FT-IR-cPAS—New Photoacoustic Measurement Technique for Analysis of Hot Gases: A Case Study on VOCs

PubMed Central

Hirschmann, Christian Bernd; Koivikko, Niina Susanna; Raittila, Jussi; Tenhunen, Jussi; Ojala, Satu; Rahkamaa-Tolonen, Katariina; Marbach, Ralf; Hirschmann, Sarah; Keiski, Riitta Liisa

2011-01-01

This article describes a new photoacoustic FT-IR system capable of operating at elevated temperatures. The key hardware component is an optical-readout cantilever microphone that can work up to 200 °C. All parts in contact with the sample gas were put into a heated oven, incl. the photoacoustic cell. The sensitivity of the built photoacoustic system was tested by measuring 18 different VOCs. At 100 ppm gas concentration, the univariate signal to noise ratios (1σ, measurement time 25.5 min, at highest peak, optical resolution 8 cm−1) of the spectra varied from minimally 19 for o-xylene up to 329 for butyl acetate. The sensitivity can be improved by multivariate analyses over broad wavelength ranges, which effectively co-adds the univariate sensitivities achievable at individual wavelengths. The multivariate limit of detection (3σ, 8.5 min, full useful wavelength range), i.e., the best possible inverse analytical sensitivity achievable at optimum calibration, was calculated using the SBC method and varied from 2.60 ppm for dichloromethane to 0.33 ppm for butyl acetate. Depending on the shape of the spectra, which often only contain a few sharp peaks, the multivariate analysis improved the analytical sensitivity by 2.2 to 9.2 times compared to the univariate case. Selectivity and multi component ability were tested by a SBC calibration including 5 VOCs and water. The average cross selectivities turned out to be less than 2% and the resulting inverse analytical sensitivities of the 5 interfering VOCs was increased by maximum factor of 2.2 compared to the single component sensitivities. Water subtraction using SBC gave the true analyte concentration with a variation coefficient of 3%, although the sample spectra (methyl ethyl ketone, 200 ppm) contained water from 1,400 to 100k ppm and for subtraction only one water spectra (10k ppm) was used. The developed device shows significant improvement to the current state-of-the-art measurement methods used in industrial VOC measurements. PMID:22163900

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.

Controlled pattern imputation for sensitivity analysis of longitudinal binary and ordinal outcomes with nonignorable dropout.

PubMed

Tang, Yongqiang

2018-04-30

The controlled imputation method refers to a class of pattern mixture models that have been commonly used as sensitivity analyses of longitudinal clinical trials with nonignorable dropout in recent years. These pattern mixture models assume that participants in the experimental arm after dropout have similar response profiles to the control participants or have worse outcomes than otherwise similar participants who remain on the experimental treatment. In spite of its popularity, the controlled imputation has not been formally developed for longitudinal binary and ordinal outcomes partially due to the lack of a natural multivariate distribution for such endpoints. In this paper, we propose 2 approaches for implementing the controlled imputation for binary and ordinal data based respectively on the sequential logistic regression and the multivariate probit model. Efficient Markov chain Monte Carlo algorithms are developed for missing data imputation by using the monotone data augmentation technique for the sequential logistic regression and a parameter-expanded monotone data augmentation scheme for the multivariate probit model. We assess the performance of the proposed procedures by simulation and the analysis of a schizophrenia clinical trial and compare them with the fully conditional specification, last observation carried forward, and baseline observation carried forward imputation methods. Copyright © 2018 John Wiley & Sons, Ltd.

Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Manfredi, Marcello; Robotti, Elisa; Quasso, Fabio; Mazzucco, Eleonora; Calabrese, Giorgio; Marengo, Emilio

2018-01-01

The authentication and traceability of hazelnuts is very important for both the consumer and the food industry, to safeguard the protected varieties and the food quality. This study investigates the use of a portable FTIR spectrometer coupled to multivariate statistical analysis for the classification of raw hazelnuts. The method discriminates hazelnuts from different origins/cultivars based on differences of the signal intensities of their IR spectra. The multivariate classification methods, namely principal component analysis (PCA) followed by linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA), with or without variable selection, allowed a very good discrimination among the groups, with PLS-DA coupled to variable selection providing the best results. Due to the fast analysis, high sensitivity, simplicity and no sample preparation, the proposed analytical methodology could be successfully used to verify the cultivar of hazelnuts, and the analysis can be performed quickly and directly on site.

Pleiotropy Analysis of Quantitative Traits at Gene Level by Multivariate Functional Linear Models

PubMed Central

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

2015-01-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks’s Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. PMID:25809955

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

PubMed

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

2015-05-01

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

Maternal Sensitivity and Child Responsiveness: Associations with Social Context, Maternal Characteristics, and Child Characteristics in a Multivariate Analysis

ERIC Educational Resources Information Center

Bornstein, Marc H.; Hendricks, Charlene; Haynes, O. Maurice; Painter, Kathleen M.

2007-01-01

This study examined unique associations of multiple distal context variables (family socioeconomic status [SES], maternal employment, and paternal parenting) and proximal maternal (personality, intelligence, and knowledge; behavior, self-perceptions, and attributions) and child (age, gender, representation, language, and sociability)…

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

PubMed

Liu, Fei; Ye, Lanhan; Peng, Jiyu; Song, Kunlin; Shen, Tingting; Zhang, Chu; He, Yong

2018-02-27

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

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

PubMed Central

Ye, Lanhan; Song, Kunlin; Shen, Tingting

2018-01-01

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

Metabolic phenotyping of urine for discriminating alcohol-dependent from social drinkers and alcohol-naive subjects.

PubMed

Mostafa, Hamza; Amin, Arwa M; Teh, Chin-Hoe; Murugaiyah, Vikneswaran; Arif, Nor Hayati; Ibrahim, Baharudin

2016-12-01

Alcohol-dependence (AD) is a ravaging public health and social problem. AD diagnosis depends on questionnaires and some biomarkers, which lack specificity and sensitivity, however, often leading to less precise diagnosis, as well as delaying treatment. This represents a great burden, not only on AD individuals but also on their families. Metabolomics using nuclear magnetic resonance spectroscopy (NMR) can provide novel techniques for the identification of novel biomarkers of AD. These putative biomarkers can facilitate early diagnosis of AD. To identify novel biomarkers able to discriminate between alcohol-dependent, non-AD alcohol drinkers and controls using metabolomics. Urine samples were collected from 30 alcohol-dependent persons who did not yet start AD treatment, 54 social drinkers and 60 controls, who were then analysed using NMR. Data analysis was done using multivariate analysis including principal component analysis (PCA) and orthogonal partial least square-discriminate analysis (OPLS-DA), followed by univariate and multivariate logistic regression to develop the discriminatory model. The reproducibility was done using intraclass correlation coefficient (ICC). The OPLS-DA revealed significant discrimination between AD and other groups with sensitivity 86.21%, specificity 97.25% and accuracy 94.93%. Six biomarkers were significantly associated with AD in the multivariate logistic regression model. These biomarkers were cis-aconitic acid, citric acid, alanine, lactic acid, 1,2-propanediol and 2-hydroxyisovaleric acid. The reproducibility of all biomarkers was excellent (0.81-1.0). This study revealed that metabolomics analysis of urine using NMR identified AD novel biomarkers which can discriminate AD from social drinkers and controls with high accuracy. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

PubMed

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

2017-05-01

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

Identifying Talent in Youth Sport: A Novel Methodology Using Higher-Dimensional Analysis.

PubMed

Till, Kevin; Jones, Ben L; Cobley, Stephen; Morley, David; O'Hara, John; Chapman, Chris; Cooke, Carlton; Beggs, Clive B

2016-01-01

Prediction of adult performance from early age talent identification in sport remains difficult. Talent identification research has generally been performed using univariate analysis, which ignores multivariate relationships. To address this issue, this study used a novel higher-dimensional model to orthogonalize multivariate anthropometric and fitness data from junior rugby league players, with the aim of differentiating future career attainment. Anthropometric and fitness data from 257 Under-15 rugby league players was collected. Players were grouped retrospectively according to their future career attainment (i.e., amateur, academy, professional). Players were blindly and randomly divided into an exploratory (n = 165) and validation dataset (n = 92). The exploratory dataset was used to develop and optimize a novel higher-dimensional model, which combined singular value decomposition (SVD) with receiver operating characteristic analysis. Once optimized, the model was tested using the validation dataset. SVD analysis revealed 60 m sprint and agility 505 performance were the most influential characteristics in distinguishing future professional players from amateur and academy players. The exploratory dataset model was able to distinguish between future amateur and professional players with a high degree of accuracy (sensitivity = 85.7%, specificity = 71.1%; p<0.001), although it could not distinguish between future professional and academy players. The validation dataset model was able to distinguish future professionals from the rest with reasonable accuracy (sensitivity = 83.3%, specificity = 63.8%; p = 0.003). Through the use of SVD analysis it was possible to objectively identify criteria to distinguish future career attainment with a sensitivity over 80% using anthropometric and fitness data alone. As such, this suggests that SVD analysis may be a useful analysis tool for research and practice within talent identification.

Identifying Talent in Youth Sport: A Novel Methodology Using Higher-Dimensional Analysis

PubMed Central

Till, Kevin; Jones, Ben L.; Cobley, Stephen; Morley, David; O'Hara, John; Chapman, Chris; Cooke, Carlton; Beggs, Clive B.

2016-01-01

Prediction of adult performance from early age talent identification in sport remains difficult. Talent identification research has generally been performed using univariate analysis, which ignores multivariate relationships. To address this issue, this study used a novel higher-dimensional model to orthogonalize multivariate anthropometric and fitness data from junior rugby league players, with the aim of differentiating future career attainment. Anthropometric and fitness data from 257 Under-15 rugby league players was collected. Players were grouped retrospectively according to their future career attainment (i.e., amateur, academy, professional). Players were blindly and randomly divided into an exploratory (n = 165) and validation dataset (n = 92). The exploratory dataset was used to develop and optimize a novel higher-dimensional model, which combined singular value decomposition (SVD) with receiver operating characteristic analysis. Once optimized, the model was tested using the validation dataset. SVD analysis revealed 60 m sprint and agility 505 performance were the most influential characteristics in distinguishing future professional players from amateur and academy players. The exploratory dataset model was able to distinguish between future amateur and professional players with a high degree of accuracy (sensitivity = 85.7%, specificity = 71.1%; p<0.001), although it could not distinguish between future professional and academy players. The validation dataset model was able to distinguish future professionals from the rest with reasonable accuracy (sensitivity = 83.3%, specificity = 63.8%; p = 0.003). Through the use of SVD analysis it was possible to objectively identify criteria to distinguish future career attainment with a sensitivity over 80% using anthropometric and fitness data alone. As such, this suggests that SVD analysis may be a useful analysis tool for research and practice within talent identification. PMID:27224653

Combining vibrational biomolecular spectroscopy with chemometric techniques for the study of response and sensitivity of molecular structures/functional groups mainly related to lipid biopolymer to various processing applications.

PubMed

Yu, Gloria Qingyu; Yu, Peiqiang

2015-09-01

The objectives of this project were to (1) combine vibrational spectroscopy with chemometric multivariate techniques to determine the effect of processing applications on molecular structural changes of lipid biopolymer that mainly related to functional groups in green- and yellow-type Crop Development Centre (CDC) pea varieties [CDC strike (green-type) vs. CDC meadow (yellow-type)] that occurred during various processing applications; (2) relatively quantify the effect of processing applications on the antisymmetric CH3 ("CH3as") and CH2 ("CH2as") (ca. 2960 and 2923 cm(-1), respectively), symmetric CH3 ("CH3s") and CH2 ("CH2s") (ca. 2873 and 2954 cm(-1), respectively) functional groups and carbonyl C=O ester (ca. 1745 cm(-1)) spectral intensities as well as their ratios of antisymmetric CH3 to antisymmetric CH2 (ratio of CH3as to CH2as), ratios of symmetric CH3 to symmetric CH2 (ratio of CH3s to CH2s), and ratios of carbonyl C=O ester peak area to total CH peak area (ratio of C=O ester to CH); and (3) illustrate non-invasive techniques to detect the sensitivity of individual molecular functional group to the various processing applications in the recently developed different types of pea varieties. The hypothesis of this research was that processing applications modified the molecular structure profiles in the processed products as opposed to original unprocessed pea seeds. The results showed that the different processing methods had different impacts on lipid molecular functional groups. Different lipid functional groups had different sensitivity to various heat processing applications. These changes were detected by advanced molecular spectroscopy with chemometric techniques which may be highly related to lipid utilization and availability. The multivariate molecular spectral analyses, cluster analysis, and principal component analysis of original spectra (without spectral parameterization) are unable to fully distinguish the structural differences in the antisymmetric and symmetric CH3 and CH2 spectral region (ca. 3001-2799 cm(-1)) and carbonyl C=O ester band region (ca. 1771-1714 cm(-1)). This result indicated that the sensitivity to detect treatment difference by multivariate analysis of cluster analysis (CLA) and principal components analysis (PCA) might be lower compared with univariate molecular spectral analysis. In the future, other more sensitive techniques such as "discriminant analysis" could be considered for discriminating and classifying structural differences. Molecular spectroscopy can be used as non-invasive technique to study processing-induced structural changes that are related to lipid compound in legume seeds.

Potential of non-invasive esophagus cancer detection based on urine surface-enhanced Raman spectroscopy

NASA Astrophysics Data System (ADS)

Huang, Shaohua; Wang, Lan; Chen, Weisheng; Feng, Shangyuan; Lin, Juqiang; Huang, Zufang; Chen, Guannan; Li, Buhong; Chen, Rong

2014-11-01

Non-invasive esophagus cancer detection based on urine surface-enhanced Raman spectroscopy (SERS) analysis was presented. Urine SERS spectra were measured on esophagus cancer patients (n = 56) and healthy volunteers (n = 36) for control analysis. Tentative assignments of the urine SERS spectra indicated some interesting esophagus cancer-specific biomolecular changes, including a decrease in the relative content of urea and an increase in the percentage of uric acid in the urine of esophagus cancer patients compared to that of healthy subjects. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was employed to analyze and differentiate the SERS spectra between normal and esophagus cancer urine. The diagnostic algorithms utilizing a multivariate analysis method achieved a diagnostic sensitivity of 89.3% and specificity of 83.3% for separating esophagus cancer samples from normal urine samples. These results from the explorative work suggested that silver nano particle-based urine SERS analysis coupled with PCA-LDA multivariate analysis has potential for non-invasive detection of esophagus cancer.

Layer-by-Layer Polyelectrolyte Encapsulation of Mycoplasma pneumoniae for Enhanced Raman Detection

PubMed Central

Rivera-Betancourt, Omar E.; Sheppard, Edward S.; Krause, Duncan C.; Dluhy, Richard A.

2014-01-01

Mycoplasma pneumoniae is a major cause of respiratory disease in humans and accounts for as much as 20% of all community-acquired pneumonia. Existing mycoplasma diagnosis is primarily limited by the poor success rate at culturing the bacteria from clinical samples. There is a critical need to develop a new platform for mycoplasma detection that has high sensitivity, specificity, and expediency. Here we report the layer-by-layer (LBL) encapsulation of M. pneumoniae cells with Ag nanoparticles in a matrix of the polyelectrolytes poly(allylamine hydrochloride) (PAH) and poly(styrene sulfonate) (PSS). We evaluated nanoparticle encapsulated mycoplasma cells as a platform for the differentiation of M. pneumoniae strains using surface enhanced Raman scattering (SERS) combined with multivariate statistical analysis. Three separate M. pneumoniae strains (M129, FH and II-3) were studied. Scanning electron microscopy and fluorescence imaging showed that the Ag nanoparticles were incorporated between the oppositely charged polyelectrolyte layers. SERS spectra showed that LBL encapsulation provides excellent spectral reproducibility. Multivariate statistical analysis of the Raman spectra differentiated the three M. pneumoniae strains with 97 – 100% specificity and sensitivity, and low (0.1 – 0.4) root mean square error. These results indicated that nanoparticle and polyelectrolyte encapsulation of M. pneumoniae is a potentially powerful platform for rapid and sensitive SERS-based bacterial identification. PMID:25017005

Approximate analysis for repeated eigenvalue problems with applications to controls-structure integrated design

NASA Technical Reports Server (NTRS)

Kenny, Sean P.; Hou, Gene J. W.

1994-01-01

A method for eigenvalue and eigenvector approximate analysis for the case of repeated eigenvalues with distinct first derivatives is presented. The approximate analysis method developed involves a reparameterization of the multivariable structural eigenvalue problem in terms of a single positive-valued parameter. The resulting equations yield first-order approximations to changes in the eigenvalues and the eigenvectors associated with the repeated eigenvalue problem. This work also presents a numerical technique that facilitates the definition of an eigenvector derivative for the case of repeated eigenvalues with repeated eigenvalue derivatives (of all orders). Examples are given which demonstrate the application of such equations for sensitivity and approximate analysis. Emphasis is placed on the application of sensitivity analysis to large-scale structural and controls-structures optimization problems.

Differentiation of benign and malignant ampullary obstruction by multi-row detector CT.

PubMed

Angthong, Wirana; Jiarakoop, Kran; Tangtiang, Kaan

2018-05-21

To determine useful CT parameters to differentiate ampullary carcinomas from benign ampullary obstruction. This study included 93 patients who underwent abdominal CT, 31 patients with ampullary carcinomas, and 62 patients with benign ampullary obstruction. Two radiologists independently evaluated CT parameters then reached consensus decisions. Statistically significant CT parameters were identified through univariate and multivariate analyses. In univariate analysis, the presence of ampullary mass, asymmetric, abrupt narrowing of distal common bile duct (CBD), dilated intrahepatic bile duct (IHD), dilated pancreatic duct (PD), peripancreatic lymphadenopathy, duodenal wall thickening, and delayed enhancement were more frequently in ampullary carcinomas observed (P < 0.05). Multivariate logistic regression analysis using significant CT parameters and clinical data from univariate analysis, and clinical symptom with jaundice (P = 0.005) was an independent predictor of ampullary carcinomas. For multivariate analysis using only significant CT parameters, abrupt narrowing of distal CBD was an independent predictor of ampullary carcinomas (P = 0.019). Among various CT criteria, abrupt narrowing of distal CBD and dilated IHD had highest sensitivity (77.4%) and highest accuracy (90.3%). The abrupt narrowing of distal CBD and dilated IHD is useful for differentiation of ampullary carcinomas from benign entity in patients without the presence of mass.

Inorganic selenium speciation analysis in Allium and Brassica vegetables by ionic liquid assisted liquid-liquid microextraction with multivariate optimization.

PubMed

Castro Grijalba, Alexander; Martinis, Estefanía M; Wuilloud, Rodolfo G

2017-03-15

A highly sensitive vortex assisted liquid-liquid microextraction (VA-LLME) method was developed for inorganic Se [Se(IV) and Se(VI)] speciation analysis in Allium and Brassica vegetables. Trihexyl(tetradecyl)phosphonium decanoate phosphonium ionic liquid (IL) was applied for the extraction of Se(IV)-ammonium pyrrolidine dithiocarbamate (APDC) complex followed by Se determination with electrothermal atomic absorption spectrometry. A complete optimization of the graphite furnace temperature program was developed for accurate determination of Se in the IL-enriched extracts and multivariate statistical optimization was performed to define the conditions for the highest extraction efficiency. Significant factors of IL-VA-LLME method were sample volume, extraction pH, extraction time and APDC concentration. High extraction efficiency (90%), a 100-fold preconcentration factor and a detection limit of 5.0ng/L were achieved. The high sensitivity obtained with preconcentration and the non-chromatographic separation of inorganic Se species in complex matrix samples such as garlic, onion, leek, broccoli and cauliflower, are the main advantages of IL-VA-LLME. Copyright © 2016 Elsevier Ltd. All rights reserved.

Use of the Analysis of the Volatile Faecal Metabolome in Screening for Colorectal Cancer

PubMed Central

2015-01-01

Diagnosis of colorectal cancer is an invasive and expensive colonoscopy, which is usually carried out after a positive screening test. Unfortunately, existing screening tests lack specificity and sensitivity, hence many unnecessary colonoscopies are performed. Here we report on a potential new screening test for colorectal cancer based on the analysis of volatile organic compounds (VOCs) in the headspace of faecal samples. Faecal samples were obtained from subjects who had a positive faecal occult blood sample (FOBT). Subjects subsequently had colonoscopies performed to classify them into low risk (non-cancer) and high risk (colorectal cancer) groups. Volatile organic compounds were analysed by selected ion flow tube mass spectrometry (SIFT-MS) and then data were analysed using both univariate and multivariate statistical methods. Ions most likely from hydrogen sulphide, dimethyl sulphide and dimethyl disulphide are statistically significantly higher in samples from high risk rather than low risk subjects. Results using multivariate methods show that the test gives a correct classification of 75% with 78% specificity and 72% sensitivity on FOBT positive samples, offering a potentially effective alternative to FOBT. PMID:26086914

Risk factors for low receptive vocabulary abilities in the preschool and early school years in the longitudinal study of Australian children.

PubMed

Christensen, Daniel; Zubrick, Stephen R; Lawrence, David; Mitrou, Francis; Taylor, Catherine L

2014-01-01

Receptive vocabulary development is a component of the human language system that emerges in the first year of life and is characterised by onward expansion throughout life. Beginning in infancy, children's receptive vocabulary knowledge builds the foundation for oral language and reading skills. The foundations for success at school are built early, hence the public health policy focus on reducing developmental inequalities before children start formal school. The underlying assumption is that children's development is stable, and therefore predictable, over time. This study investigated this assumption in relation to children's receptive vocabulary ability. We investigated the extent to which low receptive vocabulary ability at 4 years was associated with low receptive vocabulary ability at 8 years, and the predictive utility of a multivariate model that included child, maternal and family risk factors measured at 4 years. The study sample comprised 3,847 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Multivariate logistic regression was used to investigate risks for low receptive vocabulary ability from 4-8 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. In the multivariate model, substantial risk factors for receptive vocabulary delay from 4-8 years, in order of descending magnitude, were low receptive vocabulary ability at 4 years, low maternal education, and low school readiness. Moderate risk factors, in order of descending magnitude, were low maternal parenting consistency, socio-economic area disadvantage, low temperamental persistence, and NESB status. The following risk factors were not significant: One or more siblings, low family income, not reading to the child, high maternal work hours, and Aboriginal or Torres Strait Islander ethnicity. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude does not do particularly well in predicting low receptive vocabulary ability from 4-8 years.

Probabilistic sensitivity analysis for decision trees with multiple branches: use of the Dirichlet distribution in a Bayesian framework.

PubMed

Briggs, Andrew H; Ades, A E; Price, Martin J

2003-01-01

In structuring decision models of medical interventions, it is commonly recommended that only 2 branches be used for each chance node to avoid logical inconsistencies that can arise during sensitivity analyses if the branching probabilities do not sum to 1. However, information may be naturally available in an unconditional form, and structuring a tree in conditional form may complicate rather than simplify the sensitivity analysis of the unconditional probabilities. Current guidance emphasizes using probabilistic sensitivity analysis, and a method is required to provide probabilistic probabilities over multiple branches that appropriately represents uncertainty while satisfying the requirement that mutually exclusive event probabilities should sum to 1. The authors argue that the Dirichlet distribution, the multivariate equivalent of the beta distribution, is appropriate for this purpose and illustrate its use for generating a fully probabilistic transition matrix for a Markov model. Furthermore, they demonstrate that by adopting a Bayesian approach, the problem of observing zero counts for transitions of interest can be overcome.

Diagnosis of rheumatoid arthritis: multivariate analysis of biomarkers.

PubMed

Wild, Norbert; Karl, Johann; Grunert, Veit P; Schmitt, Raluca I; Garczarek, Ursula; Krause, Friedemann; Hasler, Fritz; van Riel, Piet L C M; Bayer, Peter M; Thun, Matthias; Mattey, Derek L; Sharif, Mohammed; Zolg, Werner

2008-02-01

To test if a combination of biomarkers can increase the classification power of autoantibodies to cyclic citrullinated peptides (anti-CCP) in the diagnosis of rheumatoid arthritis (RA) depending on the diagnostic situation. Biomarkers were subject to three inclusion/exclusion criteria (discrimination between RA patients and healthy blood donors, ability to identify anti-CCP-negative RA patients, specificity in a panel with major non-rheumatological diseases) before univariate ranking and multivariate analysis was carried out using a modelling panel (n = 906). To enable the evaluation of the classification power in different diagnostic settings the disease controls (n = 542) were weighted according to the admission rates in rheumatology clinics modelling a clinic panel or according to the relative prevalences of musculoskeletal disorders in the general population seen by general practitioners modelling a GP panel. Out of 131 biomarkers considered originally, we evaluated 32 biomarkers in this study, of which only seven passed the three inclusion/exclusion criteria and were combined by multivariate analysis using four different mathematical models. In the modelled clinic panel, anti-CCP was the lead marker with a sensitivity of 75.8% and a specificity of 94.0%. Due to the lack in specificity of the markers other than anti-CCP in this diagnostic setting, any gain in sensitivity by any marker combination is off-set by a corresponding loss in specificity. In the modelled GP panel, the best marker combination of anti-CCP and interleukin (IL)-6 resulted in a sensitivity gain of 7.6% (85.9% vs. 78.3%) at a minor loss in specificity of 1.6% (90.3% vs. 91.9%) compared with anti-CCP as the best single marker. Depending on the composition of the sample panel, anti-CCP alone or anti-CCP in combination with IL-6 has the highest classification power for the diagnosis of established RA.

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

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

NASA Astrophysics Data System (ADS)

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

2015-03-01

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

Fast discrimination of hydroxypropyl methyl cellulose using portable Raman spectrometer and multivariate methods

NASA Astrophysics Data System (ADS)

Song, Biao; Lu, Dan; Peng, Ming; Li, Xia; Zou, Ye; Huang, Meizhen; Lu, Feng

2017-02-01

Raman spectroscopy is developed as a fast and non-destructive method for the discrimination and classification of hydroxypropyl methyl cellulose (HPMC) samples. 44 E series and 41 K series of HPMC samples are measured by a self-developed portable Raman spectrometer (Hx-Raman) which is excited by a 785 nm diode laser and the spectrum range is 200-2700 cm-1 with a resolution (FWHM) of 6 cm-1. Multivariate analysis is applied for discrimination of E series from K series. By methods of principal components analysis (PCA) and Fisher discriminant analysis (FDA), a discrimination result with sensitivity of 90.91% and specificity of 95.12% is achieved. The corresponding receiver operating characteristic (ROC) is 0.99, indicting the accuracy of the predictive model. This result demonstrates the prospect of portable Raman spectrometer for rapid, non-destructive classification and discrimination of E series and K series samples of HPMC.

Application of reflectance spectroscopies (FTIR-ATR & FT-NIR) coupled with multivariate methods for robust in vivo detection of begomovirus infection in papaya leaves

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

Salting-out assisted liquid-liquid extraction and partial least squares regression to assay low molecular weight polycyclic aromatic hydrocarbons leached from soils and sediments

NASA Astrophysics Data System (ADS)

Bressan, Lucas P.; do Nascimento, Paulo Cícero; Schmidt, Marcella E. P.; Faccin, Henrique; de Machado, Leandro Carvalho; Bohrer, Denise

2017-02-01

A novel method was developed to determine low molecular weight polycyclic aromatic hydrocarbons in aqueous leachates from soils and sediments using a salting-out assisted liquid-liquid extraction, synchronous fluorescence spectrometry and a multivariate calibration technique. Several experimental parameters were controlled and the optimum conditions were: sodium carbonate as the salting-out agent at concentration of 2 mol L- 1, 3 mL of acetonitrile as extraction solvent, 6 mL of aqueous leachate, vortexing for 5 min and centrifuging at 4000 rpm for 5 min. The partial least squares calibration was optimized to the lowest values of root mean squared error and five latent variables were chosen for each of the targeted compounds. The regression coefficients for the true versus predicted concentrations were higher than 0.99. Figures of merit for the multivariate method were calculated, namely sensitivity, multivariate detection limit and multivariate quantification limit. The selectivity was also evaluated and other polycyclic aromatic hydrocarbons did not interfere in the analysis. Likewise, high performance liquid chromatography was used as a comparative methodology, and the regression analysis between the methods showed no statistical difference (t-test). The proposed methodology was applied to soils and sediments of a Brazilian river and the recoveries ranged from 74.3% to 105.8%. Overall, the proposed methodology was suitable for the targeted compounds, showing that the extraction method can be applied to spectrofluorometric analysis and that the multivariate calibration is also suitable for these compounds in leachates from real samples.

The Dirichlet-Multinomial Model for Multivariate Randomized Response Data and Small Samples

ERIC Educational Resources Information Center

Avetisyan, Marianna; Fox, Jean-Paul

2012-01-01

In survey sampling the randomized response (RR) technique can be used to obtain truthful answers to sensitive questions. Although the individual answers are masked due to the RR technique, individual (sensitive) response rates can be estimated when observing multivariate response data. The beta-binomial model for binary RR data will be generalized…

A multivariate relationship for the impact sensitivities of energetic N-nitrocompounds based on bond dissociation energy.

PubMed

Li, Jinshan

2010-02-15

The ZPE-corrected N-NO(2) bond dissociation energies (BDEs(ZPE)) of a series of model N-nitrocompounds and typical energetic N-nitrocompounds have been calculated using density functional theory methods. Computed results show that using the 6-31G** basis set the UB3LYP calculated BDE(ZPE) is similar to the B3PW91 but is less than the UB3P86 and that for both UB3P86 and UB3PW91 methods the 6-31G(**) calculated BDE(ZPE) is close to the 6-31++G(**). For the series of model N-nitrocompounds it is drawn from the NBO analysis that at the UB3LYP/6-31G(**) level the order of BDE(ZPE) is not only in line with that of bond order but also with that of the energy gap between N-NO(2) bond and antibond orbitals. For the typical energetic N-nitrocompounds the impact sensitivity is strongly related to the BDE(ZPE) indeed, and based on the BDEs(ZPE) calculated at different density functional theory levels this work has established a good multivariate correlation of impact sensitivity with molecular parameters, which provides a method to address the sensitivity problem.

Lower sensitivity of serum (1,3)-β-d-glucan for the diagnosis of candidaemia due to Candida parapsilosis.

PubMed

Mikulska, M; Giacobbe, D R; Furfaro, E; Mesini, A; Marchese, A; Del Bono, V; Viscoli, C

2016-07-01

The aim of this study was to evaluate the sensitivity and the levels of 1,3-β-d-glucan (BDG) among patients with candidaemia due to different Candida species. Retrospective study of all patients who had a single-species candidaemia and BDG testing performed within 48 h from the onset of candidaemia during 2009-2015 was performed. Factors influencing the sensitivity of BDG, including the presence of a central venous catheter, antifungal therapy and Candida species, were analysed in univariate and multivariate models. In all, 107 patients with the following Candida distribution were included: 46 (43%) Candida albicans, 37 (35%) Candida parapsilosis, and 24 (22%) other species. BDG sensitivity and levels were the highest in C. albicans candidaemia and lowest for C. parapsilosis (respectively, 72% and 410 pg/mL for C. albicans, 41% and 39 pg/mL for C. parapsilosis, and 63% and 149 pg/mL for other species; p 0.015 and p 0.003). In multivariate analysis, Candida species (parapsilosis versus others) was the only factor influencing the sensitivity of BDG (OR 0.3, 95% CI 0.1-0.7, p 0.006). The sensitivity of BDG in candidaemia seems highly dependent on the fungal species, with the lowest being for C. parapsilosis. Copyright © 2016 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Effect of sexual steroids on boar kinematic sperm subpopulations.

PubMed

Ayala, E M E; Aragón, M A

2017-11-01

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

Gas-water two-phase flow characterization with Electrical Resistance Tomography and Multivariate Multiscale Entropy analysis.

PubMed

Tan, Chao; Zhao, Jia; Dong, Feng

2015-03-01

Flow behavior characterization is important to understand gas-liquid two-phase flow mechanics and further establish its description model. An Electrical Resistance Tomography (ERT) provides information regarding flow conditions at different directions where the sensing electrodes implemented. We extracted the multivariate sample entropy (MSampEn) by treating ERT data as a multivariate time series. The dynamic experimental results indicate that the MSampEn is sensitive to complexity change of flow patterns including bubbly flow, stratified flow, plug flow and slug flow. MSampEn can characterize the flow behavior at different direction of two-phase flow, and reveal the transition between flow patterns when flow velocity changes. The proposed method is effective to analyze two-phase flow pattern transition by incorporating information of different scales and different spatial directions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

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.

Sensitive analytical method for simultaneous analysis of some vasoconstrictors with highly overlapped analytical signals

NASA Astrophysics Data System (ADS)

Nikolić, G. S.; Žerajić, S.; Cakić, M.

2011-10-01

Multivariate calibration method is a powerful mathematical tool that can be applied in analytical chemistry when the analytical signals are highly overlapped. The method with regression by partial least squares is proposed for the simultaneous spectrophotometric determination of adrenergic vasoconstrictors in decongestive solution containing two active components: phenyleprine hydrochloride and trimazoline hydrochloride. These sympathomimetic agents are that frequently associated in pharmaceutical formulations against the common cold. The proposed method, which is, simple and rapid, offers the advantages of sensitivity and wide range of determinations without the need for extraction of the vasoconstrictors. In order to minimize the optimal factors necessary to obtain the calibration matrix by multivariate calibration, different parameters were evaluated. The adequate selection of the spectral regions proved to be important on the number of factors. In order to simultaneously quantify both hydrochlorides among excipients, the spectral region between 250 and 290 nm was selected. A recovery for the vasoconstrictor was 98-101%. The developed method was applied to assay of two decongestive pharmaceutical preparations.

Reagent-free bacterial identification using multivariate analysis of transmission spectra

NASA Astrophysics Data System (ADS)

Smith, Jennifer M.; Huffman, Debra E.; Acosta, Dayanis; Serebrennikova, Yulia; García-Rubio, Luis; Leparc, German F.

2012-10-01

The identification of bacterial pathogens from culture is critical to the proper administration of antibiotics and patient treatment. Many of the tests currently used in the clinical microbiology laboratory for bacterial identification today can be highly sensitive and specific; however, they have the additional burdens of complexity, cost, and the need for specialized reagents. We present an innovative, reagent-free method for the identification of pathogens from culture. A clinical study has been initiated to evaluate the sensitivity and specificity of this approach. Multiwavelength transmission spectra were generated from a set of clinical isolates including Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus. Spectra of an initial training set of these target organisms were used to create identification models representing the spectral variability of each species using multivariate statistical techniques. Next, the spectra of the blinded isolates of targeted species were identified using the model achieving >94% sensitivity and >98% specificity, with 100% accuracy for P. aeruginosa and S. aureus. The results from this on-going clinical study indicate this approach is a powerful and exciting technique for identification of pathogens. The menu of models is being expanded to include other bacterial genera and species of clinical significance.

Frequency Doubling Technology Perimetry and Changes in Quality of Life of Glaucoma Patients: A Longitudinal Study.

PubMed

Abe, Ricardo Y; Gracitelli, Carolina P B; Diniz-Filho, Alberto; Zangwill, Linda M; Weinreb, Robert N; Medeiros, Felipe A

2015-07-01

To evaluate the relationship between rates of change on frequency doubling technology (FDT) perimetry and longitudinal changes in quality of life (QoL) of glaucoma patients. Prospective observational cohort study. One hundred fifty-two subjects (127 glaucoma and 25 healthy) were followed for an average of 3.2 ± 1.1 years. All subjects were evaluated with National Eye Institute Visual Function Questionnaire (NEI VFQ-25), FDT, and standard automated perimetry (SAP). Glaucoma patients had a median of 3 NEI VFQ-25, 8 FDT, and 8 SAP tests during follow-up. Mean sensitivities of the integrated binocular visual fields were estimated for FDT and SAP and used to calculate rates of change. A joint longitudinal multivariable mixed model was used to investigate the association between change in binocular mean sensitivities and change in NEI VFQ-25 Rasch-calibrated scores. There was a statistically significant correlation between change in binocular mean sensitivity for FDT and change in NEI VFQ-25 scores during follow-up in the glaucoma group. In multivariable analysis with the confounding factors, each 1 dB/year change in binocular FDT mean sensitivity corresponded to a change of 0.8 units per year in the NEI VFQ-25 scores (P = .001). For binocular SAP mean sensitivity, each 1 dB/year change was associated with 2.4 units per year change in NEI VFQ-25 scores (P < .001). The multivariable model containing baseline and rate of change information from SAP had stronger ability to predict change in NEI VFQ-25 scores compared to the equivalent model for FDT (R(2) of 50% and 30%, respectively; P = .001). SAP performed significantly better than FDT in predicting change in NEI VFQ-25 scores in our population, suggesting that it may still be the preferable perimetric technique for predicting risk of disability from the disease. Copyright © 2015 Elsevier Inc. All rights reserved.

Optical assay for biotechnology and clinical diagnosis.

PubMed

Moczko, Ewa; Cauchi, Michael; Turner, Claire; Meglinski, Igor; Piletsky, Sergey

2011-08-01

In this paper, we present an optical diagnostic assay consisting of a mixture of environmental-sensitive fluorescent dyes combined with multivariate data analysis for quantitative and qualitative examination of biological and clinical samples. The performance of the assay is based on the analysis of spectrum of the selected fluorescent dyes with the operational principle similar to electronic nose and electronic tongue systems. This approach has been successfully applied for monitoring of growing cell cultures and identification of gastrointestinal diseases in humans.

Mars approach for global sensitivity analysis of differential equation models with applications to dynamics of influenza infection.

PubMed

Lee, Yeonok; Wu, Hulin

2012-01-01

Differential equation models are widely used for the study of natural phenomena in many fields. The study usually involves unknown factors such as initial conditions and/or parameters. It is important to investigate the impact of unknown factors (parameters and initial conditions) on model outputs in order to better understand the system the model represents. Apportioning the uncertainty (variation) of output variables of a model according to the input factors is referred to as sensitivity analysis. In this paper, we focus on the global sensitivity analysis of ordinary differential equation (ODE) models over a time period using the multivariate adaptive regression spline (MARS) as a meta model based on the concept of the variance of conditional expectation (VCE). We suggest to evaluate the VCE analytically using the MARS model structure of univariate tensor-product functions which is more computationally efficient. Our simulation studies show that the MARS model approach performs very well and helps to significantly reduce the computational cost. We present an application example of sensitivity analysis of ODE models for influenza infection to further illustrate the usefulness of the proposed method.

Patterns and Predictors of Language and Literacy Abilities 4-10 Years in the Longitudinal Study of Australian Children.

PubMed

Zubrick, Stephen R; Taylor, Catherine L; Christensen, Daniel

2015-01-01

Oral language is the foundation of literacy. Naturally, policies and practices to promote children's literacy begin in early childhood and have a strong focus on developing children's oral language, especially for children with known risk factors for low language ability. The underlying assumption is that children's progress along the oral to literate continuum is stable and predictable, such that low language ability foretells low literacy ability. This study investigated patterns and predictors of children's oral language and literacy abilities at 4, 6, 8 and 10 years. The study sample comprised 2,316 to 2,792 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Six developmental patterns were observed, a stable middle-high pattern, a stable low pattern, an improving pattern, a declining pattern, a fluctuating low pattern, and a fluctuating middle-high pattern. Most children (69%) fit a stable middle-high pattern. By contrast, less than 1% of children fit a stable low pattern. These results challenged the view that children's progress along the oral to literate continuum is stable and predictable. Multivariate logistic regression was used to investigate risks for low literacy ability at 10 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. Predictors were modelled as risk variables with the lowest level of risk as the reference category. In the multivariate model, substantial risks for low literacy ability at 10 years, in order of descending magnitude, were: low school readiness, Aboriginal and/or Torres Strait Islander status and low language ability at 8 years. Moderate risks were high temperamental reactivity, low language ability at 4 years, and low language ability at 6 years. The following risk factors were not statistically significant in the multivariate model: Low maternal consistency, low family income, health care card, child not read to at home, maternal smoking, maternal education, family structure, temperamental persistence, and socio-economic area disadvantage. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude did not do particularly well in predicting low literacy ability at 10 years.

The combination of ovarian volume and outline has better diagnostic accuracy than prostate-specific antigen (PSA) concentrations in women with polycystic ovarian syndrome (PCOs).

PubMed

Bili, Eleni; Bili, Authors Eleni; Dampala, Kaliopi; Iakovou, Ioannis; Tsolakidis, Dimitrios; Giannakou, Anastasia; Tarlatzis, Basil C

2014-08-01

The aim of this study was to determine the performance of prostate specific antigen (PSA) and ultrasound parameters, such as ovarian volume and outline, in the diagnosis of polycystic ovary syndrome (PCOS). This prospective, observational, case-controlled study included 43 women with PCOS, and 40 controls. Between day 3 and 5 of the menstrual cycle, fasting serum samples were collected and transvaginal ultrasound was performed. The diagnostic performance of each parameter [total PSA (tPSA), total-to-free PSA ratio (tPSA:fPSA), ovarian volume, ovarian outline] was estimated by means of receiver operating characteristic (ROC) analysis, along with area under the curve (AUC), threshold, sensitivity, specificity as well as positive (+) and negative (-) likelihood ratios (LRs). Multivariate logistical regression models, using ovarian volume and ovarian outline, were constructed. The tPSA and tPSA:fPSA ratio resulted in AUC of 0.74 and 0.70, respectively, with moderate specificity/sensitivity and insufficient LR+/- values. In the multivariate logistic regression model, the combination of ovarian volume and outline had a sensitivity of 97.7% and a specificity of 97.5% in the diagnosis of PCOS, with +LR and -LR values of 39.1 and 0.02, respectively. In women with PCOS, tPSA and tPSA:fPSA ratio have similar diagnostic performance. The use of a multivariate logistic regression model, incorporating ovarian volume and outline, offers very good diagnostic accuracy in distinguishing women with PCOS patients from controls. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

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.

Screening for Chronic Obstructive Pulmonary Disease (COPD) in an Urban HIV Clinic: A Pilot Study

PubMed Central

Kaner, Robert J.; Glesby, Marshall J.

2015-01-01

Abstract Increased smoking and a detrimental response to tobacco smoke in the lungs of HIV/AIDS patients result in an increased risk for COPD. We aimed to determine the predictive value of a COPD screening strategy validated in the general population and to identify HIV-related factors associated with decreased lung function. Subjects at least 35 years of age at an HIV clinic in New York City completed a COPD screening questionnaire and peak flow measurement. Those with abnormal results and a random one-third of normal screens had spirometry. 235 individuals were included and 89 completed spirometry. Eleven (12%) had undiagnosed airway obstruction and 5 had COPD. A combination of a positive questionnaire and abnormal peak flow yielded a sensitivity of 20% (specificity 93%) for detection of COPD. Peak flow alone had a sensitivity of 80% (specificity 80%). Abnormal peak flow was associated with an AIDS diagnosis (p=0.04), lower nadir (p=0.001), and current CD4 counts (p=0.001). Nadir CD4 remained associated in multivariate analysis (p=0.05). Decreased FEV1 (<80% predicted) was associated with lower CD4 count nadir (p=0.04) and detectable current HIV viral load (p=0.01) in multivariate analysis. Questionnaire and peak flow together had low sensitivity, but abnormal peak flow shows potential as a screening tool for COPD in HIV/AIDS. These data suggest that lung function may be influenced by HIV-related factors. PMID:25723842

The Multi-Isotope Process (MIP) Monitor Project: FY13 Final Report

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

Meier, David E.; Coble, Jamie B.; Jordan, David V.

The Multi-Isotope Process (MIP) Monitor provides an efficient approach to monitoring the process conditions in reprocessing facilities in support of the goal of “… (minimization of) the risks of nuclear proliferation and terrorism.” The MIP Monitor measures the distribution of the radioactive isotopes in product and waste streams of a nuclear reprocessing facility. These isotopes are monitored online by gamma spectrometry and compared, in near-real-time, to spectral patterns representing “normal” process conditions using multivariate analysis and pattern recognition algorithms. The combination of multivariate analysis and gamma spectroscopy allows us to detect small changes in the gamma spectrum, which may indicatemore » changes in process conditions. By targeting multiple gamma-emitting indicator isotopes, the MIP Monitor approach is compatible with the use of small, portable, relatively high-resolution gamma detectors that may be easily deployed throughout an existing facility. The automated multivariate analysis can provide a level of data obscurity, giving a built-in information barrier to protect sensitive or proprietary operational data. Proof-of-concept simulations and experiments have been performed in previous years to demonstrate the validity of this tool in a laboratory setting for systems representing aqueous reprocessing facilities. However, pyroprocessing is emerging as an alternative to aqueous reprocessing techniques.« less

Clinical characterisation of pneumonia caused by atypical pathogens combining classic and novel predictors.

PubMed

Masiá, M; Gutiérrez, F; Padilla, S; Soldán, B; Mirete, C; Shum, C; Hernández, I; Royo, G; Martin-Hidalgo, A

2007-02-01

The aim of this study was to characterise community-acquired pneumonia (CAP) caused by atypical pathogens by combining distinctive clinical and epidemiological features and novel biological markers. A population-based prospective study of consecutive patients with CAP included investigation of biomarkers of bacterial infection, e.g., procalcitonin, C-reactive protein and lipopolysaccharide-binding protein (LBP) levels. Clinical, radiological and laboratory data for patients with CAP caused by atypical pathogens were compared by univariate and multivariate analysis with data for patients with typical pathogens and patients from whom no organisms were identified. Two predictive scoring models were developed with the most discriminatory variables from multivariate analysis. Of 493 patients, 94 had CAP caused by atypical pathogens. According to multivariate analysis, patients with atypical pneumonia were more likely to have normal white blood cell counts, have repetitive air-conditioning exposure, be aged <65 years, have elevated aspartate aminotransferase levels, have been exposed to birds, and have lower serum levels of LBP. Two different scoring systems were developed that predicted atypical pathogens with sensitivities of 35.2% and 48.8%, and specificities of 93% and 91%, respectively. The combination of selected patient characteristics and laboratory data identified up to half of the cases of atypical pneumonia with high specificity, which should help clinicians to optimise initial empirical therapy for CAP.

Integrated Multivariate Analysis with Nondetects for the Development of Human Sewage Source-Tracking Tools Using Bacteriophages of Enterococcus faecalis.

PubMed

Wangkahad, Bencharong; Mongkolsuk, Skorn; Sirikanchana, Kwanrawee

2017-02-21

We developed sewage-specific microbial source tracking (MST) tools using enterococci bacteriophages and evaluated their performance with univariate and multivariate analyses involving data below detection limits. Newly isolated Enterococci faecalis bacterial strains AIM06 (DSM100702) and SR14 (DSM100701) demonstrated 100% specificity and 90% sensitivity to human sewage without detecting 68 animal manure pooled samples of cats, chickens, cows, dogs, ducks, pigs, and pigeons. AIM06 and SR14 bacteriophages were present in human sewage at 2-4 orders of magnitude. A principal component analysis confirmed the importance of both phages as main water quality parameters. The phages presented only in the polluted water, as classified by a cluster analysis, and at median concentrations of 1.71 × 10 2 and 4.27 × 10 2 PFU/100 mL, respectively, higher than nonhost specific RYC2056 phages and sewage-specific KS148 phages (p < 0.05). Interestingly, AIM06 and SR14 phages exhibited significant correlations with each other and with total coliforms, E. coli, enterococci, and biochemical oxygen demand (Kendall's tau = 0.348 to 0.605, p < 0.05), a result supporting their roles as water quality indicators. This research demonstrates the multiregional applicability of enterococci hosts in MST application and highlights the significance of multivariate analysis with nondetects in evaluating the performance of new MST host strains.

Spectral discrimination of serum from liver cancer and liver cirrhosis using Raman spectroscopy

NASA Astrophysics Data System (ADS)

Yang, Tianyue; Li, Xiaozhou; Yu, Ting; Sun, Ruomin; Li, Siqi

2011-07-01

In this paper, Raman spectra of human serum were measured using Raman spectroscopy, then the spectra was analyzed by multivariate statistical methods of principal component analysis (PCA). Then linear discriminant analysis (LDA) was utilized to differentiate the loading score of different diseases as the diagnosing algorithm. Artificial neural network (ANN) was used for cross-validation. The diagnosis sensitivity and specificity by PCA-LDA are 88% and 79%, while that of the PCA-ANN are 89% and 95%. It can be seen that modern analyzing method is a useful tool for the analysis of serum spectra for diagnosing diseases.

[18F]DOPA PET/ceCT in diagnosis and staging of primary medullary thyroid carcinoma prior to surgery.

PubMed

Rasul, Sazan; Hartenbach, Sabrina; Rebhan, Katharina; Göllner, Adelina; Karanikas, Georgios; Mayerhoefer, Marius; Mazal, Peter; Hacker, Marcus; Hartenbach, Markus

2018-05-15

Medullary thyroid carcinoma (MTC) is characterized by a high rate of metastasis. In this study we evaluated the ability of [ 18 F]DOPA PET/ceCT to stage MTC in patients with suspicious thyroid nodules and pathologically elevated serum calcitonin (Ctn) levels prior to total thyroidectomy and lymph node (LN) dissection. A group of 32 patients with sonographically suspicious thyroid nodules and pathologically elevated basal Ctn (bCtn) and stimulated Ctn (sCtn) levels underwent DOPA PET/ceCT prior to surgery. Postoperative histology served as the standard of reference for ultrasonography and DOPA PET/ceCT region-based LN staging. Univariate and multivariate regression analyses as well as receiver operating characteristic analysis were used to evaluate the correlations between preoperative and histological parameters and postoperative tumour persistence or relapse. Primary MTC was histologically verified in all patients. Of the 32 patients, 28 showed increased DOPA decarboxylase activity in the primary tumour (sensitivity 88%, mean SUVmax 10.5). Undetected tumours were exclusively staged pT1a. The sensitivities of DOPA PET in the detection of central and lateral metastatic neck LN were 53% and 73%, in contrast to 20% and 39%, respectively, for neck ultrasonography. Preoperative bCtn and carcinoembryonic antigen levels as well as cN1b status and the number of involved neck regions on DOPA PET/ceCT were predictive of postoperative tumour persistence/relapse in the univariate regression analysis (P < 0.05). Only DOPA PET/ceCT cN1b status remained significant in the multivariate analysis (P = 0.016, relative risk 4.02). This study revealed that DOPA PET/ceCT has high sensitivity in the detection of primary MTC and superior sensitivity in the detection of LN metastases compared to ultrasonography. DOPA PET/ceCT identification of N1b status predicts postoperative tumour persistence. Thus, implementation of a DOPA-guided LN dissection might improve surgical success.

Immune Profiles to Predict Response to Desensitization Therapy in Highly HLA-Sensitized Kidney Transplant Candidates

PubMed Central

Yabu, Julie M.; Siebert, Janet C.; Maecker, Holden T.

2016-01-01

Background Kidney transplantation is the most effective treatment for end-stage kidney disease. Sensitization, the formation of human leukocyte antigen (HLA) antibodies, remains a major barrier to successful kidney transplantation. Despite the implementation of desensitization strategies, many candidates fail to respond. Current progress is hindered by the lack of biomarkers to predict response and to guide therapy. Our objective was to determine whether differences in immune and gene profiles may help identify which candidates will respond to desensitization therapy. Methods and Findings Single-cell mass cytometry by time-of-flight (CyTOF) phenotyping, gene arrays, and phosphoepitope flow cytometry were performed in a study of 20 highly sensitized kidney transplant candidates undergoing desensitization therapy. Responders to desensitization therapy were defined as 5% or greater decrease in cumulative calculated panel reactive antibody (cPRA) levels, and non-responders had 0% decrease in cPRA. Using a decision tree analysis, we found that a combination of transitional B cell and regulatory T cell (Treg) frequencies at baseline before initiation of desensitization therapy could distinguish responders from non-responders. Using a support vector machine (SVM) and longitudinal data, TRAF3IP3 transcripts and HLA-DR-CD38+CD4+ T cells could also distinguish responders from non-responders. Combining all assays in a multivariate analysis and elastic net regression model with 72 analytes, we identified seven that were highly interrelated and eleven that predicted response to desensitization therapy. Conclusions Measuring baseline and longitudinal immune and gene profiles could provide a useful strategy to distinguish responders from non-responders to desensitization therapy. This study presents the integration of novel translational studies including CyTOF immunophenotyping in a multivariate analysis model that has potential applications to predict response to desensitization, select candidates, and personalize medicine to ultimately improve overall outcomes in highly sensitized kidney transplant candidates. PMID:27078882

Immune Profiles to Predict Response to Desensitization Therapy in Highly HLA-Sensitized Kidney Transplant Candidates.

PubMed

Yabu, Julie M; Siebert, Janet C; Maecker, Holden T

2016-01-01

Kidney transplantation is the most effective treatment for end-stage kidney disease. Sensitization, the formation of human leukocyte antigen (HLA) antibodies, remains a major barrier to successful kidney transplantation. Despite the implementation of desensitization strategies, many candidates fail to respond. Current progress is hindered by the lack of biomarkers to predict response and to guide therapy. Our objective was to determine whether differences in immune and gene profiles may help identify which candidates will respond to desensitization therapy. Single-cell mass cytometry by time-of-flight (CyTOF) phenotyping, gene arrays, and phosphoepitope flow cytometry were performed in a study of 20 highly sensitized kidney transplant candidates undergoing desensitization therapy. Responders to desensitization therapy were defined as 5% or greater decrease in cumulative calculated panel reactive antibody (cPRA) levels, and non-responders had 0% decrease in cPRA. Using a decision tree analysis, we found that a combination of transitional B cell and regulatory T cell (Treg) frequencies at baseline before initiation of desensitization therapy could distinguish responders from non-responders. Using a support vector machine (SVM) and longitudinal data, TRAF3IP3 transcripts and HLA-DR-CD38+CD4+ T cells could also distinguish responders from non-responders. Combining all assays in a multivariate analysis and elastic net regression model with 72 analytes, we identified seven that were highly interrelated and eleven that predicted response to desensitization therapy. Measuring baseline and longitudinal immune and gene profiles could provide a useful strategy to distinguish responders from non-responders to desensitization therapy. This study presents the integration of novel translational studies including CyTOF immunophenotyping in a multivariate analysis model that has potential applications to predict response to desensitization, select candidates, and personalize medicine to ultimately improve overall outcomes in highly sensitized kidney transplant candidates.

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

PubMed

Linn, Kristin A; Gaonkar, Bilwaj; Satterthwaite, Theodore D; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T

2016-05-15

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases. Copyright © 2016 Elsevier Inc. 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.

Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification.

PubMed

Lee, Dongha; Jang, Changwon; Park, Hae-Jeong

2015-03-01

Signal drift in functional magnetic resonance imaging (fMRI) is an unavoidable artifact that limits classification performance in multi-voxel pattern analysis of fMRI. As conventional methods to reduce signal drift, global demeaning or proportional scaling disregards regional variations of drift, whereas voxel-wise univariate detrending is too sensitive to noisy fluctuations. To overcome these drawbacks, we propose a multivariate real-time detrending method for multiclass classification that involves spatial demeaning at each scan and the recursive detrending of drifts in the classifier outputs driven by a multiclass linear support vector machine. Experiments using binary and multiclass data showed that the linear trend estimation of the classifier output drift for each class (a weighted sum of drifts in the class-specific voxels) was more robust against voxel-wise artifacts that lead to inconsistent spatial patterns and the effect of online processing than voxel-wise detrending. The classification performance of the proposed method was significantly better, especially for multiclass data, than that of voxel-wise linear detrending, global demeaning, and classifier output detrending without demeaning. We concluded that the multivariate approach using classifier output detrending of fMRI signals with spatial demeaning preserves spatial patterns, is less sensitive than conventional methods to sample size, and increases classification performance, which is a useful feature for real-time fMRI classification. Copyright © 2014 Elsevier Inc. All rights reserved.

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

PubMed

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

2016-01-01

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

Impact of different variables on the outcome of patients with clinically confined prostate carcinoma: prediction of pathologic stage and biochemical failure using an artificial neural network.

PubMed

Ziada, A M; Lisle, T C; Snow, P B; Levine, R F; Miller, G; Crawford, E D

2001-04-15

The advent of advanced computing techniques has provided the opportunity to analyze clinical data using artificial intelligence techniques. This study was designed to determine whether a neural network could be developed using preoperative prognostic indicators to predict the pathologic stage and time of biochemical failure for patients who undergo radical prostatectomy. The preoperative information included TNM stage, prostate size, prostate specific antigen (PSA) level, biopsy results (Gleason score and percentage of positive biopsy), as well as patient age. All 309 patients underwent radical prostatectomy at the University of Colorado Health Sciences Center. The data from all patients were used to train a multilayer perceptron artificial neural network. The failure rate was defined as a rise in the PSA level > 0.2 ng/mL. The biochemical failure rate in the data base used was 14.2%. Univariate and multivariate analyses were performed to validate the results. The neural network statistics for the validation set showed a sensitivity and specificity of 79% and 81%, respectively, for the prediction of pathologic stage with an overall accuracy of 80% compared with an overall accuracy of 67% using the multivariate regression analysis. The sensitivity and specificity for the prediction of failure were 67% and 85%, respectively, demonstrating a high confidence in predicting failure. The overall accuracy rates for the artificial neural network and the multivariate analysis were similar. Neural networks can offer a convenient vehicle for clinicians to assess the preoperative risk of disease progression for patients who are about to undergo radical prostatectomy. Continued investigation of this approach with larger data sets seems warranted. Copyright 2001 American Cancer Society.

Discrimination of soft tissues using laser-induced breakdown spectroscopy in combination with k nearest neighbors (kNN) and support vector machine (SVM) classifiers

NASA Astrophysics Data System (ADS)

Li, Xiaohui; Yang, Sibo; Fan, Rongwei; Yu, Xin; Chen, Deying

2018-06-01

In this paper, discrimination of soft tissues using laser-induced breakdown spectroscopy (LIBS) in combination with multivariate statistical methods is presented. Fresh pork fat, skin, ham, loin and tenderloin muscle tissues are manually cut into slices and ablated using a 1064 nm pulsed Nd:YAG laser. Discrimination analyses between fat, skin and muscle tissues, and further between highly similar ham, loin and tenderloin muscle tissues, are performed based on the LIBS spectra in combination with multivariate statistical methods, including principal component analysis (PCA), k nearest neighbors (kNN) classification, and support vector machine (SVM) classification. Performances of the discrimination models, including accuracy, sensitivity and specificity, are evaluated using 10-fold cross validation. The classification models are optimized to achieve best discrimination performances. The fat, skin and muscle tissues can be definitely discriminated using both kNN and SVM classifiers, with accuracy of over 99.83%, sensitivity of over 0.995 and specificity of over 0.998. The highly similar ham, loin and tenderloin muscle tissues can also be discriminated with acceptable performances. The best performances are achieved with SVM classifier using Gaussian kernel function, with accuracy of 76.84%, sensitivity of over 0.742 and specificity of over 0.869. The results show that the LIBS technique assisted with multivariate statistical methods could be a powerful tool for online discrimination of soft tissues, even for tissues of high similarity, such as muscles from different parts of the animal body. This technique could be used for discrimination of tissues suffering minor clinical changes, thus may advance the diagnosis of early lesions and abnormalities.

Multiple Hypothesis Testing for Experimental Gingivitis Based on Wilcoxon Signed Rank Statistics

PubMed Central

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

2011-01-01

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

Predictive model for falling in Parkinson disease patients.

PubMed

Custodio, Nilton; Lira, David; Herrera-Perez, Eder; Montesinos, Rosa; Castro-Suarez, Sheila; Cuenca-Alfaro, Jose; Cortijo, Patricia

2016-12-01

Falls are a common complication of advancing Parkinson's disease (PD). Although numerous risk factors are known, reliable predictors of future falls are still lacking. The aim of this study was to develop a multivariate model to predict falling in PD patients. Prospective cohort with forty-nine PD patients. The area under the receiver-operating characteristic curve (AUC) was calculated to evaluate predictive performance of the purposed multivariate model. The median of PD duration and UPDRS-III score in the cohort was 6 years and 24 points, respectively. Falls occurred in 18 PD patients (30%). Predictive factors for falling identified by univariate analysis were age, PD duration, physical activity, and scores of UPDRS motor, FOG, ACE, IFS, PFAQ and GDS ( p -value < 0.001), as well as fear of falling score ( p -value = 0.04). The final multivariate model (PD duration, FOG, ACE, and physical activity) showed an AUC = 0.9282 (correctly classified = 89.83%; sensitivity = 92.68%; specificity = 83.33%). This study showed that our multivariate model have a high performance to predict falling in a sample of PD patients.

Differentiation between borderline and benign ovarian tumors: combined analysis of MRI with tumor markers for large cystic masses (≥5 cm).

PubMed

Park, Sung Yoon; Oh, Young Taik; Jung, Dae Chul

2016-05-01

There is overlap in imaging features between borderline and benign ovarian tumors. To analyze diagnostic performance of magnetic resonance imaging (MRI) combined with tumor markers for differentiating borderline from benign ovarian tumor. Ninety-nine patient with MRI and surgically confirmed ovarian tumors 5 cm or larger (borderline, n = 37; benign, n = 62) were included. On MRI, tumor size, septal number (0; 1-4; 5 or more), and presence of solid portion such as papillary projection or septal thickening 0.5 cm or larger were investigated. Serum tumor markers (carbohydrate antigen 125 [CA 125] and CA 19-9) were recorded. Multivariate analysis was conducted for assessing whether combined MRI with tumor markers could differentiate borderline from benign tumor. The diagnostic performance was also analyzed. Incidence of solid portion was 67.6% (25/37) in borderline and 3.2% (2/62) in benign tumors (P < 0.05). In all patients, without combined analysis of MRI with tumor markers, multivariate analysis revealed solid portion (P < 0.001) and CA 125 (P = 0.039) were significant for predicting borderline tumors. When combined analysis of MRI with CA 125 ((i) the presence of solid portion or (ii) CA 125 > 44.1 U/mL with septal number ≥5 for borderline tumor) is incorporated to multivariate analysis, it was only significant (P = 0.001). The sensitivity, specificity, PPV, NPV, and accuracy of combined analysis of MRI with CA 125 were 89.1%, 91.9%, 86.8%, 93.4, and 90.9%, respectively. Combined analysis of MRI with CA 125 may allow better differentiation between borderline and benign ovarian tumor compared with MRI alone. © The Foundation Acta Radiologica 2015.

Diagnostic performance of procalcitonin for hospitalised children with acute pyelonephritis presenting to the paediatric emergency department.

PubMed

Chen, Shan-Ming; Chang, Hung-Ming; Hung, Tung-Wei; Chao, Yu-Hua; Tsai, Jeng-Dau; Lue, Ko-Huang; Sheu, Ji-Nan

2013-05-01

Urinary tract infection (UTI) is a common bacterial infection in children that can result in permanent renal damage. This study prospectively assessed the diagnostic performance of procalcitonin (PCT) for predicting acute pyelonephritis (APN) among children with febrile UTI presenting to the paediatric emergency department (ED). Children aged ≤10 years with febrile UTI admitted to hospital from the paediatric ED were prospectively studied. Blood PCT, C reactive protein (CRP) and white blood cell (WBC) count were measured in the ED. Sensitivity, specificity, predictive values, multilevel likelihood ratios, receiver operating characteristic (ROC) curve analysis and multivariate logistic regression were used to assess quantitative variables for diagnosing APN. The 136 enrolled patients (56 boys and 80 girls; age range 1 month to 10 years) were divided into APN (n=87) and lower UTI (n=49) groups according to (99m)Tc-dimercaptosuccinic acid scan results. The cut-off value for maximum diagnostic performance of PCT was 1.3 ng/ml (sensitivity 86.2%, specificity 89.8%). By multivariate regression analysis, only PCT and CRP were retained as significant predictors of APN. Comparing ROC curves, PCT had a significantly greater area under the curve than CRP, WBC count and fever for differentiating between APN and lower UTI. PCT has better sensitivity and specificity than CRP and WBC count for distinguishing between APN and lower UTI. PCT is a valuable marker for predicting APN in children with febrile UTI. It may be considered in the initial investigation and therapeutic strategies for children presenting to the ED.

Texture analysis of pulmonary parenchymateous changes related to pulmonary thromboembolism in dogs - a novel approach using quantitative methods.

PubMed

Marschner, C B; Kokla, M; Amigo, J M; Rozanski, E A; Wiinberg, B; McEvoy, F J

2017-07-11

Diagnosis of pulmonary thromboembolism (PTE) in dogs relies on computed tomography pulmonary angiography (CTPA), but detailed interpretation of CTPA images is demanding for the radiologist and only large vessels may be evaluated. New approaches for better detection of smaller thrombi include dual energy computed tomography (DECT) as well as computer assisted diagnosis (CAD) techniques. The purpose of this study was to investigate the performance of quantitative texture analysis for detecting dogs with PTE using grey-level co-occurrence matrices (GLCM) and multivariate statistical classification analyses. CT images from healthy (n = 6) and diseased (n = 29) dogs with and without PTE confirmed on CTPA were segmented so that only tissue with CT numbers between -1024 and -250 Houndsfield Units (HU) was preserved. GLCM analysis and subsequent multivariate classification analyses were performed on texture parameters extracted from these images. Leave-one-dog-out cross validation and receiver operator characteristic (ROC) showed that the models generated from the texture analysis were able to predict healthy dogs with optimal levels of performance. Partial Least Square Discriminant Analysis (PLS-DA) obtained a sensitivity of 94% and a specificity of 96%, while Support Vector Machines (SVM) yielded a sensitivity of 99% and a specificity of 100%. The models, however, performed worse in classifying the type of disease in the diseased dog group: In diseased dogs with PTE sensitivities were 30% (PLS-DA) and 38% (SVM), and specificities were 80% (PLS-DA) and 89% (SVM). In diseased dogs without PTE the sensitivities of the models were 59% (PLS-DA) and 79% (SVM) and specificities were 79% (PLS-DA) and 82% (SVM). The results indicate that texture analysis of CTPA images using GLCM is an effective tool for distinguishing healthy from abnormal lung. Furthermore the texture of pulmonary parenchyma in dogs with PTE is altered, when compared to the texture of pulmonary parenchyma of healthy dogs. The models' poorer performance in classifying dogs within the diseased group, may be related to the low number of dogs compared to texture variables, a lack of balanced number of dogs within each group or a real lack of difference in the texture features among the diseased dogs.

Is there a relationship between periodontal conditions and number of medications among the elderly?

PubMed

Natto, Zuhair S; Aladmawy, Majdi; Alshaeri, Heba K; Alasqah, Mohammed; Papas, Athena

2016-03-01

To investigate possible correlations of clinical attachment level and pocket depth with number of medications in elderly individuals. Intra-oral examinations for 139 patients visiting Tufts dental clinic were done. Periodontal assessments were performed with a manual UNC-15 periodontal probe to measure probing depth (PD) and clinical attachment level (CAL) at 6 sites. Complete lists of patients' medications were obtained during the examinations. Statistical analysis involved Kruskal-Wallis, chi square and multivariate logistic regression analyses. Age and health status attained statistical significance (p< 0.05), in contingency table analysis with number of medications. Number of medications had an effect on CAL: increased attachment loss was observed when 4 or more medications were being taken by the patient. Number of medications did not have any effect on periodontal PD. In multivariate logistic regression analysis, 6 or more medications had a higher risk of attachment loss (>3mm) when compared to the no-medication group, in crude OR (1.20, 95% CI:0.22-6.64), and age adjusted (OR=1.16, 95% CI:0.21-6.45), but not with the multivariate model (OR=0.71, 95% CI:0.11-4.39). CAL seems to be more sensitive to the number of medications taken, when compared to PD. However, it is not possible to discriminate at exactly what number of drug combinations the breakdown in CAL will happen. We need to do further analysis, including more subjects, to understand the possible synergistic mechanisms for different drug and periodontal responses.

Novel risk score of contrast-induced nephropathy after percutaneous coronary intervention.

PubMed

Ji, Ling; Su, XiaoFeng; Qin, Wei; Mi, XuHua; Liu, Fei; Tang, XiaoHong; Li, Zi; Yang, LiChuan

2015-08-01

Contrast-induced nephropathy (CIN) post-percutaneous coronary intervention (PCI) is a major cause of acute kidney injury. In this study, we established a comprehensive risk score model to assess risk of CIN after PCI procedure, which could be easily used in a clinical environment. A total of 805 PCI patients, divided into analysis cohort (70%) and validation cohort (30%), were enrolled retrospectively in this study. Risk factors for CIN were identified using univariate analysis and multivariate logistic regression in the analysis cohort. Risk score model was developed based on multiple regression coefficients. Sensitivity and specificity of the new risk score system was validated in the validation cohort. Comparisons between the new risk score model and previous reported models were applied. The incidence of post-PCI CIN in the analysis cohort (n = 565) was 12%. Considerably high CIN incidence (50%) was observed in patients with chronic kidney disease (CKD). Age >75, body mass index (BMI) >25, myoglobin level, cardiac function level, hypoalbuminaemia, history of chronic kidney disease (CKD), Intra-aortic balloon pump (IABP) and peripheral vascular disease (PVD) were identified as independent risk factors of post-PCI CIN. A novel risk score model was established using multivariate regression coefficients, which showed highest sensitivity and specificity (0.917, 95%CI 0.877-0.957) compared with previous models. A new post-PCI CIN risk score model was developed based on a retrospective study of 805 patients. Application of this model might be helpful to predict CIN in patients undergoing PCI procedure. © 2015 Asian Pacific Society of Nephrology.

Novel immunological and nutritional-based prognostic index for gastric cancer.

PubMed

Sun, Kai-Yu; Xu, Jian-Bo; Chen, Shu-Ling; Yuan, Yu-Jie; Wu, Hui; Peng, Jian-Jun; Chen, Chuang-Qi; Guo, Pi; Hao, Yuan-Tao; He, Yu-Long

2015-05-21

To assess the prognostic significance of immunological and nutritional-based indices, including the prognostic nutritional index (PNI), neutrophil-lymphocyte ratio (NLR), and platelet-lymphocyte ratio in gastric cancer. We retrospectively reviewed 632 gastric cancer patients who underwent gastrectomy between 1998 and 2008. Areas under the receiver operating characteristic curve were calculated to compare the predictive ability of the indices, together with estimating the sensitivity, specificity and agreement rate. Univariate and multivariate analyses were performed to identify risk factors for overall survival (OS). Propensity score analysis was performed to adjust variables to control for selection bias. Each index could predict OS in gastric cancer patients in univariate analysis, but only PNI had independent prognostic significance in multivariate analysis before and after adjustment with propensity scoring (hazard ratio, 1.668; 95% confidence interval: 1.368-2.035). In subgroup analysis, a low PNI predicted a significantly shorter OS in patients with stage II-III disease (P = 0.019, P < 0.001), T3-T4 tumors (P < 0.001), or lymph node metastasis (P < 0.001). Canton score, a combination of PNI, NLR, and platelet, was a better indicator for OS than PNI, with the largest area under the curve for 12-, 36-, 60-mo OS and overall OS (P = 0.022, P = 0.030, P < 0.001, and P = 0.024, respectively). The maximum sensitivity, specificity, and agreement rate of Canton score for predicting prognosis were 84.6%, 34.9%, and 70.1%, respectively. PNI is an independent prognostic factor for OS in gastric cancer. Canton score can be a novel preoperative prognostic index in gastric cancer.

Algogenic substances and metabolic status in work-related Trapezius Myalgia: a multivariate explorative study.

PubMed

Gerdle, Björn; Kristiansen, Jesper; Larsson, Britt; Saltin, Bengt; Søgaard, Karen; Sjøgaard, Gisela

2014-10-28

This study compares the levels of algesic substances between subjects with trapezius myalgia (TM) and healthy controls (CON) and explores the multivariate correlation pattern between these substances, pain, and metabolic status together with relative blood flow changes reported in our previous paper (Eur J Appl Physiol 108:657-669, 2010). 43 female workers with (TM) and 19 females without (CON) trapezius myalgia were - using microdialysis - compared for differences in interstitial concentrations of interleukin-6 (IL-6), bradykinin (BKN), serotonin (5-HT), lactate dehydrogenas (LDH), substance P, and N-terminal propeptide of procollagen type I (PINP) in the trapezius muscle at rest and during repetitive/stressful work. These data were also used in multivariate analyses together with previously presented data (Eur J Appl Physiol 108:657-669, 2010): trapezius muscle blood flow, metabolite accumulation, oxygenation, and pain development and sensitivity. Substance P was significantly elevated in TM (p=0.0068). No significant differences were found in the classical algesic substances (p: 0.432-0.926). The multivariate analysis showed that blood flow related variables, interstitial concentrations of metabolic (pyruvate), and algesic (BKN and K+) substances were important for the discrimination of the subjects to one of the two groups (R2: 0.19-0.31, p<0.05). Pain intensity was positively associated with levels of 5-HT and K+ and negatively associated with oxygenation indicators and IL-6 in TM (R2: 0.24, p<0.05). A negative correlation existed in TM between mechanical pain sensitivity of trapezius and BKN and IL-6 (R2: 0.26-0.39, p<0.05). The present study increased understanding alterations in the myalgic muscle. When considering the system-wide aspects, increased concentrations of lactate, pyruvate and K+ and decreased oxygenation characterized TM compared to CON. There are three major possible explanations for this finding: the workers with pain had relatively low severity of myalgia, metabolic alterations preceded detectable alterations in levels of algesics, or peripheral sensitization and other muscle alterations existed in TM. Only SP of the investigated algesic substances was elevated in TM. Several of the algesics were of importance for the levels of pain intensity and mechanical pain sensitivity in TM. These results indicate peripheral contribution to maintenance of central nociceptive and pain mechanisms and may be important to consider when designing treatments.

Patterns and Predictors of Language and Literacy Abilities 4-10 Years in the Longitudinal Study of Australian Children

PubMed Central

Zubrick, Stephen R.; Taylor, Catherine L.; Christensen, Daniel

2015-01-01

Aims Oral language is the foundation of literacy. Naturally, policies and practices to promote children’s literacy begin in early childhood and have a strong focus on developing children’s oral language, especially for children with known risk factors for low language ability. The underlying assumption is that children’s progress along the oral to literate continuum is stable and predictable, such that low language ability foretells low literacy ability. This study investigated patterns and predictors of children’s oral language and literacy abilities at 4, 6, 8 and 10 years. The study sample comprised 2,316 to 2,792 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Six developmental patterns were observed, a stable middle-high pattern, a stable low pattern, an improving pattern, a declining pattern, a fluctuating low pattern, and a fluctuating middle-high pattern. Most children (69%) fit a stable middle-high pattern. By contrast, less than 1% of children fit a stable low pattern. These results challenged the view that children’s progress along the oral to literate continuum is stable and predictable. Findings Multivariate logistic regression was used to investigate risks for low literacy ability at 10 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. Predictors were modelled as risk variables with the lowest level of risk as the reference category. In the multivariate model, substantial risks for low literacy ability at 10 years, in order of descending magnitude, were: low school readiness, Aboriginal and/or Torres Strait Islander status and low language ability at 8 years. Moderate risks were high temperamental reactivity, low language ability at 4 years, and low language ability at 6 years. The following risk factors were not statistically significant in the multivariate model: Low maternal consistency, low family income, health care card, child not read to at home, maternal smoking, maternal education, family structure, temperamental persistence, and socio-economic area disadvantage. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude did not do particularly well in predicting low literacy ability at 10 years. PMID:26352436

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.

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Das, Bappa; Sahoo, Rabi N.; Pargal, Sourabh; Krishna, Gopal; Verma, Rakesh; Chinnusamy, Viswanathan; Sehgal, Vinay K.; Gupta, Vinod K.; Dash, Sushanta K.; Swain, Padmini

2018-03-01

In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500 nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes. Spectral data analysis was done to identify suitable spectral indices and models for sucrose estimation. Novel spectral indices in near infrared (NIR) range viz. ratio spectral index (RSI) and normalised difference spectral indices (NDSI) sensitive to sucrose, reducing sugar and total sugar content were identified which were subsequently calibrated and validated. The RSI and NDSI models had R2 values of 0.65, 0.71 and 0.67; RPD values of 1.68, 1.95 and 1.66 for sucrose, reducing sugar and total sugar, respectively for validation dataset. Different multivariate spectral models such as artificial neural network (ANN), multivariate adaptive regression splines (MARS), multiple linear regression (MLR), partial least square regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were also evaluated. The best performing multivariate models for sucrose, reducing sugars and total sugars were found to be, MARS, ANN and MARS, respectively with respect to RPD values of 2.08, 2.44, and 1.93. Results indicated that VNIR and SWIR spectroscopy combined with multivariate calibration can be used as a reliable alternative to conventional methods for measurement of sucrose, reducing sugars and total sugars of rice under water-deficit stress as this technique is fast, economic, and noninvasive.

Multivariable regression analysis of list experiment data on abortion: results from a large, randomly-selected population based study in Liberia.

PubMed

Moseson, Heidi; Gerdts, Caitlin; Dehlendorf, Christine; Hiatt, Robert A; Vittinghoff, Eric

2017-12-21

The list experiment is a promising measurement tool for eliciting truthful responses to stigmatized or sensitive health behaviors. However, investigators may be hesitant to adopt the method due to previously untestable assumptions and the perceived inability to conduct multivariable analysis. With a recently developed statistical test that can detect the presence of a design effect - the absence of which is a central assumption of the list experiment method - we sought to test the validity of a list experiment conducted on self-reported abortion in Liberia. We also aim to introduce recently developed multivariable regression estimators for the analysis of list experiment data, to explore relationships between respondent characteristics and having had an abortion - an important component of understanding the experiences of women who have abortions. To test the null hypothesis of no design effect in the Liberian list experiment data, we calculated the percentage of each respondent "type," characterized by response to the control items, and compared these percentages across treatment and control groups with a Bonferroni-adjusted alpha criterion. We then implemented two least squares and two maximum likelihood models (four total), each representing different bias-variance trade-offs, to estimate the association between respondent characteristics and abortion. We find no clear evidence of a design effect in list experiment data from Liberia (p = 0.18), affirming the first key assumption of the method. Multivariable analyses suggest a negative association between education and history of abortion. The retrospective nature of measuring lifetime experience of abortion, however, complicates interpretation of results, as the timing and safety of a respondent's abortion may have influenced her ability to pursue an education. Our work demonstrates that multivariable analyses, as well as statistical testing of a key design assumption, are possible with list experiment data, although with important limitations when considering lifetime measures. We outline how to implement this methodology with list experiment data in future research.

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

PubMed

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

2017-01-01

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

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.

Boosting Higgs pair production in the [Formula: see text] final state with multivariate techniques.

PubMed

Behr, J Katharina; Bortoletto, Daniela; Frost, James A; Hartland, Nathan P; Issever, Cigdem; Rojo, Juan

2016-01-01

The measurement of Higgs pair production will be a cornerstone of the LHC program in the coming years. Double Higgs production provides a crucial window upon the mechanism of electroweak symmetry breaking and has a unique sensitivity to the Higgs trilinear coupling. We study the feasibility of a measurement of Higgs pair production in the [Formula: see text] final state at the LHC. Our analysis is based on a combination of traditional cut-based methods with state-of-the-art multivariate techniques. We account for all relevant backgrounds, including the contributions from light and charm jet mis-identification, which are ultimately comparable in size to the irreducible 4 b QCD background. We demonstrate the robustness of our analysis strategy in a high pileup environment. For an integrated luminosity of [Formula: see text] ab[Formula: see text], a signal significance of [Formula: see text] is obtained, indicating that the [Formula: see text] final state alone could allow for the observation of double Higgs production at the High Luminosity LHC.

Multivariate class modeling techniques applied to multielement analysis for the verification of the geographical origin of chili pepper.

PubMed

Naccarato, Attilio; Furia, Emilia; Sindona, Giovanni; Tagarelli, Antonio

2016-09-01

Four class-modeling techniques (soft independent modeling of class analogy (SIMCA), unequal dispersed classes (UNEQ), potential functions (PF), and multivariate range modeling (MRM)) were applied to multielement distribution to build chemometric models able to authenticate chili pepper samples grown in Calabria respect to those grown outside of Calabria. The multivariate techniques were applied by considering both all the variables (32 elements, Al, As, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Fe, Ga, La, Li, Mg, Mn, Na, Nd, Ni, Pb, Pr, Rb, Sc, Se, Sr, Tl, Tm, V, Y, Yb, Zn) and variables selected by means of stepwise linear discriminant analysis (S-LDA). In the first case, satisfactory and comparable results in terms of CV efficiency are obtained with the use of SIMCA and MRM (82.3 and 83.2% respectively), whereas MRM performs better than SIMCA in terms of forced model efficiency (96.5%). The selection of variables by S-LDA permitted to build models characterized, in general, by a higher efficiency. MRM provided again the best results for CV efficiency (87.7% with an effective balance of sensitivity and specificity) as well as forced model efficiency (96.5%). Copyright © 2016 Elsevier Ltd. All rights reserved.

Molecular monitoring of epithelial-to-mesenchymal transition in breast cancer cells by means of Raman spectroscopy.

PubMed

Marro, M; Nieva, C; Sanz-Pamplona, R; Sierra, A

2014-09-01

In breast cancer the presence of cells undergoing the epithelial-to-mesenchymal transition is indicative of metastasis progression. Since metabolic features of breast tumour cells are critical in cancer progression and drug resistance, we hypothesized that the lipid content of malignant cells might be a useful indirect measure of cancer progression. In this study Multivariate Curve Resolution was applied to cellular Raman spectra to assess the metabolic composition of breast cancer cells undergoing the epithelial to mesenchymal transition. Multivariate Curve Resolution analysis led to the conclusion that this transition affects the lipid profile of cells, increasing tryptophan but maintaining a low fatty acid content in comparison with highly metastatic cells. Supporting those results, a Partial Least Square-Discriminant analysis was performed to test the ability of Raman spectroscopy to discriminate the initial steps of epithelial to mesenchymal transition in breast cancer cells. We achieved a high level of sensitivity and specificity, 94% and 100%, respectively. In conclusion, Raman microspectroscopy coupled with Multivariate Curve Resolution enables deconvolution and tracking of the molecular content of cancer cells during a biochemical process, being a powerful, rapid, reagent-free and non-invasive tool for identifying metabolic features of breast cancer cell aggressiveness at first stages of malignancy. Copyright © 2014 Elsevier B.V. All rights reserved.

Multivariate Models for Prediction of Skin Sensitization Hazard in Humans

EPA Science Inventory

One of ICCVAM’s highest priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary for a substance to elicit a skin sensitization reaction suggests that no single alternative me...

Multivariate models for skin sensitization hazard and potency

EPA Science Inventory

One of the top priorities being addressed by ICCVAM is the identification and validation of non-animal alternatives for skin sensitization testing. Although skin sensitization is a complex process, the key biological events have been well characterized in an adverse outcome pathw...

Multivariate Models for Prediction of Human Skin Sensitization Hazard

EPA Science Inventory

One of ICCVAM’s top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary for a substance to elicit a skin sensitization reaction suggests that no single alternative method...

Morphologic Features of Magnetic Resonance Imaging as a Surrogate of Capsular Contracture in Breast Cancer Patients With Implant-based Reconstructions.

PubMed

Tyagi, Neelam; Sutton, Elizabeth; Hunt, Margie; Zhang, Jing; Oh, Jung Hun; Apte, Aditya; Mechalakos, James; Wilgucki, Molly; Gelb, Emily; Mehrara, Babak; Matros, Evan; Ho, Alice

2017-02-01

Capsular contracture (CC) is a serious complication in patients receiving implant-based reconstruction for breast cancer. Currently, no objective methods are available for assessing CC. The goal of the present study was to identify image-based surrogates of CC using magnetic resonance imaging (MRI). We analyzed a retrospective data set of 50 patients who had undergone both a diagnostic MRI scan and a plastic surgeon's evaluation of the CC score (Baker's score) within a 6-month period after mastectomy and reconstructive surgery. The MRI scans were assessed for morphologic shape features of the implant and histogram features of the pectoralis muscle. The shape features, such as roundness, eccentricity, solidity, extent, and ratio length for the implant, were compared with the Baker score. For the pectoralis muscle, the muscle width and median, skewness, and kurtosis of the intensity were compared with the Baker score. Univariate analysis (UVA) using a Wilcoxon rank-sum test and multivariate analysis with the least absolute shrinkage and selection operator logistic regression was performed to determine significant differences in these features between the patient groups categorized according to their Baker's scores. UVA showed statistically significant differences between grade 1 and grade ≥2 for morphologic shape features and histogram features, except for volume and skewness. Only eccentricity, ratio length, and volume were borderline significant in differentiating grade ≤2 and grade ≥3. Features with P<.1 on UVA were used in the multivariate least absolute shrinkage and selection operator logistic regression analysis. Multivariate analysis showed a good level of predictive power for grade 1 versus grade ≥2 CC (area under the receiver operating characteristic curve 0.78, sensitivity 0.78, and specificity 0.82) and for grade ≤2 versus grade ≥3 CC (area under the receiver operating characteristic curve 0.75, sensitivity 0.75, and specificity 0.79). The morphologic shape features described on MR images were associated with the severity of CC. MRI has the potential to further improve the diagnostic ability of the Baker score in breast cancer patients who undergo implant reconstruction. Copyright © 2016 Elsevier Inc. All rights reserved.

Return Difference Feedback Design for Robust Uncertainty Tolerance in Stochastic Multivariable Control Systems.

DTIC Science & Technology

1984-07-01

34robustness" analysis for multiloop feedback systems. Reference [55] describes a simple method based on the Perron - Frobenius Theory of non-negative...Viewpoint, " Operator Theory : Advances and Applications, 12, pp. 277-302, 1984. - E. A. Jonckheere, "New Bound on the Sensitivity -- of the Solution of...Reidel, Dordrecht, Holland, 1984. M. G. Safonov, "Comments on Singular Value Theory in Uncertain Feedback Systems, " to appear IEEE Trans. on Automatic

Robust detection, isolation and accommodation for sensor failures

NASA Technical Reports Server (NTRS)

Emami-Naeini, A.; Akhter, M. M.; Rock, S. M.

1986-01-01

The objective is to extend the recent advances in robust control system design of multivariable systems to sensor failure detection, isolation, and accommodation (DIA), and estimator design. This effort provides analysis tools to quantify the trade-off between performance robustness and DIA sensitivity, which are to be used to achieve higher levels of performance robustness for given levels of DIA sensitivity. An innovations-based DIA scheme is used. Estimators, which depend upon a model of the process and process inputs and outputs, are used to generate these innovations. Thresholds used to determine failure detection are computed based on bounds on modeling errors, noise properties, and the class of failures. The applicability of the newly developed tools are demonstrated on a multivariable aircraft turbojet engine example. A new concept call the threshold selector was developed. It represents a significant and innovative tool for the analysis and synthesis of DiA algorithms. The estimators were made robust by introduction of an internal model and by frequency shaping. The internal mode provides asymptotically unbiased filter estimates.The incorporation of frequency shaping of the Linear Quadratic Gaussian cost functional modifies the estimator design to make it suitable for sensor failure DIA. The results are compared with previous studies which used thresholds that were selcted empirically. Comparison of these two techniques on a nonlinear dynamic engine simulation shows improved performance of the new method compared to previous techniques

Magnetic resonance imaging and magnetic resonance spectroscopy for detection of early Alzheimer's disease.

PubMed

Westman, Eric; Wahlund, Lars-Olof; Foy, Catherine; Poppe, Michaela; Cooper, Allison; Murphy, Declan; Spenger, Christian; Lovestone, Simon; Simmons, Andrew

2011-01-01

Alzheimer's disease is the most common form of neurodegenerative disorder and early detection is of great importance if new therapies are to be effectively administered. We have investigated whether the discrimination between early Alzheimer's disease (AD) and elderly healthy control subjects can be improved by adding magnetic resonance spectroscopy (MRS) measures to magnetic resonance imaging (MRI) measures. In this study 30 AD patients and 36 control subjects were included. High resolution T1-weighted axial magnetic resonance images were obtained from each subject. Automated regional volume segmentation and cortical thickness measures were determined for the images. 1H MRS was acquired from the hippocampus and LCModel was used for metabolic quantification. Altogether, this yielded 58 different volumetric, cortical thickness and metabolite ratio variables which were used for multivariate analysis to distinguish between subjects with AD and Healthy controls. Combining MRI and MRS measures resulted in a sensitivity of 97% and a specificity of 94% compared to using MRI or MRS measures alone (sensitivity: 87%, 76%, specificity: 86%, 83% respectively). Adding the MRS measures to the MRI measures more than doubled the positive likelihood ratio from 6 to 17. Adding MRS measures to a multivariate analysis of MRI measures resulted in significantly better classification than using MRI measures alone. The method shows strong potential for discriminating between Alzheimer's disease and controls.

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

NASA Astrophysics Data System (ADS)

Myoung, Se Hun; Kim, Jin-Koo

2016-03-01

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

A diagnostic analysis of the VVP single-doppler retrieval technique

NASA Technical Reports Server (NTRS)

Boccippio, Dennis J.

1995-01-01

A diagnostic analysis of the VVP (volume velocity processing) retrieval method is presented, with emphasis on understanding the technique as a linear, multivariate regression. Similarities and differences to the velocity-azimuth display and extended velocity-azimuth display retrieval techniques are discussed, using this framework. Conventional regression diagnostics are then employed to quantitatively determine situations in which the VVP technique is likely to fail. An algorithm for preparation and analysis of a robust VVP retrieval is developed and applied to synthetic and actual datasets with high temporal and spatial resolution. A fundamental (but quantifiable) limitation to some forms of VVP analysis is inadequate sampling dispersion in the n space of the multivariate regression, manifest as a collinearity between the basis functions of some fitted parameters. Such collinearity may be present either in the definition of these basis functions or in their realization in a given sampling configuration. This nonorthogonality may cause numerical instability, variance inflation (decrease in robustness), and increased sensitivity to bias from neglected wind components. It is shown that these effects prevent the application of VVP to small azimuthal sectors of data. The behavior of the VVP regression is further diagnosed over a wide range of sampling constraints, and reasonable sector limits are established.

Review-of-systems questionnaire as a predictive tool for psychogenic nonepileptic seizures.

PubMed

Robles, Liliana; Chiang, Sharon; Haneef, Zulfi

2015-04-01

Patients with refractory epilepsy undergo video-electroencephalography for seizure characterization, among whom approximately 10-30% will be discharged with the diagnosis of psychogenic nonepileptic seizures (PNESs). Clinical PNES predictors have been described but in general are not sensitive or specific. We evaluated whether multiple complaints in a routine review-of-system (ROS) questionnaire could serve as a sensitive and specific marker of PNESs. We performed a retrospective analysis of a standardized ROS questionnaire completed by patients with definite PNESs and epileptic seizures (ESs) diagnosed in our adult epilepsy monitoring unit. A multivariate analysis of covariance (MANCOVA) was used to determine whether groups with PNES and ES differed with respect to the percentage of complaints in the ROS questionnaire. Tenfold cross-validation was used to evaluate the predictive error of a logistic regression classifier for PNES status based on the percentage of positive complaints in the ROS questionnaire. A total of 44 patients were included for analysis. Patients with PNESs had a significantly higher number of complaints in the ROS questionnaire compared to patients with epilepsy. A threshold of 17% positive complaints achieved a 78% specificity and 85% sensitivity for discriminating between PNESs and ESs. We conclude that the routine ROS questionnaire may be a sensitive and specific predictive tool for discriminating between PNESs and ESs. Published by Elsevier Inc.

Analysis of Factors Influencing Diagnostic Accuracy of T-SPOT.TB for Active Tuberculosis in Clinical Practice.

PubMed

Zhang, Lifan; Shi, Xiaochun; Zhang, Yueqiu; Zhang, Yao; Huo, Feifei; Zhou, Baotong; Deng, Guohua; Liu, Xiaoqing

2017-08-10

T-SPOT.TB didn't perform a perfect diagnosis for active tuberculosis (ATB), and some factors may influence the results. We did this study to evaluate possible factors associated with the sensitivity and specificity of T-SPOT.TB, and the diagnostic parameters under varied conditions. Patients with suspected ATB were enrolled prospectively. Influencing factors of the sensitivity and specificity of T-SPOT.TB were evaluated using logistic regression models. Sensitivity, specificity, predictive values (PV), and likelihood ratios (LR) were calculated with consideration of relevant factors. Of the 865 participants, 205 (23.7%) had ATB, including 58 (28.3%) microbiologically confirmed TB and 147 (71.7%) clinically diagnosed TB. 615 (71.7%) were non-TB. 45 (5.2%) cases were clinically indeterminate and excluded from the final analysis. In multivariate analysis, serous effusion was the only independent risk factor related to lower sensitivity (OR = 0.39, 95% CI: 0.18-0.81) among patients with ATB. Among non-TB patients, age, TB history, immunosuppressive agents/glucocorticoid treatment and lymphocyte count were the independent risk factors related to specificity of T-SPOT.TB. Sensitivity, specificity, PV+, PV-, LR+ and LR- of T-SPOT.TB for diagnosis of ATB were 78.5%, 74.1%, 50.3%, 91.2%, 3.0 and 0.3, respectively. This study suggests that influencing factors of sensitivity and specificity of T-SPOT.TB should be considered for interpretation of T-SPOT.TB results.

Symptoms and signs associated with benign and malignant proximal fibular tumors: a clinicopathological analysis of 52 cases.

PubMed

Sun, Tao; Wang, Lingxiang; Guo, Changzhi; Zhang, Guochuan; Hu, Wenhai

2017-05-02

Malignant tumors in the proximal fibula are rare but life-threatening; however, biopsy is not routine due to the high risk of peroneal nerve injury. Our aim was to determine preoperative clinical indicators of malignancy. Between 2004 and 2016, 52 consecutive patients with proximal fibular tumors were retrospectively reviewed. Details of the clinicopathological characteristics including age, gender, location of tumors, the presenting symptoms, the duration of symptoms, and pathological diagnosis were collected. Descriptive statistics were calculated, and univariate and multivariate regression were performed. Of these 52 patients, 84.6% had benign tumors and 15.4% malignant tumors. The most common benign tumors were osteochondromas (46.2%), followed by enchondromas (13.5%) and giant cell tumors (13.5%). The most common malignancy was osteosarcomas (11.5%). The most common presenting symptoms were a palpable mass (52.0%) and pain (46.2%). Pain was the most sensitive (100%) and fourth specific (64%); both high skin temperature and peroneal nerve compression had the highest specificity (98%) and third sensitivity (64%); change in symptoms had the second highest specificity (89%) while 50% sensitivity. Using multivariate regression, palpable pain, high skin temperature, and peroneal nerve compression symptoms were predictors of malignancy. Most tumors in the proximal fibula are benign, and the malignancy is rare. Palpable pain, peroneal nerve compression symptoms, and high skin temperature were specific in predicting malignancy.

Distinguishing early and late brain aging from the Alzheimer's disease spectrum: consistent morphological patterns across independent samples.

PubMed

Doan, Nhat Trung; Engvig, Andreas; Zaske, Krystal; Persson, Karin; Lund, Martina Jonette; Kaufmann, Tobias; Cordova-Palomera, Aldo; Alnæs, Dag; Moberget, Torgeir; Brækhus, Anne; Barca, Maria Lage; Nordvik, Jan Egil; Engedal, Knut; Agartz, Ingrid; Selbæk, Geir; Andreassen, Ole A; Westlye, Lars T

2017-09-01

Alzheimer's disease (AD) is a debilitating age-related neurodegenerative disorder. Accurate identification of individuals at risk is complicated as AD shares cognitive and brain features with aging. We applied linked independent component analysis (LICA) on three complementary measures of gray matter structure: cortical thickness, area and gray matter density of 137 AD, 78 mild (MCI) and 38 subjective cognitive impairment patients, and 355 healthy adults aged 18-78 years to identify dissociable multivariate morphological patterns sensitive to age and diagnosis. Using the lasso classifier, we performed group classification and prediction of cognition and age at different age ranges to assess the sensitivity and diagnostic accuracy of the LICA patterns in relation to AD, as well as early and late healthy aging. Three components showed high sensitivity to the diagnosis and cognitive status of AD, with different relationships with age: one reflected an anterior-posterior gradient in thickness and gray matter density and was uniquely related to diagnosis, whereas the other two, reflecting widespread cortical thickness and medial temporal lobe volume, respectively, also correlated significantly with age. Repeating the LICA decomposition and between-subject analysis on ADNI data, including 186 AD, 395 MCI and 220 age-matched healthy controls, revealed largely consistent brain patterns and clinical associations across samples. Classification results showed that multivariate LICA-derived brain characteristics could be used to predict AD and age with high accuracy (area under ROC curve up to 0.93 for classification of AD from controls). Comparison between classifiers based on feature ranking and feature selection suggests both common and unique feature sets implicated in AD and aging, and provides evidence of distinct age-related differences in early compared to late aging. Copyright © 2017 Elsevier Inc. All rights reserved.

Ensembles of radial basis function networks for spectroscopic detection of cervical precancer

NASA Technical Reports Server (NTRS)

Tumer, K.; Ramanujam, N.; Ghosh, J.; Richards-Kortum, R.

1998-01-01

The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, noninvasively and quantitatively probes the biochemical and morphological changes that occur in precancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337-, 380-, and 460-nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from precancerous tissue samples. The use of connectionist methods such as multilayered perceptrons, radial basis function (RBF) networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated and near real-time implementation of precancer detection in the hands of nonexperts. The results are more reliable, direct, and accurate than those achieved by either human experts or multivariate statistical algorithms.

Housing Instability Among Current and Former Welfare Recipients

PubMed Central

Phinney, Robin; Danziger, Sheldon; Pollack, Harold A.; Seefeldt, Kristin

2007-01-01

Objectives. We examined correlates of eviction and homelessness among current and former welfare recipients from 1997 to 2003 in an urban Michigan community. Methods. Longitudinal cohort data were drawn from the Women’s Employment Study, a representative panel study of mothers who were receiving cash welfare in February 1997. We used logistic regression analysis to identify risk factors for both eviction and homelessness over the survey period. Results. Twenty percent (95% confidence interval [CI]=16%, 23%) of respondents were evicted and 12% (95% CI=10%, 15%) experienced homelessness at least once between fall 1997 and fall 2003. Multivariate analyses indicated 2 consistent risk factors: having less than a high school education and having used illicit drugs other than marijuana. Mental and physical health problems were significantly associated with homelessness but not evictions. A multivariate screening algorithm achieved 75% sensitivity and 67% specificity in identifying individuals at risk for homelessness. A corresponding algorithm for eviction achieved 75% sensitivity and 50% specificity. Conclusions. The high prevalence of housing instability among our respondents suggests the need to better target housing assistance and other social services to current and former welfare recipients with identifiable personal problems. PMID:17267717

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.

Insulin Sensitivity Measured With Euglycemic Clamp Is Independently Associated With Glomerular Filtration Rate in a Community-Based Cohort

PubMed Central

Nerpin, Elisabet; Risérus, Ulf; Ingelsson, Erik; Sundström, Johan; Jobs, Magnus; Larsson, Anders; Basu, Samar; Ärnlöv, Johan

2008-01-01

OBJECTIVE—To investigate the association between insulin sensitivity and glomerular filtration rate (GFR) in the community, with prespecified subgroup analyses in normoglycemic individuals with normal GFR. RESEARCH DESIGN AND METHODS—We investigated the cross-sectional association between insulin sensitivity (M/I, assessed using euglycemic clamp) and cystatin C–based GFR in a community-based cohort of elderly men (Uppsala Longitudinal Study of Adult Men [ULSAM], n = 1,070). We also investigated whether insulin sensitivity predicted the incidence of renal dysfunction at a follow-up examination after 7 years. RESULTS—Insulin sensitivity was directly related to GFR (multivariable-adjusted regression coefficient for 1-unit higher M/I 1.19 [95% CI 0.69–1.68]; P < 0.001) after adjusting for age, glucometabolic variables (fasting plasma glucose, fasting plasma insulin, and 2-h glucose after an oral glucose tolerance test), cardiovascular risk factors (hypertension, dyslipidemia, and smoking), and lifestyle factors (BMI, physical activity, and consumption of tea, coffee, and alcohol). The positive multivariable-adjusted association between insulin sensitivity and GFR also remained statistically significant in participants with normal fasting plasma glucose, normal glucose tolerance, and normal GFR (n = 443; P < 0.02). In longitudinal analyses, higher insulin sensitivity at baseline was associated with lower risk of impaired renal function (GFR <50 ml/min per 1.73 m2) during follow-up independently of glucometabolic variables (multivariable-adjusted odds ratio for 1-unit higher of M/I 0.58 [95% CI 0.40–0.84]; P < 0.004). CONCLUSIONS—Our data suggest that impaired insulin sensitivity may be involved in the development of renal dysfunction at an early stage, before the onset of diabetes or prediabetic glucose elevations. Further studies are needed in order to establish causality. PMID:18509205

A diagnostic model for the detection of sensitization to wheat allergens was developed and validated in bakery workers.

PubMed

Suarthana, Eva; Vergouwe, Yvonne; Moons, Karel G; de Monchy, Jan; Grobbee, Diederick; Heederik, Dick; Meijer, Evert

2010-09-01

To develop and validate a prediction model to detect sensitization to wheat allergens in bakery workers. The prediction model was developed in 867 Dutch bakery workers (development set, prevalence of sensitization 13%) and included questionnaire items (candidate predictors). First, principal component analysis was used to reduce the number of candidate predictors. Then, multivariable logistic regression analysis was used to develop the model. Internal validation and extent of optimism was assessed with bootstrapping. External validation was studied in 390 independent Dutch bakery workers (validation set, prevalence of sensitization 20%). The prediction model contained the predictors nasoconjunctival symptoms, asthma symptoms, shortness of breath and wheeze, work-related upper and lower respiratory symptoms, and traditional bakery. The model showed good discrimination with an area under the receiver operating characteristic (ROC) curve area of 0.76 (and 0.75 after internal validation). Application of the model in the validation set gave a reasonable discrimination (ROC area=0.69) and good calibration after a small adjustment of the model intercept. A simple model with questionnaire items only can be used to stratify bakers according to their risk of sensitization to wheat allergens. Its use may increase the cost-effectiveness of (subsequent) medical surveillance.

Weather sensitivity for zoo visitation in Toronto, Canada: a quantitative analysis of historical data

NASA Astrophysics Data System (ADS)

Hewer, Micah J.; Gough, William A.

2016-11-01

Based on a case study of the Toronto Zoo (Canada), multivariate regression analysis, involving both climatic and social variables, was employed to assess the relationship between daily weather and visitation. Zoo visitation was most sensitive to weather variability during the shoulder season, followed by the off-season and, then, the peak season. Temperature was the most influential weather variable in relation to zoo visitation, followed by precipitation and, then, wind speed. The intensity and direction of the social and climatic variables varied between seasons. Temperatures exceeding 26 °C during the shoulder season and 28 °C during the peak season suggested a behavioural threshold associated with zoo visitation, with conditions becoming too warm for certain segments of the zoo visitor market, causing visitor numbers to decline. Even light amounts of precipitation caused average visitor numbers to decline by nearly 50 %. Increasing wind speeds also demonstrated a negative influence on zoo visitation.

Determinants of Unintentional Leaks During CPAP Treatment in OSA.

PubMed

Lebret, Marius; Arnol, Nathalie; Martinot, Jean-Benoît; Lambert, Loïc; Tamisier, Renaud; Pepin, Jean-Louis; Borel, Jean-Christian

2018-04-01

Unintentional leakage from the mouth or around the mask may lead to cessation of CPAP treatment; however, the causes of unintentional leaks are poorly understood. The objectives of this study were (1) to identify determining factors of unintentional leakage and (2) to determine the effect of the type of mask (nasal/oronasal) used on unintentional leakage. Seventy-four polysomnograms from patients with OSA syndrome treated with auto-CPAP were analyzed (23 women; 56 ± 13 years; BMI, 32.9 kg/m 2 (range, 29.0-38.0 kg/m 2 ). Polysomnographic recordings were obtained under auto-CPAP, and mandibular behavior was measured with a magnetic sensor. After sleep and respiratory scoring, polysomnographic signals were computed as mean values over nonoverlapping 10-s intervals. The presence/absence of unintentional leakage was dichotomized for each 10-s interval (yes/no). Univariate and multivariate conditional regression models estimated the risk of unintentional leaks during an interval "T" based on the explanatory variables from the previous interval "T-1." A sensitivity analysis for the type of mask was then conducted. The univariate analysis showed that mandibular lowering (mouth opening), a high level of CPAP, body position (other than supine), and rapid eye movement (REM) sleep increased the risk of unintentional leaks and microarousal decreased it. In the multivariate analysis, the same variables remained independently associated with an increased risk of unintentional leakage. The sensitivity analysis showed that oronasal masks reduced the risk of unintentional leaks in cases of mouth opening and REM sleep. Mouth opening, CPAP level, sleep position, and REM sleep independently contribute to unintentional leakage. These results provide a strong rationale for the definition of phenotypes and the individual management of leaks during CPAP treatment. Copyright © 2017 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Multivariate logistic regression analysis of postoperative complications and risk model establishment of gastrectomy for gastric cancer: A single-center cohort report.

PubMed

Zhou, Jinzhe; Zhou, Yanbing; Cao, Shougen; Li, Shikuan; Wang, Hao; Niu, Zhaojian; Chen, Dong; Wang, Dongsheng; Lv, Liang; Zhang, Jian; Li, Yu; Jiao, Xuelong; Tan, Xiaojie; Zhang, Jianli; Wang, Haibo; Zhang, Bingyuan; Lu, Yun; Sun, Zhenqing

2016-01-01

Reporting of surgical complications is common, but few provide information about the severity and estimate risk factors of complications. If have, but lack of specificity. We retrospectively analyzed data on 2795 gastric cancer patients underwent surgical procedure at the Affiliated Hospital of Qingdao University between June 2007 and June 2012, established multivariate logistic regression model to predictive risk factors related to the postoperative complications according to the Clavien-Dindo classification system. Twenty-four out of 86 variables were identified statistically significant in univariate logistic regression analysis, 11 significant variables entered multivariate analysis were employed to produce the risk model. Liver cirrhosis, diabetes mellitus, Child classification, invasion of neighboring organs, combined resection, introperative transfusion, Billroth II anastomosis of reconstruction, malnutrition, surgical volume of surgeons, operating time and age were independent risk factors for postoperative complications after gastrectomy. Based on logistic regression equation, p=Exp∑BiXi / (1+Exp∑BiXi), multivariate logistic regression predictive model that calculated the risk of postoperative morbidity was developed, p = 1/(1 + e((4.810-1.287X1-0.504X2-0.500X3-0.474X4-0.405X5-0.318X6-0.316X7-0.305X8-0.278X9-0.255X10-0.138X11))). The accuracy, sensitivity and specificity of the model to predict the postoperative complications were 86.7%, 76.2% and 88.6%, respectively. This risk model based on Clavien-Dindo grading severity of complications system and logistic regression analysis can predict severe morbidity specific to an individual patient's risk factors, estimate patients' risks and benefits of gastric surgery as an accurate decision-making tool and may serve as a template for the development of risk models for other surgical groups.

Chemical fingerprinting of Arabidopsis using Fourier transform infrared (FT-IR) spectroscopic approaches.

PubMed

Gorzsás, András; Sundberg, Björn

2014-01-01

Fourier transform infrared (FT-IR) spectroscopy is a fast, sensitive, inexpensive, and nondestructive technique for chemical profiling of plant materials. In this chapter we discuss the instrumental setup, the basic principles of analysis, and the possibilities for and limitations of obtaining qualitative and semiquantitative information by FT-IR spectroscopy. We provide detailed protocols for four fully customizable techniques: (1) Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS): a sensitive and high-throughput technique for powders; (2) attenuated total reflectance (ATR) spectroscopy: a technique that requires no sample preparation and can be used for solid samples as well as for cell cultures; (3) microspectroscopy using a single element (SE) detector: a technique used for analyzing sections at low spatial resolution; and (4) microspectroscopy using a focal plane array (FPA) detector: a technique for rapid chemical profiling of plant sections at cellular resolution. Sample preparation, measurement, and data analysis steps are listed for each of the techniques to help the user collect the best quality spectra and prepare them for subsequent multivariate analysis.

The Relationship Between Serum Endocan Levels With the Presence of Slow Coronary Flow: A Cross-Sectional Study.

PubMed

Kundi, Harun; Gok, Murat; Kiziltunc, Emrullah; Topcuoglu, Canan; Cetin, Mustafa; Cicekcioglu, Hulya; Ugurlu, Burcu; Ulusoy, Feridun Vasfi

2017-07-01

The aim of this study was to investigate the relationship between endocan levels with the presence of slow coronary flow (SCF). In this cross-sectional study, a total of 88 patients, who admitted to our hospital, were included in this study. Of these, 53 patients with SCF and 35 patients with normal coronary flow were included in the final analysis. Coronary flow rates of all patients were determined by the Timi Frame Count (TFC) method. In correlation analysis, endocan levels revealed a significantly positive correlation with high sensitive C-reactive protein and corrected TFC. In multivariate logistic regression analysis, the endocan levels were found as independently associated with the presence of SCF. Finally, using a cutoff level of 2.3, endocan level predicted the presence of SCF with a sensitivity of 77.2% and specificity of 75.2%. In conclusion, our study showed that higher endocan levels were significantly and independently related to the presence of SCF.

Spot sign on 90-second delayed computed tomography angiography improves sensitivity for hematoma expansion and mortality: prospective study.

PubMed

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

2014-11-01

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

Infrared micro-spectroscopic studies of epithelial cells

PubMed Central

Romeo, Melissa; Mohlenhoff, Brian; Jennings, Michael; Diem, Max

2009-01-01

We report results from a study of human and canine mucosal cells, investigated by infrared micro-spectroscopy, and analyzed by methods of multivariate statistics. We demonstrate that the infrared spectra of individual cells are sensitive to the stage of maturation, and that a distinction between healthy and diseased cells will be possible. Since this report is written for an audience not familiar with infrared micro-spectroscopy, a short introduction into this field is presented along with a summary of principal component analysis. PMID:16797481

Dose to heart substructures is associated with non-cancer death after SBRT in stage I-II NSCLC patients.

PubMed

Stam, Barbara; Peulen, Heike; Guckenberger, Matthias; Mantel, Frederick; Hope, Andrew; Werner-Wasik, Maria; Belderbos, Jose; Grills, Inga; O'Connell, Nicolette; Sonke, Jan-Jakob

2017-06-01

To investigate potential associations between dose to heart (sub)structures and non-cancer death, in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT). 803 patients with early stage NSCLC received SBRT with predominant schedules of 3×18Gy (59%) or 4×12Gy (19%). All patients were registered to an average anatomy, their planned dose deformed accordingly, and dosimetric parameters for heart substructures were obtained. Multivariate Cox regression and a sensitivity analysis were used to identify doses to heart substructures or heart region with a significant association with non-cancer death respectively. Median follow-up was 34.8months. Two year Kaplan-Meier overall survival rate was 67%. Of the deceased patients, 26.8% died of cancer. Multivariate analysis showed that the maximum dose on the left atrium (median 6.5Gy EQD2, range=0.009-197, HR=1.005, p-value=0.035), and the dose to 90% of the superior vena cava (median 0.59Gy EQD2, range=0.003-70, HR=1.025, p-value=0.008) were significantly associated with non-cancer death. Sensitivity analysis identified the upper region of the heart (atria+vessels) to be significantly associated with non-cancer death. Doses to mainly the upper region of the heart were significantly associated with non-cancer death. Consequently, dose sparing in particular of the upper region of the heart could potentially improve outcome, and should be further studied. Copyright © 2017 Elsevier B.V. All rights reserved.

Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials.

PubMed

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

2013-05-01

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

Quality Reporting of Multivariable Regression Models in Observational Studies: Review of a Representative Sample of Articles Published in Biomedical Journals.

PubMed

Real, Jordi; Forné, Carles; Roso-Llorach, Albert; Martínez-Sánchez, Jose M

2016-05-01

Controlling for confounders is a crucial step in analytical observational studies, and multivariable models are widely used as statistical adjustment techniques. However, the validation of the assumptions of the multivariable regression models (MRMs) should be made clear in scientific reporting. The objective of this study is to review the quality of statistical reporting of the most commonly used MRMs (logistic, linear, and Cox regression) that were applied in analytical observational studies published between 2003 and 2014 by journals indexed in MEDLINE.Review of a representative sample of articles indexed in MEDLINE (n = 428) with observational design and use of MRMs (logistic, linear, and Cox regression). We assessed the quality of reporting about: model assumptions and goodness-of-fit, interactions, sensitivity analysis, crude and adjusted effect estimate, and specification of more than 1 adjusted model.The tests of underlying assumptions or goodness-of-fit of the MRMs used were described in 26.2% (95% CI: 22.0-30.3) of the articles and 18.5% (95% CI: 14.8-22.1) reported the interaction analysis. Reporting of all items assessed was higher in articles published in journals with a higher impact factor.A low percentage of articles indexed in MEDLINE that used multivariable techniques provided information demonstrating rigorous application of the model selected as an adjustment method. Given the importance of these methods to the final results and conclusions of observational studies, greater rigor is required in reporting the use of MRMs in the scientific literature.

Multivariate analysis techniques

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

Bendavid, Josh; Fisher, Wade C.; Junk, Thomas R.

2016-01-01

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

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing.

PubMed

Van Hertem, T; Bahr, C; Schlageter Tello, A; Viazzi, S; Steensels, M; Romanini, C E B; Lokhorst, C; Maltz, E; Halachmi, I; Berckmans, D

2016-09-01

The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ2, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.

Analysis and differentiation of paper samples by capillary electrophoresis and multivariate analysis.

PubMed

Fernández de la Ossa, Ma Ángeles; Ortega-Ojeda, Fernando; García-Ruiz, Carmen

2014-11-01

This work reports an investigation for the analysis of different paper samples using CE with laser-induced detection. Papers from four different manufactures (white-copy paper) and four different paper sources (white and recycled-copy papers, adhesive yellow paper notes and restaurant serviettes) were pulverized by scratching with a surgical scalpel prior to their derivatization with a fluorescent labeling agent, 8-aminopyrene-1,3,6-trisulfonic acid. Methodological conditions were evaluated, specifically the derivatization conditions with the aim to achieve the best S/N signals and the separation conditions in order to obtain optimum values of sensitivity and reproducibility. The best conditions, in terms of fastest, and easiest sample preparation procedure, minimal sample consumption, as well as the use of the simplest and fastest CE-procedure for obtaining the best analytical parameters, were applied to the analysis of the different paper samples. The registered electropherograms were pretreated (normalized and aligned) and subjected to multivariate analysis (principal component analysis). A successful discrimination among paper samples without entanglements was achieved. To the best of our knowledge, this work presents the first approach to achieve a successful differentiation among visually similar white-copy paper samples produced by different manufactures and paper from different paper sources through their direct analysis by CE-LIF and subsequent comparative study of the complete cellulose electropherogram by chemometric tools. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Accuracy of MRI for the diagnosis of metastatic cervical lymphadenopathy in patients with thyroid cancer.

PubMed

Chen, Qinghua; Raghavan, Prashant; Mukherjee, Sugoto; Jameson, Mark J; Patrie, James; Xin, Wenjun; Xian, Junfang; Wang, Zhenchang; Levine, Paul A; Wintermark, Max

2015-10-01

The aim of this study was to systematically compare a comprehensive array of magnetic resonance (MR) imaging features in terms of their sensitivity and specificity to diagnose cervical lymph node metastases in patients with thyroid cancer. The study included 41 patients with thyroid malignancy who underwent surgical excision of cervical lymph nodes and had preoperative MR imaging ≤4weeks prior to surgery. Three head and neck neuroradiologists independently evaluated all the MR images. Using the pathology results as reference, the sensitivity, specificity and interobserver agreement of each MR imaging characteristic were calculated. On multivariate analysis, no single imaging feature was significantly correlated with metastasis. In general, imaging features demonstrated high specificity, but poor sensitivity and moderate interobserver agreement at best. Commonly used MR imaging features have limited sensitivity at correctly identifying cervical lymph node metastases in patients with thyroid cancer. A negative neck MR scan should not dissuade a surgeon from performing a neck dissection in patients with thyroid carcinomas.

Proteomic Signatures of the Zebrafish (Danio rerio) Embryo: Sensitivity and Specificity in Toxicity Assessment of Chemicals.

PubMed

Hanisch, Karen; Küster, Eberhard; Altenburger, Rolf; Gündel, Ulrike

2010-01-01

Studies using embryos of the zebrafish Danio rerio (DarT) instead of adult fish for characterising the (eco-) toxic potential of chemicals have been proposed as animal replacing methods. Effect analysis at the molecular level might enhance sensitivity, specificity, and predictive value of the embryonal studies. The present paper aimed to test the potential of toxicoproteomics with zebrafish eleutheroembryos for sensitive and specific toxicity assessment. 2-DE-based toxicoproteomics was performed applying low-dose (EC(10)) exposure for 48 h with three-model substances Rotenone, 4,6-dinitro-o-cresol (DNOC) and Diclofenac. By multivariate "pattern-only" PCA and univariate statistical analyses, alterations in the embryonal proteome were detectable in nonetheless visibly intact organisms and treatment with the three substances was distinguishable at the molecular level. Toxicoproteomics enabled the enhancement of sensitivity and specificity of the embryonal toxicity assay and bear the potency to identify protein markers serving as general stress markers and early diagnosis of toxic stress.

Proteomic Signatures of the Zebrafish (Danio rerio) Embryo: Sensitivity and Specificity in Toxicity Assessment of Chemicals

PubMed Central

Hanisch, Karen; Küster, Eberhard; Altenburger, Rolf; Gündel, Ulrike

2010-01-01

Studies using embryos of the zebrafish Danio rerio (DarT) instead of adult fish for characterising the (eco-) toxic potential of chemicals have been proposed as animal replacing methods. Effect analysis at the molecular level might enhance sensitivity, specificity, and predictive value of the embryonal studies. The present paper aimed to test the potential of toxicoproteomics with zebrafish eleutheroembryos for sensitive and specific toxicity assessment. 2-DE-based toxicoproteomics was performed applying low-dose (EC10) exposure for 48 h with three-model substances Rotenone, 4,6-dinitro-o-cresol (DNOC) and Diclofenac. By multivariate “pattern-only” PCA and univariate statistical analyses, alterations in the embryonal proteome were detectable in nonetheless visibly intact organisms and treatment with the three substances was distinguishable at the molecular level. Toxicoproteomics enabled the enhancement of sensitivity and specificity of the embryonal toxicity assay and bear the potency to identify protein markers serving as general stress markers and early diagnosis of toxic stress. PMID:22084678

A blood tumor marker combination assay produces high sensitivity and specificity for cancer according to the natural history.

PubMed

Kobayashi, Tsuneo

2018-03-01

Diagnosis using a specific tumor marker is difficult because the sensitivity of this detection method is under 20%. Herein, a tumor marker combination assay, combining growth-related tumor marker and associated tumor marker (Cancer, 73(7), 1994), was employed. This double-blind tumor marker combination assay (TMCA) showed 87.5% sensitivity as the results, but a low specificity, ranging from 30 to 76%. To overcome this low specificity, we exploited complex markers, a multivariate analysis and serum fractionation by biochemical biopsy. Thus, in this study, a combination of new techniques was used to re-evaluate these serum samples. Three serum panels, containing 90, 120, and 97 samples were obtained from the Mayo Clinic. The final results showed 80-90% sensitivity, 84-85% specificity, and 83-88% accuracy. We demonstrated a notable tumor marker combination assay with high accuracy. This TMCA should be applicable for primary cancer detection and recurrence prevention. © 2018 The Author. Cancer Medicine published by John Wiley & Sons Ltd.

Apolipoprotein E4 serum concentration for increased sensitivity and specificity of diagnosis of drug treated Alzheimer's disease patients vs. drug treated parkinson's disease patients vs. age-matched normal controls.

PubMed

Goldknopf, Ira L; Park, Helen R; Sabbagh, Marwan

2012-12-01

Inasmuch as Alzheimer's disease (AD) is difficult to diagnose, patients with suspected dementias are often given FDA approved medications, including donepezil, rivastigmine, memantine HCl, or a combination, prior to diagnosis, and some respond with improved cognition. The present study demonstrates how concentrations of a select group of serum protein biomarkers can provide the basis for sensitive and specific differential diagnosis of AD in drug treated patients. Optimization is addressed by taking into account whether the patients and controls have or do not have increased risk of AD die to the presence or absence of Apolipoprotein E4. For differential diagnosis of AD, prospectively collected newly drawn blood serum samples were obtained from drug treated Alzheimer's disease and Parkinson's disease patients from a first (39 drug treated DTAD, and 31 age matched normal controls) and second medical center (56 drug treated DTPD, 47 age-matched normal controls). Analytically validated quantitative 2D gel electrophoresis (%CV ≤ 20%; LOD ≥ 0.5 ng/spot, 300 μg/ml of blood serum) was employed with patient and control sera for differential diagnosis of AD. Protein quantitation was subjected to statistical analysis by single variable Dot, Box and Whiskers and Receiver Operator Characteristics (ROC) plots for individual biomarker performance, and multivariate linear discriminant analysis for joint performance of groups of biomarkers. Protein spots were identified and characterized by LC MS/MS of in-gel trypsin digests, amino acid sequence spans of the identified peptides, and the protein spot molecular weights and isoelectric points. The single variable statistical profiles of 58 individual protein biomarker concentrations of the DTAD patient group differed from those of the normal and/or the disease control groups. Multivariate linear discriminant analysis of blood serum concentrations of the 58 proteins distinguished drug treated Alzheimer's disease (DTAD) patients from drug treated Parkinson's disease (DTPD) patients and age matched normal controls (collectively not-DTAD, DTAD Sensitivity 87.2%, Not-DTAD Specificity 87.2). Moreover, when the patients and controls were stratified into carriers or non-carriers of Alzheimer's high risk Apolipoprotein E 4 allele and/or the Apolipoprotein E4 protein, the DTAD, DTPD and control Apo E4 (+) profiles were more divergent from one another than the corresponding Apo E4 (-) profiles. Multivariate stepwise linear discriminant analysis selected 17 of the 58 biomarkers as optimal and complimentary for distinguishing Apo E4 (+) DTAD patients from Apo E4 (+) DTPD and Apo E4 (+) controls (collectively Apo E4 (+) not-DTAD, DTAD Sensitivity 100%, not-DTAD Specificity 100%) and 22 of the 58 biomarkers for distinguishing Apo E4 (-) DTAD patients from Apo E4 (-) DTPD and Apo E4 (-) controls (collectively Apo E4 (-) not-DTAD, DTAD Sensitivity 94.4%, not- DTAD Specificity 94.4%). Only 6 of the selected proteins were common to both the Apo E4 (+) and the Apo E4 (-) discriminant functions. Recombining of the results of Apo E4 (+) and Apo E4 (-) discriminations provided overall sensitivity for total DTAD of 97.4% and specificity for total not-DTAD of 95.7%. These results can form the basis of a blood test for differential diagnosis of Alzheimer's disease patients already under treatment (DTAD) by anti dementia drugs, including donepezil, rivastigmine, memantine HCl, or a combination thereof. Also, the profile differences and the rise in specificity and sensitivity obtained by handling the Apo E4 (+) and Apo E4 (-) groups separately supports the concept that they are different patient and control populations in terms of the "normal" physiology, the pathophysiology of disease, and the response to drug treatment. Taking that into account enables increased sensitivity and specificity of differential diagnosis of Alzheimer's disease.

Determination of complex formation constants by phase sensitive alternating current polarography: Cadmium-polymethacrylic acid and cadmium-polygalacturonic acid.

PubMed

Garrigosa, Anna Maria; Gusmão, Rui; Ariño, Cristina; Díaz-Cruz, José Manuel; Esteban, Miquel

2007-10-15

The use of phase sensitive alternating current polarography (ACP) for the evaluation of complex formation constants of systems where electrodic adsorption is present has been proposed. The applicability of the technique implies the previous selection of the phase angle where contribution of capacitive current is minimized. This is made using Multivariate Curve Resolution by Alternating Least Squares (MCR-ALS) in the analysis of ACP measurements at different phase angles. The method is checked by the study of the complexation of Cd by polymethacrylic (PMA) and polygalacturonic (PGA) acids, and the optimal phase angles have been ca. -10 degrees for Cd-PMA and ca. -15 degrees for Cd-PGA systems. The goodness of phase sensitive ACP has been demonstrated comparing the determined complex formation constants with those obtained by reverse pulse polarography, a technique that minimizes the electrode adsorption effects on the measured currents.

Sensitivity Analysis in Sequential Decision Models.

PubMed

Chen, Qiushi; Ayer, Turgay; Chhatwal, Jagpreet

2017-02-01

Sequential decision problems are frequently encountered in medical decision making, which are commonly solved using Markov decision processes (MDPs). Modeling guidelines recommend conducting sensitivity analyses in decision-analytic models to assess the robustness of the model results against the uncertainty in model parameters. However, standard methods of conducting sensitivity analyses cannot be directly applied to sequential decision problems because this would require evaluating all possible decision sequences, typically in the order of trillions, which is not practically feasible. As a result, most MDP-based modeling studies do not examine confidence in their recommended policies. In this study, we provide an approach to estimate uncertainty and confidence in the results of sequential decision models. First, we provide a probabilistic univariate method to identify the most sensitive parameters in MDPs. Second, we present a probabilistic multivariate approach to estimate the overall confidence in the recommended optimal policy considering joint uncertainty in the model parameters. We provide a graphical representation, which we call a policy acceptability curve, to summarize the confidence in the optimal policy by incorporating stakeholders' willingness to accept the base case policy. For a cost-effectiveness analysis, we provide an approach to construct a cost-effectiveness acceptability frontier, which shows the most cost-effective policy as well as the confidence in that for a given willingness to pay threshold. We demonstrate our approach using a simple MDP case study. We developed a method to conduct sensitivity analysis in sequential decision models, which could increase the credibility of these models among stakeholders.

Sensitivity of the Positive and Negative Syndrome Scale (PANSS) in Detecting Treatment Effects via Network Analysis.

PubMed

Esfahlani, Farnaz Zamani; Sayama, Hiroki; Visser, Katherine Frost; Strauss, Gregory P

2017-12-01

Objective: The Positive and Negative Syndrome Scale is a primary outcome measure in clinical trials examining the efficacy of antipsychotic medications. Although the Positive and Negative Syndrome Scale has demonstrated sensitivity as a measure of treatment change in studies using traditional univariate statistical approaches, its sensitivity to detecting network-level changes in dynamic relationships among symptoms has yet to be demonstrated using more sophisticated multivariate analyses. In the current study, we examined the sensitivity of the Positive and Negative Syndrome Scale to detecting antipsychotic treatment effects as revealed through network analysis. Design: Participants included 1,049 individuals diagnosed with psychotic disorders from the Phase I portion of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. Of these participants, 733 were clinically determined to be treatment-responsive and 316 were found to be treatment-resistant. Item level data from the Positive and Negative Syndrome Scale were submitted to network analysis, and macroscopic, mesoscopic, and microscopic network properties were evaluated for the treatment-responsive and treatment-resistant groups at baseline and post-phase I antipsychotic treatment. Results: Network analysis indicated that treatment-responsive patients had more densely connected symptom networks after antipsychotic treatment than did treatment-responsive patients at baseline, and that symptom centralities increased following treatment. In contrast, symptom networks of treatment-resistant patients behaved more randomly before and after treatment. Conclusions: These results suggest that the Positive and Negative Syndrome Scale is sensitive to detecting treatment effects as revealed through network analysis. Its findings also provide compelling new evidence that strongly interconnected symptom networks confer an overall greater probability of treatment responsiveness in patients with psychosis, suggesting that antipsychotics achieve their effect by enhancing a number of central symptoms, which then facilitate reduction of other highly coupled symptoms in a network-like fashion.

A Bayesian Multi-Level Factor Analytic Model of Consumer Price Sensitivities across Categories

ERIC Educational Resources Information Center

Duvvuri, Sri Devi; Gruca, Thomas S.

2010-01-01

Identifying price sensitive consumers is an important problem in marketing. We develop a Bayesian multi-level factor analytic model of the covariation among household-level price sensitivities across product categories that are substitutes. Based on a multivariate probit model of category incidence, this framework also allows the researcher to…

[Relationship between highly sensitive cardiac troponin T and sepsis and outcome in critically ill patients].

PubMed

Wang, T T; Jiang, L

2017-10-01

Objective: To investigate the prognostic value of highly sensitive cardiac Troponin T (hs-cTn T) for sepsis in critically ill patients. Methods: Patients estimated to stay in the ICU of Fuxing Hospital for more than 24h were enrolled at from March 2014 to December 2014. Serum hs-cTn T was tested within two hours. Univariate and multivariate linear regression analyses were used to determine the association of variables with the hs-cTn T. Multivariable logistic regression analysis was used to evaluate the risk factors of 28-day mortality. Results: A total of 125 patients were finally enrolled including 68 patients with sepsis and 57 without. The levels of hs-cTn T in sepsis and non-sepsis groups were significantly different[52.0(32.5, 87.5) ng/L vs 14.0(6.5, 29.0) ng/L respectively, P <0.001]. In sepsis group, hs-cTn T among common sepsis, severe sepsis and septic shock were similar. Hs-cTn T was significantly higher in non-survivors than survivors [27(13, 52)ng/L vs 44.5(28.8, 83.5)ng/L, P <0.001]. Age, sepsis, serum creatinine were independent risk factors affecting hs-cTn T by multivariate linear regression analyses. But hs-cTn T was not a risk factor for death. Conclusion: Patients with sepsis had higher serum hs-cTn T than those without sepsis. but it was not found to be associated with the severity of sepsis.

Gravitational Wave Detection of Compact Binaries Through Multivariate Analysis

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

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.

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.

Comparative artificial neural network and partial least squares models for analysis of Metronidazole, Diloxanide, Spiramycin and Cliquinol in pharmaceutical preparations.

PubMed

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

2014-09-15

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

The Irvine, Beatties, and Bresnahan (IBB) Forelimb Recovery Scale: An Assessment of Reliability and Validity

PubMed Central

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

2014-01-01

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

Comparative artificial neural network and partial least squares models for analysis of Metronidazole, Diloxanide, Spiramycin and Cliquinol in pharmaceutical preparations

NASA Astrophysics Data System (ADS)

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

2014-09-01

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

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.

Can texture analysis of tooth microwear detect within guild niche partitioning in extinct species?

NASA Astrophysics Data System (ADS)

Purnell, Mark; Nedza, Christopher; Rychlik, Leszek

2017-04-01

Recent work shows that tooth microwear analysis can be applied further back in time and deeper into the phylogenetic history of vertebrate clades than previously thought (e.g. niche partitioning in early Jurassic insectivorous mammals; Gill et al., 2014, Nature). Furthermore, quantitative approaches to analysis based on parameterization of surface roughness are increasing the robustness and repeatability of this widely used dietary proxy. Discriminating between taxa within dietary guilds has the potential to significantly increase our ability to determine resource use and partitioning in fossil vertebrates, but how sensitive is the technique? To address this question we analysed tooth microwear texture in sympatric populations of shrew species (Neomys fodiens, Neomys anomalus, Sorex araneus, Sorex minutus) from BiaŁ owieza Forest, Poland. These populations are known to exhibit varying degrees of niche partitioning (Churchfield & Rychlik, 2006, J. Zool.) with greatest overlap between the Neomys species. Sorex araneus also exhibits some niche overlap with N. anomalus, while S. minutus is the most specialised. Multivariate analysis based only on tooth microwear textures recovers the same pattern of niche partitioning. Our results also suggest that tooth textures track seasonal differences in diet. Projecting data from fossils into the multivariate dietary space defined using microwear from extant taxa demonstrates that the technique is capable of subtle dietary discrimination in extinct insectivores.

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…

Multivariate sensitivity to voice during auditory categorization.

PubMed

Lee, Yune Sang; Peelle, Jonathan E; Kraemer, David; Lloyd, Samuel; Granger, Richard

2015-09-01

Past neuroimaging studies have documented discrete regions of human temporal cortex that are more strongly activated by conspecific voice sounds than by nonvoice sounds. However, the mechanisms underlying this voice sensitivity remain unclear. In the present functional MRI study, we took a novel approach to examining voice sensitivity, in which we applied a signal detection paradigm to the assessment of multivariate pattern classification among several living and nonliving categories of auditory stimuli. Within this framework, voice sensitivity can be interpreted as a distinct neural representation of brain activity that correctly distinguishes human vocalizations from other auditory object categories. Across a series of auditory categorization tests, we found that bilateral superior and middle temporal cortex consistently exhibited robust sensitivity to human vocal sounds. Although the strongest categorization was in distinguishing human voice from other categories, subsets of these regions were also able to distinguish reliably between nonhuman categories, suggesting a general role in auditory object categorization. Our findings complement the current evidence of cortical sensitivity to human vocal sounds by revealing that the greatest sensitivity during categorization tasks is devoted to distinguishing voice from nonvoice categories within human temporal cortex. Copyright © 2015 the American Physiological Society.

Diagnostic accuracy of spot urinary protein and albumin to creatinine ratios for detection of significant proteinuria or adverse pregnancy outcome in patients with suspected pre-eclampsia: systematic review and meta-analysis

PubMed Central

Morris, R K; Riley, R D; Doug, M; Deeks, J J

2012-01-01

Objective To determine the diagnostic accuracy of two “spot urine” tests for significant proteinuria or adverse pregnancy outcome in pregnant women with suspected pre-eclampsia. Design Systematic review and meta-analysis. Data sources Searches of electronic databases 1980 to January 2011, reference list checking, hand searching of journals, and contact with experts. Inclusion criteria Diagnostic studies, in pregnant women with hypertension, that compared the urinary spot protein to creatinine ratio or albumin to creatinine ratio with urinary protein excretion over 24 hours or adverse pregnancy outcome. Study characteristics, design, and methodological and reporting quality were objectively assessed. Data extraction Study results relating to diagnostic accuracy were extracted and synthesised using multivariate random effects meta-analysis methods. Results Twenty studies, testing 2978 women (pregnancies), were included. Thirteen studies examining protein to creatinine ratio for the detection of significant proteinuria were included in the multivariate analysis. Threshold values for protein to creatinine ratio ranged between 0.13 and 0.5, with estimates of sensitivity ranging from 0.65 to 0.89 and estimates of specificity from 0.63 to 0.87; the area under the summary receiver operating characteristics curve was 0.69. On average, across all studies, the optimum threshold (that optimises sensitivity and specificity combined) seems to be between 0.30 and 0.35 inclusive. However, no threshold gave a summary estimate above 80% for both sensitivity and specificity, and considerable heterogeneity existed in diagnostic accuracy across studies at most thresholds. No studies looked at protein to creatinine ratio and adverse pregnancy outcome. For albumin to creatinine ratio, meta-analysis was not possible. Results from a single study suggested that the most predictive result, for significant proteinuria, was with the DCA 2000 quantitative analyser (>2 mg/mmol) with a summary sensitivity of 0.94 (95% confidence interval 0.86 to 0.98) and a specificity of 0.94 (0.87 to 0.98). In a single study of adverse pregnancy outcome, results for perinatal death were a sensitivity of 0.82 (0.48 to 0.98) and a specificity of 0.59 (0.51 to 0.67). Conclusion The maternal “spot urine” estimate of protein to creatinine ratio shows promising diagnostic value for significant proteinuria in suspected pre-eclampsia. The existing evidence is not, however, sufficient to determine how protein to creatinine ratio should be used in clinical practice, owing to the heterogeneity in test accuracy and prevalence across studies. Insufficient evidence is available on the use of albumin to creatinine ratio in this area. Insufficient evidence exists for either test to predict adverse pregnancy outcome. PMID:22777026

Determining venous thromboembolic risk assessment for patients with trauma: the Trauma Embolic Scoring System.

PubMed

Rogers, Frederick B; Shackford, Steven R; Horst, Michael A; Miller, Jo Ann; Wu, Daniel; Bradburn, Eric; Rogers, Amelia; Krasne, Margaret

2012-08-01

This study aimed to determine the relative "weight" of risk factors known to be associated with venous thromboembolism (VTE) for patients with trauma based on injuries and comorbidities. A retrospective review of 16,608 consecutive admissions to a trauma center was performed. Patients were separated into those who developed VTE (n = 141) versus those who did not (16,467). Univariate analysis was performed for each risk factor reported in the trauma literature. Risk factors that were shown to be significant (p < 0.05) by univariate analysis underwent multivariate analysis to develop odds ratios for VTE. The Trauma Embolic Scoring System (TESS) was derived from the multivariate coefficients. The resulting TESS was compared with a data set from the National Trauma Data Bank (2002-2006) to determine its ability to predict VTE. The multivariate analysis demonstrated that age, Injury Severity Score, obesity, ventilator use for more than 3 days, and lower-extremity trauma were significant predictors of VTE in our patient population. The TESS was from 0 to 14, with the best prediction for those patients with a score of more than 6 (sensitivity, 81.6%; specificity, 84%). Overall, the model had excellent discrimination in predicting VTE with a receiver operating characteristic curve of 0.89. The VTE rates for TESS in the National Trauma Data Bank data set were similar for all integers except for 3 and 4, in which the VTE rates were significantly higher (3, 0.2% vs. 0.6%; 4, 0.4% vs. 1.0%). The TESS provides an objective measure of classifying VTE risk for patients with trauma. The TESS could allow informed decision making regarding prophylaxis strategies in patients with trauma.

Alkalosis in Critically Ill Patients with Severe Sepsis and Septic Shock

PubMed Central

Jazrawi, Allan; Miller, Jan; Baigi, Amir; Chew, Michelle

2017-01-01

Introduction Although metabolic alkalosis is a common occurrence in intensive care units (ICUs), no study has evaluated its prevalence or outcomes in patients with severe sepsis or septic shock. Methods This is a retrospective cohort study of critically ill patients suffering from severe sepsis and septic shock admitted to the ICUs of Halmstad and Varberg County hospitals. From 910 patient records, 627 patients met the inclusion criteria. We investigated the relationship between metabolic alkalosis and mortality. Further, we studied the relationship between metabolic alkalosis and ICU length of stay (LOS). Results Metabolic alkalosis was associated with decreased 30-day and 12-month mortalities. This effect was however lost when a multivariate analysis was conducted, correcting for age, gender, pH on admission, base excess (BE) on admission, Simplified Acute Physiology Score III (SAPS III) and acute kidney injury (AKI). We then analyzed for any dose-response effect between the severity of metabolic alkalosis and mortality and found no relationship. Bivariate analysis showed that metabolic alkalosis had a significant effect on the length of ICU stay. When adjusting for age, sex, pH at admission, BE at admission, SAPS III and AKI in a multivariate analysis, metabolic alkalosis significantly contributed to prolonged ICU length of stay. In two separate sensitivity analyses pure metabolic alkalosis and late metabolic alkalosis (time of onset >48 hours) were the only significant predictor of increased ICU length of stay. Conclusion Metabolic alkalosis did not have any effect on 30-day and 12-month mortalities after adjusting for age, sex, SAPS III-score, pH and BE on admission and AKI in a multivariate analysis. The presence of metabolic alkalosis was independently associated with an increased ICU length of stay. PMID:28045915

Quantitative fibrosis parameters highly predict esophageal-gastro varices in primary biliary cirrhosis.

PubMed

Wu, Q-M; Zhao, X-Y; You, H

2016-01-01

Esophageal-gastro Varices (EGV) may develop in any histological stages of primary biliary cirrhosis (PBC). We aim to establish and validate quantitative fibrosis (qFibrosis) parameters in portal, septal and fibrillar areas as ideal predictors of EGV in PBC patients. PBC patients with liver biopsy, esophagogastroscopy and Second Harmonic Generation (SHG)/Two-photon Excited Fluorescence (TPEF) microscopy images were retrospectively enrolled in this study. qFibrosis parameters in portal, septal and fibrillar areas were acquired by computer-assisted SHG/TPEF imaging system. Independent predictor was identified using multivariate logistic regression analysis. PBC patients with liver biopsy, esophagogastroscopy and Second Harmonic Generation (SHG)/Two-photon Excited Fluorescence (TPEF) microscopy images were retrospectively enrolled in this study. qFibrosis parameters in portal, septal and fibrillar areas were acquired by computer-assisted SHG/TPEF imaging system. Independent predictor was identified using multivariate logistic regression analysis. Among the forty-nine PBC patients with qFibrosis images, twenty-nine PBC patients with both esophagogastroscopy data and qFibrosis data were selected out for EGV prognosis analysis and 44.8% (13/29) of them had EGV. The qFibrosis parameters of collagen percentage and number of crosslink in fibrillar area, short/long/thin strings number and length/width of the strings in septa area were associated with EGV (p < 0.05). Multivariate logistic analysis showed that the collagen percentage in fibrillar area ≥ 3.6% was an independent factor to predict EGV (odds ratio 6.9; 95% confidence interval 1.6-27.4). The area under receiver operating characteristic (ROC), diagnostic sensitivity and specificity was 0.9, 100% and 75% respectively. Collagen percentage in Collagen percentage in the fibrillar area as an independent predictor can highly predict EGV in PBC patients.

Sensitivity assessment of freshwater macroinvertebrates to pesticides using biological traits.

PubMed

Ippolito, A; Todeschini, R; Vighi, M

2012-03-01

Assessing the sensitivity of different species to chemicals is one of the key points in predicting the effects of toxic compounds in the environment. Trait-based predicting methods have proved to be extremely efficient for assessing the sensitivity of macroinvertebrates toward compounds with non specific toxicity (narcotics). Nevertheless, predicting the sensitivity of organisms toward compounds with specific toxicity is much more complex, since it depends on the mode of action of the chemical. The aim of this work was to predict the sensitivity of several freshwater macroinvertebrates toward three classes of plant protection products: organophosphates, carbamates and pyrethroids. Two databases were built: one with sensitivity data (retrieved, evaluated and selected from the U.S. Environmental Protection Agency ECOTOX database) and the other with biological traits. Aside from the "traditional" traits usually considered in ecological analysis (i.e. body size, respiration technique, feeding habits, etc.), multivariate analysis was used to relate the sensitivity of organisms to some other characteristics which may be involved in the process of intoxication. Results confirmed that, besides traditional biological traits, related to uptake capability (e.g. body size and body shape) some traits more related to particular metabolic characteristics or patterns have a good predictive capacity on the sensitivity to these kinds of toxic substances. For example, behavioral complexity, assumed as an indicator of nervous system complexity, proved to be an important predictor of sensitivity towards these compounds. These results confirm the need for more complex traits to predict effects of highly specific substances. One key point for achieving a complete mechanistic understanding of the process is the choice of traits, whose role in the discrimination of sensitivity should be clearly interpretable, and not only statistically significant.

Multivariate Models for Prediction of Human Skin Sensitization Hazard.

EPA Science Inventory

One of the lnteragency Coordinating Committee on the Validation of Alternative Method's (ICCVAM) top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensiti...

Robotic Versus Open Renal Transplantation in Obese Patients: Protocol for a Cost-Benefit Markov Model Analysis

PubMed Central

Puttarajappa, Chethan; Wijkstrom, Martin; Ganoza, Armando; Lopez, Roberto; Tevar, Amit

2018-01-01

Background Recent studies have reported a significant decrease in wound problems and hospital stay in obese patients undergoing renal transplantation by robotic-assisted minimally invasive techniques with no difference in graft function. Objective Due to the lack of cost-benefit studies on the use of robotic-assisted renal transplantation versus open surgical procedure, the primary aim of our study is to develop a Markov model to analyze the cost-benefit of robotic surgery versus open traditional surgery in obese patients in need of a renal transplant. Methods Electronic searches will be conducted to identify studies comparing open renal transplantation versus robotic-assisted renal transplantation. Costs associated with the two surgical techniques will incorporate the expenses of the resources used for the operations. A decision analysis model will be developed to simulate a randomized controlled trial comparing three interventional arms: (1) continuation of renal replacement therapy for patients who are considered non-suitable candidates for renal transplantation due to obesity, (2) transplant recipients undergoing open transplant surgery, and (3) transplant patients undergoing robotic-assisted renal transplantation. TreeAge Pro 2017 R1 TreeAge Software, Williamstown, MA, USA) will be used to create a Markov model and microsimulation will be used to compare costs and benefits for the two competing surgical interventions. Results The model will simulate a randomized controlled trial of adult obese patients affected by end-stage renal disease undergoing renal transplantation. The absorbing state of the model will be patients' death from any cause. By choosing death as the absorbing state, we will be able simulate the population of renal transplant recipients from the day of their randomization to transplant surgery or continuation on renal replacement therapy to their death and perform sensitivity analysis around patients' age at the time of randomization to determine if age is a critical variable for cost-benefit analysis or cost-effectiveness analysis comparing renal replacement therapy, robotic-assisted surgery or open renal transplant surgery. After running the model, one of the three competing strategies will result as the most cost-beneficial or cost-effective under common circumstances. To assess the robustness of the results of the model, a multivariable probabilistic sensitivity analysis will be performed by modifying the mean values and confidence intervals of key parameters with the main intent of assessing if the winning strategy is sensitive to rigorous and plausible variations of those values. Conclusions After running the model, one of the three competing strategies will result as the most cost-beneficial or cost-effective under common circumstances. To assess the robustness of the results of the model, a multivariable probabilistic sensitivity analysis will be performed by modifying the mean values and confidence intervals of key parameters with the main intent of assessing if the winning strategy is sensitive to rigorous and plausible variations of those values. PMID:29519780

Accuracy of ultrasound for the prediction of placenta accreta.

PubMed

Bowman, Zachary S; Eller, Alexandra G; Kennedy, Anne M; Richards, Douglas S; Winter, Thomas C; Woodward, Paula J; Silver, Robert M

2014-08-01

Ultrasound has been reported to be greater than 90% sensitive for the diagnosis of accreta. Prior studies may be subject to bias because of single expert observers, suspicion for accreta, and knowledge of risk factors. We aimed to assess the accuracy of ultrasound for the prediction of accreta. Patients with accreta at a single academic center were matched to patients with placenta previa, but no accreta, by year of delivery. Ultrasound studies with views of the placenta were collected, deidentified, blinded to clinical history, and placed in random sequence. Six investigators prospectively interpreted each study for the presence of accreta and findings reported to be associated with its diagnosis. Sensitivity, specificity, positive predictive, negative predictive value, and accuracy were calculated. Characteristics of accurate findings were compared using univariate and multivariate analyses. Six investigators examined 229 ultrasound studies from 55 patients with accreta and 56 controls for 1374 independent observations. 1205/1374 (87.7% overall, 90% controls, 84.9% cases) studies were given a diagnosis. There were 371 (27.0%) true positives; 81 (5.9%) false positives; 533 (38.8%) true negatives, 220 (16.0%) false negatives, and 169 (12.3%) with uncertain diagnosis. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 53.5%, 88.0%, 82.1%, 64.8%, and 64.8%, respectively. In multivariate analysis, true positives were more likely to have placental lacunae (odds ratio [OR], 1.5; 95% confidence interval [CI], 1.4-1.6), loss of retroplacental clear space (OR, 2.4; 95% CI, 1.1-4.9), or abnormalities on color Doppler (OR, 2.1; 95% CI, 1.8-2.4). Ultrasound for the prediction of placenta accreta may not be as sensitive as previously described. Copyright © 2014 Mosby, Inc. All rights reserved.

Discrimination of irradiated MOX fuel from UOX fuel by multivariate statistical analysis of simulated activities of gamma-emitting isotopes

NASA Astrophysics Data System (ADS)

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

2018-03-01

This paper investigates how concentrations of certain fission products and their related gamma-ray emissions can be used to discriminate between uranium oxide (UOX) and mixed oxide (MOX) type fuel. Discrimination of irradiated MOX fuel from irradiated UOX fuel is important in nuclear facilities and for transport of nuclear fuel, for purposes of both criticality safety and nuclear safeguards. Although facility operators keep records on the identity and properties of each fuel, tools for nuclear safeguards inspectors that enable independent verification of the fuel are critical in the recovery of continuity of knowledge, should it be lost. A discrimination methodology for classification of UOX and MOX fuel, based on passive gamma-ray spectroscopy data and multivariate analysis methods, is presented. Nuclear fuels and their gamma-ray emissions were simulated in the Monte Carlo code Serpent, and the resulting data was used as input to train seven different multivariate classification techniques. The trained classifiers were subsequently implemented and evaluated with respect to their capabilities to correctly predict the classes of unknown fuel items. The best results concerning successful discrimination of UOX and MOX-fuel were acquired when using non-linear classification techniques, such as the k nearest neighbors method and the Gaussian kernel support vector machine. For fuel with cooling times up to 20 years, when it is considered that gamma-rays from the isotope 134Cs can still be efficiently measured, success rates of 100% were obtained. A sensitivity analysis indicated that these methods were also robust.

The role of area-level deprivation and gender in participation in population-based faecal immunochemical test (FIT) colorectal cancer screening.

PubMed

Clarke, Nicholas; McNamara, Deirdre; Kearney, Patricia M; O'Morain, Colm A; Shearer, Nikki; Sharp, Linda

2016-12-01

This study aimed to investigate the effects of sex and deprivation on participation in a population-based faecal immunochemical test (FIT) colorectal cancer screening programme. The study population included 9785 individuals invited to participate in two rounds of a population-based biennial FIT-based screening programme, in a relatively deprived area of Dublin, Ireland. Explanatory variables included in the analysis were sex, deprivation category of area of residence and age (at end of screening). The primary outcome variable modelled was participation status in both rounds combined (with "participation" defined as having taken part in either or both rounds of screening). Poisson regression with a log link and robust error variance was used to estimate relative risks (RR) for participation. As a sensitivity analysis, data were stratified by screening round. In both the univariable and multivariable models deprivation was strongly associated with participation. Increasing affluence was associated with higher participation; participation was 26% higher in people resident in the most affluent compared to the most deprived areas (multivariable RR=1.26: 95% CI 1.21-1.30). Participation was significantly lower in males (multivariable RR=0.96: 95%CI 0.95-0.97) and generally increased with increasing age (trend per age group, multivariable RR=1.02: 95%CI, 1.01-1.02). No significant interactions between the explanatory variables were found. The effects of deprivation and sex were similar by screening round. Deprivation and male gender are independently associated with lower uptake of population-based FIT colorectal cancer screening, even in a relatively deprived setting. Development of evidence-based interventions to increase uptake in these disadvantaged groups is urgently required. Copyright © 2016. Published by Elsevier Inc.

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.

Reverse left ventricular remodeling after acute myocardial infarction: the prognostic impact of left ventricular global torsion.

PubMed

Spinelli, Letizia; Morisco, Carmine; Assante di Panzillo, Emiliano; Izzo, Raffaele; Trimarco, Bruno

2013-04-01

Reverse left ventricular (LV) remodeling (>10 % reduction in LV end-systolic volume) may occur in patients recovering for acute ST-elevation myocardial infarction (STEMI), undergoing percutaneous revascularization of infarct-related coronary artery (PCI). To detect whether LV global torsion obtained by two-dimensional speckle-tracking echocardiography was predictive of reverse LV remodeling, 75 patients with first anterior wall STEMI were studied before (T1) and after PCI (T2) and at 6-month follow-up. Two-year clinical follow-up was also accomplished. LV volumes and both LV sphericity index and conic index were obtained by three-dimensional echocardiography. Reverse remodeling was observed in 25 patients (33 %). By multivariate analysis, independent predictors of reverse LV remodeling were: LV conic index, T2 LV torsion and Δ torsion (difference between T2 and T1 LV torsion expressed as percentage of this latter). According to receiver operating characteristic analysis, 1.34°/cm for T2 LV torsion (sensitivity 88 % and specificity 80 %) and 54 % for Δ torsion (sensitivity 92 % and specificity 82 %) were the optimal cutoff values in predicting reverse LV remodeling. In up to 24 month follow-up, 4 non-fatal re-infarction, 7 hospitalization for heart failure and 4 cardiac deaths occurred. By multivariate Cox analysis, the best variable significantly associated with event-free survival rate was reverse LV remodeling with a hazard ratio = 9.9 (95 % confidence interval, 7.9-31.4, p < 0.01). In conclusion, reverse LV remodeling occurring after anterior wall STEMI is associated with favorable long-term outcome. The improvement of global LV torsion following coronary artery revascularization is the major predictor of reverse LV remodeling.

[Logistic regression model of noninvasive prediction for portal hypertensive gastropathy in patients with hepatitis B associated cirrhosis].

PubMed

Wang, Qingliang; Li, Xiaojie; Hu, Kunpeng; Zhao, Kun; Yang, Peisheng; Liu, Bo

2015-05-12

To explore the risk factors of portal hypertensive gastropathy (PHG) in patients with hepatitis B associated cirrhosis and establish a Logistic regression model of noninvasive prediction. The clinical data of 234 hospitalized patients with hepatitis B associated cirrhosis from March 2012 to March 2014 were analyzed retrospectively. The dependent variable was the occurrence of PHG while the independent variables were screened by binary Logistic analysis. Multivariate Logistic regression was used for further analysis of significant noninvasive independent variables. Logistic regression model was established and odds ratio was calculated for each factor. The accuracy, sensitivity and specificity of model were evaluated by the curve of receiver operating characteristic (ROC). According to univariate Logistic regression, the risk factors included hepatic dysfunction, albumin (ALB), bilirubin (TB), prothrombin time (PT), platelet (PLT), white blood cell (WBC), portal vein diameter, spleen index, splenic vein diameter, diameter ratio, PLT to spleen volume ratio, esophageal varices (EV) and gastric varices (GV). Multivariate analysis showed that hepatic dysfunction (X1), TB (X2), PLT (X3) and splenic vein diameter (X4) were the major occurring factors for PHG. The established regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4. The accuracy of model for PHG was 79.1% with a sensitivity of 77.2% and a specificity of 80.8%. Hepatic dysfunction, TB, PLT and splenic vein diameter are risk factors for PHG and the noninvasive predicted Logistic regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4.

Vasa previa screening strategies: a decision and cost-effectiveness analysis.

PubMed

Sinkey, R G; Odibo, A O

2018-05-22

The aim of this study is to perform a decision and cost-effectiveness analysis comparing four screening strategies for the antenatal diagnosis of vasa previa among singleton pregnancies. A decision-analytic model was constructed comparing vasa previa screening strategies. Published probabilities and costs were applied to four transvaginal screening scenarios which occurred at the time of mid-trimester ultrasound: no screening, ultrasound-indicated screening, screening pregnancies conceived by in vitro fertilization (IVF), and universal screening. Ultrasound-indicated screening was defined as performing a transvaginal ultrasound at the time of routine anatomy ultrasound in response to one of the following sonographic findings associated with an increased risk of vasa previa: low-lying placenta, marginal or velamentous cord insertion, or bilobed or succenturiate lobed placenta. The primary outcome was cost per quality adjusted life years (QALY) in U.S. dollars. The analysis was from a healthcare system perspective with a willingness to pay (WTP) threshold of $100,000 per QALY selected. One-way and multivariate sensitivity analyses (Monte-Carlo simulation) were performed. This decision-analytic model demonstrated that screening pregnancies conceived by IVF was the most cost-effective strategy with an incremental cost effectiveness ratio (ICER) of $29,186.50 / QALY. Ultrasound-indicated screening was the second most cost-effective with an ICER of $56,096.77 / QALY. These data were robust to all one-way and multivariate sensitivity analyses performed. Within our baseline assumptions, transvaginal ultrasound screening for vasa previa appears to be most cost-effective when performed among IVF pregnancies. However, both IVF and ultrasound-indicated screening strategies fall within contemporary willingness-to-pay thresholds, suggesting that both strategies may be appropriate to apply in clinical practice. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Relevant Feature Set Estimation with a Knock-out Strategy and Random Forests

PubMed Central

Ganz, Melanie; Greve, Douglas N.; Fischl, Bruce; Konukoglu, Ender

2015-01-01

Group analysis of neuroimaging data is a vital tool for identifying anatomical and functional variations related to diseases as well as normal biological processes. The analyses are often performed on a large number of highly correlated measurements using a relatively smaller number of samples. Despite the correlation structure, the most widely used approach is to analyze the data using univariate methods followed by post-hoc corrections that try to account for the data’s multivariate nature. Although widely used, this approach may fail to recover from the adverse effects of the initial analysis when local effects are not strong. Multivariate pattern analysis (MVPA) is a powerful alternative to the univariate approach for identifying relevant variations. Jointly analyzing all the measures, MVPA techniques can detect global effects even when individual local effects are too weak to detect with univariate analysis. Current approaches are successful in identifying variations that yield highly predictive and compact models. However, they suffer from lessened sensitivity and instabilities in identification of relevant variations. Furthermore, current methods’ user-defined parameters are often unintuitive and difficult to determine. In this article, we propose a novel MVPA method for group analysis of high-dimensional data that overcomes the drawbacks of the current techniques. Our approach explicitly aims to identify all relevant variations using a “knock-out” strategy and the Random Forest algorithm. In evaluations with synthetic datasets the proposed method achieved substantially higher sensitivity and accuracy than the state-of-the-art MVPA methods, and outperformed the univariate approach when the effect size is low. In experiments with real datasets the proposed method identified regions beyond the univariate approach, while other MVPA methods failed to replicate the univariate results. More importantly, in a reproducibility study with the well-known ADNI dataset the proposed method yielded higher stability and power than the univariate approach. PMID:26272728

Can P wave wavelet analysis predict atrial fibrillation after coronary artery bypass grafting?

PubMed

Vassilikos, Vassilios; Dakos, George; Chouvarda, Ioanna; Karagounis, Labros; Karvounis, Haralambos; Maglaveras, Nikolaos; Mochlas, Sotirios; Spanos, Panagiotis; Louridas, George

2003-01-01

The purpose of this study was the evaluation of Morlet wavelet analysis of the P wave as a means of predicting the development of atrial fibrillation (AF) in patients who undergo coronary artery bypass grafting (CABG). The P wave was analyzed using the Morlet wavelet in 50 patients who underwent successful CABG. Group A consisted of 17 patients, 12 men and 5 women, of mean age 66.9 +/- 5.9 years, who developed AF postoperatively. Group B consisted of 33 patients, 29 men and 4 women, mean age 62.4 +/- 7.8 years, who remained arrhythmid-free. Using custom-designed software, P wave duration and wavelet parameters expressing the mean and maximum energy of the P wave were calculated from 3-channel digital recordings derived from orthogonal ECG leads (X, Y, and Z), and the vector magnitude (VM) was determined in each of 3 frequency bands (200-160 Hz, 150-100 Hz and 90-50 Hz). Univariate logistic-regression analysis identified a history of hypertension, the mean and maximum energies in all frequency bands along the Z axis, the mean and maximum energies (expressed by the VM) in the 200-160 Hz frequency band, and the mean energy in the 150-100 Hz frequency band along the Y axis as predictors for post-CABG AF. Multivariate analysis identified hypertension, ejection fraction, and the maximum energies in the 90-50 Hz frequency band along the Z and composite-vector axes as independent predictors. This multivariate model had a sensitivity of 91% and a specificity of 65%. We conclude that the Morlet wavelet analysis of the P wave is a very sensitive method of identifying patients who are likely to develop AF after CABG. The occurrence of post-CABG AF can be explained by a different activation pattern along the Z axis.

Multivariate meta-analysis: potential and promise.

PubMed

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

2011-09-10

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

Chemometric and multivariate statistical analysis of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulfides.

PubMed

Kalegowda, Yogesh; Harmer, Sarah L

2012-03-20

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of mineral samples are complex, comprised of large mass ranges and many peaks. Consequently, characterization and classification analysis of these systems is challenging. In this study, different chemometric and statistical data evaluation methods, based on monolayer sensitive TOF-SIMS data, have been tested for the characterization and classification of copper-iron sulfide minerals (chalcopyrite, chalcocite, bornite, and pyrite) at different flotation pulp conditions (feed, conditioned feed, and Eh modified). The complex mass spectral data sets were analyzed using the following chemometric and statistical techniques: principal component analysis (PCA); principal component-discriminant functional analysis (PC-DFA); soft independent modeling of class analogy (SIMCA); and k-Nearest Neighbor (k-NN) classification. PCA was found to be an important first step in multivariate analysis, providing insight into both the relative grouping of samples and the elemental/molecular basis for those groupings. For samples exposed to oxidative conditions (at Eh ~430 mV), each technique (PCA, PC-DFA, SIMCA, and k-NN) was found to produce excellent classification. For samples at reductive conditions (at Eh ~ -200 mV SHE), k-NN and SIMCA produced the most accurate classification. Phase identification of particles that contain the same elements but a different crystal structure in a mixed multimetal mineral system has been achieved.

Exploring the spatio-temporal neural basis of face learning

PubMed Central

Yang, Ying; Xu, Yang; Jew, Carol A.; Pyles, John A.; Kass, Robert E.; Tarr, Michael J.

2017-01-01

Humans are experts at face individuation. Although previous work has identified a network of face-sensitive regions and some of the temporal signatures of face processing, as yet, we do not have a clear understanding of how such face-sensitive regions support learning at different time points. To study the joint spatio-temporal neural basis of face learning, we trained subjects to categorize two groups of novel faces and recorded their neural responses using magnetoencephalography (MEG) throughout learning. A regression analysis of neural responses in face-sensitive regions against behavioral learning curves revealed significant correlations with learning in the majority of the face-sensitive regions in the face network, mostly between 150–250 ms, but also after 300 ms. However, the effect was smaller in nonventral regions (within the superior temporal areas and prefrontal cortex) than that in the ventral regions (within the inferior occipital gyri (IOG), midfusiform gyri (mFUS) and anterior temporal lobes). A multivariate discriminant analysis also revealed that IOG and mFUS, which showed strong correlation effects with learning, exhibited significant discriminability between the two face categories at different time points both between 150–250 ms and after 300 ms. In contrast, the nonventral face-sensitive regions, where correlation effects with learning were smaller, did exhibit some significant discriminability, but mainly after 300 ms. In sum, our findings indicate that early and recurring temporal components arising from ventral face-sensitive regions are critically involved in learning new faces. PMID:28570739

Exploring the spatio-temporal neural basis of face learning.

PubMed

Yang, Ying; Xu, Yang; Jew, Carol A; Pyles, John A; Kass, Robert E; Tarr, Michael J

2017-06-01

Humans are experts at face individuation. Although previous work has identified a network of face-sensitive regions and some of the temporal signatures of face processing, as yet, we do not have a clear understanding of how such face-sensitive regions support learning at different time points. To study the joint spatio-temporal neural basis of face learning, we trained subjects to categorize two groups of novel faces and recorded their neural responses using magnetoencephalography (MEG) throughout learning. A regression analysis of neural responses in face-sensitive regions against behavioral learning curves revealed significant correlations with learning in the majority of the face-sensitive regions in the face network, mostly between 150-250 ms, but also after 300 ms. However, the effect was smaller in nonventral regions (within the superior temporal areas and prefrontal cortex) than that in the ventral regions (within the inferior occipital gyri (IOG), midfusiform gyri (mFUS) and anterior temporal lobes). A multivariate discriminant analysis also revealed that IOG and mFUS, which showed strong correlation effects with learning, exhibited significant discriminability between the two face categories at different time points both between 150-250 ms and after 300 ms. In contrast, the nonventral face-sensitive regions, where correlation effects with learning were smaller, did exhibit some significant discriminability, but mainly after 300 ms. In sum, our findings indicate that early and recurring temporal components arising from ventral face-sensitive regions are critically involved in learning new faces.

Combining Frequency Doubling Technology Perimetry and Scanning Laser Polarimetry for Glaucoma Detection.

PubMed

Mwanza, Jean-Claude; Warren, Joshua L; Hochberg, Jessica T; Budenz, Donald L; Chang, Robert T; Ramulu, Pradeep Y

2015-01-01

To determine the ability of frequency doubling technology (FDT) and scanning laser polarimetry with variable corneal compensation (GDx-VCC) to detect glaucoma when used individually and in combination. One hundred ten normal and 114 glaucomatous subjects were tested with FDT C-20-5 screening protocol and the GDx-VCC. The discriminating ability was tested for each device individually and for both devices combined using GDx-NFI, GDx-TSNIT, number of missed points of FDT, and normal or abnormal FDT. Measures of discrimination included sensitivity, specificity, area under the curve (AUC), Akaike's information criterion (AIC), and prediction confidence interval lengths. For detecting glaucoma regardless of severity, the multivariable model resulting from the combination of GDx-TSNIT, number of abnormal points on FDT (NAP-FDT), and the interaction GDx-TSNIT×NAP-FDT (AIC: 88.28, AUC: 0.959, sensitivity: 94.6%, specificity: 89.5%) outperformed the best single-variable model provided by GDx-NFI (AIC: 120.88, AUC: 0.914, sensitivity: 87.8%, specificity: 84.2%). The multivariable model combining GDx-TSNIT, NAP-FDT, and interaction GDx-TSNIT×NAP-FDT consistently provided better discriminating abilities for detecting early, moderate, and severe glaucoma than the best single-variable models. The multivariable model including GDx-TSNIT, NAP-FDT, and the interaction GDx-TSNIT×NAP-FDT provides the best glaucoma prediction compared with all other multivariable and univariable models. Combining the FDT C-20-5 screening protocol and GDx-VCC improves glaucoma detection compared with using GDx or FDT alone.

Extraversion and taste sensitivity.

PubMed

Zverev, Yuriy; Mipando, Mwapatsa

2008-03-01

The rationale for investigating the gustatory reactivity as influenced by personality dimensions was suggested by some prior findings of an association between extraversion and acuity in other sensory systems. Detection thresholds for sweet, salty, and bitter qualities of taste were measured in 60 young healthy male and female volunteers using a two-alternative forced-choice technique. Personality of the responders was assessed using the Eysenck Personality Inventory. Multivariate analysis of variance failed to demonstrate a statistically significant interaction between an extraversion-introversion score, neuroticism score, smoking, gender and age. The only reliable negative association was found between the body mass index (BMI) and taste sensitivity (Roy's largest root = 0.05, F(7436.5) = 8.34, P = 0.003). Possible reasons for lack of differences between introverts and extraverts in the values of taste detection thresholds were discussed.

Polarization in Raman spectroscopy helps explain bone brittleness in genetic mouse models

NASA Astrophysics Data System (ADS)

Makowski, Alexander J.; Pence, Isaac J.; Uppuganti, Sasidhar; Zein-Sabatto, Ahbid; Huszagh, Meredith C.; Mahadevan-Jansen, Anita; Nyman, Jeffry S.

2014-11-01

Raman spectroscopy (RS) has been extensively used to characterize bone composition. However, the link between bone biomechanics and RS measures is not well established. Here, we leveraged the sensitivity of RS polarization to organization, thereby assessing whether RS can explain differences in bone toughness in genetic mouse models for which traditional RS peak ratios are not informative. In the selected mutant mice-activating transcription factor 4 (ATF4) or matrix metalloproteinase 9 (MMP9) knock-outs-toughness is reduced but differences in bone strength do not exist between knock-out and corresponding wild-type controls. To incorporate differences in the RS of bone occurring at peak shoulders, a multivariate approach was used. Full spectrum principal components analysis of two paired, orthogonal bone orientations (relative to laser polarization) improved genotype classification and correlation to bone toughness when compared to traditional peak ratios. When applied to femurs from wild-type mice at 8 and 20 weeks of age, the principal components of orthogonal bone orientations improved age classification but not the explanation of the maturation-related increase in strength. Overall, increasing polarization information by collecting spectra from two bone orientations improves the ability of multivariate RS to explain variance in bone toughness, likely due to polarization sensitivity to organizational changes in both mineral and collagen.

Prediction of sensitivity to warfarin based on VKORC1 and CYP2C9 polymorphisms in patients from different places in Colombia.

PubMed

Cifuentes, Ricardo A; Murillo-Rojas, Juan; Avella-Vargas, Esperanza

2016-03-03

In the search to prevent hemorrhages associated with anticoagulant therapy, a major goal is to validate predictors of sensitivity to warfarin. However, previous studies in Colombia that included polymorphisms in the VKORC1 and CYP2C9 genes as predictors reported different algorithm performances to explain dose variations, and did not evaluate the prediction of sensitivity to warfarin.  To determine the accuracy of the pharmacogenetic analysis, which includes the CYP2C9 *2 and *3 and VKORC1 1639G>A polymorphisms in predicting patients' sensitivity to warfarin at the Hospital Militar Central, a reference center for patients born in different parts of Colombia.  Demographic and clinical data were obtained from 130 patients with stable doses of warfarin for more than two months. Next, their genotypes were obtained through a melting curve analysis. After verifying the Hardy-Weinberg equilibrium of the genotypes from the polymorphisms, a statistical analysis was done, which included multivariate and predictive approaches.  A pharmacogenetic model that explained 52.8% of dose variation (p<0.001) was built, which was only 4% above the performance resulting from the same data using the International Warfarin Pharmacogenetics Consortium algorithm. The model predicting the sensitivity achieved an accuracy of 77.8% and included age (p=0.003), polymorphisms *2 and *3 (p=0.002) and polymorphism 1639G>A (p<0.001) as predictors.  These results in a mixed population support the prediction of sensitivity to warfarin based on polymorphisms in VKORC1 and CYP2C9 as a valid approach in Colombian patients.

Multivariate Models for Normal and Binary Responses in Intervention Studies

ERIC Educational Resources Information Center

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

2016-01-01

Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…

Histopathology of valves in infective endocarditis, diagnostic criteria and treatment considerations.

PubMed

Brandão, Tatiana J D; Januario-da-Silva, Carolina A; Correia, Marcelo G; Zappa, Monica; Abrantes, Jaime A; Dantas, Angela M R; Golebiovski, Wilma; Barbosa, Giovanna Ianini F; Weksler, Clara; Lamas, Cristiane C

2017-04-01

Infective endocarditis (IE) is a severe disease. Pathogen isolation is fundamental so as to treat effectively and reduce morbidity and mortality. Blood and valve culture and histopathology (HP) are routinely employed for this purpose. Valve HP is the gold standard for diagnosis. To determine the sensitivity and specificity of clinical criteria for IE (the modified Duke and the St Thomas' minor modifications, STH) of blood and valve culture compared to valve HP, and to evaluate antibiotic treatment duration. Prospective case series of patients, from 2006 to 2014 with surgically treated IE. Statistical analysis was done by the R software. There were 136 clinically definite episodes of IE in 133 patients. Mean age ± SD was 43 ± 15.6 years and IE was left sided in 81.6 %. HP was definite in 96 valves examined, which were used as gold standard. Sensitivity of blood culture was 61 % (CI 0.51, 0.71) and of valve culture 15 % (CI 0.07, 0.26). The modified Duke criteria were 65 % (CI 0.55, 0.75) sensitive and 33 % specific, while the STH's sensitivity was 72 % (CI 0.61, 0.80) with similar specificity. In multivariate analysis and logistic regression, the only variable with statistical significance was duration of antibiotic therapy postoperatively. Valve HP had high sensitivity and valve culture low sensitivity in the diagnosis of IE. The STH's criteria were more sensitive than the modified Duke criteria. Valve HP should guide duration of postoperative antibiotic treatment.

The Role of Cleaning Products in Epidemic Allergic Contact Dermatitis to Methylchloroisothiazolinone/Methylisothiazolinone.

PubMed

Marrero-Alemán, Gabriel; Saavedra Santana, Pedro; Liuti, Federica; Hernández, Noelia; López-Jiménez, Esmeralda; Borrego, Leopoldo

Sensitivity to methylchloroisothiazolinone (MCI)/methylisothiazolinone (MI) has increased rapidly over recent years. This increase is mainly related to the extensive use of high concentrations of MI in cosmetic products, although a growing number of cases of occupational allergic contact dermatitis are caused by MCI/MI. The aim of this study was to examine the association between the increase in MCI/MI sensitization and the work performed by the patients in our area. A retrospective study was undertaken of the records of a total of 1179 patients who had undergone contact skin patch tests for MCI/MI from January 2005 to December 2015. A multivariate logistic regression analysis was performed to identify the factors independently associated with sensitivity to MCI/MI. A constant increase in MCI/MI sensitization was observed over the observation period. The only work associated with a significant increase in the prevalence of MCI/MI sensitization was cleaning, with 38.5% of the cleaning professionals with MCI/MI sensitization consulting for cosmetics-related dermatitis. Occupational sensitization to MCI/MI in cleaning professionals is worryingly increasing. This, in turn, could possibly account for many cases of cosmetics-associated contact dermatitis. Our findings suggest that a review of the regulations with regard to isothiazolinone concentrations in industrial and household detergents is necessary.

Weather sensitivity for zoo visitation in Toronto, Canada: a quantitative analysis of historical data.

PubMed

Hewer, Micah J; Gough, William A

2016-11-01

Based on a case study of the Toronto Zoo (Canada), multivariate regression analysis, involving both climatic and social variables, was employed to assess the relationship between daily weather and visitation. Zoo visitation was most sensitive to weather variability during the shoulder season, followed by the off-season and, then, the peak season. Temperature was the most influential weather variable in relation to zoo visitation, followed by precipitation and, then, wind speed. The intensity and direction of the social and climatic variables varied between seasons. Temperatures exceeding 26 °C during the shoulder season and 28 °C during the peak season suggested a behavioural threshold associated with zoo visitation, with conditions becoming too warm for certain segments of the zoo visitor market, causing visitor numbers to decline. Even light amounts of precipitation caused average visitor numbers to decline by nearly 50 %. Increasing wind speeds also demonstrated a negative influence on zoo visitation.

Evaluation of a panel of 28 biomarkers for the non-invasive diagnosis of endometriosis.

PubMed

Vodolazkaia, A; El-Aalamat, Y; Popovic, D; Mihalyi, A; Bossuyt, X; Kyama, C M; Fassbender, A; Bokor, A; Schols, D; Huskens, D; Meuleman, C; Peeraer, K; Tomassetti, C; Gevaert, O; Waelkens, E; Kasran, A; De Moor, B; D'Hooghe, T M

2012-09-01

At present, the only way to conclusively diagnose endometriosis is laparoscopic inspection, preferably with histological confirmation. This contributes to the delay in the diagnosis of endometriosis which is 6-11 years. So far non-invasive diagnostic approaches such as ultrasound (US), MRI or blood tests do not have sufficient diagnostic power. Our aim was to develop and validate a non-invasive diagnostic test with a high sensitivity (80% or more) for symptomatic endometriosis patients, without US evidence of endometriosis, since this is the group most in need of a non-invasive test. A total of 28 inflammatory and non-inflammatory plasma biomarkers were measured in 353 EDTA plasma samples collected at surgery from 121 controls without endometriosis at laparoscopy and from 232 women with endometriosis (minimal-mild n = 148; moderate-severe n = 84), including 175 women without preoperative US evidence of endometriosis. Surgery was done during menstrual (n = 83), follicular (n = 135) and luteal (n = 135) phases of the menstrual cycle. For analysis, the data were randomly divided into an independent training (n = 235) and a test (n = 118) data set. Statistical analysis was done using univariate and multivariate (logistic regression and least squares support vector machines (LS-SVM) approaches in training- and test data set separately to validate our findings. In the training set, two models of four biomarkers (Model 1: annexin V, VEGF, CA-125 and glycodelin; Model 2: annexin V, VEGF, CA-125 and sICAM-1) analysed in plasma, obtained during the menstrual phase, could predict US-negative endometriosis with a high sensitivity (81-90%) and an acceptable specificity (68-81%). The same two models predicted US-negative endometriosis in the independent validation test set with a high sensitivity (82%) and an acceptable specificity (63-75%). In plasma samples obtained during menstruation, multivariate analysis of four biomarkers (annexin V, VEGF, CA-125 and sICAM-1/or glycodelin) enabled the diagnosis of endometriosis undetectable by US with a sensitivity of 81-90% and a specificity of 63-81% in independent training- and test data set. The next step is to apply these models for preoperative prediction of endometriosis in an independent set of patients with infertility and/or pain without US evidence of endometriosis, scheduled for laparoscopy.

Risk factor assessment to anticipate performance in the National Developmental Screening Test in children from a disadvantaged area.

PubMed

Montes, Alejandro; Pazos, Gustavo

2016-02-01

Identifying children at risk of failing the National Developmental Screening Test by combining prevalences of children suspected of having inapparent developmental disorders (IDDs) and associated risk factors (RFs) would allow to save resources. 1. To estimate the prevalence of children suspected of having IDDs. 2. To identify associated RFs. 3. To assess three methods developed based on observed RFs and propose a pre-screening procedure. The National Developmental Screening Test was administered to 60 randomly selected children aged between 2 and 4 years old from a socioeconomically disadvantaged area from Puerto Madryn. Twenty-four biological and socioenvironmental outcome measures were assessed in order to identify potential RFs using bivariate and multivariate analyses. The likelihood of failing the screening test was estimated as follows: 1. a multivariate logistic regression model was developed; 2. a relationship was established between the number of RFs present in each child and the percentage of children who failed the test; 3. these two methods were combined. The prevalence of children suspected of having IDDs was 55.0% (95% confidence interval: 42.4%-67.6%). Six RFs were initially identified using the bivariate approach. Three of them (maternal education, number of health checkups and Z scores for height-for-age, and maternal age) were included in the logistic regression model, which has a greater explanatory power. The third method included in the assessment showed greater sensitivity and specificity (85% and 79%, respectively). The estimated prevalence of children suspected of having IDDs was four times higher than the national standards. Seven RFs were identified. Combining the analysis of risk factor accumulation and a multivariate model provides a firm basis for developing a sensitive, specific and practical pre-screening procedure for socioeconomically disadvantaged areas. Sociedad Argentina de Pediatría.

Coping with confounds in multivoxel pattern analysis: what should we do about reaction time differences? A comment on Todd, Nystrom & Cohen 2013.

PubMed

Woolgar, Alexandra; Golland, Polina; Bode, Stefan

2014-09-01

Multivoxel pattern analysis (MVPA) is a sensitive and increasingly popular method for examining differences between neural activation patterns that cannot be detected using classical mass-univariate analysis. Recently, Todd et al. ("Confounds in multivariate pattern analysis: Theory and rule representation case study", 2013, NeuroImage 77: 157-165) highlighted a potential problem for these methods: high sensitivity to confounds at the level of individual participants due to the use of directionless summary statistics. Unlike traditional mass-univariate analyses where confounding activation differences in opposite directions tend to approximately average out at group level, group level MVPA results may be driven by any activation differences that can be discriminated in individual participants. In Todd et al.'s empirical data, factoring out differences in reaction time (RT) reduced a classifier's ability to distinguish patterns of activation pertaining to two task rules. This raises two significant questions for the field: to what extent have previous multivoxel discriminations in the literature been driven by RT differences, and by what methods should future studies take RT and other confounds into account? We build on the work of Todd et al. and compare two different approaches to remove the effect of RT in MVPA. We show that in our empirical data, in contrast to that of Todd et al., the effect of RT on rule decoding is negligible, and results were not affected by the specific details of RT modelling. We discuss the meaning of and sensitivity for confounds in traditional and multivoxel approaches to fMRI analysis. We observe that the increased sensitivity of MVPA comes at a price of reduced specificity, meaning that these methods in particular call for careful consideration of what differs between our conditions of interest. We conclude that the additional complexity of the experimental design, analysis and interpretation needed for MVPA is still not a reason to favour a less sensitive approach. Copyright © 2014 Elsevier Inc. All rights reserved.

Effects of age, sex, and genotype on high-sensitivity metabolomic profiles in the fruit fly, Drosophila melanogaster

PubMed Central

Hoffman, Jessica M; Soltow, Quinlyn A; Li, Shuzhao; Sidik, Alfire; Jones, Dean P; Promislow, Daniel E L

2014-01-01

Researchers have used whole-genome sequencing and gene expression profiling to identify genes associated with age, in the hope of understanding the underlying mechanisms of senescence. But there is a substantial gap from variation in gene sequences and expression levels to variation in age or life expectancy. In an attempt to bridge this gap, here we describe the effects of age, sex, genotype, and their interactions on high-sensitivity metabolomic profiles in the fruit fly, Drosophila melanogaster. Among the 6800 features analyzed, we found that over one-quarter of all metabolites were significantly associated with age, sex, genotype, or their interactions, and multivariate analysis shows that individual metabolomic profiles are highly predictive of these traits. Using a metabolomic equivalent of gene set enrichment analysis, we identified numerous metabolic pathways that were enriched among metabolites associated with age, sex, and genotype, including pathways involving sugar and glycerophospholipid metabolism, neurotransmitters, amino acids, and the carnitine shuttle. Our results suggest that high-sensitivity metabolomic studies have excellent potential not only to reveal mechanisms that lead to senescence, but also to help us understand differences in patterns of aging among genotypes and between males and females. PMID:24636523

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.

Improving guideline sensitivity and specificity for the identification of proactive gastrostomy placement in patients with head and neck cancer.

PubMed

Brown, Teresa E; Crombie, Jane; Spurgin, Ann-Louise; Tripcony, Lee; Keller, Jacqui; Hughes, Brett G M; Dickie, Graeme; Kenny, Lizbeth Moira; Hodge, Robert A

2016-04-01

Swallowing and nutrition guidelines for patients with head and neck cancer are available for identification of proactive gastrostomy placement in patients with high nutritional risk. The purpose of this study was to investigate improvements to the validity of these guidelines. A multivariate analysis was fitted to the original dataset (n = 501) to examine the variables that may predict gastrostomy placement (eg, tumor site, treatment, sex, and age). Using these factors, the high risk category was modified and retrospectively validated in the same cohort to provide new measures of sensitivity and specificity. The following were positive predictors of gastrostomy placement: T3 (p = .01), T4 (p < .001), and chemoradiotherapy (p < .001). Laryngeal (p = .02) and skin cancer (p < .001) were negative predictors. Modification of the high risk definition improved sensitivity to 58% and maintained specificity at 92%. Minor modifications to the high risk definition in the guidelines have improved the guideline sensitivity for future use. © 2015 Wiley Periodicals, Inc. Head Neck 38: E1163-E1171, 2016. © 2015 Wiley Periodicals, Inc.

The value of FATS expression in predicting sensitivity to radiotherapy in breast cancer

PubMed Central

Zhang, Tiemei; Sun, Tao; Su, Yi; Zhao, Jing; Mu, Kun; Jin, Zhao; Gao, Ming; Liu, Juntian; Gu, Lin

2017-01-01

Purpose The fragile-site associated tumor suppressor (FATS) is a newly identified tumor suppressor involved in radiation-induced tumorigenesis. The purpose of this study was to characterize FATS expression in breast cancers about radiotherapy benefit, patient characteristics, and prognosis. Results The expression of FATS mRNA was silent or downregulated in 95.2% of breast cancer samples compared with paired normal controls (P < .0001). Negative status of FATS was correlated with higher nuclear grade (P = .01) and shorter disease-free survival (DFS) of breast cancer (P = .036). In a multivariate analysis, FATS expression showed favorable prognostic value for DFS (odds ratio, 0.532; 95% confidence interval, 0.299 to 0.947; (P = .032). Furthermore, improved survival time was seen in FATS-positive patients receiving radiotherapy (P = .006). The results of multivariate analysis revealed independent prognostic value of FATS expression in predicting longer DFS (odds ratio, 0.377; 95% confidence interval, 0.176 to 0.809; P = 0.012) for patients receiving adjuvant radiotherapy. In support of this, reduction of FATS expression in breast cancer cell lines, FATS positive group significantly sensitized than Knock-down of FATS group. Materials and Methods Tissue samples from 156 breast cancer patients and 42 controls in tumor bank were studied. FATS gene expression was evaluated using quantitative reverse transcription polymerase chain reaction (qRT-PCR). FATS function was examined in breast cancer cell lines using siRNA knock-downs and colony forming assays after irradiation. Conclusions FATS status is a biomarker in breast cancer to identify individuals likely to benefit from radiotherapy. PMID:28402275

The value of FATS expression in predicting sensitivity to radiotherapy in breast cancer.

PubMed

Zhang, Jun; Wu, Nan; Zhang, Tiemei; Sun, Tao; Su, Yi; Zhao, Jing; Mu, Kun; Jin, Zhao; Gao, Ming; Liu, Juntian; Gu, Lin

2017-06-13

The fragile-site associated tumor suppressor (FATS) is a newly identified tumor suppressor involved in radiation-induced tumorigenesis. The purpose of this study was to characterize FATS expression in breast cancers about radiotherapy benefit, patient characteristics, and prognosis. The expression of FATS mRNA was silent or downregulated in 95.2% of breast cancer samples compared with paired normal controls (P < .0001). Negative status of FATS was correlated with higher nuclear grade (P = .01) and shorter disease-free survival (DFS) of breast cancer (P = .036). In a multivariate analysis, FATS expression showed favorable prognostic value for DFS (odds ratio, 0.532; 95% confidence interval, 0.299 to 0.947; (P = .032). Furthermore, improved survival time was seen in FATS-positive patients receiving radiotherapy (P = .006). The results of multivariate analysis revealed independent prognostic value of FATS expression in predicting longer DFS (odds ratio, 0.377; 95% confidence interval, 0.176 to 0.809; P = 0.012) for patients receiving adjuvant radiotherapy. In support of this, reduction of FATS expression in breast cancer cell lines, FATS positive group significantly sensitized than Knock-down of FATS group. Tissue samples from 156 breast cancer patients and 42 controls in tumor bank were studied. FATS gene expression was evaluated using quantitative reverse transcription polymerase chain reaction (qRT-PCR). FATS function was examined in breast cancer cell lines using siRNA knock-downs and colony forming assays after irradiation. FATS status is a biomarker in breast cancer to identify individuals likely to benefit from radiotherapy.

Multivariate meta-analysis: Potential and promise

PubMed Central

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

2011-01-01

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

A Comparison of Web and Telephone Responses From a National HIV and AIDS Survey

PubMed Central

Calzavara, Liviana; Allman, Dan; Worthington, Catherine A; Tyndall, Mark; Iveniuk, James

2016-01-01

Background Response differences to survey questions are known to exist for different modes of questionnaire completion. Previous research has shown that response differences by mode are larger for sensitive and complicated questions. However, it is unknown what effect completion mode may have on HIV and AIDS survey research, which addresses particularly sensitive and stigmatized health issues. Objectives We seek to compare responses between self-selected Web and telephone respondents in terms of social desirability and item nonresponse in a national HIV and AIDS survey. Methods A survey of 2085 people in Canada aged 18 years and older was conducted to explore public knowledge, attitudes, and behaviors around HIV and AIDS in May 2011. Participants were recruited using random-digit dialing and could select to be interviewed on the telephone or self-complete through the Internet. For this paper, 15 questions considered to be either sensitive, stigma-related, or less-sensitive in nature were assessed to estimate associations between responses and mode of completion. Multivariate regression analyses were conducted for questions with significant (P≤.05) bivariate differences in responses to adjust for sociodemographic factors. As survey mode was not randomly assigned, we created a propensity score variable and included it in our multivariate models to control for mode selection bias. Results A total of 81% of participants completed the questionnaire through the Internet, and 19% completed by telephone. Telephone respondents were older, reported less education, had lower incomes, and were more likely from the province of Quebec. Overall, 2 of 13 questions assessed for social desirability and 3 of 15 questions assessed for item nonresponse were significantly associated with choice of mode in the multivariate analysis. For social desirability, Web respondents were more likely than telephone respondents to report more than 1 sexual partner in the past year (fully adjusted odds ratio (OR)=3.65, 95% CI 1.80-7.42) and more likely to have donated to charity in the past year (OR=1.63, 95% CI 1.15-2.29). For item nonresponse, Web respondents were more likely than telephone respondents to have a missing or “don’t know” response when asked about: the disease they were most concerned about (OR=3.02, 95% CI 1.67-5.47); if they had ever been tested for HIV (OR=8.04, 95% CI 2.46-26.31); and when rating their level of comfort with shopping at grocery store if the owner was known to have HIV or AIDS (OR=3.11, 95% CI 1.47-6.63). Conclusion Sociodemographic differences existed between Web and telephone respondents, but for 23 of 28 questions considered in our analysis, there were no significant differences in responses by mode. For surveys with very sensitive health content, such as HIV and AIDS, Web administration may be subject to less social desirability bias but may also have greater item nonresponse for certain questions. PMID:27473597

Prevalence of and risk factors for latex sensitization in patients with spina bifida.

PubMed

Bernardini, R; Novembre, E; Lombardi, E; Mezzetti, P; Cianferoni, A; Danti, A D; Mercurella, A; Vierucci, A

1998-11-01

We determined the prevalence of and risk factors for latex sensitization in patients with spina bifida. A total of 59 consecutive subjects 2 to 40 years old with spina bifida answered a questionnaire, and underwent a latex skin prick test and determination of serum IgE specific for latex by RAST CAP radioimmunoassay. We also noted the relationships of total serum IgE skin prick tests to common air and food allergens. In addition, skin prick plus prick tests were also done with fresh foods, including kiwi, pear, orange, almond, pineapple, apple, tomato and banana. Latex sensitization was present in 15 patients (25%) according to the presence of IgE specific to latex, as detected by a skin prick test in 9 and/or RAST CAP in 13. Five latex sensitized patients (33.3%) had clinical manifestations, such as urticaria, conjuctivitis, angioedema, rhinitis and bronchial asthma, while using a latex glove and inflating a latex balloon. Atopy was present in 21 patients (35.6%). In 14 patients (23%) 1 or more skin tests were positive for fresh foods using a prick plus prick technique. Tomato, kiwi, and pear were the most common skin test positive foods. Univariate analysis revealed that a history of 5 or more operations, atopy and positive prick plus prick tests results for pear and kiwi were significantly associated with latex sensitization. Multivariate analysis demonstrated that only atopy and a history of 5 or more operations were significantly and independently associated with latex sensitization. A fourth of the patients with spina bifida were sensitized to latex. Atopy and an elevated number of operations were significant and independent predictors of latex sensitization in these cases.

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.

REGIONAL-SCALE WIND FIELD CLASSIFICATION EMPLOYING CLUSTER ANALYSIS

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

Glascoe, L G; Glaser, R E; Chin, H S

2004-06-17

The classification of time-varying multivariate regional-scale wind fields at a specific location can assist event planning as well as consequence and risk analysis. Further, wind field classification involves data transformation and inference techniques that effectively characterize stochastic wind field variation. Such a classification scheme is potentially useful for addressing overall atmospheric transport uncertainty and meteorological parameter sensitivity issues. Different methods to classify wind fields over a location include the principal component analysis of wind data (e.g., Hardy and Walton, 1978) and the use of cluster analysis for wind data (e.g., Green et al., 1992; Kaufmann and Weber, 1996). The goalmore » of this study is to use a clustering method to classify the winds of a gridded data set, i.e, from meteorological simulations generated by a forecast model.« less

Impact and cost-effectiveness of a second tetanus toxoid, reduced diphtheria toxoid, and acellular pertussis (Tdap) vaccine dose to prevent pertussis in the United States.

PubMed

Kamiya, Hajime; Cho, Bo-Hyun; Messonnier, Mark L; Clark, Thomas A; Liang, Jennifer L

2016-04-04

The United States experienced a substantial increase in reported pertussis cases over the last decade. Since 2005, persons 11 years and older have been routinely recommended to receive a single dose of tetanus toxoid, reduced diphtheria toxoid and acellular pertussis (Tdap) vaccine. The objective of this analysis was to evaluate the potential impact and cost-effectiveness of recommending a second dose of Tdap. A static cohort model was used to calculate the epidemiologic and economic impact of adding a second dose of Tdap at age 16 or 21 years. Projected costs and outcomes were examined from a societal perspective over a 20-year period. Quality-adjusted Life Years (QALY) saved were calculated. Using baseline pertussis incidence from the National Notifiable Diseases Surveillance System, Tdap revaccination at either age 16 or 21 years would reduce outpatient visits by 433 (5%) and 285 (4%), and hospitalization cases by 7 (7%) and 5 (5%), respectively. The costs per QALY saved with a second dose of Tdap were approximately US $19.7 million (16 years) and $26.2 million (21 years). In sensitivity analyses, incidence most influenced the model; as incidence increased, the costs per QALY decreased. To a lesser degree, initial vaccine effectiveness and waning of effectiveness also affected cost outcomes. Multivariate sensitivity analyses showed that under a set of optimistic assumptions, the cost per QALY saved would be approximately $163,361 (16 years) and $204,556 (21 years). A second dose of Tdap resulted in a slight decrease in the number of cases and other outcomes, and that trend is more apparent when revaccinating at age 16 years than at age 21 years. Both revaccination strategies had high dollar per QALY saved even under optimistic assumptions in a multivariate sensitivity analysis. Published by Elsevier Ltd.

The sperm motility pattern in ecotoxicological tests. The CRYO-Ecotest as a case study.

PubMed

Fabbrocini, Adele; D'Adamo, Raffaele; Del Prete, Francesco; Maurizio, Daniela; Specchiulli, Antonietta; Oliveira, Luis F J; Silvestri, Fausto; Sansone, Giovanni

2016-01-01

Changes in environmental stressors inevitably lead to an increasing need for innovative and more flexible monitoring tools. The aim of this work has been the characterization of the motility pattern of the cryopreserved sea bream semen after exposure to a dumpsite leachate sample, for the identification of the best representative parameters to be used as endpoints in an ecotoxicological bioassay. Sperm motility has been evaluated either by visual and by computer-assisted analysis; parameters concerning motility on activation and those describing it in the times after activation (duration parameters) have been assessed, discerning them in terms of sensitivity, reliability and methodology of assessment by means of multivariate analyses. The EC50 values of the evaluated endpoints ranged between 2.3 and 4.5ml/L, except for the total motile percentage (aTM, 7.0ml/L), which proved to be the less sensitive among all the tested parameters. According to the multivariate analyses, a difference in sensitivity among "activation" endpoints in respect of "duration" ones can be inferred; on the contrary, endpoints seem to be equally informative either describing total motile sperm or the rapid sub-population, as well as the assessment methodology seems to be not discriminating. In conclusion, the CRYO-Ecotest is a multi-endpoint bioassay that can be considered a promising innovative ecotoxicological tool, characterized by a high plasticity, as its endpoints can be easy tailored each time according to the different needs of the environmental quality assessment programs. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

Seven-Month Prostate-Specific Antigen Is Prognostic in Metastatic Hormone-Sensitive Prostate Cancer Treated With Androgen Deprivation With or Without Docetaxel

PubMed Central

Harshman, Lauren C.; Chen, Yu-Hui; Liu, Glenn; Carducci, Michael A.; Jarrard, David; Dreicer, Robert; Hahn, Noah; Garcia, Jorge A.; Hussain, Maha; Shevrin, Daniel; Eisenberger, Mario; Kohli, Manish; Plimack, Elizabeth R.; Cooney, Matthew; Vogelzang, Nicholas J.; Picus, Joel; Dipaola, Robert

2018-01-01

Purpose We evaluated the relationship between prostate-specific antigen (PSA) and overall survival in the context of a prospectively randomized clinical trial comparing androgen-deprivation therapy (ADT) plus docetaxel with ADT alone for initial metastatic hormone-sensitive prostate cancer. Methods We performed a landmark survival analysis at 7 months using the E3805 Chemohormonal Therapy Versus Androgen Ablation Randomized Trial for Extensive Disease in Prostate Cancer (CHAARTED) database (ClinicalTrials.gov identifier: NCT00309985). Inclusion required at least 7 months of follow-up and PSA levels at 7 months from ADT initiation. We used the prognostic classifiers identified in a previously reported trial (Southwest Oncology Group 9346) of PSA ≤ 0.2, > 0.2 to 4, and > 4 ng/mL. Results Seven hundred nineteen of 790 patients were eligible for this subanalysis; 358 were treated with ADT plus docetaxel, and 361 were treated with ADT alone. Median follow-up time was 23.1 months. On multivariable analysis, achieving a 7-month PSA ≤ 0.2 ng/mL was more likely with docetaxel, low-volume disease, prior local therapy, and lower baseline PSAs (all P ≤ .01). Across all patients, median overall survival was significantly longer if 7-month PSA reached ≤ 0.2 ng/mL compared with > 4 ng/mL (median survival, 60.4 v 22.2 months, respectively; P < .001). On multivariable analysis, 7-month PSA ≤ 0.2 and low volume disease were prognostic of longer overall survival (all P < 0.01). The addition of docetaxel increased the likelihood of achieving a PSA ≤ 0.2 ng/mL at 7 months (45.3% v 28.8% of patients on ADT alone). Patients on ADT alone who achieved a 7-month PSA ≤ 0.2 ng/mL had the best survival and were more likely to have low-volume disease (56.7%). Conclusion PSA ≤ 0.2 ng/mL at 7 months is prognostic for longer overall survival with ADT for metastatic hormone-sensitive prostate cancer irrespective of docetaxel administration. Adding docetaxel increased the likelihood of a lower PSA and improved survival. PMID:29261442

Beneficial use of serum ferritin and heme oxygenase-1 as biomarkers in adult-onset Still's disease: A multicenter retrospective study.

PubMed

Kirino, Yohei; Kawaguchi, Yasushi; Tada, Yoshifumi; Tsukamoto, Hiroshi; Ota, Toshiyuki; Iwamoto, Masahiro; Takahashi, Hiroki; Nagasawa, Kohei; Takei, Shuji; Horiuchi, Takahiko; Ichida, Hisae; Minota, Seiji; Ueda, Atsuhisa; Ohta, Akihide; Ishigatsubo, Yoshiaki

2018-01-11

Heme oxygenase (HO)-1 is a heme-degrading enzyme highly expressed in monocyte/macrophage, serum levels of which may be promising biomarker for adult-onset Still's disease (AOSD). We here report data on the use of serum ferritin and HO-1 levels in AOSD. Under the Hypercytokinemia Study Group collaboration, we collected sera from a total of 145 AOSD patients. Three independent experts judged whether the patients were definite AOSD depending on the clinical information. These 91 'definite AOSD' patients were further divided into active, remission, and relapse groups. Forty-six cases of systemic vasculitis, sepsis, etc. were included as disease controls. Serum ferritin and HO-1 levels were measured using ELISA. Associations between clinical symptoms, serum ferritin, and HO-1 were explored. Multivariate regression analysis was performed to identify independent variables associated with definite AOSD diagnosis. Serum ferritin and HO-1 levels were significantly higher in active and relapsed AOSD cases compared to disease controls, and were reduced by the treatment. Although a significant correlation was found between serum ferritin and HO-1 levels, a discrepancy was found in some cases such as iron-deficiency anemia. Receiver operating characteristic analysis identified optimal levels of serum ferritin (>819 ng/ml; sensitivity 76.1% and specificity 73.8%), and serum HO-1 (>30.2 ng/ml; sensitivity 84.8% and specificity 83.3%) that differentiated AOSD from controls. Interestingly, 88.9% of patients with AOSD who relapsed exceeded the cut-off value of serum HO-1 > 30.2 ng/ml, but only 50.0% exceeded serum ferritin >819 ng/ml (p = .013), suggesting that serum HO-1 levels may be a convenient indicator of AOSD disease status. Multivariate analysis identified neutrophilia, RF/ANA negativity, sore throat, and elevated serum HO-1 as independent variables associated with AOSD diagnosis. We confirmed that serum ferritin and HO-1 serve as highly specific and sensitive biomarkers for AOSD. A future prospective study with large sample size is necessary to determine whether these biomarkers could be included in Yamaguchi's Criteria.

Multidisciplinary optimization of controlled space structures with global sensitivity equations

NASA Technical Reports Server (NTRS)

Padula, Sharon L.; James, Benjamin B.; Graves, Philip C.; Woodard, Stanley E.

1991-01-01

A new method for the preliminary design of controlled space structures is presented. The method coordinates standard finite element structural analysis, multivariable controls, and nonlinear programming codes and allows simultaneous optimization of the structures and control systems of a spacecraft. Global sensitivity equations are a key feature of this method. The preliminary design of a generic geostationary platform is used to demonstrate the multidisciplinary optimization method. Fifteen design variables are used to optimize truss member sizes and feedback gain values. The goal is to reduce the total mass of the structure and the vibration control system while satisfying constraints on vibration decay rate. Incorporating the nonnegligible mass of actuators causes an essential coupling between structural design variables and control design variables. The solution of the demonstration problem is an important step toward a comprehensive preliminary design capability for structures and control systems. Use of global sensitivity equations helps solve optimization problems that have a large number of design variables and a high degree of coupling between disciplines.

A Baseline for the Multivariate Comparison of Resting-State Networks

PubMed Central

Allen, Elena A.; Erhardt, Erik B.; Damaraju, Eswar; Gruner, William; Segall, Judith M.; Silva, Rogers F.; Havlicek, Martin; Rachakonda, Srinivas; Fries, Jill; Kalyanam, Ravi; Michael, Andrew M.; Caprihan, Arvind; Turner, Jessica A.; Eichele, Tom; Adelsheim, Steven; Bryan, Angela D.; Bustillo, Juan; Clark, Vincent P.; Feldstein Ewing, Sarah W.; Filbey, Francesca; Ford, Corey C.; Hutchison, Kent; Jung, Rex E.; Kiehl, Kent A.; Kodituwakku, Piyadasa; Komesu, Yuko M.; Mayer, Andrew R.; Pearlson, Godfrey D.; Phillips, John P.; Sadek, Joseph R.; Stevens, Michael; Teuscher, Ursina; Thoma, Robert J.; Calhoun, Vince D.

2011-01-01

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease. PMID:21442040

Low complements and high titre of anti-Sm antibody as predictors of histopathologically proven silent lupus nephritis without abnormal urinalysis in patients with systemic lupus erythematosus.

PubMed

Ishizaki, Jun; Saito, Kazuyoshi; Nawata, Masao; Mizuno, Yasushi; Tokunaga, Mikiko; Sawamukai, Norifumi; Tamura, Masahito; Hirata, Shintaro; Yamaoka, Kunihiro; Hasegawa, Hitoshi; Tanaka, Yoshiya

2015-03-01

The aim of this study was to clarify the clinical characteristics and predictors of silent LN (SLN), a type of LN in SLE without abnormal urinalysis or renal impairment. Of 182 patients who underwent renal biopsy, 48 did not present with abnormal urinalysis or renal impairment at the time of biopsy. The patients with LN (SLN group, n = 36) and those without LN (non-LN group, n = 12) were compared with respect to their baseline characteristics. Bivariate analysis comprised Fisher's exact test and the Mann-Whitney test, whereas multivariate analysis employed binomial logistic regression analysis. LN was histopathologically identified in 36 of 48 patients. According to the International Society of Nephrology/Renal Pathology Society classification, 72% of the SLN patients were classified as having class I/II, with a further 17% having class III/IV. Bivariate analyses indicated that platelet count, serum albumin, complement components (C3 and C4), complement haemolytic activity (CH50), anti-Sm antibody titre and anti-ribonucleoprotein antibody titre were significantly different between groups. Multivariate analysis indicated that CH50 and C3 titres were significantly lower in the SLN group, whereas anti-Sm antibody titre was significantly higher. The cut-off titre, calculated based on the receiver operating characteristic curve for CH50, was 33 U/ml, with a sensitivity and specificity of 89% and 83%, respectively. The cut-off titre for anti-Sm antibodies was 9 U/ml, with a sensitivity and specificity of 74% and 83%, respectively. Low titres of CH50 and C3 and a high titre of anti-Sm antibody were identified as predictors of SLN. © The Author 2014. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Towards practical time-of-flight secondary ion mass spectrometry lignocellulolytic enzyme assays

PubMed Central

2013-01-01

Background Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) is a surface sensitive mass spectrometry technique with potential strengths as a method for detecting enzymatic activity on solid materials. In particular, ToF-SIMS has been applied to detect the enzymatic degradation of woody lignocellulose. Proof-of-principle experiments previously demonstrated the detection of both lignin-degrading and cellulose-degrading enzymes on solvent-extracted hardwood and softwood. However, these preliminary experiments suffered from low sample throughput and were restricted to samples which had been solvent-extracted in order to minimize the potential for mass interferences between low molecular weight extractive compounds and polymeric lignocellulose components. Results The present work introduces a new, higher-throughput method for processing powdered wood samples for ToF-SIMS, meanwhile exploring likely sources of sample contamination. Multivariate analysis (MVA) including Principal Component Analysis (PCA) and Multivariate Curve Resolution (MCR) was regularly used to check for sample contamination as well as to detect extractives and enzyme activity. New data also demonstrates successful ToF-SIMS analysis of unextracted samples, placing an emphasis on identifying the low-mass secondary ion peaks related to extractives, revealing how extractives change previously established peak ratios used to describe enzyme activity, and elucidating peak intensity patterns for better detection of cellulase activity in the presence of extractives. The sensitivity of ToF-SIMS to a range of cellulase doses is also shown, along with preliminary experiments augmenting the cellulase cocktail with other proteins. Conclusions These new procedures increase the throughput of sample preparation for ToF-SIMS analysis of lignocellulose and expand the applications of the method to include unextracted lignocellulose. These are important steps towards the practical use of ToF-SIMS as a tool to screen for changes in plant composition, whether the transformation of the lignocellulose is achieved through enzyme application, plant mutagenesis, or other treatments. PMID:24034438

Combining Frequency Doubling Technology Perimetry and Scanning Laser Polarimetry for Glaucoma Detection

PubMed Central

Mwanza, Jean-Claude; Warren, Joshua L.; Hochberg, Jessica T.; Budenz, Donald L.; Chang, Robert T.; Ramulu, Pradeep Y.

2014-01-01

Purpose To determine the ability of frequency doubling technology (FDT) and scanning laser polarimetry with variable corneal compensation (GDx-VCC) to detect glaucoma when used individually and in combination. Methods One hundred and ten normal and 114 glaucomatous subjects were tested with FDT C-20-5 screening protocol and the GDx-VCC. The discriminating ability was tested for each device individually and for both devices combined using GDx-NFI, GDx-TSNIT, number of missed points of FDT, and normal or abnormal FDT. Measures of discrimination included sensitivity, specificity, area under the curve (AUC), Akaike’s information criterion (AIC), and prediction confidence interval lengths (PIL). Results For detecting glaucoma regardless of severity, the multivariable model resulting from the combination of GDX-TSNIT, number of abnormal points on FDT (NAP-FDT), and the interaction GDx-TSNIT * NAP-FDT (AIC: 88.28, AUC: 0.959, sensitivity: 94.6%, specificity: 89.5%) outperformed the best single variable model provided by GDx-NFI (AIC: 120.88, AUC: 0.914, sensitivity: 87.8%, specificity: 84.2%). The multivariable model combining GDx-TSNIT, NAPFDT, and interaction GDx-TSNIT*NAP-FDT consistently provided better discriminating abilities for detecting early, moderate and severe glaucoma than the best single variable models. Conclusions The multivariable model including GDx-TSNIT, NAP-FDT, and the interaction GDX-TSNIT * NAP-FDT provides the best glaucoma prediction compared to all other multivariable and univariable models. Combining the FDT C-20-5 screening protocol and GDx-VCC improves glaucoma detection compared to using GDx or FDT alone. PMID:24777046

Effects of intranasal oxytocin on symptoms of schizophrenia: A multivariate Bayesian meta-analysis.

PubMed

Williams, Donald R; Bürkner, Paul-Christian

2017-01-01

Schizophrenia is a heterogeneous disorder in which psychiatric symptoms are classified into two general subgroups-positive and negative symptoms. Current antipsychotic drugs are effective for treating positive symptoms, whereas negative symptoms are less responsive. Since the neuropeptide oxytocin (OT) has been shown to mediate social behavior in animals and humans, it has been used as an experimental therapeutic for treating schizophrenia and in particular negative symptoms which includes social deficits. Through eight randomized controlled trials (RCTs) and three meta-analyses, evidence for an effect of intranasal OT (IN-OT) has been inconsistent. We therefore conducted an updated meta-analysis that offers several advantages when compared to those done previously: (1) We used a multivariate analysis which allows for comparisons between symptoms and accounts for correlations between symptoms; (2) We controlled for baseline scores; (3) We used a fully Bayesian framework that allows for assessment of evidence in favor of the null hypothesis using Bayes factors; and (4) We addressed inconsistencies in the primary studies and previous meta-analyses. Eight RCTs (n=238) were included in the present study and we found that oxytocin did not improve any aspect of symptomology in schizophrenic patients and there was moderate evidence in favor of the null (no effect of oxytocin) for negative symptoms. Multivariate comparisons between symptom types revealed that oxytocin was not especially beneficial for treating negative symptoms. The effect size estimates were not moderated, publication bias was absent, and our estimates were robust to sensitivity analyses. These results suggest that IN-OT is not an effective therapeutic for schizophrenia. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting microbiologically defined infection in febrile neutropenic episodes in children: global individual participant data multivariable meta-analysis

PubMed Central

Phillips, Robert S; Sung, Lillian; Amman, Roland A; Riley, Richard D; Castagnola, Elio; Haeusler, Gabrielle M; Klaassen, Robert; Tissing, Wim J E; Lehrnbecher, Thomas; Chisholm, Julia; Hakim, Hana; Ranasinghe, Neil; Paesmans, Marianne; Hann, Ian M; Stewart, Lesley A

2016-01-01

Background: Risk-stratified management of fever with neutropenia (FN), allows intensive management of high-risk cases and early discharge of low-risk cases. No single, internationally validated, prediction model of the risk of adverse outcomes exists for children and young people. An individual patient data (IPD) meta-analysis was undertaken to devise one. Methods: The ‘Predicting Infectious Complications in Children with Cancer' (PICNICC) collaboration was formed by parent representatives, international clinical and methodological experts. Univariable and multivariable analyses, using random effects logistic regression, were undertaken to derive and internally validate a risk-prediction model for outcomes of episodes of FN based on clinical and laboratory data at presentation. Results: Data came from 22 different study groups from 15 countries, of 5127 episodes of FN in 3504 patients. There were 1070 episodes in 616 patients from seven studies available for multivariable analysis. Univariable analyses showed associations with microbiologically defined infection (MDI) in many items, including higher temperature, lower white cell counts and acute myeloid leukaemia, but not age. Patients with osteosarcoma/Ewings sarcoma and those with more severe mucositis were associated with a decreased risk of MDI. The predictive model included: malignancy type, temperature, clinically ‘severely unwell', haemoglobin, white cell count and absolute monocyte count. It showed moderate discrimination (AUROC 0.723, 95% confidence interval 0.711–0.759) and good calibration (calibration slope 0.95). The model was robust to bootstrap and cross-validation sensitivity analyses. Conclusions: This new prediction model for risk of MDI appears accurate. It requires prospective studies assessing implementation to assist clinicians and parents/patients in individualised decision making. PMID:26954719

Single Marital Status and Infectious Mortality in Women With Cervical Cancer in the United States.

PubMed

Machida, Hiroko; Eckhardt, Sarah E; Castaneda, Antonio V; Blake, Erin A; Pham, Huyen Q; Roman, Lynda D; Matsuo, Koji

2017-10-01

Unmarried status including single marital status is associated with increased mortality in women bearing malignancy. Infectious disease weights a significant proportion of mortality in patients with malignancy. Here, we examined an association of single marital status and infectious mortality in cervical cancer. This is a retrospective observational study examining 86,555 women with invasive cervical cancer identified in the Surveillance, Epidemiology, and End Results Program between 1973 and 2013. Characteristics of 18,324 single women were compared with 38,713 married women in multivariable binary logistic regression models. Propensity score matching was performed to examine cumulative risk of all-cause and infectious mortality between the 2 groups. Single marital status was significantly associated with young age, black/Hispanic ethnicity, Western US residents, uninsured status, high-grade tumor, squamous histology, and advanced-stage disease on multivariable analysis (all, P < 0.05). In a prematched model, single marital status was significantly associated with increased cumulative risk of all-cause mortality (5-year rate: 32.9% vs 29.7%, P < 0.001) and infectious mortality (0.5% vs 0.3%, P < 0.001) compared with the married status. After propensity score matching, single marital status remained an independent prognostic factor for increased cumulative risk of all-cause mortality (adjusted hazards ratio [HR], 1.15; 95% confidence interval [CI], 1.11-1.20; P < 0.001) and those of infectious mortality on multivariable analysis (adjusted HR, 1.71; 95% CI, 1.27-2.32; P < 0.001). In a sensitivity analysis for stage I disease, single marital status remained significantly increased risk of infectious mortality after propensity score matching (adjusted HR, 2.24; 95% CI, 1.34-3.73; P = 0.002). Single marital status was associated with increased infectious mortality in women with invasive cervical cancer.

Discerning mild cognitive impairment and Alzheimer Disease from normal aging: morphologic characterization based on univariate and multivariate models.

PubMed

Liao, Weiqi; Long, Xiaojing; Jiang, Chunxiang; Diao, Yanjun; Liu, Xin; Zheng, Hairong; Zhang, Lijuan

2014-05-01

Differentiating mild cognitive impairment (MCI) and Alzheimer Disease (AD) from healthy aging remains challenging. This study aimed to explore the cerebral structural alterations of subjects with MCI or AD as compared to healthy elderly based on the individual and collective effects of cerebral morphologic indices using univariate and multivariate analyses. T1-weighted images (T1WIs) were retrieved from Alzheimer Disease Neuroimaging Initiative database for 116 subjects who were categorized into groups of healthy aging, MCI, and AD. Analysis of covariance (ANCOVA) and multivariate analysis of covariance (MANCOVA) were performed to explore the intergroup morphologic alterations indexed by surface area, curvature index, cortical thickness, and subjacent white matter volume with age and sex controlled as covariates, in 34 parcellated gyri regions of interest (ROIs) for both cerebral hemispheres based on the T1WI. Statistical parameters were mapped on the anatomic images to facilitate visual inspection. Global rather than region-specific structural alterations were revealed in groups of MCI and AD relative to healthy elderly using MANCOVA. ANCOVA revealed that the cortical thickness decreased more prominently in entorhinal, temporal, and cingulate cortices and was positively correlated with patients' cognitive performance in AD group but not in MCI. The temporal lobe features marked atrophy of white matter during the disease dynamics. Significant intercorrelations were observed among the morphologic indices with univariate analysis for given ROIs. Significant global structural alterations were identified in MCI and AD based on MANCOVA model with improved sensitivity. The intercorrelation among the morphologic indices may dampen the use of individual morphological parameter in featuring cerebral structural alterations. Decrease in cortical thickness is not reflective of the cognitive performance at the early stage of AD. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

Vitamin D insufficiency and subclinical atherosclerosis in non-diabetic males living with HIV.

PubMed

Portilla, Joaquín; Moreno-Pérez, Oscar; Serna-Candel, Carmen; Escoín, Corina; Alfayate, Rocio; Reus, Sergio; Merino, Esperanza; Boix, Vicente; Giner, Livia; Sánchez-Payá, José; Picó, Antonio

2014-01-01

Vitamin D insufficiency (VDI) has been associated with increased cardiovascular risk in the non-HIV population. This study evaluates the relationship among serum 25-hydroxyvitamin D [25(OH)D] levels, cardiovascular risk factors, adipokines, antiviral therapy (ART) and subclinical atherosclerosis in HIV-infected males. A cross-sectional study in ambulatory care was made in non-diabetic patients living with HIV. VDI was defined as 25(OH)D serum levels <75 nmol/L. Fasting lipids, glucose, inflammatory markers (tumour necrosis factor-α, interleukin-6, high-sensitivity C-reactive protein) and endothelial markers (plasminogen activator inhibitor-1, or PAI-I) were measured. The common carotid artery intima-media thickness (C-IMT) was determined. A multivariate logistic regression analysis was made to identify factors associated with the presence of VDI, while multivariate linear regression analysis was used to identify factors associated with common C-IMT. Eighty-nine patients were included (age 42 ± 8 years), 18.9% were in CDC (US Centers for Disease Control and Prevention) stage C and 75 were on ART. VDI was associated with ART exposure, sedentary lifestyle, higher triglycerides levels and PAI-I. In univariate analysis, VDI was associated with greater common C-IMT. The multivariate linear regression model, adjusted by confounding factors, revealed an independent association between common C-IMT and patient age, time of exposure to protease inhibitors (PIs) and impaired fasting glucose (IFG). In contrast, there were no independent associations between common C-IMT and VDI or inflammatory and endothelial markers. VDI was not independently associated with subclinical atherosclerosis in non-diabetic males living with HIV. Older age, a longer exposure to PIs, and IFG were independent factors associated with common C-IMT in this population.

Human Adenocarcinoma Cell Line Sensitivity to Essential Oil Phytocomplexes from Pistacia Species: a Multivariate Approach.

PubMed

Buriani, Alessandro; Fortinguerra, Stefano; Sorrenti, Vincenzo; Dall'Acqua, Stefano; Innocenti, Gabbriella; Montopoli, Monica; Gabbia, Daniela; Carrara, Maria

2017-08-11

Principal component analysis (PCA) multivariate analysis was applied to study the cytotoxic activity of essential oils from various species of the Pistacia genus on human tumor cell lines. In particular, the cytotoxic activity of essential oils obtained from P. lentiscus , P. lentiscus var. chia (mastic gum), P. terebinthus , P. vera , and P. integerrima , was screened on three human adenocarcinoma cell lines: MCF-7 (breast), 2008 (ovarian), and LoVo (colon). The results indicate that all the Pistacia phytocomplexes, with the exception of mastic gum oil, induce cytotoxic effects on one or more of the three cell lines. PCA highlighted the presence of different cooperating clusters of bioactive molecules. Cluster variability among species, and even within the same species, could explain some of the differences seen among samples suggesting the presence of both common and species-specific mechanisms. Single molecules from one of the most significant clusters were tested, but only bornyl-acetate presented cytotoxic activity, although at much higher concentrations (IC 50 = 138.5 µg/mL) than those present in the essential oils, indicating that understanding of the full biological effect requires a holistic vision of the phytocomplexes with all its constituents.

Objective classification of ecological status in marine water bodies using ecotoxicological information and multivariate analysis.

PubMed

Beiras, Ricardo; Durán, Iria

2014-12-01

Some relevant shortcomings have been identified in the current approach for the classification of ecological status in marine water bodies, leading to delays in the fulfillment of the Water Framework Directive objectives. Natural variability makes difficult to settle fixed reference values and boundary values for the Ecological Quality Ratios (EQR) for the biological quality elements. Biological responses to environmental degradation are frequently of nonmonotonic nature, hampering the EQR approach. Community structure traits respond only once ecological damage has already been done and do not provide early warning signals. An alternative methodology for the classification of ecological status integrating chemical measurements, ecotoxicological bioassays and community structure traits (species richness and diversity), and using multivariate analyses (multidimensional scaling and cluster analysis), is proposed. This approach does not depend on the arbitrary definition of fixed reference values and EQR boundary values, and it is suitable to integrate nonlinear, sensitive signals of ecological degradation. As a disadvantage, this approach demands the inclusion of sampling sites representing the full range of ecological status in each monitoring campaign. National or international agencies in charge of coastal pollution monitoring have comprehensive data sets available to overcome this limitation.

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

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

DOE PAGES

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

2014-01-01

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

Demographic factors associated with moral sensitivity among nursing students.

PubMed

Tuvesson, Hanna; Lützén, Kim

2017-11-01

Today's healthcare environment is often characterized by an ethically demanding work situation, and nursing students need to prepare to meet ethical challenges in their future role. Moral sensitivity is an important aspect of the ethical decision-making process, but little is known regarding nursing students' moral sensitivity and its possible development during nursing education. The aims of this study were to investigate moral sensitivity among nursing students, differences in moral sensitivity according to sample sub-group, and the relation between demographic characteristics of nursing students and moral sensitivity. A convenience sample of 299 nursing students from one university completed a questionnaire comprising questions about demographic information and the revised Moral Sensitivity Questionnaire. With the use of SPSS, non-parametric statistics, including logistic regression models, were used to investigate the relationship between demographic characteristics and moral sensitivity. Ethical considerations: The study followed the regulations according to the Swedish Ethical Review Act and was reviewed by the Ethics Committee of South-East Sweden. The findings showed that mean scores of nursing students' moral sensitivity were found in the middle to upper segment of the rating scale. Multivariate analysis showed that gender (odds ratio = 3.32), age (odds ratio = 2.09; 1.73), and parental status (odds ratio = 0.31) were of relevance to nursing students' moral sensitivity. Academic year was found to be unrelated to moral sensitivity. These demographic aspects should be considered when designing ethics education for nursing students. Future studies should continue to investigate moral sensitivity in nursing students, such as if and how various pedagogical strategies in ethics may contribute to moral sensitivity in nursing students.

Online UV-visible spectroscopy and multivariate curve resolution as powerful tool for model-free investigation of laccase-catalysed oxidation.

PubMed

Kandelbauer, A; Kessler, W; Kessler, R W

2008-03-01

The laccase-catalysed transformation of indigo carmine (IC) with and without a redox active mediator was studied using online UV-visible spectroscopy. Deconvolution of the mixture spectra obtained during the reaction was performed on a model-free basis using multivariate curve resolution (MCR). Thereby, the time courses of educts, products, and reaction intermediates involved in the transformation were reconstructed without prior mechanistic assumptions. Furthermore, the spectral signature of a reactive intermediate which could not have been detected by a classical hard-modelling approach was extracted from the chemometric analysis. The findings suggest that the combined use of UV-visible spectroscopy and MCR may lead to unexpectedly deep mechanistic evidence otherwise buried in the experimental data. Thus, although rather an unspecific method, UV-visible spectroscopy can prove useful in the monitoring of chemical reactions when combined with MCR. This offers a wide range of chemists a cheap and readily available, highly sensitive tool for chemical reaction online monitoring.

Quantitative Trait Loci for Light Sensitivity, Body Weight, Body Size, and Morphological Eye Parameters in the Bumblebee, Bombus terrestris.

PubMed

Maebe, Kevin; Meeus, Ivan; De Riek, Jan; Smagghe, Guy

2015-01-01

Bumblebees such as Bombus terrestris are essential pollinators in natural and managed ecosystems. In addition, this species is intensively used in agriculture for its pollination services, for instance in tomato and pepper greenhouses. Here we performed a quantitative trait loci (QTL) analysis on B. terrestris using 136 microsatellite DNA markers to identify genes linked with 20 traits including light sensitivity, body size and mass, and eye and hind leg measures. By composite interval mapping (IM), we found 83 and 34 suggestive QTLs for 19 of the 20 traits at the linkage group wide significance levels of p = 0.05 and 0.01, respectively. Furthermore, we also found five significant QTLs at the genome wide significant level of p = 0.05. Individual QTLs accounted for 7.5-53.3% of the phenotypic variation. For 15 traits, at least one QTL was confirmed with multiple QTL model mapping. Multivariate principal components analysis confirmed 11 univariate suggestive QTLs but revealed three suggestive QTLs not identified by the individual traits. We also identified several candidate genes linked with light sensitivity, in particular the Phosrestin-1-like gene is a primary candidate for its phototransduction function. In conclusion, we believe that the suggestive and significant QTLs, and markers identified here, can be of use in marker-assisted breeding to improve selection towards light sensitive bumblebees, and thus also the pollination service of bumblebees.

Comparative evaluation of H&H and WFNS grading scales with modified H&H (sans systemic disease): A study on 1000 patients with subarachnoid hemorrhage.

PubMed

Aggarwal, Ashish; Dhandapani, Sivashanmugam; Praneeth, Kokkula; Sodhi, Harsimrat Bir Singh; Pal, Sudhir Singh; Gaudihalli, Sachin; Khandelwal, N; Mukherjee, Kanchan K; Tewari, M K; Gupta, Sunil Kumar; Mathuriya, S N

2018-01-01

The comparative studies on grading in subarachnoid hemorrhage (SAH) had several limitations such as the unclear grading of Glasgow Coma Scale 15 with neurological deficits in World Federation of Neurosurgical Societies (WFNS), and the inclusion of systemic disease in Hunt and Hess (H&H) scales. Their differential incremental impacts and optimum cut-off values for unfavourable outcome are unsettled. This is a prospective comparison of prognostic impacts of grading schemes to address these issues. SAH patients were assessed using WFNS, H&H (including systemic disease), modified H&H (sans systemic disease) and followed up with Glasgow Outcome Score (GOS) at 3 months. Their performance characteristics were analysed as incremental ordinal variables and different grading scale dichotomies using rank-order correlation, sensitivity, specificity, positive predictive value, negative predictive value, Youden's J and multivariate analyses. A total of 1016 patients were studied. As univariate incremental variable, H&H sans systemic disease had the best negative rank-order correlation coefficient (-0.453) with respect to lower GOS (p < 0.001). As univariate dichotomized category, WFNS grades 3-5 had the best performance index of 0.39 to suggest unfavourable GOS with a specificity of 89% and sensitivity of 51%. In multivariate incremental analysis, H&H sans systemic disease had the greatest adjusted incremental impact of 0.72 (95% confidence interval (CI) 0.54-0.91) against a lower GOS as compared to 0.6 (95% CI 0.45-0.74) and 0.55 (95% CI 0.42-0.68) for H&H and WFNS grades, respectively. In multivariate categorical analysis, H&H grades 4-5 sans systemic disease had the greatest impact on unfavourable GOS with an adjusted odds ratio of 6.06 (95% CI 3.94-9.32). To conclude, H&H grading sans systemic disease had the greatest impact on unfavourable GOS. Though systemic disease is an important prognostic factor, it should be considered distinctly from grading. Appropriate cut-off values suggesting unfavourable outcome for H&H and WFNS were 4-5 and 3-5, respectively, indicating the importance of neurological deficits in addition to level of consciousness.

Multivariate calibration in Laser-Induced Breakdown Spectroscopy quantitative analysis: The dangers of a 'black box' approach and how to avoid them

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

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

Analysis of Exhaled Breath Volatile Organic Compounds in Inflammatory Bowel Disease: A Pilot Study.

PubMed

Hicks, Lucy C; Huang, Juzheng; Kumar, Sacheen; Powles, Sam T; Orchard, Timothy R; Hanna, George B; Williams, Horace R T

2015-09-01

Distinguishing between the inflammatory bowel diseases [IBD], Crohn's disease [CD] and ulcerative colitis [UC], is important for determining management and prognosis. Selected ion flow tube mass spectrometry [SIFT-MS] may be used to analyse volatile organic compounds [VOCs] in exhaled breath: these may be altered in disease states, and distinguishing breath VOC profiles can be identified. The aim of this pilot study was to identify, quantify, and analyse VOCs present in the breath of IBD patients and controls, potentially providing insights into disease pathogenesis and complementing current diagnostic algorithms. SIFT-MS breath profiling of 56 individuals [20 UC, 18 CD, and 18 healthy controls] was undertaken. Multivariate analysis included principal components analysis and partial least squares discriminant analysis with orthogonal signal correction [OSC-PLS-DA]. Receiver operating characteristic [ROC] analysis was performed for each comparative analysis using statistically significant VOCs. OSC-PLS-DA modelling was able to distinguish both CD and UC from healthy controls and from one other with good sensitivity and specificity. ROC analysis using combinations of statistically significant VOCs [dimethyl sulphide, hydrogen sulphide, hydrogen cyanide, ammonia, butanal, and nonanal] gave integrated areas under the curve of 0.86 [CD vs healthy controls], 0.74 [UC vs healthy controls], and 0.83 [CD vs UC]. Exhaled breath VOC profiling was able to distinguish IBD patients from controls, as well as to separate UC from CD, using both multivariate and univariate statistical techniques. Copyright © 2015 European Crohn’s and Colitis Organisation (ECCO). Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Evaluation of a Multivariate Syndromic Surveillance System for West Nile Virus.

PubMed

Faverjon, Céline; Andersson, M Gunnar; Decors, Anouk; Tapprest, Jackie; Tritz, Pierre; Sandoz, Alain; Kutasi, Orsolya; Sala, Carole; Leblond, Agnès

2016-06-01

Various methods are currently used for the early detection of West Nile virus (WNV) but their outputs are not quantitative and/or do not take into account all available information. Our study aimed to test a multivariate syndromic surveillance system to evaluate if the sensitivity and the specificity of detection of WNV could be improved. Weekly time series data on nervous syndromes in horses and mortality in both horses and wild birds were used. Baselines were fitted to the three time series and used to simulate 100 years of surveillance data. WNV outbreaks were simulated and inserted into the baselines based on historical data and expert opinion. Univariate and multivariate syndromic surveillance systems were tested to gauge how well they detected the outbreaks; detection was based on an empirical Bayesian approach. The systems' performances were compared using measures of sensitivity, specificity, and area under receiver operating characteristic curve (AUC). When data sources were considered separately (i.e., univariate systems), the best detection performance was obtained using the data set of nervous symptoms in horses compared to those of bird and horse mortality (AUCs equal to 0.80, 0.75, and 0.50, respectively). A multivariate outbreak detection system that used nervous symptoms in horses and bird mortality generated the best performance (AUC = 0.87). The proposed approach is suitable for performing multivariate syndromic surveillance of WNV outbreaks. This is particularly relevant, given that a multivariate surveillance system performed better than a univariate approach. Such a surveillance system could be especially useful in serving as an alert for the possibility of human viral infections. This approach can be also used for other diseases for which multiple sources of evidence are available.

Compensation for obesity-induced insulin resistance in dogs: assessment of the effects of leptin, adiponectin, and glucagon-like peptide-1 using path analysis.

PubMed

Verkest, K R; Fleeman, L M; Morton, J M; Ishioka, K; Rand, J S

2011-07-01

The hormonal mediators of obesity-induced insulin resistance and compensatory hyperinsulinemia in dogs have not been identified. Plasma samples were obtained after a 24-h fast from 104 client-owned lean, overweight, and obese dogs. Plasma glucose and insulin concentrations were used to calculate insulin sensitivity and β-cell function with the use of the homeostasis model assessment (HOMA(insulin sensitivity) and HOMA(β-cell function), respectively). Path analysis with multivariable linear regression was used to identify whether fasting plasma leptin, adiponectin, or glucagon-like peptide-1 concentrations were associated with adiposity, insulin sensitivity, and basal insulin secretion. None of the dogs were hyperglycemic. In the final path model, adiposity was positively associated with leptin (P < 0.01) and glucagon-like peptide-1 (P = 0.04) concentrations. No significant total effect of adiposity on adiponectin in dogs (P = 0.24) was observed. If there is a direct effect of leptin on adiponectin, then our results indicate that this is a positive relationship, which at least partly counters a negative direct relationship between adiposity and adiponectin. Fasting plasma leptin concentration was directly negatively associated with fasting insulin sensitivity (P = 0.01) and positively associated with β-cell function (P < 0.01), but no direct association was observed between adiponectin concentration and either insulin sensitivity or β-cell function (P = 0.42 and 0.11, respectively). We conclude that dogs compensate effectively for obesity-induced insulin resistance. Fasting plasma leptin concentrations appear to be associated with obesity-associated changes in insulin sensitivity and compensatory hyperinsulinemia in naturally occurring obese dogs. Adiponectin does not appear to be involved in the pathophysiology of obesity-associated changes in insulin sensitivity. Copyright © 2011 Elsevier Inc. All rights reserved.

Population and High-Risk Group Screening for Glaucoma: The Los Angeles Latino Eye Study

PubMed Central

Francis, Brian A.; Vigen, Cheryl; Lai, Mei-Ying; Winarko, Jonathan; Nguyen, Betsy; Azen, Stanley

2011-01-01

Purpose. To evaluate the ability of various screening tests, both individually and in combination, to detect glaucoma in the general Latino population and high-risk subgroups. Methods. The Los Angeles Latino Eye Study is a population-based study of eye disease in Latinos 40 years of age and older. Participants (n = 6082) underwent Humphrey visual field testing (HVF), frequency doubling technology (FDT) perimetry, measurement of intraocular pressure (IOP) and central corneal thickness (CCT), and independent assessment of optic nerve vertical cup disc (C/D) ratio. Screening parameters were evaluated for three definitions of glaucoma based on optic disc, visual field, and a combination of both. Analyses were also conducted for high-risk subgroups (family history of glaucoma, diabetes mellitus, and age ≥65 years). Sensitivity, specificity, and receiver operating characteristic curves were calculated for those continuous parameters independently associated with glaucoma. Classification and regression tree (CART) analysis was used to develop a multivariate algorithm for glaucoma screening. Results. Preset cutoffs for screening parameters yielded a generally poor balance of sensitivity and specificity (sensitivity/specificity for IOP ≥21 mm Hg and C/D ≥0.8 was 0.24/0.97 and 0.60/0.98, respectively). Assessment of high-risk subgroups did not improve the sensitivity/specificity of individual screening parameters. A CART analysis using multiple screening parameters—C/D, HVF, and IOP—substantially improved the balance of sensitivity and specificity (sensitivity/specificity 0.92/0.92). Conclusions. No single screening parameter is useful for glaucoma screening. However, a combination of vertical C/D ratio, HVF, and IOP provides the best balance of sensitivity/specificity and is likely to provide the highest yield in glaucoma screening programs. PMID:21245400

Power analysis to detect treatment effects in longitudinal clinical trials for Alzheimer's disease.

PubMed

Huang, Zhiyue; Muniz-Terrera, Graciela; Tom, Brian D M

2017-09-01

Assessing cognitive and functional changes at the early stage of Alzheimer's disease (AD) and detecting treatment effects in clinical trials for early AD are challenging. Under the assumption that transformed versions of the Mini-Mental State Examination, the Clinical Dementia Rating Scale-Sum of Boxes, and the Alzheimer's Disease Assessment Scale-Cognitive Subscale tests'/components' scores are from a multivariate linear mixed-effects model, we calculated the sample sizes required to detect treatment effects on the annual rates of change in these three components in clinical trials for participants with mild cognitive impairment. Our results suggest that a large number of participants would be required to detect a clinically meaningful treatment effect in a population with preclinical or prodromal Alzheimer's disease. We found that the transformed Mini-Mental State Examination is more sensitive for detecting treatment effects in early AD than the transformed Clinical Dementia Rating Scale-Sum of Boxes and Alzheimer's Disease Assessment Scale-Cognitive Subscale. The use of optimal weights to construct powerful test statistics or sensitive composite scores/endpoints can reduce the required sample sizes needed for clinical trials. Consideration of the multivariate/joint distribution of components' scores rather than the distribution of a single composite score when designing clinical trials can lead to an increase in power and reduced sample sizes for detecting treatment effects in clinical trials for early AD.

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…

Cost-effectiveness of zoledronic acid in the prevention of skeletal-related events in patients with bone metastases secondary to advanced renal cell carcinoma: application to France, Germany, and the United Kingdom.

PubMed

Botteman, M F; Meijboom, M; Foley, I; Stephens, J M; Chen, Y M; Kaura, S

2011-12-01

The use of zoledronic acid (ZOL) has recently been shown to significantly reduce the risk of new skeletal-related events (SREs) in renal cell carcinoma (RCC) patients with bone metastases. The present exploratory study assessed the cost-effectiveness of ZOL in this population, adopting a French, German, and United Kingdom (UK) government payer perspective. This cost-effectiveness model was based on a post hoc retrospective analysis of a subset of patients with RCC who were included in a larger randomized clinical trial of patients with bone metastases secondary to a variety of cancers. In the trial, patients were randomized to receive ZOL (n = 27) or placebo (n = 19) with concomitant antineoplastic therapy every 3 weeks for 9 months (core study) plus 12 months during a study extension. Since the trial did not collect costs or data on the quality-adjusted life years (QALYs) of the patients, these outcomes had to be assumed via modeling exercises. The costs of SREs were estimated using hospital DRG tariffs. These estimates were supplemented with literature-based costs where possible. Drug, administration, and supply costs were obtained from published and internet sources. Consistent with similar economic analyses, patients were assumed to experience quality of life decrements lasting 1 month for each SRE. Uncertainty surrounding outcomes was addressed via multivariate sensitivity analyses. Patients receiving ZOL experienced 1.07 fewer SREs than patients on placebo. Patients on ZOL experienced a gain in discounted QALYs of approximately 0.1563 in France and Germany and 0.1575 in the UK. Discounted SRE-related costs were substantially lower among ZOL than placebo patients (-€ 4,196 in France, - € 3,880 in Germany, and -€ 3,355 in the UK). After taking into consideration the drug therapy costs, ZOL saved € 1,358, € 1,223, and € 719 in France, Germany, and the UK, respectively. In the multivariate sensitivity analyses, therapy with ZOL saved costs in 67-77% of simulations, depending on the country. The cost per QALY gained for ZOL versus placebo was below € 30,000 per QALY gained threshold in approximately 93-94% of multivariate sensitivity analyses simulations. The present analysis suggests that ZOL saves costs and increases QALYs compared to placebo in French, German, and UK RCC patients with bone metastases. Additional prospective research may be needed to confirm these results in a larger sample of patients.

Phenotypes determined by cluster analysis in severe or difficult-to-treat asthma.

PubMed

Schatz, Michael; Hsu, Jin-Wen Y; Zeiger, Robert S; Chen, Wansu; Dorenbaum, Alejandro; Chipps, Bradley E; Haselkorn, Tmirah

2014-06-01

Asthma phenotyping can facilitate understanding of disease pathogenesis and potential targeted therapies. To further characterize the distinguishing features of phenotypic groups in difficult-to-treat asthma. Children ages 6-11 years (n = 518) and adolescents and adults ages ≥12 years (n = 3612) with severe or difficult-to-treat asthma from The Epidemiology and Natural History of Asthma: Outcomes and Treatment Regimens (TENOR) study were evaluated in this post hoc cluster analysis. Analyzed variables included sex, race, atopy, age of asthma onset, smoking (adolescents and adults), passive smoke exposure (children), obesity, and aspirin sensitivity. Cluster analysis used the hierarchical clustering algorithm with the Ward minimum variance method. The results were compared among clusters by χ(2) analysis; variables with significant (P < .05) differences among clusters were considered as distinguishing feature candidates. Associations among clusters and asthma-related health outcomes were assessed in multivariable analyses by adjusting for socioeconomic status, environmental exposures, and intensity of therapy. Five clusters were identified in each age stratum. Sex, atopic status, and nonwhite race were distinguishing variables in both strata; passive smoke exposure was distinguishing in children and aspirin sensitivity in adolescents and adults. Clusters were not related to outcomes in children, but 2 adult and adolescent clusters distinguished by nonwhite race and aspirin sensitivity manifested poorer quality of life (P < .0001), and the aspirin-sensitive cluster experienced more frequent asthma exacerbations (P < .0001). Distinct phenotypes appear to exist in patients with severe or difficult-to-treat asthma, which is related to outcomes in adolescents and adults but not in children. The study of the therapeutic implications of these phenotypes is warranted. Copyright © 2013 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

Potential Impact on Spatial Access to Surgery Under a Low Volume Pledge: A Population-Level Analysis of Patients Undergoing Pancreatectomy

PubMed Central

Fong, Zhi Ven; Loehrer, Andrew P; Castillo, Carlos Fernández-del; Bababekov, Yanik J; Jin, Ginger; Ferrone, Cristina R; Warshaw, Andrew L; Traeger, Lara N; Hutter, Matthew M; Lillemoe, Keith D; Chang, David C

2018-01-01

Background A minimum-volume policy restricting hospitals not meeting the threshold from performing complex surgery may increase travel burden and decrease spatial access to surgery. We aim to identify vulnerable populations that would be sensitive to an added travel burden. Methods We performed a retrospective analysis of the California Office of Statewide Health Planning and Development database for patients undergoing pancreatectomy from 2005 to 2014. Number of hospitals bypassed was used as a metric for travel. Patients bypassing fewer hospitals were deemed to be more sensitive to an added travel burden. Results There were 13,374 patients who underwent a pancreatectomy, of which 2,368 (17.7%) were non-bypassers. On unadjusted analysis, patients >80 year old travelled less than their younger counterparts, bypassing a mean of 10.9 ± 9.5 hospitals compared to 14.2 ± 21.3 hospitals bypassed by the 35–49 year old age group (p<0.001). Racial minorities travelled less when compared to Non-Hispanic Whites (p<0.001). Patients identifying their payer status as self-pay (8.9 ± 15.6 hospitals bypassed) and Medicaid (10.1 ± 17.2 hospitals bypassed) also travelled less when compared to patients with private insurance (13.8 ± 20.4 hospitals bypassed, p<0.001). On multivariate analysis, advanced age, racial minority and patients with self-pay or Medicaid payer status were independently associated with increased sensitivity to an added travel burden. Conclusion In patients undergoing pancreatectomy, the elderly, racial minorities and patients with self-pay or Medicaid payer status were associated with an increased sensitivity to an added travel burden. This vulnerable cohort may be disproportionately affected by a minimum-volume policy. PMID:28504112

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

PubMed

Bonetti, Jennifer; Quarino, Lawrence

2014-05-01

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

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

PubMed Central

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

2012-01-01

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

Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models

PubMed Central

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

2014-01-01

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

A sensitive and rapid assay for 4-aminophenol in paracetamol drug and tablet formulation, by flow injection analysis with spectrophotometric detection.

PubMed

Bloomfield, M S

2002-12-06

4-Aminophenol (4AP) is the primary degradation product of paracetamol which is limited at a low level (50 ppm or 0.005% w/w) in the drug substance by the European, United States, British and German Pharmacopoeias, employing a manual colourimetric limit test. The 4AP limit is widened to 1000 ppm or 0.1% w/w for the tablet product monographs, which quote the use of a less sensitive automated HPLC method. The lower drug substance specification limit is applied to our products, (50 ppm, equivalent to 25 mug 4AP in a tablet containing 500-mg paracetamol) and the pharmacopoeial HPLC assay was not suitable at this low level due to matrix interference. For routine analysis a rapid, automated assay was required. This paper presents a highly sensitive, precise and automated method employing the technique of Flow Injection (FI) analysis to quantitatively assay low levels of this degradant. A solution of the drug substance, or an extract of the tablets, containing 4AP and paracetamol is injected into a solvent carrier stream and merged on-line with alkaline sodium nitroprusside reagent, to form a specific blue derivative which is detected spectrophotometrically at 710 nm. Standard HPLC equipment is used throughout. The procedure is fully quantitative and has been optimised for sensitivity and robustness using a multivariate experimental design (multi-level 'Central Composite' response surface) model. The method has been fully validated and is linear down to 0.01 mug ml(-1). The approach should be applicable to a range of paracetamol products.

Breath Analysis in Disease Diagnosis: Methodological Considerations and Applications

PubMed Central

Lourenço, Célia; Turner, Claire

2014-01-01

Breath analysis is a promising field with great potential for non-invasive diagnosis of a number of disease states. Analysis of the concentrations of volatile organic compounds (VOCs) in breath with an acceptable accuracy are assessed by means of using analytical techniques with high sensitivity, accuracy, precision, low response time, and low detection limit, which are desirable characteristics for the detection of VOCs in human breath. “Breath fingerprinting”, indicative of a specific clinical status, relies on the use of multivariate statistics methods with powerful in-built algorithms. The need for standardisation of sample collection and analysis is the main issue concerning breath analysis, blocking the introduction of breath tests into clinical practice. This review describes recent scientific developments in basic research and clinical applications, namely issues concerning sampling and biochemistry, highlighting the diagnostic potential of breath analysis for disease diagnosis. Several considerations that need to be taken into account in breath analysis are documented here, including the growing need for metabolomics to deal with breath profiles. PMID:24957037

Multivariate data analysis methods for the interpretation of microbial flow cytometric data.

PubMed

Davey, Hazel M; Davey, Christopher L

2011-01-01

Flow cytometry is an important technique in cell biology and immunology and has been applied by many groups to the analysis of microorganisms. This has been made possible by developments in hardware that is now sensitive enough to be used routinely for analysis of microbes. However, in contrast to advances in the technology that underpin flow cytometry, there has not been concomitant progress in the software tools required to analyse, display and disseminate the data and manual analysis, of individual samples remains a limiting aspect of the technology. We present two new data sets that illustrate common applications of flow cytometry in microbiology and demonstrate the application of manual data analysis, automated visualisation (including the first description of a new piece of software we are developing to facilitate this), genetic programming, principal components analysis and artificial neural nets to these data. The data analysis methods described here are equally applicable to flow cytometric applications with other cell types.

Breath analysis in disease diagnosis: methodological considerations and applications.

PubMed

Lourenço, Célia; Turner, Claire

2014-06-20

Breath analysis is a promising field with great potential for non-invasive diagnosis of a number of disease states. Analysis of the concentrations of volatile organic compounds (VOCs) in breath with an acceptable accuracy are assessed by means of using analytical techniques with high sensitivity, accuracy, precision, low response time, and low detection limit, which are desirable characteristics for the detection of VOCs in human breath. "Breath fingerprinting", indicative of a specific clinical status, relies on the use of multivariate statistics methods with powerful in-built algorithms. The need for standardisation of sample collection and analysis is the main issue concerning breath analysis, blocking the introduction of breath tests into clinical practice. This review describes recent scientific developments in basic research and clinical applications, namely issues concerning sampling and biochemistry, highlighting the diagnostic potential of breath analysis for disease diagnosis. Several considerations that need to be taken into account in breath analysis are documented here, including the growing need for metabolomics to deal with breath profiles.

TU-FG-201-05: Varian MPC as a Statistical Process Control Tool

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

Carver, A; Rowbottom, C

Purpose: Quality assurance in radiotherapy requires the measurement of various machine parameters to ensure they remain within permitted values over time. In Truebeam release 2.0 the Machine Performance Check (MPC) was released allowing beam output and machine axis movements to be assessed in a single test. We aim to evaluate the Varian Machine Performance Check (MPC) as a tool for Statistical Process Control (SPC). Methods: Varian’s MPC tool was used on three Truebeam and one EDGE linac for a period of approximately one year. MPC was commissioned against independent systems. After this period the data were reviewed to determine whethermore » or not the MPC was useful as a process control tool. Analyses on individual tests were analysed using Shewhart control plots, using Matlab for analysis. Principal component analysis was used to determine if a multivariate model was of any benefit in analysing the data. Results: Control charts were found to be useful to detect beam output changes, worn T-nuts and jaw calibration issues. Upper and lower control limits were defined at the 95% level. Multivariate SPC was performed using Principal Component Analysis. We found little evidence of clustering beyond that which might be naively expected such as beam uniformity and beam output. Whilst this makes multivariate analysis of little use it suggests that each test is giving independent information. Conclusion: The variety of independent parameters tested in MPC makes it a sensitive tool for routine machine QA. We have determined that using control charts in our QA programme would rapidly detect changes in machine performance. The use of control charts allows large quantities of tests to be performed on all linacs without visual inspection of all results. The use of control limits alerts users when data are inconsistent with previous measurements before they become out of specification. A. Carver has received a speaker’s honorarium from Varian.« less

Urinary tract infections following radical cystectomy and urinary diversion: a review of 1133 patients.

PubMed

Clifford, Thomas G; Katebian, Behrod; Van Horn, Christine M; Bazargani, Soroush T; Cai, Jie; Miranda, Gus; Daneshmand, Siamak; Djaladat, Hooman

2018-05-01

To investigate the incidence and microbiology of urinary tract infection (UTI) within 90 days following radical cystectomy (RC) and urinary diversion. We reviewed 1133 patients who underwent RC for bladder cancer at our institution between 2003 and 2013; 815 patients (72%) underwent orthotopic diversion, 274 (24%) ileal conduit, and 44 (4%) continent cutaneous diversion. 90-day postoperative UTI incidence, culture results, antibiotic sensitivity/resistance and treatment were recorded through retrospective review. Fisher's exact test, Kruskal-Wallis test, and multivariable analysis were performed. A total of 151 urinary tract infections were recorded in 123 patients (11%) during the first 90 days postoperatively. 21/123 (17%) had multiple infections and 25 (20%) had urosepsis in this time span. Gram-negative rods were the most common etiology (54% of positive cultures). 52% of UTI episodes led to readmission. There was no significant difference in UTI rate, etiologic microbiology (Gram-negative rods, Gram-positive cocci, fungi), or antibiotic sensitivity and resistance patterns between diversion groups. Resistance to quinolones was evident in 87.5% of Gram-positive and 35% of Gram-negative bacteria. In multivariable analysis, Charlson Comorbidity Index > 2 was associated with higher 90-day UTI rate (OR = 1.8, 95% CI 1.1-2.9, p = 0.05) and Candida UTI (OR 5.6, 95% CI 1.6-26.5, p = 0.04). UTI is a common complication and cause of readmission following radical cystectomy and urinary diversion. These infections are commonly caused by Gram-negative rods. High comorbidity index is an independent risk factor for postoperative UTI, but diversion type is not.

Predictors of Residual Disease after Unplanned Excision of Soft Tissue Sarcomas

PubMed Central

Gingrich, Alicia A.; Elias, Alexandra; Michael Lee, Chia-Yuan; Nakache, Yves-Paul N.; Li, Chin-Shang; Shah, Dhruvil R.; Boutin, Robert D.; Canter, Robert J.

2016-01-01

Background Unplanned excision of soft tissue sarcomas (STS) is an important quality of care issue given the morbidity related to tumor bed excision. Since not all patients harbor residual disease at the time of re-excision, we sought to determine predictors of residual STS following unplanned excision. Methods We identified 76 patients from a prospective database (1/1/2008 – 9/30/2014) who received a diagnosis of primary STS following unplanned excision on the trunk or extremities. We used univariable and multivariable analyses to evaluate predictors of residual STS as the primary endpoint. We calculated the sensitivity/specificity and accuracy of interval magnetic resonance imaging (MRI) to predict residual sarcoma at re-excision. Results Mean age was 52 years, and 63.2% were male. 50% had fragmented unplanned excision. Among patients undergoing re-excision, residual STS was identified in 70%. On univariable analysis, MRI showing gross disease and fragmented excision were significant predictors of residual STS (OR 10.59, 95% CI 2.14–52.49, P=0.004 and OR 3.61, 95% CI 1.09–11.94, P=0.035, respectively). On multivariable analysis, tumor size predicted distant recurrence and overall survival. When we combined equivocal and positive MRI, the sensitivity and specificity of MRI for predicting residual STS were 86.7% (95% CI 73.2–95.0%) and 57.9% (95% CI 33.5–79.8%), with an overall accuracy of 78.1% (95% CI 66.0–87.5%). Conclusions 70% of patients undergoing repeat excision after unplanned excision of STS harbor residual sarcoma. Although interval MRI and fragmented excision appear to be the most significant predictors of residual STS, the accuracy of MRI remains modest, especially given the incidence of equivocal MRI. PMID:27993214

Trait-related decision-making impairment in the three phases of bipolar disorder.

PubMed

Adida, Marc; Jollant, Fabrice; Clark, Luke; Besnier, Nathalie; Guillaume, Sébastien; Kaladjian, Arthur; Mazzola-Pomietto, Pascale; Jeanningros, Régine; Goodwin, Guy M; Azorin, Jean-Michel; Courtet, Philippe

2011-08-15

In bipolar disorder (BD), little is known about how deficits in neurocognitive functions such as decision-making are related to phase of illness. We predicted that manic, depressed, and euthymic bipolar patients (BPs) would display impaired decision-making, and we tested whether clinical characteristics could predict patients' decision-making performance. Subjects (N = 317; age range: 18-65 years) including 167 BPs (45 manic and 32 depressed inpatients, and 90 euthymic outpatients) and 150 age-, IQ-, and gender-matched healthy control (HC) participants, were included within three university psychiatric hospitals using a cross-sectional design. The relationship between predictor variables and decision-making was assessed by one-step multivariate analysis. The main outcome measures were overall decision-making ability on the Iowa Gambling Task (IGT) and an index of sensitivity to punishment frequency. Manic, depressed, and euthymic BPs selected significantly more cards from the risky decks than HCs (p < .001, p < .01, and p < .05, respectively), with no significant differences between the three BD groups. However, like HCs, BPs preferred decks that yielded infrequent penalties over those yielding frequent penalties. In multivariate analysis, decision-making impairment was significantly (p < .001) predicted by low level of education, high depressive scores, family history of BD, use of benzodiazepines, and nonuse of serotonin and norepinephrine reuptake inhibitor (SNRI) antidepressants. BPs have a trait-related impairment in decision-making that does not vary across illness phase. However, some subtle differences between the BD groups in the individual deck analyses may point to subtle state influences on reinforcement mechanisms, in addition to a more fundamental trait impairment in risk-sensitive decision making. Copyright © 2011 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Noise sensitivity, rather than noise level, predicts the non-auditory effects of noise in community samples: a population-based survey.

PubMed

Park, Jangho; Chung, Seockhoon; Lee, Jiho; Sung, Joo Hyun; Cho, Seung Woo; Sim, Chang Sun

2017-04-12

Excessive noise affects human health and interferes with daily activities. Although environmental noise may not directly cause mental illness, it may accelerate and intensify the development of latent mental disorders. Noise sensitivity (NS) is considered a moderator of non-auditory noise effects. In the present study, we aimed to assess whether NS is associated with non-auditory effects. We recruited a community sample of 1836 residents residing in Ulsan and Seoul, South Korea. From July to November 2015, participants were interviewed regarding their demographic characteristics, socioeconomic status, medical history, and NS. The non-auditory effects of noise were assessed using the Center of Epidemiologic Studies Depression, Insomnia Severity index, State Trait Anxiety Inventory state subscale, and Stress Response Inventory-Modified Form. Individual noise levels were recorded from noise maps. A three-model multivariate logistic regression analysis was performed to identify factors that might affect psychiatric illnesses. Participants ranged in age from 19 to 91 years (mean: 47.0 ± 16.1 years), and 37.9% (n = 696) were male. Participants with high NS were more likely to have been diagnosed with diabetes and hyperlipidemia and to use psychiatric medication. The multivariable analysis indicated that even after adjusting for noise-related variables, sociodemographic factors, medical illness, and duration of residence, subjects in the high NS group were more than 2 times more likely to experience depression and insomnia and 1.9 times more likely to have anxiety, compared with those in the low NS group. Noise exposure level was not identified as an explanatory value. NS increases the susceptibility and hence moderates there actions of individuals to noise. NS, rather than noise itself, is associated with an elevated susceptibility to non-auditory effects.

[Sunburn in young people: population-based study in Southern Brazil].

PubMed

Haack, Ricardo Lanzetta; Horta, Bernardo Lessa; Cesar, Juraci Almeida

2008-02-01

To assess the prevalence and risk factors for sunburn in young people. Population-based cross-sectional study using a multiple-stage sampling carried out with people living in the urban area of Pelotas, Southern Brazil, between October and December 2005. Data was collected from interviews with 1.604 subjects using a standardized pre-coded questionnaire about their family and another questionnaire applied to those aged between ten and 29 years for assessing the occurrence of sunburn episodes. Sunburn was defined as skin burning after sun exposure. Chi-square test with Yates' correction was used to compare proportions and Poisson regression with design effect control and robust adjustment of variance was applied in the multivariate analysis. Of those aged between 10 and 29 years, 1,412 reported sun exposure in the last summer. Losses and refusals were 5.5%. A total of 48.7% of the interviewees reported sunburn in the last year. The following variables were associated with sunburn in the multivariate analysis: white skin (PR=1.41; 95% CI: 1.12;1.79); higher skin sensitivity to sun exposure (PR=1.84; 95% CI: 1.64;2.06); age between 15 and 19 years (PR=1.30; 95% CI: 1.12;1.50); belonging to the higher quartile of income (PR=1.20; 95% CI: 1.01;1.42); and irregular use of sunscreens (PR=1.23; 95% CI: 1.08;1.42). The prevalence of sunburn in the population studied was high mainly among white young people with higher skin sensitivity, higher income and who used sunscreens irregularly. Sun exposure during safe times and with adequate protection should be promoted.

Clinical performance of serum prostate-specific antigen isoform [-2]proPSA (p2PSA) and its derivatives, %p2PSA and the prostate health index (PHI), in men with a family history of prostate cancer: results from a multicentre European study, the PROMEtheuS project.

PubMed

Lazzeri, Massimo; Haese, Alexander; Abrate, Alberto; de la Taille, Alexandre; Redorta, Joan Palou; McNicholas, Thomas; Lughezzani, Giovanni; Lista, Giuliana; Larcher, Alessandro; Bini, Vittorio; Cestari, Andrea; Buffi, Nicolòmaria; Graefen, Markus; Bosset, Olivier; Le Corvoisier, Philippe; Breda, Alberto; de la Torre, Pablo; Fowler, Linda; Roux, Jacques; Guazzoni, Giorgio

2013-08-01

To test the sensitivity, specificity and accuracy of serum prostate-specific antigen isoform [-2]proPSA (p2PSA), %p2PSA and the prostate health index (PHI), in men with a family history of prostate cancer (PCa) undergoing prostate biopsy for suspected PCa. To evaluate the potential reduction in unnecessary biopsies and the characteristics of potentially missed cases of PCa that would result from using serum p2PSA, %p2PSA and PHI. The analysis consisted of a nested case-control study from the PRO-PSA Multicentric European Study, the PROMEtheuS project. All patients had a first-degree relative (father, brother, son) with PCa. Multivariable logistic regression models were complemented by predictive accuracy analysis and decision-curve analysis. Of the 1026 patients included in the PROMEtheuS cohort, 158 (15.4%) had a first-degree relative with PCa. p2PSA, %p2PSA and PHI values were significantly higher (P < 0.001), and free/total PSA (%fPSA) values significantly lower (P < 0.001) in the 71 patients with PCa (44.9%) than in patients without PCa. Univariable accuracy analysis showed %p2PSA (area under the receiver-operating characteristic curve [AUC]: 0.733) and PHI (AUC: 0.733) to be the most accurate predictors of PCa at biopsy, significantly outperforming total PSA ([tPSA] AUC: 0.549), free PSA ([fPSA] AUC: 0.489) and %fPSA (AUC: 0.600) (P ≤ 0.001). For %p2PSA a threshold of 1.66 was found to have the best balance between sensitivity and specificity (70.4 and 70.1%; 95% confidence interval [CI]: 58.4-80.7 and 59.4-79.5 respectively). A PHI threshold of 40 was found to have the best balance between sensitivity and specificity (64.8 and 71.3%, respectively; 95% CI 52.5-75.8 and 60.6-80.5). At 90% sensitivity, the thresholds for %p2PSA and PHI were 1.20 and 25.5, with a specificity of 37.9 and 25.5%, respectively. At a %p2PSA threshold of 1.20, a total of 39 (24.8%) biopsies could have been avoided, but two cancers with a Gleason score (GS) of 7 would have been missed. At a PHI threshold of 25.5 a total of 27 (17.2%) biopsies could have been avoided and two (3.8%) cancers with a GS of 7 would have been missed. In multivariable logistic regression models, %p2PSA and PHI achieved independent predictor status and significantly increased the accuracy of multivariable models including PSA and prostate volume by 8.7 and 10%, respectively (P ≤ 0.001). p2PSA, %p2PSA and PHI were directly correlated with Gleason score (ρ: 0.247, P = 0.038; ρ: 0.366, P = 0.002; ρ: 0.464, P < 0.001, respectively). %p2PSA and PHI are more accurate than tPSA, fPSA and %fPSA in predicting PCa in men with a family history of PCa. Consideration of %p2PSA and PHI results in the avoidance of several unnecessary biopsies. p2PSA, %p2PSA and PHI correlate with cancer aggressiveness. © 2013 BJU International.

Prospective analysis of body mass index, physical activity and colorectal cancer risk associated with β-catenin (CTNNB1) status

PubMed Central

Morikawa, Teppei; Kuchiba, Aya; Lochhead, Paul; Nishihara, Reiko; Yamauchi, Mai; Imamura, Yu; Liao, Xiaoyun; Qian, Zhi Rong; Ng, Kimmie; Chan, Andrew T.; Meyerhardt, Jeffrey A.; Giovannucci, Edward; Fuchs, Charles S.; Ogino, Shuji

2013-01-01

Dysregulation of the WNT/β-catenin (CTNNB1) signaling pathway is implicated in colorectal carcinoma and metabolic diseases. Considering these roles and cancer prevention, we hypothesized that tumor CTNNB1 status might influence cellular sensitivity to obesity and physical activity. In clinical follow-up of 109,046 women in the Nurses' Health Study and 47,684 men in the Health Professionals Follow-up Study, there were 861 incident rectal and colon cancers with tissue immunohistochemistry data on nuclear CTNNB1 expression. Using this molecular pathological epidemiology database, we performed Cox proportional hazards regression analysis using data duplication method to assess differential associations of body mass index (BMI) or exercise activity with colorectal cancer risk according to tumor CTNNB1 status. Greater BMI was associated with a significantly higher risk of CTNNB1-negative cancer [multivariate hazard ratio (HR) =1.34; 95% confidence interval (CI), 1.18–1.53 for 5.0 kg/m2 increment; Ptrend=0.0001], but not with CTNNB1-positive cancer risk (multivariate HR =1.07; 95% CI, 0.92–1.25 for 5.0 kg/m2 increment; Ptrend=0.36; Pheterogeneity=0.027, between CTNNB1-negative and CTNNB1-positive cancer risks). Physical activity level was associated with a lower risk of CTNNB1-negative cancer (multivariate HR =0.93; 95% CI, 0.87–1.00 for 10 MET-hours/week increment; Ptrend=0.044), but not with CTNNB1-positive cancer risk (multivariate HR =0.98; 95% CI, 0.91–1.05 for 10 MET-hours/week increment; Ptrend=0.60). Our findings argue that obesity and physical inactivity are associated with a higher risk of CTNNB1-negative colorectal cancer, but not with CTNNB1-positive cancer risk. Further, they suggest that energy balance and metabolism status exerts its effect in a specific carcinogenesis pathway that is less likely dependent on WNT/CTNNB1 activation. PMID:23442321

Mouse allergen, lung function, and atopy in Puerto Rican children.

PubMed

Forno, Erick; Cloutier, Michelle M; Datta, Soma; Paul, Kathryn; Sylvia, Jody; Calvert, Deanna; Thornton-Thompson, Sherell; Wakefield, Dorothy B; Brehm, John; Hamilton, Robert G; Alvarez, María; Colón-Semidey, Angel; Acosta-Pérez, Edna; Canino, Glorisa; Celedón, Juan C

2012-01-01

To examine the relation between mouse allergen exposure and asthma in Puerto Rican children. Mus m 1, Der p 1, Bla g 2, and Fel d 1 allergens were measured in dust samples from homes of Puerto Rican children with (cases) and without (controls) asthma in Hartford, CT (n = 449) and San Juan (SJ), Puerto Rico (n = 678). Linear or logistic regression was used for the multivariate analysis of mouse allergen (Mus m 1) and lung function (FEV(1) and FEV(1)/FVC) and allergy (total IgE and skin test reactivity (STR) to ≥1 allergen) measures. Homes in SJ had lower mouse allergen levels than those in Hartford. In multivariate analyses, mouse allergen was associated with higher FEV(1) in cases in Hartford (+70.6 ml, 95% confidence interval (CI) = 8.6-132.7 ml, P = 0.03) and SJ (+45.1 ml, 95% CI =  -0.5 to 90.6 ml, P = 0.05). In multivariate analyses of controls, mouse allergen was inversely associated with STR to ≥1 allergen in non-sensitized children (odds ratio [OR] for each log-unit increment in Mus m 1 = 0.7, 95% CI = 0.5-0.9, P<0.01). In a multivariate analysis including all children at both study sites, each log-increment in mouse allergen was positively associated with FEV(1) (+28.3 ml, 95% CI = 1.4-55.2 ml, P = 0.04) and inversely associated with STR to ≥1 allergen (OR for each log-unit increment in Mus m 1 = 0.8, 95% CI = 0.6-0.9, P<0.01). Mouse allergen is associated with a higher FEV(1) and lower odds of STR to ≥1 allergen in Puerto Rican children. This may be explained by the allergen itself or correlated microbial exposures.

Mouse Allergen, Lung Function, and Atopy in Puerto Rican Children

PubMed Central

Forno, Erick; Cloutier, Michelle M.; Datta, Soma; Paul, Kathryn; Sylvia, Jody; Calvert, Deanna; Thornton-Thompson, Sherell; Wakefield, Dorothy B.; Brehm, John; Hamilton, Robert G.; Alvarez, María; Colón-Semidey, Angel; Acosta-Pérez, Edna; Canino, Glorisa; Celedón, Juan C.

2012-01-01

Objective To examine the relation between mouse allergen exposure and asthma in Puerto Rican children. Methods Mus m 1, Der p 1, Bla g 2, and Fel d 1 allergens were measured in dust samples from homes of Puerto Rican children with (cases) and without (controls) asthma in Hartford, CT (n = 449) and San Juan (SJ), Puerto Rico (n = 678). Linear or logistic regression was used for the multivariate analysis of mouse allergen (Mus m 1) and lung function (FEV1 and FEV1/FVC) and allergy (total IgE and skin test reactivity (STR) to ≥1 allergen) measures. Results Homes in SJ had lower mouse allergen levels than those in Hartford. In multivariate analyses, mouse allergen was associated with higher FEV1 in cases in Hartford (+70.6 ml, 95% confidence interval (CI) = 8.6–132.7 ml, P = 0.03) and SJ (+45.1 ml, 95% CI =  −0.5 to 90.6 ml, P = 0.05). In multivariate analyses of controls, mouse allergen was inversely associated with STR to ≥1 allergen in non-sensitized children (odds ratio [OR] for each log-unit increment in Mus m 1 = 0.7, 95% CI = 0.5–0.9, P<0.01). In a multivariate analysis including all children at both study sites, each log-increment in mouse allergen was positively associated with FEV1 (+28.3 ml, 95% CI = 1.4–55.2 ml, P = 0.04) and inversely associated with STR to ≥1 allergen (OR for each log-unit increment in Mus m 1 = 0.8, 95% CI = 0.6–0.9, P<0.01). Conclusions Mouse allergen is associated with a higher FEV1 and lower odds of STR to ≥1 allergen in Puerto Rican children. This may be explained by the allergen itself or correlated microbial exposures. PMID:22815744

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

PubMed

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

2017-02-01

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

Evaluation of risk factors for perforated peptic ulcer.

PubMed

Yamamoto, Kazuki; Takahashi, Osamu; Arioka, Hiroko; Kobayashi, Daiki

2018-02-15

The aim of this study was to evaluate the prediction factors for perforated peptic ulcer (PPU). At St. Luke's International Hospital in Tokyo, Japan, a case control study was performed between August 2004 and March 2016. All patients diagnosed with PPU were included. As control subjects, patients with age, sex and date of CT scan corresponding to those of the PPU subjects were included in the study at a proportion of 2 controls for every PPU subject. All data such as past medical histories, physical findings, and laboratory data were collected through chart reviews. Univariate analyses and multivariate analyses with logistic regression were conducted, and receiver operating characteristic curves (ROCs) were calculated to show validity. Sensitivity analyses were performed to confirm results using a stepwise method and conditional logistic regression. A total of 408 patients were included in this study; 136 were a group of patients with PPU, and 272 were a control group. Univariate analysis showed statistical significance in many categories. Four different models of multivariate analyses were conducted, and significant differences were found for muscular defense and a history of peptic ulcer disease (PUD) in all models. The conditional forced-entry analysis of muscular defense showed an odds ratio (OR) of 23.8 (95% confidence interval [CI]: 5.70-100.0), and the analysis of PUD history showed an OR of 6.40 (95% CI: 1.13-36.2). The sensitivity analysis showed consistent results, with an OR of 23.8-366.2 for muscular defense and an OR of 3.67-7.81 for PUD history. The area under the curve (AUC) of all models was high enough to confirm the results. However, anticoagulants, known risk factors for PUD, did not increase the risk for PPU in our study. The conditional forced-entry analysis of anticoagulant use showed an OR of 0.85 (95% CI: 0.03-22.3). The evaluation of prediction factors and development of a prediction rule for PPU may help our decision making in performing a CT scan for patients with acute abdominal pain.

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…

The large-scale organization of shape processing in the ventral and dorsal pathways

PubMed Central

Culham, Jody C; Plaut, David C; Behrmann, Marlene

2017-01-01

Although shape perception is considered a function of the ventral visual pathway, evidence suggests that the dorsal pathway also derives shape-based representations. In two psychophysics and neuroimaging experiments, we characterized the response properties, topographical organization and perceptual relevance of these representations. In both pathways, shape sensitivity increased from early visual cortex to extrastriate cortex but then decreased in anterior regions. Moreover, the lateral aspect of the ventral pathway and posterior regions of the dorsal pathway were sensitive to the availability of fundamental shape properties, even for unrecognizable images. This apparent representational similarity between the posterior-dorsal and lateral-ventral regions was corroborated by a multivariate analysis. Finally, as with ventral pathway, the activation profile of posterior dorsal regions was correlated with recognition performance, suggesting a possible contribution to perception. These findings challenge a strict functional dichotomy between the pathways and suggest a more distributed model of shape processing. PMID:28980938

Multidisciplinary optimization of a controlled space structure using 150 design variables

NASA Technical Reports Server (NTRS)

James, Benjamin B.

1993-01-01

A controls-structures interaction design method is presented. The method coordinates standard finite-element structural analysis, multivariable controls, and nonlinear programming codes and allows simultaneous optimization of the structure and control system of a spacecraft. Global sensitivity equations are used to account for coupling between the disciplines. Use of global sensitivity equations helps solve optimization problems that have a large number of design variables and a high degree of coupling between disciplines. The preliminary design of a generic geostationary platform is used to demonstrate the multidisciplinary optimization method. Design problems using 15, 63, and 150 design variables to optimize truss member sizes and feedback gain values are solved and the results are presented. The goal is to reduce the total mass of the structure and the vibration control system while satisfying constraints on vibration decay rate. Incorporation of the nonnegligible mass of actuators causes an essential coupling between structural design variables and control design variables.

A Search for Point Sources of EeV Photons

NASA Astrophysics Data System (ADS)

Aab, A.; Abreu, P.; Aglietta, M.; Ahlers, M.; Ahn, E. J.; Samarai, I. Al; Albuquerque, I. F. M.; Allekotte, I.; Allen, J.; Allison, P.; Almela, A.; Alvarez Castillo, J.; Alvarez-Muñiz, J.; Alves Batista, R.; Ambrosio, M.; Aminaei, A.; Anchordoqui, L.; Andringa, S.; Aramo, C.; Arqueros, F.; Asorey, H.; Assis, P.; Aublin, J.; Ave, M.; Avenier, M.; Avila, G.; Badescu, A. M.; Barber, K. B.; Bäuml, J.; Baus, C.; Beatty, J. J.; Becker, K. H.; Bellido, J. A.; Berat, C.; Bertou, X.; Biermann, P. L.; Billoir, P.; Blanco, F.; Blanco, M.; Bleve, C.; Blümer, H.; Boháčová, M.; Boncioli, D.; Bonifazi, C.; Bonino, R.; Borodai, N.; Brack, J.; Brancus, I.; Brogueira, P.; Brown, W. C.; Buchholz, P.; Bueno, A.; Buscemi, M.; Caballero-Mora, K. S.; Caccianiga, B.; Caccianiga, L.; Candusso, M.; Caramete, L.; Caruso, R.; Castellina, A.; Cataldi, G.; Cazon, L.; Cester, R.; Chavez, A. G.; Cheng, S. H.; Chiavassa, A.; Chinellato, J. A.; Chudoba, J.; Cilmo, M.; Clay, R. W.; Cocciolo, G.; Colalillo, R.; Collica, L.; Coluccia, M. R.; Conceição, R.; Contreras, F.; Cooper, M. J.; Coutu, S.; Covault, C. E.; Criss, A.; Cronin, J.; Curutiu, A.; Dallier, R.; Daniel, B.; Dasso, S.; Daumiller, K.; Dawson, B. R.; de Almeida, R. M.; De Domenico, M.; de Jong, S. J.; de Mello Neto, J. R. T.; De Mitri, I.; de Oliveira, J.; de Souza, V.; del Peral, L.; Deligny, O.; Dembinski, H.; Dhital, N.; Di Giulio, C.; Di Matteo, A.; Diaz, J. C.; Díaz Castro, M. L.; Diep, P. N.; Diogo, F.; Dobrigkeit, C.; Docters, W.; D'Olivo, J. C.; Dong, P. N.; Dorofeev, A.; Dorosti Hasankiadeh, Q.; Dova, M. T.; Ebr, J.; Engel, R.; Erdmann, M.; Erfani, M.; Escobar, C. O.; Espadanal, J.; Etchegoyen, A.; Facal San Luis, P.; Falcke, H.; Fang, K.; Farrar, G.; Fauth, A. C.; Fazzini, N.; Ferguson, A. P.; Fernandes, M.; Fick, B.; Figueira, J. M.; Filevich, A.; Filipčič, A.; Fox, B. D.; Fratu, O.; Fröhlich, U.; Fuchs, B.; Fuji, T.; Gaior, R.; García, B.; Garcia Roca, S. T.; Garcia-Gamez, D.; Garcia-Pinto, D.; Garilli, G.; Gascon Bravo, A.; Gate, F.; Gemmeke, H.; Ghia, P. L.; Giaccari, U.; Giammarchi, M.; Giller, M.; Glaser, C.; Glass, H.; Gomez Albarracin, F.; Gómez Berisso, M.; Gómez Vitale, P. F.; Gonçalves, P.; Gonzalez, J. G.; Gookin, B.; Gorgi, A.; Gorham, P.; Gouffon, P.; Grebe, S.; Griffith, N.; Grillo, A. F.; Grubb, T. D.; Guardincerri, Y.; Guarino, F.; Guedes, G. P.; Hansen, P.; Harari, D.; Harrison, T. A.; Harton, J. L.; Haungs, A.; Hebbeker, T.; Heck, D.; Heimann, P.; Herve, A. E.; Hill, G. C.; Hojvat, C.; Hollon, N.; Holt, E.; Homola, P.; Hörandel, J. R.; Horvath, P.; Hrabovský, M.; Huber, D.; Huege, T.; Insolia, A.; Isar, P. G.; Islo, K.; Jandt, I.; Jansen, S.; Jarne, C.; Josebachuili, M.; Kääpä, A.; Kambeitz, O.; Kampert, K. H.; Kasper, P.; Katkov, I.; Kégl, B.; Keilhauer, B.; Keivani, A.; Kemp, E.; Kieckhafer, R. M.; Klages, H. O.; Kleifges, M.; Kleinfeller, J.; Krause, R.; Krohm, N.; Krömer, O.; Kruppke-Hansen, D.; Kuempel, D.; Kunka, N.; La Rosa, G.; LaHurd, D.; Latronico, L.; Lauer, R.; Lauscher, M.; Lautridou, P.; Le Coz, S.; Leão, M. S. A. B.; Lebrun, D.; Lebrun, P.; Leigui de Oliveira, M. A.; Letessier-Selvon, A.; Lhenry-Yvon, I.; Link, K.; López, R.; Lopez Agüera, A.; Louedec, K.; Lozano Bahilo, J.; Lu, L.; Lucero, A.; Ludwig, M.; Lyberis, H.; Maccarone, M. C.; Malacari, M.; Maldera, S.; Maller, J.; Mandat, D.; Mantsch, P.; Mariazzi, A. G.; Marin, V.; Mariş, I. C.; Marsella, G.; Martello, D.; Martin, L.; Martinez, H.; Martínez Bravo, O.; Martraire, D.; Masías Meza, J. J.; Mathes, H. J.; Mathys, S.; Matthews, A. J.; Matthews, J.; Matthiae, G.; Maurel, D.; Maurizio, D.; Mayotte, E.; Mazur, P. O.; Medina, C.; Medina-Tanco, G.; Melissas, M.; Melo, D.; Menichetti, E.; Menshikov, A.; Messina, S.; Meyhandan, R.; Mićanović, S.; Micheletti, M. I.; Middendorf, L.; Minaya, I. A.; Miramonti, L.; Mitrica, B.; Molina-Bueno, L.; Mollerach, S.; Monasor, M.; Monnier Ragaigne, D.; Montanet, F.; Morello, C.; Moreno, J. C.; Mostafá, M.; Moura, C. A.; Muller, M. A.; Müller, G.; Münchmeyer, M.; Mussa, R.; Navarra, G.; Navas, S.; Necesal, P.; Nellen, L.; Nelles, A.; Neuser, J.; Niechciol, M.; Niemietz, L.; Niggemann, T.; Nitz, D.; Nosek, D.; Novotny, V.; Nožka, L.; Ochilo, L.; Olinto, A.; Oliveira, M.; Ortiz, M.; Pacheco, N.; Pakk Selmi-Dei, D.; Palatka, M.; Pallotta, J.; Palmieri, N.; Papenbreer, P.; Parente, G.; Parra, A.; Pastor, S.; Paul, T.; Pech, M.; Peķala, J.; Pelayo, R.; Pepe, I. M.; Perrone, L.; Pesce, R.; Petermann, E.; Peters, C.; Petrera, S.; Petrolini, A.; Petrov, Y.; Piegaia, R.; Pierog, T.; Pieroni, P.; Pimenta, M.; Pirronello, V.; Platino, M.; Plum, M.; Porcelli, A.; Porowski, C.; Privitera, P.; Prouza, M.; Purrello, V.; Quel, E. J.; Querchfeld, S.; Quinn, S.; Rautenberg, J.; Ravel, O.; Ravignani, D.; Revenu, B.; Ridky, J.; Riggi, S.; Risse, M.; Ristori, P.; Rizi, V.; Roberts, J.; Rodrigues de Carvalho, W.; Rodriguez Cabo, I.; Rodriguez Fernandez, G.; Rodriguez Rojo, J.; Rodríguez-Frías, M. D.; Ros, G.; Rosado, J.; Rossler, T.; Roth, M.; Roulet, E.; Rovero, A. C.; Rühle, C.; Saffi, S. J.; Saftoiu, A.; Salamida, F.; Salazar, H.; Salesa Greus, F.; Salina, G.; Sánchez, F.; Sanchez-Lucas, P.; Santo, C. E.; Santos, E.; Santos, E. M.; Sarazin, F.; Sarkar, B.; Sarmento, R.; Sato, R.; Scharf, N.; Scherini, V.; Schieler, H.; Schiffer, P.; Schmidt, A.; Scholten, O.; Schoorlemmer, H.; Schovánek, P.; Schulz, A.; Schulz, J.; Sciutto, S. J.; Segreto, A.; Settimo, M.; Shadkam, A.; Shellard, R. C.; Sidelnik, I.; Sigl, G.; Sima, O.; Śmiałkowski, A.; Šmída, R.; Snow, G. R.; Sommers, P.; Sorokin, J.; Squartini, R.; Srivastava, Y. N.; Stanič, S.; Stapleton, J.; Stasielak, J.; Stephan, M.; Stutz, A.; Suarez, F.; Suomijärvi, T.; Supanitsky, A. D.; Sutherland, M. S.; Swain, J.; Szadkowski, Z.; Szuba, M.; Taborda, O. A.; Tapia, A.; Tartare, M.; Thao, N. T.; Theodoro, V. M.; Tiffenberg, J.; Timmermans, C.; Todero Peixoto, C. J.; Toma, G.; Tomankova, L.; Tomé, B.; Tonachini, A.; Torralba Elipe, G.; Torres Machado, D.; Travnicek, P.; Trovato, E.; Tueros, M.; Ulrich, R.; Unger, M.; Urban, M.; Valdés Galicia, J. F.; Valiño, I.; Valore, L.; van Aar, G.; van den Berg, A. M.; van Velzen, S.; van Vliet, A.; Varela, E.; Vargas Cárdenas, B.; Varner, G.; Vázquez, J. R.; Vázquez, R. A.; Veberič, D.; Verzi, V.; Vicha, J.; Videla, M.; Villaseñor, L.; Vlcek, B.; Vorobiov, S.; Wahlberg, H.; Wainberg, O.; Walz, D.; Watson, A. A.; Weber, M.; Weidenhaupt, K.; Weindl, A.; Werner, F.; Whelan, B. J.; Widom, A.; Wiencke, L.; Wilczyńska, B.; Wilczyński, H.; Will, M.; Williams, C.; Winchen, T.; Wittkowski, D.; Wundheiler, B.; Wykes, S.; Yamamoto, T.; Yapici, T.; Younk, P.; Yuan, G.; Yushkov, A.; Zamorano, B.; Zas, E.; Zavrtanik, D.; Zavrtanik, M.; Zaw, I.; Zepeda, A.; Zhou, J.; Zhu, Y.; Zimbres Silva, M.; Ziolkowski, M.; Auger Collaboration102, The Pierre

2014-07-01

Measurements of air showers made using the hybrid technique developed with the fluorescence and surface detectors of the Pierre Auger Observatory allow a sensitive search for point sources of EeV photons anywhere in the exposed sky. A multivariate analysis reduces the background of hadronic cosmic rays. The search is sensitive to a declination band from -85° to +20°, in an energy range from 1017.3 eV to 1018.5 eV. No photon point source has been detected. An upper limit on the photon flux has been derived for every direction. The mean value of the energy flux limit that results from this, assuming a photon spectral index of -2, is 0.06 eV cm-2 s-1, and no celestial direction exceeds 0.25 eV cm-2 s-1. These upper limits constrain scenarios in which EeV cosmic ray protons are emitted by non-transient sources in the Galaxy.

Early life trauma exposure and stress sensitivity in young children.

PubMed

Grasso, Damion J; Ford, Julian D; Briggs-Gowan, Margaret J

2013-01-01

The current study replicates and extends work with adults that highlights the relationship between trauma exposure and distress in response to subsequent, nontraumatic life stressors. The sample included 213 2-4-year-old children in which 64.3% had a history of potential trauma exposure. Children were categorized into 4 groups based on trauma history and current life stress. In a multivariate analysis of variance, trauma-exposed children with current life stressors had elevated internalizing and externalizing problems compared with trauma-exposed children without current stress and nontrauma-exposed children with and without current stressors. The trauma-exposed groups with or without current stressors did not differ on posttraumatic stress disorder symptom severity. Accounting for number of traumatic events did not change these results. These findings suggest that early life trauma exposure may sensitize young children and place them at risk for internalizing or externalizing problems when exposed to subsequent, nontraumatic life stressors.

Potential of mid-infrared spectroscopy as a non-invasive diagnostic test in urine for endometrial or ovarian cancer.

PubMed

Paraskevaidi, Maria; Morais, Camilo L M; Lima, Kássio M G; Ashton, Katherine M; Stringfellow, Helen F; Martin-Hirsch, Pierre L; Martin, Francis L

2018-06-07

The current lack of an accurate, cost-effective and non-invasive test that would allow for screening and diagnosis of gynaecological carcinomas, such as endometrial and ovarian cancer, signals the necessity for alternative approaches. The potential of spectroscopic techniques in disease investigation and diagnosis has been previously demonstrated. Here, we used attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy to analyse urine samples from women with endometrial (n = 10) and ovarian cancer (n = 10), as well as from healthy individuals (n = 10). After applying multivariate analysis and classification algorithms, biomarkers of disease were pointed out and high levels of accuracy were achieved for both endometrial (95% sensitivity, 100% specificity; accuracy: 95%) and ovarian cancer (100% sensitivity, 96.3% specificity; accuracy 100%). The efficacy of this approach, in combination with the non-invasive method for urine collection, suggest a potential diagnostic tool for endometrial and ovarian cancers.

Metabolic profiling for the identification of Huntington biomarkers by on-line solid-phase extraction capillary electrophoresis mass spectrometry combined with advanced data analysis tools.

PubMed

Pont, Laura; Benavente, Fernando; Jaumot, Joaquim; Tauler, Romà; Alberch, Jordi; Ginés, Silvia; Barbosa, José; Sanz-Nebot, Victoria

2016-03-01

In this work, an untargeted metabolomic approach based on sensitive analysis by on-line solid-phase extraction capillary electrophoresis mass spectrometry (SPE-CE-MS) in combination with multivariate data analysis is proposed as an efficient method for the identification of biomarkers of Huntington's disease (HD) progression in plasma. For this purpose, plasma samples from wild-type (wt) and HD (R6/1) mice of different ages (8, 12, and 30 weeks), were analyzed by C18 -SPE-CE-MS in order to obtain the characteristic electrophoretic profiles of low molecular mass compounds. Then, multivariate curve resolution alternating least squares (MCR-ALS) was applied to the multiple full scan MS datasets. This strategy permitted the resolution of a large number of metabolites being characterized by their electrophoretic peaks and their corresponding mass spectra. A total number of 29 compounds were relevant to discriminate between wt and HD plasma samples, as well as to follow-up the HD progression. The intracellular signaling was found to be the most affected metabolic pathway in HD mice after 12 weeks of birth, when mice already showed motor coordination deficiencies and cognitive decline. This fact agreed with the atrophy and dysfunction of specific neurons, loss of several types of receptors, and changed expression of neurotransmitters. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Ratio of mean platelet volume to platelet count is a potential surrogate marker predicting liver cirrhosis.

PubMed

Iida, Hiroya; Kaibori, Masaki; Matsui, Kosuke; Ishizaki, Morihiko; Kon, Masanori

2018-01-27

To provide a simple surrogate marker predictive of liver cirrhosis (LC). Specimens from 302 patients who underwent resection for hepatocellular carcinoma between January 2006 and December 2012 were retrospectively analyzed. Based on pathologic findings, patients were divided into groups based on whether or not they had LC. Parameters associated with hepatic functional reserve were compared in these two groups using Mann-Whitney U -test for univariate analysis. Factors differing significantly in univariate analyses were entered into multivariate logistic regression analysis. There were significant differences between the LC group ( n = 100) and non-LC group ( n = 202) in prothrombin activity, concentrations of alanine aminotransferase, aspartate aminotransferase, total bilirubin, albumin, cholinesterase, type IV collagen, hyaluronic acid, indocyanine green retention rate at 15 min, maximal removal rate of technitium-99m diethylene triamine penta-acetic acid-galactosyl human serum albumin and ratio of mean platelet volume to platelet count (MPV/PLT). Multivariate analysis showed that prothrombin activity, concentrations of alanine aminotransferase, aspartate aminotransferase, total bilirubin and hyaluronic acid, and MPV/PLT ratio were factors independently predictive of LC. The area under the curve value for MPV/PLT was 0.78, with a 0.8 cutoff value having a sensitivity of 65% and a specificity of 78%. The MPV/PLT ratio, which can be determined simply from the complete blood count, may be a simple surrogate marker predicting LC.

Illness Absences Among Beryllium Sensitized Workers

PubMed Central

Watkins, Janice P.; Ellis, Elizabeth D.; Girardi, David J.; Cragle, Donna L.

2014-01-01

Objectives. This study examined absence rates among US Department of Energy workers who had beryllium sensitization (BeS) or were diagnosed with chronic beryllium disease (CBD) compared with those of other workers. Methods. We used the lymphocyte proliferation test to determine beryllium sensitivity. In addition, we applied multivariable logistic regression to compare absences from 2002 to 2011 between workers with BeS or CBD to those without, and survival analysis to compare time to first absence by beryllium sensitization status. Finally, we examined beryllium status by occupational group. Results. Fewer than 3% of the 19 305 workers were BeS, and workers with BeS or CBD had more total absences (odds ratio [OR] = 1.31; 95% confidence interval [CI] = 1.18, 1.46) and respiratory absences (OR = 1.51; 95% CI = 1.24, 1.84) than did other workers. Time to first absence for all causes and for respiratory conditions occurred earlier for workers with BeS or CBD than for other workers. Line operators and crafts personnel were at increased risk for BeS or CBD. Conclusions. Although not considered “diseased,” workers with BeS have higher absenteeism compared with nonsensitized workers. PMID:25211750

Preterm delivery at low gestational age: risk factors for short latency. A multivariated analysis

PubMed Central

Marzano, Sara; Padula, Francesco; Meloni, Paolo; Anceschi, Maurizio Marco

2008-01-01

Objective The aim of this study is to identify the risk factors for a short latency in preterm delivery at low gestational ages (GA). Study design A retrospective analysis involving, between January 2004 and May 2006, 204 singleton pregnancies with admission diagnosis of preterm labor and, in particular, 91 pregnant women admitted between 24+0 and 31+6 weeks’ gestation. Results In pregnant women with a diagnosis of preterm labor at 24-31+6 weeks’ gestation, at ROC curve, a value of considering WBC and cervical dilatation, combined in the following formula (75.237 - (2.290 * “WBC”) - (10.787 * “cervical dilatation”)) <=33.101 has a 74.2% Sensitivity and a 78.3% Specificity in predicting a latency =< 4 days (+LR 3.42 and -LR 0.33) and a 70% Sensitivity and a 84.3% Specificity in predicting GA at delivery at 24-31 weeks’ gestation (+LR 4.46 and -LR 0.36). Conclusion We suggest a more strictly monitoring and a more aggressive therapy in presence of prognostic parameters of shorter latency. PMID:22439021

Clinical value of preoperative serum CA 19-9 and CA 125 levels in predicting the resectability of hilar cholangiocarcinoma.

PubMed

Hu, Hai-Jie; Mao, Hui; Tan, Yong-Qiong; Shrestha, Anuj; Ma, Wen-Jie; Yang, Qin; Wang, Jun-Ke; Cheng, Nan-Sheng; Li, Fu-Yu

2016-01-01

To examine the predictive value of tumor markers for evaluating tumor resectability in patients with hilar cholangiocarcinoma and to explore the prognostic effect of various preoperative factors on resectability in patients with potentially resectable tumors. Patients with potentially resectable tumors judged by radiologic examination were included. The receiver operating characteristic (ROC) analysis was conducted to evaluate serum carbohydrate antigenic determinant 19-9 (CA 19-9), carbohydrate antigen 125 (CA 125) and carcino embryonie antigen levels on tumor resectability. Univariate and multivariate logistic regression models were also conducted to analysis the correlation of preoperative factors with resectability. In patients with normal bilirubin levels, ROC curve analysis calculated the ideal CA 19-9 cut-off value of 203.96 U/ml in prediction of resectability, with a sensitivity of 83.7 %, specificity of 80 %, positive predictive value of 91.1 % and negative predictive value of 66.7 %. Meanwhile, the optimal cut-off value for CA 125 to predict resectability was 25.905 U/ml (sensitivity, 78.6 %; specificity, 67.5 %). In a multivariate logistic regression model, tumor size ≤3 cm (OR 4.149, 95 % CI 1.326-12.981, P = 0.015), preoperative CA 19-9 level ≤200 U/ml (OR 20.324, 95 % CI 6.509-63.467, P < 0.001), preoperative CA 125 levels ≤26 U/ml (OR 8.209, 95 % CI 2.624-25.677, P < 0.001) were independent determinants of resectability in patients diagnosed as hilar cholangiocarcinoma. Preoperative CA 19-9 and CA 125 levels predict resectability in patients with radiological resectable hilar cholangiocarcinoma. Increased preoperative CA 19-9 levels and CA 125 levels are associated with poor resectability rate.

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.

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.

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.

Stepwise sensitivity analysis from qualitative to quantitative: Application to the terrestrial hydrological modeling of a Conjunctive Surface-Subsurface Process (CSSP) land surface model

NASA Astrophysics Data System (ADS)

Gan, Yanjun; Liang, Xin-Zhong; Duan, Qingyun; Choi, Hyun Il; Dai, Yongjiu; Wu, Huan

2015-06-01

An uncertainty quantification framework was employed to examine the sensitivities of 24 model parameters from a newly developed Conjunctive Surface-Subsurface Process (CSSP) land surface model (LSM). The sensitivity analysis (SA) was performed over 18 representative watersheds in the contiguous United States to examine the influence of model parameters in the simulation of terrestrial hydrological processes. Two normalized metrics, relative bias (RB) and Nash-Sutcliffe efficiency (NSE), were adopted to assess the fit between simulated and observed streamflow discharge (SD) and evapotranspiration (ET) for a 14 year period. SA was conducted using a multiobjective two-stage approach, in which the first stage was a qualitative SA using the Latin Hypercube-based One-At-a-Time (LH-OAT) screening, and the second stage was a quantitative SA using the Multivariate Adaptive Regression Splines (MARS)-based Sobol' sensitivity indices. This approach combines the merits of qualitative and quantitative global SA methods, and is effective and efficient for understanding and simplifying large, complex system models. Ten of the 24 parameters were identified as important across different watersheds. The contribution of each parameter to the total response variance was then quantified by Sobol' sensitivity indices. Generally, parameter interactions contribute the most to the response variance of the CSSP, and only 5 out of 24 parameters dominate model behavior. Four photosynthetic and respiratory parameters are shown to be influential to ET, whereas reference depth for saturated hydraulic conductivity is the most influential parameter for SD in most watersheds. Parameter sensitivity patterns mainly depend on hydroclimatic regime, as well as vegetation type and soil texture. This article was corrected on 26 JUN 2015. See the end of the full text for details.

PAX5 methylation detection by droplet digital PCR for ultra-sensitive deep surgical margins analysis of head and neck squamous cell carcinoma

PubMed Central

Hayashi, Masamichi; Guerrero-Preston, Rafael; Sidransky, David; Koch, Wayne M.

2015-01-01

Molecular deep surgical margin analysis has been shown to predict locoregional recurrences of head and neck squamous cell carcinoma (HNSCC). In order to improve the accuracy and versatility of the analysis, we used a highly tumor-specific methylation marker and highly sensitive detection technology to test DNA from surgical margins. Histologically cancer-negative deep surgical margin samples were prospectively collected from 82 eligible HNSCC surgeries by an imprinting procedure (n=75) and primary tissue collection (n=70). Bisulfite treated DNA from each sample was analyzed by both conventional quantitative methylation-specific polymerase chain reaction (QMSP) and QMSP by droplet digital PCR (ddQMSP) targeting PAX5 gene promoter methylation. The association between the presence of PAX5 methylation and locoregional recurrence free survival (LRFS) was evaluated. PAX5 methylation was found in 68.0% (51/75) of tumors in the imprint samples and 71.4% (50/70) in the primary tissue samples. Among cases which did not have postoperative radiation, (n=31 in imprint samples, n=29 in tissue samples), both conventional QMSP and ddQMSP revealed that PAX5 methylation positive margins was significantly associated with poor LRFS by univariate analysis. In particular, ddQMSP increased detection of the PAX5 marker from 29% to 71% in the non-radiated imprint cases. Also, PAX5 methylated imprint margins were an excellent predictor of poor LRFS (HR=3.89, 95%CI:1.19-17.52, P=0.023) by multivariate analysis. PAX5 methylation appears to be an excellent tumor-specific marker for molecular deep surgical margin analysis of HNSCC. Moreover, the ddQMSP assay displays increased sensitivity for methylation marker detection. PMID:26304463

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…

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

PubMed

Divya, O; Mishra, Ashok K

2007-05-29

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

Multidisciplinary optimization of a controlled space structure using 150 design variables

NASA Technical Reports Server (NTRS)

James, Benjamin B.

1992-01-01

A general optimization-based method for the design of large space platforms through integration of the disciplines of structural dynamics and control is presented. The method uses the global sensitivity equations approach and is especially appropriate for preliminary design problems in which the structural and control analyses are tightly coupled. The method is capable of coordinating general purpose structural analysis, multivariable control, and optimization codes, and thus, can be adapted to a variety of controls-structures integrated design projects. The method is used to minimize the total weight of a space platform while maintaining a specified vibration decay rate after slewing maneuvers.

Strain Gauge Balance Uncertainty Analysis at NASA Langley: A Technical Review

NASA Technical Reports Server (NTRS)

Tripp, John S.

1999-01-01

This paper describes a method to determine the uncertainties of measured forces and moments from multi-component force balances used in wind tunnel tests. A multivariate regression technique is first employed to estimate the uncertainties of the six balance sensitivities and 156 interaction coefficients derived from established balance calibration procedures. These uncertainties are then employed to calculate the uncertainties of force-moment values computed from observed balance output readings obtained during tests. Confidence and prediction intervals are obtained for each computed force and moment as functions of the actual measurands. Techniques are discussed for separate estimation of balance bias and precision uncertainties.

Fluorescence-based assay as a new screening tool for toxic chemicals

PubMed Central

Moczko, Ewa; Mirkes, Evgeny M.; Cáceres, César; Gorban, Alexander N.; Piletsky, Sergey

2016-01-01

Our study involves development of fluorescent cell-based diagnostic assay as a new approach in high-throughput screening method. This highly sensitive optical assay operates similarly to e-noses and e-tongues which combine semi-specific sensors and multivariate data analysis for monitoring biochemical processes. The optical assay consists of a mixture of environmental-sensitive fluorescent dyes and human skin cells that generate fluorescence spectra patterns distinctive for particular physico-chemical and physiological conditions. Using chemometric techniques the optical signal is processed providing qualitative information about analytical characteristics of the samples. This integrated approach has been successfully applied (with sensitivity of 93% and specificity of 97%) in assessing whether particular chemical agents are irritating or not for human skin. It has several advantages compared with traditional biochemical or biological assays and can impact the new way of high-throughput screening and understanding cell activity. It also can provide reliable and reproducible method for assessing a risk of exposing people to different harmful substances, identification active compounds in toxicity screening and safety assessment of drugs, cosmetic or their specific ingredients. PMID:27653274

Fluorescence-based assay as a new screening tool for toxic chemicals.

PubMed

Moczko, Ewa; Mirkes, Evgeny M; Cáceres, César; Gorban, Alexander N; Piletsky, Sergey

2016-09-22

Our study involves development of fluorescent cell-based diagnostic assay as a new approach in high-throughput screening method. This highly sensitive optical assay operates similarly to e-noses and e-tongues which combine semi-specific sensors and multivariate data analysis for monitoring biochemical processes. The optical assay consists of a mixture of environmental-sensitive fluorescent dyes and human skin cells that generate fluorescence spectra patterns distinctive for particular physico-chemical and physiological conditions. Using chemometric techniques the optical signal is processed providing qualitative information about analytical characteristics of the samples. This integrated approach has been successfully applied (with sensitivity of 93% and specificity of 97%) in assessing whether particular chemical agents are irritating or not for human skin. It has several advantages compared with traditional biochemical or biological assays and can impact the new way of high-throughput screening and understanding cell activity. It also can provide reliable and reproducible method for assessing a risk of exposing people to different harmful substances, identification active compounds in toxicity screening and safety assessment of drugs, cosmetic or their specific ingredients.

Fluorescence-based assay as a new screening tool for toxic chemicals

NASA Astrophysics Data System (ADS)

Moczko, Ewa; Mirkes, Evgeny M.; Cáceres, César; Gorban, Alexander N.; Piletsky, Sergey

2016-09-01

Our study involves development of fluorescent cell-based diagnostic assay as a new approach in high-throughput screening method. This highly sensitive optical assay operates similarly to e-noses and e-tongues which combine semi-specific sensors and multivariate data analysis for monitoring biochemical processes. The optical assay consists of a mixture of environmental-sensitive fluorescent dyes and human skin cells that generate fluorescence spectra patterns distinctive for particular physico-chemical and physiological conditions. Using chemometric techniques the optical signal is processed providing qualitative information about analytical characteristics of the samples. This integrated approach has been successfully applied (with sensitivity of 93% and specificity of 97%) in assessing whether particular chemical agents are irritating or not for human skin. It has several advantages compared with traditional biochemical or biological assays and can impact the new way of high-throughput screening and understanding cell activity. It also can provide reliable and reproducible method for assessing a risk of exposing people to different harmful substances, identification active compounds in toxicity screening and safety assessment of drugs, cosmetic or their specific ingredients.

Factors associated with postoperative C5 palsy after expansive open-door laminoplasty: retrospective cohort study using multivariable analysis.

PubMed

Tsuji, Takashi; Matsumoto, Morio; Nakamura, Masaya; Ishii, Ken; Fujita, Nobuyuki; Chiba, Kazuhiro; Watanabe, Kota

2017-09-01

The aim of the present study was to investigate the factors associated with C5 palsy by focusing on radiological parameters using multivariable analysis. The authors retrospectively assessed 190 patients with cervical spondylotic myelopathy treated by open-door laminoplasty. Four radiographic parameters-the number of expanded lamina, C3-C7 angle, lamina open angle and space anterior to the spinal cord-were evaluated to clarify the factors associated with C5 palsy. Of the 190 patients, 11 developed C5 palsy, giving an overall incidence of 5.8%. Although the number of expanded lamina, lamina open angle and space anterior to the spinal cord were significantly larger in C5 palsy group than those in non-palsy group, a multiple logistic regression analysis revealed that only the space anterior to the spinal cord (odds ratio 2.60) was a significant independent factor associated with C5 palsy. A multiple linear regression analysis indicated that the lamina open angle was associated with the space anterior to the spinal cord and the analysis identified the following equation: space anterior to the spinal cord (mm) = 1.54 + 0.09 × lamina open angle (degree). A cut-off value of 53.5° for the lamina open angle predicted the development of C5 palsy with a sensitivity of 72.7% and a specificity of 83.2%. The larger postoperative space anterior to the spinal cord, which was associated with the lamina open angle, was positively correlated with the higher incidence of C5 palsy.

Support vector machine learning-based fMRI data group analysis.

PubMed

Wang, Ze; Childress, Anna R; Wang, Jiongjiong; Detre, John A

2007-07-15

To explore the multivariate nature of fMRI data and to consider the inter-subject brain response discrepancies, a multivariate and brain response model-free method is fundamentally required. Two such methods are presented in this paper by integrating a machine learning algorithm, the support vector machine (SVM), and the random effect model. Without any brain response modeling, SVM was used to extract a whole brain spatial discriminance map (SDM), representing the brain response difference between the contrasted experimental conditions. Population inference was then obtained through the random effect analysis (RFX) or permutation testing (PMU) on the individual subjects' SDMs. Applied to arterial spin labeling (ASL) perfusion fMRI data, SDM RFX yielded lower false-positive rates in the null hypothesis test and higher detection sensitivity for synthetic activations with varying cluster size and activation strengths, compared to the univariate general linear model (GLM)-based RFX. For a sensory-motor ASL fMRI study, both SDM RFX and SDM PMU yielded similar activation patterns to GLM RFX and GLM PMU, respectively, but with higher t values and cluster extensions at the same significance level. Capitalizing on the absence of temporal noise correlation in ASL data, this study also incorporated PMU in the individual-level GLM and SVM analyses accompanied by group-level analysis through RFX or group-level PMU. Providing inferences on the probability of being activated or deactivated at each voxel, these individual-level PMU-based group analysis methods can be used to threshold the analysis results of GLM RFX, SDM RFX or SDM PMU.

Multivariate pattern analysis of fMRI data reveals deficits in distributed representations in schizophrenia

PubMed Central

Yoon, Jong H.; Tamir, Diana; Minzenberg, Michael J.; Ragland, J. Daniel; Ursu, Stefan; Carter, Cameron S.

2009-01-01

Background Multivariate pattern analysis is an alternative method of analyzing fMRI data, which is capable of decoding distributed neural representations. We applied this method to test the hypothesis of the impairment in distributed representations in schizophrenia. We also compared the results of this method with traditional GLM-based univariate analysis. Methods 19 schizophrenia and 15 control subjects viewed two runs of stimuli--exemplars of faces, scenes, objects, and scrambled images. To verify engagement with stimuli, subjects completed a 1-back matching task. A multi-voxel pattern classifier was trained to identify category-specific activity patterns on one run of fMRI data. Classification testing was conducted on the remaining run. Correlation of voxel-wise activity across runs evaluated variance over time in activity patterns. Results Patients performed the task less accurately. This group difference was reflected in the pattern analysis results with diminished classification accuracy in patients compared to controls, 59% and 72% respectively. In contrast, there was no group difference in GLM-based univariate measures. In both groups, classification accuracy was significantly correlated with behavioral measures. Both groups showed highly significant correlation between inter-run correlations and classification accuracy. Conclusions Distributed representations of visual objects are impaired in schizophrenia. This impairment is correlated with diminished task performance, suggesting that decreased integrity of cortical activity patterns is reflected in impaired behavior. Comparisons with univariate results suggest greater sensitivity of pattern analysis in detecting group differences in neural activity and reduced likelihood of non-specific factors driving these results. PMID:18822407

Association of pretreatment neutrophil-lymphocyte ratio and outcome in emergency colorectal cancer care.

PubMed

Palin, R P; Devine, A T; Hicks, G; Burke, D

2018-04-01

Introduction The association between the neutrophil-lymphocyte ratio (NLR) and outcome in elective colorectal cancer surgery is well established; the relationship between NLR and the emergency colorectal cancer patient is, as yet, unexplored. This paper evaluates the predictive quality of the NLR for outcome in the emergency colorectal cancer patient. Materials and Methods A total of 187 consecutive patients who underwent emergency surgery for colorectal cancer were included in the study. NLR was calculated from the haematological tests done on admission. Receiver operating characteristic analyses were used to determine the most suitable cut-off for NLR. Outcomes were assessed by mortality at 30 and 90 days using stepwise Cox proportional hazards regression. Results An NLR cut-off of 5 was found to have the highest sensitivity and specificity. At 30 days, age and time from admission to surgery were associated with increased mortality; a high NLR was associated with an increased risk of mortality in univariate but not multivariate analysis. At 90 days, age, NLR, time from admission to surgery and nodal status were all significantly associated with increased mortality on multivariate analysis. Conclusions Pre-operative NLR is a cheap, easily performed and useful clinical tool to aid prediction of outcome in the emergency colorectal cancer patient.

Understanding perception of active noise control system through multichannel EEG analysis.

PubMed

Bagha, Sangeeta; Tripathy, R K; Nanda, Pranati; Preetam, C; Das, Debi Prasad

2018-06-01

In this Letter, a method is proposed to investigate the effect of noise with and without active noise control (ANC) on multichannel electroencephalogram (EEG) signal. The multichannel EEG signal is recorded during different listening conditions such as silent, music, noise, ANC with background noise and ANC with both background noise and music. The multiscale analysis of EEG signal of each channel is performed using the discrete wavelet transform. The multivariate multiscale matrices are formulated based on the sub-band signals of each EEG channel. The singular value decomposition is applied to the multivariate matrices of multichannel EEG at significant scales. The singular value features at significant scales and the extreme learning machine classifier with three different activation functions are used for classification of multichannel EEG signal. The experimental results demonstrate that, for ANC with noise and ANC with noise and music classes, the proposed method has sensitivity values of 75.831% ( p < 0.001 ) and 99.31% ( p < 0.001 ), respectively. The method has an accuracy value of 83.22% for the classification of EEG signal with music and ANC with music as stimuli. The important finding of this study is that by the introduction of ANC, music can be better perceived by the human brain.

Noninvasive assessment of the risk of tobacco abuse in oral mucosa using fluorescence spectroscopy: a clinical approach

NASA Astrophysics Data System (ADS)

Nazeer, Shaiju S.; Asish, Rajashekharan; Venugopal, Chandrashekharan; Anita, Balan; Gupta, Arun Kumar; Jayasree, Ramapurath S.

2014-05-01

Tobacco abuse and alcoholism cause cancer, emphysema, and heart disease, which contribute to high death rates, globally. Society pays a significant cost for these habits whose first demonstration in many cases is in the oral cavity. Oral cavity disorders are highly curable if a screening procedure is available to diagnose them in the earliest stages. The aim of the study is to identify the severity of tobacco abuse, in oral cavity, as reflected by the emission from endogenous fluorophores and the chromophore hemoglobin. A group who had no tobacco habits and another with a history of tobacco abuse were included in this study. To compare the results with a pathological condition, a group of leukoplakia patients were also included. Emission from porphyrin and the spectral filtering modulation effect of hemoglobin were collected from different sites. Multivariate analysis strengthened the spectral features with a sensitivity of 60% to 100% and a specificity of 76% to 100% for the discrimination. Total hemoglobin and porphyrin levels of habitués and leukoplakia groups were comparable, indicating the alarming situation about the risk of tobacco abuse. Results prove that fluorescence spectroscopy along with multivariate analysis is an effective noninvasive tool for the early diagnosis of pathological changes due to tobacco abuse.

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

PubMed

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

2009-02-15

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

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

PubMed

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

2009-05-29

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

Attempted validation of the NUn score and inflammatory markers as predictors of esophageal anastomotic leak and major complications.

PubMed

Findlay, J M; Tilson, R C; Harikrishnan, A; Sgromo, B; Marshall, R E K; Maynard, N D; Gillies, R S; Middleton, M R

2015-10-01

The ability to predict complications following esophagectomy/extended total gastrectomy would be of great clinical value. A recent study demonstrated significant correlations between anastomotic leak (AL) and numerical values of C-reactive protein (CRP), white cell count (WCC) and albumin measured on postoperative day (POD) 4. A predictive model comprising all three (NUn score >10) was found to be highly sensitive and discriminant in predicting AL and complications. We attempted a retrospective validation in our center. Data were collected on all resections performed during a 5-year period (April 2008-2013) using prospectively maintained databases. Our biochemistry laboratory uses a maximum CRP value (156 mg/L), unlike that of the original study; otherwise all variables and outcome measures were comparable. Analysis was performed for all patients with complete blood results on POD4. Three hundred twenty-six patients underwent resection, of which 248 had POD4 bloods. There were 21 AL overall (6.44%); 16 among those with complete POD4 blood results (6.45%). There were 8 (2.45%) in-hospital deaths; 7 (2.82%) in those with POD4 results. No parameters were associated with AL or complication severity on univariate analysis. WCC was associated with AL in multivariate binary logistic regression with albumin and CRP (OR 1.23 [95% CI 1.03-1.47]; P = 0.021). When a binary variable of CRP ≥ 156 mg/L was used rather than an absolute value, no factors were significant. Mean NUn was 8.30 for AL, compared with 8.40 for non-AL (P = 0.710 independent t-test). NUn > 10 predicted 0 of 16 leaks (sensitivity 0.00%, specificity 94.4%, receiver operator curve [ROC] area under the curve [AUC] 0.485; P = 0.843). NUn > 7.65 was 93% sensitive and 21.6% specific. ROC for WCC alone was comparable with NUn (AUC 0.641 [0.504-0.779]; P = 0.059; WCC > 6.89 93.8% sensitive, 20.7% specific; WCC > 15 6.3% sensitive and 97% specific). There were no associations between any parameters and other complications. In a comparable cohort with the original study, we demonstrated a similar multivariate association between WCC alone on POD4 and subsequent demonstration of AL, but not albumin or CRP (measured up to 156 mg/L). The NUn score overall (calculated with this caveat) and a threshold of 10 was not found to have clinical utility in predicting AL or complications. © 2014 International Society for Diseases of the Esophagus.

On approaches to analyze the sensitivity of simulated hydrologic fluxes to model parameters in the community land model

DOE PAGES

Bao, Jie; Hou, Zhangshuan; Huang, Maoyi; ...

2015-12-04

Here, effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash-Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalizedmore » linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.« less

Principal Component Analysis of Chlorophyll Content in Tobacco, Bean and Petunia Plants Exposed to Different Tropospheric Ozone Concentrations

NASA Astrophysics Data System (ADS)

Borowiak, Klaudia; Zbierska, Janina; Budka, Anna; Kayzer, Dariusz

2014-06-01

Three plant species were assessed in this study - ozone-sensitive and -resistant tobacco, ozone-sensitive petunia and bean. Plants were exposed to ambient air conditions for several weeks in two sites differing in tropospheric ozone concentrations in the growing season of 2009. Every week chlorophyll contents were analysed. Cumulative ozone effects on the chlorophyll content in relation to other meteorological parameters were evaluated using principal component analysis, while the relation between certain days of measurements of the plants were analysed using multivariate analysis of variance. Results revealed variability between plant species response. However, some similarities were noted. Positive relations of all chlorophyll forms to cumulative ozone concentration (AOT 40) were found for all the plant species that were examined. The chlorophyll b/a ratio revealed an opposite position to ozone concentration only in the ozone-resistant tobacco cultivar. In all the plant species the highest average chlorophyll content was noted after the 7th day of the experiment. Afterwards, the plants usually revealed various responses. Ozone-sensitive tobacco revealed decrease of chlorophyll content, and after few weeks of decline again an increase was observed. Probably, due to the accommodation for the stress factor. While during first three weeks relatively high levels of chlorophyll contents were noted in ozone-resistant tobacco. Petunia revealed a slow decrease of chlorophyll content and the lowest values at the end of the experiment. A comparison between the plant species revealed the highest level of chlorophyll contents in ozone-resistant tobacco.

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

PubMed

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

2013-01-01

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

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

PubMed Central

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

2014-01-01

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

Surface-enhanced Raman spectroscopy for differentiation between benign and malignant thyroid tissues

NASA Astrophysics Data System (ADS)

Li, Zuanfang; Li, Chao; Lin, Duo; Huang, Zufang; Pan, Jianji; Chen, Guannan; Lin, Juqiang; Liu, Nenrong; Yu, Yun; Feng, Shangyuan; Chen, Rong

2014-04-01

The aim of this study was to evaluate the potential of applying silver nano-particle based surface-enhanced Raman scattering (SERS) to discriminate different types of human thyroid tissues. SERS measurements were performed on three groups of tissue samples including thyroid cancers (n = 32), nodular goiters (n = 20) and normal thyroid tissues (n = 25). Tentative assignments of the measured tissue SERS spectra suggest interesting cancer specific biomolecular differences. The principal component analysis (PCA) and linear discriminate analysis (LDA) together with the leave-one-out, cross-validated technique yielded diagnostic sensitivities of 92%, 75% and 87.5%; and specificities of 82.6%, 89.4% and 84.4%, respectively, for differentiation among normal, nodular and malignant thyroid tissue samples. This work demonstrates that tissue SERS spectroscopy associated with multivariate analysis diagnostic algorithms has great potential for detection of thyroid cancer at the molecular level.

A multi-model assessment of terrestrial biosphere model data needs

NASA Astrophysics Data System (ADS)

Gardella, A.; Cowdery, E.; De Kauwe, M. G.; Desai, A. R.; Duveneck, M.; Fer, I.; Fisher, R.; Knox, R. G.; Kooper, R.; LeBauer, D.; McCabe, T.; Minunno, F.; Raiho, A.; Serbin, S.; Shiklomanov, A. N.; Thomas, A.; Walker, A.; Dietze, M.

2017-12-01

Terrestrial biosphere models provide us with the means to simulate the impacts of climate change and their uncertainties. Going beyond direct observation and experimentation, models synthesize our current understanding of ecosystem processes and can give us insight on data needed to constrain model parameters. In previous work, we leveraged the Predictive Ecosystem Analyzer (PEcAn) to assess the contribution of different parameters to the uncertainty of the Ecosystem Demography model v2 (ED) model outputs across various North American biomes (Dietze et al., JGR-G, 2014). While this analysis identified key research priorities, the extent to which these priorities were model- and/or biome-specific was unclear. Furthermore, because the analysis only studied one model, we were unable to comment on the effect of variability in model structure to overall predictive uncertainty. Here, we expand this analysis to all biomes globally and a wide sample of models that vary in complexity: BioCro, CABLE, CLM, DALEC, ED2, FATES, G'DAY, JULES, LANDIS, LINKAGES, LPJ-GUESS, MAESPA, PRELES, SDGVM, SIPNET, and TEM. Prior to performing uncertainty analyses, model parameter uncertainties were assessed by assimilating all available trait data from the combination of the BETYdb and TRY trait databases, using an updated multivariate version of PEcAn's Hierarchical Bayesian meta-analysis. Next, sensitivity analyses were performed for all models across a range of sites globally to assess sensitivities for a range of different outputs (GPP, ET, SH, Ra, NPP, Rh, NEE, LAI) at multiple time scales from the sub-annual to the decadal. Finally, parameter uncertainties and model sensitivities were combined to evaluate the fractional contribution of each parameter to the predictive uncertainty for a specific variable at a specific site and timescale. Facilitated by PEcAn's automated workflows, this analysis represents the broadest assessment of the sensitivities and uncertainties in terrestrial models to date, and provides a comprehensive roadmap for constraining model uncertainties through model development and data collection.

Evaluating the predictive power of multivariate tensor-based morphometry in Alzheimer's disease progression via convex fused sparse group Lasso

NASA Astrophysics Data System (ADS)

Tsao, Sinchai; Gajawelli, Niharika; Zhou, Jiayu; Shi, Jie; Ye, Jieping; Wang, Yalin; Lepore, Natasha

2014-03-01

Prediction of Alzheimers disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end we combine a predictive multi-task machine learning method1 with novel MR-based multivariate morphometric surface map of the hippocampus2 to predict future cognitive scores of patients. Previous work by Zhou et al.1 has shown that a multi-task learning framework that performs prediction of all future time points (or tasks) simultaneously can be used to encode both sparsity as well as temporal smoothness. They showed that this can be used in predicting cognitive outcomes of Alzheimers Disease Neuroimaging Initiative (ADNI) subjects based on FreeSurfer-based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied Shi et al.2s recently developed multivariate tensor-based (mTBM) parametric surface analysis method to extract features from the hippocampal surface. We show that by combining the power of the multi-task framework with the sensitivity of mTBM features of the hippocampus surface, we are able to improve significantly improve predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.

Brain shaving: adaptive detection for brain PET data

NASA Astrophysics Data System (ADS)

Grecchi, Elisabetta; Doyle, Orla M.; Bertoldo, Alessandra; Pavese, Nicola; Turkheimer, Federico E.

2014-05-01

The intricacy of brain biology is such that the variation of imaging end-points in health and disease exhibits an unpredictable range of spatial distributions from the extremely localized to the very diffuse. This represents a challenge for the two standard approaches to analysis, the mass univariate and the multivariate that exhibit either strong specificity but not as good sensitivity (the former) or poor specificity and comparatively better sensitivity (the latter). In this work, we develop an analytical methodology for positron emission tomography that operates an extraction (‘shaving’) of coherent patterns of signal variation while maintaining control of the type I error. The methodology operates two rotations on the image data, one local using the wavelet transform and one global using the singular value decomposition. The control of specificity is obtained by using the gap statistic that selects, within each eigenvector, a subset of significantly coherent elements. Face-validity of the algorithm is demonstrated using a paradigmatic data-set with two radiotracers, [11C]-raclopride and [11C]-(R)-PK11195, measured on the same Huntington's disease patients, a disorder with a genetic based diagnosis. The algorithm is able to detect the two well-known separate but connected processes of dopamine neuronal loss (localized in the basal ganglia) and neuroinflammation (diffusive around the whole brain). These processes are at the two extremes of the distributional envelope, one being very sparse and the latter being perfectly Gaussian and they are not adequately detected by the univariate and the multivariate approaches.

Diagnosing perforated appendicitis in pediatric patients: a new model.

PubMed

van den Bogaard, Veerle A B; Euser, Sjoerd M; van der Ploeg, Tjeerd; de Korte, Niels; Sanders, Dave G M; de Winter, Derek; Vergroesen, Diederik; van Groningen, Krijn; de Winter, Peter

2016-03-01

Studies have investigated sensitivity and specificity of symptoms and tests for diagnosing appendicitis in children. Less is known with regard to the predictive value of these symptoms and tests with respect to the severity of appendicitis. The aim of this study was to determine the predictive value of patient's characteristics and tests for discriminating between perforated and nonperforated appendicitis in children. Pediatric patients who underwent an appendectomy at Spaarne Hospital Hoofddorp, the Netherlands, between January 1, 2009 and December 31, 2013, were included. Baseline patient's characteristics, history, physical examination, laboratory data and results of ultrasounds were collected. Univariate and multivariate logistic regressions were used to determine predictors of perforation. In total, 375 patients were included in this study of which 97 children (25.9%) had significant signs of perforation. Univariate analysis showed that age, duration of complaints, temperature, vomiting, CRP, WBC, different findings on ultrasound and the diameter of the appendix were good predictors of a perforated appendicitis. The final multivariate prediction model included temperature, CRP, clearly visible appendix and free fluids on ultrasound and diameter of the appendix and resulted in an area under the curve (AUC) of 0.91 showing sensitivity and specificity of respectively 85.2% and 81.2%. This prediction model can be used for identification of 'high-risk' children for a perforated appendicitis and might be helpful to prevent complications and longer hospitalization by bringing these children to theater earlier. Copyright © 2016 Elsevier Inc. All rights reserved.

Comparison of Various Anthropometric Indices as Risk Factors for Hearing Impairment in Asian Women.

PubMed

Kang, Seok Hui; Jung, Da Jung; Lee, Kyu Yup; Choi, Eun Woo; Do, Jun Young

2015-01-01

The objective of the present study was to examine the associations between various anthropometric measures and metabolic syndrome and hearing impairment in Asian women. We identified 11,755 women who underwent voluntary routine health checkups at Yeungnam University Hospital between June 2008 and April 2014. Among these patients, 2,485 participants were <40 years old, and 1,072 participants lacked information regarding their laboratory findings or hearing and were therefore excluded. In total 8,198 participants were recruited into our study. The AUROC value for metabolic syndrome was 0.790 for the waist to hip ratio (WHR). The cutoff value was 0.939. The sensitivity and specificity for predicting metabolic syndrome were 72.7% and 71.7%, respectively. The AUROC value for hearing loss was 0.758 for WHR. The cutoff value was 0.932. The sensitivity and specificity for predicting hearing loss were 65.8% and 73.4%, respectively. The WHR had the highest AUC and was the best predictor of metabolic syndrome and hearing loss. Univariate and multivariate linear regression analyses showed that WHR levels were positively associated with four hearing thresholds including averaged hearing threshold and low, middle, and high frequency thresholds. In addition, multivariate logistic analysis revealed that those with a high WHR had a 1.347-fold increased risk of hearing loss compared with the participants with a low WHR. Our results demonstrated that WHR may be a surrogate marker for predicting the risk of hearing loss resulting from metabolic syndrome.

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.

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

NASA Astrophysics Data System (ADS)

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

2010-05-01

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

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

Wavenumber selection based analysis in Raman spectroscopy improves skin cancer diagnostic specificity at high sensitivity levels (Conference Presentation)

NASA Astrophysics Data System (ADS)

Zhao, Jianhua; Zeng, Haishan; Kalia, Sunil; Lui, Harvey

2017-02-01

Background: Raman spectroscopy is a non-invasive optical technique which can measure molecular vibrational modes within tissue. A large-scale clinical study (n = 518) has demonstrated that real-time Raman spectroscopy could distinguish malignant from benign skin lesions with good diagnostic accuracy; this was validated by a follow-up independent study (n = 127). Objective: Most of the previous diagnostic algorithms have typically been based on analyzing the full band of the Raman spectra, either in the fingerprint or high wavenumber regions. Our objective in this presentation is to explore wavenumber selection based analysis in Raman spectroscopy for skin cancer diagnosis. Methods: A wavenumber selection algorithm was implemented using variably-sized wavenumber windows, which were determined by the correlation coefficient between wavenumbers. Wavenumber windows were chosen based on accumulated frequency from leave-one-out cross-validated stepwise regression or least and shrinkage selection operator (LASSO). The diagnostic algorithms were then generated from the selected wavenumber windows using multivariate statistical analyses, including principal component and general discriminant analysis (PC-GDA) and partial least squares (PLS). A total cohort of 645 confirmed lesions from 573 patients encompassing skin cancers, precancers and benign skin lesions were included. Lesion measurements were divided into training cohort (n = 518) and testing cohort (n = 127) according to the measurement time. Result: The area under the receiver operating characteristic curve (ROC) improved from 0.861-0.891 to 0.891-0.911 and the diagnostic specificity for sensitivity levels of 0.99-0.90 increased respectively from 0.17-0.65 to 0.20-0.75 by selecting specific wavenumber windows for analysis. Conclusion: Wavenumber selection based analysis in Raman spectroscopy improves skin cancer diagnostic specificity at high sensitivity levels.

Bivariate and multivariate analyses of the correlations between stability of the erythrocyte membrane, serum lipids and hematological variables.

PubMed

Bernardino Neto, M; de Avelar, E B; Arantes, T S; Jordão, I A; da Costa Huss, J C; de Souza, T M T; de Souza Penha, V A; da Silva, S C; de Souza, P C A; Tavares, M; Penha-Silva, N

2013-01-01

The observation that the fluidity must remain within a critical interval, outside which the stability and functionality of the cell tends to decrease, shows that stability, fluidity and function are related and that the measure of erythrocyte stability allows inferences about the fluidity or functionality of these cells. This study determined the biochemical and hematological variables that are directly or indirectly related to erythrocyte stability in a population of 71 volunteers. Data were evaluated by bivariate and multivariate analysis. The erythrocyte stability showed a greater association with hematological variables than the biochemical variables. The RDW stands out for its strong correlation with the stability of erythrocyte membrane, without being heavily influenced by other factors. Regarding the biochemical variables, the erythrocyte stability was more sensitive to LDL-C. Erythrocyte stability was significantly associated with RDW and LDL-C. Thus, the level of LDL-C is a consistent link between stability and functionality, suggesting that a measure of stability could be more one indirect parameter for assessing the risk of degenerative processes associated with high levels of LDL-C.

Predicting need for additional CT scan in children with a non-diagnostic ultrasound for appendicitis in the emergency department.

PubMed

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

2018-01-01

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

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…

Blood glucose control for patients with acute coronary syndromes in Qatar.

PubMed

Wilby, Kyle John; Elmekaty, Eman; Abdallah, Ibtihal; Habra, Masa; Al-Siyabi, Khalid

2016-01-01

Blood glucose is known to be elevated in patients presenting with acute coronary syndromes. However a gap in knowledge exists regarding effective management strategies once admitted to acute care units. It is also unknown what factors (if any) predict elevated glucose values during initial presentation. OBJECTIVES of the study were to characterize blood glucose control in patients admitted to the cardiac care unit (CCU) in Qatar and to determine predictive factors associated with high glucose levels (>10 mmol/l) on admission to the CCU. All data for this study were obtained from the CCU at Heart Hospital in Doha, Qatar. A retrospective chart review was completed for patients admitted to the CCU in Qatar from October 1st, 2012 to March 31st, 2013, of which 283 were included. Baseline characteristics (age, gender, nationality, medical history, smoking status, type of acute coronary syndrome), capillary and lab blood glucose measurements, and use of insulin were extracted. Time spent in glucose ranges of <4, 4 to <8, 8 to <10, and >10 mmol/1 was calculated manually. Univariate and multivariate logistic regression were performed to assess factors associated with high glucose on admission. The primary analysis was completed with capillary data and a sensitivity analysis was completed using laboratory data. Blood glucose values measured on admission and throughout length of stay in the CCU. Capillary blood glucose data showed majority of time was spent in the range of >10 mmol/l (41.95%), followed by 4-8 mmol/l (35.44%), then 8-10 mmol/l (21.45%), and finally <4 mmol/l (1.16%). As a sensitivity analysis, laboratory data showed very similar findings. Diabetes, hypertension, and non-smoker status predicted glucose values >10 mmol/l on admission (p < 0.05) in a univariate analysis but only diabetes remained significant in a multivariate model (OR 23.3; 95% CI, 11.5-47.3). Diabetes predicts high glucose values on hospital admission for patients with ACS and patients are not being adequately controlled throughout CCU stay.

Fourier Transform Infrared Spectroscopy (FTIR) and Multivariate Analysis for Identification of Different Vegetable Oils Used in Biodiesel Production

PubMed Central

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

2013-01-01

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

Multivariate meta-analysis for non-linear and other multi-parameter associations

PubMed Central

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

2012-01-01

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

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.

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.

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

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

A refined method for multivariate meta-analysis and meta-regression.

PubMed

Jackson, Daniel; Riley, Richard D

2014-02-20

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

Measurement of the Electroweak Single Top Quark Production Cross Section and the CKM Matrix Element $$|V_{tb}|$$ at CDF Run II

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

Larana, Bruno Casal

2010-01-01

The establishment of the electroweak single top quark production at CDF is experimentally challenging. The small single top signal hidden under large uncertain background processes makes it necessary an excellent understanding of the detector and a detailed study of the processes involved. Moreover, simple counting experiments are not sufficient to extract enough information from the candidate event sample and multivariate analysis techniques are crucial to distinguish signal from background. This thesis presents the world’s most sensitive individual search, together with CDF’s Neural Network analysis, for the combined s- and t-channel single top production. This analysis uses a dataset that correspondsmore » to an integrated luminosity of 3.2fb -1, and is based on a Boosted Decision Tree method that combines information from several input variables to construct a final powerful discriminant, reaching a sensitivity to the combined single top quark production equivalent to 5.2σ. The measured combined single top quark production cross section is 2.1 +0.7 -0.6 pb assuming a top quark mass of 175 GeV/c 2. The probability that this result comes from a background-only fluctuation (p-value) is 0.0002, which corresponds to 3.5σ.« less

Lagged segmented Poincaré plot analysis for risk stratification in patients with dilated cardiomyopathy.

PubMed

Voss, Andreas; Fischer, Claudia; Schroeder, Rico; Figulla, Hans R; Goernig, Matthias

2012-07-01

The objectives of this study were to introduce a new type of heart-rate variability analysis improving risk stratification in patients with idiopathic dilated cardiomyopathy (DCM) and to provide additional information about impaired heart beat generation in these patients. Beat-to-beat intervals (BBI) of 30-min ECGs recorded from 91 DCM patients and 21 healthy subjects were analyzed applying the lagged segmented Poincaré plot analysis (LSPPA) method. LSPPA includes the Poincaré plot reconstruction with lags of 1-100, rotating the cloud of points, its normalized segmentation adapted to their standard deviations, and finally, a frequency-dependent clustering. The lags were combined into eight different clusters representing specific frequency bands within 0.012-1.153 Hz. Statistical differences between low- and high-risk DCM could be found within the clusters II-VIII (e.g., cluster IV: 0.033-0.038 Hz; p = 0.0002; sensitivity = 85.7 %; specificity = 71.4 %). The multivariate statistics led to a sensitivity of 92.9 %, specificity of 85.7 % and an area under the curve of 92.1 % discriminating these patient groups. We introduced the LSPPA method to investigate time correlations in BBI time series. We found that LSPPA contributes considerably to risk stratification in DCM and yields the highest discriminant power in the low and very low-frequency bands.

Multivariate classification with random forests for gravitational wave searches of black hole binary coalescence

NASA Astrophysics Data System (ADS)

Baker, Paul T.; Caudill, Sarah; Hodge, Kari A.; Talukder, Dipongkar; Capano, Collin; Cornish, Neil J.

2015-03-01

Searches for gravitational waves produced by coalescing black hole binaries with total masses ≳25 M⊙ use matched filtering with templates of short duration. Non-Gaussian noise bursts in gravitational wave detector data can mimic short signals and limit the sensitivity of these searches. Previous searches have relied on empirically designed statistics incorporating signal-to-noise ratio and signal-based vetoes to separate gravitational wave candidates from noise candidates. We report on sensitivity improvements achieved using a multivariate candidate ranking statistic derived from a supervised machine learning algorithm. We apply the random forest of bagged decision trees technique to two separate searches in the high mass (≳25 M⊙ ) parameter space. For a search which is sensitive to gravitational waves from the inspiral, merger, and ringdown of binary black holes with total mass between 25 M⊙ and 100 M⊙ , we find sensitive volume improvements as high as 70±13%-109±11% when compared to the previously used ranking statistic. For a ringdown-only search which is sensitive to gravitational waves from the resultant perturbed intermediate mass black hole with mass roughly between 10 M⊙ and 600 M⊙ , we find sensitive volume improvements as high as 61±4%-241±12% when compared to the previously used ranking statistic. We also report how sensitivity improvements can differ depending on mass regime, mass ratio, and available data quality information. Finally, we describe the techniques used to tune and train the random forest classifier that can be generalized to its use in other searches for gravitational waves.

Central arterial stiffness is associated with systemic inflammation among Asians with type 2 diabetes.

PubMed

Zhang, Xiao; Liu, Jian Jun; Fang Sum, Chee; Ying, Yeoh Lee; Tavintharan, Subramaniam; Ng, Xiao Wei; Su, Chang; Low, Serena; Lee, Simon Bm; Tang, Wern Ee; Lim, Su Chi

2016-07-01

To examine the relationship between inflammation and central arterial stiffness in a type 2 diabetes Asian cohort. Central arterial stiffness was estimated by carotid-femoral pulse wave velocity and augmentation index. Linear regression model was used to evaluate the association of high-sensitivity C-reactive protein and soluble receptor for advanced glycation end products with pulse wave velocity and augmentation index. High-sensitivity C-reactive protein was analysed as a continuous variable and categories (<1, 1-3, and >3 mg/L). There is no association between high-sensitivity C-reactive protein and pulse wave velocity. Augmentation index increased with high-sensitivity C-reactive protein as a continuous variable (β = 0.328, p = 0.049) and categories (β = 1.474, p = 0.008 for high-sensitivity C-reactive protein: 1-3 mg/L and β = 1.323, p = 0.019 for high-sensitivity C-reactive protein: >3 mg/L) after multivariable adjustment. No association was observed between augmentation index and soluble receptor for advanced glycation end products. Each unit increase in natural log-transformed soluble receptor for advanced glycation end products was associated with 0.328 m/s decrease in pulse wave velocity after multivariable adjustment (p = 0.007). Elevated high-sensitivity C-reactive protein and decreased soluble receptor for advanced glycation end products are associated with augmentation index and pulse wave velocity, respectively, suggesting the potential role of systemic inflammation in the pathogenesis of central arterial stiffness in type 2 diabetes. © The Author(s) 2016.

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.

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.

Familial aggregation of MATRICS Consensus Cognitive Battery scores in a large sample of outpatients with schizophrenia and their unaffected relatives.

PubMed

Mucci, A; Galderisi, S; Green, M F; Nuechterlein, K; Rucci, P; Gibertoni, D; Rossi, A; Rocca, P; Bertolino, A; Bucci, P; Hellemann, G; Spisto, M; Palumbo, D; Aguglia, E; Amodeo, G; Amore, M; Bellomo, A; Brugnoli, R; Carpiniello, B; Dell'Osso, L; Di Fabio, F; di Giannantonio, M; Di Lorenzo, G; Marchesi, C; Monteleone, P; Montemagni, C; Oldani, L; Romano, R; Roncone, R; Stratta, P; Tenconi, E; Vita, A; Zeppegno, P; Maj, M

2018-06-01

The increased use of the MATRICS Consensus Cognitive Battery (MCCB) to investigate cognitive dysfunctions in schizophrenia fostered interest in its sensitivity in the context of family studies. As various measures of the same cognitive domains may have different power to distinguish between unaffected relatives of patients and controls, the relative sensitivity of MCCB tests for relative-control differences has to be established. We compared MCCB scores of 852 outpatients with schizophrenia (SCZ) with those of 342 unaffected relatives (REL) and a normative Italian sample of 774 healthy subjects (HCS). We examined familial aggregation of cognitive impairment by investigating within-family prediction of MCCB scores based on probands' scores. Multivariate analysis of variance was used to analyze group differences in adjusted MCCB scores. Weighted least-squares analysis was used to investigate whether probands' MCCB scores predicted REL neurocognitive performance. SCZ were significantly impaired on all MCCB domains. REL had intermediate scores between SCZ and HCS, showing a similar pattern of impairment, except for social cognition. Proband's scores significantly predicted REL MCCB scores on all domains except for visual learning. In a large sample of stable patients with schizophrenia, living in the community, and in their unaffected relatives, MCCB demonstrated sensitivity to cognitive deficits in both groups. Our findings of significant within-family prediction of MCCB scores might reflect disease-related genetic or environmental factors.

IL-8 predicts pediatric oncology patients with febrile neutropenia at low risk for bacteremia.

PubMed

Cost, Carrye R; Stegner, Martha M; Leonard, David; Leavey, Patrick

2013-04-01

Despite a low bacteremia rate, pediatric oncology patients are frequently admitted for febrile neutropenia. A pediatric risk prediction model with high sensitivity to identify patients at low risk for bacteremia is not available. We performed a single-institution prospective cohort study of pediatric oncology patients with febrile neutropenia to create a risk prediction model using clinical factors, respiratory viral infection, and cytokine expression. Pediatric oncology patients with febrile neutropenia were enrolled between March 30, 2010 and April 1, 2011 and managed per institutional protocol. Blood samples for C-reactive protein and cytokine expression and nasopharyngeal swabs for respiratory viral testing were obtained. Medical records were reviewed for clinical data. Statistical analysis utilized mixed multiple logistic regression modeling. During the 12-month period, 195 febrile neutropenia episodes were enrolled. There were 24 (12%) episodes of bacteremia. Univariate analysis revealed several factors predictive for bacteremia, and interleukin (IL)-8 was the most predictive variable in the multivariate stepwise logistic regression. Low serum IL-8 predicted patients at low risk for bacteremia with a sensitivity of 0.9 and negative predictive value of 0.98. IL-8 is a highly sensitive predictor for patients at low risk for bacteremia. IL-8 should be utilized in a multi-institution prospective trial to assign risk stratification to pediatric patients admitted with febrile neutropenia.

Development of Pattern Recognition Techniques for the Evaluation of Toxicant Impacts to Multispecies Systems

DTIC Science & Technology

1993-06-18

the exception. In the Standardized Aquatic Microcosm and the Mixed Flask Culture (MFC) microcosms, multivariate analysis and clustering methods...rule rather than the exception. In the Standardized Aquatic Microcosm and the Mixed Flask Culture (MFC) microcosms, multivariate analysis and...experiments using two microcosm protocols. We use nonmetric clustering, a multivariate pattern recognition technique developed by Matthews and Heame (1991

Impact of Weight Loss at Presentation on Survival in Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors (EGFR-TKI) Sensitive Mutant Advanced Non-small Cell Lung Cancer (NSCLC) Treated with First-line EGFR-TKI.

PubMed

Lin, Liping; Zhao, Juanjuan; Hu, Jiazhu; Huang, Fuxi; Han, Jianjun; He, Yan; Cao, Xiaolong

2018-01-01

Purpose The aim of this study is to evaluate the impact of weight loss at presentation on treatment outcomes of first-line EGFR-tyrosine kinase inhibitors (EGFR-TKI) in EGFR-TKI sensitive mutant NSCLC patients. Methods We retrospectively analyzed the clinical outcomes of 75 consecutive advanced NSCLC patients with EGFR-TKI sensitive mutations (exon 19 deletion or exon 21 L858R) received first-line gefitinib or erlotinib therapy according to weight loss status at presentation in our single center. Results Of 75 EGFR-TKI sensitive mutant NSCLC patients, 49 (65.3%) patients had no weight loss and 26 (34.7%) had weight loss at presentation, the objective response rate (ORR) to EGFR-TKI treatment were similar between the two groups (79.6% vs. 76.9%, p = 0.533). Patients without weight loss at presentation had significantly longer median progression free survival (PFS) (12.4 months vs. 7.6 months; hazard ratio [HR] 0.356, 95% confidence interval [CI] 0.212-0.596, p < 0.001) and overall survival (OS) (28.5 months vs. 20.7 months; HR 0.408, 95% CI 0.215-0.776, p = 0.006) than those with weight loss at presentation; moreover, the stratified analysis by EGFR-TKI sensitive mutation types also found similar trend between these two groups except for OS in EGFR exon 21 L858R mutation patients. Multivariate analysis identified weight loss at presentation and EGFR-TKI sensitive mutation types were independent predictive factors for PFS and OS. Conclusions Weight loss at presentation had a detrimental impact on PFS and OS in EGFR-TKI sensitive mutant advanced NSCLC patients treated with first-line EGFR-TKI. It should be considered as an important factor in the treatment decision or designing of EGFR-TKI clinical trials.

Staging research of human lung cancer tissues by high-resolution magic angle spinning proton nuclear magnetic resonance spectroscopy (HRMAS 1 H NMR) and multivariate data analysis.

PubMed

Chen, Wenxue; Lu, Shaohua; Wang, Guifang; Chen, Fener; Bai, Chunxue

2017-10-01

High-resolution magic-angle spinning proton nuclear magnetic resonance (HRMAS 1 H NMR) spectroscopy technique was employed to analyze the metabonomic characterizations of lung cancer tissues in hope to identify potential diagnostic biomarkers for malignancy detection and staging research of lung tissues. HRMAS 1 H NMR spectroscopy technique can rapidly provide important information for accurate diagnosis and staging of cancer tissues owing to its noninvasive nature and limited requirement for the samples, and thus has been acknowledged as an excellent tool to investigate tissue metabolism and provide a more realistic insight into the metabonomics of tissues when combined with multivariate data analysis (MVDA) such as component analysis and orthogonal partial least squares-discriminant analysis in particular. HRMAS 1 H NMR spectra displayed the metabonomic differences of 32 lung cancer tissues at the different stages from 32 patients. The significant changes (P < 0.05) of some important metabolites such as lipids, aspartate and choline-containing compounds in cancer tissues at the different stages had been identified. Furthermore, the combination of HRMAS 1 H NMR spectroscopy and MVDA might potentially and precisely provided for a high sensitivity, specificity, prediction accuracy in the positive identification of the staging for the cancer tissues in contrast with the pathological data in clinic. This study highlighted the potential of metabonomics in clinical settings so that the techniques might be further exploited for the diagnosis and staging prediction of lung cancer in future. © 2016 John Wiley & Sons Australia, Ltd.

Multivariate analysis for scanning tunneling spectroscopy data

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Dissecting the relationship between obesity and hyperinsulinemia: Role of insulin secretion and insulin clearance.

PubMed

Kim, Mee Kyoung; Reaven, Gerald M; Kim, Sun H

2017-02-01

The aim of this study was to better delineate the complex interrelationship among insulin resistance (IR), secretion rate (ISR), and clearance rate (ICR) to increase plasma insulin concentrations in obesity. Healthy volunteers (92 nondiabetic individuals) had an insulin suppression test to measure IR and graded-glucose infusion test to measure ISR and ICR. Obesity was defined as a body mass index (BMI) ≥30 kg/m 2 , and IR was defined as steady-state plasma glucose (SSPG) ≥10 mmol/L during the insulin suppression test. Plasma glucose and insulin concentrations, ISR, and ICR were compared in three groups: insulin sensitive/overweight; insulin sensitive/obesity; and insulin resistant/obesity. Compared with the insulin-sensitive/overweight group, the insulin-sensitive/obesity had significantly higher insulin area under the curve (AUC) and ISR AUC during the graded-glucose infusion test (P < 0.001). Glucose AUC and ICR were similar. The insulin-resistant/obesity group had higher insulin AUC and ISR AUC compared with the insulin-sensitive/obesity but also had higher glucose AUC and decreased ICR (P < 0.01). In multivariate analysis, both BMI and SSPG were significantly associated with ISR. Plasma insulin concentration and ISR are increased in individuals with obesity, irrespective of degree of IR, but a decrease in ICR is confined to the subset of individuals with IR. © 2016 The Obesity Society.

Gut Microbiota Interacts with Markers of Adipose Tissue Browning, Insulin Action and Plasma Acetate in Morbid Obesity.

PubMed

Moreno-Navarrete, José María; Serino, Matteo; Blasco-Baque, Vincent; Azalbert, Vincent; Barton, Richard H; Cardellini, Marina; Latorre, Jèssica; Ortega, Francisco; Sabater-Masdeu, Mònica; Burcelin, Rémy; Dumas, Marc-Emmanuel; Ricart, Wifredo; Federici, Massimo; Fernández-Real, José Manuel

2018-02-01

To examine the potential relationship among gene expression markers of adipose tissue browning, gut microbiota, and insulin sensitivity in humans. Gut microbiota composition and gene markers of browning are analyzed in subcutaneous (SAT) and visceral (VAT) adipose tissue from morbidly obese subjects (n = 34). Plasma acetate is measured through 1 H NMR and insulin sensitivity using euglycemic hyperinsulinemic clamp. Subjects with insulin resistance show an increase in the relative abundance (RA) of the phyla Bacteroidetes and Proteobacteria while RA of Firmicutes is decreased. In all subjects, Firmicutes RA is negatively correlated with HbA 1c and fasting triglycerides, whereas Proteobacteria RA was negatively correlated with insulin sensitivity. Firmicutes RA is positively associated with markers of brown adipocytes (PRDM16, UCP1, and DIO2) in SAT, but not in VAT. Multivariate regression analysis indicates that Firmicutes RA contributes significantly to SAT PRDM16, UCP1, and DIO2 mRNA variance after controlling for age, BMI, HbA 1c , or insulin sensitivity. Interestingly, Firmicutes RA, specifically those bacteria belonging to the Ruminococcaceae family, is positively associated with plasma acetate levels, which are also linked to SAT PRDM16 mRNA and insulin sensitivity. Gut microbiota composition is linked to adipose tissue browning and insulin action in morbidly obese subjects, possibly through circulating acetate. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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…

Ultra high performance liquid chromatography-time of flight mass spectrometry for analysis of avocado fruit metabolites: method evaluation and applicability to the analysis of ripening degrees.

PubMed

Hurtado-Fernández, Elena; Pacchiarotta, Tiziana; Gómez-Romero, María; Schoenmaker, Bart; Derks, Rico; Deelder, André M; Mayboroda, Oleg A; Carrasco-Pancorbo, Alegría; Fernández-Gutiérrez, Alberto

2011-10-21

We have developed an analytical method using UHPLC-UV/ESI-TOF MS for the comprehensive profiling of the metabolites found in the methanolic extracts of 13 different varieties of avocado at two different ripening degrees. Both chromatographic and detection parameters were optimized in order to maximize the number of compounds detected and the sensitivity. After achieving the optimum conditions, we performed a complete analytical validation of the method with respect to its linearity, sensitivity, precision, accuracy and possible matrix effects. The LODs ranged from 1.64 to 730.54 ppb (in negative polarity) for benzoic acid and chrysin, respectively, whilst they were found within the range from 0.51 to 310.23 ppb in positive polarity. The RSDs for repeatability test did not exceed 7.01% and the accuracy ranged from 97.2% to 102.0%. Our method was then applied to the analysis of real avocado samples and advanced data processing and multivariate statistical analysis (PCA, PLS-DA) were carried out to discriminate/classify the examined avocado varieties. About 200 compounds belonging to various structural classes were tentatively identified; we are certain about the identity of around 60 compounds, 20 of which have been quantified in terms of their own commercially available standard. Copyright © 2011 Elsevier B.V. All rights reserved.

Race, Ancestry, and Development of Food-Allergen Sensitization in Early Childhood

PubMed Central

Tsai, Hui-Ju; Hong, Xiumei; Liu, Xin; Wang, Guoying; Pearson, Colleen; Ortiz, Katherin; Fu, Melanie; Pongracic, Jacqueline A.; Bauchner, Howard; Wang, Xiaobin

2011-01-01

OBJECTIVE: We examined whether the risk of food-allergen sensitization varied according to self-identified race or genetic ancestry. METHODS: We studied 1104 children (mean age: 2.7 years) from an urban multiethnic birth cohort. Food sensitization was defined as specific immunoglobulin E (sIgE) levels of ≥0.35 kilo–units of allergen (kUA)/L for any of 8 common food allergens. Multivariate logistic regression analyses were used to evaluate the associations of self-identified race and genetic ancestry with food sensitization. Analyses also examined associations with numbers of food sensitizations (0, 1 or 2, and ≥3 foods) and with logarithmically transformed allergen sIgE levels. RESULTS: In this predominantly minority cohort (60.9% black and 22.5% Hispanic), 35.5% of subjects exhibited food sensitizations. In multivariate models, both self-reported black race (odds ratio [OR]: 2.34 [95% confidence interval [CI]: 1.24–4.44]) and African ancestry (in 10% increments; OR: 1.07 [95% CI: 1.02–1.14]) were associated with food sensitization. Self-reported black race (OR: 3.76 [95% CI: 1.09–12.97]) and African ancestry (OR: 1.19 [95% CI: 1.07–1.32]) were associated with a high number (≥3) of food sensitizations. African ancestry was associated with increased odds of peanut sIgE levels of ≥5 kUA/L (OR: 1.25 [95% CI: 1.01–1.52]). Similar ancestry associations were seen for egg sIgE levels of ≥2 kUA/L (OR: 1.13 [95% CI: 1.01–1.27]) and milk sIgE levels of ≥5 kUA/L (OR: 1.24 [95% CI: 0.94–1.63]), although findings were not significant for milk. CONCLUSIONS: Black children were more likely to be sensitized to food allergens and were sensitized to more foods. African ancestry was associated with peanut sensitization. PMID:21890831

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.

Quantitative methods to direct exploration based on hydrogeologic information

USGS Publications Warehouse

Graettinger, A.J.; Lee, J.; Reeves, H.W.; Dethan, D.

2006-01-01

Quantitatively Directed Exploration (QDE) approaches based on information such as model sensitivity, input data covariance and model output covariance are presented. Seven approaches for directing exploration are developed, applied, and evaluated on a synthetic hydrogeologic site. The QDE approaches evaluate input information uncertainty, subsurface model sensitivity and, most importantly, output covariance to identify the next location to sample. Spatial input parameter values and covariances are calculated with the multivariate conditional probability calculation from a limited number of samples. A variogram structure is used during data extrapolation to describe the spatial continuity, or correlation, of subsurface information. Model sensitivity can be determined by perturbing input data and evaluating output response or, as in this work, sensitivities can be programmed directly into an analysis model. Output covariance is calculated by the First-Order Second Moment (FOSM) method, which combines the covariance of input information with model sensitivity. A groundwater flow example, modeled in MODFLOW-2000, is chosen to demonstrate the seven QDE approaches. MODFLOW-2000 is used to obtain the piezometric head and the model sensitivity simultaneously. The seven QDE approaches are evaluated based on the accuracy of the modeled piezometric head after information from a QDE sample is added. For the synthetic site used in this study, the QDE approach that identifies the location of hydraulic conductivity that contributes the most to the overall piezometric head variance proved to be the best method to quantitatively direct exploration. ?? IWA Publishing 2006.

Nomograms Predicting Platinum Sensitivity, Progression-Free Survival, and Overall Survival Using Pretreatment Complete Blood Cell Counts in Epithelial Ovarian Cancer

PubMed Central

Paik, E Sun; Sohn, Insuk; Baek, Sun-Young; Shim, Minhee; Choi, Hyun Jin; Kim, Tae-Joong; Choi, Chel Hun; Lee, Jeong-Won; Kim, Byoung-Gie; Lee, Yoo-Young; Bae, Duk-Soo

2017-01-01

Purpose This study was conducted to evaluate the prognostic significance of pre-treatment complete blood cell count (CBC), including white blood cell (WBC) differential, in epithelial ovarian cancer (EOC) patients with primary debulking surgery (PDS) and to develop nomograms for platinum sensitivity, progression-free survival (PFS), and overall survival (OS). Materials and Methods We retrospectively reviewed the records of 757 patients with EOC whose primary treatment consisted of surgical debulking and chemotherapy at Samsung Medical Center from 2002 to 2012. We subsequently created nomograms for platinum sensitivity, 3-year PFS, and 5-year OS as prediction models for prognostic variables including age, stage, grade, cancer antigen 125 level, residual disease after PDS, and pre-treatment WBC differential counts. The models were then validated by 10-fold cross-validation (CV). Results In addition to stage and residual disease after PDS, which are known predictors, lymphocyte and monocyte count were found to be significant prognostic factors for platinum-sensitivity, platelet count for PFS, and neutrophil count for OS on multivariate analysis. The area under the curves of platinum sensitivity, 3-year PFS, and 5-year OS calculated by the 10-fold CV procedure were 0.7405, 0.8159, and 0.815, respectively. Conclusion Prognostic factors including pre-treatment CBC were used to develop nomograms for platinum sensitivity, 3-year PFS, and 5-year OS of patients with EOC. These nomograms can be used to better estimate individual outcomes. PMID:27669704

Nomograms Predicting Platinum Sensitivity, Progression-Free Survival, and Overall Survival Using Pretreatment Complete Blood Cell Counts in Epithelial Ovarian Cancer.

PubMed

Paik, E Sun; Sohn, Insuk; Baek, Sun-Young; Shim, Minhee; Choi, Hyun Jin; Kim, Tae-Joong; Choi, Chel Hun; Lee, Jeong-Won; Kim, Byoung-Gie; Lee, Yoo-Young; Bae, Duk-Soo

2017-07-01

This study was conducted to evaluate the prognostic significance of pre-treatment complete blood cell count (CBC), including white blood cell (WBC) differential, in epithelial ovarian cancer (EOC) patients with primary debulking surgery (PDS) and to develop nomograms for platinum sensitivity, progression-free survival (PFS), and overall survival (OS). We retrospectively reviewed the records of 757 patients with EOC whose primary treatment consisted of surgical debulking and chemotherapy at Samsung Medical Center from 2002 to 2012. We subsequently created nomograms for platinum sensitivity, 3-year PFS, and 5-year OS as prediction models for prognostic variables including age, stage, grade, cancer antigen 125 level, residual disease after PDS, and pre-treatment WBC differential counts. The models were then validated by 10-fold cross-validation (CV). In addition to stage and residual disease after PDS, which are known predictors, lymphocyte and monocyte count were found to be significant prognostic factors for platinum-sensitivity, platelet count for PFS, and neutrophil count for OS on multivariate analysis. The area under the curves of platinum sensitivity, 3-year PFS, and 5-year OS calculated by the 10-fold CV procedure were 0.7405, 0.8159, and 0.815, respectively. Prognostic factors including pre-treatment CBC were used to develop nomograms for platinum sensitivity, 3-year PFS, and 5-year OS of patients with EOC. These nomograms can be used to better estimate individual outcomes.

Prognostic value of stromal decorin expression in patients with breast cancer: a meta-analysis.

PubMed

Li, Shuang-Jiang; Chen, Da-Li; Zhang, Wen-Biao; Shen, Cheng; Che, Guo-Wei

2015-11-01

Numbers of studies have investigated the biological functions of decorin (DCN) in oncogenesis, tumor progression, angiogenesis and metastasis. Although many of them aim to highlight the prognostic value of stromal DCN expression in breast cancer, some controversial results still exist and a consensus has not been reached until now. Therefore, our meta-analysis aims to determine the prognostic significance of stromal DCN expression in breast cancer patients. PubMed, EMBASE, the Web of Science and China National Knowledge Infrastructure (CNKI) databases were searched for full-text literatures met out inclusion criteria. We applied the hazard ratio (HR) with 95% confidence interval (CI) as the appropriate summarized statistics. Q-test and I(2) statistic were employed to estimate the level of heterogeneity across the included studies. Sensitivity analysis was conducted to further identify the possible origins of heterogeneity. The publication bias was detected by Begg's test and Egger's test. There were three English literatures (involving 6 studies) included into our meta-analysis. On the one hand, both the summarized outcomes based on univariate analysis (HR: 0.513; 95% CI: 0.406-0.648; P<0.001) and multivariate analysis (HR: 0.544; 95% CI: 0.388-0.763; P<0.001) indicated that stromal DCN expression could promise the high cancer-specific survival (CSS) of breast cancer patients. On the other hand, both the summarized outcomes based on univariate analysis (HR: 0.504; 95% CI: 0.389-0.651; P<0.001) and multivariate analysis (HR: 0.568; 95% CI: 0.400-0.806; P=0.002) also indicated that stromal DCN expression was positively associated with high disease-free survival (DFS) of breast cancer patients. No significant heterogeneity or publication bias was observed within this meta-analysis. The present evidences indicate that high stromal DCN expression can significantly predict the good prognosis in patients with breast cancer. The discoveries from our meta-analysis have better be confirmed in the updated review pooling more relevant investigations in the future.

Rank estimation and the multivariate analysis of in vivo fast-scan cyclic voltammetric data

PubMed Central

Keithley, Richard B.; Carelli, Regina M.; Wightman, R. Mark

2010-01-01

Principal component regression has been used in the past to separate current contributions from different neuromodulators measured with in vivo fast-scan cyclic voltammetry. Traditionally, a percent cumulative variance approach has been used to determine the rank of the training set voltammetric matrix during model development, however this approach suffers from several disadvantages including the use of arbitrary percentages and the requirement of extreme precision of training sets. Here we propose that Malinowski’s F-test, a method based on a statistical analysis of the variance contained within the training set, can be used to improve factor selection for the analysis of in vivo fast-scan cyclic voltammetric data. These two methods of rank estimation were compared at all steps in the calibration protocol including the number of principal components retained, overall noise levels, model validation as determined using a residual analysis procedure, and predicted concentration information. By analyzing 119 training sets from two different laboratories amassed over several years, we were able to gain insight into the heterogeneity of in vivo fast-scan cyclic voltammetric data and study how differences in factor selection propagate throughout the entire principal component regression analysis procedure. Visualizing cyclic voltammetric representations of the data contained in the retained and discarded principal components showed that using Malinowski’s F-test for rank estimation of in vivo training sets allowed for noise to be more accurately removed. Malinowski’s F-test also improved the robustness of our criterion for judging multivariate model validity, even though signal-to-noise ratios of the data varied. In addition, pH change was the majority noise carrier of in vivo training sets while dopamine prediction was more sensitive to noise. PMID:20527815

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.

Sensitization to sunflower pollen and lung functions in sunflower processing workers.

PubMed

Atis, S; Tutluoglu, B; Sahin, K; Yaman, M; Küçükusta, A R; Oktay, I

2002-01-01

This study aimed to investigate whether exposure to sunflower pollen (Helianthus annuus) increases both sensitization and respiratory symptoms, and whether or not it affects lung functions in sunflower processing workers. The largest sunflower processing factories in the Thrace region of Turkey participated in this study. Workers from the units directly exposed to sunflower seed enrolled as the study group (n = 102) and workers who were not directly exposed to Helianthus annuus pollen (n = 102) were the control group. Detailed questionnaires covering respiratory and allergic symptoms were completed, and skin prick tests and lung function tests were performed. We found a very high rate (23.5%) of sensitization to Helianthus annuus in the study group compared to the controls (P<0.001). Logistic regression analysis showed that the risk of sensitization to H. annuus was increased 4.7-fold (odds ratio = 4.17, 95%) confidence interval = 1.3-16.7) if subjects were exposed to sunflower pollen in the workplace. While asthmatic symptoms and allergic skin diseases were not different between the two groups, workers in the study group had a higher rate of allergic rhinitis and conjunctivitis (P<0.05). We found that pulmonary function was significantly impaired in the study group (P<0.01). Using a multivariate analysis model, inclusion in the study group was found to be a predictive factor for impairment of lung function (P=0.002). We conclude that sunflower pollen has high allergenic potential, especially when there is close contact, and exposure to sunflower pollen in the workplace can result in impairment in lung function.

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.

Biomarkers identified by urinary metabonomics for noninvasive diagnosis of nutritional rickets.

PubMed

Wang, Maoqing; Yang, Xue; Ren, Lihong; Li, Songtao; He, Xuan; Wu, Xiaoyan; Liu, Tingting; Lin, Liqun; Li, Ying; Sun, Changhao

2014-09-05

Nutritional rickets is a worldwide public health problem; however, the current diagnostic methods retain shortcomings for accurate diagnosis of nutritional rickets. To identify urinary biomarkers associated with nutritional rickets and establish a noninvasive diagnosis method, urinary metabonomics analysis by ultra-performance liquid chromatography/quadrupole time-of-flight tandem mass spectrometry and multivariate statistical analysis were employed to investigate the metabolic alterations associated with nutritional rickets in 200 children with or without nutritional rickets. The pathophysiological changes and pathogenesis of nutritional rickets were illustrated by the identified biomarkers. By urinary metabolic profiling, 31 biomarkers of nutritional rickets were identified and five candidate biomarkers for clinical diagnosis were screened and identified by quantitative analysis and receiver operating curve analysis. Urinary levels of five candidate biomarkers were measured using mass spectrometry or commercial kits. In the validation step, the combination of phosphate and sebacic acid was able to give a noninvasive and accurate diagnostic with high sensitivity (94.0%) and specificity (71.2%). Furthermore, on the basis of the pathway analysis of biomarkers, our urinary metabonomics analysis gives new insight into the pathogenesis and pathophysiology of nutritional rickets.

Using foreground/background analysis to determine leaf and canopy chemistry

NASA Technical Reports Server (NTRS)

Pinzon, J. E.; Ustin, S. L.; Hart, Q. J.; Jacquemoud, S.; Smith, M. O.

1995-01-01

Spectral Mixture Analysis (SMA) has become a well established procedure for analyzing imaging spectrometry data, however, the technique is relatively insensitive to minor sources of spectral variation (e.g., discriminating stressed from unstressed vegetation and variations in canopy chemistry). Other statistical approaches have been tried e.g., stepwise multiple linear regression analysis to predict canopy chemistry. Grossman et al. reported that SMLR is sensitive to measurement error and that the prediction of minor chemical components are not independent of patterns observed in more dominant spectral components like water. Further, they observed that the relationships were strongly dependent on the mode of expressing reflectance (R, -log R) and whether chemistry was expressed on a weight (g/g) or are basis (g/sq m). Thus, alternative multivariate techniques need to be examined. Smith et al. reported a revised SMA that they termed Foreground/Background Analysis (FBA) that permits directing the analysis along any axis of variance by identifying vectors through the n-dimensional spectral volume orthonormal to each other. Here, we report an application of the FBA technique for the detection of canopy chemistry using a modified form of the analysis.

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability

PubMed Central

ChariDingari, Narahara; Barman, Ishan; Myakalwar, Ashwin Kumar; Tewari, Surya P.; Kumar, G. Manoj

2012-01-01

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real world applications, e.g. quality assurance and process monitoring. Specifically, variability in sample, system and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a non-linear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), due to its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data – highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples as well as in related areas of forensic and biological sample analysis. PMID:22292496

Using microRNA profiling in urine samples to develop a non-invasive test for bladder cancer.

PubMed

Mengual, Lourdes; Lozano, Juan José; Ingelmo-Torres, Mercedes; Gazquez, Cristina; Ribal, María José; Alcaraz, Antonio

2013-12-01

Current standard methods used to detect and monitor bladder urothelial cell carcinoma (UCC) are invasive or have low sensitivity. The incorporation into clinical practice of a non-invasive tool for UCC assessment would enormously improve patients' quality of life and outcome. This study aimed to examine the microRNA (miRNA) expression profiles in urines of UCC patients in order to develop a non-invasive accurate and reliable tool to diagnose and provide information on the aggressiveness of the tumor. We performed a global miRNA expression profiling analysis of the urinary cells from 40 UCC patients and controls using TaqMan Human MicroRNA Array followed by validation of 22 selected potentially diagnostic and prognostic miRNAs in a separate cohort of 277 samples using a miRCURY LNA qPCR system. miRNA-based signatures were developed by multivariate logistic regression analysis and internally cross-validated. In the initial cohort of patients, we identified 40 and 30 aberrantly expressed miRNA in UCC compared with control urines and in high compared with low grade tumors, respectively. Quantification of 22 key miRNAs in an independent cohort resulted in the identification of a six miRNA diagnostic signature with a sensitivity of 84.8% and specificity of 86.5% (AUC = 0.92) and a two miRNA prognostic model with a sensitivity of 84.95% and a specificity of 74.14% (AUC = 0.83). Internal cross-validation analysis confirmed the accuracy rates of both models, reinforcing the strength of our findings. Although the data needs to be externally validated, miRNA analysis in urine appears to be a valuable tool for the non-invasive assessment of UCC. Copyright © 2013 UICC.

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability.

PubMed

Dingari, Narahara Chari; Barman, Ishan; Myakalwar, Ashwin Kumar; Tewari, Surya P; Kumar Gundawar, Manoj

2012-03-20

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real-world applications, e.g., quality assurance and process monitoring. Specifically, variability in sample, system, and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a nonlinear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that the application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), because of its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data-highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples, as well as in related areas of forensic and biological sample analysis.

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

PubMed

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

2016-05-15

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

Risk of Falls in Parkinson's Disease: A Cross-Sectional Study of 160 Patients

PubMed Central

Contreras, Ana; Grandas, Francisco

2012-01-01

Falls are a major source of disability in Parkinson's disease. Risk factors for falling in Parkinson's disease remain unclear. To determine the relevant risk factors for falling in Parkinson's disease, we screened 160 consecutive patients with Parkinson's disease for falls and assessed 40 variables. A comparison between fallers and nonfallers was performed using statistical univariate analyses, followed by bivariate and multivariate logistic regression, receiver-operating characteristics analysis, and Kaplan-Meier curves. 38.8% of patients experienced falls since the onset of Parkinson's disease (recurrent in 67%). Tinetti Balance score and Hoehn and Yahr staging were the best independent variables associated with falls. The Tinetti Balance test predicted falls with 71% sensitivity and 79% specificity and Hoehn and Yahr staging with 77% sensitivity and 71% specificity. The risk of falls increased exponentially with age, especially from 70 years onward. Patients aged >70 years at the onset of Parkinson's disease experienced falls significantly earlier than younger patients. PMID:22292126

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.

Detection and characterization of glaucoma-like canine retinal tissues using Raman spectroscopy

NASA Astrophysics Data System (ADS)

Wang, Qi; Grozdanic, Sinisa D.; Harper, Matthew M.; Hamouche, Karl; Hamouche, Nicholas; Kecova, Helga; Lazic, Tatjana; Hernandez-Merino, Elena; Yu, Chenxu

2013-06-01

Early detection of pathological changes and progression in glaucoma and other neuroretinal diseases remains a great challenge and is critical to reduce permanent structural and functional retina and optic nerve damage. Raman spectroscopy is a sensitive technique that provides rapid biochemical characterization of tissues in a nondestructive and noninvasive fashion. In this study, spectroscopic analysis was conducted on the retinal tissues of seven beagles with acute elevation of intraocular pressure (AEIOP), six beagles with compressive optic neuropathy (CON), and five healthy beagles. Spectroscopic markers were identified associated with the different neuropathic conditions. Furthermore, the Raman spectra were subjected to multivariate discriminate analysis to classify independent tissue samples into diseased/healthy categories. The multivariate discriminant model yielded an average optimal classification accuracy of 72.6% for AEIOP and 63.4% for CON with 20 principal components being used that accounted for 87% of the total variance in the data set. A strong correlation (R2>0.92) was observed between pattern electroretinography characteristics of AEIOP dogs and Raman separation distance that measures the separation of spectra of diseased tissues from normal tissues; however, the underlining mechanism of this correlation remains to be understood. Since AEIOP mimics the pathological symptoms of acute/early-stage glaucoma, it was demonstrated that Raman spectroscopic screening has the potential to become a powerful tool for the detection and characterization of early-stage disease.

Diagnostic value of history and physical examination in patients suspected of lumbosacral nerve root compression

PubMed Central

Vroomen, P; de Krom, M C T F M; Wilmink, J; Kester, A; Knottnerus, J

2002-01-01

Objective: To evaluate patient characteristics, symptoms, and examination findings in the clinical diagnosis of lumbosacral nerve root compression causing sciatica. Methods: The study involved 274 patients with pain radiating into the leg. All had a standardised clinical assessment and magnetic resonance (MR) imaging. The associations between patient characteristics, clinical findings, and lumbosacral nerve root compression on MR imaging were analysed. Results: Nerve root compression was associated with three patient characteristics, three symptoms, and four physical examination findings (paresis, absence of tendon reflexes, a positive straight leg raising test, and increased finger-floor distance). Multivariate analysis, analysing the independent diagnostic value of the tests, showed that nerve root compression was predicted by two patient characteristics, four symptoms, and two signs (increased finger-floor distance and paresis). The straight leg raise test was not predictive. The area under the curve of the receiver-operating characteristic was 0.80 for the history items. It increased to 0.83 when the physical examination items were added. Conclusions: Various clinical findings were found to be associated with nerve root compression on MR imaging. While this set of findings agrees well with those commonly used in daily practice, the tests tended to have lower sensitivity and specificity than previously reported. Stepwise multivariate analysis showed that most of the diagnostic information revealed by physical examination findings had already been revealed by the history items. PMID:11971050

Impact of an Early Decrease in Systolic Blood Pressure on The Risk of Contrast-Induced Nephropathy after Percutaneous Coronary Intervention.

PubMed

Li, Hualong; Huang, Shuijin; He, Yiting; Liu, Yong; Liu, Yuanhui; Chen, Jiyan; Zhou, Yingling; Tan, Ning; Duan, Chongyang; Chen, Pingyan

2016-02-01

The early postprocedural period was thought to be the rush hour of contrast media excretion, causing rapid and prolonged renal hypoperfusion, which was the critical time window for contrast-induced nephropathy (CIN). 349 consecutive patients were enrolled into the study. The relation between an early postprocedural decrease in systolic blood pressure (SBP) and the risk of CIN was assessed using multivariate logistic regression. A postprocedural decrease in SBP was observed in 63% of patients and CIN developed in 28 (8.0%) patients. The CIN group had a lower postprocedural SBP (114.5±13.5 vs. 123.7±15.6mmHg, P=0.003) and a greater postprocedural decrease in SBP (16.2±19.1 vs. 5.9±18.7mmHg, P=0.005) than the no-CIN group. ROC analysis revealed that the optimum cutoff value for the SBP decrease in detecting CIN was >10mmHg (sensitivity 60.7%, specificity 59.5%, AUC=0.66). Multivariate logistic regression analysis found that a postprocedural decrease in SBP >10mmHg was a significant independent predictor of CIN (OR 2.368, 95%CI: 1.043-5.379, P=0.039), after adjustment for other risk factors. An early moderate postprocedural decrease in SBP may increase the risk of CIN in patients undergoing PCI. Copyright © 2015. Published by Elsevier B.V.

A novel practical scoring for early diagnosis of traumatic bowel injury without obvious solid organ injury in hemodynamically stable patients.

PubMed

Zarour, Ahmad; El-Menyar, Ayman; Khattabi, Mazen; Tayyem, Raed; Hamed, Osama; Mahmood, Ismail; Abdelrahman, Husham; Chiu, William; Al-Thani, Hassan

2014-01-01

To develop a scoring tool based on clinical and radiological findings for early diagnosis and intervention in hemodynamically stable patients with traumatic bowel and mesenteric injury (TBMI) without obvious solid organ injury (SOI). A retrospective analysis was conducted for all traumatic abdominal injury patients in Qatar from 2008 to 2011. Data included demographics and clinical, radiological and operative findings. Multivariate logistic regression was performed to analyze the predictors for the need of therapeutic laparotomy. A total of 105 patients met the inclusion criteria with a mean age of 33 ± 15. Motor Vehicle Crashes (58%) and fall (21%) were the major MOI. Using Receiver operating characteristic curve, Z-score of >9 was the cutoff point (AUC = 0.98) for high probability of the presence of TBMI requiring surgical intervention. Z-Score >9 was found to have sensitivity (96.7%), specificity (97.4%), PPV (93.5%) and NPV (98.7%). Multivariate regression analysis found Z-score (>9) to be an independent predictor for the need of exploratory laparotomy (OR7.0; 95% CI: 2.46-19.78, p = 0.001). This novel tool for early diagnosis of TBMI is found to be simple and helpful in selecting stable patients with free intra-abdominal fluid without SOI for exploratory Laparotomy. However, further prospective studies are warranted. Copyright © 2014 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.

The accuracy of preoperative axillary nodal staging in primary breast cancer by ultrasound is modified by nodal metastatic load and tumor biology

PubMed Central

Dihge, Looket; Grabau, Dorthe A.; Rasmussen, Rogvi W.; Bendahl, Pär-Ola; Rydén, Lisa

2016-01-01

Abstract Background The outcome of axillary ultrasound (AUS) with fine-needle aspiration biopsy (FNAB) in the diagnostic work-up of primary breast cancer has an impact on therapy decisions. We hypothesize that the accuracy of AUS is modified by nodal metastatic burden and clinico-pathological characteristics. Material and methods The performance of AUS and AUS-guided FNAB for predicting nodal metastases was assessed in a prospective breast cancer cohort subjected for surgery during 2009–2012. Predictors of accuracy were included in multivariate analysis. Results AUS had a sensitivity of 23% and a specificity of 95%, while AUS-guided FNAB obtained 73% and 100%, respectively. AUS-FNAB exclusively detected macro-metastases (median four metastases) and identified patients with more extensive nodal metastatic burden in comparison with sentinel node biopsy. The accuracy of AUS was affected by metastatic size (OR 1.11), obesity (OR 2.46), histological grade (OR 4.43), and HER2-status (OR 3.66); metastatic size and histological grade were significant in the multivariate analysis. Conclusions The clinical utility of AUS in low-risk breast cancer deserves further evaluation as the accuracy decreased with a low nodal metastatic burden. The diagnostic performance is modified by tumor and clinical characteristics. Patients with nodal disease detected by AUS-FNAB represent a group for whom neoadjuvant therapy should be considered. PMID:27050668

Conventional MRI features for predicting the clinical outcome of patients with invasive placenta

PubMed Central

Chen, Ting; Xu, Xiao-Quan; Shi, Hai-Bin; Yang, Zheng-Qiang; Zhou, Xin; Pan, Yi

2017-01-01

PURPOSE We aimed to evaluate whether morphologic magnetic resonance imaging (MRI) features could help to predict the maternal outcome after uterine artery embolization (UAE)-assisted cesarean section (CS) in patients with invasive placenta previa. METHODS We retrospectively reviewed the MRI data of 40 pregnant women who have undergone UAE-assisted cesarean section due to suspected high risk of massive hemorrhage caused by invasive placenta previa. Patients were divided into two groups based on the maternal outcome (good-outcome group: minor hemorrhage and uterus preserved; poor-outcome group: significant hemorrhage or emergency hysterectomy). Morphologic MRI features were compared between the two groups. Multivariate logistic regression analysis was used to identify the most valuable variables, and predictive value of the identified risk factor was determined. RESULTS Low signal intensity bands on T2-weighted imaging (P < 0.001), placenta percreta (P = 0.011), and placental cervical protrusion sign (P = 0.002) were more frequently observed in patients with poor outcome. Low signal intensity bands on T2-weighted imaging was the only significant predictor of poor maternal outcome in multivariate analysis (P = 0.020; odds ratio, 14.79), with 81.3% sensitivity and 84.3% specificity. CONCLUSION Low signal intensity bands on T2-weighted imaging might be a predictor of poor maternal outcome after UAE-assisted cesarean section in patients with invasive placenta previa. PMID:28345524

Severe lymphopenia during neoadjuvant chemoradiation for esophageal cancer: A propensity matched analysis of the relative risk of proton versus photon-based radiation therapy.

PubMed

Shiraishi, Yutaka; Fang, Penny; Xu, Cai; Song, Juhee; Krishnan, Sunil; Koay, Eugene J; Mehran, Reza J; Hofstetter, Wayne L; Blum-Murphy, Mariela; Ajani, Jaffer A; Komaki, Ritsuko; Minsky, Bruce; Mohan, Radhe; Hsu, Charles C; Hobbs, Brian P; Lin, Steven H

2017-12-13

Circulating lymphocytes are exquisitely sensitive to radiation exposure, even to low scattered doses which can vary drastically between radiation modalities. We compared the relative risk of radiation-induced lymphopenia between intensity modulated radiation therapy (IMRT) or proton beam therapy (PBT) in esophageal cancer (EC) patients undergoing neoadjuvant chemoradiation therapy (nCRT). EC patients treated with IMRT and PBT were propensity matched based on key clinical variables. Treatment-associated lymphopenia was graded using CTCAE v.4.0. Using matched cohorts, univariate and multivariable multiple logistic regression was used to identify factors associated with increased risk of grade 4 lymphopenia as well as characterize their relative contributions. Among the 480 patients treated with nCRT, 136 IMRT patients were propensity score matched with 136 PBT patients. In the matched groups, a greater proportion of the IMRT patients (55/136, 40.4%) developed grade 4 lymphopenia during nCRT compared with the PBT patients (24/136, 17.6%, P < 0.0001). On multivariable analysis, PBT was significantly associated with a reduction in grade 4 lymphopenia risk (odds ratio, 0.29; 95% confidence interval, 0.16-0.52; P < 0.0001). PBT is associated with significant risk reduction in grade 4 lymphopenia during nCRT in esophageal cancer. Copyright © 2017 Elsevier B.V. All rights reserved.

Hair sterol signatures coupled to multivariate data analysis reveal an increased 7β-hydroxycholesterol production in cognitive impairment.

PubMed

Son, Hyun-Hwa; Lee, Do-Yup; Seo, Hong Seog; Jeong, Jihyeon; Moon, Ju-Yeon; Lee, Jung-Eun; Chung, Bong Chul; Kim, Eosu; Choi, Man Ho

2016-01-01

Altered cholesterol metabolism could be associated with cognitive impairment. The quantitative profiling of 19 hair sterols was developed using gas chromatography-mass spectrometry coupled to multivariate data analysis. The limit of quantification of all sterols ranged from 5 to 20 ng/g, while the calibration linearity was higher than 0.98. The precision (% CV) and accuracy (% bias) ranged from 3.2% to 9.8% and from 83.2% to 119.4%, respectively. Among the sterols examined, 8 were quantitatively detected from two strands of 3-cm-long scalp hair samples of female participants, including mild cognitive impairment (MCI, n=15), Alzheimer's disease (AD, n=31), and healthy controls (HC, n=36). The cognitive impairment (MCI or AD) was correlated with a higher metabolic rate than that of HCs based on 7β-hydroxycholesterol (P<0.005). Significant negative correlations (r=-0.822) were detected between Mini-Mental State Examination (MMSE) scores and hair sample metabolic ratios of 7β-hydroxycholesterol to cholesterol, which is an accepted, sensitive, and specific tool for discriminating HCs from individuals with MCI or AD. In conclusion, improved diagnostic values can be obtained using hair sterol signatures coupled with MMSE scores. This method may prove useful for predictive diagnosis in population screening of cognitive impairment. Copyright © 2015 Elsevier Ltd. All rights reserved.

Evaluation of Uncertainty and Sensitivity in Environmental Modeling at a Radioactive Waste Management Site

NASA Astrophysics Data System (ADS)

Stockton, T. B.; Black, P. K.; Catlett, K. M.; Tauxe, J. D.

2002-05-01

Environmental modeling is an essential component in the evaluation of regulatory compliance of radioactive waste management sites (RWMSs) at the Nevada Test Site in southern Nevada, USA. For those sites that are currently operating, further goals are to support integrated decision analysis for the development of acceptance criteria for future wastes, as well as site maintenance, closure, and monitoring. At these RWMSs, the principal pathways for release of contamination to the environment are upward towards the ground surface rather than downwards towards the deep water table. Biotic processes, such as burrow excavation and plant uptake and turnover, dominate this upward transport. A combined multi-pathway contaminant transport and risk assessment model was constructed using the GoldSim modeling platform. This platform facilitates probabilistic analysis of environmental systems, and is especially well suited for assessments involving radionuclide decay chains. The model employs probabilistic definitions of key parameters governing contaminant transport, with the goals of quantifying cumulative uncertainty in the estimation of performance measures and providing information necessary to perform sensitivity analyses. This modeling differs from previous radiological performance assessments (PAs) in that the modeling parameters are intended to be representative of the current knowledge, and the uncertainty in that knowledge, of parameter values rather than reflective of a conservative assessment approach. While a conservative PA may be sufficient to demonstrate regulatory compliance, a parametrically honest PA can also be used for more general site decision-making. In particular, a parametrically honest probabilistic modeling approach allows both uncertainty and sensitivity analyses to be explicitly coupled to the decision framework using a single set of model realizations. For example, sensitivity analysis provides a guide for analyzing the value of collecting more information by quantifying the relative importance of each input parameter in predicting the model response. However, in these complex, high dimensional eco-system models, represented by the RWMS model, the dynamics of the systems can act in a non-linear manner. Quantitatively assessing the importance of input variables becomes more difficult as the dimensionality, the non-linearities, and the non-monotonicities of the model increase. Methods from data mining such as Multivariate Adaptive Regression Splines (MARS) and the Fourier Amplitude Sensitivity Test (FAST) provide tools that can be used in global sensitivity analysis in these high dimensional, non-linear situations. The enhanced interpretability of model output provided by the quantitative measures estimated by these global sensitivity analysis tools will be demonstrated using the RWMS model.

Multivariate frequency domain analysis of protein dynamics

NASA Astrophysics Data System (ADS)

Matsunaga, Yasuhiro; Fuchigami, Sotaro; Kidera, Akinori

2009-03-01

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

Imaging of polysaccharides in the tomato cell wall with Raman microspectroscopy

PubMed Central

2014-01-01

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

A refined method for multivariate meta-analysis and meta-regression

PubMed Central

Jackson, Daniel; Riley, Richard D

2014-01-01

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

Prevalence of allergic sensitization in the U.S.: Results from the National Health and Nutrition Examination Survey (NHANES) 2005–2006

PubMed Central

Salo, Päivi M.; Arbes, Samuel J.; Jaramillo, Renee; Calatroni, Agustin; Weir, Charles H.; Sever, Michelle L.; Hoppin, Jane A.; Rose, Kathryn M.; Liu, Andrew H.; Gergen, Peter J.; Mitchell, Herman E.; Zeldin, Darryl C.

2014-01-01

Background Allergic sensitization is an important risk factor for the development of atopic disease. The National Health and Nutrition Examination Survey (NHANES) 2005–2006 provides the most comprehensive information on IgE-mediated sensitization in the general US population. Objective We investigated clustering, sociodemographic and regional patterns of allergic sensitization and examined risk factors associated with IgE-mediated sensitization. Methods Data for this cross-sectional analysis were obtained from NHANES 2005–2006. Participants aged ≥1 year (N=9440) were tested for sIgEs to inhalant and food allergens; participants ≥6 years were tested for 19 sIgEs, and children aged 1–5 years for 9 sIgEs. Serum samples were analyzed using the ImmunoCAP System. Information on demographics and participant characteristics was collected by questionnaire. Results Of the study population aged 6 and older, 44.6% had detectable sIgEs, while 36.2% of children aged 1–5 years were sensitized to ≥1 allergen. Allergen-specific IgEs clustered into 7 groups that might have largely reflected biological cross-reactivity. Although sensitization to individual allergens and allergen types showed regional variation, the overall prevalence of sensitization did not differ across census regions, except in early childhood. In multivariate modeling, young age, male gender, non-Hispanic black race/ethnicity, geographic location (census region), and reported pet avoidance measures were most consistently associated with IgE-mediated sensitization. Conclusions The overall prevalence of allergic sensitization does not vary across US census regions, except in early life, although allergen-specific sensitization differs by sociodemographic and regional factors. Biological cross-reactivity may be an important, but not a sole, contributor to the clustering of allergen-specific IgEs. Clinical implications IgE-mediated sensitization shows clustering patterns and differs by sociodemographic and regional factors, but the overall prevalence of sensitization may not vary across US census regions. PMID:24522093

Checking the predictive accuracy of basic symptoms against ultra high-risk criteria and testing of a multivariable prediction model: Evidence from a prospective three-year observational study of persons at clinical high-risk for psychosis.

PubMed

Hengartner, M P; Heekeren, K; Dvorsky, D; Walitza, S; Rössler, W; Theodoridou, A

2017-09-01

The aim of this study was to critically examine the prognostic validity of various clinical high-risk (CHR) criteria alone and in combination with additional clinical characteristics. A total of 188 CHR positive persons from the region of Zurich, Switzerland (mean age 20.5 years; 60.2% male), meeting ultra high-risk (UHR) and/or basic symptoms (BS) criteria, were followed over three years. The test battery included the Structured Interview for Prodromal Syndromes (SIPS), verbal IQ and many other screening tools. Conversion to psychosis was defined according to ICD-10 criteria for schizophrenia (F20) or brief psychotic disorder (F23). Altogether n=24 persons developed manifest psychosis within three years and according to Kaplan-Meier survival analysis, the projected conversion rate was 17.5%. The predictive accuracy of UHR was statistically significant but poor (area under the curve [AUC]=0.65, P<.05), whereas BS did not predict psychosis beyond mere chance (AUC=0.52, P=.730). Sensitivity and specificity were 0.83 and 0.47 for UHR, and 0.96 and 0.09 for BS. UHR plus BS achieved an AUC=0.66, with sensitivity and specificity of 0.75 and 0.56. In comparison, baseline antipsychotic medication yielded a predictive accuracy of AUC=0.62 (sensitivity=0.42; specificity=0.82). A multivariable prediction model comprising continuous measures of positive symptoms and verbal IQ achieved a substantially improved prognostic accuracy (AUC=0.85; sensitivity=0.86; specificity=0.85; positive predictive value=0.54; negative predictive value=0.97). We showed that BS have no predictive accuracy beyond chance, while UHR criteria poorly predict conversion to psychosis. Combining BS with UHR criteria did not improve the predictive accuracy of UHR alone. In contrast, dimensional measures of both positive symptoms and verbal IQ showed excellent prognostic validity. A critical re-thinking of binary at-risk criteria is necessary in order to improve the prognosis of psychotic disorders. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Ecological effects of spring and late summer applications of lambda-cyhalothrin on freshwater microcosms.

PubMed

Van Wijngaarden, R P A; Brock, T C M; van den Brink, P J; Gylstra, R; Maund, S J

2006-02-01

The aim of the study was to compare the effects of the pyrethroid insecticide lambda-cyhalothrin (treated at 10, 25, 50, 100, 250 ng active ingredient a.i./L) on a drainage ditch ecosystem in spring and late summer. Microcosms (water volume approximately 430 L) were established using enclosures in a 50-cm-deep experimental ditch system containing communities typical of macrophyte-dominated freshwater ecosystems. Effects on macroinvertebrates, zooplankton, phytoplankton, macrophytes, and community metabolism were assessed and evaluated using univariate and multivariate statistical techniques. The macroinvertebrate community responded most clearly to treatment and, as anticipated, insects and crustaceans were among the most sensitive organisms. Statistical analysis showed that the underlying community structure was significantly different between the spring and summer experiments. However, the most sensitive species (Chaoborus obscuripes and Gammarus pulex) were abundant in spring as well as in late summer. In spring and late summer, only slight and transient effects were observed at the community level in the 10-ng/L treatment. Overall, the study did not show substantial differences in the responses of sensitive taxa between spring and late summer treatments, and effects thresholds were similar irrespective of season of treatment.

The Sensitivity of Adverse Event Cost Estimates to Diagnostic Coding Error

PubMed Central

Wardle, Gavin; Wodchis, Walter P; Laporte, Audrey; Anderson, Geoffrey M; Baker, Ross G

2012-01-01

Objective To examine the impact of diagnostic coding error on estimates of hospital costs attributable to adverse events. Data Sources Original and reabstracted medical records of 9,670 complex medical and surgical admissions at 11 hospital corporations in Ontario from 2002 to 2004. Patient specific costs, not including physician payments, were retrieved from the Ontario Case Costing Initiative database. Study Design Adverse events were identified among the original and reabstracted records using ICD10-CA (Canadian adaptation of ICD10) codes flagged as postadmission complications. Propensity score matching and multivariate regression analysis were used to estimate the cost of the adverse events and to determine the sensitivity of cost estimates to diagnostic coding error. Principal Findings Estimates of the cost of the adverse events ranged from $16,008 (metabolic derangement) to $30,176 (upper gastrointestinal bleeding). Coding errors caused the total cost attributable to the adverse events to be underestimated by 16 percent. The impact of coding error on adverse event cost estimates was highly variable at the organizational level. Conclusions Estimates of adverse event costs are highly sensitive to coding error. Adverse event costs may be significantly underestimated if the likelihood of error is ignored. PMID:22091908

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

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

2013-01-01

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

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.

Factors Associated With Provider Burnout in the NICU

PubMed Central

Phibbs, Ciaran S.; Sexton, J. Bryan; Kan, Peiyi; Sharek, Paul J.; Nisbet, Courtney C.; Rigdon, Joseph; Trockel, Mickey; Profit, Jochen

2017-01-01

BACKGROUND: NICUs vary greatly in patient acuity and volume and represent a wide array of organizational structures, but the effect of these differences on NICU providers is unknown. This study sought to test the relation between provider burnout prevalence and organizational factors in California NICUs. METHODS: Provider perceptions of burnout were obtained from 1934 nurse practitioners, physicians, registered nurses, and respiratory therapists in 41 California NICUs via a validated 4-item questionnaire based on the Maslach Burnout Inventory. The relations between burnout and organizational factors of each NICU were evaluated via t-test comparison of quartiles, univariable regression, and multivariable regression. RESULTS: Overall burnout prevalence was 26.7% ± 9.8%. Highest burnout prevalence was found among NICUs with higher average daily admissions (32.1% ± 6.4% vs 17.2% ± 6.7%, P < .001), higher average occupancy (28.1% ± 8.1% vs 19.9% ± 8.4%, P = .02), and those with electronic health records (28% ± 11% vs 18% ± 7%, P = .03). In sensitivity analysis, nursing burnout was more sensitive to organizational differences than physician burnout in multivariable modeling, significantly associated with average daily admissions, late transfer proportion, nursing hours per patient day, and mortality per 1000 infants. Burnout prevalence showed no association with proportion of high-risk patients, teaching hospital distinction, or in-house attending presence. CONCLUSIONS: Burnout is most prevalent in NICUs with high patient volume and electronic health records and may affect nurses disproportionately. Interventions to reduce burnout prevalence may be of greater importance in NICUs with ≥10 weekly admissions. PMID:28557756

Comparison of Various Anthropometric Indices as Risk Factors for Hearing Impairment in Asian Women

PubMed Central

Lee, Kyu Yup; Choi, Eun Woo; Do, Jun Young

2015-01-01

Background The objective of the present study was to examine the associations between various anthropometric measures and metabolic syndrome and hearing impairment in Asian women. Methods We identified 11,755 women who underwent voluntary routine health checkups at Yeungnam University Hospital between June 2008 and April 2014. Among these patients, 2,485 participants were <40 years old, and 1,072 participants lacked information regarding their laboratory findings or hearing and were therefore excluded. In total 8,198 participants were recruited into our study. Results The AUROC value for metabolic syndrome was 0.790 for the waist to hip ratio (WHR). The cutoff value was 0.939. The sensitivity and specificity for predicting metabolic syndrome were 72.7% and 71.7%, respectively. The AUROC value for hearing loss was 0.758 for WHR. The cutoff value was 0.932. The sensitivity and specificity for predicting hearing loss were 65.8% and 73.4%, respectively. The WHR had the highest AUC and was the best predictor of metabolic syndrome and hearing loss. Univariate and multivariate linear regression analyses showed that WHR levels were positively associated with four hearing thresholds including averaged hearing threshold and low, middle, and high frequency thresholds. In addition, multivariate logistic analysis revealed that those with a high WHR had a 1.347–fold increased risk of hearing loss compared with the participants with a low WHR. Conclusion Our results demonstrated that WHR may be a surrogate marker for predicting the risk of hearing loss resulting from metabolic syndrome. PMID:26575369

Behavioral biomarkers of aging: illustration of a multivariate approach for detecting age-related behavioral changes.

PubMed

Markowska, A L; Breckler, S J

1999-12-01

The goal of the current project is to develop a multivariate statistical strategy for the formation of behavioral indices of performance and, further, to apply this strategy to establish the relationship between age and important characteristics of performance. The strategy was to begin with a large set of measures that span a broad range of behaviors. The behavioral effects of the following variables were examined: Age (4, 12, 24, and 30 months), genotype [Fischer 344 and a hybrid (F1) of Fischer 344 and Brown Norway (F344xBN)], gender (Fischer 344 males and Fischer 344 females), long-term diet (ad lib diet or dietary restriction beginning at 4 months of age), and short-term diet (ad lib diet or dietary restriction during testing). The behavioral measures were grouped into conceptually related indicators. The indicators within a set were submitted to a principal component analysis to help identify the summary indices of performance, which were formed with the assumption that these component scores would offer more reliable and valid measures of relevant aspects of behavioral performance than would individual measures taken alone. In summary, this approach has made a number of important contributions. It has provided sensitive and selective measures of performance that indicated contributions of all variables: psychological process, age, genotype, gender, long-term and short-term diet and has increased the sensitivity of behavioral measures to age-related behavioral impairment. It has also improved task-manageability by decreasing the number of meaningful variables without losing important information, consequently providing a simplification of the pattern of changes.

CT-guided transthoracic core needle biopsy for small pulmonary lesions: diagnostic performance and adequacy for molecular testing.

PubMed

Tian, Panwen; Wang, Ye; Li, Lei; Zhou, Yongzhao; Luo, Wenxin; Li, Weimin

2017-02-01

Computed tomography (CT)-guided transthoracic needle biopsy is a well-established, minimally invasive diagnostic tool for pulmonary lesions. Few large studies have been conducted on the diagnostic performance and adequacy for molecular testing of transthoracic core needle biopsy (TCNB) for small pulmonary lesions. This study included CT-guided TCNB with 18-gauge cutting needles in 560 consecutive patients with small (≤3 cm) pulmonary lesions from January 2012 to January 2015. There were 323 males and 237 females, aged 51.8±12.7 years. The size of the pulmonary lesions was 1.8±0.6 cm. The sensitivity, specificity, accuracy and complications of the biopsies were investigated. The risk factors of diagnostic failure were assessed using univariate and multivariate analyses. The sample's adequacy for molecular testing of non-small cell lung cancer (NSCLC) was analyzed. The overall sensitivity, specificity, and accuracy for diagnosis of malignancy were 92.0% (311/338), 98.6% (219/222), and 94.6% (530/560), respectively. The incidence of bleeding complications was 22.9% (128/560), and the incidence of pneumothorax was 10.4% (58/560). Logistic multivariate regression analysis showed that the independent risk factors for diagnostic failure were a lesion size ≤1 cm [odds ratio (OR), 3.95; P=0.007], lower lobe lesions (OR, 2.83; P=0.001), and pneumothorax (OR, 1.98; P=0.004). Genetic analysis was successfully performed on 95.45% (168/176) of specimens diagnosed as NSCLC. At least 96.8% of samples with two or more passes from a lesion were sufficient for molecular testing. The diagnostic yield of small pulmonary lesions by CT-guided TCNB is high, and the procedure is relatively safe. A lesion size ≤1 cm, lower lobe lesions, and pneumothorax are independent risk factors for biopsy diagnostic failure. TCNB specimens could provide adequate tissues for molecular testing.

Are regional variations in activity of dispatcher-assisted cardiopulmonary resuscitation associated with out-of-hospital cardiac arrests outcomes? A nation-wide population-based cohort study.

PubMed

Nishi, Taiki; Kamikura, Takahisa; Funada, Akira; Myojo, Yasuhiro; Ishida, Tetsuya; Inaba, Hideo

2016-01-01

Dispatcher-assisted cardiopulmonary resuscitation (DA-CPR) impacts the rates of bystander CPR (BCPR) and survival after out-of-hospital cardiac arrests (OHCAs). This study aimed to elucidate whether regional variations in indexes for BCPR and emergency medical service (EMS) may be associated with OHCA outcomes. We conducted a population-based observational study involving 157,093 bystander-witnessed, resuscitation-attempted OHCAs without physician involvement between 2007 and 2011. For each index of BCPR and EMS, we classified the 47 prefectures into the following three groups: advanced, intermediate, and developing regions. Nominal logit analysis followed by multivariable logistic regression including OHCA backgrounds was employed to examine the association between neurologically favourable 1-month survival, and regional classifications based on BCPR- and EMS-related indexes. Logit analysis including all regional classifications revealed that the number of BLS training course participants per population or bystander's own performance of BCPR without DA-CPR was not associated with the survival. Multivariable logistic regression including the OHCA backgrounds known to be associated with survival (BCPR provision, arrest aetiology, initial rhythm, patient age, time intervals of witness-to-call and call-to-arrival at patient), the following regional classifications based on DA-CPR but not on EMS were associated with survival: sensitivity of DA-CPR [adjusted odds ratio (95% confidence intervals) for advanced region; those for intermediate region, with developing region as reference, 1.277 (1.131-1.441); 1.162 (1.058-1.277)]; the proportion of bystanders to follow DA-CPR [1.749 (1.554-1.967); 1.280 (1.188-1.380)]. Good outcomes of bystander-witnessed OHCAs correlate with regions having higher sensitivity of DA-CPR and larger proportion of bystanders to follow DA-CPR. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Diffusion-weighted imaging of the prostate: should we use quantitative metrics to better characterize focal lesions originating in the peripheral zone?

PubMed

Pierre, Thibaut; Cornud, Francois; Colléter, Loïc; Beuvon, Frédéric; Foissac, Frantz; Delongchamps, Nicolas B; Legmann, Paul

2018-05-01

To compare inter-reader concordance and accuracy of qualitative diffusion-weighted (DW) PIRADSv2.0 score with those of quantitative DW-MRI for the diagnosis of peripheral zone prostate cancer. Two radiologists independently assigned a DW-MRI-PIRADS score to 92 PZ-foci, in 74 patients (64.3±5.6 years old; median PSA level: 8 ng/ml, normal DRE in 70 men). A standardised ADCmean and nine ADC-derived parameters were measured, including ADCratios with the whole-prostate (WP-ADCratio) or the mirror-PZ (mirror-ADCratio) as reference areas. Surgical histology and MRI-TRUS fusion-biopsy were the reference for tumours and benign foci, respectively. Inter-reader agreement was assessed by the Cohen-kappa-coefficient and the intraclass correlation coefficient (ICC). Univariate-multivariate regressions determined the most predictive factor for cancer. Fifty lesions were malignant. Inter-reader concordance was fair for qualitative assessment, but excellent for quantitative assessment for all quantitative variables. At univariate analysis, ADCmean, WP-ADCratio and WL-ADCmean performed equally, but significantly better than the mirror-ADCratio (p<0.001). At multivariate analysis, the only independent variable significantly associated with malignancy was the whole-prostate-ADCratio. At a cut-off value of 0.68, sensitivity was 94-90 % and specificity was 60-38 % for readers 1 and 2, respectively. The whole-prostate-ADCratio improved the qualitative inter-reader concordance and characterisation of focal PZ-lesions. • Inter-reader concordance of DW PI-RADSv2.0 score for PZ lesions was only fair. • Using a standardised ADCmean measurement and derived DW-quantitative parameters, concordance was excellent. • The whole-prostate ADCratio performed significantly better than the mirror-ADCratio for cancer detection. • At a cut-off of 0.68, sensitivity values of WP-ADCratio were 94-90 %. • The whole-prostate ADCratio may circumvent variations of ADC metrics across centres.

The impact of resident involvement on post-operative morbidity and mortality following orthopaedic procedures: a study of 43,343 cases.

PubMed

Schoenfeld, Andrew J; Serrano, Jose A; Waterman, Brian R; Bader, Julia O; Belmont, Philip J

2013-11-01

Few studies have addressed the role of residents' participation in morbidity and mortality after orthopaedic surgery. The present study utilized the 2005-2010 National Surgical Quality Improvement Program (NSQIP) dataset to assess the risk of 30-day post-operative complications and mortality associated with resident participation in orthopaedic procedures. The NSQIP dataset was queried using codes for 12 common orthopaedic procedures. Patients identified as having received one of the procedures had their records abstracted to obtain demographic data, medical history, operative time, and resident involvement in their surgical care. Thirty-day post-operative outcomes, including complications and mortality, were assessed for all patients. A step-wise multivariate logistic regression model was constructed to evaluate the impact of resident participation on mortality- and complication-risk while controlling for other factors in the model. Primary analyses were performed comparing cases where the attending surgeon operated alone to all other case designations, while a subsequent sensitivity analysis limited inclusion to cases where resident participation was reported by post-graduate year. In the NSQIP dataset, 43,343 patients had received one of the 12 orthopaedic procedures queried. Thirty-five percent of cases were performed with resident participation. The mortality rate, overall, was 2.5 and 10 % sustained one or more complications. Multivariate analysis demonstrated a significant association between resident participation and the risk of one or more complications [OR 1.3 (95 % CI 1.1, 1.4); p < 0.001] as well as major systemic complications [OR 1.6 (95 % CI 1.3, 2.0); p < 0.001] for primary joint arthroplasty procedures only. These findings persisted even after sensitivity testing. A mild to moderate risk for complications was noted following resident involvement in joint arthroplasty procedures. No significant risk of post-operative morbidity or mortality was appreciated for the other orthopaedic procedures studied. II (Prognostic).

Anti-MCV antibodies predict radiographic progression in Greek patients with very early (<3 months duration) rheumatoid arthritis.

PubMed

Barouta, Georgia; Katsiari, Christina G; Alexiou, Ioannis; Liaskos, Christos; Varna, Areti; Bogdanos, Dimitrios P; Germenis, Anastasios E; Sakkas, Lazaros I

2017-04-01

This study aimed to assess the diagnostic and prognostic value of anti-mutated citrullinated vimentin (MCV) antibodies in very early rheumatoid arthritis (VERA) and in established rheumatoid arthritis (RA). Seventy-one patients with undifferentiated arthritis (UA) of <3 months duration, 141 with established RA, 53 with other rheumatic diseases, and 40 healthy individuals were included in the study. Anti-MCV, anti-cyclic citrullinated peptide (CCP) antibodies, and rheumatoid factor (RF) were determined and hand radiographs were recorded. Patients were assessed prospectively for 2 years, and hand radiographs were repeated. Diagnostic performance of anti-MCV was studied with receiver operating characteristic (ROC) curves and evaluation of sensitivity, specificity, and likelihood ratios. Forty-six percent of UA patients progressed to RA at 2 years. In VERA patients, sensitivity of anti-MCV was 52 %, compared to 44 % of anti-CCP and 37 % of RF, while specificity was 91 %, compared to 91 % of RF and 84 % of anti-CCP. Anti-MCV were detected in 25 % of VERA patients negative for both anti-CCP and RF. In established RA, anti-MCV did not sustain its diagnostic performance. By multivariable analysis, anti-MCV, but not anti-CCP or RF, showed significant correlation with radiographic progression in VERA patients. In established RA, anti-MCV, anti-CCP, and RF were associated with active disease (p ≤ 0.03) and joint damage (p ≤ 0.004). By multivariate analysis, the strongest factors for radiographic damage were disease duration (p = 0.000), HAQ score (p = 0.000), and RF (p = 0.002). In conclusion, in patients with very early UA, anti-MCV predict both progression to RA and radiological damage, and therefore, anti-MCV antibody testing may be useful in every day practice.

Demographics and clinical features predictive of allergic versus non-allergic rhinitis in children aged 6-18 years: A single-center experience of 1535 patients.

PubMed

La Mantia, Ignazio; Andaloro, Claudio

2017-07-01

Chronic rhinitis (CR) is one of the most common causes accounting for lost-school days, absenteeism and resource utilization in pediatric patients. Distinction between common causes of CR, allergic (AR)and non-allergic rhinitis (NAR), based upon clinical features is critical, especially in primary care settings or facilities with lack of allergen sensitivity testing, as management strategies differ considerably. The current study elucidates clinical factors, particularly facial features associated with AR and NAR using a large cohort. In a retrospective cohort analysis of pediatric patients aged 6-18 years, we assessed patient demographics, clinical symptoms, and signs associated with allergic rhinitis using multivariable regression techniques. Overall, 1490 patients (mean age: 10.11 ± 3.31 years; 48% female; 69% AR and 31% NAR) were included in the study. In multivariable regression analysis, major clinical features associated with AR were: sneezing (OR: 3.53; 95% CI: 2.35-5.32; p < 0.001), rhinorrhea (OR: 1.77; 95% CI: 1.18-2.66; p = 0.006), nasal itching (OR: 17.88; 95% CI: 11.92-26.83; p < 0.001), horizontal nasal crease (OR: 5.09; 95% CI: 1.29-20.01; p = 0.020) and conjunctivitis (OR: 4.66; 95% CI: 3.28-6.62; p < 0.001). On the contrary, we noted presence of Dennie-Morgan fold (OR: 1.67; 95% CI: 1.11-2.56; p = 0.014), moderate to severe persistent or intermittent symptoms to be likely associated with NAR than AR. In pediatric patients presenting with symptoms of rhinitis, facial hallmarks serve as an adjunct to sensitivity testing in establishing a diagnosis as well as differentiating between NAR from AR, albeit individualized upon patient history and clinical features. 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.

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...

An obstetric sphincter injury risk identification system (OSIRIS): is this a clinically useful tool?

PubMed

Webb, Sara S; Hemming, Karla; Khalfaoui, Madhi Y; Henriksen, Tine Brink; Kindberg, Sara; Stensgaard, Stine; Kettle, Christine; Ismail, Khaled M K

2017-03-01

To establish the contribution of maternal, fetal and intrapartum factors to the risk of incidence of obstetric anal sphincter injuries (OASIS) and assess the feasibility of an OASIS risk prediction model based on variables available to clinicians prior to birth. This was a population-based, retrospective cohort study using single-site data from the birth database of Aarhus University Hospital, Denmark. The participants were all women who had a singleton vaginal birth during the period 1989 to 2006. Univariate and multivariate logistic regression analyses were performed using multiple imputations for missing data and internally validated using bootstrap methods. The main outcome measures were the contributions of maternal, fetal and intrapartum events to the incidence of OASIS. A total of 71,469 women met the inclusion criteria, of whom 1,754 (2.45 %) sustained OASIS. In the multivariate analysis of variables known prior to birth, maternal age 20 - 30 years (OR 1.65, 95 % CI 1.44 - 1.89) and ≥30 years (OR 1.60, 95 % CI 1.39 - 1.85), occipitoposterior fetal position (OR 1.34, 95 % CI 1.06 - 1.70), induction/augmentation of labour (OR 1.46, 95 % CI 1.32 - 1.62), and suspected macrosomia (OR 2.20, 95 % CI 1.97 - 2.45) were independent significant predictors of OASIS, with increasing parity conferring a significant protective effect. The 'prebirth variable' model showed a 95 % sensitivity and a 24 % specificity in predicting OASIS with 1 % probability, and a 3 % sensitivity and a 99 % specificity in predicting OASIS with a 10 % probability. Our model identified several significant OASIS risk factors that are known prior to actual birth. The prognostic model shows potential for ruling out OASIS (high sensitivity with a low risk cut-off value), but is not useful for ruling in the event.

Detection of prostate cancer index lesions with multiparametric magnetic resonance imaging (mp-MRI) using whole-mount histological sections as the reference standard.

PubMed

Russo, Filippo; Regge, Daniele; Armando, Enrico; Giannini, Valentina; Vignati, Anna; Mazzetti, Simone; Manfredi, Matteo; Bollito, Enrico; Correale, Loredana; Porpiglia, Francesco

2016-07-01

To evaluate the sensitivity of multiparametric magnetic resonance imaging (mp-MRI) for detecting prostate cancer foci, including the largest (index) lesions. In all, 115 patients with biopsy confirmed prostate cancer underwent mp-MRI before radical prostatectomy. A single expert radiologist recorded all prostate cancer foci including the index lesion 'blinded' to the pathologist's biopsy report. Stained whole-mount histological sections were used as the reference standard. All lesions were contoured by an experienced uropathologist who assessed their volume and pathological Gleason score. All lesions with a volume of >0.5 mL and/or pathological Gleason score of >6 were defined as clinically significant prostate cancer. Multivariate analysis was used to ascertain the characteristics of lesions identified by MRI. In all, 104 of 115 index lesions were correctly diagnosed by mp-MRI (sensitivity 90.4%; 95% confidence interval [CI] 83.5-95.1%), including 98/105 clinically significant index lesions (93.3%; 95% CI 86.8-97.3%), among which three of three lesions had a volume of <0.5 mL and Gleason score of >6. Overall, mp-MRI detected 131/206 lesions including 13 of 68 'insignificant' prostate cancers. The multivariate logistic regression modelling showed that pathological Gleason score (odds ratio [OR] 11.7, 95% CI 2.3-59.8; P = 0.003) and lesion volume (OR 4.24, 95% CI 1.3-14.7; P = 0.022) were independently associated with the detection of index lesions at MRI. This study shows that mp-MRI has a high sensitivity for detecting clinically significant prostate cancer index lesions, while having disappointing results for the detection of small-volume, low Gleason score prostate cancer foci. Thus, mp-MRI could be used to stratify patients according to risk, allowing better treatment selection. © 2015 The Authors BJU International © 2015 BJU International Published by John Wiley & Sons Ltd.

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.

Utility of CT Findings in the Diagnosis of Cecal Volvulus.

PubMed

Dane, Bari; Hindman, Nicole; Johnson, Evan; Rosenkrantz, Andrew B

2017-10-01

The objective of our study was to assess the utility of CT features in the diagnosis of cecal volvulus. Forty-three patients undergoing CT for cecal volvulus and with surgical or clinical follow-up were included. Two radiologists (11 years and 1 year of experience) evaluated CT examinations for the following: whirl sign, abnormal cecal position, "bird beak" sign, severe cecal distention, mesenteric engorgement, a newly described "central appendix" sign (defined as abnormal appendix position near midline), and overall impression for cecal volvulus. Univariable and multivariable assessments were performed. Patients with CT examinations in which the appendix was not visible were excluded from calculations involving the central appendix sign. Fifty-one percent (n = 22) of patients had cecal volvulus. All CT findings were significantly more common in patients with cecal volvulus (p < 0.01) other than mesenteric engorgement for reader 1 (p = 0.332). Readers 1 and 2 identified the central appendix sign in 92.9% and 92.3% of patients with volvulus versus in 37.5 and 31.1% of patients without volvulus. The whirl sign exhibited a sensitivity for cecal volvulus of 90.9% for reader 1 and 95.5% for reader 2, and a specificity of 61.9% for both readers. Abnormal cecal position exhibited a sensitivity of 90.0% for reader 1 and 100.0% for reader 2 and a specificity of 66.7% and 38.1%. The bird beak sign exhibited a sensitivity of 86.4% for reader 1 and 100.0% for reader 2 and a specificity of 85.7% and 71.4%. Severe cecal distention exhibited a sensitivity of 100.0% for both readers and a specificity of 81.0% and 61.9%. Mesenteric engorgement exhibited a sensitivity of 40.9% for reader 1 and 100.0% for reader 2 and a specificity of 76.2% and 71.4%. The central appendix sign exhibited a sensitivity of 92.9% for reader 1 and 92.3% for reader 2 and a specificity of 62.5% and 68.8%. Overall impression exhibited a sensitivity of 100.0% for both readers and a specificity of 76.2% and 57.1%. At multivariable analysis, the AUC for cecal volvulus ranged from 0.787 to 0.931, and the whirl sign was an independent predictor of volvulus for both readers (p ≤ 0.014); the central appendix sign was also an independent predictor in patients with a visualized appendix for reader 2 (p ≤ 0.001). CT exhibited high diagnostic performance and very high sensitivity for cecal volvulus. The whirl sign was a significant independent predictor of volvulus for both readers.

Intimate partner violence screening and the comparative effects of screening mode on disclosure of sensitive health behaviours and exposures in clinical settings.

PubMed

Frazier, T; Yount, K M

2017-02-01

Detecting sensitive health information in clinical settings is of scientific and practical importance. The purpose of this study was to determine whether mode of screening influenced disclosure of intimate partner violence (IPV) in patterns similar to other forms of sensitive information. This cross sectional study was designed to compare effects of face-to-face vs computer self-assessment for sensitive health information on disclosure rates. Multivariate logistic regression was used for the analysis. Data were collected in 2012 from 639 eligible African American consenting women receiving services in women, infants and children (WIC) clinics. Women were randomized to complete assessments of sensitive exposures via computer-assisted self-interview (CASI) or face-to-face interview (FTFI). Those with complete information were included in the analysis (n = 616). Of 39 sensitive health exposures, reporting was higher for FTFI than CASI for exposure to IPV (7 of 7 outcomes), tobacco use (2 of 3 outcomes) and reproductive health care (2 of 3 outcomes). For example, face-to-face improved disclosure of IPV in the last year (adjusted odds ratios [aOR] = 2.27; 95% CI = 1.60-3.21) and any drug, tobacco or alcohol in the last week (aOR = 1.39; 95% CI = 1.00-1.93). Trained personnel may enhance disclosure above computer-based assessments for IPV for African American women receiving public assistance through The Special Supplemental Nutrition Program for Women, Infants and Children (WIC) Propensities to disclose sexual health behaviour and drug use by CASI may not apply to IPV in this population. The context and personal motivations influence women's decision to disclose IPV. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Differentiation between diverticulitis and colorectal cancer: quantitative CT perfusion measurements versus morphologic criteria--initial experience.

PubMed

Goh, Vicky; Halligan, Steve; Taylor, Stuart A; Burling, David; Bassett, Paul; Bartram, Clive I

2007-02-01

To determine whether computed tomographic (CT) perfusion measurements in prospectively recruited patients can be used to differentiate between diverticulitis and colorectal cancer and to compare this discrimination with that of standard morphologic criteria. After institutional review board approval and written informed consent were obtained, 60 patients (24 men, 36 women; mean age, 69 years; range, 33.5-90.4 years; 20 patients with cancer, 20 with diverticulitis, and 20 with inactive diverticular disease) underwent CT perfusion imaging at the level of the colonic abnormality, and perfusion parameters were calculated. Analysis of variance was used to investigate any differences in perfusion between the patient groups. Two independent observers also analyzed an abdominopelvic CT study obtained immediately after the CT perfusion study and noted standard morphologic criteria for differential diagnosis. The sensitivity and specificity of CT perfusion measurements for determining the diagnostic category were compared with morphologic criteria by means of multivariate analysis to identify the most discriminatory criteria. Mean blood volume, blood flow, transit time, and permeability were significantly different between patients with cancer and those with diverticulitis (P < .0001); patients with cancer had the highest blood volume, blood flow, and permeability and the shortest transit time. The most discriminatory criteria for determining diagnostic category were blood volume, transit time, permeability, and presence of pericolonic nodes (P = .05, .02, .04, and .02, respectively). Blood volume and blood flow each had a sensitivity of 80% and had specificity of 70% and 75%, respectively, for cancer in comparison with standard morphologic criteria: less than 5 cm of bowel involvement (45% sensitivity, 95% specificity), presence of a mass (85% sensitivity, 90% specificity), pericolonic inflammation (75% sensitivity, 5% specificity), and pericolonic nodes (90% sensitivity, 45% specificity). CT perfusion measurements enable differentiation and better discrimination, in comparison with morphologic criteria, between cancer and diverticulitis. (c) RSNA, 2007.

Analysis of hepatitis B surface antigen (HBsAg) using high-sensitivity HBsAg assays in hepatitis B virus carriers in whom HBsAg seroclearance was confirmed by conventional assays.

PubMed

Ozeki, Itaru; Nakajima, Tomoaki; Suii, Hirokazu; Tatsumi, Ryoji; Yamaguchi, Masakatsu; Kimura, Mutsuumi; Arakawa, Tomohiro; Kuwata, Yasuaki; Ohmura, Takumi; Hige, Shuhei; Karino, Yoshiyasu; Toyota, Joji

2018-02-01

We investigated the utility of high-sensitivity hepatitis B surface antigen (HBsAg) assays compared with conventional HBsAg assays. Using serum samples from 114 hepatitis B virus (HBV) carriers in whom HBsAg seroclearance was confirmed by conventional HBsAg assays (cut-off value, 0.05 IU/mL), the amount of HBsAg was re-examined by high-sensitivity HBsAg assays (cut-off value, 0.005 IU/mL). Cases negative for HBsAg in both assays were defined as consistent cases, and cases positive for HBsAg in the high-sensitivity HBsAg assay only were defined as discrepant cases. There were 55 (48.2%) discrepant cases, and the range of HBsAg titers determined by high-sensitivity HBsAg assays was 0.005-0.056 IU/mL. Multivariate analysis showed that the presence of nucleos(t)ide analog therapy, liver cirrhosis, and negative anti-HBs contributed to the discrepancies between the two assays. Cumulative anti-HBs positivity rates among discrepant cases were 12.7%, 17.2%, 38.8%, and 43.9% at baseline, 1 year, 3 years, and 5 years, respectively, whereas the corresponding rates among consistent cases were 50.8%, 56.0%, 61.7%, and 68.0%, respectively. Hepatitis B virus DNA negativity rates were 56.4% and 81.4% at baseline, 51.3% and 83.3% at 1 year, and 36.8% and 95.7% at 3 years, among discrepant and consistent cases, respectively. Hepatitis B surface antigen reversion was observed only in discrepant cases. Re-examination by high-sensitivity HBsAg assays revealed that HBsAg was positive in approximately 50% of cases. Cumulative anti-HBs seroconversion rates and HBV-DNA seroclearance rates were lower in these cases, suggesting a population at risk for HBsAg reversion. © 2017 The Japan Society of Hepatology.

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

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.

Sediment fingerprinting experiments to test the sensitivity of multivariate mixing models

NASA Astrophysics Data System (ADS)

Gaspar, Leticia; Blake, Will; Smith, Hugh; Navas, Ana

2014-05-01

Sediment fingerprinting techniques provide insight into the dynamics of sediment transfer processes and support for catchment management decisions. As questions being asked of fingerprinting datasets become increasingly complex, validation of model output and sensitivity tests are increasingly important. This study adopts an experimental approach to explore the validity and sensitivity of mixing model outputs for materials with contrasting geochemical and particle size composition. The experiments reported here focused on (i) the sensitivity of model output to different fingerprint selection procedures and (ii) the influence of source material particle size distributions on model output. Five soils with significantly different geochemistry, soil organic matter and particle size distributions were selected as experimental source materials. A total of twelve sediment mixtures were prepared in the laboratory by combining different quantified proportions of the < 63 µm fraction of the five source soils i.e. assuming no fluvial sorting of the mixture. The geochemistry of all source and mixture samples (5 source soils and 12 mixed soils) were analysed using X-ray fluorescence (XRF). Tracer properties were selected from 18 elements for which mass concentrations were found to be significantly different between sources. Sets of fingerprint properties that discriminate target sources were selected using a range of different independent statistical approaches (e.g. Kruskal-Wallis test, Discriminant Function Analysis (DFA), Principal Component Analysis (PCA), or correlation matrix). Summary results for the use of the mixing model with the different sets of fingerprint properties for the twelve mixed soils were reasonably consistent with the initial mixing percentages initially known. Given the experimental nature of the work and dry mixing of materials, geochemical conservative behavior was assumed for all elements, even for those that might be disregarded in aquatic systems (e.g. P). In general, the best fits between actual and modeled proportions were found using a set of nine tracer properties (Sr, Rb, Fe, Ti, Ca, Al, P, Si, K, Si) that were derived using DFA coupled with a multivariate stepwise algorithm, with errors between real and estimated value that did not exceed 6.7 % and values of GOF above 94.5 %. The second set of experiments aimed to explore the sensitivity of model output to variability in the particle size of source materials assuming that a degree of fluvial sorting of the resulting mixture took place. Most particle size correction procedures assume grain size affects are consistent across sources and tracer properties which is not always the case. Consequently, the < 40 µm fraction of selected soil mixtures was analysed to simulate the effect of selective fluvial transport of finer particles and the results were compared to those for source materials. Preliminary findings from this experiment demonstrate the sensitivity of the numerical mixing model outputs to different particle size distributions of source material and the variable impact of fluvial sorting on end member signatures used in mixing models. The results suggest that particle size correction procedures require careful scrutiny in the context of variable source characteristics.

The Multivariate Largest Lyapunov Exponent as an Age-Related Metric of Quiet Standing Balance

PubMed Central

Liu, Kun; Wang, Hongrui; Xiao, Jinzhuang

2015-01-01

The largest Lyapunov exponent has been researched as a metric of the balance ability during human quiet standing. However, the sensitivity and accuracy of this measurement method are not good enough for clinical use. The present research proposes a metric of the human body's standing balance ability based on the multivariate largest Lyapunov exponent which can quantify the human standing balance. The dynamic multivariate time series of ankle, knee, and hip were measured by multiple electrical goniometers. Thirty-six normal people of different ages participated in the test. With acquired data, the multivariate largest Lyapunov exponent was calculated. Finally, the results of the proposed approach were analysed and compared with the traditional method, for which the largest Lyapunov exponent and power spectral density from the centre of pressure were also calculated. The following conclusions can be obtained. The multivariate largest Lyapunov exponent has a higher degree of differentiation in differentiating balance in eyes-closed conditions. The MLLE value reflects the overall coordination between multisegment movements. Individuals of different ages can be distinguished by their MLLE values. The standing stability of human is reduced with the increment of age. PMID:26064182

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.

Constraining DALECv2 using multiple data streams and ecological constraints: analysis and application

DOE PAGES

Delahaies, Sylvain; Roulstone, Ian; Nichols, Nancy

2017-07-10

We use a variational method to assimilate multiple data streams into the terrestrial ecosystem carbon cycle model DALECv2 (Data Assimilation Linked Ecosystem Carbon). Ecological and dynamical constraints have recently been introduced to constrain unresolved components of this otherwise ill-posed problem. We recast these constraints as a multivariate Gaussian distribution to incorporate them into the variational framework and we demonstrate their advantage through a linear analysis. By using an adjoint method we study a linear approximation of the inverse problem: firstly we perform a sensitivity analysis of the different outputs under consideration, and secondly we use the concept of resolution matricesmore » to diagnose the nature of the ill-posedness and evaluate regularisation strategies. We then study the non-linear problem with an application to real data. Finally, we propose a modification to the model: introducing a spin-up period provides us with a built-in formulation of some ecological constraints which facilitates the variational approach.« less

Constraining DALECv2 using multiple data streams and ecological constraints: analysis and application

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

Delahaies, Sylvain; Roulstone, Ian; Nichols, Nancy

We use a variational method to assimilate multiple data streams into the terrestrial ecosystem carbon cycle model DALECv2 (Data Assimilation Linked Ecosystem Carbon). Ecological and dynamical constraints have recently been introduced to constrain unresolved components of this otherwise ill-posed problem. We recast these constraints as a multivariate Gaussian distribution to incorporate them into the variational framework and we demonstrate their advantage through a linear analysis. By using an adjoint method we study a linear approximation of the inverse problem: firstly we perform a sensitivity analysis of the different outputs under consideration, and secondly we use the concept of resolution matricesmore » to diagnose the nature of the ill-posedness and evaluate regularisation strategies. We then study the non-linear problem with an application to real data. Finally, we propose a modification to the model: introducing a spin-up period provides us with a built-in formulation of some ecological constraints which facilitates the variational approach.« less

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

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.

Impact of “Sick” and “Recovery” Roles on Brain Injury Rehabilitation Outcomes

PubMed Central

Barclay, David A.

2012-01-01

This study utilizes a multivariate, correlational, expost facto research design to examine Parsons' “sick role” as a dynamic, time-sensitive process of “sick role” and “recovery role” and the impact of this process on goal attainment (H1) and psychosocial distress (H2) of adult survivors of acquired brain injury. Measures used include the Brief Symptom Inventory-18, a Goal Attainment Scale, and an original instrument to measure sick role process. 60 survivors of ABI enrolled in community reentry rehabilitation participated. Stepwise regression analyses did not fully support the multivariate hypotheses. Two models emerged from the stepwise analyses. Goal attainment, gender, and postrehab responsibilities accounted for 40% of the shared variance of psychosocial distress. Anxiety and depression accounted for 22% of the shared variance of goal attainment with anxiety contributing to the majority of the explained variance. Bivariate analysis found sick role variables, anxiety, somatization, depression, gender, and goal attainment as significant. The study has implications for ABI rehabilitation in placing greater emphasis on sick role processes, anxiety, gender, and goal attainment in guiding program planning and future research with survivors of ABI. PMID:23119164

Differentiation of orbital lymphoma and idiopathic orbital inflammatory pseudotumor: combined diagnostic value of conventional MRI and histogram analysis of ADC maps.

PubMed

Ren, Jiliang; Yuan, Ying; Wu, Yingwei; Tao, Xiaofeng

2018-05-02

The overlap of morphological feature and mean ADC value restricted clinical application of MRI in the differential diagnosis of orbital lymphoma and idiopathic orbital inflammatory pseudotumor (IOIP). In this paper, we aimed to retrospectively evaluate the combined diagnostic value of conventional magnetic resonance imaging (MRI) and whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in the differentiation of the two lesions. In total, 18 patients with orbital lymphoma and 22 patients with IOIP were included, who underwent both conventional MRI and diffusion weighted imaging before treatment. Conventional MRI features and histogram parameters derived from ADC maps, including mean ADC (ADC mean ), median ADC (ADC median ), skewness, kurtosis, 10th, 25th, 75th and 90th percentiles of ADC (ADC 10 , ADC 25 , ADC 75 , ADC 90 ) were evaluated and compared between orbital lymphoma and IOIP. Multivariate logistic regression analysis was used to identify the most valuable variables for discriminating. Differential model was built upon the selected variables and receiver operating characteristic (ROC) analysis was also performed to determine the differential ability of the model. Multivariate logistic regression showed ADC 10 (P = 0.023) and involvement of orbit preseptal space (P = 0.029) were the most promising indexes in the discrimination of orbital lymphoma and IOIP. The logistic model defined by ADC 10 and involvement of orbit preseptal space was built, which achieved an AUC of 0.939, with sensitivity of 77.30% and specificity of 94.40%. Conventional MRI feature of involvement of orbit preseptal space and ADC histogram parameter of ADC 10 are valuable in differential diagnosis of orbital lymphoma and IOIP.

FAILURE OF RADIOACTIVE IODINE IN TREATMENT OF HYPERTHYROIDISM

PubMed Central

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

2015-01-01

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

Lower Quarter Y-Balance Test Scores and Lower Extremity Injury in NCAA Division I Athletes.

PubMed

Lai, Wilson C; Wang, Dean; Chen, James B; Vail, Jeremy; Rugg, Caitlin M; Hame, Sharon L

2017-08-01

Functional movement tests that are predictive of injury risk in National Collegiate Athletic Association (NCAA) athletes are useful tools for sports medicine professionals. The Lower Quarter Y-Balance Test (YBT-LQ) measures single-leg balance and reach distances in 3 directions. To assess whether the YBT-LQ predicts the laterality and risk of sports-related lower extremity (LE) injury in NCAA athletes. Case-control study; Level of evidence, 3. The YBT-LQ was administered to 294 NCAA Division I athletes from 21 sports during preparticipation physical examinations at a single institution. Athletes were followed prospectively over the course of the corresponding season. Correlation analysis was performed between the laterality of reach asymmetry and composite scores (CS) versus the laterality of injury. Receiver operating characteristic (ROC) analysis was used to determine the optimal asymmetry cutoff score for YBT-LQ. A multivariate regression analysis adjusting for sex, sport type, body mass index, and history of prior LE surgery was performed to assess predictors of earlier and higher rates of injury. Neither the laterality of reach asymmetry nor the CS correlated with the laterality of injury. ROC analysis found optimal cutoff scores of 2, 9, and 3 cm for anterior, posteromedial, and posterolateral reach, respectively. All of these potential cutoff scores, along with a cutoff score of 4 cm used in the majority of prior studies, were associated with poor sensitivity and specificity. Furthermore, none of the asymmetric cutoff scores were associated with earlier or increased rate of injury in the multivariate analyses. YBT-LQ scores alone do not predict LE injury in this collegiate athlete population. Sports medicine professionals should be cautioned against using the YBT-LQ alone to screen for injury risk in collegiate athletes.

Failure of radioactive iodine in the treatment of hyperthyroidism.

PubMed

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

2014-12-01

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

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…

Locating the Seventh Cervical Spinous Process: Development and Validation of a Multivariate Model Using Palpation and Personal Information.

PubMed

Ferreira, Ana Paula A; Póvoa, Luciana C; Zanier, José F C; Ferreira, Arthur S

2017-02-01

The aim of this study was to develop and validate a multivariate prediction model, guided by palpation and personal information, for locating the seventh cervical spinous process (C7SP). A single-blinded, cross-sectional study at a primary to tertiary health care center was conducted for model development and temporal validation. One-hundred sixty participants were prospectively included for model development (n = 80) and time-split validation stages (n = 80). The C7SP was located using the thorax-rib static method (TRSM). Participants underwent chest radiography for assessment of the inner body structure located with TRSM and using radio-opaque markers placed over the skin. Age, sex, height, body mass, body mass index, and vertex-marker distance (D V-M ) were used to predict the distance from the C7SP to the vertex (D V-C7 ). Multivariate linear regression modeling, limits of agreement plot, histogram of residues, receiver operating characteristic curves, and confusion tables were analyzed. The multivariate linear prediction model for D V-C7 (in centimeters) was D V-C7 = 0.986D V-M + 0.018(mass) + 0.014(age) - 1.008. Receiver operating characteristic curves had better discrimination of D V-C7 (area under the curve = 0.661; 95% confidence interval = 0.541-0.782; P = .015) than D V-M (area under the curve = 0.480; 95% confidence interval = 0.345-0.614; P = .761), with respective cutoff points at 23.40 cm (sensitivity = 41%, specificity = 63%) and 24.75 cm (sensitivity = 69%, specificity = 52%). The C7SP was correctly located more often when using predicted D V-C7 in the validation sample than when using the TRSM in the development sample: n = 53 (66%) vs n = 32 (40%), P < .001. Better accuracy was obtained when locating the C7SP by use of a multivariate model that incorporates palpation and personal information. Copyright © 2016. Published by Elsevier Inc.

Applications of time-of-flight secondary ion mass spectrometry (TOF-SIMS) and X-ray photoelectron spectroscopy (XPS) to study interactions of genetically engineered proteins with noble metal films

NASA Astrophysics Data System (ADS)

Suzuki, Noriaki

Genetically engineered proteins for inorganics (GEPIs) belong to a new class of polypeptides that are designed to have specific affinities to inorganic materials. A "gold binding protein (GBP)" was chosen as a model protein for GEPIs to study the molecular origins of binding specificity to gold using Time-of-flight secondary ion mass spectrometry (TOF-SIMS) and X-ray photoelectron spectroscopy (XPS). TOF-SIMS, a surface-sensitive analytical instrument with extremely high mass resolutions, provides information on specific amino acid-surface interactions. We used "principal component analysis (PCA)" to analyze the data. We also introduced a new multivariate technique, "hierarchical cluster analysis (HCA)" to organize the data into meaningful structures by measuring a degree of "similarity" and "dissimilarity" of the data. This report discusses a combined use of PCA and HCA to elucidate the binding specificity of GBP to Au. Based on the knowledge gained from TOF-SIMS measurements, we further investigated the nature of the interaction between selected amino acids and noble metal surfaces by using X-ray photoelectron spectroscopy (XPS). We developed a unique capability to introduce water vapor during the adsorption of a single amino acid and applied this method to study the intrinsic nature of sidechain/Au interactions. To further apply this unique research protocol, we characterized another type of GEPI, "quartz binding protein (QBP)," to identify the possible binding sites. This thesis research aims to provide experimental protocols for analyzing short peptide-substrate interface from complex spectroscopic data by using multivariate analysis techniques.

Evaluation of a Pharmacokinetic-Pharmacodynamic Model for Hypouricemic Effects of Febuxostat Using Datasets Obtained from Real-world Patients.

PubMed

Hirai, Toshinori; Itoh, Toshimasa; Kimura, Toshimi; Echizen, Hirotoshi

2018-06-06

Febuxostat is an active xanthine oxidase (XO) inhibitor that is widely used in the hyperuricemia treatment. We aimed to evaluate the predictive performance of a pharmacokinetic-pharmacodynamic (PK-PD) model for hypouricemic effects of febuxostat. Previously, we have formulated a PK--PD model for predicting hypouricemic effects of febuxostat as a function of baseline serum urate levels, body weight, renal function, and drug dose using datasets reported in preapproval studies (Hirai T et al., Biol Pharm Bull 2016; 39: 1013-21). Using an updated model with sensitivity analysis, we examined the predictive performance of the PK-PD model using datasets obtained from the medical records of patients who received febuxostat from March 2011 to December 2015 at Tokyo Women's Medical University Hospital. Multivariate regression analysis was performed to explore clinical variables to improve the predictive performance of the model. A total of 1,199 serum urate data were retrieved from 168 patients (age: 60.5 ±17.7 years, 71.4% males) who received febuxostat as hyperuricemia treatment. There was a significant correlation (r=0.68, p<0.01) between serum urate levels observed and those predicted by the modified PK-PD model. A multivariate regression analysis revealed that the predictive performance of the model may be improved further by considering comorbidities, such as diabetes mellitus, estimated glomerular filtration rate (eGFR), and co-administration of loop diuretics (r = 0.77, p<0.01). The PK-PD model may be useful for predicting individualized maintenance doses of febuxostat in real-world patients. This article is protected by copyright. All rights reserved.

Multivariate analysis of gamma spectra to characterize used nuclear fuel

DOE PAGES

Coble, Jamie; Orton, Christopher; Schwantes, Jon

2017-01-17

The Multi-Isotope Process (MIP) Monitor provides an efficient means to monitor the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of key stages in the reprocessing stream in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor; PWR and BWR, respectively), initial enrichment, burn up, and cooling time. Simulated gammamore » spectra were used in this paper to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type for the three PWR and three BWR reactor designs studied. Locally weighted PLS models were fitted on-the-fly to estimate the remaining fuel characteristics. For the simulated gamma spectra considered, burn up was predicted with 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment with approximately 2% RMSPE. Finally, this approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.« less

Multivariate analysis of gamma spectra to characterize used nuclear fuel

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

Coble, Jamie; Orton, Christopher; Schwantes, Jon

The Multi-Isotope Process (MIP) Monitor provides an efficient means to monitor the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of key stages in the reprocessing stream in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor; PWR and BWR, respectively), initial enrichment, burn up, and cooling time. Simulated gammamore » spectra were used in this paper to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type for the three PWR and three BWR reactor designs studied. Locally weighted PLS models were fitted on-the-fly to estimate the remaining fuel characteristics. For the simulated gamma spectra considered, burn up was predicted with 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment with approximately 2% RMSPE. Finally, this approach to automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and to inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters that may indicate issues with operational control or malicious activities.« less

Isomorphic red blood cells using automated urine flow cytometry is a reliable method in diagnosis of bladder cancer.

PubMed

Muto, Satoru; Sugiura, Syo-Ichiro; Nakajima, Akiko; Horiuchi, Akira; Inoue, Masahiro; Saito, Keisuke; Isotani, Shuji; Yamaguchi, Raizo; Ide, Hisamitsu; Horie, Shigeo

2014-10-01

We aimed to identify patients with a chief complaint of hematuria who could safely avoid unnecessary radiation and instrumentation in the diagnosis of bladder cancer (BC), using automated urine flow cytometry to detect isomorphic red blood cells (RBCs) in urine. We acquired urine samples from 134 patients over the age of 35 years with a chief complaint of hematuria and a positive urine occult blood test or microhematuria. The data were analyzed using the UF-1000i (®) (Sysmex Co., Ltd., Kobe, Japan) automated urine flow cytometer to determine RBC morphology, which was classified as isomorphic or dysmorphic. The patients were divided into two groups (BC versus non-BC) for statistical analysis. Multivariate logistic regression analysis was used to determine the predictive value of flow cytometry versus urine cytology, the bladder tumor antigen test, occult blood in urine test, and microhematuria test. BC was confirmed in 26 of 134 patients (19.4 %). The area under the curve for RBC count using the automated urine flow cytometer was 0.94, representing the highest reference value obtained in this study. Isomorphic RBCs were detected in all patients in the BC group. On multivariate logistic regression analysis, only isomorphic RBC morphology was significantly predictive for BC (p < 0.001). Analytical parameters such as sensitivity, specificity, positive predictive value, and negative predictive value of isomorphic RBCs in urine were 100.0, 91.7, 74.3, and 100.0 %, respectively. Detection of urinary isomorphic RBCs using automated urine flow cytometry is a reliable method in the diagnosis of BC with hematuria.

CT-based radiomics signature for differentiating Borrmann type IV gastric cancer from primary gastric lymphoma.

PubMed

Ma, Zelan; Fang, Mengjie; Huang, Yanqi; He, Lan; Chen, Xin; Liang, Cuishan; Huang, Xiaomei; Cheng, Zixuan; Dong, Di; Liang, Changhong; Xie, Jiajun; Tian, Jie; Liu, Zaiyi

2017-06-01

To evaluate the value of CT-based radiomics signature for differentiating Borrmann type IV gastric cancer (GC) from primary gastric lymphoma (PGL). 40 patients with Borrmann type IV GC and 30 patients with PGL were retrospectively recruited. 485 radiomics features were extracted and selected from the portal venous CT images to build a radiomics signature. Subjective CT findings, including gastric wall peristalsis, perigastric fat infiltration, lymphadenopathy below the renal hila and enhancement pattern, were assessed to construct a subjective findings model. The radiomics signature, subjective CT findings, age and gender were integrated into a combined model by multivariate analysis. The diagnostic performance of these three models was assessed with receiver operating characteristics curves (ROC) and were compared using DeLong test. The subjective findings model, the radiomics signature and the combined model showed a diagnostic accuracy of 81.43% (AUC [area under the curve], 0.806; 95% CI [confidence interval]: 0.696-0.917; sensitivity, 63.33%; specificity, 95.00%), 84.29% (AUC, 0.886 [95% CI: 0.809-0.963]; sensitivity, 86.67%; specificity, 82.50%), 87.14% (AUC, 0.903 [95%CI: 0.831-0.975]; sensitivity, 70.00%; specificity, 100%), respectively. There were no significant differences in AUC among these three models (P=0.051-0.422). Radiomics analysis has the potential to accurately differentiate Borrmann type IV GC from PGL. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

RSV-hRV co-infection is a risk factor for recurrent bronchial obstruction and early sensitization 3 years after bronchiolitis.

PubMed

Amat, Flore; Plantard, Chloé; Mulliez, Aurélien; Petit, Isabelle; Rochette, Emmanuelle; Verdan, Matthieu; Henquell, Cécile; Labbé, Guillaume; Heraud, Marie Christine; Evrard, Bertrand; Labbé, André

2018-05-01

To assess risk factors of recurrent bronchial obstruction and allergic sensitization 3 years after an episode of acute bronchiolitis, whether after ambulatory care treatment or hospitalization. A monocentric prospective longitudinal study including infants aged under 1 year with acute bronchiolitis was performed, with clinical (severity score), biological (serum Krebs von den Lungen 6 antigen), and viral (14 virus by naso-pharyngeal suction detection) assessments. Follow-up included a quaterly telephone interview, and a final clinical examination at 3 years. Biological markers of atopy were also measured in peripheral blood, including specific IgEs towards aero- and food allergens. Complete data were available for 154 children. 46.8% of them had recurrent wheezing (RW). No difference was found according to initial severity, care at home or in the hospital, respiratory virus involved, or existence of co-infection. A familial history of atopy was identified as a risk factor for recurrent bronchial obstruction (60% for RW infants versus 39%, P = 0.02), as living in an apartment (35% versus 15%, P = 0.002). 18.6% of the infants were sensitized, with 48.1% of them sensitized to aeroallergens and 81.5% to food allergens. Multivariate analysis confirmed that a familial history of atopy (P = 0.02) and initial co-infection RSV-hRV (P = 0.02) were correlated with the risk of sensitization to aeroallergens at 3 years. Familial history of atopy and RSV-hRV co-infection are risk factors for recurrent bronchial obstruction and sensitization. © 2018 Wiley Periodicals, Inc.

Raman exfoliative cytology for oral precancer diagnosis

NASA Astrophysics Data System (ADS)

Sahu, Aditi; Gera, Poonam; Pai, Venkatesh; Dubey, Abhishek; Tyagi, Gunjan; Waghmare, Mandavi; Pagare, Sandeep; Mahimkar, Manoj; Murali Krishna, C.

2017-11-01

Oral premalignant lesions (OPLs) such as leukoplakia, erythroplakia, and oral submucous fibrosis, often precede oral cancer. Screening and management of these premalignant conditions can improve prognosis. Raman spectroscopy has previously demonstrated potential in the diagnosis of oral premalignant conditions (in vivo), detected viral infection, and identified cancer in both oral and cervical exfoliated cells (ex vivo). The potential of Raman exfoliative cytology (REC) in identifying premalignant conditions was investigated. Oral exfoliated samples were collected from healthy volunteers (n=20), healthy volunteers with tobacco habits (n=20), and oral premalignant conditions (n=27, OPL) using Cytobrush. Spectra were acquired using Raman microprobe. Spectral acquisition parameters were: λex: 785 nm, laser power: 40 mW, acquisition time: 15 s, and average: 3. Postspectral acquisition, cell pellet was subjected to Pap staining. Multivariate analysis was carried out using principal component analysis and principal component-linear discriminant analysis using both spectra- and patient-wise approaches in three- and two-group models. OPLs could be identified with ˜77% (spectra-wise) and ˜70% (patient-wise) sensitivity in the three-group model while with 86% (spectra-wise) and 83% (patient-wise) in the two-group model. Use of histopathologically confirmed premalignant cases and better sampling devices may help in development of improved standard models and also enhance the sensitivity of the method. Future longitudinal studies can help validate potential of REC in screening and monitoring high-risk populations and prognosis prediction of premalignant lesions.

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

Load cell having strain gauges of arbitrary location

DOEpatents

Spletzer, Barry [Albuquerque, NM

2007-03-13

A load cell utilizes a plurality of strain gauges mounted upon the load cell body such that there are six independent load-strain relations. Load is determined by applying the inverse of a load-strain sensitivity matrix to a measured strain vector. The sensitivity matrix is determined by performing a multivariate regression technique on a set of known loads correlated to the resulting strains. Temperature compensation is achieved by configuring the strain gauges as co-located orthogonal pairs.

The Classification of Ground Roasted Decaffeinated Coffee Using UV-VIS Spectroscopy and SIMCA Method

NASA Astrophysics Data System (ADS)

Yulia, M.; Asnaning, A. R.; Suhandy, D.

2018-05-01

In this work, an investigation on the classification between decaffeinated and non- decaffeinated coffee samples using UV-VIS spectroscopy and SIMCA method was investigated. Total 200 samples of ground roasted coffee were used (100 samples for decaffeinated coffee and 100 samples for non-decaffeinated coffee). After extraction and dilution, the spectra of coffee samples solution were acquired using a UV-VIS spectrometer (Genesys™ 10S UV-VIS, Thermo Scientific, USA) in the range of 190-1100 nm. The multivariate analyses of the spectra were performed using principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA). The SIMCA model showed that the classification between decaffeinated and non-decaffeinated coffee samples was detected with 100% sensitivity and specificity.

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.

Correlation of Early Recurrence With In Vitro Adenosine Triphosphate Based Chemotherapy Response Assay in Pancreas Cancer With Postoperative Gemcitabine Chemotherapy.

PubMed

Park, Joon Seong; Kim, Jae Keun; Yoon, Dong Sup

2016-11-01

Gemcitabine-based regimens represent the standard systemic first line treatment in patients after pancreatic resection. However, the clinical impact of gemcitabine varies significantly in individuals because of chemoresistance. An in vitro adenosine triphosphate based chemotherapy response assay (ATP-CRA) was designed to evaluate the sensitivity of cancer cells to various chemotherapeutic agents. This study investigated the correlation between in vitro gemcitabine sensitivity of tumor cells and early recurrence after curative resection. From January 2007 to December 2010, the ATP-CRA for gemcitabine was tested in 64 patients surgically treated for pancreas cancer at Gangnam Severance Hospital, Seoul, Korea. We analyzed the relationship between chemosensitivity and early systemic recurrence in patients with pancreas cancer to predict disease-free survival (DFS) after curative resection in pancreas cancer. The mean cell death rate (CDR) was 20.0 (±14.5) and divided into two groups according to the mean values of the CDR. Lymphovascular invasion was more frequently shown in gemcitabine resistance group without statistical significance. In univariate and multivariate analysis, advanced tumor stage and gemcitabine sensitive group (CDR ≥ 20) were identified as independent prognostic factors for DFS. Gemcitabine sensitivity measured by ATP-CRA was well correlated with in vivo drug responsibility to predict early recurrence following gemcitabine-based adjuvant chemotherapy in patients with pancreas cancer. © 2016 Wiley Periodicals, Inc.

Ascertainment of Outpatient Visits by Patients with Diabetes: The National Ambulatory Medical Care Survey (NAMCS) and the National Hospital Ambulatory Medical Care Survey (NHAMCS)

PubMed Central

Asao, Keiko; McEwen, Laura N.; Lee, Joyce M.; Herman, William H.

2015-01-01

Aims To estimate and evaluate the sensitivity and specificity of providers’ diagnosis codes and medication lists to identify outpatient visits by patients with diabetes. Methods We used data from the 2006 to 2010 National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey. We assessed the sensitivity and specificity of providers’ diagnoses and medication lists to identify patients with diabetes, using the checkbox for diabetes as the gold standard. We then examined differences in sensitivity by patients’ characteristics using multivariate logistic regression models. Results The checkbox identified 12,647 outpatient visits by adults with diabetes among the 70,352 visits used for this analysis. The sensitivity and specificity of providers’ diagnoses or listed diabetes medications were 72.3% (95% CI: 70.8% to 73.8%) and 99.2% (99.1% to 99.4%), respectively. Diabetic patients ≥75 years pf age, women, non-Hispanics, and those with private insurance or Medicare were more likely to be missed by providers’ diagnoses and medication lists. Diabetic patients who had more diagnosis codes and medications recorded, had glucose or hemoglobin A1c measured, or made office- rather than hospital-outpatient visits were less likely to be missed. Conclusions Providers’ diagnosis codes and medication lists fail to identify approximately one quarter of outpatient visits by patients with diabetes. PMID:25891975

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

TERT promoter mutations contribute to IDH mutations in predicting differential responses to adjuvant therapies in WHO grade II and III diffuse gliomas

PubMed Central

Ding, Xiao-Jie; Qin, Zhi-Yong; Hong, Christopher S.; Chen, Ling-Chao; Zhang, Xin; Zhao, Fang-Ping; Wang, Yin; Wang, Yang; Zhou, Liang-Fu; Zhuang, Zhengping; Ng, Ho-Keung; Yan, Hai; Yao, Yu; Mao, Ying

2015-01-01

IDH mutations frequently occur in WHO grade II and III diffuse gliomas and have favorable prognosis compared to wild-type tumors. However, whether IDH mutations in WHO grade II and II diffuse gliomas predict enhanced sensitivity to adjuvant radiation (RT) or chemotherapy (CHT) is still being debated. Recent studies have identified recurrent mutations in the promoter region of telomerase reverse transcriptase (TERT) in gliomas. We previously demonstrated that TERT promoter mutations may be promising biomarkers in glioma survival prognostication when combined with IDH mutations. This study analyzed IDH and TERT promoter mutations in 295 WHO grade II and III diffuse gliomas treated with or without adjuvant therapies to explore their impact on the sensitivity of tumors to genotoxic therapies. IDH mutations were found in 216 (73.2%) patients and TERT promoter mutations were found in 112 (38%) patients. In multivariate analysis, IDH mutations (p < 0.001) were independent prognostic factors for PFS and OS in patients receiving genotoxic therapies while TERT promoter mutations were not. In univariate analysis, IDH and TERT promoter mutations were not significant prognostic factors in patients who did not receive genotoxic therapies. Adjuvant RT and CHT were factors independently impacting PFS (RT p = 0.001, CHT p = 0.026) in IDH mutated WHO grade II and III diffuse gliomas but not in IDH wild-type group. Univariate and multivariate analyses demonstrated TERT promoter mutations further stratified IDH wild-type WHO grade II and III diffuse gliomas into two subgroups with different responses to genotoxic therapies. Adjuvant RT and CHT were significant parameters influencing PFS in the IDH wt/TERT mut subgroup (RT p = 0.015, CHT p = 0.015) but not in the IDH wt/TERT wt subgroup. Our data demonstrated that IDH mutated WHO grade II and III diffuse gliomas had better PFS and OS than their IDH wild-type counterparts when genotoxic therapies were administered after surgery. Importantly, we also found that TERT promoter mutations further stratify IDH wild-type WHO grade II and III diffuse gliomas into two subgroups with different responses to adjuvant therapies. Taken together, TERT promoter mutations may predict enhanced sensitivity to genotoxic therapies in IDH wild-type WHO grade II and III diffuse gliomas and may justify intensified treatment in this subgroup. PMID:26314843

A study to derive a clinical decision rule for triage of emergency department patients with chest pain: design and methodology

PubMed Central

Hess, Erik P; Wells, George A; Jaffe, Allan; Stiell, Ian G

2008-01-01

Background Chest pain is the second most common chief complaint in North American emergency departments. Data from the U.S. suggest that 2.1% of patients with acute myocardial infarction and 2.3% of patients with unstable angina are misdiagnosed, with slightly higher rates reported in a recent Canadian study (4.6% and 6.4%, respectively). Information obtained from the history, 12-lead ECG, and a single set of cardiac enzymes is unable to identify patients who are safe for early discharge with sufficient sensitivity. The 2007 ACC/AHA guidelines for UA/NSTEMI do not identify patients at low risk for adverse cardiac events who can be safely discharged without provocative testing. As a result large numbers of low risk patients are triaged to chest pain observation units and undergo provocative testing, at significant cost to the healthcare system. Clinical decision rules use clinical findings (history, physical exam, test results) to suggest a diagnostic or therapeutic course of action. Currently no methodologically robust clinical decision rule identifies patients safe for early discharge. Methods/design The goal of this study is to derive a clinical decision rule which will allow emergency physicians to accurately identify patients with chest pain who are safe for early discharge. The study will utilize a prospective cohort design. Standardized clinical variables will be collected on all patients at least 25 years of age complaining of chest pain prior to provocative testing. Variables strongly associated with the composite outcome acute myocardial infarction, revascularization, or death will be further analyzed with multivariable analysis to derive the clinical rule. Specific aims are to: i) apply standardized clinical assessments to patients with chest pain, incorporating results of early cardiac testing; ii) determine the inter-observer reliability of the clinical information; iii) determine the statistical association between the clinical findings and the composite outcome; and iv) use multivariable analysis to derive a highly sensitive clinical decision rule to guide triage decisions. Discussion The study will derive a highly sensitive clinical decision rule to identify low risk patients safe for early discharge. This will improve patient care, lower healthcare costs, and enhance flow in our busy and overcrowded emergency departments. PMID:18254973

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

Performance of Ultrasound in the Diagnosis of Gout in a Multicenter Study: Comparison With Monosodium Urate Monohydrate Crystal Analysis as the Gold Standard.

PubMed

Ogdie, Alexis; Taylor, William J; Neogi, Tuhina; Fransen, Jaap; Jansen, Tim L; Schumacher, H Ralph; Louthrenoo, Worawit; Vazquez-Mellado, Janitzia; Eliseev, Maxim; McCarthy, Geraldine; Stamp, Lisa K; Perez-Ruiz, Fernando; Sivera, Francisca; Ea, Hang-Korng; Gerritsen, Martijn; Cagnotto, Giovanni; Cavagna, Lorenzo; Lin, Chingtsai; Chou, Yin-Yi; Tausche, Anne-Kathrin; Lima Gomes Ochtrop, Manuella; Janssen, Matthijs; Chen, Jiunn-Horng; Slot, Ole; Lazovskis, Juris; White, Douglas; Cimmino, Marco A; Uhlig, Till; Dalbeth, Nicola

2017-02-01

To examine the performance of ultrasound (US) for the diagnosis of gout using the presence of monosodium urate monohydrate (MSU) crystals as the gold standard. We analyzed data from the Study for Updated Gout Classification Criteria (SUGAR), a large, multicenter observational cross-sectional study of consecutive subjects with at least 1 swollen joint who conceivably may have gout. All subjects underwent arthrocentesis; cases were subjects with confirmed MSU crystals. Rheumatologists or radiologists who were blinded with regard to the results of the MSU crystal analysis performed US on 1 or more clinically affected joints. US findings of interest were double contour sign, tophus, and snowstorm appearance. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Multivariable logistic regression models were used to examine factors associated with positive US results among subjects with gout. US was performed in 824 subjects (416 cases and 408 controls). The sensitivity, specificity, PPV, and NPV for the presence of any 1 of the features were 76.9%, 84.3%, 83.3%, and 78.2%, respectively. Sensitivity was higher among subjects with a disease duration of ≥2 years and among subjects with subcutaneous nodules on examination (suspected tophus). Associations with a positive US finding included suspected clinical tophus (odds ratio [OR] 4.77 [95% confidence interval (95% CI) 2.23-10.21]), any abnormality on plain radiography (OR 4.68 [95% CI 2.68-8.17]), and serum urate level (OR 1.31 [95% CI 1.06-1.62]). US features of MSU crystal deposition had high specificity and high PPV but more limited sensitivity for early gout. The specificity remained high in subjects with early disease and without clinical signs of tophi. © 2016, American College of Rheumatology.

Assessment of HPV-mRNA test to predict recurrent disease in patients previously treated for CIN 2/3.

PubMed

Frega, Antonio; Sesti, Francesco; Lombardi, Danila; Votano, Sergio; Sopracordevole, Francesco; Catalano, Angelica; Milazzo, Giusi Natalia; Lombardo, Riccardo; Assorgi, Chiara; Olivola, Sara; Chiusuri, Valentina; Ricciardi, Enzo; French, Deborah; Moscarini, Massimo

2014-05-01

The use of HPV-mRNA test in the follow-up after LEEP is still matter of debate, with regard to its capacity of prediction relapse. The aim of the present study is to evaluate the reliability of HPV-mRNA test to predict the residual and recurrent disease, and its accuracy in the follow-up of patients treated for CIN 2/3. Multicenter prospective cohort study. Patients who underwent LEEP after a biopsy diagnosing CIN 2/3 were followed at 3, 6, 12, 24 and 36 months. Each check up included cytology, colposcopy, HPV-DNA test (LiPA) and HPV-mRNA test (PreTect HPV Proofer Kit NorChip). Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), of HPV-DNA test and HPV-mRNA test to predict relapse, recurrent and residual disease. Using multiple logistic regression, the statistical significant variables as assessed in univariate analysis were entered and investigated as predictors of relapse disease. The mRNA-test in predicting a residual disease had a sensitivity of 52% and a NPV of 91%, whereas DNA-test had 100% and 100%, respectively. On the contrary in the prediction of recurrent disease mRNA-test had a sensitivity and a NPV of 73.5% and 97%, whereas DNA-test had 44% and 93%. On the multivariate analysis, age, cytology, HPV DNA and mRNA test achieved the role of independent predictors of relapse. HPV-mRNA test has a higher sensitivity and a higher NPV in predicting recurrent disease, for this reason it should be used in the follow-up of patients treated with LEEP for CIN 2/3 in order to individualize the timing of check up. Copyright © 2014 Elsevier B.V. All rights reserved.

Performance of Ultrasound in the Diagnosis of Gout in a Multi-Center Study: Comparison with Monosodium Urate Crystal Analysis as the Gold Standard

PubMed Central

Ogdie, Alexis; Taylor, William J; Neogi, Tuhina; Fransen, Jaap; Jansen, Tim L; Schumacher, H. Ralph; Louthrenoo, Worawit; Vazquez-Mellado, Janitzia; Eliseev, Maxim; McCarthy, Geraldine; Stamp, Lisa K.; Perez-Ruiz, Fernando; Sivera, Francisca; Ea, Hang-Korng; Gerritsen, Martijn; Cagnotto, Giovanni; Cavagna, Lorenzo; Lin, Chingtsai; Chou, Yin-Yi; Tausche, Anne-Kathrin; Ochtrop, Manuella Lima Gomes; Janssen, Matthijs; Chen, Jiunn-Horng; Slot, Ole; Lazovskis, Juris; White, Douglas; Cimmino, Marco A.; Uhlig, Till; Dalbeth, Nicola

2017-01-01

Objectives To examine the performance of ultrasound for the diagnosis of gout using presence of monosodium urate (MSU) crystals as the gold standard. Methods We analyzed data from the Study for Updated Gout Classification Criteria (SUGAR), a large, multi-center observational cross-sectional study of consecutive subjects with at least one swollen joint who conceivably may have gout. All subjects underwent arthrocentesis; cases were subjects with MSU crystal confirmation. Rheumatologists or radiologists, blinded to the results of the MSU crystal analysis, performed ultrasound on one or more clinically affected joints. Ultrasound findings of interest were: double contour sign (DCS), tophus, and ‘snowstorm’ appearance. Sensitivity, specificity, positive and negative predictive values (PPV and NPV) were calculated. Multivariable logistic regression models were used to examine factors associated with positive ultrasound results among subjects with gout. Results Ultrasound was performed in 824 subjects (416 cases and 408 controls). The sensitivity, specificity, PPV and NPV for the presence of any one of the features were 76.9%, 84.3%, 83.3% and 78.1% respectively. Sensitivity was higher among subjects with disease ≥2 years duration and among subjects with subcutaneous nodules on exam (suspected tophus). Associations with a positive ultrasound finding included suspected clinical tophus (odds ratio 4.77; 95% CI 2.23–10.21), any abnormal plain film radiograph (4.68; 2.68–8.17) and serum urate (1.31; 1.06–1.62). Conclusions Ultrasound features of MSU crystal deposition had high specificity and high positive predictive value but more limited sensitivity for early gout. The specificity remained high in subjects with early disease and without clinical signs of tophi. PMID:27748084

Longer-Term Investigation of the Value of 18F-FDG-PET and Magnetic Resonance Imaging for Predicting the Conversion of Mild Cognitive Impairment to Alzheimer's Disease: A Multicenter Study.

PubMed

Inui, Yoshitaka; Ito, Kengo; Kato, Takashi

2017-01-01

The value of fluorine-18-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) and magnetic resonance imaging (MRI) for predicting conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD) in longer-term is unclear. To evaluate longer-term prediction of MCI to AD conversion using 18F-FDG-PET and MRI in a multicenter study. One-hundred and fourteen patients with MCI were followed for 5 years. They underwent clinical and neuropsychological examinations, 18F-FDG-PET, and MRI at baseline. PET images were visually classified into predefined dementia patterns. PET scores were calculated as a semi quantitative index. For structural MRI, z-scores in medial temporal area were calculated by automated volume-based morphometry (VBM). Overall, 72% patients with amnestic MCI progressed to AD during the 5-year follow-up. The diagnostic accuracy of PET scores over 5 years was 60% with 53% sensitivity and 84% specificity. Visual interpretation of PET images predicted conversion to AD with an overall 82% diagnostic accuracy, 94% sensitivity, and 53% specificity. The accuracy of VBM analysis presented little fluctuation through 5 years and it was highest (73%) at the 5-year follow-up, with 79% sensitivity and 63% specificity. The best performance (87.9% diagnostic accuracy, 89.8% sensitivity, and 82.4% specificity) was with a combination identified using multivariate logistic regression analysis that included PET visual interpretation, educational level, and neuropsychological tests as predictors. 18F-FDG-PET visual assessment showed high performance for predicting conversion to AD from MCI, particularly in combination with neuropsychological tests. PET scores showed high diagnostic specificity. Structural MRI focused on the medial temporal area showed stable predictive value throughout the 5-year course.

Higher serum levels of uric acid are associated with a reduced insulin clearance in non-diabetic individuals.

PubMed

Fiorentino, Teresa Vanessa; Sesti, Franz; Succurro, Elena; Pedace, Elisabetta; Andreozzi, Francesco; Sciacqua, Angela; Hribal, Marta Letizia; Perticone, Francesco; Sesti, Giorgio

2018-05-17

Decreased insulin clearance has been reported to be associated with insulin resistance-related disorders and incident type 2 diabetes. The aim of this study was to evaluate whether higher levels of uric acid (UA), a known risk factor of type 2 diabetes, are associated with a reduced insulin clearance. 440 non-diabetic individuals were stratified in tertiles according to serum UA levels. Insulin clearance and skeletal muscle insulin sensitivity were assessed by euglycemic hyperinsulinemic clamp. Hepatic insulin resistance was estimated by the liver IR index. Subjects with higher levels of UA displayed an unfavorable metabolic phenotype with a worse lipid profile, increased levels of 2-h post-load glucose levels, fasting, and 2-h post-load insulin levels, hsCRP, liver IR index, and lower levels of eGFR and skeletal muscle insulin sensitivity, in comparison to individuals with lower UA levels. Moreover, subjects with higher UA concentrations exhibited decreased levels of insulin clearance even after adjustment for age, gender, BMI, eGFR, and skeletal muscle insulin sensitivity. In a multivariate regression analysis model including several confounding factors, UA concentration was an independent predictor of insulin clearance (β = - 0.145; P = 0.03). However, when liver IR index was included in the model, the independent association between UA levels and insulin clearance was not retained. Accordingly, in a mediation analysis, liver IR index was a mediator of the negative effects of UA levels on insulin clearance (t = - 2.55, P = 0.01). Higher serum levels of UA may affect insulin clearance by impairing hepatic insulin sensitivity.

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.

Evaluation of an inpatient fall risk screening tool to identify the most critical fall risk factors in inpatients.

PubMed

Hou, Wen-Hsuan; Kang, Chun-Mei; Ho, Mu-Hsing; Kuo, Jessie Ming-Chuan; Chen, Hsiao-Lien; Chang, Wen-Yin

2017-03-01

To evaluate the accuracy of the inpatient fall risk screening tool and to identify the most critical fall risk factors in inpatients. Variations exist in several screening tools applied in acute care hospitals for examining risk factors for falls and identifying high-risk inpatients. Secondary data analysis. A subset of inpatient data for the period from June 2011-June 2014 was extracted from the nursing information system and adverse event reporting system of an 818-bed teaching medical centre in Taipei. Data were analysed using descriptive statistics, receiver operating characteristic curve analysis and logistic regression analysis. During the study period, 205 fallers and 37,232 nonfallers were identified. The results revealed that the inpatient fall risk screening tool (cut-off point of ≥3) had a low sensitivity level (60%), satisfactory specificity (87%), a positive predictive value of 2·0% and a negative predictive value of 99%. The receiver operating characteristic curve analysis revealed an area under the curve of 0·805 (sensitivity, 71·8%; specificity, 78%). To increase the sensitivity values, the Youden index suggests at least 1·5 points to be the most suitable cut-off point for the inpatient fall risk screening tool. Multivariate logistic regression analysis revealed a considerably increased fall risk in patients with impaired balance and impaired elimination. The fall risk factor was also significantly associated with days of hospital stay and with admission to surgical wards. The findings can raise awareness about the two most critical risk factors for falls among future clinical nurses and other healthcare professionals and thus facilitate the development of fall prevention interventions. This study highlights the needs for redefining the cut-off points of the inpatient fall risk screening tool to effectively identify inpatients at a high risk of falls. Furthermore, inpatients with impaired balance and impaired elimination should be closely monitored by nurses to prevent falling during hospitalisations. © 2016 John Wiley & Sons Ltd.

Iodine concentration: a new, important characteristic of the spot sign that predicts haematoma expansion.

PubMed

Fu, Fan; Sun, Shengjun; Liu, Liping; Li, Jianying; Su, Yaping; Li, Yingying

2018-04-19

The computed tomography angiography (CTA) spot sign is a validated predictor of haematoma expansion (HE) in spontaneous intracerebral haemorrhage (SICH). We investigated whether defining the iodine concentration (IC) inside the spot sign and the haematoma on Gemstone spectral imaging (GSI) would improve its sensitivity and specificity for predicting HE. From 2014 to 2016, we prospectively enrolled 65 SICH patients who underwent single-phase spectral CTA within 6 h. Logistic regression was performed to assess the risk factors for HE. The predictive performance of individual spot sign characteristics was examined via receiver operating characteristic (ROC) analysis. The spot sign was detected in 46.1% (30/65) of patients. ROC analysis indicated that IC inside the spot sign had the greatest area under the ROC curve for HE (0.858; 95% confidence interval, 0.727-0.989; p = 0.003). Multivariate analysis found that spot sign with higher IC (i.e. IC > 7.82 100 μg/ml) was an independent predictor of HE (odds ratio = 34.27; 95% confidence interval, 5.608-209.41; p < 0.001) with sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 0.81, 0.75, 0.90 and 0.60, respectively; while the spot sign showed sensitivity, specificity, PPV and NPV of 0.81, 0.79, 0.73 and 0.86. Logistic regression analysis indicated that the IC in haematomas was independently associated with HE (odds ratio = 1.525; 95% confidence interval, 1.041-2.235; p = 0.030). ICs in haematoma and in spot sign were all independently associated with HE. IC analysis in spectral imaging may help to identify SICH patients for targeted haemostatic therapy. • Iodine concentration in spot sign and haematoma can predict haematoma expansion • Spectral imaging could measure the IC inside the spot sign and haematoma • IC in spot sign improved the positive predictive value (PPV) cf. CTA.

MIDAS: Regionally linear multivariate discriminative statistical mapping.

PubMed

Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos

2018-07-01

Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the statistical significance of the derived statistic by analytically approximating its null distribution without the need for computationally expensive permutation tests. The proposed framework was extensively validated using simulated atrophy in structural magnetic resonance imaging (MRI) and further tested using data from a task-based functional MRI study as well as a structural MRI study of cognitive performance. The performance of the proposed framework was evaluated against standard voxel-wise general linear models and other information mapping methods. The experimental results showed that MIDAS achieves relatively higher sensitivity and specificity in detecting group differences. Together, our results demonstrate the potential of the proposed approach to efficiently map effects of interest in both structural and functional data. Copyright © 2018. Published by Elsevier Inc.

Toxicity risk assessment of mercury, DDT and arsenic legacy pollution in sediments: A triad approach under low concentration conditions.

PubMed

Marziali, L; Rosignoli, F; Drago, A; Pascariello, S; Valsecchi, L; Rossaro, B; Guzzella, L

2017-09-01

The determination of sediment toxicity is challenging due to site-specific factors affecting pollutants distribution and bioavailability, especially when contamination levels are close to expected non-effect concentrations. Different lines of evidence and sensitive tools are necessary for a proper toxicity risk assessment. We examined the case study of the Toce River (Northern Italy), where past industrial activities determined Hg, DDT and As enrichment in sediments. A triad approach comprising chemical, ecotoxicological and ecological analyses (benthic invertebrates) was carried out for risk assessment of residual contamination in river sediments. A "blank" site upstream from the industrial site was selected to compare the other sites downstream. Sediment, water and benthic invertebrate samplings were carried out following standard protocols. Results emphasized that despite the emissions of the industrial site ceased about 20years ago, sediments in the downstream section of the river remain contaminated by Hg, DDT and As with concentrations exceeding Threshold Effect Concentrations. A chronic whole-sediment test with Chironomus riparius showed decreased development rate and a lower number of eggs per mass in the contaminated sediments. Benthic community was analyzed with the calculation of integrated (STAR_ICMi) and stressor-specific metrics (SPEAR pesticide and mean sensitivity to Hg), but no significant differences were found between upstream and downstream sites. On the other hand, multivariate analysis (partial Redundancy Analysis and variation partitioning) emphasized a slight impact on invertebrate community, accounting for 5% variation in taxa composition. Results show that legacy contaminants in sediments, even at low concentrations, may be bioavailable and possibly toxic for benthic invertebrates. At low concentration levels, sensitive and site-specific tools need to be developed for a proper risk analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

Costs and cost-effectiveness of training traditional birth attendants to reduce neonatal mortality in the Lufwanyama Neonatal Survival study (LUNESP).

PubMed

Sabin, Lora L; Knapp, Anna B; MacLeod, William B; Phiri-Mazala, Grace; Kasimba, Joshua; Hamer, Davidson H; Gill, Christopher J

2012-01-01

The Lufwanyama Neonatal Survival Project ("LUNESP") was a cluster randomized, controlled trial that showed that training traditional birth attendants (TBAs) to perform interventions targeting birth asphyxia, hypothermia, and neonatal sepsis reduced all-cause neonatal mortality by 45%. This companion analysis was undertaken to analyze intervention costs and cost-effectiveness, and factors that might improve cost-effectiveness. We calculated LUNESP's financial and economic costs and the economic cost of implementation for a forecasted ten-year program (2011-2020). In each case, we calculated the incremental cost per death avoided and disability-adjusted life years (DALYs) averted in real 2011 US dollars. The forecasted 10-year program analysis included a base case as well as 'conservative' and 'optimistic' scenarios. Uncertainty was characterized using one-way sensitivity analyses and a multivariate probabilistic sensitivity analysis. The estimated financial and economic costs of LUNESP were $118,574 and $127,756, respectively, or $49,469 and $53,550 per year. Fixed costs accounted for nearly 90% of total costs. For the 10-year program, discounted total and annual program costs were $256,455 and $26,834 respectively; for the base case, optimistic, and conservative scenarios, the estimated cost per death avoided was $1,866, $591, and $3,024, and cost per DALY averted was $74, $24, and $120, respectively. Outcomes were robust to variations in local costs, but sensitive to variations in intervention effect size, number of births attended by TBAs, and the extent of foreign consultants' participation. Based on established guidelines, the strategy of using trained TBAs to reduce neonatal mortality was 'highly cost effective'. We strongly recommend consideration of this approach for other remote rural populations with limited access to health care.

Melamine detection by mid- and near-infrared (MIR/NIR) spectroscopy: a quick and sensitive method for dairy products analysis including liquid milk, infant formula, and milk powder.

PubMed

Balabin, Roman M; Smirnov, Sergey V

2011-07-15

Melamine (2,4,6-triamino-1,3,5-triazine) is a nitrogen-rich chemical implicated in the pet and human food recalls and in the global food safety scares involving milk products. Due to the serious health concerns associated with melamine consumption and the extensive scope of affected products, rapid and sensitive methods to detect melamine's presence are essential. We propose the use of spectroscopy data-produced by near-infrared (near-IR/NIR) and mid-infrared (mid-IR/MIR) spectroscopies, in particular-for melamine detection in complex dairy matrixes. None of the up-to-date reported IR-based methods for melamine detection has unambiguously shown its wide applicability to different dairy products as well as limit of detection (LOD) below 1 ppm on independent sample set. It was found that infrared spectroscopy is an effective tool to detect melamine in dairy products, such as infant formula, milk powder, or liquid milk. ALOD below 1 ppm (0.76±0.11 ppm) can be reached if a correct spectrum preprocessing (pretreatment) technique and a correct multivariate (MDA) algorithm-partial least squares regression (PLS), polynomial PLS (Poly-PLS), artificial neural network (ANN), support vector regression (SVR), or least squares support vector machine (LS-SVM)-are used for spectrum analysis. The relationship between MIR/NIR spectrum of milk products and melamine content is nonlinear. Thus, nonlinear regression methods are needed to correctly predict the triazine-derivative content of milk products. It can be concluded that mid- and near-infrared spectroscopy can be regarded as a quick, sensitive, robust, and low-cost method for liquid milk, infant formula, and milk powder analysis. Copyright © 2011 Elsevier B.V. All rights reserved.

[Effectiveness of a clinimetric scale for diagnosing vulvovaginal candidosis].

PubMed

Reyna Figueroa, Jesús; Morales Rangel, Verónica; Ortiz Ibarra, Federico Javier; Casanova Román, Gerardo; Beltrán Zúñiga, Magdalena

2004-05-01

Vulvovaginal candidosis is one of the most frequent infections of the female genital system. It is believed that 75% of women in reproductive life have suffered from vulvovaginal candidosis at least once in their life. To evaluate the effectiveness of a clinimetric scale based on clinical characteristics and factors of risk to diagnose vulvovaginal candidosis and to establish the cut points. A questionnaire was elaborated by means of consensus, there were questions about symptoms and risk factors for the diagnosis. It was applied to women in reproductive age, besides the cervicovaginal culture. The resulting questionnaire was evaluated by means of the analysis of sensitivity according to the criteria of Feinstein. The determination of points of cut was made by means of curves ROC as well as sensitivity, specificity, positive and negative predictive value. The univariable analysis was made by means of the test of chi2 and the exact test of Fisher. We used likelihood ratio for association of variables and the multi-variate logistic regression analysis was made to fit variable potentials. One-hundred forty-two women answered the questionnaire, 39 (27%) had positive isolation to Candida. Vulvar edema (3.49 OR IC 95% 1.16-10.43 p = 0.02) and ardor (OR 2.40 IC 95% 0.88-6.51 p = 0.08) were significant. None of the risk factors were statistically significant. It was reported with sensitivity of 76%, specificity of 38%, VPP 32% and VPN 81% for the = 60 record. The clinimetric scale is brief, valid and easy to apply. It can be used independently of the educative or cultural factors that limit the accomplishment of other forms of diagnosis, such as the vaginal flow exam and the cervicovaginal culture.

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.

Costs and Cost-Effectiveness of Training Traditional Birth Attendants to Reduce Neonatal Mortality in the Lufwanyama Neonatal Survival Study (LUNESP)

PubMed Central

Sabin, Lora L.; Knapp, Anna B.; MacLeod, William B.; Phiri-Mazala, Grace; Kasimba, Joshua; Hamer, Davidson H.; Gill, Christopher J.

2012-01-01

Background The Lufwanyama Neonatal Survival Project (“LUNESP”) was a cluster randomized, controlled trial that showed that training traditional birth attendants (TBAs) to perform interventions targeting birth asphyxia, hypothermia, and neonatal sepsis reduced all-cause neonatal mortality by 45%. This companion analysis was undertaken to analyze intervention costs and cost-effectiveness, and factors that might improve cost-effectiveness. Methods and Findings We calculated LUNESP's financial and economic costs and the economic cost of implementation for a forecasted ten-year program (2011–2020). In each case, we calculated the incremental cost per death avoided and disability-adjusted life years (DALYs) averted in real 2011 US dollars. The forecasted 10-year program analysis included a base case as well as ‘conservative’ and ‘optimistic’ scenarios. Uncertainty was characterized using one-way sensitivity analyses and a multivariate probabilistic sensitivity analysis. The estimated financial and economic costs of LUNESP were $118,574 and $127,756, respectively, or $49,469 and $53,550 per year. Fixed costs accounted for nearly 90% of total costs. For the 10-year program, discounted total and annual program costs were $256,455 and $26,834 respectively; for the base case, optimistic, and conservative scenarios, the estimated cost per death avoided was $1,866, $591, and $3,024, and cost per DALY averted was $74, $24, and $120, respectively. Outcomes were robust to variations in local costs, but sensitive to variations in intervention effect size, number of births attended by TBAs, and the extent of foreign consultants' participation. Conclusions Based on established guidelines, the strategy of using trained TBAs to reduce neonatal mortality was ‘highly cost effective’. We strongly recommend consideration of this approach for other remote rural populations with limited access to health care. PMID:22545117

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.

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.

The effect of constraints on the analytical figures of merit achieved by extended multivariate curve resolution-alternating least-squares.

PubMed

Pellegrino Vidal, Rocío B; Allegrini, Franco; Olivieri, Alejandro C

2018-03-20

Multivariate curve resolution-alternating least-squares (MCR-ALS) is the model of choice when dealing with some non-trilinear arrays, specifically when the data are of chromatographic origin. To drive the iterative procedure to chemically interpretable solutions, the use of constraints becomes essential. In this work, both simulated and experimental data have been analyzed by MCR-ALS, applying chemically reasonable constraints, and investigating the relationship between selectivity, analytical sensitivity (γ) and root mean square error of prediction (RMSEP). As the selectivity in the instrumental modes decreases, the estimated values for γ did not fully represent the predictive model capabilities, judged from the obtained RMSEP values. Since the available sensitivity expressions have been developed by error propagation theory in unconstrained systems, there is a need of developing new expressions or analytical indicators. They should not only consider the specific profiles retrieved by MCR-ALS, but also the constraints under which the latter ones have been obtained. Copyright © 2017 Elsevier B.V. All rights reserved.

Clinical performance of the Prostate Health Index (PHI) for the prediction of prostate cancer in obese men: data from the PROMEtheuS project, a multicentre European prospective study.

PubMed

Abrate, Alberto; Lazzeri, Massimo; Lughezzani, Giovanni; Buffi, Nicolòmaria; Bini, Vittorio; Haese, Alexander; de la Taille, Alexandre; McNicholas, Thomas; Redorta, Joan Palou; Gadda, Giulio M; Lista, Giuliana; Kinzikeeva, Ella; Fossati, Nicola; Larcher, Alessandro; Dell'Oglio, Paolo; Mistretta, Francesco; Freschi, Massimo; Guazzoni, Giorgio

2015-04-01

To test serum prostate-specific antigen (PSA) isoform [-2]proPSA (p2PSA), p2PSA/free PSA (%p2PSA) and Prostate Health Index (PHI) accuracy in predicting prostate cancer in obese men and to test whether PHI is more accurate than PSA in predicting prostate cancer in obese patients. The analysis consisted of a nested case-control study from the pro-PSA Multicentric European Study (PROMEtheuS) project. The study is registered at http://www.controlled-trials.com/ISRCTN04707454. The primary outcome was to test sensitivity, specificity and accuracy (clinical validity) of serum p2PSA, %p2PSA and PHI, in determining prostate cancer at prostate biopsy in obese men [body mass index (BMI) ≥30 kg/m(2) ], compared with total PSA (tPSA), free PSA (fPSA) and fPSA/tPSA ratio (%fPSA). The number of avoidable prostate biopsies (clinical utility) was also assessed. Multivariable logistic regression models were complemented by predictive accuracy analysis and decision-curve analysis. Of the 965 patients, 383 (39.7%) were normal weight (BMI <25 kg/m(2) ), 440 (45.6%) were overweight (BMI 25-29.9 kg/m(2) ) and 142 (14.7%) were obese (BMI ≥30 kg/m(2) ). Among obese patients, prostate cancer was found in 65 patients (45.8%), with a higher percentage of Gleason score ≥7 diseases (67.7%). PSA, p2PSA, %p2PSA and PHI were significantly higher, and %fPSA significantly lower in patients with prostate cancer (P < 0.001). In multivariable logistic regression models, PHI significantly increased accuracy of the base multivariable model by 8.8% (P = 0.007). At a PHI threshold of 35.7, 46 (32.4%) biopsies could have been avoided. In obese patients, PHI is significantly more accurate than current tests in predicting prostate cancer. © 2014 The Authors. BJU International © 2014 BJU International.

Knowledge of Human Papillomavirus and Acceptability to Vaccinate in Adolescents and Young Adults of the Moroccan Population.

PubMed

Zouheir, Yassine; Daouam, Samira; Hamdi, Salsabil; Alaoui, Abdelaaziz; Fechtali, Taoufiq

2016-06-01

Human papillomavirus (HPV) infection is estimated to play an etiologic role in 99.7% of cervical cancer. Vaccines can prevent up to 70% of the cervical cancer caused by HPV 16 and 18. The present study was designed to define the knowledge of HPV and HPV vaccine acceptability among Moroccan youth. DESIGN, SETTING, PARTICIPANTS, INTERVENTIONS, AND MAIN OUTCOME MEASURES: A nationwide anonymous questionnaire with a sample of 688 adolescents (12-17 years) and 356 young adults (18-30 years) was organized, that asked about HPV, origin of cervical cancer, Papanicolaou (Pap) test, and acceptability of HPV vaccine. Data were analyzed using univariate and multivariate logistic regression methods. Overall, a low frequency (213/1044 = 20%) of HPV knowledge was observed among the studied population. A multivariate model analysis showed that age, educational level, and knowledge of the Pap test remained significantly associated factors with HPV knowledge. Additionally, only 27% (282/1044) of participants were willing to accept HPV vaccination. Highest acceptability was observed among young adults compared with adolescents (166/356 = 46.6% vs 116/688 = 16.9%). Sixty-two percent (103/165) of male participants accepted the HPV vaccine compared with only 20.4% (179/879) of female participants. Educational level, type of school, and knowledge of the Pap test were associated factors with HPV vaccine acceptability in a multivariate model analysis. The present study showed a low level of HPV knowledge and HPV vaccine acceptability among Moroccan youth. Promotion of activities and sensitization are required to maximize public awareness in the future. This objective can be achieved with the use of media, active efforts by health care providers, and introduction of sexual education in school programs. Copyright © 2015 North American Society for Pediatric and Adolescent Gynecology. Published by Elsevier Inc. All rights reserved.

A Multi-Variable Approach to Diagnosing the Monthly Covariability of the Amazonian Radiative and Convective Diurnal Cycles

NASA Astrophysics Data System (ADS)

Dodson, J. B.; Taylor, P. C.

2016-12-01

The diurnal cycle of convection (CDC) greatly influences the water, radiative, and energy budgets in convectively active regions. For example, previous research of the Amazonian CDC has identified significant monthly covariability between the satellite-observed radiative and precipitation diurnal and multiple reanalysis-derived atmospheric state variables (ASVs) representing convective instability. However, disagreements between retrospective analysis products (reanalyses) over monthly ASV anomalies create significant uncertainty in the resulting covariability. Satellite observations of convective clouds can be used to characterize monthly anomalies in convective activity. CloudSat observes multiple properties of both deep convective cores and the associated anvils, and so is useful as an alternative to the use of reanalyses. CloudSat cannot observe the full diurnal cycle, but it can detect differences between daytime and nighttime convection. Initial efforts to use CloudSat data to characterize convective activity showed that the results are highly dependent on the choice of variable used to characterize the cloud. This is caused by a series of inverse relationships between convective frequency, cloud top height, radar reflectivity vertical profile, and other variables. A single, multi-variable index for convective activity based on CloudSat data may be useful to clarify the results. Principal component analysis (PCA) provides a method to create a multivariable index, where the first principal component (PC1) corresponds with convective instability. The time series of PC1 can then be used as a proxy for monthly variability in convective activity. The primary challenge presented involves determining the utility of PCA for creating a robust index for convective activity that accounts for the complex relationships of multiple convective cloud variables, and yields information about the interactions between convection, the convective environment, and radiation beyond the previous single-variable approaches. The choice of variables used to calculate PC1 may influence any results based on PC1, so it is necessary to test the sensitivity of the results to different variable combinations.

Use of Neuroanatomical Pattern Classification to Identify Subjects in At-Risk Mental States of Psychosis and Predict Disease Transition

PubMed Central

Koutsouleris, Nikolaos; Meisenzahl, Eva M.; Davatzikos, Christos; Bottlender, Ronald; Frodl, Thomas; Scheuerecker, Johanna; Schmitt, Gisela; Zetzsche, Thomas; Decker, Petra; Reiser, Maximilian; Möller, Hans-Jürgen; Gaser, Christian

2014-01-01

Context Identification of individuals at high risk of developing psychosis has relied on prodromal symptomatology. Recently, machine learning algorithms have been successfully used for magnetic resonance imaging–based diagnostic classification of neuropsychiatric patient populations. Objective To determine whether multivariate neuroanatomical pattern classification facilitates identification of individuals in different at-risk mental states (ARMS) of psychosis and enables the prediction of disease transition at the individual level. Design Multivariate neuroanatomical pattern classification was performed on the structural magnetic resonance imaging data of individuals in early or late ARMS vs healthy controls (HCs). The predictive power of the method was then evaluated by categorizing the baseline imaging data of individuals with transition to psychosis vs those without transition vs HCs after 4 years of clinical follow-up. Classification generalizability was estimated by cross-validation and by categorizing an independent cohort of 45 new HCs. Setting Departments of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany. Participants The first classification analysis included 20 early and 25 late at-risk individuals and 25 matched HCs. The second analysis consisted of 15 individuals with transition, 18 without transition, and 17 matched HCs. Main Outcome Measures Specificity, sensitivity, and accuracy of classification. Results The 3-group, cross-validated classification accuracies of the first analysis were 86% (HCs vs the rest), 91% (early at-risk individuals vs the rest), and 86% (late at-risk individuals vs the rest). The accuracies in the second analysis were 90% (HCs vs the rest), 88% (individuals with transition vs the rest), and 86% (individuals without transition vs the rest). Independent HCs were correctly classified in 96% (first analysis) and 93% (second analysis) of cases. Conclusions Different ARMSs and their clinical outcomes may be reliably identified on an individual basis by assessing patterns of whole-brain neuroanatomical abnormalities. These patterns may serve as valuable biomarkers for the clinician to guide early detection in the prodromal phase of psychosis. PMID:19581561

Mass spectrometry-based metabolic profiling of gemcitabine-sensitive and gemcitabine-resistant pancreatic cancer cells.

PubMed

Fujimura, Yoshinori; Ikenaga, Naoki; Ohuchida, Kenoki; Setoyama, Daiki; Irie, Miho; Miura, Daisuke; Wariishi, Hiroyuki; Murata, Masaharu; Mizumoto, Kazuhiro; Hashizume, Makoto; Tanaka, Masao

2014-03-01

Gemcitabine resistance (GR) is one of the critical issues for therapy for pancreatic cancer, but the mechanism still remains unclear. Our aim was to increase the understanding of GR by metabolic profiling approach. To establish GR cells, 2 human pancreatic cancer cell lines, SUIT-2 and CAPAN-1, were exposed to increasing concentration of gemcitabine. Both parental and chemoresistant cells obtained by this treatment were subjected to metabolic profiling based on liquid chromatography-mass spectrometry. Multivariate statistical analyses, both principal component analysis and orthogonal partial least squares discriminant analysis, distinguished metabolic signature of responsiveness and resistance to gemcitabine in both SUIT-2 and CAPAN-1 cells. Among significantly different (P < 0.005) metabolite peaks between parental and GR cells, we identified metabolites related to several metabolic pathways such as amino acid, nucleotide, energy, cofactor, and vitamin pathways. Decreases in glutamine and proline levels as well as increases in aspartate, hydroxyproline, creatine, and creatinine levels were observed in chemoresistant cells from both cell lines. These results suggest that metabolic profiling can isolate distinct features of pancreatic cancer in the metabolome of gemcitabine-sensitive and GR cells. These findings may contribute to the biomarker discovery and an enhanced understanding of GR in pancreatic cancer.

Recent advances in the use of laser-induced breakdown spectroscopy (LIBS) as a rapid point-of-care pathogen diagnostic

NASA Astrophysics Data System (ADS)

Rehse, Steven; Trojand, Daniel; Putnam, Russell; Gillies, Derek; Woodman, Ryan; Sheikh, Khadija; Daabous, Andrew

2013-05-01

There is a well-known and urgent need in the fields of medicine, environmental health and safety, food-processing, and defense/security to develop new 21st Century technologies for the rapid and sensitive identification of bacterial pathogens. In only the last five years, the use of a real-time elemental (atomic) analysis performed with laser-induced breakdown spectroscopy (LIBS) has made tremendous progress in becoming a viable technology for rapid bacterial pathogen detection and identification. In this talk we will show how this laser-based optical emission spectroscopic technique is able to sensitively assay the elemental composition of bacterial cells in situ. We will also present the latest achievements of our lab to fully develop LIBS-based bacterial sensing including simulation of a rapid urinary tract infection diagnosis and investigation of a variety of autonomous multivariate analysis algorithms. Lastly, we will show how this technology is now ready to be transitioned from the laboratory to field-portable and potentially man-portable instrumentation. The introduction of such a technology into popular use could very well transform the field of bacterial biosensing - a market valued at approximately 10 billion/year world-wide. Funding for this project was provided in part by a Natural Sciences and Engineering Research Council of Canada Discovery Grant.

The correlation between highly sensitive C-reactive protein levels and erectile function among men with late-onset hypogonadism.

PubMed

Shigehara, Kazuyoshi; Konaka, Hiroyuki; Ijima, Masashi; Nohara, Takahiro; Narimoto, Kazutaka; Izumi, Koji; Kadono, Yoshifumi; Kitagawa, Yasuhide; Mizokami, Atsushi; Namiki, Mikio

2016-12-01

We investigated the correlation between highly sensitive C-reactive protein (hs-CRP) levels and erectile function, and assessed the clinical role of hs-CRP levels in men with late-onset hypogonadism (LOH) syndrome. For 77 participants, we assessed Sexual Health Inventory for men (SHIM) score, Aging Male Symptoms (AMS) score and International Prostate Symptom Score (IPSS). We also evaluated free testosterone (FT), hs-CRP, total cholesterol, triglyceride levels, high density lipoprotein cholesterol, hemoglobin A1c, body mass index, waist size and blood pressure. We attempted to identify parameters correlated with SHIM score and to determine the factors affecting cardiovascular risk based on hs-CRP levels. A Spearman rank correlation test revealed that age, AMS score, IPSS and hs-CRP levels were significantly correlated with SHIM score. Age-adjusted analysis revealed that hs-CRP and IPSS were the independent factors affecting SHIM score (r= -0.304 and -0.322, respectively). Seventeen patients belonged to the moderate to high risk group for cardiovascular disease, whereas the remaining 60 belonged to the low risk group. Age, FT value and SHIM score showed significant differences between the two groups. A multivariate regression analysis demonstrated that SHIM score was an independent factor affecting cardiovascular risk (OR: 0.796; 95%CI: 0.637-0.995).

Rationalizing context-dependent performance of dynamic RNA regulatory devices.

PubMed

Kent, Ross; Halliwell, Samantha; Young, Kate; Swainston, Neil; Dixon, Neil

2018-06-21

The ability of RNA to sense, regulate and store information is an attractive attribute for a variety of functional applications including the development of regulatory control devices for synthetic biology. RNA folding and function is known to be highly context sensitive, which limits the modularity and reuse of RNA regulatory devices to control different heterologous sequences and genes. We explored the cause and effect of sequence context sensitivity for translational ON riboswitches located in the 5' UTR, by constructing and screening a library of N-terminal synonymous codon variants. By altering the N-terminal codon usage we were able to obtain RNA devices with a broad range of functional performance properties (ON, OFF, fold-change). Linear regression and calculated metrics were used to rationalize the major determining features leading to optimal riboswitch performance, and to identify multiple interactions between the explanatory metrics. Finally, partial least squared (PLS) analysis was employed in order to understand the metrics and their respective effect on performance. This PLS model was shown to provide good explanation of our library. This study provides a novel multi-variant analysis framework by which to rationalize the codon context performance of allosteric RNA-devices. The framework will also serve as a platform for future riboswitch context engineering endeavors.

Simultaneous determination of vitamin B12 and its derivatives using some of multivariate calibration 1 (MVC1) techniques

NASA Astrophysics Data System (ADS)

Samadi-Maybodi, Abdolraouf; Darzi, S. K. Hassani Nejad

2008-10-01

Resolution of binary mixtures of vitamin B12, methylcobalamin and B12 coenzyme with minimum sample pre-treatment and without analyte separation has been successfully achieved by methods of partial least squares algorithm with one dependent variable (PLS1), orthogonal signal correction/partial least squares (OSC/PLS), principal component regression (PCR) and hybrid linear analysis (HLA). Data of analysis were obtained from UV-vis spectra. The UV-vis spectra of the vitamin B12, methylcobalamin and B12 coenzyme were recorded in the same spectral conditions. The method of central composite design was used in the ranges of 10-80 mg L -1 for vitamin B12 and methylcobalamin and 20-130 mg L -1 for B12 coenzyme. The models refinement procedure and validation were performed by cross-validation. The minimum root mean square error of prediction (RMSEP) was 2.26 mg L -1 for vitamin B12 with PLS1, 1.33 mg L -1 for methylcobalamin with OSC/PLS and 3.24 mg L -1 for B12 coenzyme with HLA techniques. Figures of merit such as selectivity, sensitivity, analytical sensitivity and LOD were determined for three compounds. The procedure was successfully applied to simultaneous determination of three compounds in synthetic mixtures and in a pharmaceutical formulation.

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.

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.

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.

Detection of Motor Impairment in Parkinson's Disease Via Mobile Touchscreen Typing.

PubMed

Arroyo-Gallego, Teresa; Ledesma-Carbayo, Maria Jesus; Sanchez-Ferro, Alvaro; Butterworth, Ian; Mendoza, Carlos S; Matarazzo, Michele; Montero, Paloma; Lopez-Blanco, Roberto; Puertas-Martin, Veronica; Trincado, Rocio; Giancardo, Luca

2017-09-01

Mobile technology is opening a wide range of opportunities for transforming the standard of care for chronic disorders. Using smartphones as tools for longitudinally tracking symptoms could enable personalization of drug regimens and improve patient monitoring. Parkinson's disease (PD) is an ideal candidate for these tools. At present, evaluation of PD signs requires trained experts to quantify motor impairment in the clinic, limiting the frequency and quality of the information available for understanding the status and progression of the disease. Mobile technology can help clinical decision making by completing the information of motor status between hospital visits. This paper presents an algorithm to detect PD by analyzing the typing activity on smartphones independently of the content of the typed text. We propose a set of touchscreen typing features based on a covariance, skewness, and kurtosis analysis of the timing information of the data to capture PD motor signs. We tested these features, both independently and in a multivariate framework, in a population of 21 PD and 23 control subjects, achieving a sensitivity/specificity of 0.81/0.81 for the best performing feature and 0.73/0.84 for the best multivariate method. The results of the alternating finger-tapping, an established motor test, measured in our cohort are 0.75/0.78. This paper contributes to the development of a home-based, high-compliance, and high-frequency PD motor test by analysis of routine typing on touchscreens.

Prevalence and risk factors for Staphylococcus aureus and methicillin-resistant Staphylococcus aureus nasal carriage inpatients in a tertiary care hospital's chest clinic in Turkey.

PubMed

Oguzkaya-Artan, M; Artan, C; Baykan, Z

2016-01-01

We aimed to determine the prevalence and associated risk factors for nasal methicillin-sensitive and methicillin-resistant Staphylococcus aureus (MSSA/MRSA) carriage among patients admitted to a chest clinic of a tertiary care hospital in this study. Nasal samples were taken from anterior nares were cultured in CHROMagar S. aureus plates, MRSA was determined by disc diffusion method (cefoxitin 30 μg) according to the Clinical and Laboratory Standards Institute guidelines and CHROMagar MRSA plates. A questionnaire was applied to determine the demographic characteristics of the participants and risk factors for carriage. Fisher's exact test, univariate and multivariate logistic regression analysis were used. A P < 0.05 indicated a statistically significant difference. This is a cross-sectional study covering all the patients (n = 431) admitted to Kayseri Training and Research Hospital's Chest Clinic from January 1st to 31st 2014. Of all these patients 55 (12.8%) were nasal S. aureus carriers. MRSA positivity was in five among these patients. In multivariate analysis, being under 65 years of age (odds ratio [OR], 1.9; 95% confidence interval [CI], 1.0-3.3), and having prosthesis (OR, 4.8; 95% CI, 1.6-13.9) were found as risk factors for MSSA colonization. The prevalence of nasal carriage of MSSA was low in our study population. The only risk factors playing role in carriage were found as being under the age of 65 and having prosthesis.

Low enhancement on multiphase contrast-enhanced CT images: an independent predictor of the presence of high tumor grade of clear cell renal cell carcinoma.

PubMed

Zhu, Ye-Hua; Wang, Xun; Zhang, Jin; Chen, Yong-Hui; Kong, Wen; Huang, Yi-Ran

2014-09-01

The purpose of this study was to assess the relation between tumor enhancement on multiphase contrast-enhanced CT images and Fuhrman grade of clear cell renal cell carcinoma. A single-institution retrospective review was conducted on the records of 255 patients who underwent radical or partial nephrectomy and received a histologic diagnosis of clear cell renal cell carcinoma. Two radiologists recorded the radiographic features of each patient, including the attenuation value of the lesion, lesion size, calcification within the lesion, cystic versus solid appearance, and margin regularity. Parameters representing the extent of tumor enhancement were defined and calculated. The association between tumor enhancement and Fuhrman grade was analyzed, and multivariate analysis was performed to find independent predictors of high tumor grade. Significant differences existed in tumor enhancement among different Fuhrman grades (p < 0.001). High-grade tumors had significantly lower enhancement (p < 0.001). The enhancement parameter had a sensitivity of 0.84 and specificity of 0.93 in prediction of high tumor grade. In the multivariate analysis, more advanced age, irregular margin, and low tumor enhancement were the three independent predictors of high tumor grade. Tumor enhancement of clear cell renal cell carcinoma on multiphase contrast-enhanced CT images is associated with Fuhrman grade. Low tumor enhancement in the corticomedullary phase is an independent predictor of high tumor grade. This system may be helpful in clinical decision making about the care of patients treated by nonsurgical approaches.

Pancreatic thickness as a predictive factor for postoperative pancreatic fistula after distal pancreatectomy using an endopath stapler.

PubMed

Okano, Keiichi; Oshima, Minoru; Kakinoki, Keitaro; Yamamoto, Naoki; Akamoto, Shintaro; Yachida, Shinichi; Hagiike, Masanobu; Kamada, Hideki; Masaki, Tsutomu; Suzuki, Yasuyuki

2013-02-01

No consistent risk factor has yet been established for the development of pancreatic fistula (PF) after distal pancreatectomy (DP) with a stapler. A total of 31 consecutive patients underwent DP with an endopath stapler between June 2006 and December 2010 using a slow parenchymal flattening technique. The risk factors for PF after DP with an endopath stapler were identified based on univariate and multivariate analyses. Clinical PF developed in 7 of 31 (22 %) patients who underwent DP with a stapler. The pancreata were significantly thicker at the transection line in patients with PF (19.4 ± 1.47 mm) in comparison to patients without PF (12.6 ± 0.79 mm; p = 0.0003). A 16-mm cut-off for pancreatic thickness was established based on the receiver operating characteristic (ROC) curve; the area under the ROC curve was 0.875 (p = 0.0215). Pancreatic thickness (p = 0.0006) and blood transfusion (p = 0.028) were associated with postoperative PF in a univariate analysis. Pancreatic thickness was the only significant independent factor (odds ratio 9.99; p = 0.036) according to a multivariate analysis with a specificity of 72 %, and a sensitivity of 85 %. Pancreatic thickness is a significant independent risk factor for PF development after DP with an endopath stapler. The stapler technique is thus considered to be an appropriate modality in patients with a pancreatic thicknesses of <16 mm.

Shifting chronic disease management from hospitals to primary care in Estonian health system: analysis of national panel data.

PubMed

Atun, Rifat; Gurol-Urganci, Ipek; Hone, Thomas; Pell, Lisa; Stokes, Jonathan; Habicht, Triin; Lukka, Kaija; Raaper, Elin; Habicht, Jarno

2016-12-01

Following independence from the Soviet Union in 1991, Estonia introduced a national insurance system, consolidated the number of health care providers, and introduced family medicine centred primary health care (PHC) to strengthen the health system. Using routinely collected health billing records for 2005-2012, we examine health system utilisation for seven ambulatory care sensitive conditions (ACSCs) (asthma, chronic obstructive pulmonary disease [COPD], depression, Type 2 diabetes, heart failure, hypertension, and ischemic heart disease [IHD]), and by patient characteristics (gender, age, and number of co-morbidities). The data set contained 552 822 individuals. We use patient level data to test the significance of trends, and employ multivariate regression analysis to evaluate the probability of inpatient admission while controlling for patient characteristics, health system supply-side variables, and PHC use. Over the study period, utilisation of PHC increased, whilst inpatient admissions fell. Service mix in PHC changed with increases in phone, email, nurse, and follow-up (vs initial) consultations. Healthcare utilisation for diabetes, depression, IHD and hypertension shifted to PHC, whilst for COPD, heart failure and asthma utilisation in outpatient and inpatient settings increased. Multivariate regression indicates higher probability of inpatient admission for males, older patient and especially those with multimorbidity, but protective effect for PHC, with significantly lower hospital admission for those utilising PHC services. Our findings suggest health system reforms in Estonia have influenced the shift of ACSCs from secondary to primary care, with PHC having a protective effect in reducing hospital admissions.

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

USGS Publications Warehouse

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

2001-01-01

Six treatments of eastern Kansas tallgrass prairie - native prairie, hayed, mowed, grazed, burned and untreated - were studied to examine the biophysical effects of land management practices on grasslands. On each treatment, measurements of plant biomass, leaf area index, plant cover, leaf moisture and soil moisture were collected. In addition, measurements were taken of the Normalized Difference Vegetation Index (NDVI), which is derived from spectral reflectance measurements. Measurements were taken in mid-June, mid-July and late summer of 1990 and 1991. Multivariate analysis of variance was used to determine whether there were differences in the set of variables among treatments and years. Follow-up tests included univariate t-tests to determine which variables were contributing to any significant difference. Results showed a significant difference (p < 0.0005) among treatments in the composite of parameters during each of the months sampled. In most treatment types, there was a significant difference between years within each month. The univariate tests showed, however, that only some variables, primarily soil moisture, were contributing to this difference. We conclude that biomass and % plant cover show the best potential to serve as long-term indicators of grassland condition as they generally were sensitive to effects of different land management practices but not to yearly change in weather conditions. NDVI was insensitive to precipitation differences between years in July for most treatments, but was not in the native prairie. Choice of sampling time is important for these parameters to serve effectively as indicators.

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

Different levels of UV-B resistance in Vaccinium corymbosum cultivars reveal distinct backgrounds of phenylpropanoid metabolites.

PubMed

Luengo Escobar, Ana; Magnum de Oliveira Silva, Franklin; Acevedo, Patricio; Nunes-Nesi, Adriano; Alberdi, Miren; Reyes-Díaz, Marjorie

2017-09-01

UV-B radiation induces several physiological and biochemical effects that can influence regulatory plant processes. Vaccinium corymbosum responds differently to UV-B radiation depending on the UV-B resistance of cultivars, according to their physiological and biochemical features. In this work, the effect of two levels of UV-B radiation during long-term exposure on the phenylpropanoid biosynthesis, and the expression of genes associated with flavonoid biosynthesis as well as the absolute quantification of secondary metabolites were studied in two contrasting UV-B-resistant cultivars (Legacy, resistant and Bluegold, sensitive). Multivariate analyses were performed to understand the role of phenylpropanoids in UV-B defense mechanisms. The amount of phenylpropanoid compounds was generally higher in Legacy than in Bluegold. Different expression levels of flavonoid biosynthetic genes for both cultivars were transiently induced, showing that even in longer period of UV-B exposure; plants are still adjusting their phenylpropanoids at the transcription levels. Multivariate analysis in Legacy indicated no significant correlation between gene expression and the levels of the flavonoids and phenolic acids. By contrast, in the Bluegold cultivar higher number of correlations between secondary metabolite and transcript levels was found. Taken together, the results indicated different adjustments between the cultivars for a successful UV-B acclimation. While the sensitive cultivar depends on metabolite adjustments to respond to UV-B exposure, the resistant cultivar also possesses an intrinsically higher antioxidant and UV-B screening capacity. Thus, we conclude that UV-B resistance involves not only metabolite level adjustments during the acclimation period, but also depends on the intrinsic metabolic status of the plant and metabolic features of the phenylpropanoid compounds. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

aMMP-8 in correlation to caries and periodontal condition in adolescents-results of the epidemiologic LIFE child study.

PubMed

Schmidt, Jana; Guder, Ulrike; Kreuz, Markus; Löffler, Markus; Kiess, Wieland; Hirsch, Christian; Ziebolz, Dirk; Haak, Rainer

2018-01-01

The suitability of a chairside aMMP-8 test in determination of periodontal inflammation and caries in adolescents was assessed. Secondly, the influence of orthodontic treatment on aMMP-8 test result was analyzed. Within the LIFE Child study, 434 adolescents (10 to 18 years) were included. Clinical dental examinations comprised caries experience (DMF/T-Index), signs of periodontal inflammation (probing pocket depth, PPD; community periodontal index of treatment needs; CPITN) at six index teeth and oral hygiene (OH). Information about orthodontic appliances (OA) and socioeconomic status (SES) were obtained by validated questionnaires. Test's sensitivity and specificity to detect periodontal inflammation and carious lesions were evaluated. The influence of OA on the test result was analyzed (multivariate model). No associations between age, gender, SES or OH, and test outcome were found (p > 0.05). Positive test results correlated to periodontal findings (CPITN, mean PPD; p < 0.001). However, for the detection of ≥ 1 site(s) with PPD ≥ 4 mm, the test's sensitivity and specificity were found to be 61 and 69%, respectively. Multivariate analysis revealed a higher probability for a positive test result in cases of fixed OA (odds ratio 5.02, 95% confidence interval 1.90-13.19). The test had no diagnostic value considering carious lesions. The chairside aMMP-8 test does not reliably identify adolescents with periodontal inflammation. Positive test results were more frequent in case of OA. The chairside aMMP-8 test is no appropriate tool to screen children and adolescents neither for periodontal inflammation nor for carious lesions.

Lack of emergency medical services documentation is associated with poor patient outcomes: a validation of audit filters for prehospital trauma care.

PubMed

Laudermilch, Dann J; Schiff, Melissa A; Nathens, Avery B; Rosengart, Matthew R

2010-02-01

Our previous Delphi study identified several audit filters considered sensitive to deviations in prehospital trauma care and potentially useful in conducting performance improvement, a process currently recommended by the American College of Surgeons Committee on Trauma. This study validates 2 of those proposed audit filters. We studied 4,744 trauma patients using the electronic records of the Central Region Trauma registry and Emergency Medical Services (EMS) patient logs for the period January 1, 2002, to December 31, 2004. We studied whether requests by on-scene Basic Life Support (BLS) for Advanced Life Support (ALS) assistance or failure by EMS personnel to record basic patient physiology at the scene was associated with increased in-hospital mortality. We performed multivariate analyses, including a propensity score quintile approach, adjusting for differences in case mix and clustering by hospital. Overall mortality was 6.1%. A total of 28.2% (n = 1,337) of EMS records were missing patient scene physiologic data. Multivariate analysis revealed that patients missing 1 or more measures of patient physiology at the scene had increased risk of death (adjusted odds ratio = 2.15; 95% CI, 1.13 to 4.10). In 17.4% (n = 402) of cases BLS requested ALS assistance. Patients for whom BLS requested ALS had a similar risk of death as patients for whom ALS was initially dispatched (odds ratio = 1.04; 95% CI, 0.51 to 2.15). Failure of EMS to document basic measures of scene physiology is associated with increased mortality. This deviation in care can serve as a sensitive audit filter for performance improvement. The need by BLS for ALS assistance was not associated with increased mortality.

A FABP-ulous 'rule out' strategy? Heart fatty acid binding protein and troponin for rapid exclusion of acute myocardial infarction.

PubMed

Body, Richard; McDowell, Garry; Carley, Simon; Wibberley, Christopher; Ferguson, Jamie; Mackway-Jones, Kevin

2011-08-01

Many Emergency Departments (EDs) utilise 'triple marker' testing with CK-MB, myoglobin and troponin I (cTnI) to exclude acute myocardial infarction (AMI) within hours of presentation. We evaluated the ability of 8 biomarkers to rapidly exclude AMI at the point of presentation and investigated whether 'triple marker' testing represents the optimal multimarker strategy. We recruited patients who presented to the ED with suspected cardiac chest pain occurring within 24 h. Blood was drawn at the time of presentation. Diagnostic value was assessed by calculating the area under the ROC curve (AUC) and a multivariate model was constructed by logistic regression. The primary outcome was a diagnosis of AMI, established by ≥12-h troponin testing in all patients. 705 included patients underwent venepuncture a median of 3.5 h after symptom onset. Heart fatty acid binding protein (H-FABP) had an AUC of 0.86 (95% CI 0.82-0.90), which was significantly higher than any other biomarker including cTnI. While no single biomarker could enable exclusion of AMI, multivariate analysis identified cTnI and H-FABP as the optimal biomarker combination. Combined with clinical risk stratification, this strategy had a sensitivity of 96.9%, specificity of 54.7%, PPV 32.4% and NPV 98.8%. We have derived an algorithm that would enable AMI to be immediately excluded in 315 (44.7%) patients at the cost of missing 6 AMIs per 1000 patients treated. While the risk is likely to be unacceptable for clinical implementation, we have highlighted an area for future development using serial testing and increasingly sensitive assays. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Critical elements on fitting the Bayesian multivariate Poisson Lognormal model

NASA Astrophysics Data System (ADS)

Zamzuri, Zamira Hasanah binti

2015-10-01

Motivated by a problem on fitting multivariate models to traffic accident data, a detailed discussion of the Multivariate Poisson Lognormal (MPL) model is presented. This paper reveals three critical elements on fitting the MPL model: the setting of initial estimates, hyperparameters and tuning parameters. These issues have not been highlighted in the literature. Based on simulation studies conducted, we have shown that to use the Univariate Poisson Model (UPM) estimates as starting values, at least 20,000 iterations are needed to obtain reliable final estimates. We also illustrated the sensitivity of the specific hyperparameter, which if it is not given extra attention, may affect the final estimates. The last issue is regarding the tuning parameters where they depend on the acceptance rate. Finally, a heuristic algorithm to fit the MPL model is presented. This acts as a guide to ensure that the model works satisfactorily given any data set.

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.

Highly sensitive time-resolved thermography and multivariate image analysis of the cerebral cortex for intrasurgical diagnostics

NASA Astrophysics Data System (ADS)

Hollmach, Julia; Hoffmann, Nico; Schnabel, Christian; Küchler, Saskia; Sobottka, Stephan; Kirsch, Matthias; Schackert, Gabriele; Koch, Edmund; Steiner, Gerald

2013-03-01

Time-resolved thermography is a novel method to assess thermal variations and heterogeneities in tissue and blood. The recent generation of thermal cameras provides a sensitivity of less than mK. This high sensitivity in conjunction with non-invasive, label-free and radiation-free monitoring makes thermography a promising tool for intrasurgical diagnostics. In brain surgery, time-resolved thermography can be employed to distinguish between normal and anomalous tissue. In this study, we investigated and discussed the potential of time-resolved thermography in neurosurgery for the intraoperative detection and demarcation of tumor borders. Algorithms for segmentation, reduction of movement artifacts and image fusion were developed. The preprocessed image stacks were subjected to discrete wavelet transform to examine individual frequency components. K-means clustering was used for image evaluation to reveal similarities within the image sequence. The image evaluation shows significant differences for both types of tissue. Tumor and normal tissues have different time characteristics in heat production and transfer. Furthermore, tumor could be highlighted. These results demonstrate that time-resolved thermography is able to support the detection of tumors in a contactless manner without any side effects for the tissue. The intraoperative usage of time-resolved thermography improves the accuracy of tumor resections to prevent irreversible brain damage during surgery.

Pretreatment 14-3-3 epsilon level is predictive for advanced extranodal NK/T cell lymphoma therapeutic response to asparaginase-based chemotherapy.

PubMed

Qiu, Yajuan; Zhou, Zhiyuan; Li, Zhaoming; Lu, Lisha; Li, Ling; Li, Xin; Wang, Xinhua; Zhang, Mingzhi

2017-03-01

The aim of the present study was to identify the potential relevant biomarkers to predict the therapeutic response of advanced extranodal natural killer/T cell lymphoma(ENKTL) treated with asparaginase-based treatment. Proteomic technology is used to identify differentially expressed proteins between chemotherapy-resistant and chemotherapy-sensitive patients. Then enzyme-linked immunosorbent assay is used to validate the predictive value of selective biomarkers. A total of 61 upregulated and 22 downregulated proteins are identified in chemotherapy-resistant patients compared with chemotherapy-sensitive patients. Furthermore, they validated that pretreatment high level 14-3-3 epsilon(ε)(≥61.95 ng/mL, 84.0 and 95.2% for sensitivity and specificity, respectively) is associated with poor 2-year overall survival (OS) (5.3 vs 68.8%, p<0.0001) and PFS (4.5 vs 76.9%, p<0.0001). In multivariate survival analysis, pretreatment high level 14-3-3 epsilon significantly is correlated with both inferior OS (p = 0.033) and PFS (p = 0.005). These findings indicate that pretreatment high level 14-3-3 epsilon is an independent predictor of chemotherapy-resistance and poor prognosis for patients with advanced ENKTL in the era of asparaginase. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Interpersonal sensitivity associated with return to work status following sick leave: a cross-sectional study among Japanese workers with major depressive disorder.

PubMed

Ogawa, Tetsuo; Shigemura, Jun; Yoshino, Aihide; Nomura, Soichiro

2013-04-01

We examined the relationship between return to work (RTW) from sick leave (SL) and personality traits in workers with major depressive disorder (MDD). Eighty-eight Japanese individuals with ≥2 weeks of SL episode and with ≥2 months of pharmacotherapy history were assessed. Measurements included Mini-International Neuropsychiatric Interview (MINI), Hamilton Rating Scale for Depression (HAM-D), Neuroticism, and Interpersonal Sensitivity Measure (IPSM). Multivariate analyses were conducted to clarify the association between personality traits and RTW status. In order to minimize the state effect of depressive symptoms to personality traits, we performed an additional analysis among a subgroup of subjects in remission (HAM-D ≤7). Thirty-seven subjects (42.0%) had returned to work. Among whole subjects, factors associated with RTW status were: shorter SL duration in the past 5 years, longer treatment duration of the recent major depressive episode, HAM-D ≤7, and IPSM ≤94. In the subgroup of remission subjects (n=53), factors associated with RTW status were: IPSM ≤94, no comorbid current anxiety disorder, and shorter SL duration in the past 5 years. Low interpersonal sensitivity, along with depression remission, was associated with post-SL RTW status among workers with MDD. Copyright © 2012 Elsevier B.V. All rights reserved.

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.

Dissociation between the Prevalence of Atopy and Allergic Disease in Rural China among Children and Adults

PubMed Central

Kim, Jennifer S; Ouyang, Fengxiu; Pongracic, Jacqueline A; Fang, Yaping; Wang, Binyan; Liu, Xue; Xing, Houxun; Caruso, Deanna; Liu, Xin; Zhang, Shanchun; Xu, Xiping; Wang, Xiaobin

2009-01-01

Background The prevalence of allergic diseases is increasing worldwide, but the reasons are not well understood. Previous studies suggest that this trend may be associated with lifestyle and urbanization. Objective To describe patterns of sensitization and allergic disease in an unselected agricultural Chinese population. Methods The data was derived from a community-based twin study in Anqing, China. Skin prick testing was performed to foods and aeroallergens. Atopy was defined as sensitization to ≥1 allergen. Allergic disease was ascertained by self-report. The analysis was stratified by sex and age (children [11-17 years] and adults [≥18 years]) and included 1059 same-sex twin pairs. Results Of 2118 subjects, 57.6% were male (n=1220). Ages ranged from 11-71 years; 43.3% were children (n=918). Atopy was observed in 47.2% (n=999) of participants. The most common sensitizing foods were shellfish (16.7%) and peanut (12.3%). The most common sensitizing aeroallergens were dust mite (30.6%) and cockroach (25.2%). Birth order and zygosity had no effect on sensitization rates. Multivariate logistic regression models revealed risk factors for sensitization include age for foods and sex for aeroallergens. The rates of food allergy and asthma were estimated to be <1%. Conclusions Atopic sensitization was common in this rural farming Chinese population, particularly to shellfish, peanut, dust mite, and cockroach. The prevalence of allergic disease, in contrast, was quite low. Clinical Implications Allergen sensitization was far more common than the rate of self-reported allergic disease in this community. Evidence of sensitization is an inadequate marker of allergic disease and better correlates with clinical disease are needed. Capsule summary Among this large unselected Chinese rural farming community, atopy was observed in nearly half of the study subjects, but the rate of allergic disease was comparatively very low. PMID:18805578

High MICs for Vancomycin and Daptomycin and Complicated Catheter-Related Bloodstream Infections with Methicillin-Sensitive Staphylococcus aureus

PubMed Central

Viedma, Esther; Chaves, Fernando; Lalueza, Antonio; Fortún, Jesús; Loza, Elena; Pujol, Miquel; Ardanuy, Carmen; Morales, Isabel; de Cueto, Marina; Resino-Foz, Elena; Morales-Cartagena, Alejandra; Rico, Alicia; Romero, María P.; Orellana, María Ángeles; López-Medrano, Francisco; Fernández-Ruiz, Mario; Aguado, José María

2016-01-01

We investigated the prognostic role of high MICs for antistaphylococcal agents in patients with methicillin-sensitive Staphylococcus aureus catheter-related bloodstream infection (MSSA CRBSI). We prospectively reviewed 83 episodes from 5 centers in Spain during April 2011–June 2014 that had optimized clinical management and analyzed the relationship between E-test MICs for vancomycin, daptomycin, oxacillin, and linezolid and development of complicated bacteremia by using multivariate analysis. Complicated MSSA CRBSI occurred in 26 (31.3%) patients; MICs for vancomycin and daptomycin were higher in these patients (optimal cutoff values for predictive accuracy = 1.5 μg/mL and 0.5 μg/mL). High MICs for vancomycin (hazard ratio 2.4, 95% CI 1.2–5.5) and daptomycin (hazard ratio 2.4, 95% CI 1.1–5.9) were independent risk factors for development of complicated MSSA CRBSI. Our data suggest that patients with MSSA CRBSI caused by strains that have high MICs for vancomycin or daptomycin are at increased risk for complications. PMID:27192097

Distributed and opposing effects of incidental learning in the human brain.

PubMed

Hall, Michelle G; Naughtin, Claire K; Mattingley, Jason B; Dux, Paul E

2018-06-01

Incidental learning affords a behavioural advantage when sensory information matches regularities that have previously been encountered. Previous studies have taken a focused approach by probing the involvement of specific candidate brain regions underlying incidentally acquired memory representations, as well as expectation effects on early sensory representations. Here, we investigated the broader extent of the brain's sensitivity to violations and fulfilments of expectations, using an incidental learning paradigm in which the contingencies between target locations and target identities were manipulated without participants' overt knowledge. Multivariate analysis of functional magnetic resonance imaging data was applied to compare the consistency of neural activity for visual events that the contingency manipulation rendered likely versus unlikely. We observed widespread sensitivity to expectations across frontal, temporal, occipital, and sub-cortical areas. These activation clusters showed distinct response profiles, such that some regions displayed more reliable activation patterns under fulfilled expectations, whereas others showed more reliable patterns when expectations were violated. These findings reveal that expectations affect multiple stages of information processing during visual decision making, rather than early sensory processing stages alone. Copyright © 2018 Elsevier Inc. All rights reserved.

HPLC and chemometrics-assisted UV-spectroscopy methods for the simultaneous determination of ambroxol and doxycycline in capsule

NASA Astrophysics Data System (ADS)

Hadad, Ghada M.; El-Gindy, Alaa; Mahmoud, Waleed M. M.

2008-08-01

High-performance liquid chromatography (HPLC) and multivariate spectrophotometric methods are described for the simultaneous determination of ambroxol hydrochloride (AM) and doxycycline (DX) in combined pharmaceutical capsules. The chromatographic separation was achieved on reversed-phase C 18 analytical column with a mobile phase consisting of a mixture of 20 mM potassium dihydrogen phosphate, pH 6-acetonitrile in ratio of (1:1, v/v) and UV detection at 245 nm. Also, the resolution has been accomplished by using numerical spectrophotometric methods as classical least squares (CLS), principal component regression (PCR) and partial least squares (PLS-1) applied to the UV spectra of the mixture and graphical spectrophotometric method as first derivative of the ratio spectra ( 1DD) method. Analytical figures of merit (FOM), such as sensitivity, selectivity, analytical sensitivity, limit of quantitation and limit of detection were determined for CLS, PLS-1 and PCR methods. The proposed methods were validated and successfully applied for the analysis of pharmaceutical formulation and laboratory-prepared mixtures containing the two component combination.

HPLC and chemometrics-assisted UV-spectroscopy methods for the simultaneous determination of ambroxol and doxycycline in capsule.

PubMed

Hadad, Ghada M; El-Gindy, Alaa; Mahmoud, Waleed M M

2008-08-01

High-performance liquid chromatography (HPLC) and multivariate spectrophotometric methods are described for the simultaneous determination of ambroxol hydrochloride (AM) and doxycycline (DX) in combined pharmaceutical capsules. The chromatographic separation was achieved on reversed-phase C(18) analytical column with a mobile phase consisting of a mixture of 20mM potassium dihydrogen phosphate, pH 6-acetonitrile in ratio of (1:1, v/v) and UV detection at 245 nm. Also, the resolution has been accomplished by using numerical spectrophotometric methods as classical least squares (CLS), principal component regression (PCR) and partial least squares (PLS-1) applied to the UV spectra of the mixture and graphical spectrophotometric method as first derivative of the ratio spectra ((1)DD) method. Analytical figures of merit (FOM), such as sensitivity, selectivity, analytical sensitivity, limit of quantitation and limit of detection were determined for CLS, PLS-1 and PCR methods. The proposed methods were validated and successfully applied for the analysis of pharmaceutical formulation and laboratory-prepared mixtures containing the two component combination.

Body mass index is associated with type 2 diabetes mellitus in Chinese elderly.

PubMed

Zhao, Qianping; Laukkanen, Jari A; Li, Qifu; Li, Gang

2017-01-01

There is limited information on the association between metabolic syndrome components including body mass index (BMI) and type 2 diabetes mellitus in elderly Chinese population. Therefore, we investigated whether components of metabolic syndrome are associated with type 2 diabetes mellitus in elderly. A total of 479 hospitalized patients (aged 65-95 years) with recently diagnosed type 2 diabetes mellitus were studied retrospectively in a cross-sectional study and compared with 183 subjects with prediabetes and 62 subjects without glucose metabolism abnormalities. BMI (24.69±3.59 versus 23.92±3.08 and 23.56±3.25 kg/m 2 ), blood pressure, cholesterol, triglyceride, liver enzymes and prevalence of fatty liver were higher in patients with type 2 diabetes mellitus as compared with elderly subjects with prediabetes or normal glucose metabolism separately (all P <0.05). Multivariable regression analysis showed that BMI was associated positively with insulin resistance and inversely with insulin sensitivity in type 2 diabetes mellitus group (all P <0.05). Higher BMI was associated with increased insulin resistance and decreased insulin sensitivity in elderly Asian population with type 2 diabetes mellitus.

[Analysis of correlative factors of sterility in males undergoing routine sperm inspection by masturbation].

PubMed

Zhou, Liyuan; Shi, Xiaobo; Wang, Xin; Liu, Dan

2012-07-01

To investigate the factors influencing sterility in males undergoing routine sperm inspection by masturbation. Scales for demographic data, self-compiled infertility questionnaire, Symptom Checklist-90 (SCL-90) , and sexual life subscale of Olson Marital Quality Questionnaire (ENRICH) were assessed in 220 cases of sterility in males who had undergone sperm examination after ejaculation. The total SCL-90 scores and the factor scores of anxiety, phobia, somatization, obsessive compulsive behavior, interpersonal-sensitivity, hostility, and depression were significantly higher than the norm (P<0.05). The total SCL-90 score of 69 males was higher than 160, implying that 31.36% of the sterile males had negative emotions. The total score was related to wife's attitude, semen collecting room, ejaculation situation, and the general state of sexual life. The ejaculation situation was subjected to a multivariate linear regression model. About 1/3 of males with sterility problems undergoing routine semen examination by masturbation have negative emotions such as anxiety, phobia, somatization, and interpersonal sensitivities. The defective ejaculation may be the influential factor at the stage.

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.

Cost effectiveness analysis of immunotherapy in patients with grass pollen allergic rhinoconjunctivitis in Germany.

PubMed

Westerhout, K Y; Verheggen, B G; Schreder, C H; Augustin, M

2012-01-01

An economic evaluation was conducted to assess the outcomes and costs as well as cost-effectiveness of the following grass-pollen immunotherapies: OA (Oralair; Stallergenes S.A., Antony, France) vs GRZ (Grazax; ALK-Abelló, Hørsholm, Denmark), and ALD (Alk Depot SQ; ALK-Abelló) (immunotherapy agents alongside symptomatic medication) and symptomatic treatment alone for grass pollen allergic rhinoconjunctivitis. The costs and outcomes of 3-year treatment were assessed for a period of 9 years using a Markov model. Treatment efficacy was estimated using an indirect comparison of available clinical trials with placebo as a common comparator. Estimates for immunotherapy discontinuation, occurrence of asthma, health state utilities, drug costs, resource use, and healthcare costs were derived from published sources. The analysis was conducted from the insurant's perspective including public and private health insurance payments and co-payments by insurants. Outcomes were reported as quality-adjusted life years (QALYs) and symptom-free days. The uncertainty around incremental model results was tested by means of extensive deterministic univariate and probabilistic multivariate sensitivity analyses. In the base case analysis the model predicted a cost-utility ratio of OA vs symptomatic treatment of €14,728 per QALY; incremental costs were €1356 (95%CI: €1230; €1484) and incremental QALYs 0.092 (95%CI: 0.052; 0.140). OA was the dominant strategy compared to GRZ and ALD, with estimated incremental costs of -€1142 (95%CI: -€1255; -€1038) and -€54 (95%CI: -€188; €85) and incremental QALYs of 0.015 (95%CI: -0.025; 0.056) and 0.027 (95%CI: -0.022; 0.075), respectively. At a willingness-to-pay threshold of €20,000, the probability of OA being the most cost-effective treatment was predicted to be 79%. Univariate sensitivity analyses show that incremental outcomes were moderately sensitive to changes in efficacy estimates. The main study limitation was the requirement of an indirect comparison involving several steps to assess relative treatment effects. The analysis suggests OA to be cost-effective compared to GRZ and ALD, and a symptomatic treatment. Sensitivity analyses showed that uncertainty surrounding treatment efficacy estimates affected the model outcomes.

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.

Two-dimensional NMR spectroscopy strongly enhances soil organic matter composition analysis

NASA Astrophysics Data System (ADS)

Soucemarianadin, Laure; Erhagen, Björn; Öquist, Mats; Nilsson, Mats; Hedenström, Mattias; Schleucher, Jürgen

2016-04-01

Soil organic matter (SOM) is the largest terrestrial carbon pool and strongly affects soil properties. With climate change, understanding SOM processes and turnover and how they could be affected by increasing temperatures becomes critical. This is particularly key for organic soils as they represent a huge carbon pool in very sensitive ecosystems, like boreal ecosystems and peatlands. Nevertheless, characterization of SOM molecular composition, which is essential to elucidate soil carbon processes, is not easily achieved, and further advancements in that area are greatly needed. Solid-state one-dimensional (1D) 13C nuclear magnetic resonance (NMR) spectroscopy is often used to characterize its molecular composition, but only provides data on a few major functional groups, which regroup many different molecular fragments. For instance, in the carbohydrates region, signals of all monosaccharides present in many different polymers overlap. This overlap thwarts attempts to identify molecular moieties, resulting in insufficient information to characterize SOM composition. Here we show that two-dimensional (2D) liquid-state 1H-13C NMR spectra provided much richer data on the composition of boreal plant litter and organic surface soil. The 2D spectra indeed resolved overlaps observed in 1D 13C spectra and displayed signals from hundreds of identifiable molecular groups. For example, in the aromatics region, signals from individual lignin units could be recognized. It was hence possible to follow the fate of specific structural moieties in soils. We observed differences between litter and soil samples, and were able to relate them to the decomposition of identifiable moieties. Sample preparation and data acquisition were both simple and fast. Further, using multivariate data analysis, we aimed at linking the detailed chemical fingerprints of SOM to turnover rates in a soil incubation experiment. With the multivariate models, we were able to identify specific molecular moieties correlated to variability in the temperature response of organic matter decomposition, as assessed by Q10. Thus, 2D NMR methods, and their combination with multivariate analysis, can greatly improve analysis of litter and SOM composition, thereby facilitating elucidation of their roles in biogeochemical and ecological processes that are so critical to foresee associated feedback mechanisms on SOM turnover as a result of global environmental change.

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