USDA-ARS?s Scientific Manuscript database

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

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

Search for WH associated production in 5.3 fb -1 of p p ¯ collisions at the Fermilab Tevatron

DOE PAGES

Abazov, V.M.; Abbott, B.; Acharya, B.S.; ...

2011-03-01

We present a search for associated production of Higgs and W bosons in collisions at a center of mass energy of in 5.3 fb -1 of integrated luminosity recorded by the D0 experiment. Multivariate analysis techniques are applied to events containing one lepton, an imbalance in transverse energy, and one or two b-tagged jets to discriminate a potential WH signal from Standard Model backgrounds. We observe good agreement between data and expected backgrounds, and set an upper limit of 4.5 (at 95% confidence level and for m H=115 GeV) on the ratio of the WH cross section multiplied by themore » branching fraction of H → bb¯ to its Standard Model prediction, which is consistent with an expected limit of 4.8.« less

Sleep and nutritional deprivation and performance of house officers.

PubMed

Hawkins, M R; Vichick, D A; Silsby, H D; Kruzich, D J; Butler, R

1985-07-01

A study was conducted by the authors to compare cognitive functioning in acutely and chronically sleep-deprived house officers. A multivariate analysis of variance revealed significant deficits in primary mental tasks involving basic rote memory, language, and numeric skills as well as in tasks requiring high-order cognitive functioning and traditional intellective abilities. These deficits existed only for the acutely sleep-deprived group. The finding of deficits in individuals who reported five hours or less of sleep in a 24-hour period suggests that the minimum standard of four hours that has been considered by some to be adequate for satisfactory performance may be insufficient for more complex cognitive functioning.

Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

PubMed

Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

2017-12-01

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis (PCA) and cluster analysis (CA). Copyright © 2017 Elsevier Ltd. All rights reserved.

Advance notification letters increase adherence in colorectal cancer screening: a population-based randomized trial.

PubMed

van Roon, A H C; Hol, L; Wilschut, J A; Reijerink, J C I Y; van Vuuren, A J; van Ballegooijen, M; Habbema, J D F; van Leerdam, M E; Kuipers, Ernst J

2011-06-01

The population benefit of screening depends not only on the effectiveness of the test, but also on adherence, which, for colorectal cancer (CRC) screening remains low. An advance notification letter may increase adherence, however, no population-based randomized trials have been conducted to provide evidence of this. In 2008, a representative sample of the Dutch population (aged 50-74 years) was randomized. All 2493 invitees in group A were sent an advance notification letter, followed two weeks later by a standard invitation. The 2507 invitees in group B only received the standard invitation. Non-respondents in both groups were sent a reminder 6 weeks after the invitation. The advance notification letters resulted in a significantly higher adherence (64.4% versus 61.1%, p-value 0.019). Multivariate logistic regression analysis showed no significant interactions between group and age, sex, or socio-economic status. Cost analysis showed that the incremental cost per additional detected advanced neoplasia due to sending an advance notification letter was € 957. This population-based randomized trial demonstrates that sending an advance notification letter significantly increases adherence by 3.3%. The incremental cost per additional detected advanced neoplasia is acceptable. We therefore recommend that such letters are incorporated within the standard CRC-screening invitation process. Copyright © 2011 Elsevier Inc. All rights reserved.

Numerical analysis of the accuracy of bivariate quantile distributions utilizing copulas compared to the GUM supplement 2 for oil pressure balance uncertainties

NASA Astrophysics Data System (ADS)

Ramnath, Vishal

2017-11-01

In the field of pressure metrology the effective area is Ae = A0 (1 + λP) where A0 is the zero-pressure area and λ is the distortion coefficient and the conventional practise is to construct univariate probability density functions (PDFs) for A0 and λ. As a result analytical generalized non-Gaussian bivariate joint PDFs has not featured prominently in pressure metrology. Recently extended lambda distribution based quantile functions have been successfully utilized for summarizing univariate arbitrary PDF distributions of gas pressure balances. Motivated by this development we investigate the feasibility and utility of extending and applying quantile functions to systems which naturally exhibit bivariate PDFs. Our approach is to utilize the GUM Supplement 1 methodology to solve and generate Monte Carlo based multivariate uncertainty data for an oil based pressure balance laboratory standard that is used to generate known high pressures, and which are in turn cross-floated against another pressure balance transfer standard in order to deduce the transfer standard's respective area. We then numerically analyse the uncertainty data by formulating and constructing an approximate bivariate quantile distribution that directly couples A0 and λ in order to compare and contrast its accuracy to an exact GUM Supplement 2 based uncertainty quantification analysis.

Relationship between non-standard work arrangements and work-related accident absence in Belgium

PubMed Central

Alali, Hanan; Braeckman, Lutgart; Van Hecke, Tanja; De Clercq, Bart; Janssens, Heidi; Wahab, Magd Abdel

2017-01-01

Objectives: The main objective of this study is to examine the relationship between indicators of non-standard work arrangements, including precarious contract, long working hours, multiple jobs, shift work, and work-related accident absence, using a representative Belgian sample and considering several socio-demographic and work characteristics. Methods: This study was based on the data of the fifth European Working Conditions Survey (EWCS). For the analysis, the sample was restricted to 3343 respondents from Belgium who were all employed workers. The associations between non-standard work arrangements and work-related accident absence were studied with multivariate logistic regression modeling techniques while adjusting for several confounders. Results: During the last 12 months, about 11.7% of workers were absent from work because of work-related accident. A multivariate regression model showed an increased injury risk for those performing shift work (OR 1.546, 95% CI 1.074-2.224). The relationship between contract type and occupational injuries was not significant (OR 1.163, 95% CI 0.739-1.831). Furthermore, no statistically significant differences were observed for those performing long working hours (OR 1.217, 95% CI 0.638-2.321) and those performing multiple jobs (OR 1.361, 95% CI 0.827-2.240) in relation to work-related accident absence. Those who rated their health as bad, low educated workers, workers from the construction sector, and those exposed to biomechanical exposure (BM) were more frequent victims of work-related accident absence. No significant gender difference was observed. Conclusion: Indicators of non-standard work arrangements under this study, except shift work, were not significantly associated with work-related accident absence. To reduce the burden of occupational injuries, not only risk reduction strategies and interventions are needed but also policy efforts are to be undertaken to limit shift work. In general, preventive measures and more training on the job are needed to ensure the safety and well-being of all workers. PMID:28111414

Relationship between non-standard work arrangements and work-related accident absence in Belgium.

PubMed

Alali, Hanan; Braeckman, Lutgart; Van Hecke, Tanja; De Clercq, Bart; Janssens, Heidi; Wahab, Magd Abdel

2017-03-28

The main objective of this study is to examine the relationship between indicators of non-standard work arrangements, including precarious contract, long working hours, multiple jobs, shift work, and work-related accident absence, using a representative Belgian sample and considering several socio-demographic and work characteristics. This study was based on the data of the fifth European Working Conditions Survey (EWCS). For the analysis, the sample was restricted to 3343 respondents from Belgium who were all employed workers. The associations between non-standard work arrangements and work-related accident absence were studied with multivariate logistic regression modeling techniques while adjusting for several confounders. During the last 12 months, about 11.7% of workers were absent from work because of work-related accident. A multivariate regression model showed an increased injury risk for those performing shift work (OR 1.546, 95% CI 1.074-2.224). The relationship between contract type and occupational injuries was not significant (OR 1.163, 95% CI 0.739-1.831). Furthermore, no statistically significant differences were observed for those performing long working hours (OR 1.217, 95% CI 0.638-2.321) and those performing multiple jobs (OR 1.361, 95% CI 0.827-2.240) in relation to work-related accident absence. Those who rated their health as bad, low educated workers, workers from the construction sector, and those exposed to biomechanical exposure (BM) were more frequent victims of work-related accident absence. No significant gender difference was observed. Indicators of non-standard work arrangements under this study, except shift work, were not significantly associated with work-related accident absence. To reduce the burden of occupational injuries, not only risk reduction strategies and interventions are needed but also policy efforts are to be undertaken to limit shift work. In general, preventive measures and more training on the job are needed to ensure the safety and well-being of all workers.

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.

Heterogeneity Coefficients for Mahalanobis' D as a Multivariate Effect Size.

PubMed

Del Giudice, Marco

2017-01-01

The Mahalanobis distance D is the multivariate generalization of Cohen's d and can be used as a standardized effect size for multivariate differences between groups. An important issue in the interpretation of D is heterogeneity, that is, the extent to which contributions to the overall effect size are concentrated in a small subset of variables rather than evenly distributed across the whole set. Here I present two heterogeneity coefficients for D based on the Gini coefficient, a well-known index of inequality among values of a distribution. I discuss the properties and limitations of the two coefficients and illustrate their use by reanalyzing some published findings from studies of gender differences.

Obstetric attending physician characteristics and their impact on vacuum and forceps delivery rates: University of California at San Francisco experience from 1977 to 1999.

PubMed

Chang, Anne Lynn S; Noah, Melinda Scully; Laros, Russell K

2002-06-01

The objective of our study was to determine the impact of obstetric attending physician characteristics (eg, region of previous residency training, sex, year of graduation from residency) on the rates of vacuum and forceps delivery at our institution. The analysis was based on 19,897 vaginal deliveries that were performed by 171 attending physicians and 160 resident physicians between 1977 and 1999 at the University of California at San Francisco Medical Center. Z -tests and multivariate logistic regression were performed on a perinatal database that contained standard obstetric variables. Male attending physicians had a higher percentage of forceps deliveries compared with female attending physicians (11.1% vs 6.6%; P <.001); female attending physicians had a higher percentage of vacuum deliveries compared with male attending physicians (9.8% vs 5.1%; P <.001). However, multivariate regression analysis revealed that only the year in which the procedure was performed affected both the forceps and vacuum delivery rates (P <.041). The region of previous residency training of the attending physician affected the vacuum delivery rate (P <.0001) but not the forceps delivery rate (P >.06) in multivariate logistic regression analysis. Factors such as the sex of the obstetric attending physician, the sex of the resident, and the year of graduation from residency for the obstetric attending physician did not have a significant impact on the forceps or vacuum delivery rates (all P >.05). Our study is the first to report that the apparent gender differences in forceps and vacuum delivery rates among obstetric attending physicians was due to the year in which the procedure was performed and not due to sex per se. We also found that the region of previous residency training for the obstetric attending physician significantly influenced the vacuum delivery rate.

Predictive factors for survival and correlation to toxicity in advanced Stage III non-small cell lung cancer patients with concurrent chemoradiation.

PubMed

Kim, Yong-Hyub; Ahn, Sung-Ja; Kim, Young-Chul; Kim, Kyu-Sik; Oh, In-Jae; Ban, Hee-Jung; Chung, Woong-Ki; Nam, Taek-Keun; Yoon, Mee Sun; Jeong, Jae-Uk; Song, Ju-Young

2016-02-01

Concurrent chemoradiotherapy is the standard treatment for locally advanced Stage III non-small cell lung cancer in patients with a good performance status and minimal weight loss. This study aimed to define subgroups with different survival outcomes and identify correlations with the radiation-related toxicities. We retrospectively reviewed 381 locally advanced Stage III non-small cell lung cancer patients with a good performance status or weight loss of <10% who received concurrent chemoradiotherapy between 2004 and 2011. Three-dimensional conformal radiotherapy was administered once daily, combined with weekly chemotherapy. The Kaplan-Meier method was used for survival comparison and Cox regression for multivariate analysis. Multivariate analysis was performed using all variables with P values <0.1 from the univariate analysis. Median survival of all patients was 24 months. Age > 75 years, the diffusion lung capacity for carbon monoxide ≤80%, gross tumor volume ≥100 cm(3) and subcarinal nodal involvement were the statistically significant predictive factors for poor overall survival both in univariate and multivariate analyses. Patients were classified into four groups according to these four predictive factors. The median survival times were 36, 29, 18 and 14 months in Groups I, II, III and IV, respectively (P < 0.001). Rates of esophageal or lung toxicity ≥Grade 3 were 5.9, 14.1, 12.5 and 22.2%, respectively. The radiotherapy interruption rate differed significantly between the prognostic subgroups; 8.8, 15.4, 22.7 and 30.6%, respectively (P = 0.017). Severe toxicity and interruption of radiotherapy were more frequent in patients with multiple adverse predictive factors. To maintain the survival benefit in patients with concurrent chemoradiotherapy, strategies to reduce treatment-related toxicities need to be deeply considered. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Compliance with Standard Precautions and Associated Factors among Healthcare Workers in Gondar University Comprehensive Specialized Hospital, Northwest Ethiopia

PubMed Central

Haile, Tariku Gebre

2017-01-01

Background. In many studies, compliance with standard precautions among healthcare workers was reported to be inadequate. Objective. The aim of this study was to assess compliance with standard precautions and associated factors among healthcare workers in northwest Ethiopia. Methods. An institution-based cross-sectional study was conducted from March 01 to April 30, 2014. Simple random sampling technique was used to select participants. Data were entered into Epi info 3.5.1 and were exported to SPSS version 20.0 for statistical analysis. Multivariate logistic regression analyses were computed and adjusted odds ratio with 95% confidence interval was calculated to identify associated factors. Results. The proportion of healthcare workers who always comply with standard precautions was found to be 12%. Being a female healthcare worker (AOR [95% CI] 2.18 [1.12–4.23]), higher infection risk perception (AOR [95% CI] 3.46 [1.67–7.18]), training on standard precautions (AOR [95% CI] 2.90 [1.20–7.02]), accessibility of personal protective equipment (AOR [95% CI] 2.87 [1.41–5.86]), and management support (AOR [95% CI] 2.23 [1.11–4.53]) were found to be statistically significant. Conclusion and Recommendation. Compliance with standard precautions among the healthcare workers is very low. Interventions which include training of healthcare workers on standard precautions and consistent management support are recommended. PMID:28191020

MULTIVARIATE CURVE RESOLUTION OF NMR SPECTROSCOPY METABONOMIC DATA

EPA Science Inventory

Sandia National Laboratories is working with the EPA to evaluate and develop mathematical tools for analysis of the collected NMR spectroscopy data. Initially, we have focused on the use of Multivariate Curve Resolution (MCR) also known as molecular factor analysis (MFA), a tech...

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

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

Drunk driving detection based on classification of multivariate time series.

PubMed

Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

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

A model for incomplete longitudinal multivariate ordinal data.

PubMed

Liu, Li C

2008-12-30

In studies where multiple outcome items are repeatedly measured over time, missing data often occur. A longitudinal item response theory model is proposed for analysis of multivariate ordinal outcomes that are repeatedly measured. Under the MAR assumption, this model accommodates missing data at any level (missing item at any time point and/or missing time point). It allows for multiple random subject effects and the estimation of item discrimination parameters for the multiple outcome items. The covariates in the model can be at any level. Assuming either a probit or logistic response function, maximum marginal likelihood estimation is described utilizing multidimensional Gauss-Hermite quadrature for integration of the random effects. An iterative Fisher-scoring solution, which provides standard errors for all model parameters, is used. A data set from a longitudinal prevention study is used to motivate the application of the proposed model. In this study, multiple ordinal items of health behavior are repeatedly measured over time. Because of a planned missing design, subjects answered only two-third of all items at a given point. Copyright 2008 John Wiley & Sons, Ltd.

Conceptual and statistical problems associated with the use of diversity indices in ecology.

PubMed

Barrantes, Gilbert; Sandoval, Luis

2009-09-01

Diversity indices, particularly the Shannon-Wiener index, have extensively been used in analyzing patterns of diversity at different geographic and ecological scales. These indices have serious conceptual and statistical problems which make comparisons of species richness or species abundances across communities nearly impossible. There is often no a single statistical method that retains all information needed to answer even a simple question. However, multivariate analyses could be used instead of diversity indices, such as cluster analyses or multiple regressions. More complex multivariate analyses, such as Canonical Correspondence Analysis, provide very valuable information on environmental variables associated to the presence and abundance of the species in a community. In addition, particular hypotheses associated to changes in species richness across localities, or change in abundance of one, or a group of species can be tested using univariate, bivariate, and/or rarefaction statistical tests. The rarefaction method has proved to be robust to standardize all samples to a common size. Even the simplest method as reporting the number of species per taxonomic category possibly provides more information than a diversity index value.

Impact of hospital transfer on surgical outcomes of intestinal atresia.

PubMed

Erickson, T; Vana, P G; Blanco, B A; Brownlee, S A; Paddock, H N; Kuo, P C; Kothari, A N

2017-03-01

Examine effects of hospital transfer into a quaternary care center on surgical outcomes of intestinal atresia. Children <1 yo principally diagnosed with intestinal atresia were identified using the Kids' Inpatient Database (2012). Exposure variable was patient transfer status. Outcomes measured were inpatient mortality, hospital length of stay (LOS) and discharge status. Linearized standard errors, design-based F tests, and multivariable logistic regression were performed. 1672 weighted discharges represented a national cohort. The highest income group and those with private insurance had significantly lower odds of transfer (OR:0.53 and 0.74, p < 0.05). Rural patients had significantly higher transfer rates (OR: 2.73, p < 0.05). Multivariate analysis revealed no difference in mortality (OR:0.71, p = 0.464) or non-home discharge (OR: 0.79, p = 0.166), but showed prolonged LOS (OR:1.79, p < 0.05) amongst transferred patients. Significant differences in hospital LOS and treatment access reveal a potential healthcare gap. Post-acute care resources should be improved for transferred patients. Copyright © 2016 Elsevier Inc. All rights reserved.

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

PubMed

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

2015-12-30

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

The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data

PubMed Central

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

2015-01-01

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

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis.

PubMed

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

2007-01-01

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

Moving beyond Univariate Post-Hoc Testing in Exercise Science: A Primer on Descriptive Discriminate Analysis

ERIC Educational Resources Information Center

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

2016-01-01

There has been a recent call to improve data reporting in kinesiology journals, including the appropriate use of univariate and multivariate analysis techniques. For example, a multivariate analysis of variance (MANOVA) with univariate post hocs and a Bonferroni correction is frequently used to investigate group differences on multiple dependent…

Angiotensin-converting enzyme inhibitor and statin use and incident mobility limitation in community-dwelling older adults: the Health, Aging and Body Composition study.

PubMed

Gray, Shelly L; Boudreau, Robert M; Newman, Anne B; Studenski, Stephanie A; Shorr, Ronald I; Bauer, Douglas C; Simonsick, Eleanor M; Hanlon, Joseph T

2011-12-01

To evaluate whether the use of angiotensin-converting enzyme (ACE) inhibitors and statins is associated with a lower risk of incident mobility limitation in older community dwelling adults. Longitudinal cohort study. Health, Aging and Body Composition (Health ABC) study. Three thousand fifty-five participants who were well functioning at baseline (no mobility limitations). Summated standardized daily doses (low, medium, high) and duration of ACE inhibitor and statin use were computed. Mobility limitation (two consecutive self-reports of having any difficulty walking one-quarter of a mile or climbing 10 steps without resting) was assessed every 6 months after baseline. Multivariable Cox proportional hazards analyses were conducted, adjusting for demographics, health status, and health behaviors. At baseline, 15.2% used ACE inhibitors and 12.9% used statins; use of both was greater than 25% by Year 6. Over 6.5 years of follow-up, 49.8% had developed mobility limitation. In separate multivariable models, neither ACE inhibitor (multivariate hazard ratio (HR) = 0.95, 95% confidence interval (CI) = 0.82-1.09) nor statin use (multivariate HR = 1.02, 95% CI = 0.87-1.17) was associated with lower risk of mobility limitation. Similar findings were seen in analyses examining dose-response and duration-response relationships and a sensitivity analysis restricted to those with hypertension. ACE inhibitors and statins widely prescribed to treat hypertension and hypercholesterolemia, respectively, do not lower risk of mobility limitation, an important indicator of quality of life. © 2011, Copyright the Authors Journal compilation © 2011, The American Geriatrics Society.

Development Of A Multivariate Prognostic Model For Pain And Activity Limitation In People With Low Back Disorders Receiving Physiotherapy.

PubMed

Ford, Jon J; Richards BPhysio, Matt C; Surkitt BPhysio, Luke D; Chan BPhysio, Alexander Yp; Slater, Sarah L; Taylor, Nicholas F; Hahne, Andrew J

2018-05-28

To identify predictors for back pain, leg pain and activity limitation in patients with early persistent low back disorders. Prospective inception cohort study; Setting: primary care private physiotherapy clinics in Melbourne, Australia. 300 adults aged 18-65 years with low back and/or referred leg pain of ≥6-weeks and ≤6-months duration. Not applicable. Numerical rating scales for back pain and leg pain as well as the Oswestry Disability Scale. Prognostic factors included sociodemographics, treatment related factors, subjective/physical examination, subgrouping factors and standardized questionnaires. Univariate analysis followed by generalized estimating equations were used to develop a multivariate prognostic model for back pain, leg pain and activity limitation. Fifty-eight prognostic factors progressed to the multivariate stage where 15 showed significant (p<0.05) associations with at least one of the three outcomes. There were five indicators of positive outcome (two types of low back disorder subgroups, paresthesia below waist, walking as an easing factor and low transversus abdominis tone) and 10 indicators of negative outcome (both parents born overseas, deep leg symptoms, longer sick leave duration, high multifidus tone, clinically determined inflammation, higher back and leg pain severity, lower lifting capacity, lower work capacity and higher pain drawing percentage coverage). The preliminary model identifying predictors of low back disorders explained up to 37% of the variance in outcome. This study evaluated a comprehensive range of prognostic factors reflective of both the biomedical and psychosocial domains of low back disorders. The preliminary multivariate model requires further validation before being considered for clinical use. Copyright © 2018. Published by Elsevier Inc.

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

Zlotnikov, Michael

We develop a polynomial reduction procedure that transforms any gauge fixed CHY amplitude integrand for n scattering particles into a σ-moduli multivariate polynomial of what we call the standard form. We show that a standard form polynomial must have a specific ladder type monomial structure, which has finite size at any n, with highest multivariate degree given by (n – 3)(n – 4)/2. This set of monomials spans a complete basis for polynomials with rational coefficients in kinematic data on the support of scattering equations. Subsequently, at tree and one-loop level, we employ the global residue theorem to derive amore » prescription that evaluates any CHY amplitude by means of collecting simple residues at infinity only. Furthermore, the prescription is then applied explicitly to some tree and one-loop amplitude examples.« less

Standard Error of Linear Observed-Score Equating for the NEAT Design with Nonnormally Distributed Data

ERIC Educational Resources Information Center

Zu, Jiyun; Yuan, Ke-Hai

2012-01-01

In the nonequivalent groups with anchor test (NEAT) design, the standard error of linear observed-score equating is commonly estimated by an estimator derived assuming multivariate normality. However, real data are seldom normally distributed, causing this normal estimator to be inconsistent. A general estimator, which does not rely on the…

Multivariate meta-analysis using individual participant data

PubMed Central

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

2016-01-01

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

Relationship between visual field progression and baseline refraction in primary open-angle glaucoma.

PubMed

Naito, Tomoko; Yoshikawa, Keiji; Mizoue, Shiro; Nanno, Mami; Kimura, Tairo; Suzumura, Hirotaka; Umeda, Yuzo; Shiraga, Fumio

2016-01-01

To analyze the relationship between visual field (VF) progression and baseline refraction in Japanese patients with primary open-angle glaucoma (POAG) including normal-tension glaucoma. In this retrospective study, the subjects were patients with POAG who had undergone VF tests at least ten times with a Humphrey Field Analyzer (Swedish interactive thresholding algorithm standard, Central 30-2 program). VF progression was defined as a significantly negative value of mean deviation (MD) slope at the final VF test. Multivariate logistic regression models were applied to detect an association between MD slope deterioration and baseline refraction. A total of 156 eyes of 156 patients were included in this analysis. Significant deterioration of MD slope was observed in 70 eyes of 70 patients (44.9%), whereas no significant deterioration was evident in 86 eyes of 86 patients (55.1%). The eyes with VF progression had significantly higher baseline refraction compared to those without apparent VF progression (-1.9±3.8 diopter [D] vs -3.5±3.4 D, P=0.0048) (mean ± standard deviation). When subject eyes were classified into four groups by the level of baseline refraction applying spherical equivalent (SE): no myopia (SE > -1D), mild myopia (-1D ≥ SE > -3D), moderate myopia (-3D ≥ SE > -6D), and severe myopia (-6D ≥ SE), the Cochran-Armitage trend analysis showed a decreasing trend in the proportion of MD slope deterioration with increasing severity of myopia (P=0.0002). The multivariate analysis revealed that baseline refraction (P=0.0108, odds ratio [OR]: 1.13, 95% confidence interval [CI]: 1.03-1.25) and intraocular pressure reduction rate (P=0.0150, OR: 0.97, 95% CI: 0.94-0.99) had a significant association with MD slope deterioration. In the current analysis of Japanese patients with POAG, baseline refraction was a factor significantly associated with MD slope deterioration as well as intraocular pressure reduction rate. When baseline refraction was classified into four groups, MD slope in myopia groups was less deteriorated as compared to those in the emmetropic/hyperopic group.

Resolving Identification Issues of Saraca asoca from Its Adulterant and Commercial Samples Using Phytochemical Markers

PubMed Central

Hegde, Satisha; Hegde, Harsha Vasudev; Jalalpure, Sunil Satyappa; Peram, Malleswara Rao; Pai, Sandeep Ramachandra; Roy, Subarna

2017-01-01

Saraca asoca (Roxb.) De Wilde (Ashoka) is a highly valued endangered medicinal tree species from Western Ghats of India. Besides treating cardiac and circulatory problems, S. asoca provides immense relief in gynecological disorders. Higher price and demand, in contrast to the smaller population size of the plant, have motivated adulteration with other plants such as Polyalthia longifolia (Sonnerat) Thwaites. The fundamental concerns in quality control of S. asoca arise due to its part of medicinal value (Bark) and the chemical composition. Phytochemical fingerprinting with proper selection of analytical markers is a promising method in addressing quality control issues. In the present study, high-performance liquid chromatography of phenolic compounds (gallic acid, catechin, and epicatechin) coupled to multivariate analysis was used. Five samples each of S. asoca, P. longifolia from two localities alongside five commercial market samples showed evidence of adulteration. Subsequently, multivariate hierarchical cluster analysis and principal component analysis was established to discriminate the adulterants of S. asoca. The proposed method ascertains identification of S. asoca from its putative adulterant P. longifolia and commercial market samples. The data generated may also serve as baseline data to form a quality standard for pharmacopoeias. SUMMARY Simultaneous quantification of gallic acid, catechin, epicatechin from Saraca asoca by high-performance liquid chromatographyDetection of S. asoca from adulterant and commercial samplesUse of analytical method along with a statistical tool for addressing quality issues. Abbreviations used: HPLC: High Performance Liquid Chromatography; RP-HPLC: Reverse Phase High Performance Liquid Chromatography; CAT: Catechin; EPI: Epicatechin; GA: Gallic acid; PCA: Principal Component Analysis. PMID:28808391

Association of tumor growth on nude mice and poor clinical outcome in soft tissue sarcoma patients.

PubMed

Budach, W; Budach, V

2001-09-01

Permanent growth in nude mice (PGNM) may be associated with poor clinical outcome. We tested this hypothesis in a group of soft tissue sarcoma (STS) patients. Small chunks from fresh tumor biopsies of 81 patients with STS were transplanted subcutaneously into NMRI-nu/nu nude mice. Tumor cell lines exhibiting growth in nude mice for more than three tumor passages were considered as permanently established. Clinical outcome of all patients was monitored with a median follow-up of 38 months. 39/81 (48%) STSs exhibited PGNM. High grade, high S-phase proportion, and aneuploidy were significant predictors of PGNM. Overall survival (OS) at 3 years was 21% (+7% standard error of median) for STS patients with PGNM and 53% (+/-8%) for patients without PGNM (P<0.01). Considering only patients without distant metastasis at the time of biopsy (n = 49), 3-year-OS was 25% (+/-10%) and 71% (+/-9%) for STS with PGNM and without PGNM, respectively (P<0.01). In the univariate analysis, PGNM, aneuploidy high S-phase proportion, tumor location at the trunk, high tumor grade, and non-liposarcoma histology were associated with reduced survival time. In the multivariate analysis, aneuploidy and tumor location at the trunk were the only independent predictors of overall survival. Permanent growth of STS on nude mice is associated with poor clinical outcome in the univariate analysis, but is not an independent predictor of survival in the multivariate analysis due to a strong co-correlation to other known adverse prognostic factors.

Solution identification and quantitative analysis of fiber-capacitive drop analyzer based on multivariate statistical methods

NASA Astrophysics Data System (ADS)

Chen, Zhe; Qiu, Zurong; Huo, Xinming; Fan, Yuming; Li, Xinghua

2017-03-01

A fiber-capacitive drop analyzer is an instrument which monitors a growing droplet to produce a capacitive opto-tensiotrace (COT). Each COT is an integration of fiber light intensity signals and capacitance signals and can reflect the unique physicochemical property of a liquid. In this study, we propose a solution analytical and concentration quantitative method based on multivariate statistical methods. Eight characteristic values are extracted from each COT. A series of COT characteristic values of training solutions at different concentrations compose a data library of this kind of solution. A two-stage linear discriminant analysis is applied to analyze different solution libraries and establish discriminant functions. Test solutions can be discriminated by these functions. After determining the variety of test solutions, Spearman correlation test and principal components analysis are used to filter and reduce dimensions of eight characteristic values, producing a new representative parameter. A cubic spline interpolation function is built between the parameters and concentrations, based on which we can calculate the concentration of the test solution. Methanol, ethanol, n-propanol, and saline solutions are taken as experimental subjects in this paper. For each solution, nine or ten different concentrations are chosen to be the standard library, and the other two concentrations compose the test group. By using the methods mentioned above, all eight test solutions are correctly identified and the average relative error of quantitative analysis is 1.11%. The method proposed is feasible which enlarges the applicable scope of recognizing liquids based on the COT and improves the concentration quantitative precision, as well.

Television Viewing and Its Association with Sedentary Behaviors, Self-Rated Heath and Academic Performance among Secondary School Students in Peru.

PubMed

Sharma, Bimala; Cosme Chavez, Rosemary; Jeong, Ae Suk; Nam, Eun Woo

2017-04-05

The study assessed television viewing >2 h a day and its association with sedentary behaviors, self-rated health, and academic performance among secondary school adolescents. A cross-sectional survey was conducted among randomly selected students in Lima in 2015. We measured self-reported responses of students using a standard questionnaire, and conducted in-depth interviews with 10 parents and 10 teachers. Chi-square test, correlation and multivariate logistic regression analysis were performed among 1234 students, and thematic analysis technique was used for qualitative information. A total of 23.1% adolescents reported watching television >2 h a day. Qualitative findings also show that adolescents spend most of their leisure time watching television, playing video games or using the Internet. Television viewing had a significant positive correlation with video game use in males and older adolescents, with Internet use in both sexes, and a negative correlation with self-rated health and academic performance in females. Multivariate logistic regression analysis shows that television viewing >2 h a day, independent of physical activity was associated with video games use >2 h a day, Internet use >2 h a day, poor/fair self-rated health and poor self-reported academic performance. Television viewing time and sex had a significant interaction effect on both video game use >2 h a day and Internet use >2 h a day. Reducing television viewing time may be an effective strategy for improving health and academic performance in adolescents.

Socioeconomic status and prevalence of self-reported diabetes among adults in Tehran: results from a large population-based cross-sectional study (Urban HEART-2).

PubMed

Asadi-Lari, M; Khosravi, A; Nedjat, S; Mansournia, M A; Majdzadeh, R; Mohammad, K; Vaez-Mahdavi, M R; Faghihzadeh, S; Haeri Mehrizi, A A; Cheraghian, B

2016-05-01

Diabetes mellitus is an important public health challenge worldwide. The prevalence of type 2 diabetes varies across countries. The aim of this study is to estimate the prevalence of type 2 diabetes and to determine related factors including socioeconomic factors in a large random sample of Tehran population in 2011. In this cross-sectional study, 91,814 individuals aged over 20 years were selected randomly based on a multistage, cluster sampling. All participants were interviewed by trained personnel using standard questionnaires. Prevalence and Townsend deprivation indexes were calculated. Principal component analysis (PCA) was used to construct wealth index. Logistic regression model was used in multivariate analysis. The estimated prevalence of self-reported diabetes was 4.98 % overall, 4.76 %in men and 5.19 % in women (P < 0.003). In multivariate analysis, age, marital status (married and divorced/widow) and BMI were positively associated with the prevalence of self-reported diabetes. Of the socioeconomic variables, educational level and wealth status were negatively and Townsend Index was positively associated with diabetes. Our study findings highlight low reported prevalence of diabetes among adults in Tehran. Subjects with low socioeconomic status (SES) had a higher prevalence of type 2 diabetes. Weight gain and obesity were the most important risk factors associated with type 2 diabetes. Wealth index and educational level were better socioeconomic indicators for presenting the inequality in diabetes prevalence in relation to Townsend deprivation index.

Short-course whole-brain radiotherapy (WBRT) for brain metastases due to small-cell lung cancer (SCLC).

PubMed

Bohlen, Guenther; Meyners, Thekla; Kieckebusch, Susanne; Lohynska, Radka; Veninga, Theo; Stalpers, Lukas J A; Schild, Steven E; Rades, Dirk

2010-04-01

Many patients with brain metastases due to SCLC have a poor survival prognosis. The most common treatment is whole-brain radiotherapy (WBRT). This retrospective study compares short-course WBRT with 5x4Gy in 1 week to standard WBRT with 10x3Gy in 2 weeks. Forty-four SCLC patients receiving WBRT with 5x4Gy were compared to 102 patients receiving 10x3Gy for survival (OS) and local (intracerebral) control (LC). Seven further potential prognostic factors were investigated: age, gender, Karnofsky Performance Score (KPS), number of brain metastases, extracerebral metastases, interval from tumor diagnosis to WBRT, RPA (Recursive Partitioning Analysis) class. After 5x4Gy, 12-month OS was 15%, versus 22% after 10x3Gy (p=0.69). On multivariate analysis, improved OS was associated with age or=70 (p<0.001), <4 brain metastases (p=0.011), and RPA class 1 (p<0.001). 12-month LC was 34% after 5x4Gy versus 25% after 10x3Gy (p=0.32). On multivariate analysis, improved LC was associated with KPS >or=70 (p<0.001), <4 brain metastases (p=0.027), and RPA class 1 (p<0.001). In patients with brain metastases due to SCLC, short-course WBRT with 5x4Gy provided similar outcomes as 10x3Gy and appears preferable, particularly for patients with poor estimated survival.

Television Viewing and Its Association with Sedentary Behaviors, Self-Rated Health and Academic Performance among Secondary School Students in Peru

PubMed Central

Sharma, Bimala; Cosme Chavez, Rosemary; Jeong, Ae Suk; Nam, Eun Woo

2017-01-01

The study assessed television viewing >2 h a day and its association with sedentary behaviors, self-rated health, and academic performance among secondary school adolescents. A cross-sectional survey was conducted among randomly selected students in Lima in 2015. We measured self-reported responses of students using a standard questionnaire, and conducted in-depth interviews with 10 parents and 10 teachers. Chi-square test, correlation and multivariate logistic regression analysis were performed among 1234 students, and thematic analysis technique was used for qualitative information. A total of 23.1% adolescents reported watching television >2 h a day. Qualitative findings also show that adolescents spend most of their leisure time watching television, playing video games or using the Internet. Television viewing had a significant positive correlation with video game use in males and older adolescents, with Internet use in both sexes, and a negative correlation with self-rated health and academic performance in females. Multivariate logistic regression analysis shows that television viewing >2 h a day, independent of physical activity was associated with video games use >2 h a day, Internet use >2 h a day, poor/fair self-rated health and poor self-reported academic performance. Television viewing time and sex had a significant interaction effect on both video game use >2 h a day and Internet use >2 h a day. Reducing television viewing time may be an effective strategy for improving health and academic performance in adolescents. PMID:28379202

Disparities in the surgical treatment of colorectal liver metastases.

PubMed

Munene, Gitonga; Parker, Robyn D; Shaheen, Abdel Aziz; Myers, Robert P; Quan, May Lynn; Ball, Chad G; Dixon, Elijah

2013-01-01

Hepatectomy is an accepted standard of care for patients with resectable colorectal liver metastases (CLM). Given that it is unclear whether disparities exist between different patient populations, a population-based analysis was performed to analyze this issue with regards to resection rates and surgical mortality in patients with CLM. Using the Nationwide Inpatient Sample, characteristics and outcomes of adult patients with a diagnosis of colorectal cancer and colorectal metastases that subsequently underwent a liver resection during the years 1993-2007 were identified. Multivariate analysis was used to determine the effects of demographic and clinical covariables on resection rates and in-hospital mortality. Incident colorectal and liver metastases were identified in 138,565 patients; 3,528 patients (2.6%) underwent subsequent resection. African American and Hispanic race were associated with lower resection rates compared to Caucasian patients (adjusted OR 0.61 (0.52 - 0.71) and 0.81 (0.68 - 0.96) respectively). Medicaid insurance was associated with decreased resection rates compared to private insurance (AOR 0.47 (0.40 - 0.56)). The overall inpatient mortality rate was 3.1%. Multivariate analysis determined that mortality rate was correlated to both insurance status and geographic region. The national resection rate is significantly lower than has been reported by most case series. Race and insurance status appear to be correlated to the likelihood of surgical resection. In-hospital mortality is equivalent to the rates reported elsewhere, but is correlated to insurance status and region.

Hybrid least squares multivariate spectral analysis methods

DOEpatents

Haaland, David M.

2002-01-01

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

The association of longitudinal trend of fasting plasma glucose with retinal microvasculature in people without established diabetes.

PubMed

Hu, Yin; Niu, Yong; Wang, Dandan; Wang, Ying; Holden, Brien A; He, Mingguang

2015-01-22

Structural changes of retinal vasculature, such as altered retinal vascular calibers, are considered as early signs of systemic vascular damage. We examined the associations of 5-year mean level, longitudinal trend, and fluctuation in fasting plasma glucose (FPG) with retinal vascular caliber in people without established diabetes. A prospective study was conducted in a cohort of Chinese people age ≥40 years in Guangzhou, southern China. The FPG was measured at baseline in 2008 and annually until 2012. In 2012, retinal vascular caliber was assessed using standard fundus photographs and validated software. A total of 3645 baseline nondiabetic participants with baseline and follow-up data on FPG for 3 or more visits was included for statistical analysis. The associations of retinal vascular caliber with 5-year mean FPG level, longitudinal FPG trend (slope of linear regression-FPG), and fluctuation (standard deviation and root mean square error of FPG) were analyzed using multivariable linear regression analyses. Multivariate regression models adjusted for baseline FPG and other potential confounders showed that a 10% annual increase in FPG was associated independently with a 2.65-μm narrowing in retinal arterioles (P = 0.008) and a 3.47-μm widening in venules (P = 0. 0.004). Associations with mean FPG level and fluctuation were not statistically significant. Annual rising trend in FPG, but not its mean level or fluctuation, is associated with altered retinal vasculature in nondiabetic people. Copyright 2015 The Association for Research in Vision and Ophthalmology, Inc.

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

PubMed

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

2001-11-01

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

Multi-Sample Cluster Analysis Using Akaike’s Information Criterion.

DTIC Science & Technology

1982-12-20

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

Docking and multivariate methods to explore HIV-1 drug-resistance: a comparative analysis

NASA Astrophysics Data System (ADS)

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

2008-05-01

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

SUGGESTIONS FOR OPTIMIZED PLANNING OF MULTIVARIATE MONITORING OF ATMOSPHERIC POLLUTION

EPA Science Inventory

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

Multivariate Meta-Analysis Using Individual Participant Data

ERIC Educational Resources Information Center

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

2015-01-01

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

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

PubMed Central

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

2013-01-01

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

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

PubMed

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

2013-10-15

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

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

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

Loveday, D.L.; Craggs, C.

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

Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review.

PubMed

Maione, Camila; Barbosa, Rommel Melgaço

2018-01-24

Rice is one of the most important staple foods around the world. Authentication of rice is one of the most addressed concerns in the present literature, which includes recognition of its geographical origin and variety, certification of organic rice and many other issues. Good results have been achieved by multivariate data analysis and data mining techniques when combined with specific parameters for ascertaining authenticity and many other useful characteristics of rice, such as quality, yield and others. This paper brings a review of the recent research projects on discrimination and authentication of rice using multivariate data analysis and data mining techniques. We found that data obtained from image processing, molecular and atomic spectroscopy, elemental fingerprinting, genetic markers, molecular content and others are promising sources of information regarding geographical origin, variety and other aspects of rice, being widely used combined with multivariate data analysis techniques. Principal component analysis and linear discriminant analysis are the preferred methods, but several other data classification techniques such as support vector machines, artificial neural networks and others are also frequently present in some studies and show high performance for discrimination of rice.

Comparison of the efficacy of pharmacoinvasive management for ST-segment elevation myocardial infarction in smokers versus non-smokers (from the Trial of Routine Angioplasty and Stenting After Fibrinolysis to Enhance Reperfusion in Acute Myocardial Infarction).

PubMed

Tan, Nigel S; Goodman, Shaun G; Cantor, Warren J; Tan, Mary K; Yan, Raymond T; Bagnall, Alan J; Mehta, Shamir R; Fitchett, David; Strauss, Bradley H; Yan, Andrew T

2014-10-01

Compared with non-smokers, cigarette smokers with ST-segment elevation myocardial infarctions derive greater benefit from fibrinolytic therapy. However, it is not known whether the optimal treatment strategy after fibrinolysis differs on the basis of smoking status. The Trial of Routine Angioplasty and Stenting After Fibrinolysis to Enhance Reperfusion in Acute Myocardial Infarction (TRANSFER-AMI) randomized patients with ST-segment elevation myocardial infarctions to a routine early invasive (pharmacoinvasive) versus a standard (early transfer only for rescue percutaneous coronary intervention or delayed angiography) strategy after fibrinolysis. The efficacy of these strategies was compared in 1,051 patients on the basis of their smoking status. Treatment heterogeneity was assessed between smokers and non-smokers, and multivariable analysis was performed to evaluate for an interaction between smoking status and treatment strategy after adjusting for baseline Global Registry of Acute Coronary Events (GRACE) risk score. Smokers (n=448) were younger, had fewer cardiovascular risk factors, and had lower GRACE risk scores. They had a lower rate of the primary composite end point of 30-day mortality, reinfarction, recurrent ischemia, heart failure, or cardiogenic shock and fewer deaths or reinfarctions at 6 months and 1 year. Smoking status was not a significant predictor of either primary or secondary end points in multivariable analysis. Pharmacoinvasive management reduced the primary end point compared with standard therapy in smokers (7.7% vs 13.6%, p=0.04) and non-smokers (13.1% vs 19.7%, p=0.03). Smoking status did not modify treatment effect on any measured outcomes (p>0.10 for all). In conclusion, compared with non-smokers, current smokers receiving either standard or early invasive management of ST-segment elevation myocardial infarction after fibrinolysis have more favorable outcomes, which is likely attributable to their better baseline risk profile. The beneficial treatment effect of a pharmacoinvasive strategy is consistent in smokers and non-smokers. Copyright © 2014 Elsevier Inc. All rights reserved.

The methylation status of RASSF1A promoter predicts responsiveness to chemotherapy and eventual cure in hepatoblastoma patients.

PubMed

Honda, Shohei; Haruta, Masayuki; Sugawara, Waka; Sasaki, Fumiaki; Ohira, Miki; Matsunaga, Tadashi; Yamaoka, Hiroaki; Horie, Hiroshi; Ohnuma, Naomi; Nakagawara, Akira; Hiyama, Eiso; Todo, Satoru; Kaneko, Yasuhiko

2008-09-01

Despite the progress of therapy, outcomes of advanced hepatoblastoma patients who are refractory to standard preoperative chemotherapy remain unsatisfactory. To improve the mortality rate, novel prognostic markers are needed for better therapy planning. We examined the methylation status of 13 candidate tumor suppressor genes in 20 hepatoblastoma tumors by conventional methylation-specific PCR (MSP) and found hypermethylation in 3 of the 13 genes. We analyzed the methylation status of these 3 genes (RASSF1A, SOCS1 and CASP8) in 97 tumors and found hypermethylation in 30.9, 33.0 and 15.5%, respectively. Univariate analysis showed that only the methylation status of RASSF1A but not the other 2 genes predicted the outcome, and multivariate analysis showed a weak contribution of RASSF1A methylation to overall survival. Using quantitative MSP, we found RASSF1A methylation in 44.3% of the 97 tumors. CTNNB1 mutation was detected in 67.0% of the 97 tumors. While univariate analysis demonstrated RASSF1A methylation, CTNNB1 mutation and other clinicopathological variables as prognostic factors, multivariate analysis identified RASSF1A methylation (p = 0.043; relative risk 9.39) and the disease stage (p = 0.002; relative risk 7.67) but not CTNNB1 mutation as independent prognostic factors. In survival analysis of 33 patients in stage 3B or 4, patients with unmethylated tumor had better overall survival than those with methylated tumor (p = 0.035). RASSF1A methylation may be a promising molecular-genetic marker to predict the treatment outcome and may be used to stratify patients when clinical trials are carried out.

A hybrid approach identifies metabolic signatures of high-producers for chinese hamster ovary clone selection and process optimization.

PubMed

Popp, Oliver; Müller, Dirk; Didzus, Katharina; Paul, Wolfgang; Lipsmeier, Florian; Kirchner, Florian; Niklas, Jens; Mauch, Klaus; Beaucamp, Nicola

2016-09-01

In-depth characterization of high-producer cell lines and bioprocesses is vital to ensure robust and consistent production of recombinant therapeutic proteins in high quantity and quality for clinical applications. This requires applying appropriate methods during bioprocess development to enable meaningful characterization of CHO clones and processes. Here, we present a novel hybrid approach for supporting comprehensive characterization of metabolic clone performance. The approach combines metabolite profiling with multivariate data analysis and fluxomics to enable a data-driven mechanistic analysis of key metabolic traits associated with desired cell phenotypes. We applied the methodology to quantify and compare metabolic performance in a set of 10 recombinant CHO-K1 producer clones and a host cell line. The comprehensive characterization enabled us to derive an extended set of clone performance criteria that not only captured growth and product formation, but also incorporated information on intracellular clone physiology and on metabolic changes during the process. These criteria served to establish a quantitative clone ranking and allowed us to identify metabolic differences between high-producing CHO-K1 clones yielding comparably high product titers. Through multivariate data analysis of the combined metabolite and flux data we uncovered common metabolic traits characteristic of high-producer clones in the screening setup. This included high intracellular rates of glutamine synthesis, low cysteine uptake, reduced excretion of aspartate and glutamate, and low intracellular degradation rates of branched-chain amino acids and of histidine. Finally, the above approach was integrated into a workflow that enables standardized high-content selection of CHO producer clones in a high-throughput fashion. In conclusion, the combination of quantitative metabolite profiling, multivariate data analysis, and mechanistic network model simulations can identify metabolic traits characteristic of high-performance clones and enables informed decisions on which clones provide a good match for a particular process platform. The proposed approach also provides a mechanistic link between observed clone phenotype, process setup, and feeding regimes, and thereby offers concrete starting points for subsequent process optimization. Biotechnol. Bioeng. 2016;113: 2005-2019. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

[Application of multivariate analysis to the serum mineral and trace element content on differentiation of healthy subjects].

PubMed

Rodríguez Rodríguez, E; Henríquez Sánchez, P; López Blanco, F; Díaz Romero, C; Serra Majem, L

2004-01-01

Serum concentrations of Na, K, Ca, Mg, Fe, Cu, Zn, Se, Mn and P were determined in apparently health individuals representing of the population of the Canary Islands. Multivariate analysis was applied on the data matrix in order to differentiate the individuals according several criteria such as gender, age, island and province of residence, smoking and drinking habits and physical exercise. 395 serum samples (187 men and 208 women) were analyzed mean age of 38.4 +/- 20.0 years. Individuals data about age, gender, weight, height, alcohol consumption, smoking habits and physical exercise were recorded using standardized questionnaires. The determination of minerals was carried out by flame emission spectrometry (Na and K) and atomic absorption spectrometry with flame air/acetylene (Ca, Mg, Fe, Cu, Zn), hybride generation (Se) and graphite furnace (Mn). The P was determined by a colorimetric method. The sex and age of individuals influenced on the serum concentrations of some minerals, Cu and Fe, and P and Se, respectively. The island of residence influenced the mean concentrations of the most the minerals analysed. The smoking and drinking habits do not seem to influence the mean contents of the minerals in an important manner. Physical exercise had significant influence on the P, Cu and Mn concentrations in serum. The water for consumption influenced on the serum concentrations of the electrolytes and Ca and Mg, but it did not affect the concentrations of the trace elements. Applying discriminant analysis the individuals lower 18 years were reasonably well differentiated (89% of the individuals correctly classified) from the rest of individuals. A tendency for differentiation of individuals according to the island of residence was also observed. A low differentiation of the individuals according to the sex, province or island or residence and habits or life style was observed after application of multivariate analysis techniques. However, the adults were reasonably differentiated from the children and adolescent, and the inhabitants of Lanzarote and La Palma tend to separate from the rest of the individuals of their province.

Elevated amniotic fluid lactate predicts labor disorders and cesarean delivery in nulliparous women at term.

PubMed

Murphy, Martina; Butler, Michelle; Coughlan, Barbara; Brennan, Donal; O'Herlihy, Colm; Robson, Michael

2015-11-01

We sought to assess amniotic fluid lactate (AFL) at diagnosis of spontaneous labor at term (≥37 weeks) as a predictor of labor disorders (dystocia) and cesarean delivery (CD). This was a single-institution, prospective cohort study of 905 singleton, cephalic, term (≥37 weeks) nulliparous women in spontaneous labor. A standard management of labor (active management of labor) including a standard oxytocin regimen up to a maximum dose of 30 mU/min was applied. AFL was measured using a point-of-care device (LMU061; ObsteCare, Stockholm, Sweden). Labor arrest in the first stage of labor was defined as the need for oxytocin when cervical dilatation was <1 cm/h over 2 hours and in the second stage of labor by poor descent and rotation over 1 hour. Standard statistical analysis included analysis of variance, Pearson correlations, and binary logistic regression. Unsupervised decision tree analysis with 10-fold cross-validation was used to identify AFL thresholds. AFL was normally distributed and did not correlate with age, body mass index, or gestation. Unsupervised decision tree analysis demonstrated that AFL could be divided into 3 groups: 0-4.9 mmol/L (n = 118), 5.0-9.9 mmol/L (n = 707), and ≥10.0 mmol/L (n = 80). Increasing AFL was associated with higher total oxytocin dose (P = .001), labor disorders (P = .005), and CD (P ≤ .001). Multivariable regression analysis demonstrated that women with AFL ≥5.0-9.9 mmol/L (odds ratio [OR], 1.6; 95% confidence interval [CI], 1.06-2.39) and AFL ≥10.0 mmol/L (OR, 1.72; 95% CI, 1.01-2.93) were independent predictors of a labor disorder. AFL ≥5.0-9.9 mmol/L did not predict CD but multivariable analysis confirmed that AFL ≥10.0 mmol/L was an independent predictor of CD (OR, 3.35; 95% CI, 1.73-6.46). AFL ≥5.0-9.9 mmol/L had a sensitivity of 89% in predicting a labor disorder and a sensitivity of 93% in predicting CD with a 97% negative predictive value. AFL ≥10.0 mmol/L was highly specific but lacked sensitivity for CD. There was no difference in birthweight of infants according to labor disorder and delivery method. AFL at diagnosis of labor in spontaneously laboring single cephalic nulliparous term women is an independent predictor of a labor disorder and CD. These data suggest that women with AFL between 5.0-9.9 mmol/L with a labor disorder may be amenable to correction using the active management of labor protocol. Copyright © 2015 Elsevier Inc. All rights reserved.

Rapid analysis of glucose, fructose, sucrose, and maltose in honeys from different geographic regions using fourier transform infrared spectroscopy and multivariate analysis.

PubMed

Wang, Jun; Kliks, Michael M; Jun, Soojin; Jackson, Mel; Li, Qing X

2010-03-01

Quantitative analysis of glucose, fructose, sucrose, and maltose in different geographic origin honey samples in the world using the Fourier transform infrared (FTIR) spectroscopy and chemometrics such as partial least squares (PLS) and principal component regression was studied. The calibration series consisted of 45 standard mixtures, which were made up of glucose, fructose, sucrose, and maltose. There were distinct peak variations of all sugar mixtures in the spectral "fingerprint" region between 1500 and 800 cm(-1). The calibration model was successfully validated using 7 synthetic blend sets of sugars. The PLS 2nd-derivative model showed the highest degree of prediction accuracy with a highest R(2) value of 0.999. Along with the canonical variate analysis, the calibration model further validated by high-performance liquid chromatography measurements for commercial honey samples demonstrates that FTIR can qualitatively and quantitatively determine the presence of glucose, fructose, sucrose, and maltose in multiple regional honey samples.

Nutritional Intervention: A Secondary Analysis of Its Effect on Malnourished Colombian Pre-Schoolers.

ERIC Educational Resources Information Center

Bejar, Isaac I.

1981-01-01

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

A Multivariate Descriptive Model of Motivation for Orthodontic Treatment.

ERIC Educational Resources Information Center

Hackett, Paul M. W.; And Others

1993-01-01

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

Exploring Pattern of Socialisation Conditions and Human Development by Nonlinear Multivariate Analysis.

ERIC Educational Resources Information Center

Grundmann, Matthias

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

Univariate Analysis of Multivariate Outcomes in Educational Psychology.

ERIC Educational Resources Information Center

Hubble, L. M.

1984-01-01

The author examined the prevalence of multiple operational definitions of outcome constructs and an estimate of the incidence of Type I error rates when univariate procedures were applied to multiple variables in educational psychology. Multiple operational definitions of constructs were advocated and wider use of multivariate analysis was…

Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

ERIC Educational Resources Information Center

Warner, Rebecca M.

2007-01-01

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

Evaluation of Meterorite Amono Acid Analysis Data Using Multivariate Techniques

NASA Technical Reports Server (NTRS)

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

1999-01-01

The amino acid distributions in the Murchison carbonaceous chondrite, Mars meteorite ALH84001, and ice from the Allan Hills region of Antarctica are shown, using a multivariate technique known as Principal Component Analysis (PCA), to be statistically distinct from the average amino acid compostion of 101 terrestrial protein superfamilies.

MULTIVARIATE ANALYSIS ON LEVELS OF SELECTED METALS, PARTICULATE MATTER, VOC, AND HOUSEHOLD CHARACTERISTICS AND ACTIVITIES FROM THE MIDWESTERN STATES NHEXAS

EPA Science Inventory

Microenvironmental and biological/personal monitoring information were collected during the National Human Exposure Assessment Survey (NHEXAS), conducted in the six states comprising U.S. EPA Region Five. They have been analyzed by multivariate analysis techniques with general ...

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

PubMed

Ma, Yan; Mazumdar, Madhu

2011-10-30

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

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.

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

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

Determination of thiamine HCl and pyridoxine HCl in pharmaceutical preparations using UV-visible spectrophotometry and genetic algorithm based multivariate calibration methods.

PubMed

Ozdemir, Durmus; Dinc, Erdal

2004-07-01

Simultaneous determination of binary mixtures pyridoxine hydrochloride and thiamine hydrochloride in a vitamin combination using UV-visible spectrophotometry and classical least squares (CLS) and three newly developed genetic algorithm (GA) based multivariate calibration methods was demonstrated. The three genetic multivariate calibration methods are Genetic Classical Least Squares (GCLS), Genetic Inverse Least Squares (GILS) and Genetic Regression (GR). The sample data set contains the UV-visible spectra of 30 synthetic mixtures (8 to 40 microg/ml) of these vitamins and 10 tablets containing 250 mg from each vitamin. The spectra cover the range from 200 to 330 nm in 0.1 nm intervals. Several calibration models were built with the four methods for the two components. Overall, the standard error of calibration (SEC) and the standard error of prediction (SEP) for the synthetic data were in the range of <0.01 and 0.43 microg/ml for all the four methods. The SEP values for the tablets were in the range of 2.91 and 11.51 mg/tablets. A comparison of genetic algorithm selected wavelengths for each component using GR method was also included.

An improvement of drought monitoring through the use of a multivariate magnitude index

NASA Astrophysics Data System (ADS)

Real-Rangel, R. A.; Alcocer-Yamanaka, V. H.; Pedrozo-Acuña, A.; Breña-Naranjo, J. A.; Ocón-Gutiérrez, A. R.

2017-12-01

In drought monitoring activities it is widely acknowledged that the severity of an event is determined in relation to monthly values of univariate indices of one or more hydrological variables. Normally, these indices are estimated using temporal windows from 1 to 12 months or more to aggregate the effects of deficits in the variable of interest. However, the use of these temporal windows may lead to a misperception of both, the drought event intensity and the timing of its occurrence. In this context, this work presents the implementation of a trivariate drought magnitude index, considering key hydrological variables (e.g., precipitation, soil moisture and runoff) using for this the framework of the Multivariate Standardized Drought Index (MSDI). Despite the popularity and simplicity of the concept of drought magnitude for standardized drought indices, its implementation in drought monitoring and early warning systems has not been reported. This approach has been tested for operational purposes in the recently launched Multivariate Drought Monitor of Mexico (MOSEMM) and the results shows that the inclusion of a Magnitude index facilitates the drought detection and, thus, improves the decision making process for emergency managers.

Time Series Model Identification by Estimating Information.

DTIC Science & Technology

1982-11-01

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

Genomic Analysis of Complex Microbial Communities in Wounds

DTIC Science & Technology

2012-01-01

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

Prognostic implications of 62Cu-diacetyl-bis (N4-methylthiosemicarbazone) PET/CT in patients with glioma.

PubMed

Toriihara, Akira; Ohtake, Makoto; Tateishi, Kensuke; Hino-Shishikura, Ayako; Yoneyama, Tomohiro; Kitazume, Yoshio; Inoue, Tomio; Kawahara, Nobutaka; Tateishi, Ukihide

2018-05-01

The potential of positron emission tomography/computed tomography using 62 Cu-diacetyl-bis (N 4 -methylthiosemicarbazone) ( 62 Cu-ATSM PET/CT), which was originally developed as a hypoxic tracer, to predict therapeutic resistance and prognosis has been reported in various cancers. Our purpose was to investigate prognostic value of 62 Cu-ATSM PET/CT in patients with glioma, compared to PET/CT using 2-deoxy-2-[ 18 F]fluoro-D-glucose ( 18 F-FDG). 56 patients with glioma of World Health Organization grade 2-4 were enrolled. All participants had undergone both 62 Cu-ATSM PET/CT and 18 F-FDG PET/CT within mean 33.5 days prior to treatment. Maximum standardized uptake value and tumor/background ratio were calculated within areas of increased radiotracer uptake. The prognostic significance for progression-free survival and overall survival were assessed by log-rank test and Cox's proportional hazards model. Disease progression and death were confirmed in 37 and 27 patients in follow-up periods, respectively. In univariate analysis, there was significant difference of both progression-free survival and overall survival in age, tumor grade, history of chemoradiotherapy, maximum standardized uptake value and tumor/background ratio calculated using 62 Cu-ATSM PET/CT. Multivariate analysis revealed that maximum standardized uptake value calculated using 62 Cu-ATSM PET/CT was an independent predictor of both progression-free survival and overall survival (p < 0.05). In a subgroup analysis including patients of grade 4 glioma, only the maximum standardized uptake values calculated using 62 Cu-ATSM PET/CT showed significant difference of progression-free survival (p < 0.05). 62 Cu-ATSM PET/CT is a more promising imaging method to predict prognosis of patients with glioma compared to 18 F-FDG PET/CT.

Permeability Surface Area Product Using Perfusion Computed Tomography Is a Valuable Prognostic Factor in Glioblastomas Treated with Radiotherapy Plus Concomitant and Adjuvant Temozolomide.

PubMed

Saito, Taiichi; Sugiyama, Kazuhiko; Ikawa, Fusao; Yamasaki, Fumiyuki; Ishifuro, Minoru; Takayasu, Takeshi; Nosaka, Ryo; Nishibuchi, Ikuno; Muragaki, Yoshihiro; Kawamata, Takakazu; Kurisu, Kaoru

2017-01-01

The current standard treatment protocol for patients with newly diagnosed glioblastoma (GBM) includes surgery, radiotherapy, and concomitant and adjuvant temozolomide (TMZ). We hypothesized that the permeability surface area product (PS) from a perfusion computed tomography (PCT) study is associated with sensitivity to TMZ. The aim of this study was to determine whether PS values were correlated with prognosis of GBM patients who received the standard treatment protocol. This study included 36 patients with GBM that were newly diagnosed between October 2005 and September 2014 and who underwent preoperative PCT study and the standard treatment protocol. We measured the maximum value of relative cerebral blood volume (rCBVmax) and the maximum PS value (PSmax). We statistically examined the relationship between PSmax and prognosis using survival analysis, including other clinicopathologic factors (age, Karnofsky performance status [KPS], extent of resection, O6-methylguanine-DNA methyltransferase [MGMT] status, second-line use of bevacizumab, and rCBVmax). Log-rank tests revealed that age, KPS, MGMT status, and PSmax were significantly correlated with overall survival. Multivariate analysis using the Cox regression model showed that PSmax was the most significant prognostic factor. Receiver operating characteristic curve analysis showed that PSmax had the highest accuracy in differentiating longtime survivors (LTSs) (surviving more than 2 years) from non-LTSs. At a cutoff point of 8.26 mL/100 g/min, sensitivity and specificity were 90% and 70%, respectively. PSmax from PCT study can help predict survival time in patients with GBM receiving the standard treatment protocol. Survival may be related to sensitivity to TMZ. Copyright © 2016 Elsevier Inc. All rights reserved.

Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids

PubMed Central

Ali, S. M.; Mehmood, C. A; Khan, B.; Jawad, M.; Farid, U; Jadoon, J. K.; Ali, M.; Tareen, N. K.; Usman, S.; Majid, M.; Anwar, S. M.

2016-01-01

In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion. PMID:27314229

Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids.

PubMed

Ali, S M; Mehmood, C A; Khan, B; Jawad, M; Farid, U; Jadoon, J K; Ali, M; Tareen, N K; Usman, S; Majid, M; Anwar, S M

2016-01-01

In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion.

Polynomial reduction and evaluation of tree- and loop-level CHY amplitudes

DOE PAGES

Zlotnikov, Michael

2016-08-24

We develop a polynomial reduction procedure that transforms any gauge fixed CHY amplitude integrand for n scattering particles into a σ-moduli multivariate polynomial of what we call the standard form. We show that a standard form polynomial must have a specific ladder type monomial structure, which has finite size at any n, with highest multivariate degree given by (n – 3)(n – 4)/2. This set of monomials spans a complete basis for polynomials with rational coefficients in kinematic data on the support of scattering equations. Subsequently, at tree and one-loop level, we employ the global residue theorem to derive amore » prescription that evaluates any CHY amplitude by means of collecting simple residues at infinity only. Furthermore, the prescription is then applied explicitly to some tree and one-loop amplitude examples.« less

The proliferation marker Ki67, but not neuroendocrine expression, is an independent factor in the prediction of prognosis of primary prostate cancer patients

PubMed Central

Pascale, Mariarosa; Aversa, Cinzia; Barbazza, Renzo; Marongiu, Barbara; Siracusano, Salvatore; Stoffel, Flavio; Sulfaro, Sando; Roggero, Enrico; Stanta, Giorgio

2016-01-01

Abstract Background Neuroendocrine markers, which could indicate for aggressive variants of prostate cancer and Ki67 (a well-known marker in oncology for defining tumor proliferation), have already been associated with clinical outcome in prostate cancer. The aim of this study was to investigate the prognostic value of those markers in primary prostate cancer patients. Patients and methods NSE (neuron specific enolase), ChrA (chromogranin A), Syp (Synaptophysin) and Ki67 staining were performed by immunohistochemistry. Then, the prognostic impact of their expression on overall survival was investigated in 166 primary prostate cancer patients by univariate and multivariate analyses. Results NSE, ChrA, Syp and Ki67 were positive in 50, 45, 54 and 146 out of 166 patients, respectively. In Kaplan-Meier analysis only diffuse NSE staining (negative vs diffuse, p = 0.004) and Ki67 (≤ 10% vs > 10%, p < 0.0001) were significantly associated with overall survival. Ki67 expression, but not NSE, resulted as an independent prognostic factor for overall survival in multivariate analysis. Conclusions A prognostic model incorporating Ki67 expression with clinical-pathological covariates could provide additional prognostic information. Ki67 may thus improve prediction of prostate cancer outcome based on standard clinical-pathological parameters improving prognosis and management of prostate cancer patients. PMID:27679548

Red blood cell alloimmunization among sickle cell Kuwaiti Arab patients who received red blood cell transfusion.

PubMed

Ameen, Reem; Al Shemmari, Salem; Al-Bashir, Abdulaziz

2009-08-01

Sickle cell disease (SCD) is common in the Arabian Gulf region. Most cases require a red blood cell (RBC) transfusion, increasing the potential for RBC alloantibody development. The incidence of RBC alloimmunization among Kuwaiti Arab SCD patients is not yet known. This study retrospectively assessed the effect of using two different matching protocols on the incidence of alloimmunization among multiply transfused Kuwaiti Arab SCD patients. A total of 233 Kuwaiti Arab SCD patients were divided into two groups: Group 1 (n = 110) received RBC transfusion through standard ABO- and D-matched nonleukoreduced blood; Group 2 (n = 123) received RBCs matched for ABO, Rh, and K1 poststorage-leukoreduced blood. Multivariate analysis was performed on the factors associated with RBC alloimmunization and antibody specificity. Sixty-five percent of patients in Group 1 developed clinically significant RBC alloantibody with an increased prevalence in females; in patients in Group 2, 23.6% developed RBC alloantibodies (p = 0.01). In Group 1, 72 patients (65.5%) had alloantibodies directed against Rh and Kell systems (p = 0.01). Multivariate analysis further confirmed the results, showing that blood transfusion type and sex have significant effects on the rate of alloimmunizations. This study confirms the importance of selecting RBCs matched for Rh and Kell to reduce the risk of alloimmunizations among Kuwaiti Arab SCD patients.

Analysis of the Risk Factors for Aerobic Vaginitis: A Case-Control Study.

PubMed

Geng, Nv; Wu, Wenjuan; Fan, Aiping; Han, Cha; Wang, Chen; Wang, Yingmei; Xue, Fengxia

2015-06-09

Aerobic vaginitis (AV) is a newly defined clinical entity which may interfere with women's reproductive health and have negative effects on pregnancy. This study was to identify the risk factors for AV. Participants in this case-control study included healthy women and women with AV. All participants completed a standardized questionnaire covering sociodemographic factors, sexual behaviors, personal hygiene habits and health behaviors. Uni- and multivariate logistic regression analyses were used for statistical evaluation. A total of 290 women of reproductive age were enrolled. In the multivariate analysis, unmarried status (odds ratio [OR] 2.606, 95% confidence interval [CI] 1.257-5.402), use of an intrauterine device (OR 4.989, 95% CI 1.922-12.952), long-term use of antibiotics (OR 11.176, 95% CI 1.363-91.666) and frequent vaginal douching (OR 4.689, 95% CI 1.363-16.135) were independent risk factors for AV. On the contrary, consistent condom use (OR 0.546, 95% CI 0.301-0.991) and college-level education or above (OR 0.255, 95% CI 0.131-0.497) were independent protective factors. Measures that may be considered to prevent AV include enhancing education to improve women's knowledge related to reproductive health, especially unmarried women, encouraging them to consistently use condoms as a contraceptive method, to avoid long-term use of antibiotics and to stop frequent vaginal douching. © 2015 S. Karger AG, Basel.

Kidney donation after circulatory death in a country with a high number of brain dead donors: 10-year experience in Belgium.

PubMed

Jochmans, Ina; Darius, Tom; Kuypers, Dirk; Monbaliu, Diethard; Goffin, Eric; Mourad, Michel; Ledinh, Hieu; Weekers, Laurent; Peeters, Patrick; Randon, Caren; Bosmans, Jean-Louis; Roeyen, Geert; Abramowicz, Daniel; Hoang, Anh-Dung; De Pauw, Luc; Rahmel, Axel; Squifflet, Jean-Paul; Pirenne, Jacques

2012-08-01

Worldwide shortage of standard brain dead donors (DBD) has revived the use of kidneys donated after circulatory death (DCD). We reviewed the Belgian DCD kidney transplant (KT) experience since its reintroduction in 2000. Risk factors for delayed graft function (DGF) were identified using multivariate analysis. Five-year patient/graft survival was assessed using Kaplan-Meier curves. The evolution of the kidney donor type and the impact of DCDs on the total KT activity in Belgium were compared with the Netherlands. Between 2000 and 2009, 287 DCD KT were performed. Primary nonfunction occurred in 1% and DGF in 31%. Five-year patient and death-censored graft survival were 93% and 95%, respectively. In multivariate analysis, cold storage (versus machine perfusion), cold ischemic time, and histidine-tryptophan-ketoglutarate solution were independent risk factors for the development of DGF. Despite an increased number of DCD donations and transplantations, the total number of deceased KT did not increase significantly. This could suggest a shift from DBDs to DCDs. To increase KT activity, Belgium should further expand controlled DCD programs while simultaneously improve the identification of all potential DBDs and avoid their referral for donation as DCDs before brain death occurs. Furthermore, living donation remains underused. © 2012 The Authors. Transplant International © 2012 European Society for Organ Transplantation.

Magnitude and Determinants of the Ratio between Prevalence of Low Vision and Blindness in Rapid Assessment of Avoidable Blindness Surveys.

PubMed

Kaphle, Dinesh; Lewallen, Susan

2017-10-01

To determine the magnitude and determinants of the ratio between prevalence of low vision and prevalence of blindness in rapid assessment of avoidable blindness (RAAB) surveys globally. Standard RAAB reports were downloaded from the repository or requested from principal investigators. Potential predictor variables included prevalence of uncorrected refractive error (URE) as well as gross domestic product (GDP) per capita, health expenditure per capita of the country across World Bank regions. Univariate and multivariate linear regression were used to investigate the correlation between potential predictor variables and the ratio. The results of 94 surveys from 43 countries showed that the ratio ranged from 1.35 in Mozambique to 11.03 in India with a median value of 3.90 (Interquartile range 3.06;5.38). Univariate regression analysis showed that prevalence of URE (p = 0.04), logarithm of GDP per capita (p = 0.01) and logarithm of health expenditure per capita (p = 0.03) were significantly associated with the higher ratio. However, only prevalence of URE was found to be significant in multivariate regression analysis (p = 0.03). There is a wide variation in the ratio of the prevalence of low vision to the prevalence of blindness. Eye care service utilization indicators such as the prevalence of URE may explain some of the variation across the regions.

Use of metabolomics and lipidomics to evaluate the hypocholestreolemic effect of Proanthocyanidins from grape seed in a pig model.

PubMed

Quifer-Rada, Paola; Choy, Ying Yng; Calvert, Christopher C; Waterhouse, Andrew L; Lamuela-Raventos, Rosa M

2016-10-01

This work aims to evaluate changes in the fecal metabolomic profile due to grape seed extract (GSE) intake by untargeted and targeted analysis using high resolution mass spectrometry in conjunction with multivariate statistics. An intervention study with six crossbred female pigs was performed. The pigs followed a standard diet for 3 days, then they were fed with a supplemented diet containing 1% (w/w) of MegaNatural® Gold grape seed extract for 6 days. Fresh pig fecal samples were collected daily. A combination of untargeted high resolution mass spectrometry, multivariate analysis (PLS-DA), data-dependent MS/MS scan, and accurate mass database matching was used to measure the effect of the treatment on fecal composition. The resultant PLS-DA models showed a good discrimination among classes with great robustness and predictability. A total of 14 metabolites related to the GSE consumption were identified including biliary acid, dicarboxylic fatty acid, cholesterol metabolites, purine metabolites, and eicosanoid metabolites among others. Moreover, targeted metabolomics using GC-MS showed that cholesterol and its metabolites fecal excretion was increased due to the proanthocyanidins from grape seed extract. The results show that oligomeric procyanidins from GSE modifies bile acid and steroid excretion, which could exert a hypocholesterolemic effect. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Predictor variables for a half marathon race time in recreational male runners

PubMed Central

Rüst, Christoph Alexander; Knechtle, Beat; Knechtle, Patrizia; Barandun, Ursula; Lepers, Romuald; Rosemann, Thomas

2011-01-01

The aim of this study was to investigate predictor variables of anthropometry, training, and previous experience in order to predict a half marathon race time for future novice recreational male half marathoners. Eighty-four male finishers in the ‘Half Marathon Basel’ completed the race distance within (mean and standard deviation, SD) 103.9 (16.5) min, running at a speed of 12.7 (1.9) km/h. After multivariate analysis of the anthropometric characteristics, body mass index (r = 0.56), suprailiacal (r = 0.36) and medial calf skin fold (r = 0.53) were related to race time. For the variables of training and previous experience, speed in running of the training sessions (r = −0.54) were associated with race time. After multivariate analysis of both the significant anthropometric and training variables, body mass index (P = 0.0150) and speed in running during training (P = 0.0045) were related to race time. Race time in a half marathon might be partially predicted by the following equation (r2 = 0.44): Race time (min) = 72.91 + 3.045 * (body mass index, kg/m2) −3.884 * (speed in running during training, km/h) for recreational male runners. To conclude, variables of both anthropometry and training were related to half marathon race time in recreational male half marathoners and cannot be reduced to one single predictor variable. PMID:24198577

Predictor variables for a half marathon race time in recreational male runners.

PubMed

Rüst, Christoph Alexander; Knechtle, Beat; Knechtle, Patrizia; Barandun, Ursula; Lepers, Romuald; Rosemann, Thomas

2011-01-01

The aim of this study was to investigate predictor variables of anthropometry, training, and previous experience in order to predict a half marathon race time for future novice recreational male half marathoners. Eighty-four male finishers in the 'Half Marathon Basel' completed the race distance within (mean and standard deviation, SD) 103.9 (16.5) min, running at a speed of 12.7 (1.9) km/h. After multivariate analysis of the anthropometric characteristics, body mass index (r = 0.56), suprailiacal (r = 0.36) and medial calf skin fold (r = 0.53) were related to race time. For the variables of training and previous experience, speed in running of the training sessions (r = -0.54) were associated with race time. After multivariate analysis of both the significant anthropometric and training variables, body mass index (P = 0.0150) and speed in running during training (P = 0.0045) were related to race time. Race time in a half marathon might be partially predicted by the following equation (r(2) = 0.44): Race time (min) = 72.91 + 3.045 * (body mass index, kg/m(2)) -3.884 * (speed in running during training, km/h) for recreational male runners. To conclude, variables of both anthropometry and training were related to half marathon race time in recreational male half marathoners and cannot be reduced to one single predictor variable.

Seroepidemiology of cytomegalovirus infection in pregnant women in Durango City, Mexico.

PubMed

Alvarado-Esquivel, Cosme; Hernández-Tinoco, Jesús; Sánchez-Anguiano, Luis Francisco; Ramos-Nevárez, Agar; Cerrillo-Soto, Sandra Margarita; Estrada-Martínez, Sergio; Martínez-Ramírez, Lucio; Pérez-Álamos, Alma Rosa; Guido-Arreola, Carlos Alberto

2014-09-05

Cytomegalovirus causes congenital infections all around the world. The seroepidemiology of cytomegalovirus infection in pregnant women in Mexico is largely unknown. We sought to determine the seroprevalence of cytomegalovirus infection in pregnant women in Durango City, Mexico; and to determine seroprevalence association with socio-demographic, clinical and behavioral characteristics of pregnant women. Through a cross-sectional study design, 343 pregnant women were examined for anti-cytomegalovirus IgG and IgM antibodies in Durango City, Mexico. We used a standardized questionnaire to obtain the general characteristics of the pregnant women. Multivariate analysis was performed to determine the association of cytomegalovirus infection with the characteristics of the pregnant women. Anti-CMV IgG and IgM antibodies were detected in 225 (65.6%) and in none of the 343 pregnant women studied, respectively. Multivariate analysis showed that CMV exposure was associated with increasing age (OR = 1.67; 95% CI: 1.01-2.76; P = 0.04). Other women characteristics including socioeconomic status, education, blood transfusion, transplantation, sexual promiscuity and number of previous pregnancies or deliveries did not show an association with CMV exposure. This is the first seroepidemiology study of CMV infection in pregnant women in Mexico. A number of known factors associated with CMV infection were not associated with CMV exposure in the women studied. Further studies to determine routes of CMV infection in pregnant women in Mexico are needed.

Application of near-infrared spectroscopy for the rapid quality assessment of Radix Paeoniae Rubra

NASA Astrophysics Data System (ADS)

Zhan, Hao; Fang, Jing; Tang, Liying; Yang, Hongjun; Li, Hua; Wang, Zhuju; Yang, Bin; Wu, Hongwei; Fu, Meihong

2017-08-01

Near-infrared (NIR) spectroscopy with multivariate analysis was used to quantify gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra, and the feasibility to classify the samples originating from different areas was investigated. A new high-performance liquid chromatography method was developed and validated to analyze gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra as the reference. Partial least squares (PLS), principal component regression (PCR), and stepwise multivariate linear regression (SMLR) were performed to calibrate the regression model. Different data pretreatments such as derivatives (1st and 2nd), multiplicative scatter correction, standard normal variate, Savitzky-Golay filter, and Norris derivative filter were applied to remove the systematic errors. The performance of the model was evaluated according to the root mean square of calibration (RMSEC), root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), and correlation coefficient (r). The results show that compared to PCR and SMLR, PLS had a lower RMSEC, RMSECV, and RMSEP and higher r for all the four analytes. PLS coupled with proper pretreatments showed good performance in both the fitting and predicting results. Furthermore, the original areas of Radix Paeoniae Rubra samples were partly distinguished by principal component analysis. This study shows that NIR with PLS is a reliable, inexpensive, and rapid tool for the quality assessment of Radix Paeoniae Rubra.

In situ X-ray diffraction analysis of (CF x) n batteries: signal extraction by multivariate analysis

DOE PAGES

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

2007-11-10

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

Hybrid least squares multivariate spectral analysis methods

DOEpatents

Haaland, David M.

2004-03-23

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

Integration of vessel traits, wood density, and height in angiosperm shrubs and trees.

PubMed

Martínez-Cabrera, Hugo I; Schenk, H Jochen; Cevallos-Ferriz, Sergio R S; Jones, Cynthia S

2011-05-01

Trees and shrubs tend to occupy different niches within and across ecosystems; therefore, traits related to their resource use and life history are expected to differ. Here we analyzed how growth form is related to variation in integration among vessel traits, wood density, and height. We also considered the ecological and evolutionary consequences of such differences. In a sample of 200 woody plant species (65 shrubs and 135 trees) from Argentina, Mexico, and the United States, standardized major axis (SMA) regression, correlation analyses, and ANOVA were used to determine whether relationships among traits differed between growth forms. The influence of phylogenetic relationships was examined with a phylogenetic ANOVA and phylogenetically independent contrasts (PICs). A principal component analysis was conducted to determine whether trees and shrubs occupy different portions of multivariate trait space. Wood density did not differ between shrubs and trees, but there were significant differences in vessel diameter, vessel density, theoretical conductivity, and as expected, height. In addition, relationships between vessel traits and wood density differed between growth forms. Trees showed coordination among vessel traits, wood density, and height, but in shrubs, wood density and vessel traits were independent. These results hold when phylogenetic relationships were considered. In the multivariate analyses, these differences translated as significantly different positions in multivariate trait space occupied by shrubs and trees. Differences in trait integration between growth forms suggest that evolution of growth form in some lineages might be associated with the degree of trait interrelation.

Socio-demographic and clinical profiles of paranoid and nonparanoid schizophrenia: a prospective, multicenter study in China.

PubMed

Xiang, Yu-Tao; Wang, Chuan-Yue; Chiu, Helen F K; Weng, Yong-Zhen; Bo, Qi-Jing; Chan, Sandra S M; Lee, Edwin H M; Ungvari, Gabor S

2011-07-01

This study aimed to explore the socio-demographic and clinical characteristics of paranoid and nonparanoid subtypes of schizophrenia. In a multicenter, randomized, controlled, longitudinal study, 374 clinically stable schizophrenia patients were interviewed at entry with standardized assessment instruments and followed for 12-26 months. In the multivariate analysis, male sex, married marital status, urban abode, and more frequent relapse over the study period were independently associated with paranoid schizophrenia. The socio-demographic and clinical characteristics of Chinese patients with the paranoid subtype of schizophrenia are different from those of their Caucasian counterparts who are more likely to be women and have a better outcome. © 2010 Wiley Periodicals, Inc.

Standardized Uptake Decrease on [18F]-Fluorodeoxyglucose Positron Emission Tomography After Neoadjuvant Chemotherapy Is a Prognostic Classifier for Long-Term Outcome After Multimodality Treatment: Secondary Analysis of a Randomized Trial for Resectable Stage IIIA/B Non-Small-Cell Lung Cancer.

PubMed

Pöttgen, Christoph; Gauler, Thomas; Bellendorf, Alexander; Guberina, Maja; Bockisch, Andreas; Schwenzer, Nina; Heinzelmann, Frank; Cordes, Sebastian; Schuler, Martin H; Welter, Stefan; Stamatis, Georgios; Friedel, Godehard; Darwiche, Kaid; Jöckel, Karl-Heinz; Eberhardt, Wilfried; Stuschke, Martin

2016-07-20

A confirmatory analysis was performed to determine the prognostic value of metabolic response during induction chemotherapy followed by bimodality/trimodality treatment of patients with operable locally advanced non-small-cell lung cancer. Patients with potentially operable stage IIIA(N2) or selected stage IIIB non-small-cell lung cancer received three cycles of cisplatin/paclitaxel (induction chemotherapy) followed by neoadjuvant radiochemotherapy (RCT) to 45 Gy (1.5 Gy twice per day concurrent cisplatin/vinorelbine) within the ESPATUE (Phase III Study of Surgery Versus Definitive Concurrent Chemoradiotherapy Boost in Patients With Resectable Stage IIIA[N2] and Selected IIIB Non-Small-Cell Lung Cancer After Induction Chemotherapy and Concurrent Chemoradiotherapy) trial. Positron emission tomography scans were recommended before (t0) and after (t2) induction chemotherapy. Patients who were eligible for surgery after neoadjuvant RCT were randomly assigned to definitive RCT or surgery. The prognostic value of percentage of maximum standardized uptake value (%SUVmax) remaining in the primary tumor after induction chemotherapy-%SUVremaining = SUVmax(t2)/SUVmax(t0)-was assessed by proportional hazard analysis and receiver operating characteristic analysis. Overall, 161 patients were randomly assigned (155 from the Essen and Tübingen centers), and 124 of these received positron emission tomography scans at t0 and t2. %SUVremaining as a continuous variable was prognostic for the three end points of overall survival, progression-free survival, and freedom from extracerebral progression in univariable and multivariable analysis (P < .016). The respective hazard ratios per 50% increase in %SUVremaining from multivariable analysis were 2.3 (95% CI, 1.5 to 3.4; P < .001), 1.8 (95% CI, 1.3 to 2.5; P < .001), and 1.8 (95% CI, 1.2 to 2.7; P = .006) for the three end points. %SUVremaining dichotomized at a cut point of maximum sum of sensitivity and specificity from receiver operating characteristic analysis at 36 months was also prognostic. Exploratory analysis revealed that %SUVremaining was likewise prognostic for overall survival in both treatment arms and was more closely associated with extracerebral distant metastases (P = .016) than with isolated locoregional relapses (P = .97). %SUVremaining is a predictor for survival and other end points after multimodality treatment and can serve as a parameter for treatment stratification after induction chemotherapy or for evaluation of adjuvant new systemic treatment options for high-risk patients. © 2016 by American Society of Clinical Oncology.

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

Treesearch

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

1996-01-01

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

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

ERIC Educational Resources Information Center

Tchumtchoua, Sylvie; Dey, Dipak K.

2012-01-01

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

Use of Multivariate Linkage Analysis for Dissection of a Complex Cognitive Trait

PubMed Central

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

2003-01-01

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

The association between body mass index and severe biliary infections: a multivariate analysis.

PubMed

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

2012-11-01

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

Multivariate meta-analysis using individual participant data.

PubMed

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

2015-06-01

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

Multivariate analysis 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.

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.

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

PubMed

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

2017-10-01

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

A Statistical Discrimination Experiment for Eurasian Events Using a Twenty-Seven-Station Network

DTIC Science & Technology

1980-07-08

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

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

DTIC Science & Technology

2015-01-01

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

Multivariate optimum interpolation of surface pressure and winds over oceans

NASA Technical Reports Server (NTRS)

Bloom, S. C.

1984-01-01

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

Nonlinear multivariate and time series analysis by neural network methods

NASA Astrophysics Data System (ADS)

Hsieh, William W.

2004-03-01

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

History and physical examination findings predictive of testicular torsion: an attempt to promote clinical diagnosis by house staff.

PubMed

Srinivasan, Arun; Cinman, Nadya; Feber, Kevin M; Gitlin, Jordan; Palmer, Lane S

2011-08-01

To standardize the history and physical examination of boys who present with acute scrotum and identify parameters that best predict testicular torsion. Over a 5-month period, a standardized history and physical examination form with 22 items was used for all boys presenting with scrotal pain. Management decisions for radiological evaluation and surgical intervention were based on the results. Data were statistically analyzed in correlation with the eventual diagnosis. Of the 79 boys evaluated, 8 (10.1%) had testicular torsion. On univariate analysis, age, worsening pain, nausea/vomiting, severe pain at rest, absence of ipsilateral cremaster reflex, abnormal testicular position and scrotal skin changes were statistically predictive of torsion. After multivariate analysis and adjusting for confounding effect of other co-existing variables, absence of ipsilateral cremaster reflex (P < 0.001), nausea/vomiting (P < 0.05) and scrotal skin changes (P < 0.001) were the only consistent predictive factors of testicular torsion. An accurate history and physical examination of boys with acute scrotum should be primary in deciding upon further radiographic or surgical evaluation. While several forces have led to less consistent overnight resident staffing, consistent and reliable clinical evaluation of the acute scrotum using a standardized approach should reduce error, improve patient care and potentially reduce health care costs. Copyright © 2011 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.

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

PubMed

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

2009-05-30

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

Application of multivariate statistical techniques for differentiation of ripe banana flour based on the composition of elements.

PubMed

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

2009-01-01

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

PYCHEM: a multivariate analysis package for python.

PubMed

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

2006-10-15

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

Borrowing of strength and study weights in multivariate and network meta-analysis.

PubMed

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

2017-12-01

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

Borrowing of strength and study weights in multivariate and network meta-analysis

PubMed Central

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

2016-01-01

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

A framework for multivariate data-based at-site flood frequency analysis: Essentiality of the conjugal application of parametric and nonparametric approaches

NASA Astrophysics Data System (ADS)

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

2015-06-01

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

Cluster-based exposure variation analysis

PubMed Central

2013-01-01

Background Static posture, repetitive movements and lack of physical variation are known risk factors for work-related musculoskeletal disorders, and thus needs to be properly assessed in occupational studies. The aims of this study were (i) to investigate the effectiveness of a conventional exposure variation analysis (EVA) in discriminating exposure time lines and (ii) to compare it with a new cluster-based method for analysis of exposure variation. Methods For this purpose, we simulated a repeated cyclic exposure varying within each cycle between “low” and “high” exposure levels in a “near” or “far” range, and with “low” or “high” velocities (exposure change rates). The duration of each cycle was also manipulated by selecting a “small” or “large” standard deviation of the cycle time. Theses parameters reflected three dimensions of exposure variation, i.e. range, frequency and temporal similarity. Each simulation trace included two realizations of 100 concatenated cycles with either low (ρ = 0.1), medium (ρ = 0.5) or high (ρ = 0.9) correlation between the realizations. These traces were analyzed by conventional EVA, and a novel cluster-based EVA (C-EVA). Principal component analysis (PCA) was applied on the marginal distributions of 1) the EVA of each of the realizations (univariate approach), 2) a combination of the EVA of both realizations (multivariate approach) and 3) C-EVA. The least number of principal components describing more than 90% of variability in each case was selected and the projection of marginal distributions along the selected principal component was calculated. A linear classifier was then applied to these projections to discriminate between the simulated exposure patterns, and the accuracy of classified realizations was determined. Results C-EVA classified exposures more correctly than univariate and multivariate EVA approaches; classification accuracy was 49%, 47% and 52% for EVA (univariate and multivariate), and C-EVA, respectively (p < 0.001). All three methods performed poorly in discriminating exposure patterns differing with respect to the variability in cycle time duration. Conclusion While C-EVA had a higher accuracy than conventional EVA, both failed to detect differences in temporal similarity. The data-driven optimality of data reduction and the capability of handling multiple exposure time lines in a single analysis are the advantages of the C-EVA. PMID:23557439

Consumption of psychoactive substances in educational institutions: an inquiry into the state of affairs in the schools of Córdoba.

PubMed

Lucchese, M S M; Burrone, M S; Enders, J E; Fernández, A R

2014-01-01

This study describes and analyses the consumption of psychoactive substances in educational institutions, the school environment conditions and its relation to the school standing of the students. In the first stage, a quantitative evaluation was performed, based on the records of the Second National Survey of Secondary School Students carried out in Córdoba in 2005; the second stage used a qualitative approach. A multistage probabilistic sample of 4593 students was used for the quantitative assessment. The analysis comprised summary measurements, multivariate and factorial correspondence analysis, in all cases with a significance level of p < 0.05. For the qualitative stage, an ethnographic approach was applied. The state schools were chosen using an intentional, cumulative and sequential sampling method. Ten in-depth interviews were carried out to gather qualitative data that was analyzed using the comparative constant method. Results evince that consumption is lower among morning-shift students and that grade repetition and behavior problems are associated to consumption of illegal drugs. Furthermore, it was detected that students in night-shift schools with low academic and disciplinary demand standards have a higher probability of consumption. It is clear that as academic standards decrease, consumption increases.

Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling.

PubMed

Tao, Ran; Zeng, Donglin; Franceschini, Nora; North, Kari E; Boerwinkle, Eric; Lin, Dan-Yu

2015-06-01

High-throughput DNA sequencing allows for the genotyping of common and rare variants for genetic association studies. At the present time and for the foreseeable future, it is not economically feasible to sequence all individuals in a large cohort. A cost-effective strategy is to sequence those individuals with extreme values of a quantitative trait. We consider the design under which the sampling depends on multiple quantitative traits. Under such trait-dependent sampling, standard linear regression analysis can result in bias of parameter estimation, inflation of type I error, and loss of power. We construct a likelihood function that properly reflects the sampling mechanism and utilizes all available data. We implement a computationally efficient EM algorithm and establish the theoretical properties of the resulting maximum likelihood estimators. Our methods can be used to perform separate inference on each trait or simultaneous inference on multiple traits. We pay special attention to gene-level association tests for rare variants. We demonstrate the superiority of the proposed methods over standard linear regression through extensive simulation studies. We provide applications to the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study and the National Heart, Lung, and Blood Institute Exome Sequencing Project.

Analytical Problems and Suggestions in the Analysis of Behavioral Economic Demand Curves.

PubMed

Yu, Jihnhee; Liu, Liu; Collins, R Lorraine; Vincent, Paula C; Epstein, Leonard H

2014-01-01

Behavioral economic demand curves (Hursh, Raslear, Shurtleff, Bauman, & Simmons, 1988) are innovative approaches to characterize the relationships between consumption of a substance and its price. In this article, we investigate common analytical issues in the use of behavioral economic demand curves, which can cause inconsistent interpretations of demand curves, and then we provide methodological suggestions to address those analytical issues. We first demonstrate that log transformation with different added values for handling zeros changes model parameter estimates dramatically. Second, demand curves are often analyzed using an overparameterized model that results in an inefficient use of the available data and a lack of assessment of the variability among individuals. To address these issues, we apply a nonlinear mixed effects model based on multivariate error structures that has not been used previously to analyze behavioral economic demand curves in the literature. We also propose analytical formulas for the relevant standard errors of derived values such as P max, O max, and elasticity. The proposed model stabilizes the derived values regardless of using different added increments and provides substantially smaller standard errors. We illustrate the data analysis procedure using data from a relative reinforcement efficacy study of simulated marijuana purchasing.

Causality networks from multivariate time series and application to epilepsy.

PubMed

Siggiridou, Elsa; Koutlis, Christos; Tsimpiris, Alkiviadis; Kimiskidis, Vasilios K; Kugiumtzis, Dimitris

2015-08-01

Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. For this, realizations on high dimensional coupled dynamical systems are considered and the performance of the Granger causality measures is evaluated, seeking for the measures that form networks closest to the true network of the dynamical system. In particular, the comparison focuses on Granger causality measures that reduce the state space dimension when many variables are observed. Further, the linear and nonlinear Granger causality measures of dimension reduction are compared to a standard Granger causality measure on electroencephalographic (EEG) recordings containing episodes of epileptiform discharges.

Kernel canonical-correlation Granger causality for multiple time series

NASA Astrophysics Data System (ADS)

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

2011-04-01

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

Multivariate geometry as an approach to algal community analysis

USGS Publications Warehouse

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

1973-01-01

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

Prognostic determinants of community-acquired bloodstream infection in type 2 diabetic patients in ED.

PubMed

Yo, Chia-Hung; Lee, Meng-Tse Gabriel; Gi, Weng-Tein; Chang, Shy-Shin; Tsai, Kuang-Chau; Chen, Shyr-Chyr; Lee, Chien-Chang

2014-12-01

The objective of the study is to describe the epidemiology and outcome of community-acquired bloodstream infection (BSI) in type 2 diabetic patients in emergency department (ED). All patients admitted to the ED of the university hospital from June 2010 to June 2011 with a history of type 2 diabetes mellitus and microbiologically documented BSI were retrospectively enrolled. Demographic characteristics, Charlson comorbidity index, antibiotic therapy, clinical severity, microbiological etiology, and diabetes-related complications were recorded in a standardized form. The major outcome measure was 30-day survival. χ2 Or Student t test was used for univariate analysis, and Cox proportional hazards models were used for multivariate analysis. Among 250 enrolled emergency patients with BSI, the overall 30-day mortality rate was 15.5%. Twenty-seven patients (10.7%) developed diabetic ketoacidosis (DKA), and 22 patients (8.8%) developed hyperosmolar hyperglycemic state. On univariate analysis, DKA rather than hyperosmolar hyperglycemic state was associated with adverse outcome. Other risk factors include higher mean glycated hemoglobin level, presence of underlying malignancy, long-term use of steroids, lower respiratory tract infection, and higher Charlson scores. Multivariate analysis identified 3 independent risk factors for early mortality when severity, comorbidity, age, and sex were under control: DKA (hazard ratio, 3.89; 95% confidence interval, 1.6-8.9), inappropriate antibiotics (2.25, 1.05-4.82), and chronic use of steroid (3.89, 1.1-13.2). In type 2 diabetic patients with BSI, a substantial proportion of patients developed DKA. This condition was probably underrecognized by clinicians and constituted an independent risk factor for short-term mortality. Other identified risk factors are potentially correctable and may allow preventive efforts to individuals at greatest potential benefit. Copyright © 2014 Elsevier Inc. All rights reserved.

The Effects of Geography and Spatial Behavior on Health Care Utilization among the Residents of a Rural Region

PubMed Central

Arcury, Thomas A; Gesler, Wilbert M; Preisser, John S; Sherman, Jill; Spencer, John; Perin, Jamie

2005-01-01

Objective This analysis determines the importance of geography and spatial behavior as predisposing and enabling factors in rural health care utilization, controlling for demographic, social, cultural, and health status factors. Data Sources A survey of 1,059 adults in 12 rural Appalachian North Carolina counties. Study Design This cross-sectional study used a three-stage sampling design stratified by county and ethnicity. Preliminary analysis of health services utilization compared weighted proportions of number of health care visits in the previous 12 months for regular check-up care, chronic care, and acute care across geographic, sociodemographic, cultural, and health variables. Multivariable logistic models identified independent correlates of health services utilization. Data Collection Methods Respondents answered standard survey questions. They located places in which they engaged health related and normal day-to-day activities; these data were entered into a geographic information system for analysis. Principal Findings Several geographic and spatial behavior factors, including having a driver's license, use of provided rides, and distance for regular care, were significantly related to health care utilization for regular check-up and chronic care in the bivariate analysis. In the multivariate model, having a driver's license and distance for regular care remained significant, as did several predisposing (age, gender, ethnicity), enabling (household income), and need (physical and mental health measures, number of conditions). Geographic measures, as predisposing and enabling factors, were related to regular check-up and chronic care, but not to acute care visits. Conclusions These results show the importance of geographic and spatial behavior factors in rural health care utilization. They also indicate continuing inequity in rural health care utilization that must be addressed in public policy. PMID:15663706

Study and interpretation of chemical composition of rainwater in selected urban and rural locations in India using multivariate analysis

NASA Astrophysics Data System (ADS)

Chakraborty, Bidisha; Gupta, Abhik

2018-04-01

Rainwater is an important untapped resource for all water managers and can be collected and used personally for all uses and simultaneously diverted to ground for recharge of depleting aquifers. Rain water is the most purest form of water until it is contaminated by the atmospheric pollution. Evaluation of rainwater quality analysis is also essential for non-potable applications and to match quality to specific uses. Rainwater quality analysis is, therefore, carried out to understand the problems of rainwater contamination with various pollutants. Rainwater samples were collected from the pre-monsoon season of March 2010 to post-monsoon of October 2013, from seven sampling sites namely Irongmara, Badarpur, Bongaigaon, Dolaigaon, BGR Township, Kolkata and Kharagpur, which characterised typical suburban, urban and industrialised locations respectively. A total of 943 samples were collected during this period from the sampling sites, taking utmost care in sampling and storage were analysed for heavy metals determination. Results for pH, EC, Pb, Cd, Ni, Zn, Cr and Co were reported in this study. The samples were collected using PVC bottles. The highest concentration of elements was observed at the beginning of the rainfall season when large amounts of dust accumulated in the atmosphere scavenged by rain. The values of pH in rainwater samples were relatively within the World Health Organization (WHO) standard for drinking water. Multivariate statistical analysis especially varimax rotation was applied to bring to focus the hidden yet important variables which influence the rainwater quality. It is also observed that rainwater contamination may not be restricted to industrial areas alone but vehicular emission may also contribute significantly in certain areas.

Pre-Stroke Use of Beta-Blockers Does Not Lower Post-Stroke Infection Rate: An Exploratory Analysis of the Preventive Antibiotics in Stroke Study.

PubMed

Westendorp, Willeke F; Vermeij, Jan-Dirk; Brouwer, Matthijs C; Roos, Y B W E M; Nederkoorn, Paul J; van de Beek, Diederik

2016-01-01

Stroke-associated infections occur frequently and are associated with unfavorable outcome. Previous cohort studies suggest a protective effect of beta-blockers (BBs) against infections. A sympathetic drive may increase immune suppression and infections. This study is aimed at investigating the association between BB treatment at baseline and post-stroke infection in the Preventive Antibiotics in Stroke Study (PASS), a prospective clinical trial. We performed an exploratory analysis in PASS, 2,538 patients with acute phase of stroke (24 h after onset) were randomized to ceftriaxone (intravenous, 2 g per day for 4 days) in addition to stroke unit care, or standard stroke unit care without preventive antibiotic treatment. All clinical data, including use of BBs, was prospectively collected. Infection was diagnosed by the treating physician, and independently by an expert panel blinded for all other data. Multivariable analysis was performed to investigate the relation between BB treatment and infection rate. Infection, as defined by the physician, occurred in 348 of 2,538 patients (14%). Multivariable analysis showed that the use of BBs at baseline was associated with the development of infection during clinical course (adjusted OR (aOR) 1.61, 95% CI 1.19-2.18; p < 0.01). BB use at baseline was also associated with the development of pneumonia (aOR 1.56, 95% CI 1.05-2.30; p = 0.03). Baseline BB use was not associated with mortality (aOR 1.14, 95% CI 0.84-1.53; p = 0.41) or unfavorable outcome at 3 months (aOR 1.10, 95% CI 0.89-1.35; p = 0.39). Patients treated with BBs prior to stroke have a higher rate of infection and pneumonia. © 2016 S. Karger AG, Basel.

Use of multivariate statistics to identify unreliable data obtained using CASA.

PubMed

Martínez, Luis Becerril; Crispín, Rubén Huerta; Mendoza, Maximino Méndez; Gallegos, Oswaldo Hernández; Martínez, Andrés Aragón

2013-06-01

In order to identify unreliable data in a dataset of motility parameters obtained from a pilot study acquired by a veterinarian with experience in boar semen handling, but without experience in the operation of a computer assisted sperm analysis (CASA) system, a multivariate graphical and statistical analysis was performed. Sixteen boar semen samples were aliquoted then incubated with varying concentrations of progesterone from 0 to 3.33 µg/ml and analyzed in a CASA system. After standardization of the data, Chernoff faces were pictured for each measurement, and a principal component analysis (PCA) was used to reduce the dimensionality and pre-process the data before hierarchical clustering. The first twelve individual measurements showed abnormal features when Chernoff faces were drawn. PCA revealed that principal components 1 and 2 explained 63.08% of the variance in the dataset. Values of principal components for each individual measurement of semen samples were mapped to identify differences among treatment or among boars. Twelve individual measurements presented low values of principal component 1. Confidence ellipses on the map of principal components showed no statistically significant effects for treatment or boar. Hierarchical clustering realized on two first principal components produced three clusters. Cluster 1 contained evaluations of the two first samples in each treatment, each one of a different boar. With the exception of one individual measurement, all other measurements in cluster 1 were the same as observed in abnormal Chernoff faces. Unreliable data in cluster 1 are probably related to the operator inexperience with a CASA system. These findings could be used to objectively evaluate the skill level of an operator of a CASA system. This may be particularly useful in the quality control of semen analysis using CASA systems.

Prescribing potentially inappropriate medication (PIM) in Germany's elderly as indicated by the PRISCUS list. An analysis based on regional claims data.

PubMed

Schubert, Ingrid; Küpper-Nybelen, Jutta; Ihle, Peter; Thürmann, Petra

2013-07-01

The aim of this study was to estimate the prevalence of potentially inappropriate medication (PIM) in the elderly as indicated by Germany's recently published list (PRISCUS) and to assess factors independently associated with PIM prescribing, both overall and separately for therapeutic groups. Claims data analysis (Health Insurance Sample AOK Hesse/KV Hesse, 18.75% random sample of insurants from AOK Hesse, Germany) is used in the study. The study population is composed of 73,665 insurants >64 years of age continuously insured in the last quarter of 2009 and either continuously insured or deceased in 2010. Prevalence estimates are standardized to the population of Germany (31 December 2010). The variables age, sex, polypharmacy, hospital stay and nursing care are assessed for their independent association with general PIM prescription and among 11 therapeutic subgroups using multivariate logistic regression analysis. In 2010, 22.0% of the elderly received at least one PIM prescription (men: 18.3%, women: 24.8%). The highest PIM prevalence was observed for antidepressants (6.5%), antihypertensives (3.8%) and antiarrhythmic drugs (3.5%). Amitriptyline, tetrazepam, doxepin, acetyldigoxin, doxazosin and etoricoxib were the most frequently prescribed PIMs. Multivariate analyses indicate that women (OR 1.39; 95% CI: 1.34-1.44) and persons with extreme polypharmacy (≥10 vs. <5 drugs: OR 5.16; 95% CI: 4.87-5.47) were at higher risk for receiving a PRISCUS-PIM. Risk analysis for therapeutic groups shows divergent associations. PRISCUS-PIMs are widely used. Educational programs should focus on drugs with high treatment prevalence and call professionals' attention to those elderly patients who are at special risk for inappropriate medication. Copyright © 2013 John Wiley & Sons, Ltd.

Comparison of Optimum Interpolation and Cressman Analyses

NASA Technical Reports Server (NTRS)

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

1984-01-01

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

Comparison of Optimum Interpolation and Cressman Analyses

NASA Technical Reports Server (NTRS)

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

1985-01-01

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

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

ERIC Educational Resources Information Center

Anderson, John R.

2012-01-01

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

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

ERIC Educational Resources Information Center

Martin, James L.

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

Missing Data and Multiple Imputation in the Context of Multivariate Analysis of Variance

ERIC Educational Resources Information Center

Finch, W. Holmes

2016-01-01

Multivariate analysis of variance (MANOVA) is widely used in educational research to compare means on multiple dependent variables across groups. Researchers faced with the problem of missing data often use multiple imputation of values in place of the missing observations. This study compares the performance of 2 methods for combining p values in…

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

ERIC Educational Resources Information Center

Dinov, Ivo D.; Christou, Nicolas

2011-01-01

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

Bias and Precision of Measures of Association for a Fixed-Effect Multivariate Analysis of Variance Model

ERIC Educational Resources Information Center

Kim, Soyoung; Olejnik, Stephen

2005-01-01

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

Multivariate analysis of climate along the southern coast of Alaska—some forestry implications.

Treesearch

Wilbur A. Farr; John S. Hard

1987-01-01

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

Imaging mass spectrometry data reduction: automated feature identification and extraction.

PubMed

McDonnell, Liam A; van Remoortere, Alexandra; de Velde, Nico; van Zeijl, René J M; Deelder, André M

2010-12-01

Imaging MS now enables the parallel analysis of hundreds of biomolecules, spanning multiple molecular classes, which allows tissues to be described by their molecular content and distribution. When combined with advanced data analysis routines, tissues can be analyzed and classified based solely on their molecular content. Such molecular histology techniques have been used to distinguish regions with differential molecular signatures that could not be distinguished using established histologic tools. However, its potential to provide an independent, complementary analysis of clinical tissues has been limited by the very large file sizes and large number of discrete variables associated with imaging MS experiments. Here we demonstrate data reduction tools, based on automated feature identification and extraction, for peptide, protein, and lipid imaging MS, using multiple imaging MS technologies, that reduce data loads and the number of variables by >100×, and that highlight highly-localized features that can be missed using standard data analysis strategies. It is then demonstrated how these capabilities enable multivariate analysis on large imaging MS datasets spanning multiple tissues. Copyright © 2010 American Society for Mass Spectrometry. Published by Elsevier Inc. All rights reserved.

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

PubMed Central

Neupane, Binod; Beyene, Joseph

2015-01-01

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

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

PubMed

Neupane, Binod; Beyene, Joseph

2015-01-01

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

Proposed Clinical Decision Rules to Diagnose Acute Rhinosinusitis Among Adults in Primary Care.

PubMed

Ebell, Mark H; Hansen, Jens Georg

2017-07-01

To reduce inappropriate antibiotic prescribing, we sought to develop a clinical decision rule for the diagnosis of acute rhinosinusitis and acute bacterial rhinosinusitis. Multivariate analysis and classification and regression tree (CART) analysis were used to develop clinical decision rules for the diagnosis of acute rhinosinusitis, defined using 3 different reference standards (purulent antral puncture fluid or abnormal finding on a computed tomographic (CT) scan; for acute bacterial rhinosinusitis, we used a positive bacterial culture of antral fluid). Signs, symptoms, C-reactive protein (CRP), and reference standard tests were prospectively recorded in 175 Danish patients aged 18 to 65 years seeking care for suspected acute rhinosinusitis. For each reference standard, we developed 2 clinical decision rules: a point score based on a logistic regression model and an algorithm based on a CART model. We identified low-, moderate-, and high-risk groups for acute rhinosinusitis or acute bacterial rhinosinusitis for each clinical decision rule. The point scores each had between 5 and 6 predictors, and an area under the receiver operating characteristic curve (AUROCC) between 0.721 and 0.767. For positive bacterial culture as the reference standard, low-, moderate-, and high-risk groups had a 16%, 49%, and 73% likelihood of acute bacterial rhinosinusitis, respectively. CART models had an AUROCC ranging from 0.783 to 0.827. For positive bacterial culture as the reference standard, low-, moderate-, and high-risk groups had a likelihood of acute bacterial rhinosinusitis of 6%, 31%, and 59% respectively. We have developed a series of clinical decision rules integrating signs, symptoms, and CRP to diagnose acute rhinosinusitis and acute bacterial rhinosinusitis with good accuracy. They now require prospective validation and an assessment of their effect on clinical and process outcomes. © 2017 Annals of Family Medicine, Inc.

Cerebral Small Vessel Disease Burden Is Associated with Motor Performance of Lower and Upper Extremities in Community-Dwelling Populations

PubMed Central

Su, Ning; Zhai, Fei-Fei; Zhou, Li-Xin; Ni, Jun; Yao, Ming; Li, Ming-Li; Jin, Zheng-Yu; Gong, Gao-Lang; Zhang, Shu-Yang; Cui, Li-Ying; Tian, Feng; Zhu, Yi-Cheng

2017-01-01

Objective: To investigate the correlation between cerebral small vessel disease (CSVD) burden and motor performance of lower and upper extremities in community-dwelling populations. Methods: We performed a cross-sectional analysis on 770 participants enrolled in the Shunyi study, which is a population-based cohort study. CSVD burden, including white matter hyperintensities (WMH), lacunes, cerebral microbleeds (CMBs), perivascular spaces (PVS), and brain atrophy were measured using 3T magnetic resonance imaging. All participants underwent quantitative motor assessment of lower and upper extremities, which included 3-m walking speed, 5-repeat chair-stand time, 10-repeat pronation–supination time, and 10-repeat finger-tapping time. Data on demographic characteristics, vascular risk factors, and cognitive functions were collected. General linear model analysis was performed to identify potential correlations between motor performance measures and imaging markers of CSVD after controlling for confounding factors. Results: For motor performance of the lower extremities, WMH was negatively associated with gait speed (standardized β = -0.092, p = 0.022) and positively associated with chair-stand time (standardized β = 0.153, p < 0.0001, surviving FDR correction). For motor performance of the upper extremities, pronation–supination time was positively associated with WMH (standardized β = 0.155, p < 0.0001, surviving FDR correction) and negatively with brain parenchymal fraction (BPF; standardized β = -0.125, p = 0.011, surviving FDR correction). Only BPF was found to be negatively associated with finger-tapping time (standardized β = -0.123, p = 0.012). However, lacunes, CMBs, or PVS were not found to be associated with motor performance of lower or upper extremities in multivariable analysis. Conclusion: Our findings suggest that cerebral microstructural changes related to CSVD may affect motor performance of both lower and upper extremities. WMH and brain atrophy are most strongly associated with motor function deterioration in community-dwelling populations. PMID:29021757

Cerebral Small Vessel Disease Burden Is Associated with Motor Performance of Lower and Upper Extremities in Community-Dwelling Populations.

PubMed

Su, Ning; Zhai, Fei-Fei; Zhou, Li-Xin; Ni, Jun; Yao, Ming; Li, Ming-Li; Jin, Zheng-Yu; Gong, Gao-Lang; Zhang, Shu-Yang; Cui, Li-Ying; Tian, Feng; Zhu, Yi-Cheng

2017-01-01

Objective: To investigate the correlation between cerebral small vessel disease (CSVD) burden and motor performance of lower and upper extremities in community-dwelling populations. Methods: We performed a cross-sectional analysis on 770 participants enrolled in the Shunyi study, which is a population-based cohort study. CSVD burden, including white matter hyperintensities (WMH), lacunes, cerebral microbleeds (CMBs), perivascular spaces (PVS), and brain atrophy were measured using 3T magnetic resonance imaging. All participants underwent quantitative motor assessment of lower and upper extremities, which included 3-m walking speed, 5-repeat chair-stand time, 10-repeat pronation-supination time, and 10-repeat finger-tapping time. Data on demographic characteristics, vascular risk factors, and cognitive functions were collected. General linear model analysis was performed to identify potential correlations between motor performance measures and imaging markers of CSVD after controlling for confounding factors. Results: For motor performance of the lower extremities, WMH was negatively associated with gait speed (standardized β = -0.092, p = 0.022) and positively associated with chair-stand time (standardized β = 0.153, p < 0.0001, surviving FDR correction). For motor performance of the upper extremities, pronation-supination time was positively associated with WMH (standardized β = 0.155, p < 0.0001, surviving FDR correction) and negatively with brain parenchymal fraction (BPF; standardized β = -0.125, p = 0.011, surviving FDR correction). Only BPF was found to be negatively associated with finger-tapping time (standardized β = -0.123, p = 0.012). However, lacunes, CMBs, or PVS were not found to be associated with motor performance of lower or upper extremities in multivariable analysis. Conclusion: Our findings suggest that cerebral microstructural changes related to CSVD may affect motor performance of both lower and upper extremities. WMH and brain atrophy are most strongly associated with motor function deterioration in community-dwelling populations.

Small Renal Masses in Close Proximity to the Collecting System and Renal Sinus Are Enriched for Malignancy and High Fuhrman Grade and Should Be Considered for Early Intervention.

PubMed

Correa, Andres F; Toussi, Amir; Amin, Milon; Hrebinko, Ronald L; Gayed, Bishoy A; Parwani, Anil V; Maranchie, Jodi K

2018-02-05

Recent reports show a correlation between renal tumor radiographic characteristics and pathologic features. We hypothesize that a more central location within the relatively hypoxic renal medulla might confer a more aggressive tumor phenotype. To test this, radiographic tumor characteristics were compared with tumor grade and histology. We retrospectively reviewed renal masses <4 cm in diameter that underwent resection between 2008 and 2013. Tumor location was recorded using standard R.E.N.A.L. Nephrometry Score. Multivariate logistic regression was performed to compare independent anatomic features with incidence of malignancy and high nuclear grade. A total of 334 renal tumors had information available for analysis. Univariate analysis showed that increasing endophycity and proximity to the collecting system (<4 mm) were predictors of malignancy and high-grade features. In multivariate analysis, proximity to the collecting system <4 mm remained the as the only anatomical variable predictive of malignancy (odds ratio [OR], 3.58; 95% confidence interval [CI], 1.06-12.05; P = .04) and high nuclear grade (OR, 2.81; 95% CI, 1.44-5.51; P = .003). Malignancy and high tumor grade occur with much greater frequency when tumors are located deep in the kidney, in close proximity to the collecting system and renal sinus. Ninety-six percent of small renal masses in this region were cancers and nearly half were Fuhrman Grade 3 or 4, suggesting that these small centrally located tumors should be targeted for early intervention. Copyright © 2018 Elsevier Inc. All rights reserved.

Temporal performance assessment of wastewater treatment plants by using multivariate statistical analysis.

PubMed

Ebrahimi, Milad; Gerber, Erin L; Rockaway, Thomas D

2017-05-15

For most water treatment plants, a significant number of performance data variables are attained on a time series basis. Due to the interconnectedness of the variables, it is often difficult to assess over-arching trends and quantify operational performance. The objective of this study was to establish simple and reliable predictive models to correlate target variables with specific measured parameters. This study presents a multivariate analysis of the physicochemical parameters of municipal wastewater. Fifteen quality and quantity parameters were analyzed using data recorded from 2010 to 2016. To determine the overall quality condition of raw and treated wastewater, a Wastewater Quality Index (WWQI) was developed. The index summarizes a large amount of measured quality parameters into a single water quality term by considering pre-established quality limitation standards. To identify treatment process performance, the interdependencies between the variables were determined by using Principal Component Analysis (PCA). The five extracted components from the 15 variables accounted for 75.25% of total dataset information and adequately represented the organic, nutrient, oxygen demanding, and ion activity loadings of influent and effluent streams. The study also utilized the model to predict quality parameters such as Biological Oxygen Demand (BOD), Total Phosphorus (TP), and WWQI. High accuracies ranging from 71% to 97% were achieved for fitting the models with the training dataset and relative prediction percentage errors less than 9% were achieved for the testing dataset. The presented techniques and procedures in this paper provide an assessment framework for the wastewater treatment monitoring programs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Glycolytic activity in breast cancer using 18F-FDG PET/CT as prognostic predictor: A molecular phenotype approach.

PubMed

Garcia Vicente, A M; Soriano Castrejón, A; Amo-Salas, M; Lopez Fidalgo, J F; Muñoz Sanchez, M M; Alvarez Cabellos, R; Espinosa Aunion, R; Muñoz Madero, V

2016-01-01

To explore the relationship between basal (18)F-FDG uptake in breast tumors and survival in patients with breast cancer (BC) using a molecular phenotype approach. This prospective and multicentre study included 193 women diagnosed with BC. All patients underwent an (18)F-FDG PET/CT prior to treatment. Maximum standardized uptake value (SUVmax) in tumor (T), lymph nodes (N), and the N/T index was obtained in all the cases. Metabolic stage was established. As regards biological prognostic parameters, tumors were classified into molecular sub-types and risk categories. Overall survival (OS) and disease free survival (DFS) were obtained. An analysis was performed on the relationship between semi-quantitative metabolic parameters with molecular phenotypes and risk categories. The effect of molecular sub-type and risk categories in prognosis was analyzed using Kaplan-Meier and univariate and multivariate tests. Statistical differences were found in both SUVT and SUVN, according to the molecular sub-types and risk classifications, with higher semi-quantitative values in more biologically aggressive tumors. No statistical differences were observed with respect to the N/T index. Kaplan-Meier analysis revealed that risk categories were significantly related to DFS and OS. In the multivariate analysis, metabolic stage and risk phenotype showed a significant association with DFS. High-risk phenotype category showed a worst prognosis with respect to the other categories with higher SUVmax in primary tumor and lymph nodes. Copyright © 2015 Elsevier España, S.L.U. and SEMNIM. All rights reserved.

The Risk of Developing Diabetes Mellitus in Patients with Psoriatic Arthritis: A Cohort Study.

PubMed

Eder, Lihi; Chandran, Vinod; Cook, Richard; Gladman, Dafna D

2017-03-01

To estimate the prevalence of diabetes mellitus (DM) in patients with psoriatic arthritis (PsA) in comparison with the general population and to assess whether the level of disease activity over time predicts the development of DM in these patients. A cohort analysis was conducted in patients followed in a large PsA clinic from 1978 to 2014. The prevalence of DM in the patients was compared with the general population of Ontario, Canada, and the age-standardized prevalence ratio (SPR) was calculated. For the assessment of risk factors for DM, time-weighted arithmetic mean (AM) levels of PsA-related disease activity measures were assessed as predictors for the development of DM. Multivariable Cox proportional hazards models were used to compute HR for incident DM after controlling for potential confounders. A total of 1305 patients were included in the analysis. The SPR of DM in PsA compared with the general population in Ontario was 1.43 (p = 0.002). Of the 1065 patients who were included in the time-to-event analysis, 73 patients were observed to develop DM. Based on multivariable analyses, AM tender joint count (HR 1.53, 95% CI 1.08-2.18, p = 0.02) and AM erythrocyte sedimentation rate (HR 1.21, 95% CI 1.03-1.41, p = 0.02) predicted the development of DM. The prevalence of DM is higher in patients with PsA compared with the general population. Patients with elevated levels of disease activity are at higher risk of developing DM.

MULTIVARIATE ANALYSES (CONONICAL CORRELATION AND PARTIAL LEAST SQUARE, PLS) TO MODEL AND ASSESS THE ASSOCIATION OF LANDSCAPE METRICS TO SURFACE WATER CHEMICAL AND BIOLOGICAL PROPERTIES USING SAVANNAH RIVER BASIN DATA.

EPA Science Inventory

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

Multivariate Density Estimation and Remote Sensing

NASA Technical Reports Server (NTRS)

Scott, D. W.

1983-01-01

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

Comprehensive drought characteristics analysis based on a nonlinear multivariate drought index

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Systematic review of the methodological quality of controlled trials evaluating Chinese herbal medicine in patients with rheumatoid arthritis

PubMed Central

Pan, Xin; Lopez-Olivo, Maria A; Song, Juhee; Pratt, Gregory; Suarez-Almazor, Maria E

2017-01-01

Objectives We appraised the methodological and reporting quality of randomised controlled clinical trials (RCTs) evaluating the efficacy and safety of Chinese herbal medicine (CHM) in patients with rheumatoid arthritis (RA). Design For this systematic review, electronic databases were searched from inception until June 2015. The search was limited to humans and non-case report studies, but was not limited by language, year of publication or type of publication. Two independent reviewers selected RCTs, evaluating CHM in RA (herbals and decoctions). Descriptive statistics were used to report on risk of bias and their adherence to reporting standards. Multivariable logistic regression analysis was performed to determine study characteristics associated with high or unclear risk of bias. Results Out of 2342 unique citations, we selected 119 RCTs including 18 919 patients: 10 108 patients received CHM alone and 6550 received one of 11 treatment combinations. A high risk of bias was observed across all domains: 21% had a high risk for selection bias (11% from sequence generation and 30% from allocation concealment), 85% for performance bias, 89% for detection bias, 4% for attrition bias and 40% for reporting bias. In multivariable analysis, fewer authors were associated with selection bias (allocation concealment), performance bias and attrition bias, and earlier year of publication and funding source not reported or disclosed were associated with selection bias (sequence generation). Studies published in non-English language were associated with reporting bias. Poor adherence to recommended reporting standards (<60% of the studies not providing sufficient information) was observed in 11 of the 23 sections evaluated. Limitations Study quality and data extraction were performed by one reviewer and cross-checked by a second reviewer. Translation to English was performed by one reviewer in 85% of the included studies. Conclusions Studies evaluating CHM often fail to meet expected methodological criteria, and high-quality evidence is lacking. PMID:28249848

DNA Mismatch Repair Status Predicts Need for Future Colorectal Surgery for Metachronous Neoplasms in Young Individuals Undergoing Colorectal Cancer Resection.

PubMed

Aronson, Melyssa; Holter, Spring; Semotiuk, Kara; Winter, Laura; Pollett, Aaron; Gallinger, Steven; Cohen, Zane; Gryfe, Robert

2015-07-01

The treatment of colorectal cancer in young patients involves both management of the incident cancer and consideration of the possibility of Lynch syndrome and the development of metachronous colorectal cancers. This study aims to assess the prognostic role of DNA mismatch repair deficiency and extended colorectal resection for metachronous colorectal neoplasia risk in young patients with colorectal cancer. This is a retrospective review of 285 patients identified in our GI cancer registry with colorectal cancer diagnosed at 35 years or younger in the absence of polyposis. Using univariate and multivariate analysis, we assessed the prognostic role of mismatch repair deficiency and standard clinicopathologic characteristics, including the extent of resection, on the rate of developing metachronous colorectal neoplasia requiring resection. Mismatch repair deficiency was identified in biospecimens from 44% of patients and was significantly associated with an increased risk for metachronous colorectal neoplasia requiring resection (10-year cumulative risk, 13.5% ± 4.2%) compared with 56% of patients with mismatch repair-intact colorectal cancer (10-year cumulative risk, 5.8% ± 3.3%; p = 0.011). In multivariate analysis, mismatch repair deficiency was associated with a HR of 3.65 (95% CI, 1.44-9.21; p = 0.006) for metachronous colorectal neoplasia, whereas extended resection with ileorectal or ileosigmoid anastomosis significantly decreased the risk of metachronous colorectal neoplasia (HR, 0.21; 95% CI, 0.05-0.90; p = 0.036). This study had a retrospective design, and, therefore, recommendations for colorectal cancer surgery and screening were not fully standardized. Quality of life after colorectal cancer surgery was not assessed. Young patients with colorectal cancer with molecular hallmarks of Lynch syndrome were at significantly higher risk for the development of subsequent colorectal neoplasia. This risk was significantly reduced in those who underwent extended resection compared with segmental resection.

64Cu-DOTATATE PET/MRI for Detection of Activated Macrophages in Carotid Atherosclerotic Plaques: Studies in Patients Undergoing Endarterectomy.

PubMed

Pedersen, Sune Folke; Sandholt, Benjamin Vikjær; Keller, Sune Høgild; Hansen, Adam Espe; Clemmensen, Andreas Ettrup; Sillesen, Henrik; Højgaard, Liselotte; Ripa, Rasmus Sejersten; Kjær, Andreas

2015-07-01

A feature of vulnerable atherosclerotic plaques of the carotid artery is high activity and abundance of lesion macrophages. There is consensus that this is of importance for plaque vulnerability, which may lead to clinical events, such as stroke and transient ischemic attack. We used positron emission tomography (PET) and the novel PET ligand [(64)Cu] [1,4,7,10-tetraazacyclododecane-N,N',N″,N‴-tetraacetic acid]-d-Phe1,Tyr3-octreotate ((64)Cu-DOTATATE) to specifically target macrophages via the somatostatin receptor subtype-2 in vivo. Ten patients underwent simultaneous PET/MRI to measure (64)Cu-DOTATATE uptake in carotid artery plaques before carotid endarterectomy. (64)Cu-DOTATATE uptake was significantly higher in symptomatic plaque versus the contralateral carotid artery (P<0.001). Subsequently, a total of 62 plaque segments were assessed for gene expression of selected markers of plaque vulnerability using real-time quantitative polymerase chain reaction. These results were compared with in vivo (64)Cu-DOTATATE uptake calculated as the mean standardized uptake value. Univariate analysis of real-time quantitative polymerase chain reaction and PET showed that cluster of differentiation 163 (CD163) and CD68 gene expression correlated significantly but weakly with mean standardized uptake value in scans performed 85 minutes post injection (P<0.001 and P=0.015, respectively). Subsequent multivariate analysis showed that CD163 correlated independently with (64)Cu-DOTATATE uptake (P=0.031) whereas CD68 did not contribute significantly to the final model. The novel PET tracer (64)Cu-DOTATATE accumulates in atherosclerotic plaques of the carotid artery. CD163 gene expression correlated independently with (64)Cu-DOTATATE uptake measured by real-time quantitative polymerase chain reaction in the final multivariate model, indicating that (64)Cu-DOTATATE PET is detecting alternatively activated macrophages. This association could potentially improve noninvasive identification and characterization of vulnerable plaques. © 2015 The Authors.

Two-part models with stochastic processes for modelling longitudinal semicontinuous data: Computationally efficient inference and modelling the overall marginal mean.

PubMed

Yiu, Sean; Tom, Brian Dm

2017-01-01

Several researchers have described two-part models with patient-specific stochastic processes for analysing longitudinal semicontinuous data. In theory, such models can offer greater flexibility than the standard two-part model with patient-specific random effects. However, in practice, the high dimensional integrations involved in the marginal likelihood (i.e. integrated over the stochastic processes) significantly complicates model fitting. Thus, non-standard computationally intensive procedures based on simulating the marginal likelihood have so far only been proposed. In this paper, we describe an efficient method of implementation by demonstrating how the high dimensional integrations involved in the marginal likelihood can be computed efficiently. Specifically, by using a property of the multivariate normal distribution and the standard marginal cumulative distribution function identity, we transform the marginal likelihood so that the high dimensional integrations are contained in the cumulative distribution function of a multivariate normal distribution, which can then be efficiently evaluated. Hence, maximum likelihood estimation can be used to obtain parameter estimates and asymptotic standard errors (from the observed information matrix) of model parameters. We describe our proposed efficient implementation procedure for the standard two-part model parameterisation and when it is of interest to directly model the overall marginal mean. The methodology is applied on a psoriatic arthritis data set concerning functional disability.

Marginal analysis in assessing factors contributing time to physician in the Emergency Department using operations data.

PubMed

Pathan, Sameer A; Bhutta, Zain A; Moinudheen, Jibin; Jenkins, Dominic; Silva, Ashwin D; Sharma, Yogdutt; Saleh, Warda A; Khudabakhsh, Zeenat; Irfan, Furqan B; Thomas, Stephen H

2016-01-01

Background: Standard Emergency Department (ED) operations goals include minimization of the time interval (tMD) between patients' initial ED presentation and initial physician evaluation. This study assessed factors known (or suspected) to influence tMD with a two-step goal. The first step was generation of a multivariate model identifying parameters associated with prolongation of tMD at a single study center. The second step was the use of a study center-specific multivariate tMD model as a basis for predictive marginal probability analysis; the marginal model allowed for prediction of the degree of ED operations benefit that would be affected with specific ED operations improvements. Methods: The study was conducted using one month (May 2015) of data obtained from an ED administrative database (EDAD) in an urban academic tertiary ED with an annual census of approximately 500,000; during the study month, the ED saw 39,593 cases. The EDAD data were used to generate a multivariate linear regression model assessing the various demographic and operational covariates' effects on the dependent variable tMD. Predictive marginal probability analysis was used to calculate the relative contributions of key covariates as well as demonstrate the likely tMD impact on modifying those covariates with operational improvements. Analyses were conducted with Stata 14MP, with significance defined at p  < 0.05 and confidence intervals (CIs) reported at the 95% level. Results: In an acceptable linear regression model that accounted for just over half of the overall variance in tMD (adjusted r 2 0.51), important contributors to tMD included shift census ( p  = 0.008), shift time of day ( p  = 0.002), and physician coverage n ( p  = 0.004). These strong associations remained even after adjusting for each other and other covariates. Marginal predictive probability analysis was used to predict the overall tMD impact (improvement from 50 to 43 minutes, p  < 0.001) of consistent staffing with 22 physicians. Conclusions: The analysis identified expected variables contributing to tMD with regression demonstrating significance and effect magnitude of alterations in covariates including patient census, shift time of day, and number of physicians. Marginal analysis provided operationally useful demonstration of the need to adjust physician coverage numbers, prompting changes at the study ED. The methods used in this analysis may prove useful in other EDs wishing to analyze operations information with the goal of predicting which interventions may have the most benefit.

Validation of the conceptual research utilization scale: an application of the standards for educational and psychological testing in healthcare.

PubMed

Squires, Janet E; Estabrooks, Carole A; Newburn-Cook, Christine V; Gierl, Mark

2011-05-19

There is a lack of acceptable, reliable, and valid survey instruments to measure conceptual research utilization (CRU). In this study, we investigated the psychometric properties of a newly developed scale (the CRU Scale). We used the Standards for Educational and Psychological Testing as a validation framework to assess four sources of validity evidence: content, response processes, internal structure, and relations to other variables. A panel of nine international research utilization experts performed a formal content validity assessment. To determine response process validity, we conducted a series of one-on-one scale administration sessions with 10 healthcare aides. Internal structure and relations to other variables validity was examined using CRU Scale response data from a sample of 707 healthcare aides working in 30 urban Canadian nursing homes. Principal components analysis and confirmatory factor analyses were conducted to determine internal structure. Relations to other variables were examined using: (1) bivariate correlations; (2) change in mean values of CRU with increasing levels of other kinds of research utilization; and (3) multivariate linear regression. Content validity index scores for the five items ranged from 0.55 to 1.00. The principal components analysis predicted a 5-item 1-factor model. This was inconsistent with the findings from the confirmatory factor analysis, which showed best fit for a 4-item 1-factor model. Bivariate associations between CRU and other kinds of research utilization were statistically significant (p < 0.01) for the latent CRU scale score and all five CRU items. The CRU scale score was also shown to be significant predictor of overall research utilization in multivariate linear regression. The CRU scale showed acceptable initial psychometric properties with respect to responses from healthcare aides in nursing homes. Based on our validity, reliability, and acceptability analyses, we recommend using a reduced (four-item) version of the CRU scale to yield sound assessments of CRU by healthcare aides. Refinement to the wording of one item is also needed. Planned future research will include: latent scale scoring, identification of variables that predict and are outcomes to conceptual research use, and longitudinal work to determine CRU Scale sensitivity to change.

Screening Household Members of Acute Brucellosis Cases in Endemic Areas and Risk Factors for Brucellosis.

PubMed

Deniz, Secil; Baykam, Nurcan; Celikbas, Aysel; Yilmaz, Sirin Menekse; Guzel, Tugba Cirkin; Dokuzoguz, Basak; Ergonul, Onder

2015-08-01

Early diagnosis and treatment of acute brucellosis cases were targeted by screening the household members of the index cases. We also aimed to describe the causal relations of brucellosis in an endemic region. A cross-sectional study was performed among household members (29 index cases, 113 household members). Brucellosis was diagnosed on the basis of clinical findings, serum agglutinin titer of ≥1/160 in standard tube agglutination test (STA), or a positive blood culture. Index cases were defined as patients who had been admitted to the clinic on suspicion of brucellosis and then confirmed as brucellosis cases. The people who lived in the same house as the index cases were defined as household members. The risk factors for seropositivity were studied by multivariate analysis. Independent variables of gender, consuming fresh cheese, blood groups, dealing with husbandry, and contact with the placenta of infected animals were included to the model. Backward and forward selections were performed. Nineteen out of 113 (17%) screened individuals had agglutination titers ≥1/160. The mean ages of index cases and household members were 43 years (standard deviation [SD] 18) and 29 years (SD 19), respectively. In multivariate analysis, consuming fresh cheese (odds ratio [OR]=3.1, confidence interval [CI] 1.07-9.68, p=0.049), blood group A (OR=2.6, CI 1.18-5.96, p=0.018), contact with the placenta of the infected animals (OR=3.7, CI 1.42-9.68, p=0.007), and age >30 years (OR=2.8, CI 1.25-6.51, p=0.13) were found to be associated with brucellosis. In univariate analysis, the individuals with blood group B were protected from brucella infection (p=0.013). In conclusion, screening of the people in brucellosis-endemic areas should be considered for early diagnosis and treatment. To our knowledge, blood groups were studied for the first time by this study. Higher prevalence of brucellosis among the individuals with blood group A and less prevalence among the individuals with blood group B should be considered for further studies on pathogenesis.

Comparison of Survival Outcomes Among Cancer Patients Treated In and Out of Clinical Trials

PubMed Central

2014-01-01

Background Clinical trials test the efficacy of a treatment in a select patient population. We examined whether cancer clinical trial patients were similar to nontrial, “real-world” patients with respect to presenting characteristics and survival. Methods We reviewed the SWOG national clinical trials consortium database to identify candidate trials. Demographic factors, stage, and overall survival for patients in the standard arms were compared with nontrial control subjects selected from the Surveillance, Epidemiology, and End Results program. Multivariable survival analyses using Cox regression were conducted. The survival functions from aggregate data across all studies were compared separately by prognosis (≥50% vs <50% average 2-year survival). All statistical tests were two-sided. Results We analyzed 21 SWOG studies (11 good prognosis and 10 poor prognosis) comprising 5190 patients enrolled from 1987 to 2007. Trial patients were younger than nontrial patients (P < .001). In multivariable analysis, trial participation was not associated with improved overall survival for all 11 good-prognosis studies but was associated with better survival for nine of 10 poor-prognosis studies (P < .001). The impact of trial participation on overall survival endured for only 1 year. Conclusions Trial participation was associated with better survival in the first year after diagnosis, likely because of eligibility criteria that excluded higher comorbidity patients from trials. Similar survival patterns between trial and nontrial patients after the first year suggest that trial standard arm outcomes are generalizable over the long term and may improve confidence that trial treatment effects will translate to the real-world setting. Reducing eligibility criteria would improve access to clinical trials. PMID:24627276

Costs associated with Eribulin treatment for patients with metastatic breast cancer in a comprehensive cancer center in France.

PubMed

Hurtaud, Aline; Donnadieu, Anne; Escalup, Laurence; Cottu, Paul H; Baffert, Sandrine

2016-12-01

There is no standard recommendation for metastatic breast cancer treatment (MBC) after two chemotherapy regimens. Eribulin (Halaven ® ) has shown a significant improvement in overall survival (OS) in this setting. Its use may however be hampered by its cost, which is up to three times the cost of other standard drugs. We report the clinical outcomes and health care costs of a large series of consecutive MBC patients treated with Eribulin. A monocentric retrospective study was conducted at Institut Curie over 1 year (August 2012 to August 2013). Data from patient's medical records were extracted to estimate treatment and outcome patterns, and direct medical costs until the end of treatment were measured. Factors affecting cost variability were identified by multiple linear regressions and factors linked to OS by a multivariate Cox model. We included 87 MBC patients. The median OS was 10.7 months (95%CI = 8.0-13.3). By multivariate Cox analysis, independent factors of poor prognosis were an Eastern Cooperative Oncology Group (ECOG) performance status of 3, a number of metastatic sites ≥ 4 and the need for hospitalization. Per-patient costs during whole treatment were €18,694 [CI 95%: 16,028-21,360], and €2581 [CI 95%: 2226-3038] per month. Eribulin administration contributed to 79% of per-patient costs. Innovative and expensive drugs often appear to be the main cost drivers in cancer treatment, particularly for MBC. There is an urgent need to assess clinical practice benefits. Copyright © 2016 Elsevier Ltd. All rights reserved.

Distribution, sources and contamination assessment of heavy metals in surface sediments of the South Yellow Sea and northern part of the East China Sea.

PubMed

Lu, Jian; Li, Anchun; Huang, Peng

2017-11-15

Surface sediment samples collected from the South Yellow Sea and northern part of the East China Sea during spring and autumn, respectively, were analyzed for grain size, aluminum, and heavy metals (Cr, Ni, Cu, Zn, and Pb) to evaluate heavy metal levels and the contamination status. The results showed that all of the heavy metal concentrations met the standard criteria of the Chinese National Standard Criteria for Marine Sediment Quality. Both the EFs and a multivariate analysis (PCA) indicated that Cr, Ni, Cu, and Zn were mainly from natural contributions, while Pb was influenced by anthropogenic inputs, especially during autumn. The geoaccumulation index of Pb near the mouth of the Yangtze River suggested that the pollution degree in autumn was heavier than that in spring, which might be caused by the greater river discharge in summer and more heavy metal adsorption with finer grain sizes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Measurement of the single-top-quark t-channel cross section in pp collisions at $$ \\sqrt{s}=7 $$ TeV

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

Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.

A measurement of the single-top-quark t-channel production cross section in pp collisions at sqrt(s) = 7 TeV with the CMS detector at the LHC is presented. Two different and complementary approaches have been followed. The first approach exploits the distributions of the pseudorapidity of the recoil jet and reconstructed top-quark mass using background estimates determined from control samples in data. The second approach is based on multivariate analysis techniques that probe the compatibility of the candidate events with the signal. Data have been collected for the muon and electron final states, corresponding to integrated luminosities of 1.17 and 1.56 inversemore » femtobarns, respectively. The single-top-quark production cross section in the t-channel is measured to be 67.2 +/- 6.1 pb, in agreement with the approximate next-to-next-to-leading-order standard model prediction. Using the standard model electroweak couplings, the CKM matrix element abs(V[tb

Risk Factors for Anthroponotic Cutaneous Leishmaniasis at the Household Level in Kabul, Afghanistan

PubMed Central

Reithinger, Richard; Mohsen, Mohammad; Leslie, Toby

2010-01-01

Background Kabul, Afghanistan, is the largest focus of anthroponotic cutaneous leishmaniasis (ACL) in the world. ACL is a protozoan disease transmitted to humans by the bite of phlebotomine sand flies. Although not fatal, ACL can lead to considerable stigmatization of affected populations. Methods Using data from a standardized survey of 872 households in 4 wards of Kabul, Afghanistan, univariate and multivariate logistic regression analyses tested associations between presence of active ACL and ACL scars with 15 household-level variables. Findings Univariate analyses showed that active ACL was positively associated with household member's age, ACL prevalence, and brick wall type, but negatively associated with household number of rooms, bednet use, and proportion of windows with screens. Multivariate analysis showed a positive association between active ACL and household member's age, ACL prevalence, and brick wall type, and a negative association with household proportion of windows with screens. Conclusion Household-level charateristics were shown to be risk factors for ACL. Monitoring a selected number of household characteristics could assist in rapid assessments of household-level variation in risk of ACL. ACL prevention and control programs should consider improving house construction, including smoothing of walls and screening of windows. PMID:20351787

Development of Raman microspectroscopy for automated detection and imaging of basal cell carcinoma

NASA Astrophysics Data System (ADS)

Larraona-Puy, Marta; Ghita, Adrian; Zoladek, Alina; Perkins, William; Varma, Sandeep; Leach, Iain H.; Koloydenko, Alexey A.; Williams, Hywel; Notingher, Ioan

2009-09-01

We investigate the potential of Raman microspectroscopy (RMS) for automated evaluation of excised skin tissue during Mohs micrographic surgery (MMS). The main aim is to develop an automated method for imaging and diagnosis of basal cell carcinoma (BCC) regions. Selected Raman bands responsible for the largest spectral differences between BCC and normal skin regions and linear discriminant analysis (LDA) are used to build a multivariate supervised classification model. The model is based on 329 Raman spectra measured on skin tissue obtained from 20 patients. BCC is discriminated from healthy tissue with 90+/-9% sensitivity and 85+/-9% specificity in a 70% to 30% split cross-validation algorithm. This multivariate model is then applied on tissue sections from new patients to image tumor regions. The RMS images show excellent correlation with the gold standard of histopathology sections, BCC being detected in all positive sections. We demonstrate the potential of RMS as an automated objective method for tumor evaluation during MMS. The replacement of current histopathology during MMS by a ``generalization'' of the proposed technique may improve the feasibility and efficacy of MMS, leading to a wider use according to clinical need.

Optimization of large animal MI models; a systematic analysis of control groups from preclinical studies.

PubMed

Zwetsloot, P P; Kouwenberg, L H J A; Sena, E S; Eding, J E; den Ruijter, H M; Sluijter, J P G; Pasterkamp, G; Doevendans, P A; Hoefer, I E; Chamuleau, S A J; van Hout, G P J; Jansen Of Lorkeers, S J

2017-10-27

Large animal models are essential for the development of novel therapeutics for myocardial infarction. To optimize translation, we need to assess the effect of experimental design on disease outcome and model experimental design to resemble the clinical course of MI. The aim of this study is therefore to systematically investigate how experimental decisions affect outcome measurements in large animal MI models. We used control animal-data from two independent meta-analyses of large animal MI models. All variables of interest were pre-defined. We performed univariable and multivariable meta-regression to analyze whether these variables influenced infarct size and ejection fraction. Our analyses incorporated 246 relevant studies. Multivariable meta-regression revealed that infarct size and cardiac function were influenced independently by choice of species, sex, co-medication, occlusion type, occluded vessel, quantification method, ischemia duration and follow-up duration. We provide strong systematic evidence that commonly used endpoints significantly depend on study design and biological variation. This makes direct comparison of different study-results difficult and calls for standardized models. Researchers should take this into account when designing large animal studies to most closely mimic the clinical course of MI and enable translational success.

Multivariate study of parameters in the determination of pesticide residues in apple by headspace solid phase microextraction coupled to gas chromatography-mass spectrometry using experimental factorial design.

PubMed

Abdulra'uf, Lukman Bola; Tan, Guan Huat

2013-12-15

Solid-phase microextraction (SPME) is a solvent-less sample preparation method which combines sample preparation, isolation, concentration and enrichment into one step. In this study, multivariate strategy was used to determine the significance of the factors affecting the solid phase microextraction of pesticide residues (fenobucarb, diazinon, chlorothalonil and chlorpyrifos) using a randomised factorial design. The interactions and effects of temperature, time and salt addition on the efficiency of the extraction of the pesticide residues were evaluated using 2(3) factorial designs. The analytes were extracted with 100 μm PDMS fibres according to the factorial design matrix and desorbed into a gas chromatography-mass spectrometry detector. The developed method was applied for the analysis of apple samples and the limits of detection were between 0.01 and 0.2 μg kg(-)(1), which were lower than the MRLs for apples. The relative standard deviations (RSD) were between 0.1% and 13.37% with average recovery of 80-105%. The linearity ranges from 0.5-50 μg kg(-)(1) with correlation coefficient greater than 0.99. Copyright © 2013 Elsevier Ltd. All rights reserved.

Sensorimotor and postural control factors associated with driving safety in a community-dwelling older driver population.

PubMed

Lacherez, Philippe; Wood, Joanne M; Anstey, Kaarin J; Lord, Stephen R

2014-02-01

To establish whether sensorimotor function and balance are associated with on-road driving performance in older adults. The performance of 270 community-living adults aged 70-88 years recruited via the electoral roll was measured on a battery of peripheral sensation, strength, flexibility, reaction time, and balance tests and on a standardized measure of on-road driving performance. Forty-seven participants (17.4%) were classified as unsafe based on their driving assessment. Unsafe driving was associated with reduced peripheral sensation, lower limb weakness, reduced neck range of motion, slow reaction time, and poor balance in univariate analyses. Multivariate logistic regression analysis identified poor vibration sensitivity, reduced quadriceps strength, and increased sway on a foam surface with eyes closed as significant and independent risk factors for unsafe driving. These variables classified participants into safe and unsafe drivers with a sensitivity of 74% and specificity of 70%. A number of sensorimotor and balance measures were associated with driver safety and the multivariate model comprising measures of sensation, strength, and balance was highly predictive of unsafe driving in this sample. These findings highlight important determinants of driver safety and may assist in developing efficacious driver safety strategies for older drivers.

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.

Postoperative complications of contemporary open and robot-assisted laparoscopic radical prostatectomy using standardized reporting systems.

PubMed

Pompe, Raisa S; Beyer, Burkhard; Haese, Alexander; Preisser, Felix; Michl, Uwe; Steuber, Thomas; Graefen, Markus; Huland, Hartwig; Karakiewicz, Pierre I; Tilki, Derya

2018-05-04

To analyze time trends and contemporary rates of postoperative complications after RP and to compare the complication profile of ORP and RALP using standardized reporting systems. Retrospective analysis of 13,924 RP patients in a single institution (2005 to 2015). Complications were collected during hospital stay and via standardized questionnaire 3 months after and grouped into eight schemes. Since 2013, the revised Clavien-Dindo classification was used (n = 4,379). Annual incidence rates of different complications were graphically displayed. Multivariable logistic regression analyses compared complications between ORP and RALP after inverse probability of treatment weighting (IPTW). After introduction of standardized classification systems, complication rates have increased with a contemporary rate of 20.6% (2013 - 2015). While minor Clavien-Dindo grades represented the majority (I: 10.6%; II: 7.9%), severe complications (grades IV-V) were rare (<1%). In logistic regression analyses after IPTW, RALP was associated with less blood loss, shorter catheterization time and lower risk for Clavien-Dindo grade II and III complications. Our results emphasize the importance of standardized reporting systems for quality control and comparison across approaches or institutions. Contemporary complication rates in a high volume center remain low and are most frequently minor Clavien-Dindo grades. RALP had a slightly better complication profile compared to ORP. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Optimization of Dissolution Compartments in a Biorelevant Dissolution Apparatus Golem v2, Supported by Multivariate Analysis.

PubMed

Stupák, Ivan; Pavloková, Sylvie; Vysloužil, Jakub; Dohnal, Jiří; Čulen, Martin

2017-11-23

Biorelevant dissolution instruments represent an important tool for pharmaceutical research and development. These instruments are designed to simulate the dissolution of drug formulations in conditions most closely mimicking the gastrointestinal tract. In this work, we focused on the optimization of dissolution compartments/vessels for an updated version of the biorelevant dissolution apparatus-Golem v2. We designed eight compartments of uniform size but different inner geometry. The dissolution performance of the compartments was tested using immediate release caffeine tablets and evaluated by standard statistical methods and principal component analysis. Based on two phases of dissolution testing (using 250 and 100 mL of dissolution medium), we selected two compartment types yielding the highest measurement reproducibility. We also confirmed a statistically ssignificant effect of agitation rate and dissolution volume on the extent of drug dissolved and measurement reproducibility.