Substituent effect study on experimental ¹³C NMR chemical shifts of (3-(substituted phenyl)-cis-4,5-dihydroisoxazole-4,5-diyl)bis(methylene)diacetate derivatives.

PubMed

Kara, Yesim S

2015-12-05

Eleven novel (3-(substituted phenyl)-cis-4,5-dihydroisoxazole-4,5-diyl)bis(methylene) diacetate derivatives were synthesized in the present study. These dihydroisoxazole derivatives were characterized by IR, (1)H NMR, (13)C NMR and elemental analyses. Their (13)C NMR spectra were measured in Deuterochloroform (CDCl3). The correlation analysis for the substituent-induced chemical shift (SCS) with Hammett substituent constant (σ), inductive substituent constant (σI), different of resonance substituent constants (σR, σR(o)) and Swain-Lupton substituent parameters (F, R) were performed using SSP (single substituent parameter), and DSP (dual substituent parameter) methods, as well as single and multiple regression analysis. From the result of regression analysis, the effect of substituent on the (13)C NMR chemical shifts was explained. Copyright © 2015 Elsevier B.V. All rights reserved.

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

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

Anderson, Ryan B.; Clegg, Samuel M.; Frydenvang, Jens

We report that accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response ofmore » an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “submodel” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. Lastly, the sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.« less

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

DOE PAGES

Anderson, Ryan B.; Clegg, Samuel M.; Frydenvang, Jens; ...

2016-12-15

We report that accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response ofmore » an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “submodel” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. Lastly, the sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.« less

Procedures for adjusting regional regression models of urban-runoff quality using local data

USGS Publications Warehouse

Hoos, A.B.; Sisolak, J.K.

1993-01-01

Statistical operations termed model-adjustment procedures (MAP?s) can be used to incorporate local data into existing regression models to improve the prediction of urban-runoff quality. Each MAP is a form of regression analysis in which the local data base is used as a calibration data set. Regression coefficients are determined from the local data base, and the resulting `adjusted? regression models can then be used to predict storm-runoff quality at unmonitored sites. The response variable in the regression analyses is the observed load or mean concentration of a constituent in storm runoff for a single storm. The set of explanatory variables used in the regression analyses is different for each MAP, but always includes the predicted value of load or mean concentration from a regional regression model. The four MAP?s examined in this study were: single-factor regression against the regional model prediction, P, (termed MAP-lF-P), regression against P,, (termed MAP-R-P), regression against P, and additional local variables (termed MAP-R-P+nV), and a weighted combination of P, and a local-regression prediction (termed MAP-W). The procedures were tested by means of split-sample analysis, using data from three cities included in the Nationwide Urban Runoff Program: Denver, Colorado; Bellevue, Washington; and Knoxville, Tennessee. The MAP that provided the greatest predictive accuracy for the verification data set differed among the three test data bases and among model types (MAP-W for Denver and Knoxville, MAP-lF-P and MAP-R-P for Bellevue load models, and MAP-R-P+nV for Bellevue concentration models) and, in many cases, was not clearly indicated by the values of standard error of estimate for the calibration data set. A scheme to guide MAP selection, based on exploratory data analysis of the calibration data set, is presented and tested. The MAP?s were tested for sensitivity to the size of a calibration data set. As expected, predictive accuracy of all MAP?s for the verification data set decreased as the calibration data-set size decreased, but predictive accuracy was not as sensitive for the MAP?s as it was for the local regression models.

Individual risk factors for deep infection and compromised fracture healing after intramedullary nailing of tibial shaft fractures: a single centre experience of 480 patients.

PubMed

Metsemakers, W-J; Handojo, K; Reynders, P; Sermon, A; Vanderschot, P; Nijs, S

2015-04-01

Despite modern advances in the treatment of tibial shaft fractures, complications including nonunion, malunion, and infection remain relatively frequent. A better understanding of these injuries and its complications could lead to prevention rather than treatment strategies. A retrospective study was performed to identify risk factors for deep infection and compromised fracture healing after intramedullary nailing (IMN) of tibial shaft fractures. Between January 2000 and January 2012, 480 consecutive patients with 486 tibial shaft fractures were enrolled in the study. Statistical analysis was performed to determine predictors of deep infection and compromised fracture healing. Compromised fracture healing was subdivided in delayed union and nonunion. The following independent variables were selected for analysis: age, sex, smoking, obesity, diabetes, American Society of Anaesthesiologists (ASA) classification, polytrauma, fracture type, open fractures, Gustilo type, primary external fixation (EF), time to nailing (TTN) and reaming. As primary statistical evaluation we performed a univariate analysis, followed by a multiple logistic regression model. Univariate regression analysis revealed similar risk factors for delayed union and nonunion, including fracture type, open fractures and Gustilo type. Factors affecting the occurrence of deep infection in this model were primary EF, a prolonged TTN, open fractures and Gustilo type. Multiple logistic regression analysis revealed polytrauma as the single risk factor for nonunion. With respect to delayed union, no risk factors could be identified. In the same statistical model, deep infection was correlated with primary EF. The purpose of this study was to evaluate risk factors of poor outcome after IMN of tibial shaft fractures. The univariate regression analysis showed that the nature of complications after tibial shaft nailing could be multifactorial. This was not confirmed in a multiple logistic regression model, which only revealed polytrauma and primary EF as risk factors for nonunion and deep infection, respectively. Future strategies should focus on prevention in high-risk populations such as polytrauma patients treated with EF. Copyright © 2014 Elsevier Ltd. All rights reserved.

A general framework for the use of logistic regression models in meta-analysis.

PubMed

Simmonds, Mark C; Higgins, Julian Pt

2016-12-01

Where individual participant data are available for every randomised trial in a meta-analysis of dichotomous event outcomes, "one-stage" random-effects logistic regression models have been proposed as a way to analyse these data. Such models can also be used even when individual participant data are not available and we have only summary contingency table data. One benefit of this one-stage regression model over conventional meta-analysis methods is that it maximises the correct binomial likelihood for the data and so does not require the common assumption that effect estimates are normally distributed. A second benefit of using this model is that it may be applied, with only minor modification, in a range of meta-analytic scenarios, including meta-regression, network meta-analyses and meta-analyses of diagnostic test accuracy. This single model can potentially replace the variety of often complex methods used in these areas. This paper considers, with a range of meta-analysis examples, how random-effects logistic regression models may be used in a number of different types of meta-analyses. This one-stage approach is compared with widely used meta-analysis methods including Bayesian network meta-analysis and the bivariate and hierarchical summary receiver operating characteristic (ROC) models for meta-analyses of diagnostic test accuracy. © The Author(s) 2014.

Learning investment indicators through data extension

NASA Astrophysics Data System (ADS)

Dvořák, Marek

2017-07-01

Stock prices in the form of time series were analysed using single and multivariate statistical methods. After simple data preprocessing in the form of logarithmic differences, we augmented this single variate time series to a multivariate representation. This method makes use of sliding windows to calculate several dozen of new variables using simple statistic tools like first and second moments as well as more complicated statistic, like auto-regression coefficients and residual analysis, followed by an optional quadratic transformation that was further used for data extension. These were used as a explanatory variables in a regularized logistic LASSO regression which tried to estimate Buy-Sell Index (BSI) from real stock market data.

Predicting Air Permeability of Handloom Fabrics: A Comparative Analysis of Regression and Artificial Neural Network Models

NASA Astrophysics Data System (ADS)

Mitra, Ashis; Majumdar, Prabal Kumar; Bannerjee, Debamalya

2013-03-01

This paper presents a comparative analysis of two modeling methodologies for the prediction of air permeability of plain woven handloom cotton fabrics. Four basic fabric constructional parameters namely ends per inch, picks per inch, warp count and weft count have been used as inputs for artificial neural network (ANN) and regression models. Out of the four regression models tried, interaction model showed very good prediction performance with a meager mean absolute error of 2.017 %. However, ANN models demonstrated superiority over the regression models both in terms of correlation coefficient and mean absolute error. The ANN model with 10 nodes in the single hidden layer showed very good correlation coefficient of 0.982 and 0.929 and mean absolute error of only 0.923 and 2.043 % for training and testing data respectively.

Advanced statistics: linear regression, part II: multiple linear regression.

PubMed

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Impact of covariate models on the assessment of the air pollution-mortality association in a single- and multipollutant context.

PubMed

Sacks, Jason D; Ito, Kazuhiko; Wilson, William E; Neas, Lucas M

2012-10-01

With the advent of multicity studies, uniform statistical approaches have been developed to examine air pollution-mortality associations across cities. To assess the sensitivity of the air pollution-mortality association to different model specifications in a single and multipollutant context, the authors applied various regression models developed in previous multicity time-series studies of air pollution and mortality to data from Philadelphia, Pennsylvania (May 1992-September 1995). Single-pollutant analyses used daily cardiovascular mortality, fine particulate matter (particles with an aerodynamic diameter ≤2.5 µm; PM(2.5)), speciated PM(2.5), and gaseous pollutant data, while multipollutant analyses used source factors identified through principal component analysis. In single-pollutant analyses, risk estimates were relatively consistent across models for most PM(2.5) components and gaseous pollutants. However, risk estimates were inconsistent for ozone in all-year and warm-season analyses. Principal component analysis yielded factors with species associated with traffic, crustal material, residual oil, and coal. Risk estimates for these factors exhibited less sensitivity to alternative regression models compared with single-pollutant models. Factors associated with traffic and crustal material showed consistently positive associations in the warm season, while the coal combustion factor showed consistently positive associations in the cold season. Overall, mortality risk estimates examined using a source-oriented approach yielded more stable and precise risk estimates, compared with single-pollutant analyses.

THE DISTRIBUTION OF COOK’S D STATISTIC

PubMed Central

Muller, Keith E.; Mok, Mario Chen

2013-01-01

Cook (1977) proposed a diagnostic to quantify the impact of deleting an observation on the estimated regression coefficients of a General Linear Univariate Model (GLUM). Simulations of models with Gaussian response and predictors demonstrate that his suggestion of comparing the diagnostic to the median of the F for overall regression captures an erratically varying proportion of the values. We describe the exact distribution of Cook’s statistic for a GLUM with Gaussian predictors and response. We also present computational forms, simple approximations, and asymptotic results. A simulation supports the accuracy of the results. The methods allow accurate evaluation of a single value or the maximum value from a regression analysis. The approximations work well for a single value, but less well for the maximum. In contrast, the cut-point suggested by Cook provides widely varying tail probabilities. As with all diagnostics, the data analyst must use scientific judgment in deciding how to treat highlighted observations. PMID:24363487

Impact of job characteristics on psychological health of Chinese single working women.

PubMed

Yeung, D Y; Tang, C S

2001-01-01

This study aims at investigating the impact of individual and contextual job characteristics of control, psychological and physical demand, and security on psychological distress of 193 Chinese single working women in Hong Kong. The mediating role of job satisfaction in the job characteristics-distress relation is also assessed. Multiple regression analysis results show that job satisfaction mediates the effects of job control and security in predicting psychological distress; whereas psychological job demand has an independent effect on mental distress after considering the effect of job satisfaction. This main effect model indicates that psychological distress is best predicted by small company size, high psychological job demand, and low job satisfaction. Results from a separate regression analysis fails to support the overall combined effect of job demand-control on psychological distress. However, a significant physical job demand-control interaction effect on mental distress is noted, which reduces slightly after controlling the effect of job satisfaction.

Who Gets Market Supplements? Gender Differences within a Large Canadian University

ERIC Educational Resources Information Center

Doucet, Christine; Durand, Claire; Smith, Michael

2008-01-01

This study examines the gender pay gap among university faculty by analyzing gender differences in one component of faculty members' salaries--"market premiums." The data were collected during the Fall of 2002 using a survey of faculty at a single Canadian research university. Correspondence analysis and logistic regression analysis were…

Liquid scintillation counting for /sup 14/C uptake of single algal cells isolated from natural samples

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

Rivkin, R.B.; Seliger, H.H.

1981-07-01

Short term rates of /sup 14/C uptake for single cells and small numbers of isolated algal cells of five phytoplankton species from natural populations were measured by liquid scintillation counting. Regression analysis of uptake rates per cell for cells isolated from unialgal cultures of seven species of dinoflagellates, ranging in volume from ca. 10/sup 3/ to 10/sup 7/ ..mu..m/sup 3/, gave results identical to uptake rates per cell measured by conventional /sup 14/C techniques. Relative standard errors or regression coefficients ranged between 3 and 10%, indicating that for any species there was little variation in photosynthesis per cell.

Missing data imputation: focusing on single imputation.

PubMed

Zhang, Zhongheng

2016-01-01

Complete case analysis is widely used for handling missing data, and it is the default method in many statistical packages. However, this method may introduce bias and some useful information will be omitted from analysis. Therefore, many imputation methods are developed to make gap end. The present article focuses on single imputation. Imputations with mean, median and mode are simple but, like complete case analysis, can introduce bias on mean and deviation. Furthermore, they ignore relationship with other variables. Regression imputation can preserve relationship between missing values and other variables. There are many sophisticated methods exist to handle missing values in longitudinal data. This article focuses primarily on how to implement R code to perform single imputation, while avoiding complex mathematical calculations.

Missing data imputation: focusing on single imputation

PubMed Central

2016-01-01

Complete case analysis is widely used for handling missing data, and it is the default method in many statistical packages. However, this method may introduce bias and some useful information will be omitted from analysis. Therefore, many imputation methods are developed to make gap end. The present article focuses on single imputation. Imputations with mean, median and mode are simple but, like complete case analysis, can introduce bias on mean and deviation. Furthermore, they ignore relationship with other variables. Regression imputation can preserve relationship between missing values and other variables. There are many sophisticated methods exist to handle missing values in longitudinal data. This article focuses primarily on how to implement R code to perform single imputation, while avoiding complex mathematical calculations. PMID:26855945

Analysis of the discriminative methods for diagnosis of benign and malignant solitary pulmonary nodules based on serum markers.

PubMed

Wang, Wanping; Liu, Mingyue; Wang, Jing; Tian, Rui; Dong, Junqiang; Liu, Qi; Zhao, Xianping; Wang, Yuanfang

2014-01-01

Screening indexes of tumor serum markers for benign and malignant solitary pulmonary nodules (SPNs) were analyzed to find the optimum method for diagnosis. Enzyme-linked immunosorbent assays, an automatic immune analyzer and radioimmunoassay methods were used to examine the levels of 8 serum markers in 164 SPN patients, and the sensitivity for differential diagnosis of malignant or benign SPN was compared for detection using a single plasma marker or a combination of markers. The results for serological indicators that closely relate to benign and malignant SPNs were screened using the Fisher discriminant analysis and a non-conditional logistic regression analysis method, respectively. The results were then verified by the k-means clustering analysis method. The sensitivity when using a combination of serum markers to detect SPN was higher than that using a single marker. By Fisher discriminant analysis, cytokeratin 19 fragments (CYFRA21-1), carbohydrate antigen 125 (CA125), squamous cell carcinoma antigen (SCC) and breast cancer antigen (CA153), which relate to the benign and malignant SPNs, were screened. Through non-conditional logistic regression analysis, CYFRA21-1, SCC and CA153 were obtained. Using the k-means clustering analysis, the cophenetic correlation coefficient (0.940) obtained by the Fisher discriminant analysis was higher than that obtained with logistic regression analysis (0.875). This study indicated that the Fisher discriminant analysis functioned better in screening out serum markers to recognize the benign and malignant SPN. The combined detection of CYFRA21-1, CA125, SCC and CA153 is an effective way to distinguish benign and malignant SPN, and will find an important clinical application in the early diagnosis of SPN. © 2014 S. Karger GmbH, Freiburg.

Comparison of the Relationship between Women' Empowerment and Fertility between Single-child and Multi-child Families

PubMed Central

Saberi, Tahereh; Ehsanpour, Soheila; Mahaki, Behzad; Kohan, Shahnaz

2018-01-01

Background: The reduction in fertility and increase in the number of single-child families in Iran will result in an increased risk of population aging. One of the factors affecting fertility is women's empowerment. This study aimed to evaluate the relationship between women's empowerment and fertility in single-child and multi-child families. Materials and Methods: This case-control study was conducted among 350 women (120 who had only 1 child as case group and 230 who had 2 or more children as control group) of 15–49 years of age in Isfahan, Iran, in 2016. For data collection, a 2-part questionnaire was designed. Data were analyzed using independent t-test, Chi-square test, and logistic regression analysis. Results: The difference between average scores of women's empowerment in the case group 54.08 (9.88) and control group 51.47 (8.57) was significant (p = 0.002). Simple logistic regression analysis showed that under diploma education, compared to postgraduate education, (OR = 0.21, p = 0.001) and being a housewife, compared to being employed, (OR = 0.45, p = 0.004) decreased the odds of having only 1 child. Multiple logistic regression results showed that the relationship between women's empowerment and fertility was not significant (p = 0.265). Conclusions: Although women in single-child families were more empowered, this was not the main reason for their preference to have only 1 child. In fact, educated and employed women postpone marriage and childbearing and limit fertility to only 1 child despite their desire. PMID:29628961

Air Leakage of US Homes: Regression Analysis and Improvements from Retrofit

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

Chan, Wanyu R.; Joh, Jeffrey; Sherman, Max H.

2012-08-01

LBNL Residential Diagnostics Database (ResDB) contains blower door measurements and other diagnostic test results of homes in United States. Of these, approximately 134,000 single-family detached homes have sufficient information for the analysis of air leakage in relation to a number of housing characteristics. We performed regression analysis to consider the correlation between normalized leakage and a number of explanatory variables: IECC climate zone, floor area, height, year built, foundation type, duct location, and other characteristics. The regression model explains 68% of the observed variability in normalized leakage. ResDB also contains the before and after retrofit air leakage measurements of approximatelymore » 23,000 homes that participated in weatherization assistant programs (WAPs) or residential energy efficiency programs. The two types of programs achieve rather similar reductions in normalized leakage: 30% for WAPs and 20% for other energy programs.« less

Transition from a multiport technique to a single-port technique for lung cancer surgery: is lymph node dissection inferior using the single-port technique?†.

PubMed

Liu, Chia-Chuan; Shih, Chih-Shiun; Pennarun, Nicolas; Cheng, Chih-Tao

2016-01-01

The feasibility and radicalism of lymph node dissection for lung cancer surgery by a single-port technique has frequently been challenged. We performed a retrospective cohort study to investigate this issue. Two chest surgeons initiated multiple-port thoracoscopic surgery in a 180-bed cancer centre in 2005 and shifted to a single-port technique gradually after 2010. Data, including demographic and clinical information, from 389 patients receiving multiport thoracoscopic lobectomy or segmentectomy and 149 consecutive patients undergoing either single-port lobectomy or segmentectomy for primary non-small-cell lung cancer were retrieved and entered for statistical analysis by multivariable linear regression models and Box-Cox transformed multivariable analysis. The mean number of total dissected lymph nodes in the lobectomy group was 28.5 ± 11.7 for the single-port group versus 25.2 ± 11.3 for the multiport group; the mean number of total dissected lymph nodes in the segmentectomy group was 19.5 ± 10.8 for the single-port group versus 17.9 ± 10.3 for the multiport group. In linear multivariable and after Box-Cox transformed multivariable analyses, the single-port approach was still associated with a higher total number of dissected lymph nodes. The total number of dissected lymph nodes for primary lung cancer surgery by single-port video-assisted thoracoscopic surgery (VATS) was higher than by multiport VATS in univariable, multivariable linear regression and Box-Cox transformed multivariable analyses. This study confirmed that highly effective lymph node dissection could be achieved through single-port VATS in our setting. © The Author 2015. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Evaluation of methodology for the analysis of 'time-to-event' data in pharmacogenomic genome-wide association studies.

PubMed

Syed, Hamzah; Jorgensen, Andrea L; Morris, Andrew P

2016-06-01

To evaluate the power to detect associations between SNPs and time-to-event outcomes across a range of pharmacogenomic study designs while comparing alternative regression approaches. Simulations were conducted to compare Cox proportional hazards modeling accounting for censoring and logistic regression modeling of a dichotomized outcome at the end of the study. The Cox proportional hazards model was demonstrated to be more powerful than the logistic regression analysis. The difference in power between the approaches was highly dependent on the rate of censoring. Initial evaluation of single-nucleotide polymorphism association signals using computationally efficient software with dichotomized outcomes provides an effective screening tool for some design scenarios, and thus has important implications for the development of analytical protocols in pharmacogenomic studies.

A tandem regression-outlier analysis of a ligand cellular system for key structural modifications around ligand binding.

PubMed

Lin, Ying-Ting

2013-04-30

A tandem technique of hard equipment is often used for the chemical analysis of a single cell to first isolate and then detect the wanted identities. The first part is the separation of wanted chemicals from the bulk of a cell; the second part is the actual detection of the important identities. To identify the key structural modifications around ligand binding, the present study aims to develop a counterpart of tandem technique for cheminformatics. A statistical regression and its outliers act as a computational technique for separation. A PPARγ (peroxisome proliferator-activated receptor gamma) agonist cellular system was subjected to such an investigation. Results show that this tandem regression-outlier analysis, or the prioritization of the context equations tagged with features of the outliers, is an effective regression technique of cheminformatics to detect key structural modifications, as well as their tendency of impact to ligand binding. The key structural modifications around ligand binding are effectively extracted or characterized out of cellular reactions. This is because molecular binding is the paramount factor in such ligand cellular system and key structural modifications around ligand binding are expected to create outliers. Therefore, such outliers can be captured by this tandem regression-outlier analysis.

A generalized least squares regression approach for computing effect sizes in single-case research: application examples.

PubMed

Maggin, Daniel M; Swaminathan, Hariharan; Rogers, Helen J; O'Keeffe, Breda V; Sugai, George; Horner, Robert H

2011-06-01

A new method for deriving effect sizes from single-case designs is proposed. The strategy is applicable to small-sample time-series data with autoregressive errors. The method uses Generalized Least Squares (GLS) to model the autocorrelation of the data and estimate regression parameters to produce an effect size that represents the magnitude of treatment effect from baseline to treatment phases in standard deviation units. In this paper, the method is applied to two published examples using common single case designs (i.e., withdrawal and multiple-baseline). The results from these studies are described, and the method is compared to ten desirable criteria for single-case effect sizes. Based on the results of this application, we conclude with observations about the use of GLS as a support to visual analysis, provide recommendations for future research, and describe implications for practice. Copyright © 2011 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Effect Size Measure and Analysis of Single Subject Designs

ERIC Educational Resources Information Center

Society for Research on Educational Effectiveness, 2013

2013-01-01

One of the vexing problems in the analysis of SSD is in the assessment of the effect of intervention. Serial dependence notwithstanding, the linear model approach that has been advanced involves, in general, the fitting of regression lines (or curves) to the set of observations within each phase of the design and comparing the parameters of these…

A Meta-Analysis of Peer-Mediated Interventions for Young Children with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Zhang, Jie; Wheeler, John J.

2011-01-01

This meta-analysis investigated the efficacy of peer-mediated interventions for promoting social interactions among children from birth to eight years of age diagnosed with ASD. Forty-five single-subject design studies were analyzed and the effect sizes were calculated by the regression model developed by Allison and Gorman (1993). The overall…

Data Analysis of Criteria Governing Selection of Active Guard/Reserve Colonel

DTIC Science & Technology

2014-09-01

20  Figure 7.  Graphically depicts the Marital Status breakdown of the packets submitted by Married (M); Divorced (D); Single (S); Widowed...Status and compares them to the number of packets selected within the each group. Married (M); Divorced (D); Single (S); Widowed (W...logistic regression to examine the determining factors of poverty in Kenya. The study 8 digs deeper than the three indicators commonly thought to

Determination of glomerular filtration rate (GFR) from fractional renal accumulation of iodinated contrast material: a convenient and rapid single-kidney CT-GFR technique.

PubMed

Yuan, XiaoDong; Tang, Wei; Shi, WenWei; Yu, Libao; Zhang, Jing; Yuan, Qing; You, Shan; Wu, Ning; Ao, Guokun; Ma, Tingting

2018-07-01

To develop a convenient and rapid single-kidney CT-GFR technique. One hundred and twelve patients referred for multiphasic renal CT and 99mTc-DTPA renal dynamic imaging Gates-GFR measurement were prospectively included and randomly divided into two groups of 56 patients each: the training group and the validation group. On the basis of the nephrographic phase images, the fractional renal accumulation (FRA) was calculated and correlated with the Gates-GFR in the training group. From this correlation a formula was derived for single-kidney CT-GFR calculation, which was validated by a paired t test and linear regression analysis with the single-kidney Gates-GFR in the validation group. In the training group, the FRA (x-axis) correlated well (r = 0.95, p < 0.001) with single-kidney Gates-GFR (y-axis), producing a regression equation of y = 1665x + 1.5 for single-kidney CT-GFR calculation. In the validation group, the difference between the methods of single-kidney GFR measurements was 0.38 ± 5.57 mL/min (p = 0.471); the regression line is identical to the diagonal (intercept = 0 and slope = 1) (p = 0.727 and p = 0.473, respectively), with a standard deviation of residuals of 5.56 mL/min. A convenient and rapid single-kidney CT-GFR technique was presented and validated in this investigation. • The new CT-GFR method takes about 2.5 min of patient time. • The CT-GFR method demonstrated identical results to the Gates-GFR method. • The CT-GFR method is based on the fractional renal accumulation of iodinated CM. • The CT-GFR method is achieved without additional radiation dose to the patient.

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.

Bias due to two-stage residual-outcome regression analysis in genetic association studies.

PubMed

Demissie, Serkalem; Cupples, L Adrienne

2011-11-01

Association studies of risk factors and complex diseases require careful assessment of potential confounding factors. Two-stage regression analysis, sometimes referred to as residual- or adjusted-outcome analysis, has been increasingly used in association studies of single nucleotide polymorphisms (SNPs) and quantitative traits. In this analysis, first, a residual-outcome is calculated from a regression of the outcome variable on covariates and then the relationship between the adjusted-outcome and the SNP is evaluated by a simple linear regression of the adjusted-outcome on the SNP. In this article, we examine the performance of this two-stage analysis as compared with multiple linear regression (MLR) analysis. Our findings show that when a SNP and a covariate are correlated, the two-stage approach results in biased genotypic effect and loss of power. Bias is always toward the null and increases with the squared-correlation between the SNP and the covariate (). For example, for , 0.1, and 0.5, two-stage analysis results in, respectively, 0, 10, and 50% attenuation in the SNP effect. As expected, MLR was always unbiased. Since individual SNPs often show little or no correlation with covariates, a two-stage analysis is expected to perform as well as MLR in many genetic studies; however, it produces considerably different results from MLR and may lead to incorrect conclusions when independent variables are highly correlated. While a useful alternative to MLR under , the two -stage approach has serious limitations. Its use as a simple substitute for MLR should be avoided. © 2011 Wiley Periodicals, Inc.

Quantitative laser-induced breakdown spectroscopy data using peak area step-wise regression analysis: an alternative method for interpretation of Mars science laboratory results

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

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

2008-01-01

The ChemCam instrument on the Mars Science Laboratory (MSL) will include a laser-induced breakdown spectrometer (LIBS) to quantify major and minor elemental compositions. The traditional analytical chemistry approach to calibration curves for these data regresses a single diagnostic peak area against concentration for each element. This approach contrasts with a new multivariate method in which elemental concentrations are predicted by step-wise multiple regression analysis based on areas of a specific set of diagnostic peaks for each element. The method is tested on LIBS data from igneous and metamorphosed rocks. Between 4 and 13 partial regression coefficients are needed to describemore » each elemental abundance accurately (i.e., with a regression line of R{sup 2} > 0.9995 for the relationship between predicted and measured elemental concentration) for all major and minor elements studied. Validation plots suggest that the method is limited at present by the small data set, and will work best for prediction of concentration when a wide variety of compositions and rock types has been analyzed.« less

Structural Analysis of Women’s Heptathlon

PubMed Central

Gassmann, Freya; Fröhlich, Michael; Emrich, Eike

2016-01-01

The heptathlon comprises the results of seven single disciplines, assuming an equal influence from each discipline, depending on the measured performance. Data analysis was based on the data recorded for the individual performances of the 10 winning heptathletes in the World Athletics Championships from 1987 to 2013 and the Olympic Games from 1988 to 2012. In addition to descriptive analysis methods, correlations, bivariate and multivariate linear regressions, and panel data regressions were used. The transformation of the performances from seconds, centimeters, and meters into points showed that the individual disciplines do not equally affect the overall competition result. The currently valid conversion formula for the run, jump, and throw disciplines prefers the sprint and jump disciplines but penalizes the athletes performing in the 800 m run, javelin throw, and shotput disciplines. Furthermore, 21% to 48% of the variance of the sum of points can be attributed to the performances in the disciplines of long jump, 200 m sprint, 100 m hurdles, and high jump. To balance the effects of the single disciplines in the heptathlon, the formula to calculate points should be reevaluated. PMID:29910260

Determinants of single family residential water use across scales in four western US cities.

PubMed

Chang, Heejun; Bonnette, Matthew Ryan; Stoker, Philip; Crow-Miller, Britt; Wentz, Elizabeth

2017-10-15

A growing body of literature examines urban water sustainability with increasing evidence that locally-based physical and social spatial interactions contribute to water use. These studies however are based on single-city analysis and often fail to consider whether these interactions occur more generally. We examine a multi-city comparison using a common set of spatially-explicit water, socioeconomic, and biophysical data. We investigate the relative importance of variables for explaining the variations of single family residential (SFR) water uses at Census Block Group (CBG) and Census Tract (CT) scales in four representative western US cities - Austin, Phoenix, Portland, and Salt Lake City, - which cover a wide range of climate and development density. We used both ordinary least squares regression and spatial error regression models to identify the influence of spatial dependence on water use patterns. Our results show that older downtown areas show lower water use than newer suburban areas in all four cities. Tax assessed value and building age are the main determinants of SFR water use across the four cities regardless of the scale. Impervious surface area becomes an important variable for summer water use in all cities, and it is important in all seasons for arid environments such as Phoenix. CT level analysis shows better model predictability than CBG analysis. In all cities, seasons, and spatial scales, spatial error regression models better explain the variations of SFR water use. Such a spatially-varying relationship of urban water consumption provides additional evidence for the need to integrate urban land use planning and municipal water planning. Copyright © 2017 Elsevier B.V. All rights reserved.

Taguchi Based Regression Analysis of End-Wall Film Cooling in a Gas Turbine Cascade with Single Row of Holes

NASA Astrophysics Data System (ADS)

Ravi, D.; Parammasivam, K. M.

2016-09-01

Numerical investigations were conducted on a turbine cascade, with end-wall cooling by a single row of cylindrical holes, inclined at 30°. The mainstream fluid was hot air and the coolant was CO2 gas. Based on the Reynolds number, the flow was turbulent at the inlet. The film hole row position, its pitch and blowing ratio was varied with five different values. Taguchi approach was used in designing a L25 orthogonal array (OA) for these parameters. The end-wall averaged film cooling effectiveness (bar η) was chosen as the quality characteristic. CFD analyses were carried out using Ansys Fluent on computational domains designed with inputs from OA. Experiments were conducted for one chosen OA configuration and the computational results were found to correlate well with experimental measurements. The responses from the CFD analyses were fed to the statistical tool to develop a correlation for bar η using regression analysis.

Identification of rs7350481 at chromosome 11q23.3 as a novel susceptibility locus for metabolic syndrome in Japanese individuals by an exome-wide association study.

PubMed

Yamada, Yoshiji; Sakuma, Jun; Takeuchi, Ichiro; Yasukochi, Yoshiki; Kato, Kimihiko; Oguri, Mitsutoshi; Fujimaki, Tetsuo; Horibe, Hideki; Muramatsu, Masaaki; Sawabe, Motoji; Fujiwara, Yoshinori; Taniguchi, Yu; Obuchi, Shuichi; Kawai, Hisashi; Shinkai, Shoji; Mori, Seijiro; Arai, Tomio; Tanaka, Masashi

2017-06-13

We have performed exome-wide association studies to identify genetic variants that influence body mass index or confer susceptibility to obesity or metabolic syndrome in Japanese. The exome-wide association study for body mass index included 12,890 subjects, and those for obesity and metabolic syndrome included 12,968 subjects (3954 individuals with obesity, 9014 controls) and 6817 subjects (3998 individuals with MetS, 2819 controls), respectively. Exome-wide association studies were performed with Illumina HumanExome-12 DNA Analysis BeadChip or Infinium Exome-24 BeadChip arrays. The relation of genotypes of single nucleotide polymorphisms to body mass index was examined by linear regression analysis, and that of allele frequencies of single nucleotide polymorphisms to obesity or metabolic syndrome was evaluated with Fisher's exact test. The exome-wide association studies identified six, 11, and 40 single nucleotide polymorphisms as being significantly associated with body mass index, obesity (P <1.21 × 10-6), or metabolic syndrome (P <1.20 × 10-6), respectively. Subsequent multivariable logistic regression analysis with adjustment for age and sex revealed that three and five single nucleotide polymorphisms were related (P < 0.05) to obesity or metabolic syndrome, respectively, with one of these latter polymorphisms-rs7350481 (C/T) at chromosome 11q23.3-also being significantly (P < 3.13 × 10-4) associated with metabolic syndrome. The polymorphism rs7350481 may thus be a novel susceptibility locus for metabolic syndrome in Japanese. In addition, single nucleotide polymorphisms in three genes (CROT, TSC1, RIN3) and at four loci (ANKK1, ZNF804B, CSRNP3, 17p11.2) were implicated as candidate determinants of obesity and metabolic syndrome, respectively.

[A case-control study on the risk factors of work-related acute pesticide poisoning among farmers from Jiangsu province].

PubMed

Tu, Zhi-bin; Cui, Meng-jing; Yao, Hong-yan; Hu, Guo-qing; Xiang, Hui-yun; Stallones, Lorann; Zhang, Xu-jun

2012-04-01

To explore the risk factors on cases regarding work-related acute pesticide poisoning among farmers of Jiangsu province. A population-based, 1:2 matched case-control study was carried out, with 121 patients as case-group paired by 242 persons with same gender, district and age less then difference of 3 years, as controls. Cases were the ones who had suffered from work-related acute pesticide poisoning. A unified questionnaire was used. Data base was established by EpiData 3.1, and SPSS 16.0 was used for both data single factor and multi-conditional logistics regression analysis. Results from the single factor logistic regression analysis showed that the related risk factors were: lack of safety guidance, lack of readable labels before praying pesticides, no regression during application, using hand to wipe sweat, using leaking knapsack, body contaminated during application and continuing to work when feeling ill after the contact of pesticides. Results from multi-conditional logistic regression analysis indicated that the lack of safety guidance (OR=2.25, 95%CI: 1.35-3.74), no readable labels before praying pesticides (OR=1.95, 95%CI: 1.19-3.18), wiping the sweat by hand during application (OR=1.97, 95%CI: 1.20-3.24) and using leaking knapsack during application (OR=1.82, 95%CI:1.10-3.01) were risk factors for the occurrence of work-related acute pesticide poisoning. The lack of safety guidance, no readable labels before praying pesticides, wiping the sweat by hand or using leaking knapsack during application were correlated to the occurrence of work-related acute pesticide poisoning.

Using Multilevel Modeling in Language Assessment Research: A Conceptual Introduction

ERIC Educational Resources Information Center

Barkaoui, Khaled

2013-01-01

This article critiques traditional single-level statistical approaches (e.g., multiple regression analysis) to examining relationships between language test scores and variables in the assessment setting. It highlights the conceptual, methodological, and statistical problems associated with these techniques in dealing with multilevel or nested…

The Relative Impact of Educational Attainment and Fatherlessness on Criminality.

ERIC Educational Resources Information Center

Koski, Douglas D.

1996-01-01

Regression analysis of 40 years of data on median income, education, divorce rate, and female-headed households was conducted to determine their influence on crime rates, especially homicide. Educational attainment had a significant bearing on criminality. Single parenting was less significant than low income. (SK)

Techniques for detecting effects of urban and rural land-use practices on stream-water chemistry in selected watersheds in Texas, Minnesota,and Illinois

USGS Publications Warehouse

Walker, J.F.

1993-01-01

Selected statistical techniques were applied to three urban watersheds in Texas and Minnesota and three rural watersheds in Illinois. For the urban watersheds, single- and paired-site data-collection strategies were considered. The paired-site strategy was much more effective than the singlesite strategy for detecting changes. Analysis of storm load regression residuals demonstrated the potential utility of regressions for variability reduction. For the rural watersheds, none of the selected techniques were effective at identifying changes, primarily due to a small degree of management-practice implementation, potential errors introduced through the estimation of storm load, and small sample sizes. A Monte Carlo sensitivity analysis was used to determine the percent change in water chemistry that could be detected for each watershed. In most instances, the use of regressions improved the ability to detect changes.

Prescription-drug-related risk in driving: comparing conventional and lasso shrinkage logistic regressions.

PubMed

Avalos, Marta; Adroher, Nuria Duran; Lagarde, Emmanuel; Thiessard, Frantz; Grandvalet, Yves; Contrand, Benjamin; Orriols, Ludivine

2012-09-01

Large data sets with many variables provide particular challenges when constructing analytic models. Lasso-related methods provide a useful tool, although one that remains unfamiliar to most epidemiologists. We illustrate the application of lasso methods in an analysis of the impact of prescribed drugs on the risk of a road traffic crash, using a large French nationwide database (PLoS Med 2010;7:e1000366). In the original case-control study, the authors analyzed each exposure separately. We use the lasso method, which can simultaneously perform estimation and variable selection in a single model. We compare point estimates and confidence intervals using (1) a separate logistic regression model for each drug with a Bonferroni correction and (2) lasso shrinkage logistic regression analysis. Shrinkage regression had little effect on (bias corrected) point estimates, but led to less conservative results, noticeably for drugs with moderate levels of exposure. Carbamates, carboxamide derivative and fatty acid derivative antiepileptics, drugs used in opioid dependence, and mineral supplements of potassium showed stronger associations. Lasso is a relevant method in the analysis of databases with large number of exposures and can be recommended as an alternative to conventional strategies.

Role of anthropometric data in the prediction of 4-stranded hamstring graft size in anterior cruciate ligament reconstruction.

PubMed

Ho, Sean Wei Loong; Tan, Teong Jin Lester; Lee, Keng Thiam

2016-03-01

To evaluate whether pre-operative anthropometric data can predict the optimal diameter and length of hamstring tendon autograft for anterior cruciate ligament (ACL) reconstruction. This was a cohort study that involved 169 patients who underwent single-bundle ACL reconstruction (single surgeon) with 4-stranded MM Gracilis and MM Semi-Tendinosus autografts. Height, weight, body mass index (BMI), gender, race, age and -smoking status were recorded pre-operatively. Intra-operatively, the diameter and functional length of the 4-stranded autograft was recorded. Multiple regression analysis was used to determine the relationship between the anthropometric measurements and the length and diameter of the implanted autografts. The strongest correlation between 4-stranded hamstring autograft diameter was height and weight. This correlation was stronger in females than males. BMI had a moderate correlation with the diameter of the graft in females. Females had a significantly smaller graft both in diameter and length when compared with males. Linear regression models did not show any significant correlation between hamstring autograft length with height and weight (p>0.05). Simple regression analysis demonstrated that height and weight can be used to predict hamstring graft diameter. The following regression equation was obtained for females: Graft diameter=0.012+0.034*Height+0.026*Weight (R2=0.358, p=0.004) The following regression equation was obtained for males: Graft diameter=5.130+0.012*Height+0.007*Weight (R2=0.086, p=0.002). Pre-operative anthropometric data has a positive correlation with the diameter of 4 stranded hamstring autografts but no significant correlation with the length. This data can be utilised to predict the autograft diameter and may be useful for pre-operative planning and patient counseling for graft selection.

Single-Nucleotide Polymorphisms Associated with Skin Naphthyl–Keratin Adduct Levels in Workers Exposed to Naphthalene

PubMed Central

Jiang, Rong; French, John E.; Stober, Vandy P.; Kang-Sickel, Juei-Chuan C.; Zou, Fei

2012-01-01

Background: Individual genetic variation that results in differences in systemic response to xenobiotic exposure is not accounted for as a predictor of outcome in current exposure assessment models. Objective: We developed a strategy to investigate individual differences in single-nucleotide polymorphisms (SNPs) as genetic markers associated with naphthyl–keratin adduct (NKA) levels measured in the skin of workers exposed to naphthalene. Methods: The SNP-association analysis was conducted in PLINK using candidate-gene analysis and genome-wide analysis. We identified significant SNP–NKA associations and investigated the potential impact of these SNPs along with personal and workplace factors on NKA levels using a multiple linear regression model and the Pratt index. Results: In candidate-gene analysis, a SNP (rs4852279) located near the CYP26B1 gene contributed to the 2-naphthyl–keratin adduct (2NKA) level. In the multiple linear regression model, the SNP rs4852279, dermal exposure, exposure time, task replacing foam, age, and ethnicity all were significant predictors of 2NKA level. In genome-wide analysis, no single SNP reached genome-wide significance for NKA levels (all p ≥ 1.05 × 10–5). Pathway and network analyses of SNPs associated with NKA levels were predicted to be involved in the regulation of cellular processes and homeostasis. Conclusions: These results provide evidence that a quantitative biomarker can be used as an intermediate phenotype when investigating the association between genetic markers and exposure–dose relationship in a small, well-characterized exposed worker population. PMID:22391508

Gender Role Conflict, Interest in Casual Sex, and Relationship Satisfaction Among Gay Men

PubMed Central

Sanchez, Fráncisco J.; Bocklandt, Sven; Vilain, Eric

2010-01-01

This study compared single (n = 129) and partnered gay men (n = 114) to determine if they differed in their concerns over traditional masculine roles and interest in casual sex, and to measure the relationship between concerns over masculine roles and interest in casual sex. Additionally, a regression model to predict relationship satisfaction was tested. Participants were recruited at two Southern California Gay Pride festivals. Group comparisons showed single men were more restrictive in their affectionate behavior with other men (effect-size r = .14) and were more interested in casual sex than partnered men (effect-size r = .13); and partnered men were more concerned with being successful, powerful, and competitive than single men (effect-size r = .20). Different masculine roles were predictive of interest in casual sex among the two groups of men. Finally, a hierarchical regression analysis found that interest in casual sex and the length of one’s current relationship served as unique predictors of relationship satisfaction among the partnered gay men (Cohen’s f2 = .52). PMID:20721305

Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models.

PubMed

Riley, Richard D; Ensor, Joie; Jackson, Dan; Burke, Danielle L

2017-01-01

Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher's information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis).

Satisfaction of active duty soldiers with family dental care.

PubMed

Chisick, M C

1997-02-01

In the fall of 1992, a random, worldwide sample of 6,442 married and single parent soldiers completed a self-administered survey on satisfaction with 22 attributes of family dental care. Simple descriptive statistics for each attribute were derived, as was a composite overall satisfaction score using factor analysis. Composite scores were regressed on demographics, annual dental utilization, and access barriers to identify those factors having an impact on a soldier's overall satisfaction with family dental care. Separate regression models were constructed for single parents, childless couples, and couples with children. Results show below-average satisfaction with nearly all attributes of family dental care, with access attributes having the lowest average satisfaction scores. Factors influencing satisfaction with family dental care varied by family type with one exception: dependent dental utilization within the past year contributed positively to satisfaction across all family types.

A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples.

PubMed

Li, Yankun; Shao, Xueguang; Cai, Wensheng

2007-04-15

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.

Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.

PubMed

Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A

2016-01-01

Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.

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

USGS Publications Warehouse

Law, George S.; Tasker, Gary D.

2003-01-01

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

Estimation of stature from radiologic anthropometry of the lumbar vertebral dimensions in Chinese.

PubMed

Zhang, Kui; Chang, Yun-feng; Fan, Fei; Deng, Zhen-hua

2015-11-01

The recent study was to assess the relationship between the radiologic anthropometry of the lumbar vertebral dimensions and stature in Chinese and to develop regression formulae to estimate stature from these dimensions. A total of 412 normal, healthy volunteers, comprising 206 males and 206 females, were recruited. The linear regression analysis were performed to assess the correlation between the stature and lengths of various segments of the lumbar vertebral column. Among the regression equations created for single variable, the predictive value was greatest for the reconstruction of stature from the lumbar segment in both sexes and subgroup analysis. When individual vertebral body was used, the heights of posterior vertebral body of L3 gave the most accurate results for male group, the heights of central vertebral body of L1 provided the most accurate results for female group and female group with age above 45 years, the heights of central vertebral body of L3 gave the most accurate results for the groups with age from 20-45 years for both sexes and the male group with age above 45 years. The heights of anterior vertebral body of L5 gave the less accurate results except for the heights of anterior vertebral body of L4 provided the less accurate result for the male group with age above 45 years. As expected, multiple regression equations were more successful than equations derived from a single variable. The research observations suggest lumbar vertebral dimensions to be useful in stature estimation among Chinese population. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

An overview of longitudinal data analysis methods for neurological research.

PubMed

Locascio, Joseph J; Atri, Alireza

2011-01-01

The purpose of this article is to provide a concise, broad and readily accessible overview of longitudinal data analysis methods, aimed to be a practical guide for clinical investigators in neurology. In general, we advise that older, traditional methods, including (1) simple regression of the dependent variable on a time measure, (2) analyzing a single summary subject level number that indexes changes for each subject and (3) a general linear model approach with a fixed-subject effect, should be reserved for quick, simple or preliminary analyses. We advocate the general use of mixed-random and fixed-effect regression models for analyses of most longitudinal clinical studies. Under restrictive situations or to provide validation, we recommend: (1) repeated-measure analysis of covariance (ANCOVA), (2) ANCOVA for two time points, (3) generalized estimating equations and (4) latent growth curve/structural equation models.

Predicting Spanking of Younger and Older Children by Mothers and Fathers.

ERIC Educational Resources Information Center

Day, Randal D.; Peterson, Gary W.; McCracken, Coleen

1998-01-01

Parents' characteristics that influence the incidence of spanking are investigated. Differences between boys and girls, mothers and fathers, older and younger children, Black and White, and married versus single women, as well as attributes of the child, the parent, and the social context are explored using multiple regression analysis. Profiles…

Effects of eye artifact removal methods on single trial P300 detection, a comparative study.

PubMed

Ghaderi, Foad; Kim, Su Kyoung; Kirchner, Elsa Andrea

2014-01-15

Electroencephalographic signals are commonly contaminated by eye artifacts, even if recorded under controlled conditions. The objective of this work was to quantitatively compare standard artifact removal methods (regression, filtered regression, Infomax, and second order blind identification (SOBI)) and two artifact identification approaches for independent component analysis (ICA) methods, i.e. ADJUST and correlation. To this end, eye artifacts were removed and the cleaned datasets were used for single trial classification of P300 (a type of event related potentials elicited using the oddball paradigm). Statistical analysis of the results confirms that the combination of Infomax and ADJUST provides a relatively better performance (0.6% improvement on average of all subject) while the combination of SOBI and correlation performs the worst. Low-pass filtering the data at lower cutoffs (here 4 Hz) can also improve the classification accuracy. Without requiring any artifact reference channel, the combination of Infomax and ADJUST improves the classification performance more than the other methods for both examined filtering cutoffs, i.e., 4 Hz and 25 Hz. Copyright © 2013 Elsevier B.V. All rights reserved.

Comparison of bioavailable vanadium in alfalfa rhizosphere soil extracted by an improved BCR procedure and EDTA, HCl, and NaNO₃ single extractions in a pot experiment with V-Cd treatments.

PubMed

Yang, Jie; Teng, Yanguo; Zuo, Rui; Song, Liuting

2015-06-01

The BCR sequential extraction procedure was compared with EDTA, HCl, and NaNO3 single extractions for evaluating vanadium bioavailability in alfalfa rhizosphere soil. The amounts of vanadium extracted by these methods were in the following order: BCR (bioavailable V) > EDTA ≈ HCl > NaNO3. Both correlation analysis and stepwise regression were adopted to illustrate the extractable vanadium between different reagents. The correlation coefficients between extracted vanadium and the vanadium contents in alfalfa roots were R NaNO3 = 0.948, R HCl = 0.902, R EDTA = 0.816, and R bioavailable V = 0.819. The stepwise multiple regression equation of the NaNO3 extraction was the most significant at a 95 % confidence interval. The influence of pH, total organic carbon, and cadmium content of soil to vanadium bioavailability were not definite. In summary, both the BCR sequential extraction and the single extraction methods were valid approaches for predicting vanadium bioavailability in alfalfa rhizosphere soil, especially the single extractions.

Addressing data privacy in matched studies via virtual pooling.

PubMed

Saha-Chaudhuri, P; Weinberg, C R

2017-09-07

Data confidentiality and shared use of research data are two desirable but sometimes conflicting goals in research with multi-center studies and distributed data. While ideal for straightforward analysis, confidentiality restrictions forbid creation of a single dataset that includes covariate information of all participants. Current approaches such as aggregate data sharing, distributed regression, meta-analysis and score-based methods can have important limitations. We propose a novel application of an existing epidemiologic tool, specimen pooling, to enable confidentiality-preserving analysis of data arising from a matched case-control, multi-center design. Instead of pooling specimens prior to assay, we apply the methodology to virtually pool (aggregate) covariates within nodes. Such virtual pooling retains most of the information used in an analysis with individual data and since individual participant data is not shared externally, within-node virtual pooling preserves data confidentiality. We show that aggregated covariate levels can be used in a conditional logistic regression model to estimate individual-level odds ratios of interest. The parameter estimates from the standard conditional logistic regression are compared to the estimates based on a conditional logistic regression model with aggregated data. The parameter estimates are shown to be similar to those without pooling and to have comparable standard errors and confidence interval coverage. Virtual data pooling can be used to maintain confidentiality of data from multi-center study and can be particularly useful in research with large-scale distributed data.

Comparisons of estimates of annual exceedance-probability discharges for small drainage basins in Iowa, based on data through water year 2013

USGS Publications Warehouse

Eash, David A.

2015-01-01

An examination was conducted to understand why the 1987 single-variable RREs seem to provide better accuracy and less bias than either of the 2013 multi- or single-variable RREs. A comparison of 1-percent annual exceedance-probability regression lines for hydrologic regions 1-4 from the 1987 single-variable RREs and for flood regions 1-3 from the 2013 single-variable RREs indicates that the 1987 single-variable regional-regression lines generally have steeper slopes and lower discharges when compared to 2013 single-variable regional-regression lines for corresponding areas of Iowa. The combination of the definition of hydrologic regions, the lower discharges, and the steeper slopes of regression lines associated with the 1987 single-variable RREs seem to provide better accuracy and less bias when compared to the 2013 multi- or single-variable RREs; better accuracy and less bias was determined particularly for drainage areas less than 2 mi2, and also for some drainage areas between 2 and 20 mi2. The 2013 multi- and single-variable RREs are considered to provide better accuracy and less bias for larger drainage areas. Results of this study indicate that additional research is needed to address the curvilinear relation between drainage area and AEPDs for areas of Iowa.

Building and verifying a severity prediction model of acute pancreatitis (AP) based on BISAP, MEWS and routine test indexes.

PubMed

Ye, Jiang-Feng; Zhao, Yu-Xin; Ju, Jian; Wang, Wei

2017-10-01

To discuss the value of the Bedside Index for Severity in Acute Pancreatitis (BISAP), Modified Early Warning Score (MEWS), serum Ca2+, similarly hereinafter, and red cell distribution width (RDW) for predicting the severity grade of acute pancreatitis and to develop and verify a more accurate scoring system to predict the severity of AP. In 302 patients with AP, we calculated BISAP and MEWS scores and conducted regression analyses on the relationships of BISAP scoring, RDW, MEWS, and serum Ca2+ with the severity of AP using single-factor logistics. The variables with statistical significance in the single-factor logistic regression were used in a multi-factor logistic regression model; forward stepwise regression was used to screen variables and build a multi-factor prediction model. A receiver operating characteristic curve (ROC curve) was constructed, and the significance of multi- and single-factor prediction models in predicting the severity of AP using the area under the ROC curve (AUC) was evaluated. The internal validity of the model was verified through bootstrapping. Among 302 patients with AP, 209 had mild acute pancreatitis (MAP) and 93 had severe acute pancreatitis (SAP). According to single-factor logistic regression analysis, we found that BISAP, MEWS and serum Ca2+ are prediction indexes of the severity of AP (P-value<0.001), whereas RDW is not a prediction index of AP severity (P-value>0.05). The multi-factor logistic regression analysis showed that BISAP and serum Ca2+ are independent prediction indexes of AP severity (P-value<0.001), and MEWS is not an independent prediction index of AP severity (P-value>0.05); BISAP is negatively related to serum Ca2+ (r=-0.330, P-value<0.001). The constructed model is as follows: ln()=7.306+1.151*BISAP-4.516*serum Ca2+. The predictive ability of each model for SAP follows the order of the combined BISAP and serum Ca2+ prediction model>Ca2+>BISAP. There is no statistical significance for the predictive ability of BISAP and serum Ca2+ (P-value>0.05); however, there is remarkable statistical significance for the predictive ability using the newly built prediction model as well as BISAP and serum Ca2+ individually (P-value<0.01). Verification of the internal validity of the models by bootstrapping is favorable. BISAP and serum Ca2+ have high predictive value for the severity of AP. However, the model built by combining BISAP and serum Ca2+ is remarkably superior to those of BISAP and serum Ca2+ individually. Furthermore, this model is simple, practical and appropriate for clinical use. Copyright © 2016. Published by Elsevier Masson SAS.

Influence of temperature on the spreading velocity of simplified-step adhesive systems.

PubMed

Pazinatto, Flávia Bittencourt; Marquezini, Luiz; Atta, Maria Teresa

2006-01-01

Flowability and viscosity vary for different adhesive systems owing to differences in their composition. These characteristics can be modified by environmental temperature. The purpose of this study was to determine the influence of temperature on the spreading (flow capacity) of simplified-step adhesive systems. Spreading velocities of adhesive systems (Adper Single Bond and Single Bond Plus [3M ESPE, St. Paul, MN, USA]; Prime & Bond 2.1 and Prime & Bond NT [Dentsply Indústria e Comércio Ltda, Petrópolis, RJ, Brazil]; Adper Prompt [3M ESPE]; and One Up Bond F [Tokuyama Corp, Tokyo, Japan]) were analyzed at intervals of 10, 15, 20, and 30 seconds at both 25 degrees C and 37 degrees C by placing 10 microL drops on a glass slide surface with an inclination of 45 degrees. The spreading of each adhesive system was measured in millimeters per second. Data were analyzed by two-way analysis of variance and Student-Newman-Keuls tests. Regression analysis was used to determine a correlation between spreading velocity and time. Statistical significance was considered at a confidence level of 95%. Temperature influenced the spreading velocity, increasing it for Single Bond and Prime & Bond 2.1 and decreasing it for Adper Prompt (p < .05). No differences on spreading were observed for the other adhesives studied (p >.05). Regression analysis of each adhesive system demonstrated an inverse correlation between mean spreading velocity and time (R2 = .999) on both temperatures. Temperature increases yielded an increase of spreading for Single Bond and Prime & Bond 2.1. The influence of temperature on the spreading velocity was material dependent. Environmental temperature can influence the rate of spreading of the adhesive system in clinically relevant times and may influence adhesive thickness on cavity walls.

Factors favoring regain of the lost vertical spinal height through posterior spinal fusion in adolescent idiopathic scoliosis.

PubMed

Shi, Benlong; Mao, Saihu; Xu, Leilei; Sun, Xu; Liu, Zhen; Zhu, Zezhang; Lam, Tsz Ping; Cheng, Jack Cy; Ng, Bobby; Qiu, Yong

2016-07-04

Height gain is a common beneficial consequence following correction surgery in adolescent idiopathic scoliosis (AIS), yet little is known concerning factors favoring regain of the lost vertical spinal height (SH) through posterior spinal fusion. A consecutive series of AIS patients from February 2013 to August 2015 were reviewed. Surgical changes in SH (ΔSH), as well as the multiple coronal and sagittal deformity parameters were measured and correlated. Factors associated with ΔSH were identified through Pearson correlation analysis and multivariate regression analysis. A total of 172 single curve and 104 double curve patients were reviewed. The ΔSH averaged 2.5 ± 0.9 cm in single curve group and 2.9 ± 1.0 cm in double curve group. The multivariate regression analysis revealed the following pre-operative variables contributed significantly to ΔSH: pre-op Cobb angle, pre-op TK (single curve group only), pre-op GK (double curve group only) and pre-op LL (double curve group only) (p < 0.05). Thus change in height (in cm) = 0.044 × (pre-op Cobb angle) + 0.012 × (pre-op TK) (Single curve, adjusted R(2) = 0.549) or 0.923 + 0.021 × (pre-op Cobb angle1) + 0.028 × (pre-op Cobb angle2) + 0.015 × (pre-op GK)-0.012 × (pre-op LL) (Double curve, adjusted R(2) = 0.563). Severer pre-operative coronal Cobb angle and greater sagittal curves were beneficial factors favoring more contribution to the surgical lengthening effect in vertical spinal height in AIS.

Regression with Small Data Sets: A Case Study using Code Surrogates in Additive Manufacturing

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

Kamath, C.; Fan, Y. J.

There has been an increasing interest in recent years in the mining of massive data sets whose sizes are measured in terabytes. While it is easy to collect such large data sets in some application domains, there are others where collecting even a single data point can be very expensive, so the resulting data sets have only tens or hundreds of samples. For example, when complex computer simulations are used to understand a scientific phenomenon, we want to run the simulation for many different values of the input parameters and analyze the resulting output. The data set relating the simulationmore » inputs and outputs is typically quite small, especially when each run of the simulation is expensive. However, regression techniques can still be used on such data sets to build an inexpensive \\surrogate" that could provide an approximate output for a given set of inputs. A good surrogate can be very useful in sensitivity analysis, uncertainty analysis, and in designing experiments. In this paper, we compare different regression techniques to determine how well they predict melt-pool characteristics in the problem domain of additive manufacturing. Our analysis indicates that some of the commonly used regression methods do perform quite well even on small data sets.« less

A successful backward step correlates with hip flexion moment of supporting limb in elderly people.

PubMed

Takeuchi, Yahiko

2018-01-01

The objective of this study was to determine the positional relationship between the center of mass (COM) and the center of pressure (COP) at the time of step landing, and to examine their relationship with the joint moments exerted by the supporting limb, with regard to factors of the successful backward step response. The study population comprised 8 community-dwelling elderly people that were observed to take successive multi steps after the landing of a backward stepping. Using a motion capture system and force plate, we measured the COM, COP and COM-COP deviation distance on landing during backward stepping. In addition, we measured the moment of the supporting limb joint during backward stepping. The multi-step data were compared with data from instances when only one step was taken (single-step). Variables that differed significantly between the single- and multi-step data were used as objective variables and the joint moments of the supporting limb were used as explanatory variables in single regression analyses. The COM-COP deviation in the anteroposterior was significantly larger in the single-step. A regression analysis with COM-COP deviation as the objective variable obtained a significant regression equation in the hip flexion moment (R2 = 0.74). The hip flexion moment of supporting limb was shown to be a significant explanatory variable in both the PS and SS phases for the relationship with COM-COP distance. This study found that to create an appropriate backward step response after an external disturbance (i.e. the ability to stop after 1 step), posterior braking of the COM by a hip flexion moment are important during the single-limbed standing phase.

Can multi-slice or navigator-gated R2* MRI replace single-slice breath-hold acquisition for hepatic iron quantification?

PubMed

Loeffler, Ralf B; McCarville, M Beth; Wagstaff, Anne W; Smeltzer, Matthew P; Krafft, Axel J; Song, Ruitian; Hankins, Jane S; Hillenbrand, Claudia M

2017-01-01

Liver R2* values calculated from multi-gradient echo (mGRE) magnetic resonance images (MRI) are strongly correlated with hepatic iron concentration (HIC) as shown in several independently derived biopsy calibration studies. These calibrations were established for axial single-slice breath-hold imaging at the location of the portal vein. Scanning in multi-slice mode makes the exam more efficient, since whole-liver coverage can be achieved with two breath-holds and the optimal slice can be selected afterward. Navigator echoes remove the need for breath-holds and allow use in sedated patients. To evaluate if the existing biopsy calibrations can be applied to multi-slice and navigator-controlled mGRE imaging in children with hepatic iron overload, by testing if there is a bias-free correlation between single-slice R2* and multi-slice or multi-slice navigator controlled R2*. This study included MRI data from 71 patients with transfusional iron overload, who received an MRI exam to estimate HIC using gradient echo sequences. Patient scans contained 2 or 3 of the following imaging methods used for analysis: single-slice images (n = 71), multi-slice images (n = 69) and navigator-controlled images (n = 17). Small and large blood corrected region of interests were selected on axial images of the liver to obtain R2* values for all data sets. Bland-Altman and linear regression analysis were used to compare R2* values from single-slice images to those of multi-slice images and navigator-controlled images. Bland-Altman analysis showed that all imaging method comparisons were strongly associated with each other and had high correlation coefficients (0.98 ≤ r ≤ 1.00) with P-values ≤0.0001. Linear regression yielded slopes that were close to 1. We found that navigator-gated or breath-held multi-slice R2* MRI for HIC determination measures R2* values comparable to the biopsy-validated single-slice, single breath-hold scan. We conclude that these three R2* methods can be interchangeably used in existing R2*-HIC calibrations.

Allometric scaling: analysis of LD50 data.

PubMed

Burzala-Kowalczyk, Lidia; Jongbloed, Geurt

2011-04-01

The need to identify toxicologically equivalent doses across different species is a major issue in toxicology and risk assessment. In this article, we investigate interspecies scaling based on the allometric equation applied to the single, oral LD (50) data previously analyzed by Rhomberg and Wolff. We focus on the statistical approach, namely, regression analysis of the mentioned data. In contrast to Rhomberg and Wolff's analysis of species pairs, we perform an overall analysis based on the whole data set. From our study it follows that if one assumes one single scaling rule for all species and substances in the data set, then β = 1 is the most natural choice among a set of candidates known in the literature. In fact, we obtain quite narrow confidence intervals for this parameter. However, the estimate of the variance in the model is relatively high, resulting in rather wide prediction intervals. © 2010 Society for Risk Analysis.

Associated factors of radiation pneumonitis induced by precise radiotherapy in 186 elderly patients with esophageal cancer.

PubMed

Cui, Zhen; Tian, Ye; He, Bin; Li, Hongwei; Li, Duojie; Liu, Jingjing; Cai, Hanfei; Lou, Jianjun; Jiang, Hao; Shen, Xueming; Peng, Kaigui

2015-01-01

Radiation pneumonitis is one of the most severe complications of esophageal cancer. To explore the factors correlated to radiation pneumonitis induced by precise radiotherapy for elderly patients with esophageal cancer. The retrospective analysis was used to collect clinical data from 186 elderly patients with esophageal cancer. The incidence of radiation pneumonitis was observed, followed by statistical analysis through ANVON or multiple regression analysis. 27 in 186 cases of esophageal cancer suffered from radiation pneumonitis, with incidence of 14.52%. The single factor analysis showed that, Karnofsky performance status (KPS) score, chronic obstructive pulmonary disease, concurrent chemoradiotherapy, gross tumor volume (GTV) dose, lung V20, mean lung dose (MLD) and planning target volume (PTV) were associated with radiation pneumonitis. The logistic regression analysis indicated that, concurrent chemoradiotherapy, GTV dose, lung V20 and PTV were the independent factors of radiation pneumonitis. The concurrent chemoradiotherapy, GTV dose, lung V20, MLD and PTV are the major risk factors of radiation pneumonitis for elderly patients with esophageal cancer.

[Comparison of predictive effect between the single auto regressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) combination model on the incidence of scarlet fever].

PubMed

Zhu, Yu; Xia, Jie-lai; Wang, Jing

2009-09-01

Application of the 'single auto regressive integrated moving average (ARIMA) model' and the 'ARIMA-generalized regression neural network (GRNN) combination model' in the research of the incidence of scarlet fever. Establish the auto regressive integrated moving average model based on the data of the monthly incidence on scarlet fever of one city, from 2000 to 2006. The fitting values of the ARIMA model was used as input of the GRNN, and the actual values were used as output of the GRNN. After training the GRNN, the effect of the single ARIMA model and the ARIMA-GRNN combination model was then compared. The mean error rate (MER) of the single ARIMA model and the ARIMA-GRNN combination model were 31.6%, 28.7% respectively and the determination coefficient (R(2)) of the two models were 0.801, 0.872 respectively. The fitting efficacy of the ARIMA-GRNN combination model was better than the single ARIMA, which had practical value in the research on time series data such as the incidence of scarlet fever.

Spacecraft platform cost estimating relationships

NASA Technical Reports Server (NTRS)

Gruhl, W. M.

1972-01-01

The three main cost areas of unmanned satellite development are discussed. The areas are identified as: (1) the spacecraft platform (SCP), (2) the payload or experiments, and (3) the postlaunch ground equipment and operations. The SCP normally accounts for over half of the total project cost and accurate estimates of SCP costs are required early in project planning as a basis for determining total project budget requirements. The development of single formula SCP cost estimating relationships (CER) from readily available data by statistical linear regression analysis is described. The advantages of single formula CER are presented.

Single Group, Pre- and Post-Test Research Designs: Some Methodological Concerns

ERIC Educational Resources Information Center

Marsden, Emma; Torgerson, Carole J.

2012-01-01

This article provides two illustrations of some of the factors that can influence findings from pre- and post-test research designs in evaluation studies, including regression to the mean (RTM), maturation, history and test effects. The first illustration involves a re-analysis of data from a study by Marsden (2004), in which pre-test scores are…

An Overview of Longitudinal Data Analysis Methods for Neurological Research

PubMed Central

Locascio, Joseph J.; Atri, Alireza

2011-01-01

The purpose of this article is to provide a concise, broad and readily accessible overview of longitudinal data analysis methods, aimed to be a practical guide for clinical investigators in neurology. In general, we advise that older, traditional methods, including (1) simple regression of the dependent variable on a time measure, (2) analyzing a single summary subject level number that indexes changes for each subject and (3) a general linear model approach with a fixed-subject effect, should be reserved for quick, simple or preliminary analyses. We advocate the general use of mixed-random and fixed-effect regression models for analyses of most longitudinal clinical studies. Under restrictive situations or to provide validation, we recommend: (1) repeated-measure analysis of covariance (ANCOVA), (2) ANCOVA for two time points, (3) generalized estimating equations and (4) latent growth curve/structural equation models. PMID:22203825

Relationship between sharps disposal containers and Clostridium difficile infections in acute care hospitals.

PubMed

Pogorzelska-Maziarz, Monika

2015-10-01

Sharps disposal containers are ubiquitous in health care facilities; however, there is paucity of data on their potential role in pathogen transmission. This study assessed the relationship between use of single-use versus reusable sharps containers and rates of Clostridium difficile infections in a national sample of hospitals. A 2013 survey of 1,990 hospitals collected data on the use of sharps containers. Responses were linked to the 2012 Medicare Provider Analysis and Review dataset. Bivariate and multivariable negative binomial regression were conducted to examine differences in C difficile rates between hospitals using single-use versus reusable containers. There were 604 hospitals who completed the survey; of these, 539 provided data on use of sharps containers in 2012 (27% response rate). Hospitals had, on average, 289 beds (SD ± 203) and were predominantly non-for-profit (67%) and nonteaching (63%). Most used reusable sharps containers (72%). In bivariate regression, hospitals using single-use containers had significantly lower rates of C difficile versus hospitals using reusable containers (incidence rate ratio [IRR] = 0.846, P = .001). This relationship persisted in multivariable regression (IRR = 0.870, P = .003) after controlling for other hospital characteristics. This is the first study to show a link between use of single-use sharps containers and lower C difficile rates. Future research should investigate the potential for environmental contamination of reusable containers and the role they may play in pathogen transmission. Copyright © 2015 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Assessing host-specificity of Escherichia coli using a supervised learning logic-regression-based analysis of single nucleotide polymorphisms in intergenic regions.

PubMed

Zhi, Shuai; Li, Qiaozhi; Yasui, Yutaka; Edge, Thomas; Topp, Edward; Neumann, Norman F

2015-11-01

Host specificity in E. coli is widely debated. Herein, we used supervised learning logic-regression-based analysis of intergenic DNA sequence variability in E. coli in an attempt to identify single nucleotide polymorphism (SNP) biomarkers of E. coli that are associated with natural selection and evolution toward host specificity. Seven-hundred and eighty strains of E. coli were isolated from 15 different animal hosts. We utilized logic regression for analyzing DNA sequence data of three intergenic regions (flanked by the genes uspC-flhDC, csgBAC-csgDEFG, and asnS-ompF) to identify genetic biomarkers that could potentially discriminate E. coli based on host sources. Across 15 different animal hosts, logic regression successfully discriminated E. coli based on animal host source with relatively high specificity (i.e., among the samples of the non-target animal host, the proportion that correctly did not have the host-specific marker pattern) and sensitivity (i.e., among the samples from a given animal host, the proportion that correctly had the host-specific marker pattern), even after fivefold cross validation. Permutation tests confirmed that for most animals, host specific intergenic biomarkers identified by logic regression in E. coli were significantly associated with animal host source. The highest level of biomarker sensitivity was observed in deer isolates, with 82% of all deer E. coli isolates displaying a unique SNP pattern that was 98% specific to deer. Fifty-three percent of human isolates displayed a unique biomarker pattern that was 98% specific to humans. Twenty-nine percent of cattle isolates displayed a unique biomarker that was 97% specific to cattle. Interestingly, even within a related host group (i.e., Family: Canidae [domestic dogs and coyotes]), highly specific SNP biomarkers (98% and 99% specificity for dog and coyotes, respectively) were observed, with 21% of dog E. coli isolates displaying a unique dog biomarker and 61% of coyote isolates displaying a unique coyote biomarker. Application of a supervised learning method, such as logic regression, to DNA sequence analysis at certain intergenic regions demonstrates that some E. coli strains may evolve to become host-specific. Copyright © 2015 Elsevier Inc. All rights reserved.

Effect of marital status on treatment and survival of extremity soft tissue sarcoma

PubMed Central

Alamanda, V. K.; Song, Y.; Holt, G. E.

2014-01-01

Background Spousal support has been hypothesized as providing important psychosocial support for patients and as such has been noted to provide a survival advantage in a number of chronic diseases and cancers. However, the specific effect of marital status on survival in soft tissue sarcomas (STSs) of the extremity has not been explored in detail. Patients and methods A total of 7384 patients were evaluated for this study using a Surveillance, Epidemiology, and End Results (SEER) registry query for patients over 20 years old with extremity STS diagnosed between 2004 and 2009. Survival outcomes were analyzed using Gray's test after patients were stratified by marital status. The Fine and Gray model, a multivariable regression model, was used to assess whether marital status was an independent predictor of sarcoma specific death. Statistical significance was maintained at P < 0.05. Results Analysis of the SEER database showed that single patients were more likely to die of their STS and at a faster rate than married patients. No differences were noted in tumor size and tumor site on presentation between married and single patients. However, single patients presented with higher grade tumors more frequently (P = 0.013), received less radiotherapy (P < 0.001), and had less surgery carried out (P < 0.001), compared with their married peers. Regression analysis showed that after accounting for tumor size, grade, site, histology, use of radiotherapy, age, gender, region where the patients were from, and income, being single continued to serve as an independent predictor of sarcoma-specific death; P < 0.0001. Conclusion Overall survival is worse for single patients, when compared with married patients, with STS. Single patients do not undergo surgical resection or receive radiation therapy as frequently as their married counterparts. Social support systems and barriers to care should be evaluated at time of diagnosis and addressed in single patients to potentially improve survival outcomes. PMID:24504446

Valid Statistical Analysis for Logistic Regression with Multiple Sources

NASA Astrophysics Data System (ADS)

Fienberg, Stephen E.; Nardi, Yuval; Slavković, Aleksandra B.

Considerable effort has gone into understanding issues of privacy protection of individual information in single databases, and various solutions have been proposed depending on the nature of the data, the ways in which the database will be used and the precise nature of the privacy protection being offered. Once data are merged across sources, however, the nature of the problem becomes far more complex and a number of privacy issues arise for the linked individual files that go well beyond those that are considered with regard to the data within individual sources. In the paper, we propose an approach that gives full statistical analysis on the combined database without actually combining it. We focus mainly on logistic regression, but the method and tools described may be applied essentially to other statistical models as well.

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

Learning effect and test-retest variability of pulsar perimetry.

PubMed

Salvetat, Maria Letizia; Zeppieri, Marco; Parisi, Lucia; Johnson, Chris A; Sampaolesi, Roberto; Brusini, Paolo

2013-03-01

To assess Pulsar Perimetry learning effect and test-retest variability (TRV) in normal (NORM), ocular hypertension (OHT), glaucomatous optic neuropathy (GON), and primary open-angle glaucoma (POAG) eyes. This multicenter prospective study included 43 NORM, 38 OHT, 33 GON, and 36 POAG patients. All patients underwent standard automated perimetry and Pulsar Contrast Perimetry using white stimuli modulated in phase and counterphase at 30 Hz (CP-T30W test). The learning effect and TRV for Pulsar Perimetry were assessed for 3 consecutive visual fields (VFs). The learning effect were evaluated by comparing results from the first session with the other 2. TRV was assessed by calculating the mean of the differences (in absolute value) between retests for each combination of single tests. TRV was calculated for Mean Sensitivity, Mean Defect, and single Mean Sensitivity for each 66 test locations. Influence of age, VF eccentricity, and loss severity on TRV were assessed using linear regression analysis and analysis of variance. The learning effect was not significant in any group (analysis of variance, P>0.05). TRV for Mean Sensitivity and Mean Defect was significantly lower in NORM and OHT (0.6 ± 0.5 spatial resolution contrast units) than in GON and POAG (0.9 ± 0.5 and 1.0 ± 0.8 spatial resolution contrast units, respectively) (Kruskal-Wallis test, P=0.04); however, the differences in NORM among age groups was not significant (Kruskal-Wallis test, P>0.05). Slight significant differences were found for the single Mean Sensitivity TRV among single locations (Duncan test, P<0.05). For POAG, TRV significantly increased with decreasing Mean Sensitivity and increasing Mean Defect (linear regression analysis, P<0.01). The Pulsar Perimetry CP-T30W test did not show significant learning effect in patients with standard automated perimetry experience. TRV for global indices was generally low, and was not related to patient age; it was only slightly affected by VF defect eccentricity, and significantly influenced by VF loss severity.

Predicting attention-deficit/hyperactivity disorder severity from psychosocial stress and stress-response genes: a random forest regression approach

PubMed Central

van der Meer, D; Hoekstra, P J; van Donkelaar, M; Bralten, J; Oosterlaan, J; Heslenfeld, D; Faraone, S V; Franke, B; Buitelaar, J K; Hartman, C A

2017-01-01

Identifying genetic variants contributing to attention-deficit/hyperactivity disorder (ADHD) is complicated by the involvement of numerous common genetic variants with small effects, interacting with each other as well as with environmental factors, such as stress exposure. Random forest regression is well suited to explore this complexity, as it allows for the analysis of many predictors simultaneously, taking into account any higher-order interactions among them. Using random forest regression, we predicted ADHD severity, measured by Conners’ Parent Rating Scales, from 686 adolescents and young adults (of which 281 were diagnosed with ADHD). The analysis included 17 374 single-nucleotide polymorphisms (SNPs) across 29 genes previously linked to hypothalamic–pituitary–adrenal (HPA) axis activity, together with information on exposure to 24 individual long-term difficulties or stressful life events. The model explained 12.5% of variance in ADHD severity. The most important SNP, which also showed the strongest interaction with stress exposure, was located in a region regulating the expression of telomerase reverse transcriptase (TERT). Other high-ranking SNPs were found in or near NPSR1, ESR1, GABRA6, PER3, NR3C2 and DRD4. Chronic stressors were more influential than single, severe, life events. Top hits were partly shared with conduct problems. We conclude that random forest regression may be used to investigate how multiple genetic and environmental factors jointly contribute to ADHD. It is able to implicate novel SNPs of interest, interacting with stress exposure, and may explain inconsistent findings in ADHD genetics. This exploratory approach may be best combined with more hypothesis-driven research; top predictors and their interactions with one another should be replicated in independent samples. PMID:28585928

Visual Acuity Is Correlated with the Area of the Foveal Avascular Zone in Diabetic Retinopathy and Retinal Vein Occlusion.

PubMed

Balaratnasingam, Chandrakumar; Inoue, Maiko; Ahn, Seungjun; McCann, Jesse; Dhrami-Gavazi, Elona; Yannuzzi, Lawrence A; Freund, K Bailey

2016-11-01

To determine if the area of the foveal avascular zone (FAZ) is correlated with visual acuity (VA) in diabetic retinopathy (DR) and retinal vein occlusion (RVO). Cross-sectional study. Ninety-five eyes of 66 subjects with DR (65 eyes), branch retinal vein occlusion (19 eyes), and central retinal vein occlusion (11 eyes). Structural optical coherence tomography (OCT; Spectralis, Heidelberg Engineering) and OCT angiography (OCTA; Avanti, Optovue RTVue XR) data from a single visit were analyzed. FAZ area, point thickness of central fovea, central 1-mm subfield thickness, the occurrence of intraretinal cysts, ellipsoid zone disruption, and disorganization of retinal inner layers (DRIL) length were measured. VA was also recorded. Correlations between FAZ area and VA were explored using regression models. Main outcome measure was VA. Mean age was 62.9±13.2 years. There was no difference in demographic and OCT-derived anatomic measurements between branch retinal vein occlusion and central retinal vein occlusion groups (all P ≥ 0.058); therefore, data from the 2 groups were pooled together to a single RVO group for further statistical comparisons. Univariate and multiple regression analysis showed that the area of the FAZ was significantly correlated with VA in DR and RVO (all P ≤ 0.003). The relationship between FAZ area and VA varied with age (P = 0.026) such that for a constant FAZ area, an increase in patient age was associated with poorer vision (rise in logarithm of the minimum angle of resolution visual acuity). Disruption of the ellipsoid zone was significantly correlated with VA in univariate and multiple regression analysis (both P < 0.001). Occurrence of intraretinal cysts, DRIL length, and lens status were significantly correlated with VA in the univariate regression analysis (P ≤ 0.018) but not the multiple regression analysis (P ≥ 0.210). Remaining variables evaluated in this study were not predictive of VA (all P ≥ 0.225). The area of the FAZ is significantly correlated with VA in DR and RVO and this relationship is modulated by patient age. Further study about FAZ area and VA correlations during the natural course of retinal vascular diseases and following treatment is warranted. Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

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

PubMed

Laurens, L M L; Wolfrum, E J

2013-12-18

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

A classical regression framework for mediation analysis: fitting one model to estimate mediation effects.

PubMed

Saunders, Christina T; Blume, Jeffrey D

2017-10-26

Mediation analysis explores the degree to which an exposure's effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects. Because these effects are often simple functions of the EMCs, an analytical expression for their model-based variance follows directly. Given this formula, it is instructive to revisit the performance of routinely used variance approximations (e.g., delta method and resampling methods). Requiring the fit of only one model reduces the computation time required for complex mediation analyses and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations, as would be required in the Baron-Kenny framework. Using data from the BRAIN-ICU study, we provide examples to illustrate the advantages of this framework and compare it with the existing approaches. © The Author 2017. Published by Oxford University Press.

Comparison of Maximum Likelihood Estimation Approach and Regression Approach in Detecting Quantitative Trait Lco Using RAPD Markers

Treesearch

Changren Weng; Thomas L. Kubisiak; C. Dana Nelson; James P. Geaghan; Michael Stine

1999-01-01

Single marker regression and single marker maximum likelihood estimation were tied to detect quantitative trait loci (QTLs) controlling the early height growth of longleaf pine and slash pine using a ((longleaf pine x slash pine) x slash pine) BC, population consisting of 83 progeny. Maximum likelihood estimation was found to be more power than regression and could...

[Induced abortion: a comparison between married and single women residing in the city of São Paulo in 2008].

PubMed

de Souza e Silva, Rebeca; Andreoni, Solange

2012-07-01

The scope of this study was to evaluate the association between having had an induced abortion and marital status (being single or legally married) in women residing in the city of São Paulo. This analysis is derived from a broader population survey on abortion conducted in 2008. In this study we focus on the subset of 389 single and legally married women between 15 and 49 years of age. Logistic regression models were used to evaluate the association between induced abortion and being single or married, monitoring age, education, income, number of live births, contraceptive use and acceptance of the practice of abortion. Being single was the only characteristic associated with having had an induced abortion, in other words, when faced with a pregnancy single women were four times more likely to have an abortion than married women (OR=3.9; p=0.009).

Single-parent households and children's educational achievement: A state-level analysis.

PubMed

Amato, Paul R; Patterson, Sarah; Beattie, Brett

2015-09-01

Although many studies have examined associations between family structure and children's educational achievement at the individual level, few studies have considered how the increase in single-parent households may have affected children's educational achievement at the population level. We examined changes in the percentage of children living with single parents between 1990 and 2011 and state mathematics and reading scores on the National Assessment of Educational Progress. Regression models with state and year fixed effects revealed that changes in the percentage of children living with single parents were not associated with test scores. Increases in maternal education, however, were associated with improvements in children's test scores during this period. These results do not support the notion that increases in single parenthood have had serious consequences for U.S. children's school achievement. Copyright © 2015 Elsevier Inc. All rights reserved.

Genetic aspect of Alzheimer disease: Results of complex segregation analysis

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

Sadonvick, A.D.; Lee, I.M.L.; Bailey-Wilson, J.E.

1994-09-01

The study was designed to evaluate the possibility that a single major locus will explain the segregation of Alzheimer disease (AD). The data were from the population-based AD Genetic Database and consisted of 402 consecutive, unrelated probands, diagnosed to have either `probable` or `autopsy confirmed` AD and their 2,245 first-degree relatives. In this analysis, a relative was considered affected with AD only when there were sufficient medical/autopsy data to support diagnosis of AD being the most likely cause of the dementia. Transmission probability models allowing for a genotype-dependent and logistically distributed age-of-onset were used. The program REGTL in the S.A.G.E.more » computer program package was used for a complex segregation analysis. The models included correction for single ascertainment. Regressive familial effects were not estimated. The data were analyzed to test for single major locus (SML), random transmission and no transmission (environmental) hypotheses. The results of the complex segregation analysis showed that (1) the SML was the best fit, and (2) the non-genetic models could be rejected.« less

Single versus multiple sets of resistance exercise: a meta-regression.

PubMed

Krieger, James W

2009-09-01

There has been considerable debate over the optimal number of sets per exercise to improve musculoskeletal strength during a resistance exercise program. The purpose of this study was to use hierarchical, random-effects meta-regression to compare the effects of single and multiple sets per exercise on dynamic strength. English-language studies comparing single with multiple sets per exercise, while controlling for other variables, were considered eligible for inclusion. The analysis comprised 92 effect sizes (ESs) nested within 30 treatment groups and 14 studies. Multiple sets were associated with a larger ES than a single set (difference = 0.26 +/- 0.05; confidence interval [CI]: 0.15, 0.37; p < 0.0001). In a dose-response model, 2 to 3 sets per exercise were associated with a significantly greater ES than 1 set (difference = 0.25 +/- 0.06; CI: 0.14, 0.37; p = 0.0001). There was no significant difference between 1 set per exercise and 4 to 6 sets per exercise (difference = 0.35 +/- 0.25; CI: -0.05, 0.74; p = 0.17) or between 2 to 3 sets per exercise and 4 to 6 sets per exercise (difference = 0.09 +/- 0.20; CI: -0.31, 0.50; p = 0.64). There were no interactions between set volume and training program duration, subject training status, or whether the upper or lower body was trained. Sensitivity analysis revealed no highly influential studies, and no evidence of publication bias was observed. In conclusion, 2 to 3 sets per exercise are associated with 46% greater strength gains than 1 set, in both trained and untrained subjects.

Measurements of the talus in the assessment of population affinity.

PubMed

Bidmos, Mubarak A; Dayal, Manisha R; Adegboye, Oyelola A

2018-06-01

As part of their routine work, forensic anthropologists are expected to report population affinity as part of the biological profile of an individual. The skull is the most widely used bone for the estimation of population affinity but it is not always present in a forensic case. Thus, other bones that preserve well have been shown to give a good indication of either the sex or population affinity of an individual. In this study, the potential of measurements of the talus was investigated for the purpose of estimating population affinity in South Africans. Nine measurements from two hundred and twenty tali of South African Africans (SAA) and South African Whites (SAW) from the Raymond A. Dart Collection of Human Skeletons were used. Direct and step-wise discriminant function and logistic regression analyses were carried out using SPSS and SAS. Talar length was the best single variable for discriminating between these two groups for males while in females the head height was the best single predictor. Average accuracies for correct population affinity classification using logistic regression analysis were higher than those obtained from discriminant function analysis. This study was the first of its type to employ discriminant function analyses and logistic regression analyses to estimate the population affinity of an individual from the talus. Thus these equations can now be used by South African anthropologists when estimating the population affinity of dismembered or damaged or incomplete skeletal remains of SAA and SAW. Copyright © 2018 Elsevier B.V. All rights reserved.

Design of Experiment and Analysis for the Joint Dynamic Allocation of Fires and Sensors (JDAFS) Simulation

DTIC Science & Technology

2007-06-01

other databases such as MySQL , Oracle , and Derby will be added to future versions of the program. Setting a factor requires more than changing a single...Non-Penetrating vs . Penetrating Results.............106 a. Coverage...Interaction Profile for D U-2 and C RQ-4 .......................................................89 Figure 59. R-Squared vs . Number of Regression Tree

High and low frequency unfolded partial least squares regression based on empirical mode decomposition for quantitative analysis of fuel oil samples.

PubMed

Bian, Xihui; Li, Shujuan; Lin, Ligang; Tan, Xiaoyao; Fan, Qingjie; Li, Ming

2016-06-21

Accurate prediction of the model is fundamental to the successful analysis of complex samples. To utilize abundant information embedded over frequency and time domains, a novel regression model is presented for quantitative analysis of hydrocarbon contents in the fuel oil samples. The proposed method named as high and low frequency unfolded PLSR (HLUPLSR), which integrates empirical mode decomposition (EMD) and unfolded strategy with partial least squares regression (PLSR). In the proposed method, the original signals are firstly decomposed into a finite number of intrinsic mode functions (IMFs) and a residue by EMD. Secondly, the former high frequency IMFs are summed as a high frequency matrix and the latter IMFs and residue are summed as a low frequency matrix. Finally, the two matrices are unfolded to an extended matrix in variable dimension, and then the PLSR model is built between the extended matrix and the target values. Coupled with Ultraviolet (UV) spectroscopy, HLUPLSR has been applied to determine hydrocarbon contents of light gas oil and diesel fuels samples. Comparing with single PLSR and other signal processing techniques, the proposed method shows superiority in prediction ability and better model interpretation. Therefore, HLUPLSR method provides a promising tool for quantitative analysis of complex samples. Copyright © 2016 Elsevier B.V. All rights reserved.

Identification of molecular markers associated with mite resistance in coconut (Cocos nucifera L.).

PubMed

Shalini, K V; Manjunatha, S; Lebrun, P; Berger, A; Baudouin, L; Pirany, N; Ranganath, R M; Prasad, D Theertha

2007-01-01

Coconut mite (Aceria guerreronis 'Keifer') has become a major threat to Indian coconut (Coçcos nucifera L.) cultivators and the processing industry. Chemical and biological control measures have proved to be costly, ineffective, and ecologically undesirable. Planting mite-resistant coconut cultivars is the most effective method of preventing yield loss and should form a major component of any integrated pest management stratagem. Coconut genotypes, and mite-resistant and -susceptible accessions were collected from different parts of South India. Thirty-two simple sequence repeat (SSR) and 7 RAPD primers were used for molecular analyses. In single-marker analysis, 9 SSR and 4 RAPD markers associated with mite resistance were identified. In stepwise multiple regression analysis of SSRs, a combination of 6 markers showed 100% association with mite infestation. Stepwise multiple regression analysis for RAPD data revealed that a combination of 3 markers accounted for 83.86% of mite resistance in the selected materials. Combined stepwise multiple regression analysis of RAPD and SSR data showed that a combination of 5 markers explained 100% of the association with mite resistance in coconut. Markers associated with mite resistance are important in coconut breeding programs and will facilitate the selection of mite-resistant plants at an early stage as well as mother plants for breeding programs.

Accuracy of Dual-Energy Virtual Monochromatic CT Numbers: Comparison between the Single-Source Projection-Based and Dual-Source Image-Based Methods.

PubMed

Ueguchi, Takashi; Ogihara, Ryota; Yamada, Sachiko

2018-03-21

To investigate the accuracy of dual-energy virtual monochromatic computed tomography (CT) numbers obtained by two typical hardware and software implementations: the single-source projection-based method and the dual-source image-based method. A phantom with different tissue equivalent inserts was scanned with both single-source and dual-source scanners. A fast kVp-switching feature was used on the single-source scanner, whereas a tin filter was used on the dual-source scanner. Virtual monochromatic CT images of the phantom at energy levels of 60, 100, and 140 keV were obtained by both projection-based (on the single-source scanner) and image-based (on the dual-source scanner) methods. The accuracy of virtual monochromatic CT numbers for all inserts was assessed by comparing measured values to their corresponding true values. Linear regression analysis was performed to evaluate the dependency of measured CT numbers on tissue attenuation, method, and their interaction. Root mean square values of systematic error over all inserts at 60, 100, and 140 keV were approximately 53, 21, and 29 Hounsfield unit (HU) with the single-source projection-based method, and 46, 7, and 6 HU with the dual-source image-based method, respectively. Linear regression analysis revealed that the interaction between the attenuation and the method had a statistically significant effect on the measured CT numbers at 100 and 140 keV. There were attenuation-, method-, and energy level-dependent systematic errors in the measured virtual monochromatic CT numbers. CT number reproducibility was comparable between the two scanners, and CT numbers had better accuracy with the dual-source image-based method at 100 and 140 keV. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Kernel machine SNP set analysis provides new insight into the association between obesity and polymorphisms located on the chromosomal 16q.12.2 region: Tehran Lipid and Glucose Study.

PubMed

Javanrouh, Niloufar; Daneshpour, Maryam S; Soltanian, Ali Reza; Tapak, Leili

2018-06-05

Obesity is a serious health problem that leads to low quality of life and early mortality. To the purpose of prevention and gene therapy for such a worldwide disease, genome wide association study is a powerful tool for finding SNPs associated with increased risk of obesity. To conduct an association analysis, kernel machine regression is a generalized regression method, has an advantage of considering the epistasis effects as well as the correlation between individuals due to unknown factors. In this study, information of the people who participated in Tehran cardio-metabolic genetic study was used. They were genotyped for the chromosomal region, evaluation 986 variations located at 16q12.2; build 38hg. Kernel machine regression and single SNP analysis were used to assess the association between obesity and SNPs genotyped data. We found that associated SNP sets with obesity, were almost in the FTO (P = 0.01), AIKTIP (P = 0.02) and MMP2 (P = 0.02) genes. Moreover, two SNPs, i.e., rs10521296 and rs11647470, showed significant association with obesity using kernel regression (P = 0.02). In conclusion, significant sets were randomly distributed throughout the region with more density around the FTO, AIKTIP and MMP2 genes. Furthermore, two intergenic SNPs showed significant association after using kernel machine regression. Therefore, more studies have to be conducted to assess their functionality or precise mechanism. Copyright © 2018 Elsevier B.V. All rights reserved.

Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses

PubMed Central

Nickerson, Lisa D.; Smith, Stephen M.; Öngür, Döst; Beckmann, Christian F.

2017-01-01

Independent Component Analysis (ICA) is one of the most popular techniques for the analysis of resting state FMRI data because it has several advantageous properties when compared with other techniques. Most notably, in contrast to a conventional seed-based correlation analysis, it is model-free and multivariate, thus switching the focus from evaluating the functional connectivity of single brain regions identified a priori to evaluating brain connectivity in terms of all brain resting state networks (RSNs) that simultaneously engage in oscillatory activity. Furthermore, typical seed-based analysis characterizes RSNs in terms of spatially distributed patterns of correlation (typically by means of simple Pearson's coefficients) and thereby confounds together amplitude information of oscillatory activity and noise. ICA and other regression techniques, on the other hand, retain magnitude information and therefore can be sensitive to both changes in the spatially distributed nature of correlations (differences in the spatial pattern or “shape”) as well as the amplitude of the network activity. Furthermore, motion can mimic amplitude effects so it is crucial to use a technique that retains such information to ensure that connectivity differences are accurately localized. In this work, we investigate the dual regression approach that is frequently applied with group ICA to assess group differences in resting state functional connectivity of brain networks. We show how ignoring amplitude effects and how excessive motion corrupts connectivity maps and results in spurious connectivity differences. We also show how to implement the dual regression to retain amplitude information and how to use dual regression outputs to identify potential motion effects. Two key findings are that using a technique that retains magnitude information, e.g., dual regression, and using strict motion criteria are crucial for controlling both network amplitude and motion-related amplitude effects, respectively, in resting state connectivity analyses. We illustrate these concepts using realistic simulated resting state FMRI data and in vivo data acquired in healthy subjects and patients with bipolar disorder and schizophrenia. PMID:28348512

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.

Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions

PubMed Central

Fernandes, Bruno J. T.; Roque, Alexandre

2018-01-01

Height and weight are measurements explored to tracking nutritional diseases, energy expenditure, clinical conditions, drug dosages, and infusion rates. Many patients are not ambulant or may be unable to communicate, and a sequence of these factors may not allow accurate estimation or measurements; in those cases, it can be estimated approximately by anthropometric means. Different groups have proposed different linear or non-linear equations which coefficients are obtained by using single or multiple linear regressions. In this paper, we present a complete study of the application of different learning models to estimate height and weight from anthropometric measurements: support vector regression, Gaussian process, and artificial neural networks. The predicted values are significantly more accurate than that obtained with conventional linear regressions. In all the cases, the predictions are non-sensitive to ethnicity, and to gender, if more than two anthropometric parameters are analyzed. The learning model analysis creates new opportunities for anthropometric applications in industry, textile technology, security, and health care. PMID:29651366

An index of effluent aquatic toxicity designed by partial least squares regression, using acute and chronic tests and expert judgements.

PubMed

Vindimian, Éric; Garric, Jeanne; Flammarion, Patrick; Thybaud, Éric; Babut, Marc

1999-10-01

The evaluation of the ecotoxicity of effluents requires a battery of biological tests on several species. In order to derive a summary parameter from such a battery, a single endpoint was calculated for all the tests: the EC10, obtained by nonlinear regression, with bootstrap evaluation of the confidence intervals. Principal component analysis was used to characterize and visualize the correlation between the tests. The table of the toxicity of the effluents was then submitted to a panel of experts, who classified the effluents according to the test results. Partial least squares (PLS) regression was used to fit the average value of the experts' judgements to the toxicity data, using a simple equation. Furthermore, PLS regression on partial data sets and other considerations resulted in an optimum battery, with two chronic tests and one acute test. The index is intended to be used for the classification of effluents based on their toxicity to aquatic species. Copyright © 1999 SETAC.

An index of effluent aquatic toxicity designed by partial least squares regression, using acute and chronic tests and expert judgments

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

Vindimian, E.; Garric, J.; Flammarion, P.

1999-10-01

The evaluation of the ecotoxicity of effluents requires a battery of biological tests on several species. In order to derive a summary parameter from such a battery, a single endpoint was calculated for all the tests: the EC10, obtained by nonlinear regression, with bootstrap evaluation of the confidence intervals. Principal component analysis was used to characterize and visualize the correlation between the tests. The table of the toxicity of the effluents was then submitted to a panel of experts, who classified the effluents according to the test results. Partial least squares (PLS) regression was used to fit the average valuemore » of the experts' judgments to the toxicity data, using a simple equation. Furthermore, PLS regression on partial data sets and other considerations resulted in an optimum battery, with two chronic tests and one acute test. The index is intended to be used for the classification of effluents based on their toxicity to aquatic species.« less

Multi Objective Optimization of Multi Wall Carbon Nanotube Based Nanogrinding Wheel Using Grey Relational and Regression Analysis

NASA Astrophysics Data System (ADS)

Sethuramalingam, Prabhu; Vinayagam, Babu Kupusamy

2016-07-01

Carbon nanotube mixed grinding wheel is used in the grinding process to analyze the surface characteristics of AISI D2 tool steel material. Till now no work has been carried out using carbon nanotube based grinding wheel. Carbon nanotube based grinding wheel has excellent thermal conductivity and good mechanical properties which are used to improve the surface finish of the workpiece. In the present study, the multi response optimization of process parameters like surface roughness and metal removal rate of grinding process of single wall carbon nanotube (CNT) in mixed cutting fluids is undertaken using orthogonal array with grey relational analysis. Experiments are performed with designated grinding conditions obtained using the L9 orthogonal array. Based on the results of the grey relational analysis, a set of optimum grinding parameters is obtained. Using the analysis of variance approach the significant machining parameters are found. Empirical model for the prediction of output parameters has been developed using regression analysis and the results are compared empirically, for conditions of with and without CNT grinding wheel in grinding process.

Laboratory- and field-based testing as predictors of skating performance in competitive-level female ice hockey.

PubMed

Henriksson, Tommy; Vescovi, Jason D; Fjellman-Wiklund, Anncristine; Gilenstam, Kajsa

2016-01-01

The purpose of this study was to examine whether field-based and/or laboratory-based assessments are valid tools for predicting key performance characteristics of skating in competitive-level female hockey players. Cross-sectional study. Twenty-three female ice hockey players aged 15-25 years (body mass: 66.1±6.3 kg; height: 169.5±5.5 cm), with 10.6±3.2 years playing experience volunteered to participate in the study. The field-based assessments included 20 m sprint, squat jump, countermovement jump, 30-second repeated jump test, standing long jump, single-leg standing long jump, 20 m shuttle run test, isometric leg pull, one-repetition maximum bench press, and one-repetition maximum squats. The laboratory-based assessments included body composition (dual energy X-ray absorptiometry), maximal aerobic power, and isokinetic strength (Biodex). The on-ice tests included agility cornering s-turn, cone agility skate, transition agility skate, and modified repeat skate sprint. Data were analyzed using stepwise multivariate linear regression analysis. Linear regression analysis was used to establish the relationship between key performance characteristics of skating and the predictor variables. Regression models (adj R (2)) for the on-ice variables ranged from 0.244 to 0.663 for the field-based assessments and from 0.136 to 0.420 for the laboratory-based assessments. Single-leg tests were the strongest predictors for key performance characteristics of skating. Single leg standing long jump alone explained 57.1%, 38.1%, and 29.1% of the variance in skating time during transition agility skate, agility cornering s-turn, and modified repeat skate sprint, respectively. Isokinetic peak torque in the quadriceps at 90° explained 42.0% and 32.2% of the variance in skating time during agility cornering s-turn and modified repeat skate sprint, respectively. Field-based assessments, particularly single-leg tests, are an adequate substitute to more expensive and time-consuming laboratory assessments if the purpose is to gain knowledge about key performance characteristics of skating.

Laboratory- and field-based testing as predictors of skating performance in competitive-level female ice hockey

PubMed Central

Henriksson, Tommy; Vescovi, Jason D; Fjellman-Wiklund, Anncristine; Gilenstam, Kajsa

2016-01-01

Objectives The purpose of this study was to examine whether field-based and/or laboratory-based assessments are valid tools for predicting key performance characteristics of skating in competitive-level female hockey players. Design Cross-sectional study. Methods Twenty-three female ice hockey players aged 15–25 years (body mass: 66.1±6.3 kg; height: 169.5±5.5 cm), with 10.6±3.2 years playing experience volunteered to participate in the study. The field-based assessments included 20 m sprint, squat jump, countermovement jump, 30-second repeated jump test, standing long jump, single-leg standing long jump, 20 m shuttle run test, isometric leg pull, one-repetition maximum bench press, and one-repetition maximum squats. The laboratory-based assessments included body composition (dual energy X-ray absorptiometry), maximal aerobic power, and isokinetic strength (Biodex). The on-ice tests included agility cornering s-turn, cone agility skate, transition agility skate, and modified repeat skate sprint. Data were analyzed using stepwise multivariate linear regression analysis. Linear regression analysis was used to establish the relationship between key performance characteristics of skating and the predictor variables. Results Regression models (adj R2) for the on-ice variables ranged from 0.244 to 0.663 for the field-based assessments and from 0.136 to 0.420 for the laboratory-based assessments. Single-leg tests were the strongest predictors for key performance characteristics of skating. Single leg standing long jump alone explained 57.1%, 38.1%, and 29.1% of the variance in skating time during transition agility skate, agility cornering s-turn, and modified repeat skate sprint, respectively. Isokinetic peak torque in the quadriceps at 90° explained 42.0% and 32.2% of the variance in skating time during agility cornering s-turn and modified repeat skate sprint, respectively. Conclusion Field-based assessments, particularly single-leg tests, are an adequate substitute to more expensive and time-consuming laboratory assessments if the purpose is to gain knowledge about key performance characteristics of skating. PMID:27574474

SigEMD: A powerful method for differential gene expression analysis in single-cell RNA sequencing data.

PubMed

Wang, Tianyu; Nabavi, Sheida

2018-04-24

Differential gene expression analysis is one of the significant efforts in single cell RNA sequencing (scRNAseq) analysis to discover the specific changes in expression levels of individual cell types. Since scRNAseq exhibits multimodality, large amounts of zero counts, and sparsity, it is different from the traditional bulk RNA sequencing (RNAseq) data. The new challenges of scRNAseq data promote the development of new methods for identifying differentially expressed (DE) genes. In this study, we proposed a new method, SigEMD, that combines a data imputation approach, a logistic regression model and a nonparametric method based on the Earth Mover's Distance, to precisely and efficiently identify DE genes in scRNAseq data. The regression model and data imputation are used to reduce the impact of large amounts of zero counts, and the nonparametric method is used to improve the sensitivity of detecting DE genes from multimodal scRNAseq data. By additionally employing gene interaction network information to adjust the final states of DE genes, we further reduce the false positives of calling DE genes. We used simulated datasets and real datasets to evaluate the detection accuracy of the proposed method and to compare its performance with those of other differential expression analysis methods. Results indicate that the proposed method has an overall powerful performance in terms of precision in detection, sensitivity, and specificity. Copyright © 2018 Elsevier Inc. All rights reserved.

Low-level lead exposure and the IQ of children. A meta-analysis of modern studies

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

Needleman, H.L.; Gatsonis, C.A.

1990-02-02

We identified 24 modern studies of childhood exposures to lead in relation to IQ. From this population, 12 that employed multiple regression analysis with IQ as the dependent variable and lead as the main effect and that controlled for nonlead covariates were selected for a quantitative, integrated review or meta-analysis. The studies were grouped according to type of tissue analyzed for lead. There were 7 blood and 5 tooth lead studies. Within each group, we obtained joint P values by two different methods and average effect sizes as measured by the partial correlation coefficients. We also investigated the sensitivity ofmore » the results to any single study. The sample sizes ranged from 75 to 724. The sign of the regression coefficient for lead was negative in 11 of 12 studies. The negative partial r's for lead ranged from -.27 to -.003. The power to find an effect was limited, below 0.6 in 7 of 12 studies. The joint P values for the blood lead studies were less than .0001 for both methods of analysis (95% confidence interval for group partial r, -.15 {plus minus} .05), while for the tooth lead studies they were .0005 and .004, respectively (95% confidence interval for group partial r, -.08 {plus minus} .05). The hypothesis that lead impairs children's IQ at low dose is strongly supported by this quantitative review. The effect is robust to the impact of any single study.« less

The role of chemometrics in single and sequential extraction assays: a review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques.

PubMed

Giacomino, Agnese; Abollino, Ornella; Malandrino, Mery; Mentasti, Edoardo

2011-03-04

Single and sequential extraction procedures are used for studying element mobility and availability in solid matrices, like soils, sediments, sludge, and airborne particulate matter. In the first part of this review we reported an overview on these procedures and described the applications of chemometric uni- and bivariate techniques and of multivariate pattern recognition techniques based on variable reduction to the experimental results obtained. The second part of the review deals with the use of chemometrics not only for the visualization and interpretation of data, but also for the investigation of the effects of experimental conditions on the response, the optimization of their values and the calculation of element fractionation. We will describe the principles of the multivariate chemometric techniques considered, the aims for which they were applied and the key findings obtained. The following topics will be critically addressed: pattern recognition by cluster analysis (CA), linear discriminant analysis (LDA) and other less common techniques; modelling by multiple linear regression (MLR); investigation of spatial distribution of variables by geostatistics; calculation of fractionation patterns by a mixture resolution method (Chemometric Identification of Substrates and Element Distributions, CISED); optimization and characterization of extraction procedures by experimental design; other multivariate techniques less commonly applied. Copyright © 2010 Elsevier B.V. All rights reserved.

EPHA2 Polymorphisms in Estonian Patients with Age-Related Cataract.

PubMed

Celojevic, Dragana; Abramsson, Alexandra; Seibt Palmér, Mona; Tasa, Gunnar; Juronen, Erkki; Zetterberg, Henrik; Zetterberg, Madeleine

2016-01-01

Ephrin receptors (Ephs) are tyrosine kinases that together with their ligands, ephrins, are considered important in cell-cell communication, especially during embryogenesis but also for epithelium homeostasis. Studies have demonstrated the involvement of mutations or common variants of the gene encoding Eph receptor A2 (EPHA2), in congenital cataract and in age-related cataract. This study investigated a number of disease-associated single nucleotide polymorphisms (SNPs) in EPHA2 in patients with age-related cataract. The study included 491 Estonian patients who had surgery for age-related cataract, classified as nuclear, cortical, posterior subcapsular and mixed lens opacities, and 185 controls of the same ethnical origin. Seven SNPs in EPHA2 (rs7543472, rs11260867, rs7548209, rs3768293, rs6603867, rs6678616, rs477558) were genotyped using TaqMan Allelic Discrimination. Statistical analyses for single factor associations used χ(2)-test and logistic regression was performed including relevant covariates (age, sex and smoking). In single-SNP allele analysis, only the rs7543472 showed a borderline significant association with risk of cataract (p = 0.048). Regression analysis with known risk factors for cataract showed no significant associations of the studied SNPs with cataract. Stratification by cataract subtype did not alter the results. Adjusted odds ratios were between 0.82 and 1.16 (95% confidence interval 0.61-1.60). The present study does not support a major role of EphA2 in cataractogenesis in an Estonian population.

[Assessment of the quality of life of patients with age-related macular degeneration after photodynamic therapy].

PubMed

Kyo, Tetsuhiro; Matsumoto, Yoko; Tochigi, Kasumi; Yuzawa, Mitsuko; Yamaguchi, Takuhiro; Komoto, Atsushi; Shimozuma, Kojiro; Fukuhara, Shunichi

2006-09-01

To quantify quality of life (QOL) changes in patients who have received a single session of photodynamic therapy (PDT) for subfoveal choroidal neovascularization, secondary to age-related macular degeneration (AMD), and to identify factors that correlate with the QOL changes. The QOL changes in 88 patients with AMD were scored with the 25-Item National Eye Institute Visual Function Questionnaire (VFQ-25) before and 3 months after a single PDT with routine ophthalmologic examinations. We used multiple regression analysis to evaluate VFQ-25 sub-scale scores and ophthalmologic findings in these patients before PDT, to identify impact on the effectiveness of PDT. We also evaluated changes in ophthalmologic findings influencing the QOL score. The sub-scale scores for both 'mental health' (p = 0.02) and 'role limitation' (p = 0.03) improved significantly in all 88 cases, but only 'mental health' improved significantly in 34 cases in which PDT was effective. Multiple regression analysis in all 88 cases revealed that the factors contributing significantly to improvement in 'mental health' were a lower pre-PDT 'mental health' score (p < 0.01) and the presence of fibrous tissue (p = 0.01) before the PDT session. The lower the role limitation before PDT (p < 0.01), the more significant was the improvement in this score. Although no baseline sub-scale score was identified as predicting the effectiveness of a single PDT session, the scores for both 'mental health' and 'role limitation' improved.

Best single-slice location to measure visceral adipose tissue on paediatric CT scans and the relationship between anthropometric measurements, gender and VAT volume in children.

PubMed

O'Connor, Michelle; Ryan, John; Foley, Shane

2015-10-01

Visceral adipose tissue (VAT) is a significant risk factor for obesity-related metabolic diseases. This study investigates (1) the best single CT slice location for predicting total abdominal VAT volume in paediatrics and (2) the relationship between waist circumference (WC), sagittal diameter (SD), gender and VAT volume. A random sample of 130 paediatric abdomen CT scans, stratified according to age and gender, was collected. Three readers measured VAT area at each intervertebral level between T12 and S1 using ImageJ analysis (National Institute of Health, Bethesda, MD) software by thresholding -190 to -30 HU and manually segmenting VAT. Single-slice VAT measurements were correlated with total VAT volume to identify the most representative slice. WC and SD were measured at L3-L4 and L4-L5 slices, respectively. Regression analysis was used to evaluate WC, SD and gender as VAT volume predictors. Interviewer and intraviewer reliability were excellent (intraclass correlation coefficient = 0.99). Although VAT measured at multiple slices correlated strongly with abdominal VAT, only one slice in females at L2-L3 and two slices in males at L1-L2 and L5-S1 were strongly correlated across all age groups. Linear regression analysis showed that WC was strongly correlated with VAT volume (beta = 0.970, p < 0.001). Single-slice VAT measurements are highly reproducible. Measurements performed at L2-L3 in females and L1-L2 or L5-S1 in males were most representative of VAT. WC is indicative of VAT. VAT should be measured at L2-L3 in female children and at either L1-L2 or L5-S1 in males. WC is a strong indicator of VAT in children.

Pointwise influence matrices for functional-response regression.

PubMed

Reiss, Philip T; Huang, Lei; Wu, Pei-Shien; Chen, Huaihou; Colcombe, Stan

2017-12-01

We extend the notion of an influence or hat matrix to regression with functional responses and scalar predictors. For responses depending linearly on a set of predictors, our definition is shown to reduce to the conventional influence matrix for linear models. The pointwise degrees of freedom, the trace of the pointwise influence matrix, are shown to have an adaptivity property that motivates a two-step bivariate smoother for modeling nonlinear dependence on a single predictor. This procedure adapts to varying complexity of the nonlinear model at different locations along the function, and thereby achieves better performance than competing tensor product smoothers in an analysis of the development of white matter microstructure in the brain. © 2017, The International Biometric Society.

Evaluation of the Risk Factors for a Rotator Cuff Retear After Repair Surgery.

PubMed

Lee, Yeong Seok; Jeong, Jeung Yeol; Park, Chan-Deok; Kang, Seung Gyoon; Yoo, Jae Chul

2017-07-01

A retear is a significant clinical problem after rotator cuff repair. However, no study has evaluated the retear rate with regard to the extent of footprint coverage. To evaluate the preoperative and intraoperative factors for a retear after rotator cuff repair, and to confirm the relationship with the extent of footprint coverage. Cohort study; Level of evidence, 3. Data were retrospectively collected from 693 patients who underwent arthroscopic rotator cuff repair between January 2006 and December 2014. All repairs were classified into 4 types of completeness of repair according to the amount of footprint coverage at the end of surgery. All patients underwent magnetic resonance imaging (MRI) after a mean postoperative duration of 5.4 months. Preoperative demographic data, functional scores, range of motion, and global fatty degeneration on preoperative MRI and intraoperative variables including the tear size, completeness of rotator cuff repair, concomitant subscapularis repair, number of suture anchors used, repair technique (single-row or transosseous-equivalent double-row repair), and surgical duration were evaluated. Furthermore, the factors associated with failure using the single-row technique and transosseous-equivalent double-row technique were analyzed separately. The retear rate was 7.22%. Univariate analysis revealed that rotator cuff retears were affected by age; the presence of inflammatory arthritis; the completeness of rotator cuff repair; the initial tear size; the number of suture anchors; mean operative time; functional visual analog scale scores; Simple Shoulder Test findings; American Shoulder and Elbow Surgeons scores; and fatty degeneration of the supraspinatus, infraspinatus, and subscapularis. Multivariate logistic regression analysis revealed patient age, initial tear size, and fatty degeneration of the supraspinatus as independent risk factors for a rotator cuff retear. Multivariate logistic regression analysis of the single-row group revealed patient age and fatty degeneration of the supraspinatus as independent risk factors for a rotator cuff retear. Multivariate logistic regression analysis of the transosseous-equivalent double-row group revealed a frozen shoulder as an independent risk factor for a rotator cuff retear. Our results suggest that patient age, initial tear size, and fatty degeneration of the supraspinatus are independent risk factors for a rotator cuff retear, whereas the completeness of rotator cuff repair based on the extent of footprint coverage and repair technique are not.

Spatial prediction of landslides using a hybrid machine learning approach based on Random Subspace and Classification and Regression Trees

NASA Astrophysics Data System (ADS)

Pham, Binh Thai; Prakash, Indra; Tien Bui, Dieu

2018-02-01

A hybrid machine learning approach of Random Subspace (RSS) and Classification And Regression Trees (CART) is proposed to develop a model named RSSCART for spatial prediction of landslides. This model is a combination of the RSS method which is known as an efficient ensemble technique and the CART which is a state of the art classifier. The Luc Yen district of Yen Bai province, a prominent landslide prone area of Viet Nam, was selected for the model development. Performance of the RSSCART model was evaluated through the Receiver Operating Characteristic (ROC) curve, statistical analysis methods, and the Chi Square test. Results were compared with other benchmark landslide models namely Support Vector Machines (SVM), single CART, Naïve Bayes Trees (NBT), and Logistic Regression (LR). In the development of model, ten important landslide affecting factors related with geomorphology, geology and geo-environment were considered namely slope angles, elevation, slope aspect, curvature, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Performance of the RSSCART model (AUC = 0.841) is the best compared with other popular landslide models namely SVM (0.835), single CART (0.822), NBT (0.821), and LR (0.723). These results indicate that performance of the RSSCART is a promising method for spatial landslide prediction.

Effect of removing the common mode errors on linear regression analysis of noise amplitudes in position time series of a regional GPS network & a case study of GPS stations in Southern California

NASA Astrophysics Data System (ADS)

Jiang, Weiping; Ma, Jun; Li, Zhao; Zhou, Xiaohui; Zhou, Boye

2018-05-01

The analysis of the correlations between the noise in different components of GPS stations has positive significance to those trying to obtain more accurate uncertainty of velocity with respect to station motion. Previous research into noise in GPS position time series focused mainly on single component evaluation, which affects the acquisition of precise station positions, the velocity field, and its uncertainty. In this study, before and after removing the common-mode error (CME), we performed one-dimensional linear regression analysis of the noise amplitude vectors in different components of 126 GPS stations with a combination of white noise, flicker noise, and random walking noise in Southern California. The results show that, on the one hand, there are above-moderate degrees of correlation between the white noise amplitude vectors in all components of the stations before and after removal of the CME, while the correlations between flicker noise amplitude vectors in horizontal and vertical components are enhanced from un-correlated to moderately correlated by removing the CME. On the other hand, the significance tests show that, all of the obtained linear regression equations, which represent a unique function of the noise amplitude in any two components, are of practical value after removing the CME. According to the noise amplitude estimates in two components and the linear regression equations, more accurate noise amplitudes can be acquired in the two components.

SPSS and SAS programs for comparing Pearson correlations and OLS regression coefficients.

PubMed

Weaver, Bruce; Wuensch, Karl L

2013-09-01

Several procedures that use summary data to test hypotheses about Pearson correlations and ordinary least squares regression coefficients have been described in various books and articles. To our knowledge, however, no single resource describes all of the most common tests. Furthermore, many of these tests have not yet been implemented in popular statistical software packages such as SPSS and SAS. In this article, we describe all of the most common tests and provide SPSS and SAS programs to perform them. When they are applicable, our code also computes 100 × (1 - α)% confidence intervals corresponding to the tests. For testing hypotheses about independent regression coefficients, we demonstrate one method that uses summary data and another that uses raw data (i.e., Potthoff analysis). When the raw data are available, the latter method is preferred, because use of summary data entails some loss of precision due to rounding.

Optimization to the Culture Conditions for Phellinus Production with Regression Analysis and Gene-Set Based Genetic Algorithm

PubMed Central

Li, Zhongwei; Xin, Yuezhen; Wang, Xun; Sun, Beibei; Xia, Shengyu; Li, Hui

2016-01-01

Phellinus is a kind of fungus and is known as one of the elemental components in drugs to avoid cancers. With the purpose of finding optimized culture conditions for Phellinus production in the laboratory, plenty of experiments focusing on single factor were operated and large scale of experimental data were generated. In this work, we use the data collected from experiments for regression analysis, and then a mathematical model of predicting Phellinus production is achieved. Subsequently, a gene-set based genetic algorithm is developed to optimize the values of parameters involved in culture conditions, including inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time, and rotation speed. These optimized values of the parameters have accordance with biological experimental results, which indicate that our method has a good predictability for culture conditions optimization. PMID:27610365

The Health of the Nation’s Custodial Grandfathers and Older Single Fathers: Findings From the Behavior Risk Factor Surveillance System

PubMed Central

Whitley, Deborah M.; Fuller-Thomson, Esme

2015-01-01

Two important parent groups are solo grandfathers and single fathers raising children alone. The health of male caregivers raising children has received little attention by scholars. Investigating the health of single male caregivers raises awareness about their physical vulnerability. This study uses the 2012 Behavioral Risk Factor Surveillance System to compare health characteristics of 82 solo grandfathers with 396 single fathers aged 50 years and older. The findings suggest that grandfathers exhibited a high prevalence for various health conditions, including diabetes (44%), heart attack (27%), chronic obstructive pulmonary disease (23%), and stroke (6%). Almost half of grandfathers rated their health as fair/poor (47%), and nearly two in five had functional limitations (38%). Although older single fathers had better health characteristics than grandfathers, their health profile was poorer than population norms. Logistic regression analysis suggests that solo grandfathers are more at risk for poor health outcomes than older single fathers. Practice interventions to minimize health risks are discussed. PMID:26669777

A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery.

PubMed

Tan, Kok Chooi; Lim, Hwee San; Matjafri, Mohd Zubir; Abdullah, Khiruddin

2012-06-01

Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.

Multiple linear regression to estimate time-frequency electrophysiological responses in single trials

PubMed Central

Hu, L.; Zhang, Z.G.; Mouraux, A.; Iannetti, G.D.

2015-01-01

Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations. These modulations can be detected when single-trial waveforms are analysed in the time-frequency domain, and consist in stimulus-induced decreases (event-related desynchronization, ERD) or increases (event-related synchronization, ERS) of synchrony in the activity of the underlying neuronal populations. ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding. ERD and ERS are commonly estimated by averaging the time-frequency decomposition of single trials. However, their trial-to-trial variability that can reflect physiologically-important information is lost by across-trial averaging. Here, we aim to (1) develop novel approaches to explore single-trial parameters (including latency, frequency and magnitude) of ERP/ERD/ERS; (2) disclose the relationship between estimated single-trial parameters and other experimental factors (e.g., perceived intensity). We found that (1) stimulus-elicited ERP/ERD/ERS can be correctly separated using principal component analysis (PCA) decomposition with Varimax rotation on the single-trial time-frequency distributions; (2) time-frequency multiple linear regression with dispersion term (TF-MLRd) enhances the signal-to-noise ratio of ERP/ERD/ERS in single trials, and provides an unbiased estimation of their latency, frequency, and magnitude at single-trial level; (3) these estimates can be meaningfully correlated with each other and with other experimental factors at single-trial level (e.g., perceived stimulus intensity and ERP magnitude). The methods described in this article allow exploring fully non-phase-locked stimulus-induced cortical oscillations, obtaining single-trial estimate of response latency, frequency, and magnitude. This permits within-subject statistical comparisons, correlation with pre-stimulus features, and integration of simultaneously-recorded EEG and fMRI. PMID:25665966

Longitudinal changes in tooth/single-implant relationship and bone topography: an 8-year retrospective analysis.

PubMed

Chang, Moontaek; Wennström, Jan L

2012-06-01

To evaluate longitudinal changes in tooth/implant relationship and bone topography at single implants with a microthreaded, conical marginal portion (Astra Tech ST® implants, Astra Tech AB, Mölndal, Sweden). Thirty-one subjects with single implant-supported restorations in the esthetic zone were included. Radiographs obtained at crown installation and 1, 5, and 8 years of follow-up were analyzed with regard to changes in (1) bone level at the implant and adjacent teeth and (2) vertical position of adjacent teeth relative to the single implant. The mean marginal bone loss amounted to 0.1 mm at both implants and adjacent teeth during the 8 years of follow-up. Regression analysis failed to identify significant explanatory factors for observed variance in bone level change at the adjacent tooth surfaces. Vertical change in position of the teeth relative to the implants was more frequent and significantly greater in incisor compared with premolar tooth region but not associated with gender or age. The marginal bone level at teeth adjacent to single implants with a microthreaded conical marginal part was not influenced by horizontal and vertical tooth-implant distances. Continuous eruption of adjacent teeth may result in infraocclusal positioning of a single-implant restoration. © 2010 Wiley Periodicals, Inc.

Understanding Child Stunting in India: A Comprehensive Analysis of Socio-Economic, Nutritional and Environmental Determinants Using Additive Quantile Regression

PubMed Central

Fenske, Nora; Burns, Jacob; Hothorn, Torsten; Rehfuess, Eva A.

2013-01-01

Background Most attempts to address undernutrition, responsible for one third of global child deaths, have fallen behind expectations. This suggests that the assumptions underlying current modelling and intervention practices should be revisited. Objective We undertook a comprehensive analysis of the determinants of child stunting in India, and explored whether the established focus on linear effects of single risks is appropriate. Design Using cross-sectional data for children aged 0–24 months from the Indian National Family Health Survey for 2005/2006, we populated an evidence-based diagram of immediate, intermediate and underlying determinants of stunting. We modelled linear, non-linear, spatial and age-varying effects of these determinants using additive quantile regression for four quantiles of the Z-score of standardized height-for-age and logistic regression for stunting and severe stunting. Results At least one variable within each of eleven groups of determinants was significantly associated with height-for-age in the 35% Z-score quantile regression. The non-modifiable risk factors child age and sex, and the protective factors household wealth, maternal education and BMI showed the largest effects. Being a twin or multiple birth was associated with dramatically decreased height-for-age. Maternal age, maternal BMI, birth order and number of antenatal visits influenced child stunting in non-linear ways. Findings across the four quantile and two logistic regression models were largely comparable. Conclusions Our analysis confirms the multifactorial nature of child stunting. It emphasizes the need to pursue a systems-based approach and to consider non-linear effects, and suggests that differential effects across the height-for-age distribution do not play a major role. PMID:24223839

Understanding child stunting in India: a comprehensive analysis of socio-economic, nutritional and environmental determinants using additive quantile regression.

PubMed

Fenske, Nora; Burns, Jacob; Hothorn, Torsten; Rehfuess, Eva A

2013-01-01

Most attempts to address undernutrition, responsible for one third of global child deaths, have fallen behind expectations. This suggests that the assumptions underlying current modelling and intervention practices should be revisited. We undertook a comprehensive analysis of the determinants of child stunting in India, and explored whether the established focus on linear effects of single risks is appropriate. Using cross-sectional data for children aged 0-24 months from the Indian National Family Health Survey for 2005/2006, we populated an evidence-based diagram of immediate, intermediate and underlying determinants of stunting. We modelled linear, non-linear, spatial and age-varying effects of these determinants using additive quantile regression for four quantiles of the Z-score of standardized height-for-age and logistic regression for stunting and severe stunting. At least one variable within each of eleven groups of determinants was significantly associated with height-for-age in the 35% Z-score quantile regression. The non-modifiable risk factors child age and sex, and the protective factors household wealth, maternal education and BMI showed the largest effects. Being a twin or multiple birth was associated with dramatically decreased height-for-age. Maternal age, maternal BMI, birth order and number of antenatal visits influenced child stunting in non-linear ways. Findings across the four quantile and two logistic regression models were largely comparable. Our analysis confirms the multifactorial nature of child stunting. It emphasizes the need to pursue a systems-based approach and to consider non-linear effects, and suggests that differential effects across the height-for-age distribution do not play a major role.

A Functional Varying-Coefficient Single-Index Model for Functional Response Data

PubMed Central

Li, Jialiang; Huang, Chao; Zhu, Hongtu

2016-01-01

Motivated by the analysis of imaging data, we propose a novel functional varying-coefficient single index model (FVCSIM) to carry out the regression analysis of functional response data on a set of covariates of interest. FVCSIM represents a new extension of varying-coefficient single index models for scalar responses collected from cross-sectional and longitudinal studies. An efficient estimation procedure is developed to iteratively estimate varying coefficient functions, link functions, index parameter vectors, and the covariance function of individual functions. We systematically examine the asymptotic properties of all estimators including the weak convergence of the estimated varying coefficient functions, the asymptotic distribution of the estimated index parameter vectors, and the uniform convergence rate of the estimated covariance function and their spectrum. Simulation studies are carried out to assess the finite-sample performance of the proposed procedure. We apply FVCSIM to investigating the development of white matter diffusivities along the corpus callosum skeleton obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. PMID:29200540

A Functional Varying-Coefficient Single-Index Model for Functional Response Data.

PubMed

Li, Jialiang; Huang, Chao; Zhu, Hongtu

2017-01-01

Motivated by the analysis of imaging data, we propose a novel functional varying-coefficient single index model (FVCSIM) to carry out the regression analysis of functional response data on a set of covariates of interest. FVCSIM represents a new extension of varying-coefficient single index models for scalar responses collected from cross-sectional and longitudinal studies. An efficient estimation procedure is developed to iteratively estimate varying coefficient functions, link functions, index parameter vectors, and the covariance function of individual functions. We systematically examine the asymptotic properties of all estimators including the weak convergence of the estimated varying coefficient functions, the asymptotic distribution of the estimated index parameter vectors, and the uniform convergence rate of the estimated covariance function and their spectrum. Simulation studies are carried out to assess the finite-sample performance of the proposed procedure. We apply FVCSIM to investigating the development of white matter diffusivities along the corpus callosum skeleton obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) study.

Part-Time Community-College Faculty and the Desire for Full-Time Tenure-Track Positions: Results of a Single Institution Case Study

ERIC Educational Resources Information Center

Jacoby, Dan

2005-01-01

According to data derived from a community-college survey in the state of Washington, the majority of part-time faculty prefer full-time work. Using a logit regression analysis, the study reported in this paper suggests that typical part-timers enter their part-time teaching situations with the intent of becoming full-time, but gradually become…

Measuring multi-joint stiffness during single movements: numerical validation of a novel time-frequency approach.

PubMed

Piovesan, Davide; Pierobon, Alberto; DiZio, Paul; Lackner, James R

2012-01-01

This study presents and validates a Time-Frequency technique for measuring 2-dimensional multijoint arm stiffness throughout a single planar movement as well as during static posture. It is proposed as an alternative to current regressive methods which require numerous repetitions to obtain average stiffness on a small segment of the hand trajectory. The method is based on the analysis of the reassigned spectrogram of the arm's response to impulsive perturbations and can estimate arm stiffness on a trial-by-trial basis. Analytic and empirical methods are first derived and tested through modal analysis on synthetic data. The technique's accuracy and robustness are assessed by modeling the estimation of stiffness time profiles changing at different rates and affected by different noise levels. Our method obtains results comparable with two well-known regressive techniques. We also test how the technique can identify the viscoelastic component of non-linear and higher than second order systems with a non-parametrical approach. The technique proposed here is very impervious to noise and can be used easily for both postural and movement tasks. Estimations of stiffness profiles are possible with only one perturbation, making our method a useful tool for estimating limb stiffness during motor learning and adaptation tasks, and for understanding the modulation of stiffness in individuals with neurodegenerative diseases.

Surveillance of antimicrobial resistance in clinical isolates of Pasteurella multocida and Streptococcus suis from Ontario swine.

PubMed

Glass-Kaastra, Shiona K; Pearl, David L; Reid-Smith, Richard J; McEwen, Beverly; Slavic, Durda; Fairles, Jim; McEwen, Scott A

2014-10-01

Susceptibility results for Pasteurella multocida and Streptococcus suis isolated from swine clinical samples were obtained from January 1998 to October 2010 from the Animal Health Laboratory at the University of Guelph, Guelph, Ontario, and used to describe variation in antimicrobial resistance (AMR) to 4 drugs of importance in the Ontario swine industry: ampicillin, tetracycline, tiamulin, and trimethoprim-sulfamethoxazole. Four temporal data-analysis options were used: visualization of trends in 12-month rolling averages, logistic-regression modeling, temporal-scan statistics, and a scan with the "What's strange about recent events?" (WSARE) algorithm. The AMR trends varied among the antimicrobial drugs for a single pathogen and between pathogens for a single antimicrobial, suggesting that pathogen-specific AMR surveillance may be preferable to indicator data. The 4 methods provided complementary and, at times, redundant results. The most appropriate combination of analysis methods for surveillance using these data included temporal-scan statistics with a visualization method (rolling-average or predicted-probability plots following logistic-regression models). The WSARE algorithm provided interesting results for quality control and has the potential to detect new resistance patterns; however, missing data created problems for displaying the results in a way that would be meaningful to all surveillance stakeholders.

Surveillance of antimicrobial resistance in clinical isolates of Pasteurella multocida and Streptococcus suis from Ontario swine

PubMed Central

Glass-Kaastra, Shiona K.; Pearl, David L.; Reid-Smith, Richard J.; McEwen, Beverly; Slavic, Durda; Fairles, Jim; McEwen, Scott A.

2014-01-01

Susceptibility results for Pasteurella multocida and Streptococcus suis isolated from swine clinical samples were obtained from January 1998 to October 2010 from the Animal Health Laboratory at the University of Guelph, Guelph, Ontario, and used to describe variation in antimicrobial resistance (AMR) to 4 drugs of importance in the Ontario swine industry: ampicillin, tetracycline, tiamulin, and trimethoprim–sulfamethoxazole. Four temporal data-analysis options were used: visualization of trends in 12-month rolling averages, logistic-regression modeling, temporal-scan statistics, and a scan with the “What’s strange about recent events?” (WSARE) algorithm. The AMR trends varied among the antimicrobial drugs for a single pathogen and between pathogens for a single antimicrobial, suggesting that pathogen-specific AMR surveillance may be preferable to indicator data. The 4 methods provided complementary and, at times, redundant results. The most appropriate combination of analysis methods for surveillance using these data included temporal-scan statistics with a visualization method (rolling-average or predicted-probability plots following logistic-regression models). The WSARE algorithm provided interesting results for quality control and has the potential to detect new resistance patterns; however, missing data created problems for displaying the results in a way that would be meaningful to all surveillance stakeholders. PMID:25355992

A novel method to estimate changes in stress-induced salivary α-amylase using heart rate variability and respiratory rate, as measured in a non-contact manner using a single radar attached to the back of a chair.

PubMed

Matsui, Takemi; Katayose, Satoshi

2014-08-01

The authors have developed a non-contact system which estimates changes in salivary α-amylase (sAA ratio) induced by stress. Before and after stressful sound exposure, a single 24 GHz compact radar which is attached to the back of a chair measures the low frequency (LF) component of heart rate variability and respiratory rate, α-amylase in the subjects' buccal secretions was measured by using an α-amylase assay kit. Using multiple regression analysis, sAA ratio was estimated using stress-induced LF change (LF ratio) and stress-induced respiratory rate change (respiratory rate ratio). Twelve healthy subjects were tested (12 males, 22 ± 2 years), who were exposed to audio stimuli with a composite tone of 2120 Hz and 2130 Hz sine waves at a sound pressure level of 95 dB after a silent period through a headphone. The result showed that sAA ratio estimated using multiple regression analysis significantly correlated with measured sAA ratio (R = 0.76, p < 0.01). This indicates that the system may serve for a stress management in the future.

Use of magnetic resonance imaging to predict the body composition of pigs in vivo.

PubMed

Kremer, P V; Förster, M; Scholz, A M

2013-06-01

The objective of the study was to evaluate whether magnetic resonance imaging (MRI) offers the opportunity to reliably analyze body composition of pigs in vivo. Therefore, the relation between areas of loin eye muscle and its back fat based on MRI images were used to predict body composition values measured by dual energy X-ray absorptiometry (DXA). During the study, a total of 77 pigs were studied by MRI and DXA, with a BW ranging between 42 and 102 kg. The pigs originated from different extensive or conventional breeds or crossbreds such as Cerdo Iberico, Duroc, German Landrace, German Large White, Hampshire and Pietrain. A Siemens Magnetom Open was used for MRI in the thorax region between 13th and 14th vertebrae in order to measure the loin eye area (MRI-LA) and the above back fat area (MRI-FA) of both body sides, whereas a whole body scan was performed by DXA with a GE Lunar DPX-IQ in order to measure the amount and percentage of fat tissue (DXA-FM; DXA-%FM) and lean tissue mass (DXA-LM; DXA-%LM). A linear single regression analysis was performed to quantify the linear relationships between MRI- and DXA-derived traits. In addition, a stepwise regression procedure was carried out to calculate (multiple) regression equations between MRI and DXA variables (including BW). Single regression analyses showed high relationships between DXA-%FM and MRI-FA (R 2 = 0.89, √MSE = 2.39%), DXA-FM and MRI-FA (R 2 = 0.82, √MSE = 2757 g) and DXA-LM and MRI-LA (R 2 = 0.82, √MSE = 4018 g). Only DXA-%LM and MRI-LA did not show any relationship (R 2 = 0). As a result of the multiple regression analysis, DXA-LM and DXA-FM were both highly related to MRI-LA, MRI-FA and BW (R 2 = 0.96; √MSE = 1784 g, and R 2 = 0.95, √MSE = 1496 g). Therefore, it can be concluded that the use of MRI-derived images provides exact information about important 'carcass-traits' in pigs and may be used to reliably predict the body composition in vivo.

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.

Epistasis analysis for quantitative traits by functional regression model.

PubMed

Zhang, Futao; Boerwinkle, Eric; Xiong, Momiao

2014-06-01

The critical barrier in interaction analysis for rare variants is that most traditional statistical methods for testing interactions were originally designed for testing the interaction between common variants and are difficult to apply to rare variants because of their prohibitive computational time and poor ability. The great challenges for successful detection of interactions with next-generation sequencing (NGS) data are (1) lack of methods for interaction analysis with rare variants, (2) severe multiple testing, and (3) time-consuming computations. To meet these challenges, we shift the paradigm of interaction analysis between two loci to interaction analysis between two sets of loci or genomic regions and collectively test interactions between all possible pairs of SNPs within two genomic regions. In other words, we take a genome region as a basic unit of interaction analysis and use high-dimensional data reduction and functional data analysis techniques to develop a novel functional regression model to collectively test interactions between all possible pairs of single nucleotide polymorphisms (SNPs) within two genome regions. By intensive simulations, we demonstrate that the functional regression models for interaction analysis of the quantitative trait have the correct type 1 error rates and a much better ability to detect interactions than the current pairwise interaction analysis. The proposed method was applied to exome sequence data from the NHLBI's Exome Sequencing Project (ESP) and CHARGE-S study. We discovered 27 pairs of genes showing significant interactions after applying the Bonferroni correction (P-values < 4.58 × 10(-10)) in the ESP, and 11 were replicated in the CHARGE-S study. © 2014 Zhang et al.; Published by Cold Spring Harbor Laboratory Press.

Non-homogeneous hybrid rocket fuel for enhanced regression rates utilizing partial entrainment

NASA Astrophysics Data System (ADS)

Boronowsky, Kenny

A concept was developed and tested to enhance the performance and regression rate of hydroxyl terminated polybutadiene (HTPB), a commonly used hybrid rocket fuel. By adding small nodules of paraffin into the HTPB fuel, a non-homogeneous mixture was created resulting in increased regression rates. The goal was to develop a fuel with a simplified single core geometry and a tailorable regression rate. The new fuel would benefit from the structural stability of HTPB yet not suffer from the large void fraction representative of typical HTPB core geometries. Regression rates were compared between traditional HTPB single core grains, 85% HTPB mixed with 15% (by weight) paraffin cores, 70% HTPB mixed with 30% paraffin cores, and plain paraffin single core grains. Each fuel combination was tested at oxidizer flow rates, ranging from 0.9 - 3.3 g/s of gaseous oxygen, in a small scale hybrid test rocket and average regression rates were measured. While large uncertainties were present in the experimental setup, the overall data showed that the regression rate was enhanced as paraffin concentration increased. While further testing would be required at larger scales of interest, the trends are encouraging. Inclusion of paraffin nodules in the HTPB grain may produce a greater advantage than other more noxious additives in current use. In addition, it may lead to safer rocket motors with higher integrated thrust due to the decreased void fraction.

CANcer-specific Evaluation System (CANES): a high-accuracy platform, for preclinical single/multi-biomarker discovery

PubMed Central

Kwon, Min-Seok; Nam, Seungyoon; Lee, Sungyoung; Ahn, Young Zoo; Chang, Hae Ryung; Kim, Yon Hui; Park, Taesung

2017-01-01

The recent creation of enormous, cancer-related “Big Data” public depositories represents a powerful means for understanding tumorigenesis. However, a consistently accurate system for clinically evaluating single/multi-biomarkers remains lacking, and it has been asserted that oft-failed clinical advancement of biomarkers occurs within the very early stages of biomarker assessment. To address these challenges, we developed a clinically testable, web-based tool, CANcer-specific single/multi-biomarker Evaluation System (CANES), to evaluate biomarker effectiveness, across 2,134 whole transcriptome datasets, from 94,147 biological samples (from 18 tumor types). For user-provided single/multi-biomarkers, CANES evaluates the performance of single/multi-biomarker candidates, based on four classification methods, support vector machine, random forest, neural networks, and classification and regression trees. In addition, CANES offers several advantages over earlier analysis tools, including: 1) survival analysis; 2) evaluation of mature miRNAs as markers for user-defined diagnostic or prognostic purposes; and 3) provision of a “pan-cancer” summary view, based on each single marker. We believe that such “landscape” evaluation of single/multi-biomarkers, for diagnostic therapeutic/prognostic decision-making, will be highly valuable for the discovery and “repurposing” of existing biomarkers (and their specific targeted therapies), leading to improved patient therapeutic stratification, a key component of targeted therapy success for the avoidance of therapy resistance. PMID:29050243

[The related factors of head and neck mocosal melanoma with lymph node metastasis].

PubMed

Yin, G F; Guo, W; Chen, X H; Huang, Z G

2017-12-05

Objective: To investigate the related factors of mucosal melanoma of head and neck with lymph node metastasis for early diagnosis and further treatments. Method: A retrospective analysis of 117 cases of head and neck mucosal malignant melanoma patients which received surgical treatment was performed. Eleven cases of patients with pathologically confirmed lymph node metastasis and 33 cases without lymph node metastasis (1∶3) were randomly selected to analyze. The related factors of lymph node metastasis of head and neck mucosal melanoma patients including age, gender, whether the existence of recurrence, bone invasion, lesion location were analyzed. The single factor and logistic regression analysis were performed, P <0.05 difference was statistically significant. Result: The lymph node metastasis rate of head and neck mucosal melanoma was 9.40%(11/117), the single factor analysis showed that there were 3 factors to be associated with lymph node metastasis, which was recurrence ( P =0.0000), bone invasion ( P =0.001), primary position ( P =0.007). Recurrence ( P =0.021) was a risk factor for lymph node metastasis according to the Logistic regression analysis, and the impact of bone invasion ( P =0.487) and primary location ( P =0.367) remained to be further explored. Conclusion: The patients of head and neck mucosal melanoma with the presence of recurrent usually accompanied by a further progression of the disease, such as lymph node metastasis, so for recurrent patients should pay special attention to the situation of lymph node and choose the reasonable treatment. Copyright© by the Editorial Department of Journal of Clinical Otorhinolaryngology Head and Neck Surgery.

Least Squares Moving-Window Spectral Analysis.

PubMed

Lee, Young Jong

2017-08-01

Least squares regression is proposed as a moving-windows method for analysis of a series of spectra acquired as a function of external perturbation. The least squares moving-window (LSMW) method can be considered an extended form of the Savitzky-Golay differentiation for nonuniform perturbation spacing. LSMW is characterized in terms of moving-window size, perturbation spacing type, and intensity noise. Simulation results from LSMW are compared with results from other numerical differentiation methods, such as single-interval differentiation, autocorrelation moving-window, and perturbation correlation moving-window methods. It is demonstrated that this simple LSMW method can be useful for quantitative analysis of nonuniformly spaced spectral data with high frequency noise.

Practical aspects of estimating energy components in rodents

PubMed Central

van Klinken, Jan B.; van den Berg, Sjoerd A. A.; van Dijk, Ko Willems

2013-01-01

Recently there has been an increasing interest in exploiting computational and statistical techniques for the purpose of component analysis of indirect calorimetry data. Using these methods it becomes possible to dissect daily energy expenditure into its components and to assess the dynamic response of the resting metabolic rate (RMR) to nutritional and pharmacological manipulations. To perform robust component analysis, however, is not straightforward and typically requires the tuning of parameters and the preprocessing of data. Moreover the degree of accuracy that can be attained by these methods depends on the configuration of the system, which must be properly taken into account when setting up experimental studies. Here, we review the methods of Kalman filtering, linear, and penalized spline regression, and minimal energy expenditure estimation in the context of component analysis and discuss their results on high resolution datasets from mice and rats. In addition, we investigate the effect of the sample time, the accuracy of the activity sensor, and the washout time of the chamber on the estimation accuracy. We found that on the high resolution data there was a strong correlation between the results of Kalman filtering and penalized spline (P-spline) regression, except for the activity respiratory quotient (RQ). For low resolution data the basal metabolic rate (BMR) and resting RQ could still be estimated accurately with P-spline regression, having a strong correlation with the high resolution estimate (R2 > 0.997; sample time of 9 min). In contrast, the thermic effect of food (TEF) and activity related energy expenditure (AEE) were more sensitive to a reduction in the sample rate (R2 > 0.97). In conclusion, for component analysis on data generated by single channel systems with continuous data acquisition both Kalman filtering and P-spline regression can be used, while for low resolution data from multichannel systems P-spline regression gives more robust results. PMID:23641217

Potential for Bias When Estimating Critical Windows for Air Pollution in Children's Health.

PubMed

Wilson, Ander; Chiu, Yueh-Hsiu Mathilda; Hsu, Hsiao-Hsien Leon; Wright, Robert O; Wright, Rosalind J; Coull, Brent A

2017-12-01

Evidence supports an association between maternal exposure to air pollution during pregnancy and children's health outcomes. Recent interest has focused on identifying critical windows of vulnerability. An analysis based on a distributed lag model (DLM) can yield estimates of a critical window that are different from those from an analysis that regresses the outcome on each of the 3 trimester-average exposures (TAEs). Using a simulation study, we assessed bias in estimates of critical windows obtained using 3 regression approaches: 1) 3 separate models to estimate the association with each of the 3 TAEs; 2) a single model to jointly estimate the association between the outcome and all 3 TAEs; and 3) a DLM. We used weekly fine-particulate-matter exposure data for 238 births in a birth cohort in and around Boston, Massachusetts, and a simulated outcome and time-varying exposure effect. Estimates using separate models for each TAE were biased and identified incorrect windows. This bias arose from seasonal trends in particulate matter that induced correlation between TAEs. Including all TAEs in a single model reduced bias. DLM produced unbiased estimates and added flexibility to identify windows. Analysis of body mass index z score and fat mass in the same cohort highlighted inconsistent estimates from the 3 methods. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Bayesian dose-response analysis for epidemiological studies with complex uncertainty in dose estimation.

PubMed

Kwon, Deukwoo; Hoffman, F Owen; Moroz, Brian E; Simon, Steven L

2016-02-10

Most conventional risk analysis methods rely on a single best estimate of exposure per person, which does not allow for adjustment for exposure-related uncertainty. Here, we propose a Bayesian model averaging method to properly quantify the relationship between radiation dose and disease outcomes by accounting for shared and unshared uncertainty in estimated dose. Our Bayesian risk analysis method utilizes multiple realizations of sets (vectors) of doses generated by a two-dimensional Monte Carlo simulation method that properly separates shared and unshared errors in dose estimation. The exposure model used in this work is taken from a study of the risk of thyroid nodules among a cohort of 2376 subjects who were exposed to fallout from nuclear testing in Kazakhstan. We assessed the performance of our method through an extensive series of simulations and comparisons against conventional regression risk analysis methods. When the estimated doses contain relatively small amounts of uncertainty, the Bayesian method using multiple a priori plausible draws of dose vectors gave similar results to the conventional regression-based methods of dose-response analysis. However, when large and complex mixtures of shared and unshared uncertainties are present, the Bayesian method using multiple dose vectors had significantly lower relative bias than conventional regression-based risk analysis methods and better coverage, that is, a markedly increased capability to include the true risk coefficient within the 95% credible interval of the Bayesian-based risk estimate. An evaluation of the dose-response using our method is presented for an epidemiological study of thyroid disease following radiation exposure. Copyright © 2015 John Wiley & Sons, Ltd.

Subsonic Aircraft With Regression and Neural-Network Approximators Designed

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Hopkins, Dale A.

2004-01-01

At the NASA Glenn Research Center, NASA Langley Research Center's Flight Optimization System (FLOPS) and the design optimization testbed COMETBOARDS with regression and neural-network-analysis approximators have been coupled to obtain a preliminary aircraft design methodology. For a subsonic aircraft, the optimal design, that is the airframe-engine combination, is obtained by the simulation. The aircraft is powered by two high-bypass-ratio engines with a nominal thrust of about 35,000 lbf. It is to carry 150 passengers at a cruise speed of Mach 0.8 over a range of 3000 n mi and to operate on a 6000-ft runway. The aircraft design utilized a neural network and a regression-approximations-based analysis tool, along with a multioptimizer cascade algorithm that uses sequential linear programming, sequential quadratic programming, the method of feasible directions, and then sequential quadratic programming again. Optimal aircraft weight versus the number of design iterations is shown. The central processing unit (CPU) time to solution is given. It is shown that the regression-method-based analyzer exhibited a smoother convergence pattern than the FLOPS code. The optimum weight obtained by the approximation technique and the FLOPS code differed by 1.3 percent. Prediction by the approximation technique exhibited no error for the aircraft wing area and turbine entry temperature, whereas it was within 2 percent for most other parameters. Cascade strategy was required by FLOPS as well as the approximators. The regression method had a tendency to hug the data points, whereas the neural network exhibited a propensity to follow a mean path. The performance of the neural network and regression methods was considered adequate. It was at about the same level for small, standard, and large models with redundancy ratios (defined as the number of input-output pairs to the number of unknown coefficients) of 14, 28, and 57, respectively. In an SGI octane workstation (Silicon Graphics, Inc., Mountainview, CA), the regression training required a fraction of a CPU second, whereas neural network training was between 1 and 9 min, as given. For a single analysis cycle, the 3-sec CPU time required by the FLOPS code was reduced to milliseconds by the approximators. For design calculations, the time with the FLOPS code was 34 min. It was reduced to 2 sec with the regression method and to 4 min by the neural network technique. The performance of the regression and neural network methods was found to be satisfactory for the analysis and design optimization of the subsonic aircraft.

Direct evidence on the immune-mediated spontaneous regression of human cancer: an incentive for pharmaceutical companies to develop a novel anti-cancer vaccine.

PubMed

Saleh, F; Renno, W; Klepacek, I; Ibrahim, G; Dashti, H; Asfar, S; Behbehani, A; Al-Sayer, H; Dashti, A; Kerry, Crotty

2005-01-01

To develop an effective pharmaceutical treatment for a disease, we need to fully understand the biological behavior of that disease, especially when dealing with cancer. The current available treatment for cancer may help in lessening the burden of the disease or, on certain occasions, in increasing the survival of the patient. However, a total eradication of cancer remains the researchers' hope. Some of the discoveries in the field of medicine relied on observations of natural events. Among these events is the spontaneous regression of cancer. It has been argued that such regression could be immunologically-mediated, but no direct evidence has been shown to support such an argument. We, hereby, provide compelling evidence that spontaneous cancer regression in humans is immunologically-mediated, hoping that the results from this study would stimulate the pharmaceutical industry to focus more on cancer vaccine immunotherapy. Our results showed that patients with >3 primary melanomas (very rare group among cancer patients) develop significant histopathological spontaneous regression of further melanomas that they could acquire during their life (P=0.0080) as compared to patients with single primary melanoma where the phenomenon of spontaneous regression is absent or minimal. It seems that such regression resulted from the repeated exposure to the tumor which mimics a self-immunization process. Analysis of the regressing tumors revealed heavy infiltration by T lymphocytes as compared to non-regressing tumors (P<0.0001), the predominant of which were T cytotoxic rather than T helper. Mature dendritic cells were also found in significant number (P<0.0001) in the regressing tumors as compared to the non regressing ones, which demonstrate an active involvement of the different arms of the immune system in the multiple primary melanoma patients in the process of tumor regression. Also, MHC expression was significantly higher in the regressing versus the non-regressing tumors (P <0.0001), which reflects a proper tumor antigen expression. Associated with tumor regression was also loss of the melanoma common tumor antigen Melan A/ MART-1 in the multiple primary melanoma patients as compared to the single primary ones (P=0.0041). Furthermore, loss of Melan A/ MART-1 in the regressing tumors significantly correlated with the presence of Melan A/ MART-1-specific CTLs in the peripheral blood of these patients (P=0.03), which adds to the evidence that the phenomenon of regression seen in these patients was immunologically-mediated and tumor-specific. Such correlation was also seen in another rare group of melanoma patients, namely those with occult primary melanoma. The lesson that we could learn from nature in this study is that inducing cancer regression using the different arms of the immune system is possible. Also, developing a novel cancer vaccine is not out of reach.

Association of the FGA and SLC6A4 genes with autistic spectrum disorder in a Korean population.

PubMed

Ro, Myungja; Won, Seongsik; Kang, Hyunjun; Kim, Su-Yeon; Lee, Seung Ku; Nam, Min; Bang, Hee Jung; Yang, Jae Won; Choi, Kyung-Sik; Kim, Su Kang; Chung, Joo-Ho; Kwack, Kyubum

2013-01-01

Autism spectrum disorder (ASD) is a neurobiological disorder characterized by distinctive impairments in cognitive function, language, and behavior. Linkage and population studies suggest a genetic association between solute carrier family 6 member 4 (SLC6A4) variants and ASD. Logistic regression was used to identify associations between single-nucleotide polymorphisms (SNPs) and ASD with 3 alternative models (additive, dominant, and recessive). Linear regression analysis was performed to determine the influence of SNPs on Childhood Autism Rating Scale (CARS) scores as a quantitative phenotype. In the present study, we examined the associations of SNPs in the SLC6A4 gene and the fibrinogen alpha chain (FGA) gene. Logistic regression analysis showed a significant association between the risk of ASD and rs2070025 and rs2070011 in the FGA gene. The gene-gene interaction between SLC6A4 and FGA was not significantly associated with ASD susceptibility. However, polymorphisms in both SLC6A4 and the FGA gene significantly affected the symptoms of ASD. Our findings indicate that FGA and SLC6A4 gene interactions may contribute to the phenotypes of ASD rather than the incidence of ASD. © 2013 S. Karger AG, Basel.

Cooperation without culture? The null effect of generalized trust on intentional homicide: a cross-national panel analysis, 1995-2009.

PubMed

Robbins, Blaine

2013-01-01

Sociologists, political scientists, and economists all suggest that culture plays a pivotal role in the development of large-scale cooperation. In this study, I used generalized trust as a measure of culture to explore if and how culture impacts intentional homicide, my operationalization of cooperation. I compiled multiple cross-national data sets and used pooled time-series linear regression, single-equation instrumental-variables linear regression, and fixed- and random-effects estimation techniques on an unbalanced panel of 118 countries and 232 observations spread over a 15-year time period. Results suggest that culture and large-scale cooperation form a tenuous relationship, while economic factors such as development, inequality, and geopolitics appear to drive large-scale cooperation.

Comparison of the clinical efficacy between single-agent and dual-agent concurrent chemoradiotherapy in the treatment of unresectable esophageal squamous cell carcinoma: a multicenter retrospective analysis.

PubMed

Li, Jie; Gong, Youling; Diao, Peng; Huang, Qingmei; Wen, Yixue; Lin, Binwei; Cai, Hongwei; Tian, Honggang; He, Bing; Ji, Lanlan; Guo, Ping; Miao, Jidong; Du, Xiaobo

2018-01-22

Some Chinese patients with esophageal squamous cell carcinomaare often treated with single-agent concurrent chemoradiotherapy. However, no results have been reported from randomized controlled clinical trials comparing single-agent with double-agent concurrent chemoradiotherapy. It therefore remains unclear whether these regimens are equally clinically effective. In this study, we retrospectively analyzed and compared the therapeutic effects of single-agent and double-agent concurrent chemoradiotherapy in patients with unresectable esophageal squamous cell carcinoma. This study enrolled 168 patients who received definitive concurrent chemoradiotherapy for locally advanced unresectable esophageal squamous carcinoma at 10 hospitals between 2010 and 2015. We evaluated survival time and toxicity. The Kaplan-Meier method was used to estimate survival data. The log-rank test was used in univariate analysis A Cox proportional hazards regression model was used to conduct a multivariate analysis of the effects of prognostic factors on survival. In this study, 100 (59.5%) and 68 patients (40.5%) received single-agent and dual-agent combination chemoradiotherapy, respectively. The estimate 5-year progression-free survival (PFS) rate and overall survival (OS) rate of dual-agent therapy was higher than that of single-agent therapy (52.5% and 40.9%, 78.2% and 60.7%, respectively), but there were no significant differences (P = 0.367 and 0.161, respectively). Multivariate analysis showed that sex, age,and radiotherapy dose had no significant effects on OS or PFS. Only disease stage was associated with OS and PFS in the multivariable analysis (P = 0.006 and 0.003, respectively). In dual-agent group, the incidence of acute toxicity and the incidence of 3 and4 grade toxicity were higher than single-agent group. The 5-year PFS and OS rates of dual-agent therapy were higher than those of single-agent concurrent chemoradiotherapy for patients with unresectable esophageal squamous cell carcinoma; however, there were no significant differences in univariate analysis and multivariable analysis. Single-agent concurrent chemotherapy had less toxicity than a double-drug regimen. Therefore, we suggest that single therapis not inferior to dual therapy y. In the future, we aim to confirm our hypothesis through a prospective randomized study.

Learning curve of single port laparoscopic cholecystectomy determined using the non-linear ordinary least squares method based on a non-linear regression model: An analysis of 150 consecutive patients.

PubMed

Han, Hyung Joon; Choi, Sae Byeol; Park, Man Sik; Lee, Jin Suk; Kim, Wan Bae; Song, Tae Jin; Choi, Sang Yong

2011-07-01

Single port laparoscopic surgery has come to the forefront of minimally invasive surgery. For those familiar with conventional techniques, however, this type of operation demands a different type of eye/hand coordination and involves unfamiliar working instruments. Herein, the authors describe the learning curve and the clinical outcomes of single port laparoscopic cholecystectomy for 150 consecutive patients with benign gallbladder disease. All patients underwent single port laparoscopic cholecystectomy using a homemade glove port by one of five operators with different levels of experiences of laparoscopic surgery. The learning curve for each operator was fitted using the non-linear ordinary least squares method based on a non-linear regression model. Mean operating time was 77.6 ± 28.5 min. Fourteen patients (6.0%) were converted to conventional laparoscopic cholecystectomy. Complications occurred in 15 patients (10.0%), as follows: bile duct injury (n = 2), surgical site infection (n = 8), seroma (n = 2), and wound pain (n = 3). One operator achieved a learning curve plateau at 61.4 min per procedure after 8.5 cases and his time improved by 95.3 min as compared with initial operation time. Younger surgeons showed significant decreases in mean operation time and achieved stable mean operation times. In particular, younger surgeons showed significant decreases in operation times after 20 cases. Experienced laparoscopic surgeons can safely perform single port laparoscopic cholecystectomy using conventional or angled laparoscopic instruments. The present study shows that an operator can overcome the single port laparoscopic cholecystectomy learning curve in about eight cases.

Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study.

PubMed

Lee, Poh Foong; Kan, Donica Pei Xin; Croarkin, Paul; Phang, Cheng Kar; Doruk, Deniz

2018-01-01

There is an unmet need for practical and reliable biomarkers for mood disorders in young adults. Identifying the brain activity associated with the early signs of depressive disorders could have important diagnostic and therapeutic implications. In this study we sought to investigate the EEG characteristics in young adults with newly identified depressive symptoms. Based on the initial screening, a total of 100 participants (n = 50 euthymic, n = 50 depressive) underwent 32-channel EEG acquisition. Simple logistic regression and C-statistic were used to explore if EEG power could be used to discriminate between the groups. The strongest EEG predictors of mood using multivariate logistic regression models. Simple logistic regression analysis with subsequent C-statistics revealed that only high-alpha and beta power originating from the left central cortex (C3) have a reliable discriminative value (ROC curve >0.7 (70%)) for differentiating the depressive group from the euthymic group. Multivariate regression analysis showed that the single most significant predictor of group (depressive vs. euthymic) is the high-alpha power over C3 (p = 0.03). The present findings suggest that EEG is a useful tool in the identification of neurophysiological correlates of depressive symptoms in young adults with no previous psychiatric history. Our results could guide future studies investigating the early neurophysiological changes and surrogate outcomes in depression. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Generalized Least Squares Regression Approach for Computing Effect Sizes in Single-Case Research: Application Examples

ERIC Educational Resources Information Center

Maggin, Daniel M.; Swaminathan, Hariharan; Rogers, Helen J.; O'Keeffe, Breda V.; Sugai, George; Horner, Robert H.

2011-01-01

A new method for deriving effect sizes from single-case designs is proposed. The strategy is applicable to small-sample time-series data with autoregressive errors. The method uses Generalized Least Squares (GLS) to model the autocorrelation of the data and estimate regression parameters to produce an effect size that represents the magnitude of…

Understanding Scaling Relations in Fracture and Mechanical Deformation of Single Crystal and Polycrystalline Silicon by Performing Atomistic Simulations at Mesoscale

DTIC Science & Technology

2009-07-16

0.25 0.26 -0.85 1 SSR SSE R SSTO SSTO = = − 2 2 ˆ( ) : Regression sum of square, ˆwhere : mean value, : value from the fitted line ˆ...Error sum of square : Total sum of square i i i i SSR Y Y Y Y SSE Y Y SSTO SSE SSR = − = − = + ∑ ∑ Statistical analysis: Coefficient of correlation

Mean Expected Error in Prediction of Total Body Water: A True Accuracy Comparison between Bioimpedance Spectroscopy and Single Frequency Regression Equations

PubMed Central

Abtahi, Shirin; Abtahi, Farhad; Ellegård, Lars; Johannsson, Gudmundur; Bosaeus, Ingvar

2015-01-01

For several decades electrical bioimpedance (EBI) has been used to assess body fluid distribution and body composition. Despite the development of several different approaches for assessing total body water (TBW), it remains uncertain whether bioimpedance spectroscopic (BIS) approaches are more accurate than single frequency regression equations. The main objective of this study was to answer this question by calculating the expected accuracy of a single measurement for different EBI methods. The results of this study showed that all methods produced similarly high correlation and concordance coefficients, indicating good accuracy as a method. Even the limits of agreement produced from the Bland-Altman analysis indicated that the performance of single frequency, Sun's prediction equations, at population level was close to the performance of both BIS methods; however, when comparing the Mean Absolute Percentage Error value between the single frequency prediction equations and the BIS methods, a significant difference was obtained, indicating slightly better accuracy for the BIS methods. Despite the higher accuracy of BIS methods over 50 kHz prediction equations at both population and individual level, the magnitude of the improvement was small. Such slight improvement in accuracy of BIS methods is suggested insufficient to warrant their clinical use where the most accurate predictions of TBW are required, for example, when assessing over-fluidic status on dialysis. To reach expected errors below 4-5%, novel and individualized approaches must be developed to improve the accuracy of bioimpedance-based methods for the advent of innovative personalized health monitoring applications. PMID:26137489

Concordance of macular pigment measurements obtained using customized heterochromatic flicker photometry, dual-wavelength autofluorescence, and single-wavelength reflectance.

PubMed

Dennison, Jessica L; Stack, Jim; Beatty, Stephen; Nolan, John M

2013-11-01

This study compares in vivo measurements of macular pigment (MP) obtained using customized heterochromatic flicker photometry (cHFP; Macular Metrics Densitometer(™)), dual-wavelength fundus autofluorescence (Heidelberg Spectralis(®) HRA + OCT MultiColor) and single-wavelength fundus reflectance (Zeiss Visucam(®) 200). MP was measured in one eye of 62 subjects on each device. Data from 49 subjects (79%) was suitable for analysis. Agreement between the Densitometer and Spectralis was investigated at various eccentricities using a variety of quantitative and graphical methods, including: Pearson correlation coefficient to measure degree of scatter (precision), accuracy coefficient, concordance correlation coefficient (ccc), paired t-test, scatter and Bland-Altman plots. The relationship between max MP from the Visucam and central MP from the Spectralis and Densitometer was investigated using regression methods. Agreement was strong between the Densitometer and Spectralis at all central eccentricities (e.g. at 0.25° eccentricity: accuracy = 0.97, precision = 0.90, ccc = 0.87). Regression analysis showed a very weak relationship between the Visucam and Densitometer (e.g. Visucam max on Densitometer central MP: R(2) = 0.008, p = 0.843). Regression analysis also demonstrated a weak relationship between MP measured by the Spectralis and Visucam (e.g. Visucam max on Spectralis central MP: R(2) = 0.047, p = 0.348). MP values obtained using the Heidelberg Spectralis are comparable to MP values obtained using the Densitometer. In contrast, MP values obtained using the Zeiss Visucam are not comparable with either the Densitometer or the Spectralis MP measuring devices. Taking cHFP as the current standard to which other MP measuring devices should be compared, the Spectralis is suitable for use in a clinical and research setting, whereas the Visucam is not. Copyright © 2013 Elsevier Ltd. All rights reserved.

A matching framework to improve causal inference in interrupted time-series analysis.

PubMed

Linden, Ariel

2018-04-01

Interrupted time-series analysis (ITSA) is a popular evaluation methodology in which a single treatment unit's outcome is studied over time and the intervention is expected to "interrupt" the level and/or trend of the outcome, subsequent to its introduction. When ITSA is implemented without a comparison group, the internal validity may be quite poor. Therefore, adding a comparable control group to serve as the counterfactual is always preferred. This paper introduces a novel matching framework, ITSAMATCH, to create a comparable control group by matching directly on covariates and then use these matches in the outcomes model. We evaluate the effect of California's Proposition 99 (passed in 1988) for reducing cigarette sales, by comparing California to other states not exposed to smoking reduction initiatives. We compare ITSAMATCH results to 2 commonly used matching approaches, synthetic controls (SYNTH), and regression adjustment; SYNTH reweights nontreated units to make them comparable to the treated unit, and regression adjusts covariates directly. Methods are compared by assessing covariate balance and treatment effects. Both ITSAMATCH and SYNTH achieved covariate balance and estimated similar treatment effects. The regression model found no treatment effect and produced inconsistent covariate adjustment. While the matching framework achieved results comparable to SYNTH, it has the advantage of being technically less complicated, while producing statistical estimates that are straightforward to interpret. Conversely, regression adjustment may "adjust away" a treatment effect. Given its advantages, ITSAMATCH should be considered as a primary approach for evaluating treatment effects in multiple-group time-series analysis. © 2017 John Wiley & Sons, Ltd.

Bayesian logistic regression in detection of gene-steroid interaction for cancer at PDLIM5 locus.

PubMed

Wang, Ke-Sheng; Owusu, Daniel; Pan, Yue; Xie, Changchun

2016-06-01

The PDZ and LIM domain 5 (PDLIM5) gene may play a role in cancer, bipolar disorder, major depression, alcohol dependence and schizophrenia; however, little is known about the interaction effect of steroid and PDLIM5 gene on cancer. This study examined 47 single-nucleotide polymorphisms (SNPs) within the PDLIM5 gene in the Marshfield sample with 716 cancer patients (any diagnosed cancer, excluding minor skin cancer) and 2848 noncancer controls. Multiple logistic regression model in PLINK software was used to examine the association of each SNP with cancer. Bayesian logistic regression in PROC GENMOD in SAS statistical software, ver. 9.4 was used to detect gene- steroid interactions influencing cancer. Single marker analysis using PLINK identified 12 SNPs associated with cancer (P< 0.05); especially, SNP rs6532496 revealed the strongest association with cancer (P = 6.84 × 10⁻³); while the next best signal was rs951613 (P = 7.46 × 10⁻³). Classic logistic regression in PROC GENMOD showed that both rs6532496 and rs951613 revealed strong gene-steroid interaction effects (OR=2.18, 95% CI=1.31-3.63 with P = 2.9 × 10⁻³ for rs6532496 and OR=2.07, 95% CI=1.24-3.45 with P = 5.43 × 10⁻³ for rs951613, respectively). Results from Bayesian logistic regression showed stronger interaction effects (OR=2.26, 95% CI=1.2-3.38 for rs6532496 and OR=2.14, 95% CI=1.14-3.2 for rs951613, respectively). All the 12 SNPs associated with cancer revealed significant gene-steroid interaction effects (P < 0.05); whereas 13 SNPs showed gene-steroid interaction effects without main effect on cancer. SNP rs4634230 revealed the strongest gene-steroid interaction effect (OR=2.49, 95% CI=1.5-4.13 with P = 4.0 × 10⁻⁴ based on the classic logistic regression and OR=2.59, 95% CI=1.4-3.97 from Bayesian logistic regression; respectively). This study provides evidence of common genetic variants within the PDLIM5 gene and interactions between PLDIM5 gene polymorphisms and steroid use influencing cancer.

Genetic variation of the androgen receptor and risk of myocardial infarction and ischemic stroke in women.

PubMed

Rexrode, Kathryn M; Ridker, Paul M; Hegener, Hillary H; Buring, Julie E; Manson, JoAnn E; Zee, Robert Y L

2008-05-01

Androgen receptors (AR) are expressed in endothelial cells and vascular smooth-muscle cells. Some studies suggest an association between AR gene variation and risk of cardiovascular disease (CVD) in men; however, the relationship has not been examined in women. Six haplotype block-tagging single nucleotide polymorphisms (rs962458, rs6152, rs1204038, rs2361634, rs1337080, rs1337082), as well as the cysteine, adenine, guanine (CAG) microsatellite in exon 1, of the AR gene were evaluated among 300 white postmenopausal women who developed CVD (158 myocardial infarctions and 142 ischemic strokes) and an equal number of matched controls within the Women's Health Study. Genotype distributions were similar between cases and controls, and genotypes were not significantly related to risk of CVD, myocardial infarctions or ischemic stroke in conditional logistic regression models. Seven common haplotypes were observed, but distributions did not differ between cases and controls nor were significant associations observed in logistic regression analysis. The median CAG repeat length was 21. In conditional logistic regression, there was no association between the number of alleles with CAG repeat length >or=21 (or >or=22) and risk of CVD, myocardial infarctions or ischemic stroke. No association between AR genetic variation, as measured by haplotype-tagging single nucleotide polymorphisms and CAG repeat number, and risk of CVD was observed in women.

Development of a mobbing short scale in the Gutenberg Health Study.

PubMed

Garthus-Niegel, Susan; Nübling, Matthias; Letzel, Stephan; Hegewald, Janice; Wagner, Mandy; Wild, Philipp S; Blettner, Maria; Zwiener, Isabella; Latza, Ute; Jankowiak, Sylvia; Liebers, Falk; Seidler, Andreas

2016-01-01

Despite its highly detrimental potential, most standard questionnaires assessing psychosocial stress at work do not include mobbing as a risk factor. In the German standard version of COPSOQ, mobbing is assessed with a single item. In the Gutenberg Health Study, this version was used together with a newly developed short scale based on the Leymann Inventory of Psychological Terror. The purpose of the present study was to evaluate the psychometric properties of these two measures, to compare them and to test their differential impact on relevant outcome parameters. This analysis is based on a population-based sample of 1441 employees participating in the Gutenberg Health Study. Exploratory and confirmatory factor analyses and reliability analyses were used to assess the mobbing scale. To determine their predictive validities, multiple linear regression analyses with six outcome parameters and log-binomial regression models for two of the outcome aspects were run. Factor analyses of the five-item scale confirmed a one-factor solution, reliability was α = 0.65. Both the single-item and the five-item scales were associated with all six outcome scales. Effect sizes were similar for both mobbing measures. Mobbing is an important risk factor for health-related outcomes. For the purpose of psychosocial risk assessment in the workplace, both the single-item and the five-item constructs were psychometrically appropriate. Associations with outcomes were about equivalent. However, the single item has the advantage of parsimony, whereas the five-item construct depicts several distinct forms of mobbing.

Factors Impacting Growth in Infants with Single Ventricle Physiology: A Report from Pediatric Heart Network Infant Single Ventricle Trial

PubMed Central

Williams, Richard V.; Zak, Victor; Ravishankar, Chitra; Altmann, Karen; Anderson, Jeffrey; Atz, Andrew M.; Dunbar-Masterson, Carolyn; Ghanayem, Nancy; Lambert, Linda; Lurito, Karen; Medoff-Cooper, Barbara; Margossian, Renee; Pemberton, Victoria L.; Russell, Jennifer; Stylianou, Mario; Hsu, Daphne

2011-01-01

Objectives To describe growth patterns in infants with single ventricle physiology and determine factors influencing growth. Study design Data from 230 subjects enrolled in the Pediatric Heart Network Infant Single Ventricle Enalapril Trial were used to assess factors influencing change in weight-for-age z-score (Δz) from study enrollment (0.7 ± 0.4 months) to pre-superior cavopulmonary connection (SCPC) (5.1 ± 1.8 months, period 1), and pre-SCPC to final study visit (14.1 ± 0.9 months, period 2). Predictor variables included patient characteristics, feeding regimen, clinical center, and medical factors during neonatal (period 1) and SCPC hospitalizations (period 2). Univariate regression analysis was performed, followed by backward stepwise regression and bootstrapping reliability to inform a final multivariable model. Results Weights were available for 197/230 subjects for period 1 and 173/197 for period 2. For period 1, greater gestational age, younger age at study enrollment, tube feeding at neonatal discharge, and clinical center were associated with a greater negative Δz (poorer growth) in multivariable modeling (adjusted R2 = 0.39, p < 0.001). For period 2, younger age at SCPC and greater daily caloric intake were associated with greater positive Δz (better growth) (R2 = 0.10, p = 0.002). Conclusions Aggressive nutritional support and earlier SCPC are modifiable factors associated with a favorable change in weight-for-age z-score. PMID:21784436

A kernel regression approach to gene-gene interaction detection for case-control studies.

PubMed

Larson, Nicholas B; Schaid, Daniel J

2013-11-01

Gene-gene interactions are increasingly being addressed as a potentially important contributor to the variability of complex traits. Consequently, attentions have moved beyond single locus analysis of association to more complex genetic models. Although several single-marker approaches toward interaction analysis have been developed, such methods suffer from very high testing dimensionality and do not take advantage of existing information, notably the definition of genes as functional units. Here, we propose a comprehensive family of gene-level score tests for identifying genetic elements of disease risk, in particular pairwise gene-gene interactions. Using kernel machine methods, we devise score-based variance component tests under a generalized linear mixed model framework. We conducted simulations based upon coalescent genetic models to evaluate the performance of our approach under a variety of disease models. These simulations indicate that our methods are generally higher powered than alternative gene-level approaches and at worst competitive with exhaustive SNP-level (where SNP is single-nucleotide polymorphism) analyses. Furthermore, we observe that simulated epistatic effects resulted in significant marginal testing results for the involved genes regardless of whether or not true main effects were present. We detail the benefits of our methods and discuss potential genome-wide analysis strategies for gene-gene interaction analysis in a case-control study design. © 2013 WILEY PERIODICALS, INC.

Meta-analysis of haplotype-association studies: comparison of methods and empirical evaluation of the literature

PubMed Central

2011-01-01

Background Meta-analysis is a popular methodology in several fields of medical research, including genetic association studies. However, the methods used for meta-analysis of association studies that report haplotypes have not been studied in detail. In this work, methods for performing meta-analysis of haplotype association studies are summarized, compared and presented in a unified framework along with an empirical evaluation of the literature. Results We present multivariate methods that use summary-based data as well as methods that use binary and count data in a generalized linear mixed model framework (logistic regression, multinomial regression and Poisson regression). The methods presented here avoid the inflation of the type I error rate that could be the result of the traditional approach of comparing a haplotype against the remaining ones, whereas, they can be fitted using standard software. Moreover, formal global tests are presented for assessing the statistical significance of the overall association. Although the methods presented here assume that the haplotypes are directly observed, they can be easily extended to allow for such an uncertainty by weighting the haplotypes by their probability. Conclusions An empirical evaluation of the published literature and a comparison against the meta-analyses that use single nucleotide polymorphisms, suggests that the studies reporting meta-analysis of haplotypes contain approximately half of the included studies and produce significant results twice more often. We show that this excess of statistically significant results, stems from the sub-optimal method of analysis used and, in approximately half of the cases, the statistical significance is refuted if the data are properly re-analyzed. Illustrative examples of code are given in Stata and it is anticipated that the methods developed in this work will be widely applied in the meta-analysis of haplotype association studies. PMID:21247440

Advanced glycation end products and antioxidant status in type 2 diabetic patients with and without peripheral artery disease.

PubMed

Lapolla, Annunziata; Piarulli, Francesco; Sartore, Giovanni; Ceriello, Antonio; Ragazzi, Eugenio; Reitano, Rachele; Baccarin, Lorenzo; Laverda, Barbara; Fedele, Domenico

2007-03-01

Advanced glycation end products (AGEs), pentosidine and malondialdehyde (MDA), are elevated in type 2 diabetic subjects with coronary and carotid angiopathy. We investigated the relationship of AGEs, MDA, total reactive antioxidant potentials (TRAPs), and vitamin E in type 2 diabetic patients with and without peripheral artery disease (PAD). AGEs, pentosidine, MDA, TRAP, vitamin E, and ankle-brachial index (ABI) were measured in 99 consecutive type 2 diabetic subjects and 20 control subjects. AGEs, pentosidine, and MDA were higher and vitamin E and TRAP were lower in patients with PAD (ABI <0.9) than in patients without PAD (ABI >0.9) (P < 0.001). After multiple regression analysis, a correlation between AGEs and pentosidine, as independent variables, and ABI, as the dependent variable, was found in both patients with and without PAD (r = 0.9198, P < 0.001 and r = 0.5764, P < 0.001, respectively) but not in control subjects. When individual regression coefficients were evaluated, only that due to pentosidine was confirmed as significant. For patients with PAD, considering TRAP, vitamin E, and MDA as independent variables and ABI as the dependent variable produced an overall significant regression (r = 0.6913, P < 0.001). The regression coefficients for TRAP and vitamin E were not significant, indicating that the model is best explained by a single linear regression between MDA and ABI. These findings were also confirmed by principal component analysis. Results show that pentosidine and MDA are strongly associated with PAD in type 2 diabetic patients.

Albumin, a marker for post-operative myocardial damage in cardiac surgery.

PubMed

van Beek, Dianne E C; van der Horst, Iwan C C; de Geus, A Fred; Mariani, Massimo A; Scheeren, Thomas W L

2018-06-06

Low serum albumin (SA) is a prognostic factor for poor outcome after cardiac surgery. The aim of this study was to estimate the association between pre-operative SA, early post-operative SA and postoperative myocardial injury. This single center cohort study included adult patients undergoing cardiac surgery during 4 consecutive years. Postoperative myocardial damage was defined by calculating the area under the curve (AUC) of troponin (Tn) values during the first 72 h after surgery and its association with SA analyzed using linear regression and with multivariable linear regression to account for patient related and procedural confounders. The association between SA and the secondary outcomes (peri-operative myocardial infarction [PMI], requiring ventilation >24 h, rhythm disturbances, 30-day mortality) was studied using (multivariable) log binomial regression analysis. In total 2757 patients were included. The mean pre-operative SA was 29 ± 13 g/l and the mean post-operative SA was 26 ± 6 g/l. Post-operative SA levels (on average 26 min after surgery) were inversely associated with postoperative myocardial damage in both univariable analysis (regression coefficient - 0.019, 95%CI -0.022/-0.015, p < 0.005) and after adjustment for patient related and surgical confounders (regression coefficient - 0.014 [95% CI -0.020/-0.008], p < 0.0005). Post-operative albumin levels were significantly correlated with the amount of postoperative myocardial damage in patients undergoing cardiac surgery independent of typical confounders. Copyright © 2018. Published by Elsevier Inc.

Analyzing Whitebark Pine Distribution in the Northern Rocky Mountains in Support of Grizzly Bear Recovery

NASA Astrophysics Data System (ADS)

Lawrence, R.; Landenburger, L.; Jewett, J.

2007-12-01

Whitebark pine seeds have long been identified as the most significant vegetative food source for grizzly bears in the Greater Yellowstone Ecosystem (GYE) and, hence, a crucial element of suitable grizzly bear habitat. The overall health and status of whitebark pine in the GYE is currently threatened by mountain pine beetle infestations and the spread of whitepine blister rust. Whitebark pine distribution (presence/absence) was mapped for the GYE using Landsat 7 Enhanced Thematic Mapper (ETM+) imagery and topographic data as part of a long-term inter-agency monitoring program. Logistic regression was compared with classification tree analysis (CTA) with and without boosting. Overall comparative classification accuracies for the central portion of the GYE covering three ETM+ images along a single path ranged from 91.6% using logistic regression to 95.8% with See5's CTA algorithm with the maximum 99 boosts. The analysis is being extended to the entire northern Rocky Mountain Ecosystem and extended over decadal time scales. The analysis is being extended to the entire northern Rocky Mountain Ecosystem and extended over decadal time scales.

Longitudinal flying qualities criteria for single-pilot instrument flight operations

NASA Technical Reports Server (NTRS)

Stengel, R. F.; Bar-Gill, A.

1983-01-01

Modern estimation and control theory, flight testing, and statistical analysis were used to deduce flying qualities criteria for General Aviation Single Pilot Instrument Flight Rule (SPIFR) operations. The principal concern is that unsatisfactory aircraft dynamic response combined with high navigation/communication workload can produce problems of safety and efficiency. To alleviate these problems. The relative importance of these factors must be determined. This objective was achieved by flying SPIFR tasks with different aircraft dynamic configurations and assessing the effects of such variations under these conditions. The experimental results yielded quantitative indicators of pilot's performance and workload, and for each of them, multivariate regression was applied to evaluate several candidate flying qualities criteria.

MAGMA: Generalized Gene-Set Analysis of GWAS Data

PubMed Central

de Leeuw, Christiaan A.; Mooij, Joris M.; Heskes, Tom; Posthuma, Danielle

2015-01-01

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn’s Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn’s Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn’s Disease data was found to be considerably faster as well. PMID:25885710

MAGMA: generalized gene-set analysis of GWAS data.

PubMed

de Leeuw, Christiaan A; Mooij, Joris M; Heskes, Tom; Posthuma, Danielle

2015-04-01

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn's Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn's Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn's Disease data was found to be considerably faster as well.

Nonlinear multivariate and time series analysis by neural network methods

NASA Astrophysics Data System (ADS)

Hsieh, William W.

2004-03-01

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

Factors Influencing Cecal Intubation Time during Retrograde Approach Single-Balloon Enteroscopy

PubMed Central

Chen, Peng-Jen; Shih, Yu-Lueng; Huang, Hsin-Hung; Hsieh, Tsai-Yuan

2014-01-01

Background and Aim. The predisposing factors for prolonged cecal intubation time (CIT) during colonoscopy have been well identified. However, the factors influencing CIT during retrograde SBE have not been addressed. The aim of this study was to determine the factors influencing CIT during retrograde SBE. Methods. We investigated patients who underwent retrograde SBE at a medical center from January 2011 to March 2014. The medical charts and SBE reports were reviewed. The patients' characteristics and procedure-associated data were recorded. These data were analyzed with univariate analysis as well as multivariate logistic regression analysis to identify the possible predisposing factors. Results. We enrolled 66 patients into this study. The median CIT was 17.4 minutes. With univariate analysis, there was no statistical difference in age, sex, BMI, or history of abdominal surgery, except for bowel preparation (P = 0.021). Multivariate logistic regression analysis showed that inadequate bowel preparation (odds ratio 30.2, 95% confidence interval 4.63–196.54; P < 0.001) was the independent predisposing factors for prolonged CIT during retrograde SBE. Conclusions. For experienced endoscopist, inadequate bowel preparation was the independent predisposing factor for prolonged CIT during retrograde SBE. PMID:25505904

Association between Prenatal Environmental Factors and Child Autism: A Case Control Study in Tianjin, China.

PubMed

Gao, Lei; Xi, Qian Qian; Wu, Jun; Han, Yu; Dai, Wei; Su, Yuan Yuan; Zhang, Xin

2015-09-01

To investigate the association between autism and prenatal environmental risk factors. A case-control study was conducted among 193 children with autism from the special educational schools and 733 typical development controls matched by age and gender by using questionnaire in Tianjin from 2007 to 2012. Statistical analysis included quick unbiased efficient statistical tree (QUEST) and logistic regression in SPSS 20.0. There were four predictors by QUEST and the logistic regression analysis, maternal air conditioner use during pregnancy (OR=0.316, 95% CI: 0.215-0.463) was the single first-level node (χ²=50.994, P=0.000); newborn complications (OR=4.277, 95% CI: 2.314-7.908) and paternal consumption of freshwater fish (OR=0.383, 95% CI: 0.256-0.573) were second-layer predictors (χ²=45.248, P=0.000; χ²=24.212, P=0.000); and maternal depression (OR=4.822, 95% CI: 3.047-7.631) was the single third-level predictor (χ²=23.835, P=0.000). The prediction accuracy of the tree was 89.2%. The air conditioner use during pregnancy and paternal freshwater fish diet might be beneficial for the prevention of autism, while newborn complications and maternal depression might be the risk factors. Copyright © 2015 The Editorial Board of Biomedical and Environmental Sciences. Published by China CDC. All rights reserved.

Risk Factors Related with Retroperitoneal Laparoscopic Converted to Open Nephrectomy for Nonfunctioning Renal Tuberculosis.

PubMed

Xu, Bo; Hu, Jinghai; Chen, Anxiang; Hao, Yuanyuan; Liu, GuoHui; Wang, Chunxi; Wang, Xiaoqing

2017-06-01

The present study was designed to investigate the risk factors affecting the conversion to open surgery in retroperitoneal laparoscopic nephrectomy of nonfunctioning renal tuberculosis (TB). The records of 144 patients who underwent a retroperitoneal laparoscopic nephrectomy procedure by a single surgeon were retrospectively reviewed. The following factors, including age, sex, body mass index (BMI), diabetes status, hypertension status, side of kidney, size of kidney, degree of calcification, mild perirenal extravasation, contralateral hydronephrosis, the time of anti-TB, and surgeon experience were analyzed. Univariate and multivariate logistic regression analyses were used for statistical assessment. Twenty-three patients were converted to open surgery and the conversion rate was 15.97%. In univariate analysis, BMI ≥35 kg/m 2 (p = 0.023), hypertension (p = 0.011), diabetes (p = 0.003), and kidney size (p = 0.032) were the main factors of conversion to open surgery. Sex, age, side, anti-TB time, calcification, mild extravasation, and surgeon experience were not significantly related. In multivariate regression analysis, BMI ≥35 kg/m 2 , hypertension, diabetes, and enlargement of kidney were the most important factors for conversion to open surgery. Depending on the results achieved by a single surgeon, BMI ≥30 kg/m 2 , diabetes, hypertension, and enlargement of kidney significantly increased the conversion risk in retroperitoneal laparoscopic nephrectomy for nonfunctioning renal TB.

Measuring Multi-Joint Stiffness during Single Movements: Numerical Validation of a Novel Time-Frequency Approach

PubMed Central

Piovesan, Davide; Pierobon, Alberto; DiZio, Paul; Lackner, James R.

2012-01-01

This study presents and validates a Time-Frequency technique for measuring 2-dimensional multijoint arm stiffness throughout a single planar movement as well as during static posture. It is proposed as an alternative to current regressive methods which require numerous repetitions to obtain average stiffness on a small segment of the hand trajectory. The method is based on the analysis of the reassigned spectrogram of the arm's response to impulsive perturbations and can estimate arm stiffness on a trial-by-trial basis. Analytic and empirical methods are first derived and tested through modal analysis on synthetic data. The technique's accuracy and robustness are assessed by modeling the estimation of stiffness time profiles changing at different rates and affected by different noise levels. Our method obtains results comparable with two well-known regressive techniques. We also test how the technique can identify the viscoelastic component of non-linear and higher than second order systems with a non-parametrical approach. The technique proposed here is very impervious to noise and can be used easily for both postural and movement tasks. Estimations of stiffness profiles are possible with only one perturbation, making our method a useful tool for estimating limb stiffness during motor learning and adaptation tasks, and for understanding the modulation of stiffness in individuals with neurodegenerative diseases. PMID:22448233

The Necessity-Concerns-Framework: A Multidimensional Theory Benefits from Multidimensional Analysis

PubMed Central

Phillips, L. Alison; Diefenbach, Michael; Kronish, Ian M.; Negron, Rennie M.; Horowitz, Carol R.

2014-01-01

Background Patients’ medication-related concerns and necessity-beliefs predict adherence. Evaluation of the potentially complex interplay of these two dimensions has been limited because of methods that reduce them to a single dimension (difference scores). Purpose We use polynomial regression to assess the multidimensional effect of stroke-event survivors’ medication-related concerns and necessity-beliefs on their adherence to stroke-prevention medication. Methods Survivors (n=600) rated their concerns, necessity-beliefs, and adherence to medication. Confirmatory and exploratory polynomial regression determined the best-fitting multidimensional model. Results As posited by the Necessity-Concerns Framework (NCF), the greatest and lowest adherence was reported by those with strong necessity-beliefs/weak concerns and strong concerns/weak necessity-beliefs, respectively. However, as could not be assessed using a difference-score model, patients with ambivalent beliefs were less adherent than those exhibiting indifference. Conclusions Polynomial regression allows for assessment of the multidimensional nature of the NCF. Clinicians/Researchers should be aware that concerns and necessity dimensions are not polar opposites. PMID:24500078

The necessity-concerns framework: a multidimensional theory benefits from multidimensional analysis.

PubMed

Phillips, L Alison; Diefenbach, Michael A; Kronish, Ian M; Negron, Rennie M; Horowitz, Carol R

2014-08-01

Patients' medication-related concerns and necessity-beliefs predict adherence. Evaluation of the potentially complex interplay of these two dimensions has been limited because of methods that reduce them to a single dimension (difference scores). We use polynomial regression to assess the multidimensional effect of stroke-event survivors' medication-related concerns and necessity beliefs on their adherence to stroke-prevention medication. Survivors (n = 600) rated their concerns, necessity beliefs, and adherence to medication. Confirmatory and exploratory polynomial regression determined the best-fitting multidimensional model. As posited by the necessity-concerns framework (NCF), the greatest and lowest adherence was reported by those necessity weak concerns and strong concerns/weak Necessity-Beliefs, respectively. However, as could not be assessed using a difference-score model, patients with ambivalent beliefs were less adherent than those exhibiting indifference. Polynomial regression allows for assessment of the multidimensional nature of the NCF. Clinicians/Researchers should be aware that concerns and necessity dimensions are not polar opposites.

Computational tools for exact conditional logistic regression.

PubMed

Corcoran, C; Mehta, C; Patel, N; Senchaudhuri, P

Logistic regression analyses are often challenged by the inability of unconditional likelihood-based approximations to yield consistent, valid estimates and p-values for model parameters. This can be due to sparseness or separability in the data. Conditional logistic regression, though useful in such situations, can also be computationally unfeasible when the sample size or number of explanatory covariates is large. We review recent developments that allow efficient approximate conditional inference, including Monte Carlo sampling and saddlepoint approximations. We demonstrate through real examples that these methods enable the analysis of significantly larger and more complex data sets. We find in this investigation that for these moderately large data sets Monte Carlo seems a better alternative, as it provides unbiased estimates of the exact results and can be executed in less CPU time than can the single saddlepoint approximation. Moreover, the double saddlepoint approximation, while computationally the easiest to obtain, offers little practical advantage. It produces unreliable results and cannot be computed when a maximum likelihood solution does not exist. Copyright 2001 John Wiley & Sons, Ltd.

Quantifying prosthetic gait deviation using simple outcome measures

PubMed Central

Kark, Lauren; Odell, Ross; McIntosh, Andrew S; Simmons, Anne

2016-01-01

AIM: To develop a subset of simple outcome measures to quantify prosthetic gait deviation without needing three-dimensional gait analysis (3DGA). METHODS: Eight unilateral, transfemoral amputees and 12 unilateral, transtibial amputees were recruited. Twenty-eight able-bodied controls were recruited. All participants underwent 3DGA, the timed-up-and-go test and the six-minute walk test (6MWT). The lower-limb amputees also completed the Prosthesis Evaluation Questionnaire. Results from 3DGA were summarised using the gait deviation index (GDI), which was subsequently regressed, using stepwise regression, against the other measures. RESULTS: Step-length (SL), self-selected walking speed (SSWS) and the distance walked during the 6MWT (6MWD) were significantly correlated with GDI. The 6MWD was the strongest, single predictor of the GDI, followed by SL and SSWS. The predictive ability of the regression equations were improved following inclusion of self-report data related to mobility and prosthetic utility. CONCLUSION: This study offers a practicable alternative to quantifying kinematic deviation without the need to conduct complete 3DGA. PMID:27335814

Cascade Optimization Strategy with Neural Network and Regression Approximations Demonstrated on a Preliminary Aircraft Engine Design

NASA Technical Reports Server (NTRS)

Hopkins, Dale A.; Patnaik, Surya N.

2000-01-01

A preliminary aircraft engine design methodology is being developed that utilizes a cascade optimization strategy together with neural network and regression approximation methods. The cascade strategy employs different optimization algorithms in a specified sequence. The neural network and regression methods are used to approximate solutions obtained from the NASA Engine Performance Program (NEPP), which implements engine thermodynamic cycle and performance analysis models. The new methodology is proving to be more robust and computationally efficient than the conventional optimization approach of using a single optimization algorithm with direct reanalysis. The methodology has been demonstrated on a preliminary design problem for a novel subsonic turbofan engine concept that incorporates a wave rotor as a cycle-topping device. Computations of maximum thrust were obtained for a specific design point in the engine mission profile. The results (depicted in the figure) show a significant improvement in the maximum thrust obtained using the new methodology in comparison to benchmark solutions obtained using NEPP in a manual design mode.

Fluoroscopy Learning Curve in Hip Arthroscopy-A Single Surgeon's Experience.

PubMed

Smith, Kevin M; Duplantier, Neil L; Crump, Kimbelyn H; Delgado, Domenica A; Sullivan, Stephanie L; McCulloch, Patrick C; Harris, Joshua D

2017-10-01

To determine if (1) absorbed radiation dose and (2) fluoroscopy time decreased with experience over the first 100 cases of a single surgeon's hip arthroscopy practice. Subjects who underwent hip arthroscopy for symptomatic femoroacetabular impingement and labral injury were eligible for analysis. Inclusion criteria included the first 100 subjects who underwent hip arthroscopy by a single surgeon (December 2013 to December 2014). Subject demographics, procedure details, fluoroscopy absorbed dose (milligray [mGy]), and time were recorded. Subjects were categorized by date of surgery to one of 4 possible groups (25 per group). One-way analysis of variance was used to determine if a significant difference in dose (mGy) or time was present between groups. Simple linear regression analysis was performed to determine the relation between case number and both radiation dose and fluoroscopy time. Subjects underwent labral repair (n = 93), cam osteoplasty (n = 90), and pincer acetabuloplasty (n = 65). There was a significant (P < .001 for both) linear regression between case number and both radiation dose and fluoroscopy time. A significant difference in mGy was observed between groups, group 1 the highest and group 4 the lowest amounts of radiation (P = .003). Comparing individual groups, group 4 was found to have a significantly lower amount of radiation than group 1 (P = .002), though it was not significantly lower than that of group 2 (P = .09) or group 3 (P = .08). A significant difference in fluoroscopy time was observed between groups, group 1 the highest and group 4 the lowest times (P = .05). Comparing individual groups, group 4 was found to have a significantly lower fluoroscopy time than group 1 (P = .039). Correction for weight, height, and body mass index all revealed the same findings: significant (P < .05) differences in both dose and time across groups. The absorbed dose of radiation and fluoroscopy time decreased significantly over the first 100 cases of a single surgeon's hip arthroscopy practice learning curve. Level IV, therapeutic, retrospective, noncomparative case series. Copyright © 2017 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

Polymorphisms within the FANCA gene associate with premature ovarian failure in Korean women.

PubMed

Pyun, Jung-A; Kim, Sunshin; Cha, Dong Hyun; Kwack, KyuBum

2014-05-01

This study investigated whether polymorphisms within the Fanconi anemia complementation group A (FANCA) gene contribute to the increased risk of premature ovarian failure (POF) in Korean women. Ninety-eight women with POF and 218 controls participated in this study. Genomic DNA from peripheral blood was isolated, and GoldenGate genotyping assay was used to identify single nucleotide polymorphisms (SNPs) within the FANCA gene. Two significant SNPs (rs1006547 and rs2239359; P < 0.05) were identified by logistic regression analysis, but results were insignificant after Bonferroni correction. Six SNPs formed a linkage disequilibrium block, and three main haplotypes were found. Two of three haplotypes (AAAGAA and GGGAGG) distributed highly in the POF group, whereas the remaining haplotype (GGAAGG) distributed highly in the control group by logistic regression analysis (highest odds ratio, 2.515; 95% CI, 1.515-4.175; P = 0.00036). Our observations suggest that genetic variations in the FANCA gene may increase the risk for POF in Korean women.

A non-linear regression analysis program for describing electrophysiological data with multiple functions using Microsoft Excel.

PubMed

Brown, Angus M

2006-04-01

The objective of this present study was to demonstrate a method for fitting complex electrophysiological data with multiple functions using the SOLVER add-in of the ubiquitous spreadsheet Microsoft Excel. SOLVER minimizes the difference between the sum of the squares of the data to be fit and the function(s) describing the data using an iterative generalized reduced gradient method. While it is a straightforward procedure to fit data with linear functions, and we have previously demonstrated a method of non-linear regression analysis of experimental data based upon a single function, it is more complex to fit data with multiple functions, usually requiring specialized expensive computer software. In this paper we describe an easily understood program for fitting experimentally acquired data, in this case the stimulus-evoked compound action potential from the mouse optic nerve, with multiple Gaussian functions. The program is flexible and can be applied to describe data with a wide variety of user-input functions.

Genotype-phenotype association study via new multi-task learning model

PubMed Central

Huo, Zhouyuan; Shen, Dinggang

2018-01-01

Research on the associations between genetic variations and imaging phenotypes is developing with the advance in high-throughput genotype and brain image techniques. Regression analysis of single nucleotide polymorphisms (SNPs) and imaging measures as quantitative traits (QTs) has been proposed to identify the quantitative trait loci (QTL) via multi-task learning models. Recent studies consider the interlinked structures within SNPs and imaging QTs through group lasso, e.g. ℓ2,1-norm, leading to better predictive results and insights of SNPs. However, group sparsity is not enough for representing the correlation between multiple tasks and ℓ2,1-norm regularization is not robust either. In this paper, we propose a new multi-task learning model to analyze the associations between SNPs and QTs. We suppose that low-rank structure is also beneficial to uncover the correlation between genetic variations and imaging phenotypes. Finally, we conduct regression analysis of SNPs and QTs. Experimental results show that our model is more accurate in prediction than compared methods and presents new insights of SNPs. PMID:29218896

A canonical correlation neural network for multicollinearity and functional data.

PubMed

Gou, Zhenkun; Fyfe, Colin

2004-03-01

We review a recent neural implementation of Canonical Correlation Analysis and show, using ideas suggested by Ridge Regression, how to make the algorithm robust. The network is shown to operate on data sets which exhibit multicollinearity. We develop a second model which not only performs as well on multicollinear data but also on general data sets. This model allows us to vary a single parameter so that the network is capable of performing Partial Least Squares regression (at one extreme) to Canonical Correlation Analysis (at the other)and every intermediate operation between the two. On multicollinear data, the parameter setting is shown to be important but on more general data no particular parameter setting is required. Finally, we develop a second penalty term which acts on such data as a smoother in that the resulting weight vectors are much smoother and more interpretable than the weights without the robustification term. We illustrate our algorithms on both artificial and real data.

Risk Factors for Developing Scoliosis in Cerebral Palsy: A Cross-Sectional Descriptive Study.

PubMed

Bertoncelli, Carlo M; Solla, Federico; Loughenbury, Peter R; Tsirikos, Athanasios I; Bertoncelli, Domenico; Rampal, Virginie

2017-06-01

This study aims to identify the risk factors leading to the development of severe scoliosis among children with cerebral palsy. A cross-sectional descriptive study of 70 children (aged 12-18 years) with severe spastic and/or dystonic cerebral palsy treated in a single specialist unit is described. Statistical analysis included Fisher exact test and logistic regression analysis to identify risk factors. Severe scoliosis is more likely to occur in patients with intractable epilepsy ( P = .008), poor gross motor functional assessment scores ( P = .018), limb spasticity ( P = .045), a history of previous hip surgery ( P = .048), and nonambulatory patients ( P = .013). Logistic regression model confirms the major risk factors are previous hip surgery ( P = .001), moderate to severe epilepsy ( P = .007), and female gender ( P = .03). History of previous hip surgery, intractable epilepsy, and female gender are predictors of developing severe scoliosis in children with cerebral palsy. This knowledge should aid in the early diagnosis of scoliosis and timely referral to specialist services.

Corona Enhancement and Mosaic Architecture for Prognosis and Selection Between of Liver Resection Versus Transcatheter Arterial Chemoembolization in Single Hepatocellular Carcinomas >5 cm Without Extrahepatic Metastases

PubMed Central

Li, Meng; Xin, Yongjie; Fu, Sirui; Liu, Zaiyi; Li, Yong; Hu, Baoshan; Chen, Shuting; Liang, Changhong; Lu, Ligong

2016-01-01

Abstract Corona enhancement and mosaic architecture are 2 radiologic features of hepatocellular carcinoma (HCC). However, neither their prognostic values nor their impacts on the selection of liver resection (LR) versus transcatheter arterial chemoembolization (TACE) as treatment modalities have been established. We retrospectively analyzed 275 patients with a single HCC lesion >5 cm without extrahepatic metastasis treated with LR or TACE. In LR patients, the overall survival (OS) and time to progression (TTP) were compared between corona enhancement negative (corona−) versus positive (corona+) and mosaic architecture negative (mosaic−) versus positive (mosaic+) patients. Furthermore, by the combination of corona and mosaic, LR patients were divided into negative for both corona and mosaic patterns (LR−/−), positive for only 1 feature (LR+/−), and positive for both (LR+/+); their OS and TTP were compared to those of the TACE group. Cox regression was performed to identify independent factors for OS. In the survival plots for LR, corona− had better OS and TTP than corona+, and mosaic− had better OS than mosaic+. There was no significant difference in TTP between the subgroups. On Cox regression analysis, corona enhancement, but not mosaic architecture, was a significant factor for OS, whereas neither were a significant factor for TTP. In TACE patients, neither corona nor mosaic patterns had significant correlations with OS or TTP. In the whole population, LR−/ and LR+/− subgroups had similar OS, which was better than the LR+/+ and TACE groups. Moreover, LR−/− and LR+/− patients had better TTP than TACE patients, but there were no differences between the LR−/− versus LR+/−, LR−/ versus LR+/+, LR+/− versus LR+/+, and LR+/+ versus TACE groups. On Cox regression analysis, the presence of corona/mosaic patterns was an independent prognostic factor for OS. Our results showed that, for patients with a single HCC >5 cm without extrahepatic metastasis, corona and mosaic patterns are indicators of limited LR efficacy. When both of the features are present, TACE can be used instead of LR with no negative influence on survival. PMID:26765441

Corona Enhancement and Mosaic Architecture for Prognosis and Selection Between of Liver Resection Versus Transcatheter Arterial Chemoembolization in Single Hepatocellular Carcinomas >5 cm Without Extrahepatic Metastases: An Imaging-Based Retrospective Study.

PubMed

Li, Meng; Xin, Yongjie; Fu, Sirui; Liu, Zaiyi; Li, Yong; Hu, Baoshan; Chen, Shuting; Liang, Changhong; Lu, Ligong

2016-01-01

Corona enhancement and mosaic architecture are 2 radiologic features of hepatocellular carcinoma (HCC). However, neither their prognostic values nor their impacts on the selection of liver resection (LR) versus transcatheter arterial chemoembolization (TACE) as treatment modalities have been established.We retrospectively analyzed 275 patients with a single HCC lesion >5 cm without extrahepatic metastasis treated with LR or TACE. In LR patients, the overall survival (OS) and time to progression (TTP) were compared between corona enhancement negative (corona-) versus positive (corona+) and mosaic architecture negative (mosaic-) versus positive (mosaic+) patients. Furthermore, by the combination of corona and mosaic, LR patients were divided into negative for both corona and mosaic patterns (LR-/-), positive for only 1 feature (LR+/-), and positive for both (LR+/+); their OS and TTP were compared to those of the TACE group. Cox regression was performed to identify independent factors for OS.In the survival plots for LR, corona- had better OS and TTP than corona+, and mosaic- had better OS than mosaic+. There was no significant difference in TTP between the subgroups. On Cox regression analysis, corona enhancement, but not mosaic architecture, was a significant factor for OS, whereas neither were a significant factor for TTP. In TACE patients, neither corona nor mosaic patterns had significant correlations with OS or TTP. In the whole population, LR-/ and LR+/- subgroups had similar OS, which was better than the LR+/+ and TACE groups. Moreover, LR-/- and LR+/- patients had better TTP than TACE patients, but there were no differences between the LR-/- versus LR+/-, LR-/ versus LR+/+, LR+/- versus LR+/+, and LR+/+ versus TACE groups. On Cox regression analysis, the presence of corona/mosaic patterns was an independent prognostic factor for OS.Our results showed that, for patients with a single HCC >5 cm without extrahepatic metastasis, corona and mosaic patterns are indicators of limited LR efficacy. When both of the features are present, TACE can be used instead of LR with no negative influence on survival.

Separation mechanism of nortriptyline and amytriptyline in RPLC

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

Gritti, Fabrice; Guiochon, Georges A

2005-08-01

The single and the competitive equilibrium isotherms of nortriptyline and amytriptyline were acquired by frontal analysis (FA) on the C{sub 18}-bonded discovery column, using a 28/72 (v/v) mixture of acetonitrile and water buffered with phosphate (20 mM, pH 2.70). The adsorption energy distributions (AED) of each compound were calculated from the raw adsorption data. Both the fitting of the adsorption data using multi-linear regression analysis and the AEDs are consistent with a trimodal isotherm model. The single-component isotherm data fit well to the tri-Langmuir isotherm model. The extension to a competitive two-component tri-Langmuir isotherm model based on the best parametersmore » of the single-component isotherms does not account well for the breakthrough curves nor for the overloaded band profiles measured for mixtures of nortriptyline and amytriptyline. However, it was possible to derive adjusted parameters of a competitive tri-Langmuir model based on the fitting of the adsorption data obtained for these mixtures. A very good agreement was then found between the calculated and the experimental overloaded band profiles of all the mixtures injected.« less

Impact of routine surgical ward and intensive care unit admission surveillance cultures on hospital-wide nosocomial methicillin-resistant Staphylococcus aureus infections in a university hospital: an interrupted time-series analysis.

PubMed

Chaberny, Iris F; Schwab, Frank; Ziesing, Stefan; Suerbaum, Sebastian; Gastmeier, Petra

2008-12-01

To determine whether a routine admission screening in surgical wards and intensive care units (ICUs) was effective in reducing methicillin-resistant Staphylococcus aureus (MRSA) infections-particularly nosocomial MRSA infections-for the whole hospital. The study used a single-centre prospective quasi-experimental design to evaluate the effect of the MRSA screening policy on the incidence density of MRSA-infected/nosocomial MRSA-infected patients/1000 patient-days (pd) in the whole hospital. The effect on incidence density was calculated by a segmented regression analysis of interrupted time series with 30 months prior to and 24 months after a 6 month implementation period. The MRSA screening policy had a highly significant hospital-wide effect on the incidence density of MRSA infections. It showed a significant change in both level [-0.163 MRSA-infected patients/1000 pd, 95% confidence interval (CI): -0.276 to -0.050] and slope (-0.01 MRSA-infected patients/1000 pd per month, 95% CI: -0.018 to -0.003) after the implementation of the MRSA screening policy. A decrease in the MRSA infections by 57% is a conservative estimate of the reduction between the last month before (0.417 MRSA-infected patients/1000 pd) and month 24 after the implementation of the MRSA screening policy (0.18 MRSA-infected patients/1000 pd). Equivalent results were found in the analysis of nosocomial MRSA-infected patients/1000 pd. This is the first hospital-wide study that investigates the impact of introducing admission screening in ICUs and non-ICUs as a single intervention to prevent MRSA infections performed with a time-series regression analysis. Admission screening is a potent tool in controlling the spread of MRSA infections in hospitals.

Association of HS6ST3 gene polymorphisms with obesity and triglycerides: gene x gender interaction.

PubMed

Wang, Ke-Sheng; Wang, Liang; Liu, Xuefeng; Zeng, Min

2013-12-01

The heparan sulfate 6-O-sulfotransferase 3 (HS6ST3) gene is involved in heparan sulphate and heparin metabolism, and has been reported to be associated with diabetic retinopathy in type 2 diabetes.We hypothesized that HS6ST3 gene polymorphisms might play an important role in obesity and related phenotypes (such as triglycerides). We examined genetic associations of 117 single-nucleotide polymorphisms (SNPs) within the HS6ST3 gene with obesity and triglycerides using two Caucasian samples: the Marshfield sample (1442 obesity cases and 2122 controls), and the Health aging and body composition (Health ABC) sample (305 cases and 1336 controls). Logistic regression analysis of obesity as a binary trait and linear regression analysis of triglycerides as a continuous trait, adjusted for age and sex, were performed using PLINK. Single marker analysis showed that six SNPs in the Marshfield sample and one SNP in the Health ABC sample were associated with obesity (P < 0.05). SNP rs535812 revealed a stronger association with obesity in meta-analysis of these two samples (P = 0.0105). The T-A haplotype from rs878950 and rs9525149 revealed significant association with obesity in the Marshfield sample (P = 0.012). Moreover, nine SNPs showed associations with triglycerides in the Marshfield sample (P < 0.05) and the best signal was rs1927796 (P = 0.00858). In addition, rs7331762 showed a strong gene x gender interaction (P = 0.00956) for obesity while rs1927796 showed a strong gene x gender interaction (P = 0.000625) for triglycerides in the Marshfield sample. These findings contribute new insights into the pathogenesis of obesity and triglycerides and demonstrate the importance of gender differences in the aetiology.

Cooperation without Culture? The Null Effect of Generalized Trust on Intentional Homicide: A Cross-National Panel Analysis, 1995–2009

PubMed Central

Robbins, Blaine

2013-01-01

Sociologists, political scientists, and economists all suggest that culture plays a pivotal role in the development of large-scale cooperation. In this study, I used generalized trust as a measure of culture to explore if and how culture impacts intentional homicide, my operationalization of cooperation. I compiled multiple cross-national data sets and used pooled time-series linear regression, single-equation instrumental-variables linear regression, and fixed- and random-effects estimation techniques on an unbalanced panel of 118 countries and 232 observations spread over a 15-year time period. Results suggest that culture and large-scale cooperation form a tenuous relationship, while economic factors such as development, inequality, and geopolitics appear to drive large-scale cooperation. PMID:23527211

Single-breath CO2 analysis as a predictor of lung volume in a healthy animal model during controlled ventilation.

PubMed

Stenz, R I; Grenier, B; Thompson, J E; Arnold, J H

1998-08-01

To examine the utility of single-breath CO2 analysis as a measure of lung volume. A prospective, animal cohort study comparing 21 parameters derived from single-breath CO2 analysis with lung volume measurements determined by nitrogen washout in animals during controlled ventilation. An animal laboratory in a university-affiliated medical center. Seven healthy lambs. The single-breath CO2 analysis station consists of a mainstream capnometer, a variable orifice pneumotachometer, a signal processor and computer software with capability for both on- and off-line data analysis. Twenty-one derived components of the CO2 expirogram were evaluated as predictors of lung volume. Lung volume was manipulated by 3 cm H2O incremental increases in positive end-expiratory pressure from 0 to 21 cm H2O, and ranged between 147 and 942 mL. Fifty-five measurements of lung volume were available for comparison with derived variables from the CO2 expirogam. Stepwise linear regression identified four variables that were most predictive of lung volume: a) dynamic lung compliance; b) the slope of phase 3; c) the slope of phase 2 divided by the mixed expired CO2 tension; and d) airway deadspace. The multivariate equation was highly statistically significant and explained 94% of the variance (adjusted r2 =.94, p < .0001). The bias and precision of the calculated lung volume was .00 and 51, respectively. The mean percent difference for the lung volume estimate derived from the single-breath CO2 analysis station was 0.79%. Our data indicate that analysis of the CO2 expirogram can yield accurate information about lung volume. Specifically, four variables derived from a plot of expired CO2 concentration vs. expired volume predict changes in lung volume in healthy lambs with an adjusted coefficient of determination of .94. Prospective application of this technology in the setting of lung injury and rapidly changing physiology is essential in determining the clinical usefulness of the technique.

How useful is determination of anti-factor Xa activity to guide bridging therapy with enoxaparin? A pilot study.

PubMed

Hammerstingl, Christoph; Omran, Heyder; Tripp, Christian; Poetzsch, Bernd

2009-02-01

Low-molecular-weight heparins (LMWH) are commonly used as peri-procedural bridging anticoagulants. The usefulness of measurement of anti-factor Xa activity (anti-Xa) to guide bridging therapy with LMWH is unknown. It was the objective of this study to determine levels of anti-Xa during standard bridging therapy with enoxaparin, and to examine predictors for residual anti-Xa. Consecutive patients receiving enoxaparin at a dosage of 1 mg/kg body weight/12 hours for temporary interruption of phenprocoumon were prospectively enrolled to the study. Blood-samples were obtained 14 hours after LMWH-application immediately pre- procedurally. Procedural details, clinical and demographic data were collected and subsequently analyzed. Seventy patients were included (age 75.2 +/- 10.8 years, Cr Cl 55.7 +/- 21.7ml/min, body mass index [BMI] 27.1 +/- 4.9). LMWH- therapy was for a mean of 4.2 +/- 1.6 days; overall anti-Xa was 0.58 +/- 0.32 U/ml. In 37 (52.8%) of patients anti-Xa was > or U/ml, including 10 (14.3%) patients with anti-Xa > 1U/ml. Linear regression analysis of single variables and logistic multivariable regression analysis failed to prove a correlation between anti-Xa and single or combined factors. No major bleeding, no thromboembolism and four (5.7%) minor haemorrhages were observed. When bridging OAC with therapeutic doses of enoxaparin a high percentage of patients undergo interventions with high residual anti-Xa. The levels of anti-Xa vary largely and are independent of single or combined clinical variables. Since the anti-Xa-related outcome of patients receiving bridging therapy with LMWH is not investigated, no firm recommendation on the usefulness of monitoring of anti-Xa can be given at this stage.

Intratumoral IL-12 and TNF-alpha-loaded microspheres lead to regression of breast cancer and systemic antitumor immunity.

PubMed

Sabel, Michael S; Skitzki, Joseph; Stoolman, Lloyd; Egilmez, Nejat K; Mathiowitz, Edith; Bailey, Nicola; Chang, Wen-Jian; Chang, Alfred E

2004-02-01

Local, sustained delivery of cytokines at a tumor can enhance induction of antitumor immunity and may be a feasible neoadjuvant immunotherapy for breast cancer. We evaluated the ability of intratumoral poly-lactic-acid-encapsulated microspheres (PLAM) containing interleukin 12 (IL-12), tumor necrosis factor alpha (TNF-alpha), and granulocyte-macrophage colony stimulating factor (GM-CSF) in a murine model of breast cancer to generate a specific antitumor response. BALB/c mice with established MT-901 tumors underwent resection or treatment with a single intratumoral injection of PLAM containing IL-12, TNF-alpha, or GM-CSF, alone or in combination. Two weeks later, lymph nodes and spleens were harvested, activated with anti-CD3 monoclonal antibodies (mAb) and rhIL-2, and assessed for antitumor reactivity by an interferon gamma (IFNgamma) release assay. Tumor-infiltrating lymphocyte (TIL) analysis was performed on days 2 and 5 after treatment by mechanically processing the tumors to create a single cell suspension, followed by three-color fluorescence-activated cell sorter (FACS) analysis. Intratumoral injection of cytokine-loaded PLAM significantly suppressed tumor growth, with the combination of IL-12 and TNF-alpha leading to increased infiltration by polymorphonuclear cells and CD8+ T-cells in comparison with controls. The induction of tumor-specific reactive T-cells in the nodes and spleens, as measured by IFN-gamma production, was highest with IL-12 and TNF-alpha. This treatment resulted in resistance to tumor rechallenge. A single intratumoral injection of IL-12 and TNF-alpha-loaded PLAM into a breast tumor leads to infiltration by polymorphonuclear cells and CD8+ T-cells with subsequent tumor regression. In addition, this local therapy induces specific antitumor T-cells in the lymph nodes and spleens, resulting in memory immune response.

Do active surveillance and contact precautions reduce MRSA acquisition? A prospective interrupted time series.

PubMed

Marshall, Caroline; Richards, Michael; McBryde, Emma

2013-01-01

Consensus for methicillin-resistant Staphylococcus aureus (MRSA) control has still not been reached. We hypothesised that use of rapid MRSA detection followed by contact precautions and single room isolation would reduce MRSA acquisition. This study was a pre-planned prospective interrupted time series comparing rapid PCR detection and use of long sleeved gowns and gloves (contact precautions) plus single room isolation or cohorting of MRSA colonised patients with a control group. The study took place in a medical-surgical intensive care unit of a tertiary adult hospital between May 21(st) 2007 and September 21(st) 2009. The primary outcome was the rate of MRSA acquisition. A segmented regression analysis was performed to determine the trend in MRSA acquisition rates before and after the intervention. The rate of MRSA acquisition was 18.5 per 1000 at risk patient days in the control phase and 7.9 per 1000 at-risk patient days in the intervention phase, with an adjusted hazard ratio 0.39 (95% CI 0.24 to 0.62). Segmented regression analysis showed a decline in MRSA acquisition of 7% per month in the intervention phase, (95%CI 1.9% to 12.8% reduction) which was a significant change in slope compared with the control phase. Secondary analysis found prior exposure to anaerobically active antibiotics and colonization pressure were associated with increased acquisition risk. Contact precautions with single room isolation or cohorting were associated with a 60% reduction in MRSA acquisition. While this study was a quasi-experimental design, many measures were taken to strengthen the study, such as accounting for differences in colonisation pressure, hand hygiene compliance and individual risk factors across the groups, and confining the study to one centre to reduce variation in transmission. Use of two research nurses may limit its generalisability to units in which this level of support is available.

Three-year incidence of Nd:YAG capsulotomy and posterior capsule opacification and its relationship to monofocal acrylic IOL biomaterial: a UK Real World Evidence study.

PubMed

Ursell, Paul G; Dhariwal, Mukesh; Majirska, Katarina; Ender, Frank; Kalson-Ray, Shoshannah; Venerus, Alessandra; Miglio, Cristiana; Bouchet, Christine

2018-06-11

To evaluate 3-year incidence of Nd:YAG capsulotomy and PCO and compare the effect of different IOL materials. Data were retrospectively collected from seven UK ophthalmology clinics using Medisoft electronic medical records. Eyes from patients ≥65 years undergoing cataract surgery with implantation of acrylic monofocal IOLs during 2010-2013 and 3-year follow-up were analysed. Nd:YAG capsulotomy and PCO incidence proportions were reported for 3 IOL cohorts: AcrySof, other hydrophobic and hydrophilic acrylic IOLs. Unadjusted/adjusted odds ratios (OR) of Nd:YAG capsulotomy were calculated through logistic regression for non-AcrySof cohorts versus AcrySof. A sub-group analysis in single-piece IOLs (>90% of sample eyes) was also performed. The AcrySof cohort included 13,329 eyes, non-AcrySof hydrophobic 19,025 and non-AcrySof hydrophilic 19,808. The 3-year Nd:YAG capsulotomy incidence (95% CI) for AcrySof (2.4%, 2.2-2.7%) was approximately two times lower than non-AcrySof hydrophobic IOLs (4.4%, 4.1-4.7%) and approximately fourfold lower than non-AcrySof hydrophilic IOLs (10.9%, 10.5-11.3%). Trends were similar in PCO incidence (AcrySof: 4.7%; non-AcrySof hydrophobic: 6.3%; non-AcrySof hydrophilic: 14.8%). Also in the analysis restricted to single-piece IOLs, the pattern remained (2.4% vs 5.1% vs. 10.9%, respectively). Adjusted regression analysis showed a approximately two and fivefold increased odds of Nd:YAG for non-AcrySof hydrophobic and hydrophilic acrylic IOLs respectively vs. AcrySof IOLs. Nd:YAG capsulotomy ORs were similar and remained statistically significant in the single-piece IOL sub-group. Real-world evidence shows that within 3 years following implantation, AcrySof IOLs are significantly superior in reducing Nd:YAG capsulotomy and PCO incidence compared to other hydrophilic and hydrophobic acrylic IOLs.

Pharmacokinetics of isochlorgenic acid C in rats by HPLC-MS: Absolute bioavailability and dose proportionality.

PubMed

Huang, Li Hua; Xiong, Xiao Hong; Zhong, Yun Ming; Cen, Mei Feng; Cheng, Xuan Ge; Wang, Gui Xiang; Zang, Lin Quan; Wang, Su Jun

2016-06-05

Isochlorgenic acid C (IAC), one of the bioactive compounds of Lonicera japonica, exhibited diverse pharmacological effects. However, its pharmacokinetic properties and bioavailability remained unresolved. To determine the absolute bioavailability in rats and the dose proportionality on the pharmacokinetics of single oral dose of IAC. A validated HPLC-MS method was developed for the determination of IAC in rat plasma. Plasma concentration versus time data were generated following oral and intravenous dosing. The pharmacokinetic analysis was performed using DAS 3.0 software analysis. Absolute bioavailability in rats was determined by comparing pharmacokinetic data after administration of single oral (5, 10 and 25mgkg(-1)) and intravenous (5mgkg(-1)) doses of IAC. The dose proportionality of AUC(0-∞) and Cmax were analyzed by linear regression. Experimental data showed that absolute oral bioavailability of IAC in rats across the doses ranged between 14.4% and 16.9%. The regression analysis of AUC(0-∞) and Cmax at the three doses (5, 10 and 25mgkg(-1)) indicated that the equations were y=35.23x+117.20 (r=0.998) and y=121.03x+255.74 (r=0.995), respectively. A new HPLC-MS method was developed to determine the bioavailability and the dose proportionality of IAC. Bioavailability of IAC in rats was poor and both Cmax and AUC(0-∞) of IAC had a positive correlation with dose. Evaluation of the pharmacokinetics of IAC will be useful in assessing concentration-effect relationships for the potential therapeutic applications of IAC. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

GWAS with longitudinal phenotypes: performance of approximate procedures

PubMed Central

Sikorska, Karolina; Montazeri, Nahid Mostafavi; Uitterlinden, André; Rivadeneira, Fernando; Eilers, Paul HC; Lesaffre, Emmanuel

2015-01-01

Analysis of genome-wide association studies with longitudinal data using standard procedures, such as linear mixed model (LMM) fitting, leads to discouragingly long computation times. There is a need to speed up the computations significantly. In our previous work (Sikorska et al: Fast linear mixed model computations for genome-wide association studies with longitudinal data. Stat Med 2012; 32.1: 165–180), we proposed the conditional two-step (CTS) approach as a fast method providing an approximation to the P-value for the longitudinal single-nucleotide polymorphism (SNP) effect. In the first step a reduced conditional LMM is fit, omitting all the SNP terms. In the second step, the estimated random slopes are regressed on SNPs. The CTS has been applied to the bone mineral density data from the Rotterdam Study and proved to work very well even in unbalanced situations. In another article (Sikorska et al: GWAS on your notebook: fast semi-parallel linear and logistic regression for genome-wide association studies. BMC Bioinformatics 2013; 14: 166), we suggested semi-parallel computations, greatly speeding up fitting many linear regressions. Combining CTS with fast linear regression reduces the computation time from several weeks to a few minutes on a single computer. Here, we explore further the properties of the CTS both analytically and by simulations. We investigate the performance of our proposal in comparison with a related but different approach, the two-step procedure. It is analytically shown that for the balanced case, under mild assumptions, the P-value provided by the CTS is the same as from the LMM. For unbalanced data and in realistic situations, simulations show that the CTS method does not inflate the type I error rate and implies only a minimal loss of power. PMID:25712081

Integrative Analysis of High-throughput Cancer Studies with Contrasted Penalization

PubMed Central

Shi, Xingjie; Liu, Jin; Huang, Jian; Zhou, Yong; Shia, BenChang; Ma, Shuangge

2015-01-01

In cancer studies with high-throughput genetic and genomic measurements, integrative analysis provides a way to effectively pool and analyze heterogeneous raw data from multiple independent studies and outperforms “classic” meta-analysis and single-dataset analysis. When marker selection is of interest, the genetic basis of multiple datasets can be described using the homogeneity model or the heterogeneity model. In this study, we consider marker selection under the heterogeneity model, which includes the homogeneity model as a special case and can be more flexible. Penalization methods have been developed in the literature for marker selection. This study advances from the published ones by introducing the contrast penalties, which can accommodate the within- and across-dataset structures of covariates/regression coefficients and, by doing so, further improve marker selection performance. Specifically, we develop a penalization method that accommodates the across-dataset structures by smoothing over regression coefficients. An effective iterative algorithm, which calls an inner coordinate descent iteration, is developed. Simulation shows that the proposed method outperforms the benchmark with more accurate marker identification. The analysis of breast cancer and lung cancer prognosis studies with gene expression measurements shows that the proposed method identifies genes different from those using the benchmark and has better prediction performance. PMID:24395534

Association of genetic variations in the serotonin and dopamine systems with aggressive behavior in the Chinese adolescent population: Single- and multiple-risk genetic variants.

PubMed

Chang, Hongjuan; Yan, Qiuge; Tang, Lina; Huang, Juan; Ma, Yuqiao; Ye, Xiaozhou; Wu, Chunxia; Wu, Linguo; Yu, Yizhen

2018-01-01

Genetic predisposition is an important factor leading to aggressive behavior. However, the relationship between genetic polymorphisms and aggressive behavior has not been elucidated. We identified candidate genes located in the dopaminergic and serotonin system (DRD3, DRD4, and FEV) that had been previously reported to be associated with aggressive behavior. We investigated 14 tag single-nucleotide polymorphisms (SNPs) using a multi-analytic strategy combining logistic regression (LR) and classification and regression tree (CART) to explore higher-order interactions between these SNPs and aggressive behavior in 318 patients and 558 controls. Both LR and CART analyses suggested that the rs16859448 polymorphism is the strongest individual factor associated with aggressive behavior risk. In CART analysis, individuals carrying the combined genotypes of rs16859448TT/GT-rs11246228CT/TT-rs3773679TT had the highest risk, while rs16859448GG-rs2134655CT had the lowest risk (OR = 5.25, 95% CI: 2.53-10.86). This study adds to the growing evidence on the association of single- and multiple-risk variants in DRD3, DRD4, and FEV with aggressive behavior in Chinese adolescents. However, the aggressive behavior scale used to diagnose aggression in this study did not account for comorbid conditions; therefore, further studies are needed to confirm our observations. Copyright © 2017 Elsevier B.V. All rights reserved.

Complications and Short-Term Explantation Rate Following Artificial Urinary Sphincter Implantation: Results from a Large Middle European Multi-Institutional Case Series.

PubMed

Kretschmer, Alexander; Hüsch, Tanja; Thomsen, Frauke; Kronlachner, Dominik; Obaje, Alice; Anding, Ralf; Pottek, Tobias; Rose, Achim; Olianas, Roberto; Friedl, Alexander; Hübner, Wilhelm; Homberg, Roland; Pfitzenmaier, Jesco; Grein, Ulrich; Queissert, Fabian; Naumann, Carsten Maik; Schweiger, Josef; Wotzka, Carola; Nyarangi-Dix, Joanne N; Hofmann, Torben; Seiler, Roland; Haferkamp, Axel; Bauer, Ricarda M

2016-01-01

Background/Aims/Objectives: To analyze perioperative complication and short-term explantation rates after perineal or penoscrotal single-cuff and double-cuff artificial urinary sphincter (AUS) implantation in a large middle European multi-institutional patient cohort. 467 male patients with stress urinary incontinence underwent implantation of a perineal single-cuff (n = 152), penoscrotal single-cuff (n = 99), or perineal double-cuff (n = 216) AUS between 2010 and 2012. Postoperative complications and 6-month explantation rates were assessed. For statistical analysis, Fisher's exact test and Kruskal-Wallis rank sum test, and a multiple logistic regression model were used (p < 0.05). Compared to perineal single-cuff AUS, penoscrotal single-cuff implantation led to significantly increased short-term explantation rates (8.6% (perineal) vs. 19.2% (penoscrotal), p = 0.019). The postoperative infection rate was significantly higher after double-cuff compared to single-cuff implantation (6.0% (single-cuff) vs. 13.9% (double-cuff), p = 0.019). The short-term explantation rate after primary double-cuff placement was 6.5% (p = 0.543 vs. perineal single-cuff). In multivariate analysis, the penoscrotal approach (p = 0.004), intraoperative complications (p = 0.005), postoperative bleeding (p = 0.011), and perioperative infection (p < 0.001) were independent risk factors for short-term explantation. Providing data from a large contemporary multi-institutional patient cohort from high-volume and low-volume institutions, our results reflect the current standard of care in middle Europe. We indicate that the penoscrotal approach is an independent risk factor for increased short-term explantation rates. © 2016 S. Karger AG, Basel.

Determination of Single-Kidney Glomerular Filtration Rate in Human Subjects by Using CT

PubMed Central

Kwon, Soon Hyo; Saad, Ahmed; Herrmann, Sandra M.; Textor, Stephen C.

2015-01-01

Purpose To test the hypothesis that computed tomography (CT)–derived measurements of single-kidney glomerular filtration rate (GFR) obtained in human subjects with 64-section CT agree with those obtained with iothalamate clearance, a rigorous reference standard. Materials and Methods The institutional review board approved this HIPAA-compliant study, and written informed consent was obtained. Ninety-six patients (age range, 51–73 years; 46 men, 50 women) with essential (n = 56) or renovascular (n = 40) hypertension were prospectively studied in controlled conditions (involving sodium intake and renin-angiotensin blockade). Single-kidney perfusion, volume, and GFR were measured by using multidetector CT time-attenuation curves and were compared with GFR measured by using iothalamate clearance, as assigned to the right and left kidney according to relative volumes. The reproducibility of CT GFR over a 3-month period (n = 21) was assessed in patients with renal artery stenosis who were undergoing stable medical treatment. Statistical analysis included the t test, Wilcoxon signed rank test, linear regression, and Bland-Altman analysis. Results CT GFR values were similar to those of iothalamate clearance (mean ± standard deviation, 38.2 mL/min ± 18 vs 41.6 mL/min ± 17; P = .062). Stenotic kidney CT GFR in patients with renal artery stenosis was lower than contralateral kidney GFR or essential hypertension single-kidney GFR (mean, 23.1 mL/min ± 13 vs 36.9 mL/min ± 17 [P = .0008] and 45.2 mL/min ± 16 [P = .019], respectively), as was iothalamate clearance (mean, 26.9 mL/min ± 14 vs 38.5 mL/min ± 15 [P = .0004] and 49.0 mL/min ± 14 [P = .001], respectively). CT GFR correlated well with iothalamate GFR (linear regression, CT GFR = 0.88*iothalamate GFR, r2 = 0.89, P < .0001), and Bland-Altman analysis was used to confirm the agreement. CT GFR was also moderately reproducible in medically treated patients with renal artery stenosis (concordance coefficient correlation, 0.835) but was unaffected by revascularization (mean, 25.3 mL/min ± 15.2 vs 30.3 mL/min ± 18.5; P = .097). Conclusion CT assessments of single-kidney GFR are reproducible and agree well with a reference standard. CT can be useful to obtain minimally invasive estimates of bilateral single-kidney function in human subjects. © RSNA, 2015 PMID:25848903

Self-reported work ability and work performance in workers with chronic nonspecific musculoskeletal pain.

PubMed

de Vries, Haitze J; Reneman, Michiel F; Groothoff, Johan W; Geertzen, Jan H B; Brouwer, Sandra

2013-03-01

To assess self-reported work ability and work performance of workers who stay at work despite chronic nonspecific musculoskeletal pain (CMP), and to explore which variables were associated with these outcomes. In a cross-sectional study we assessed work ability (Work Ability Index, single item scale 0-10) and work performance (Health and Work Performance Questionnaire, scale 0-10) among 119 workers who continued work while having CMP. Scores of work ability and work performance were categorized into excellent (10), good (9), moderate (8) and poor (0-7). Hierarchical multiple regression and logistic regression analysis was used to analyze the relation of socio-demographic, pain-related, personal- and work-related variables with work ability and work performance. Mean work ability and work performance were 7.1 and 7.7 (poor to moderate). Hierarchical multiple regression analysis revealed that higher work ability scores were associated with lower age, better general health perception, and higher pain self-efficacy beliefs (R(2) = 42 %). Higher work performance was associated with lower age, higher pain self-efficacy beliefs, lower physical work demand category and part-time work (R(2) = 37 %). Logistic regression analysis revealed that work ability ≥8 was significantly explained by age (OR = 0.90), general health perception (OR = 1.04) and pain self-efficacy (OR = 1.15). Work performance ≥8 was explained by pain self-efficacy (OR = 1.11). Many workers with CMP who stay at work report poor to moderate work ability and work performance. Our findings suggest that a subgroup of workers with CMP can stay at work with high work ability and performance, especially when they have high beliefs of pain self-efficacy. Our results further show that not the pain itself, but personal and work-related factors relate to work ability and work performance.

Selection of higher order regression models in the analysis of multi-factorial transcription data.

PubMed

Prazeres da Costa, Olivia; Hoffman, Arthur; Rey, Johannes W; Mansmann, Ulrich; Buch, Thorsten; Tresch, Achim

2014-01-01

Many studies examine gene expression data that has been obtained under the influence of multiple factors, such as genetic background, environmental conditions, or exposure to diseases. The interplay of multiple factors may lead to effect modification and confounding. Higher order linear regression models can account for these effects. We present a new methodology for linear model selection and apply it to microarray data of bone marrow-derived macrophages. This experiment investigates the influence of three variable factors: the genetic background of the mice from which the macrophages were obtained, Yersinia enterocolitica infection (two strains, and a mock control), and treatment/non-treatment with interferon-γ. We set up four different linear regression models in a hierarchical order. We introduce the eruption plot as a new practical tool for model selection complementary to global testing. It visually compares the size and significance of effect estimates between two nested models. Using this methodology we were able to select the most appropriate model by keeping only relevant factors showing additional explanatory power. Application to experimental data allowed us to qualify the interaction of factors as either neutral (no interaction), alleviating (co-occurring effects are weaker than expected from the single effects), or aggravating (stronger than expected). We find a biologically meaningful gene cluster of putative C2TA target genes that appear to be co-regulated with MHC class II genes. We introduced the eruption plot as a tool for visual model comparison to identify relevant higher order interactions in the analysis of expression data obtained under the influence of multiple factors. We conclude that model selection in higher order linear regression models should generally be performed for the analysis of multi-factorial microarray data.

The effectiveness of manual and mechanical instrumentation for the retreatment of three different root canal filling materials.

PubMed

Somma, Francesco; Cammarota, Giuseppe; Plotino, Gianluca; Grande, Nicola M; Pameijer, Cornelis H

2008-04-01

The aim of this study was to compare the effectiveness of the Mtwo R (Sweden & Martina, Padova, Italy), ProTaper retreatment files (Dentsply-Maillefer, Ballaigues, Switzerland), and a Hedström manual technique in the removal of three different filling materials (gutta-percha, Resilon [Resilon Research LLC, Madison, CT], and EndoRez [Ultradent Products Inc, South Jordan, UT]) during retreatment. Ninety single-rooted straight premolars were instrumented and randomly divided into 9 groups of 10 teeth each (n = 10) with regards to filling material and instrument used. For all roots, the following data were recorded: procedural errors, time of retreatment, apically extruded material, canal wall cleanliness through optical stereomicroscopy (OSM), and scanning electron microscopy (SEM). A linear regression analysis and three logistic regression analyses were performed to assess the level of significance set at p = 0.05. The results indicated that the overall regression models were statistically significant. The Mtwo R, ProTaper retreatment files, and Resilon filling material had a positive impact in reducing the time for retreatment. Both ProTaper retreatment files and Mtwo R showed a greater extrusion of debris. For both OSM and SEM logistic regression models, the root canal apical third had the greatest impact on the score values. EndoRez filling material resulted in cleaner root canal walls using OSM analysis, whereas Resilon filling material and both engine-driven NiTi rotary techniques resulted in less clean root canal walls according to SEM analysis. In conclusion, all instruments left remnants of filling material and debris on the root canal walls irrespective of the root filling material used. Both the engine-driven NiTi rotary systems proved to be safe and fast devices for the removal of endodontic filling material.

Generating linear regression model to predict motor functions by use of laser range finder during TUG.

PubMed

Adachi, Daiki; Nishiguchi, Shu; Fukutani, Naoto; Hotta, Takayuki; Tashiro, Yuto; Morino, Saori; Shirooka, Hidehiko; Nozaki, Yuma; Hirata, Hinako; Yamaguchi, Moe; Yorozu, Ayanori; Takahashi, Masaki; Aoyama, Tomoki

2017-05-01

The purpose of this study was to investigate which spatial and temporal parameters of the Timed Up and Go (TUG) test are associated with motor function in elderly individuals. This study included 99 community-dwelling women aged 72.9 ± 6.3 years. Step length, step width, single support time, variability of the aforementioned parameters, gait velocity, cadence, reaction time from starting signal to first step, and minimum distance between the foot and a marker placed to 3 in front of the chair were measured using our analysis system. The 10-m walk test, five times sit-to-stand (FTSTS) test, and one-leg standing (OLS) test were used to assess motor function. Stepwise multivariate linear regression analysis was used to determine which TUG test parameters were associated with each motor function test. Finally, we calculated a predictive model for each motor function test using each regression coefficient. In stepwise linear regression analysis, step length and cadence were significantly associated with the 10-m walk test, FTSTS and OLS test. Reaction time was associated with the FTSTS test, and step width was associated with the OLS test. Each predictive model showed a strong correlation with the 10-m walk test and OLS test (P < 0.01), which was not significant higher correlation than TUG test time. We showed which TUG test parameters were associated with each motor function test. Moreover, the TUG test time regarded as the lower extremity function and mobility has strong predictive ability in each motor function test. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis.

PubMed

Ono, Tomohiro; Nakamura, Mitsuhiro; Hirose, Yoshinori; Kitsuda, Kenji; Ono, Yuka; Ishigaki, Takashi; Hiraoka, Masahiro

2017-09-01

To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D-CT scanning. The three-dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D-CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root-mean-square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D-CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Detecting sea-level hazards: Simple regression-based methods for calculating the acceleration of sea level

USGS Publications Warehouse

Doran, Kara S.; Howd, Peter A.; Sallenger,, Asbury H.

2016-01-04

Recent studies, and most of their predecessors, use tide gage data to quantify SL acceleration, ASL(t). In the current study, three techniques were used to calculate acceleration from tide gage data, and of those examined, it was determined that the two techniques based on sliding a regression window through the time series are more robust compared to the technique that fits a single quadratic form to the entire time series, particularly if there is temporal variation in the magnitude of the acceleration. The single-fit quadratic regression method has been the most commonly used technique in determining acceleration in tide gage data. The inability of the single-fit method to account for time-varying acceleration may explain some of the inconsistent findings between investigators. Properly quantifying ASL(t) from field measurements is of particular importance in evaluating numerical models of past, present, and future SLR resulting from anticipated climate change.

Phosphatidylserine-targeting antibodies augment the anti-tumorigenic activity of anti-PD-1 therapy by enhancing immune activation and downregulating pro-oncogenic factors induced by T-cell checkpoint inhibition in murine triple-negative breast cancers.

PubMed

Gray, Michael J; Gong, Jian; Hatch, Michaela M S; Nguyen, Van; Hughes, Christopher C W; Hutchins, Jeff T; Freimark, Bruce D

2016-05-11

The purpose of this study was to investigate the potential of antibody-directed immunotherapy targeting the aminophospholipid phosphatidylserine, which promotes immunosuppression when exposed in the tumor microenvironment, alone and in combination with antibody treatment towards the T-cell checkpoint inhibitor PD-1 in breast carcinomas, including triple-negative breast cancers. Immune-competent mice bearing syngeneic EMT-6 or E0771 tumors were subjected to treatments comprising of a phosphatidylserine-targeting and an anti-PD-1 antibody either as single or combinational treatments. Anti-tumor effects were determined by tumor growth inhibition and changes in overall survival accompanying each treatment. The generation of a tumor-specific immune response in animals undergoing complete tumor regression was assessed by secondary tumor cell challenge and splenocyte-produced IFNγ in the presence or absence of irradiated tumor cells. Changes in the presence of tumor-infiltrating lymphocytes were assessed by flow cytometry, while mRNA-based immune profiling was determined using NanoString PanCancer Immune Profiling Panel analysis. Treatment by a phosphatidylserine-targeting antibody inhibits in-vivo growth and significantly enhances the anti-tumor activity of antibody-mediated PD-1 therapy, including providing a distinct survival advantage over treatment by either single agent. Animals in which complete tumor regression occurred with combination treatments were resistant to secondary tumor challenge and presented heightened expression levels of splenocyte-produced IFNγ. Combinational treatment by a phosphatidylserine-targeting antibody with anti-PD-1 therapy increased the number of tumor-infiltrating lymphocytes more than that observed with single-arm therapies. Finally, immunoprofiling analysis revealed that the combination of anti-phosphatidylserine targeting antibody and anti-PD-1 therapy enhanced tumor-infiltrating lymphocytes, and increased expression of pro-immunosurveillance-associated cytokines while significantly decreasing expression of pro-tumorigenic cytokines that were induced by single anti-PD-1 therapy. Our data suggest that antibody therapy targeting phosphatidylserine-associated immunosuppression, which has activity as a single agent, can significantly enhance immunotherapies targeting the PD-1 pathway in murine breast neoplasms, including triple-negative breast cancers.

Clinical features and risk factor analysis for lower extremity deep venous thrombosis in Chinese neurosurgical patients

PubMed Central

Guo, Fuyou; Shashikiran, Tagilapalli; Chen, Xi; Yang, Lei; Liu, Xianzhi; Song, Laijun

2015-01-01

Background: Deep venous thrombosis (DVT) contributes significantly to the morbidity and mortality of neurosurgical patients; however, no data regarding lower extremity DVT in postoperative Chinese neurosurgical patients have been reported. Materials and Methods: From January 2012 to December 2013, 196 patients without preoperative DVT who underwent neurosurgical operations were evaluated by color Doppler ultrasonography and D-dimer level measurements on the 3rd, 7th, and 14th days after surgery. Follow-up clinical data were recorded to determine the incidence of lower extremity DVT in postoperative neurosurgical patients and to analyze related clinical features. First, a single factor analysis, Chi-square test, was used to select statistically significant factors. Then, a multivariate analysis, binary logistic regression analysis, was used to determine risk factors for lower extremity DVT in postoperative neurosurgical patients. Results: Lower extremity DVT occurred in 61 patients, and the incidence of DVT was 31.1% in the enrolled Chinese neurosurgical patients. The common symptoms of DVT were limb swelling and lower extremity pain as well as increased soft tissue tension. The common sites of venous involvement were the calf muscle and peroneal and posterior tibial veins. The single factor analysis showed statistically significant differences in DVT risk factors, including age, hypertension, smoking status, operation time, a bedridden or paralyzed state, the presence of a tumor, postoperative dehydration, and glucocorticoid treatment, between the two groups (P < 0.05). The binary logistic regression analysis showed that an age greater than 50 years, hypertension, a bedridden or paralyzed state, the presence of a tumor, and postoperative dehydration were risk factors for lower extremity DVT in postoperative neurosurgical patients. Conclusions: Lower extremity DVT was a common complication following craniotomy in the enrolled Chinese neurosurgical patients. Multiple factors were identified as predictive of DVT in neurosurgical patients, including the presence of a tumor, an age greater than 50 years, hypertension, and immobility. PMID:26752303

Risk of Revision Was Not Reduced by a Double-bundle ACL Reconstruction Technique: Results From the Scandinavian Registers.

PubMed

Aga, Cathrine; Kartus, Jüri-Tomas; Lind, Martin; Lygre, Stein Håkon Låstad; Granan, Lars-Petter; Engebretsen, Lars

2017-10-01

Double-bundle anterior cruciate ligament (ACL) reconstruction has demonstrated improved biomechanical properties and moderately better objective outcomes compared with single-bundle reconstructions. This could make an impact on the rerupture rate and reduce the risk of revisions in patients undergoing double-bundle ACL reconstruction compared with patients reconstructed with a traditional single-bundle technique. The National Knee Ligament Registers in Scandinavia provide information that can be used to evaluate the revision outcome after ACL reconstructions. The purposes of the study were (1) to compare the risk of revision between double-bundle and single-bundle reconstructions, reconstructed with autologous hamstring tendon grafts; (2) to compare the risk of revision between double-bundle hamstring tendon and single-bundle bone-patellar tendon-bone autografts; and (3) to compare the hazard ratios for the same two research questions after Cox regression analysis was performed. Data collection of primary ACL reconstructions from the National Knee Ligament Registers in Denmark, Norway, and Sweden from July 1, 2005, to December 31, 2014, was retrospectively analyzed. A total of 60,775 patients were included in the study; 994 patients were reconstructed with double-bundle hamstring tendon grafts, 51,991 with single-bundle hamstring tendon grafts, and 7790 with single-bundle bone-patellar tendon-bone grafts. The double-bundle ACL-reconstructed patients were compared with the two other groups. The risk of revision for each research question was detected by the risk ratio, hazard ratio, and the corresponding 95% confidence intervals. Kaplan-Meier analysis was used to estimate survival at 1, 2, and 5 years for the three different groups. Furthermore, a Cox proportional hazard regression model was applied and the hazard ratios were adjusted for country, age, sex, meniscal or chondral injury, and utilized fixation devices on the femoral and tibial sides. There were no differences in the crude risk of revision between the patients undergoing the double-bundle technique and the two other groups. A total of 3.7% patients were revised in the double-bundle group (37 of 994 patients) versus 3.8% in the single-bundle hamstring tendon group (1952 of 51,991; risk ratio, 1.01; 95% confidence interval (CI), 0.73-1.39; p = 0.96), and 2.8% of the patients were revised in the bone-patellar tendon-bone group (219 of the 7790 bone-patellar tendon-bone patients; risk ratio, 0.76; 95% CI, 0.54-1.06; p = 0.11). Cox regression analysis with adjustment for country, age, sex, menisci or cartilage injury, and utilized fixation device on the femoral and tibial sides, did not reveal any further difference in the risk of revision between the single-bundle hamstring tendon and double-bundle hamstring tendon groups (hazard ratio, 1.18; 95% CI, 0.85-1.62; p = 0.33), but the adjusted hazard ratio showed a lower risk of revision in the single-bundle bone-patellar tendon-bone group compared with the double-bundle group (hazard ratio, 0.62; 95% CI, 0.43-0.90; p = 0.01). Comparisons of the graft revision rates reported separately for each country revealed that double-bundle hamstring tendon reconstructions in Sweden had a lower hazard ratio compared with the single-bundle hamstring tendon reconstructions (hazard ratio, 1.00 versus 1.89; 95% CI, 1.09-3.29; p = 0.02). Survival at 5 years after index surgery was 96.0% for the double-bundle group, 95.4% for the single-bundle hamstring tendon group, and 97.0% for the single-bundle bone-patellar tendon-bone group. Based on the data from all three national registers, the risk of revision was not influenced by the reconstruction technique in terms of using single- or double-bundle hamstring tendons, although national differences in survival existed. Using bone-patellar tendon-bone grafts lowered the risk of revision compared with double-bundle hamstring tendon grafts. These findings should be considered when deciding what reconstruction technique to use in ACL-deficient knees. Future studies identifying the reasons for graft rerupture in single- and double-bundle reconstructions would be of interest to understand the findings of the present study. Level III, therapeutic study.

Single Motherhood, Alcohol Dependence, and Smoking During Pregnancy: A Propensity Score Analysis.

PubMed

Waldron, Mary; Bucholz, Kathleen K; Lian, Min; Lessov-Schlaggar, Christina N; Miller, Ruth Huang; Lynskey, Michael T; Knopik, Valerie S; Madden, Pamela A F; Heath, Andrew C

2017-09-01

Few studies linking single motherhood and maternal smoking during pregnancy consider correlated risk from problem substance use beyond history of smoking and concurrent use of alcohol. In the present study, we used propensity score methods to examine whether the risk of smoking during pregnancy associated with single motherhood is the result of potential confounders, including alcohol dependence. Data were drawn from mothers participating in a birth cohort study of their female like-sex twin offspring (n = 257 African ancestry; n = 1,711 European or other ancestry). We conducted standard logistic regression models predicting smoking during pregnancy from single motherhood at twins' birth, followed by propensity score analyses comparing single-mother and two-parent families stratified by predicted probability of single motherhood. In standard models, single motherhood predicted increased risk of smoking during pregnancy in European ancestry but not African ancestry families. In propensity score analyses, rates of smoking during pregnancy were elevated in single-mother relative to two-parent European ancestry families across much of the spectrum a priori risk of single motherhood. Among African ancestry families, within-strata comparisons of smoking during pregnancy by single-mother status were nonsignificant. These findings highlight single motherhood as a unique risk factor for smoking during pregnancy in European ancestry mothers, over and above alcohol dependence. Additional research is needed to identify risks, beyond single motherhood, associated with smoking during pregnancy in African ancestry mothers.

Resolving the percentage of component terrains within single resolution elements

NASA Technical Reports Server (NTRS)

Marsh, S. E.; Switzer, P.; Kowalik, W. S.; Lyon, R. J. P.

1980-01-01

An approximate maximum likelihood technique employing a widely available discriminant analysis program is discussed that has been developed for resolving the percentage of component terrains within single resolution elements. The method uses all four channels of Landsat data simultaneously and does not require prior knowledge of the percentage of components in mixed pixels. It was tested in five cases that were chosen to represent mixtures of outcrop, soil and vegetation which would typically be encountered in geologic studies with Landsat data. For all five cases, the method proved to be superior to single band weighted average and linear regression techniques and permitted an estimate of the total area occupied by component terrains to within plus or minus 6% of the true area covered. Its major drawback is a consistent overestimation of the pixel component percent of the darker materials (vegetation) and an underestimation of the pixel component percent of the brighter materials (sand).

Sewage sludge disintegration by combined treatment of alkaline+high pressure homogenization.

PubMed

Zhang, Yuxuan; Zhang, Panyue; Zhang, Guangming; Ma, Weifang; Wu, Hao; Ma, Boqiang

2012-11-01

Alkaline pretreatment combined with high pressure homogenization (HPH) was applied to promote sewage sludge disintegration. For sewage sludge with a total solid content of 1.82%, sludge disintegration degree (DD(COD)) with combined treatment was higher than the sum of DD(COD) with single alkaline and single HPH treatment. NaOH dosage ⩽0.04mol/L, homogenization pressure ⩽60MPa and a single homogenization cycle were the suitable conditions for combined sludge treatment. The combined sludge treatment showed a maximum DD(COD) of 59.26%. By regression analysis, the combined sludge disintegration model was established as 11-DD(COD)=0.713C(0.334)P(0.234)N(0.119), showing that the effect of operating parameters on sludge disintegration followed the order: NaOH dosage>homogenization pressure>number of homogenization cycle. The energy efficiency with combined sludge treatment significantly increased compared with that with single HPH treatment, and the high energy efficiency was achieved at low homogenization pressure with a single homogenization cycle. Copyright © 2012 Elsevier Ltd. All rights reserved.

Determination of the chemical parameters and manufacturer of divins from their broadband transmission spectra

NASA Astrophysics Data System (ADS)

Khodasevich, M. A.; Sinitsyn, G. V.; Skorbanova, E. A.; Rogovaya, M. V.; Kambur, E. I.; Aseev, V. A.

2016-06-01

Analysis of multiparametric data on transmission spectra of 24 divins (Moldovan cognacs) in the 190-2600 nm range allows identification of outliers and their removal from a sample under study in the following consideration. The principal component analysis and classification tree with a single-rank predictor constructed in the 2D space of principal components allow classification of divin manufacturers. It is shown that the accuracy of syringaldehyde, ethyl acetate, vanillin, and gallic acid concentrations in divins calculated with the regression to latent structures depends on the sample volume and is 3, 6, 16, and 20%, respectively, which is acceptable for the application.

Demographic and psychosocial risk factors for preterm delivery in an active duty pregnant population.

PubMed

Evans, M A; Rosen, L N

2000-01-01

The effects of work climate, pregnancy transitions stress, maternal medical conditions, health risk behaviors, psychological health, and demographic characteristics were examined among 269 pregnant military women. The study found that single and separated/divorced military women were at greater risk for preterm delivery than married women. Unmarried participants were more likely to belong to ethnic minorities, were lower ranking, less educated, and reported a greater number of medical conditions than married participants. Psychosocial variables distinguished the three marital status groups--married, single, and separated/divorced--but none of these variables was related to preterm delivery. In a logistic regression analysis, marital status was a more significant predictor of preterm delivery than were medical conditions.

Survival analysis of postoperative nausea and vomiting in patients receiving patient-controlled epidural analgesia.

PubMed

Lee, Shang-Yi; Hung, Chih-Jen; Chen, Chih-Chieh; Wu, Chih-Cheng

2014-11-01

Postoperative nausea and vomiting as well as postoperative pain are two major concerns when patients undergo surgery and receive anesthetics. Various models and predictive methods have been developed to investigate the risk factors of postoperative nausea and vomiting, and different types of preventive managements have subsequently been developed. However, there continues to be a wide variation in the previously reported incidence rates of postoperative nausea and vomiting. This may have occurred because patients were assessed at different time points, coupled with the overall limitation of the statistical methods used. However, using survival analysis with Cox regression, and thus factoring in these time effects, may solve this statistical limitation and reveal risk factors related to the occurrence of postoperative nausea and vomiting in the following period. In this retrospective, observational, uni-institutional study, we analyzed the results of 229 patients who received patient-controlled epidural analgesia following surgery from June 2007 to December 2007. We investigated the risk factors for the occurrence of postoperative nausea and vomiting, and also assessed the effect of evaluating patients at different time points using the Cox proportional hazards model. Furthermore, the results of this inquiry were compared with those results using logistic regression. The overall incidence of postoperative nausea and vomiting in our study was 35.4%. Using logistic regression, we found that only sex, but not the total doses and the average dose of opioids, had significant effects on the occurrence of postoperative nausea and vomiting at some time points. Cox regression showed that, when patients consumed a higher average dose of opioids, this correlated with a higher incidence of postoperative nausea and vomiting with a hazard ratio of 1.286. Survival analysis using Cox regression showed that the average consumption of opioids played an important role in postoperative nausea and vomiting, a result not found by logistic regression. Therefore, the incidence of postoperative nausea and vomiting in patients cannot be reliably determined on the basis of a single visit at one point in time. Copyright © 2014. Published by Elsevier Taiwan.

Factor complexity of crash occurrence: An empirical demonstration using boosted regression trees.

PubMed

Chung, Yi-Shih

2013-12-01

Factor complexity is a characteristic of traffic crashes. This paper proposes a novel method, namely boosted regression trees (BRT), to investigate the complex and nonlinear relationships in high-variance traffic crash data. The Taiwanese 2004-2005 single-vehicle motorcycle crash data are used to demonstrate the utility of BRT. Traditional logistic regression and classification and regression tree (CART) models are also used to compare their estimation results and external validities. Both the in-sample cross-validation and out-of-sample validation results show that an increase in tree complexity provides improved, although declining, classification performance, indicating a limited factor complexity of single-vehicle motorcycle crashes. The effects of crucial variables including geographical, time, and sociodemographic factors explain some fatal crashes. Relatively unique fatal crashes are better approximated by interactive terms, especially combinations of behavioral factors. BRT models generally provide improved transferability than conventional logistic regression and CART models. This study also discusses the implications of the results for devising safety policies. Copyright © 2012 Elsevier Ltd. All rights reserved.

[Influencing factors on depression among medical staff in Hunan province under ordinal regression analysis].

PubMed

Liu, Zhi-yu; Zhong, Meng; Hai, Yan; Du, Qi-yun; Wang, Ai-hua; Xie, Dong-hua

2012-11-01

To understand the situation of depression and its related influencing factors among medical staff in Hunan province. Data were collected through random sampling with multi-stage stratified cluster. Wilcoxon rank sum test, Kruskal-Wallis H test and Ordinal regression analysis were used for data analysis by SPSS 17.0 software. This survey was including 16,000 medical personnel with 14, 988 valid questionnaires and the effective rate was 93.68%. from the single factor analysis showed that factors as: level of the hospital grading, gender, education background, age, occupation, title, departments, the number of continue education, income, working overtime every week, the frequency of night work, the number of patients treated in the emergency room etc., had statistical significances (P < 0.05). Data from ordinal regression showed that the probabilities related to depression that clinicians and nurses suffering from were 1.58 times more than the pharmacists (OR = 1.58, 95%CI: 1.30 - 1.92). The probability among those whose income was less than 2000 Yuan/month was 2.19 times of the ones whose earned more than 3000 Yuan/month (OR = 2.19, 95%CI: 2.05 - 2.35). The higher the numbers of days with working overtime every week, the frequencies of night work, and the numbers of patients being treated at the emergency room, with more probabilities of the people with depression seen in our study. Depression seemed to be common among doctors and nurses. We suggested that the government need to increase the monthly income and to reduce the workload and intensity, lessen the overworking time, etc.

Marital status and survival in patients with renal cell carcinoma.

PubMed

Li, Yan; Zhu, Ming-Xi; Qi, Si-Hua

2018-04-01

Previous studies have shown that marital status is an independent prognostic factor for survival in several types of cancer. In this study, we investigated the effects of marital status on survival outcomes among renal cell carcinoma (RCC) patients.We identified patients diagnosed with RCC between 1973 and 2013 from the Surveillance, Epidemiology and End Results (SEER) database. Kaplan-Meier analysis and Cox regression were used to identify the effects of marital status on overall survival (OS) and cancer-specific survival (CSS).We enrolled 97,662 eligible RCC patients, including 64,884 married patients, and 32,778 unmarried (9831 divorced/separated, 9692 widowed, and 13,255 single) patients at diagnosis. The 5-year OS and CSS rates of the married, separated/divorced, widowed, and single patients were 73.7%, 69.5%, 58.3%, and 73.2% (OS), and 82.2%, 80.7%, 75.7%, and 83.3% (CSS), respectively. Multivariate Cox regression showed that, compared with married patients, widowed individuals showed poorer OS (hazard ratio, 1.419; 95% confidence interval, 1.370-1.469) and CSS (hazard ratio, 1.210; 95% confidence interval, 1.144-1.279). Stratified analyses and multivariate Cox regression showed that, in the insured and uninsured groups, married patients had better survival outcomes while widowed patients suffered worse OS outcomes; however, this trend was not significant for CSS.In RCC patients, married patients had better survival outcomes while widowed patients tended to suffer worse survival outcomes in terms of both OS and CSS.

Marital status and survival in patients with renal cell carcinoma

PubMed Central

Li, Yan; Zhu, Ming-xi; Qi, Si-hua

2018-01-01

Abstract Previous studies have shown that marital status is an independent prognostic factor for survival in several types of cancer. In this study, we investigated the effects of marital status on survival outcomes among renal cell carcinoma (RCC) patients. We identified patients diagnosed with RCC between 1973 and 2013 from the Surveillance, Epidemiology and End Results (SEER) database. Kaplan–Meier analysis and Cox regression were used to identify the effects of marital status on overall survival (OS) and cancer-specific survival (CSS). We enrolled 97,662 eligible RCC patients, including 64,884 married patients, and 32,778 unmarried (9831 divorced/separated, 9692 widowed, and 13,255 single) patients at diagnosis. The 5-year OS and CSS rates of the married, separated/divorced, widowed, and single patients were 73.7%, 69.5%, 58.3%, and 73.2% (OS), and 82.2%, 80.7%, 75.7%, and 83.3% (CSS), respectively. Multivariate Cox regression showed that, compared with married patients, widowed individuals showed poorer OS (hazard ratio, 1.419; 95% confidence interval, 1.370–1.469) and CSS (hazard ratio, 1.210; 95% confidence interval, 1.144–1.279). Stratified analyses and multivariate Cox regression showed that, in the insured and uninsured groups, married patients had better survival outcomes while widowed patients suffered worse OS outcomes; however, this trend was not significant for CSS. In RCC patients, married patients had better survival outcomes while widowed patients tended to suffer worse survival outcomes in terms of both OS and CSS. PMID:29668592

Automated single-trial assessment of laser-evoked potentials as an objective functional diagnostic tool for the nociceptive system.

PubMed

Hatem, S M; Hu, L; Ragé, M; Gierasimowicz, A; Plaghki, L; Bouhassira, D; Attal, N; Iannetti, G D; Mouraux, A

2012-12-01

To assess the clinical usefulness of an automated analysis of event-related potentials (ERPs). Nociceptive laser-evoked potentials (LEPs) and non-nociceptive somatosensory electrically-evoked potentials (SEPs) were recorded in 37 patients with syringomyelia and 21 controls. LEP and SEP peak amplitudes and latencies were estimated using a single-trial automated approach based on time-frequency wavelet filtering and multiple linear regression, as well as a conventional approach based on visual inspection. The amplitudes and latencies of normal and abnormal LEP and SEP peaks were identified reliably using both approaches, with similar sensitivity and specificity. Because the automated approach provided an unbiased solution to account for average waveforms where no ERP could be identified visually, it revealed significant differences between patients and controls that were not revealed using the visual approach. The automated analysis of ERPs characterized reliably and objectively LEP and SEP waveforms in patients. The automated single-trial analysis can be used to characterize normal and abnormal ERPs with a similar sensitivity and specificity as visual inspection. While this does not justify its use in a routine clinical setting, the technique could be useful to avoid observer-dependent biases in clinical research. Copyright © 2012 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Multi-variant study of obesity risk genes in African Americans: The Jackson Heart Study.

PubMed

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

2016-11-30

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

Principal component regression analysis with SPSS.

PubMed

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Risk factors for an additional port in single-incision laparoscopic cholecystectomy in patients with cholecystitis.

PubMed

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

2017-01-01

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

An Optimization of Inventory Demand Forecasting in University Healthcare Centre

NASA Astrophysics Data System (ADS)

Bon, A. T.; Ng, T. K.

2017-01-01

Healthcare industry becomes an important field for human beings nowadays as it concerns about one’s health. With that, forecasting demand for health services is an important step in managerial decision making for all healthcare organizations. Hence, a case study was conducted in University Health Centre to collect historical demand data of Panadol 650mg for 68 months from January 2009 until August 2014. The aim of the research is to optimize the overall inventory demand through forecasting techniques. Quantitative forecasting or time series forecasting model was used in the case study to forecast future data as a function of past data. Furthermore, the data pattern needs to be identified first before applying the forecasting techniques. Trend is the data pattern and then ten forecasting techniques are applied using Risk Simulator Software. Lastly, the best forecasting techniques will be find out with the least forecasting error. Among the ten forecasting techniques include single moving average, single exponential smoothing, double moving average, double exponential smoothing, regression, Holt-Winter’s additive, Seasonal additive, Holt-Winter’s multiplicative, seasonal multiplicative and Autoregressive Integrated Moving Average (ARIMA). According to the forecasting accuracy measurement, the best forecasting technique is regression analysis.

Water quality of storm runoff and comparison of procedures for estimating storm-runoff loads, volume, event-mean concentrations, and the mean load for a storm for selected properties and constituents for Colorado Springs, southeastern Colorado, 1992

USGS Publications Warehouse

Von Guerard, Paul; Weiss, W.B.

1995-01-01

The U.S. Environmental Protection Agency requires that municipalities that have a population of 100,000 or greater obtain National Pollutant Discharge Elimination System permits to characterize the quality of their storm runoff. In 1992, the U.S. Geological Survey, in cooperation with the Colorado Springs City Engineering Division, began a study to characterize the water quality of storm runoff and to evaluate procedures for the estimation of storm-runoff loads, volume and event-mean concentrations for selected properties and constituents. Precipitation, streamflow, and water-quality data were collected during 1992 at five sites in Colorado Springs. Thirty-five samples were collected, seven at each of the five sites. At each site, three samples were collected for permitting purposes; two of the samples were collected during rainfall runoff, and one sample was collected during snowmelt runoff. Four additional samples were collected at each site to obtain a large enough sample size to estimate storm-runoff loads, volume, and event-mean concentrations for selected properties and constituents using linear-regression procedures developed using data from the Nationwide Urban Runoff Program (NURP). Storm-water samples were analyzed for as many as 186 properties and constituents. The constituents measured include total-recoverable metals, vola-tile-organic compounds, acid-base/neutral organic compounds, and pesticides. Storm runoff sampled had large concentrations of chemical oxygen demand and 5-day biochemical oxygen demand. Chemical oxygen demand ranged from 100 to 830 milligrams per liter, and 5.-day biochemical oxygen demand ranged from 14 to 260 milligrams per liter. Total-organic carbon concentrations ranged from 18 to 240 milligrams per liter. The total-recoverable metals lead and zinc had the largest concentrations of the total-recoverable metals analyzed. Concentrations of lead ranged from 23 to 350 micrograms per liter, and concentrations of zinc ranged from 110 to 1,400 micrograms per liter. The data for 30 storms representing rainfall runoff from 5 drainage basins were used to develop single-storm local-regression models. The response variables, storm-runoff loads, volume, and event-mean concentrations were modeled using explanatory variables for climatic, physical, and land-use characteristics. The r2 for models that use ordinary least-squares regression ranged from 0.57 to 0.86 for storm-runoff loads and volume and from 0.25 to 0.63 for storm-runoff event-mean concentrations. Except for cadmium, standard errors of estimate ranged from 43 to 115 percent for storm- runoff loads and volume and from 35 to 66 percent for storm-runoff event-mean concentrations. Eleven of the 30 concentrations collected during rainfall runoff for total-recoverable cadmium were censored (less than) concentrations. Ordinary least-squares regression should not be used with censored data; however, censored data can be included with uncensored data using tobit regression. Standard errors of estimate for storm-runoff load and event-mean concentration for total-recoverable cadmium, computed using tobit regression, are 247 and 171 percent. Estimates from single-storm regional-regression models, developed from the Nationwide Urban Runoff Program data base, were compared with observed storm-runoff loads, volume, and event-mean concentrations determined from samples collected in the study area. Single-storm regional-regression models tended to overestimate storm-runoff loads, volume, and event-mean con-centrations. Therefore, single-storm local- and regional-regression models were combined using model-adjustment procedures to take advantage of the strengths of both models while minimizing the deficiencies of each model. Procedures were used to develop single-stormregression equations that were adjusted using local data and estimates from single-storm regional-regression equations. Single-storm regression models developed using model- adjustment proce

[Influence of sample surface roughness on mathematical model of NIR quantitative analysis of wood density].

PubMed

Huang, An-Min; Fei, Ben-Hua; Jiang, Ze-Hui; Hse, Chung-Yun

2007-09-01

Near infrared spectroscopy is widely used as a quantitative method, and the main multivariate techniques consist of regression methods used to build prediction models, however, the accuracy of analysis results will be affected by many factors. In the present paper, the influence of different sample roughness on the mathematical model of NIR quantitative analysis of wood density was studied. The result of experiments showed that if the roughness of predicted samples was consistent with that of calibrated samples, the result was good, otherwise the error would be much higher. The roughness-mixed model was more flexible and adaptable to different sample roughness. The prediction ability of the roughness-mixed model was much better than that of the single-roughness model.

A secure distributed logistic regression protocol for the detection of rare adverse drug events

PubMed Central

El Emam, Khaled; Samet, Saeed; Arbuckle, Luk; Tamblyn, Robyn; Earle, Craig; Kantarcioglu, Murat

2013-01-01

Background There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. Objective To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. Methods We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. Results The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. Conclusion The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for correlations among patients within sites through generalized estimating equations, and to accommodate other link functions by extending it to generalized linear models. PMID:22871397

A secure distributed logistic regression protocol for the detection of rare adverse drug events.

PubMed

El Emam, Khaled; Samet, Saeed; Arbuckle, Luk; Tamblyn, Robyn; Earle, Craig; Kantarcioglu, Murat

2013-05-01

There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for correlations among patients within sites through generalized estimating equations, and to accommodate other link functions by extending it to generalized linear models.

Synthesis, spectral studies and antimicrobial activities of some 2-naphthyl pyrazoline derivatives

NASA Astrophysics Data System (ADS)

Sakthinathan, S. P.; Vanangamudi, G.; Thirunarayanan, G.

A series of 2-naphthyl pyrazolines were synthesized by the cyclization of 2-naphthyl chalcones and phenylhydrazine hydrochloride in the presence of sodium acetate. The yields of pyrazoline derivatives are more than 80%. The synthesized pyrazolines were characterized by their physical constants, IR, 1H, 13C and MS spectra. From the IR and NMR spectra the Cdbnd N (cm-1) stretches, the pyrazoline ring proton chemical shifts (ppm) of δ, Hb and Hc and also the carbon chemical shifts (ppm) of δCdbnd N are correlated with Hammett substituent constants, F and R, and Swain-Lupton's parameters using single and multi-regression analyses. From the results of linear regression analysis, the effect of substituents on the group frequencies has been predicted. The antimicrobial activities of all synthesized pyrazolines have been studied.

Feature Selection for Ridge Regression with Provable Guarantees.

PubMed

Paul, Saurabh; Drineas, Petros

2016-04-01

We introduce single-set spectral sparsification as a deterministic sampling-based feature selection technique for regularized least-squares classification, which is the classification analog to ridge regression. The method is unsupervised and gives worst-case guarantees of the generalization power of the classification function after feature selection with respect to the classification function obtained using all features. We also introduce leverage-score sampling as an unsupervised randomized feature selection method for ridge regression. We provide risk bounds for both single-set spectral sparsification and leverage-score sampling on ridge regression in the fixed design setting and show that the risk in the sampled space is comparable to the risk in the full-feature space. We perform experiments on synthetic and real-world data sets; a subset of TechTC-300 data sets, to support our theory. Experimental results indicate that the proposed methods perform better than the existing feature selection methods.

The subcutaneous abdominal fat and not the intraabdominal fat compartment is associated with anovulation in women with obesity and infertility.

PubMed

Kuchenbecker, Walter K H; Groen, Henk; Zijlstra, Tineke M; Bolster, Johanna H T; Slart, Riemer H J; van der Jagt, Erik J; Kobold, Anneke C Muller; Wolffenbuttel, Bruce H R; Land, Jolande A; Hoek, Annemieke

2010-05-01

Abdominal fat contributes to anovulation. We compared body fat distribution measurements and their contribution to anovulation in obese ovulatory and anovulatory infertile women. Seventeen ovulatory and 40 anovulatory women (age, 30 +/- 4 yr; body mass index, 37.7 +/- 6.1 kg/m(2)) participated. Body fat distribution was measured by anthropometrics, dual-energy x-ray absorptiometry, and single-sliced abdominal computed tomography scan. Multiple logistic regression analysis was applied to determine which fat compartments significantly contributed to anovulation. Anovulatory women had a higher waist circumference (113 +/- 11 vs. 104 +/- 9 cm; P < 0.01) and significantly more trunk fat (23.0 +/- 5.3 vs. 19.1 +/- 4.2 kg; P < 0.01) and abdominal fat (4.4 +/- 1.3 kg vs. 3.5 +/- 0.9 kg; P < 0.05) on dual-energy x-ray absorptiometry scan than ovulatory women despite similar body mass index. The volume of intraabdominal fat on single-sliced abdominal computed tomography scan was not significantly different between the two groups (203 +/- 56 vs. 195 +/- 71 cm(3); P = 0.65), but anovulatory women had significantly more sc abdominal fat (SAF) (992 +/- 198 vs. 864 +/- 146 cm(3); P < 0.05). After multiple logistic regression analysis, only trunk fat, abdominal fat, and SAF were associated with anovulation. Abdominal fat is increased in anovulatory women due to a significant increase in SAF and not in intraabdominal fat. SAF and especially abdominal and trunk fat accumulation are associated with anovulation.

Regression Analysis by Example. 5th Edition

ERIC Educational Resources Information Center

Chatterjee, Samprit; Hadi, Ali S.

2012-01-01

Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…

Climate change and epidemics in Chinese history: A multi-scalar analysis.

PubMed

Lee, Harry F; Fei, Jie; Chan, Christopher Y S; Pei, Qing; Jia, Xin; Yue, Ricci P H

2017-02-01

This study seeks to provide further insight regarding the relationship of climate-epidemics in Chinese history through a multi-scalar analysis. Based on 5961 epidemic incidents in China during 1370-1909 CE we applied Ordinary Least Square regression and panel data regression to verify the climate-epidemic nexus over a range of spatial scales (country, macro region, and province). Results show that epidemic outbreaks were negatively correlated with the temperature in historical China at various geographic levels, while a stark reduction in the correlational strength was observed at lower geographic levels. Furthermore, cooling drove up epidemic outbreaks in northern and central China, where population pressure reached a clear threshold for amplifying the vulnerability of epidemic outbreaks to climate change. Our findings help to illustrate the modifiable areal unit and the uncertain geographic context problems in climate-epidemics research. Researchers need to consider the scale effect in the course of statistical analyses, which are currently predominantly conducted on a national/single scale; and also the importance of how the study area is delineated, an issue which is rarely discussed in the climate-epidemics literature. Future research may leverage our results and provide a cross-analysis with those derived from spatial analysis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Analysis of quality of life and influencing factors in 197 Chinese patients with port-wine stains

PubMed Central

Wang, Juan; Zhu, Yu-you; Wang, Zhong-ying; Yao, Xiu-hua; Zhang, Lan-fang; Lv, Hong; Zhang, Si-ping; Hu, Bai

2017-01-01

Abstract Port-wine stains (PWS) are congenital capillary malformations, usually occurring on the face, neck, and other exposed parts of the skin, that have serious psychological and social impact on the patient. Most researchers focus on the treatment of PWS, but the quality of life (QoL) of PWS patients is seldom researched. The objective of this study is to evaluate the QoL of patients with PWS on exposed parts and explore the factors influencing the QoL of PWS patients. The QoL of 197 cases with PWS on exposed parts were prospectively studied using the Dermatology Life Quality Index questionnaire (DLQI), and the factors influencing the patients’ QoL were analyzed by single-factor analysis and multiple-factor logistic regression analysis. The reliability and validity of the QoL of PWS patients were then assessed by DLQI. A total of 197 valid questionnaires were collected. The DLQI scores in PWS cases ranged from 2 to 16, with 2 to 5 in 52.29% (103/197), 6 to 10 in 42.13% (83/197), and 11 to 20 in 5.58% (11/197). The main score elements of the DLQI focused on symptoms and feelings, daily activities, and social entertainment. Single-factor analysis and multiple-factor logistic regression analysis showed that the main influencing factors were female sex, skin hypertrophy, and lesion area >30 cm2. The inter-item correlation averaged 47.46% and the Cronbach α was 0.740, indicating high internal consistency. Correlation of the 6 dimensions of the DLQI questionnaires with the total scores showed that the Spearman correlation coefficient r ranged from 0.550 to 0.782 (P < .001), with symptoms and feelings having a correlation coefficient of 0.782 and a high correlation with total scores. This study shows that PWS has mild to moderate influence on the QoL of most patients, mainly on daily activities, social entertainment, and feelings. PMID:29390578

Articular Cartilage of the Human Knee Joint: In Vivo Multicomponent T2 Analysis at 3.0 T

PubMed Central

Choi, Kwang Won; Samsonov, Alexey; Spencer, Richard G.; Wilson, John J.; Block, Walter F.; Kijowski, Richard

2015-01-01

Purpose To compare multicomponent T2 parameters of the articular cartilage of the knee joint measured by using multicomponent driven equilibrium single-shot observation of T1 and T2 (mcDESPOT) in asymptomatic volunteers and patients with osteoarthritis. Materials and Methods This prospective study was performed with institutional review board approval and with written informed consent from all subjects. The mcDESPOT sequence was performed in the knee joint of 13 asymptomatic volunteers and 14 patients with osteoarthritis of the knee. Single-component T2 (T2Single), T2 of the fast-relaxing water component (T2F) and of the slow-relaxing water component (T2S), and the fraction of the fast-relaxing water component (FF) of cartilage were measured. Wilcoxon rank-sum tests and multivariate linear regression models were used to compare mcDESPOT parameters between volunteers and patients with osteoarthritis. Receiver operating characteristic analysis was used to assess diagnostic performance with mcDESPOT parameters for distinguishing morphologically normal cartilage from morphologically degenerative cartilage identified at magnetic resonance imaging in eight cartilage subsections of the knee joint. Results Higher cartilage T2Single (P < .001), lower cartilage FF (P < .001), and similar cartilage T2F (P = .079) and T2S (P = .124) values were seen in patients with osteoarthritis compared with those in asymptomatic volunteers. Differences in T2Single and FF remained significant (P < .05) after consideration of age differences between groups of subjects. Diagnostic performance was higher with FF than with T2Single for distinguishing between normal and degenerative cartilage (P < .05), with greater areas under the curve at receiver operating characteristic analysis. Conclusion Patients with osteoarthritis of the knee had significantly higher cartilage T2Single and significantly lower cartilage FF than did asymptomatic volunteers, and receiver operating characteristic analysis results suggested that FF may allow greater diagnostic performance than that with T2Single for distinguishing between normal and degenerative cartilage. © RSNA, 2015 Online supplemental material is available for this article. PMID:26024307

A systematic study on the influencing parameters and improvement of quantitative analysis of multi-component with single marker method using notoginseng as research subject.

PubMed

Wang, Chao-Qun; Jia, Xiu-Hong; Zhu, Shu; Komatsu, Katsuko; Wang, Xuan; Cai, Shao-Qing

2015-03-01

A new quantitative analysis of multi-component with single marker (QAMS) method for 11 saponins (ginsenosides Rg1, Rb1, Rg2, Rh1, Rf, Re and Rd; notoginsenosides R1, R4, Fa and K) in notoginseng was established, when 6 of these saponins were individually used as internal referring substances to investigate the influences of chemical structure, concentrations of quantitative components, and purities of the standard substances on the accuracy of the QAMS method. The results showed that the concentration of the analyte in sample solution was the major influencing parameter, whereas the other parameters had minimal influence on the accuracy of the QAMS method. A new method for calculating the relative correction factors by linear regression was established (linear regression method), which demonstrated to decrease standard method differences of the QAMS method from 1.20%±0.02% - 23.29%±3.23% to 0.10%±0.09% - 8.84%±2.85% in comparison with the previous method. And the differences between external standard method and the QAMS method using relative correction factors calculated by linear regression method were below 5% in the quantitative determination of Rg1, Re, R1, Rd and Fa in 24 notoginseng samples and Rb1 in 21 notoginseng samples. And the differences were mostly below 10% in the quantitative determination of Rf, Rg2, R4 and N-K (the differences of these 4 constituents bigger because their contents lower) in all the 24 notoginseng samples. The results indicated that the contents assayed by the new QAMS method could be considered as accurate as those assayed by external standard method. In addition, a method for determining applicable concentration ranges of the quantitative components assayed by QAMS method was established for the first time, which could ensure its high accuracy and could be applied to QAMS methods of other TCMs. The present study demonstrated the practicability of the application of the QAMS method for the quantitative analysis of multi-component and the quality control of TCMs and TCM prescriptions. Copyright © 2014 Elsevier B.V. All rights reserved.

Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications

PubMed Central

Huang, Jian; Zhang, Cun-Hui

2013-01-01

The ℓ1-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weighted ℓ1-penalized estimators for convex loss functions of a general form, including the generalized linear models. We study the estimation, prediction, selection and sparsity properties of the weighted ℓ1-penalized estimator in sparse, high-dimensional settings where the number of predictors p can be much larger than the sample size n. Adaptive Lasso is considered as a special case. A multistage method is developed to approximate concave regularized estimation by applying an adaptive Lasso recursively. We provide prediction and estimation oracle inequalities for single- and multi-stage estimators, a general selection consistency theorem, and an upper bound for the dimension of the Lasso estimator. Important models including the linear regression, logistic regression and log-linear models are used throughout to illustrate the applications of the general results. PMID:24348100

Estimation of nutrients and organic matter in Korean swine slurry using multiple regression analysis of physical and chemical properties.

PubMed

Suresh, Arumuganainar; Choi, Hong Lim

2011-10-01

Swine waste land application has increased due to organic fertilization, but excess application in an arable system can cause environmental risk. Therefore, in situ characterizations of such resources are important prior to application. To explore this, 41 swine slurry samples were collected from Korea, and wide differences were observed in the physico-biochemical properties. However, significant (P<0.001) multiple property correlations (R²) were obtained between nutrients with specific gravity (SG), electrical conductivity (EC), total solids (TS) and pH. The different combinations of hydrometer, EC meter, drying oven and pH meter were found useful to estimate Mn, Fe, Ca, K, Al, Na, N and 5-day biochemical oxygen demands (BOD₅) at improved R² values of 0.83, 0.82, 0.77, 0.75, 0.67, 0.47, 0.88 and 0.70, respectively. The results from this study suggest that multiple property regressions can facilitate the prediction of micronutrients and organic matter much better than a single property regression for livestock waste. Copyright © 2011 Elsevier Ltd. All rights reserved.

Quantitative Analysis of Single and Mix Food Antiseptics Basing on SERS Spectra with PLSR Method

NASA Astrophysics Data System (ADS)

Hou, Mengjing; Huang, Yu; Ma, Lingwei; Zhang, Zhengjun

2016-06-01

Usage and dosage of food antiseptics are very concerned due to their decisive influence in food safety. Surface-enhanced Raman scattering (SERS) effect was employed in this research to realize trace potassium sorbate (PS) and sodium benzoate (SB) detection. HfO2 ultrathin film-coated Ag NR array was fabricated as SERS substrate. Protected by HfO2 film, the SERS substrate possesses good acid resistance, which enables it to be applicable in acidic environment where PS and SB work. Regression relationship between SERS spectra of 0.3~10 mg/L PS solution and their concentration was calibrated by partial least squares regression (PLSR) method, and the concentration prediction performance was quite satisfactory. Furthermore, mixture solution of PS and SB was also quantitatively analyzed by PLSR method. Spectrum data of characteristic peak sections corresponding to PS and SB was used to establish the regression models of these two solutes, respectively, and their concentrations were determined accurately despite their characteristic peak sections overlapping. It is possible that the unique modeling process of PLSR method prevented the overlapped Raman signal from reducing the model accuracy.

Association analysis of multiple traits by an approach of combining P values.

PubMed

Chen, Lili; Wang, Yong; Zhou, Yajing

2018-03-01

Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants.

Single-nucleotide polymorphisms of MMP2 in MMP/TIMP pathways associated with the risk of alcohol-induced osteonecrosis of the femoral head in Chinese males: A case-control study.

PubMed

Yu, Yan; Xie, Zhilan; Wang, Jihan; Chen, Chu; Du, Shuli; Chen, Peng; Li, Bin; Jin, Tianbo; Zhao, Heping

2016-12-01

The proportion of alcohol-induced osteonecrosis of the femoral head (ONFH) in all ONFH patients was 30.7%, with males prevailing among the ONFH patients in mainland China (70.1%). Matrix metalloproteinase 2 (MMP2), a member of the MMP gene family, encodes the enzyme MMP2, which can promote osteoclast migration, attachment, and bone matrix degradation. In this case-control study, we aimed to investigate the association between MMP2 and the alcohol-induced ONFH in Chinese males.In total, 299 patients with alcohol-induced ONFH and 396 healthy controls were recruited for a case-control association study. Five single-nucleotide polymorphisms within the MMP2 locus were genotyped and examined for their correlation with the risk of alcohol-induced ONFH and treatment response using Pearson χ test and unconditional logistic regression analysis. We identified 3 risk alleles for carriers: the allele "T" of rs243849 increased the risk of alcohol-induced ONFH in the allele model, the log-additive model without adjustment, and the log-additive model with adjustment for age. Conversely, the genotypes "CC" in rs7201 and "CC" in rs243832 decreased the risk of alcohol-induced ONFH, as revealed by the recessive model. After the Bonferroni multiple adjustment, no significant association was found. Furthermore, the haplotype analysis showed that the "TT" haplotype of MMP2 was more frequent among patients with alcohol-induced ONFH by unconditional logistic regression analysis adjusted for age.In conclusion, there may be an association between MMP2 and the risk of alcohol-induced ONFH in North-Chinese males. However, studies on larger populations are needed to confirm this hypothesis; these data may provide a theoretical foundation for future studies.

A Multiomics Approach to Identify Genes Associated with Childhood Asthma Risk and Morbidity.

PubMed

Forno, Erick; Wang, Ting; Yan, Qi; Brehm, John; Acosta-Perez, Edna; Colon-Semidey, Angel; Alvarez, Maria; Boutaoui, Nadia; Cloutier, Michelle M; Alcorn, John F; Canino, Glorisa; Chen, Wei; Celedón, Juan C

2017-10-01

Childhood asthma is a complex disease. In this study, we aim to identify genes associated with childhood asthma through a multiomics "vertical" approach that integrates multiple analytical steps using linear and logistic regression models. In a case-control study of childhood asthma in Puerto Ricans (n = 1,127), we used adjusted linear or logistic regression models to evaluate associations between several analytical steps of omics data, including genome-wide (GW) genotype data, GW methylation, GW expression profiling, cytokine levels, asthma-intermediate phenotypes, and asthma status. At each point, only the top genes/single-nucleotide polymorphisms/probes/cytokines were carried forward for subsequent analysis. In step 1, asthma modified the gene expression-protein level association for 1,645 genes; pathway analysis showed an enrichment of these genes in the cytokine signaling system (n = 269 genes). In steps 2-3, expression levels of 40 genes were associated with intermediate phenotypes (asthma onset age, forced expiratory volume in 1 second, exacerbations, eosinophil counts, and skin test reactivity); of those, methylation of seven genes was also associated with asthma. Of these seven candidate genes, IL5RA was also significant in analytical steps 4-8. We then measured plasma IL-5 receptor α levels, which were associated with asthma age of onset and moderate-severe exacerbations. In addition, in silico database analysis showed that several of our identified IL5RA single-nucleotide polymorphisms are associated with transcription factors related to asthma and atopy. This approach integrates several analytical steps and is able to identify biologically relevant asthma-related genes, such as IL5RA. It differs from other methods that rely on complex statistical models with various assumptions.

Multicomponent blood lipid analysis by means of near infrared spectroscopy, in geese.

PubMed

Bazar, George; Eles, Viktoria; Kovacs, Zoltan; Romvari, Robert; Szabo, Andras

2016-08-01

This study provides accurate near infrared (NIR) spectroscopic models on some laboratory determined clinicochemical parameters (i.e. total lipid (5.57±1.95 g/l), triglyceride (2.59±1.36 mmol/l), total cholesterol (3.81±0.68 mmol/l), high density lipoprotein (HDL) cholesterol (2.45±0.58 mmol/l)) of blood serum samples of fattened geese. To increase the performance of multivariate chemometrics, samples significantly deviating from the regression models implying laboratory error were excluded from the final calibration datasets. Reference data of excluded samples having outlier spectra in principal component analysis were not marked as false. Samples deviating from the regression models but having non outlier spectra in PCA were identified as having false reference constituent values. Based on the NIR selection methods, 5% of the reference measurement data were rated as doubtful. The achieved models reached R(2) of 0.864, 0.966, 0.850, 0.793, and RMSE of 0.639 g/l, 0.232 mmol/l, 0.210 mmol/l, 0.241 mmol/l for total lipid, triglyceride, total cholesterol and HDL cholesterol, respectively, during independent validation. Classical analytical techniques focus on single constituents and often require chemicals, time-consuming measurements, and experienced technicians. NIR technique provides a quick, cost effective, non-hazardous alternative method for analysis of several constituents based on one single spectrum of each sample, and it also offers the possibility for looking at the laboratory reference data critically. Evaluation of reference data to identify and exclude falsely analyzed samples can provide warning feedback to the reference laboratory, especially in the case of analyses where laboratory methods are not perfectly suited to the subjected material and there is an increased chance of laboratory error. Copyright © 2016 Elsevier B.V. All rights reserved.

Dermatological comorbidity in psoriasis: results from a large-scale cohort of employees.

PubMed

Zander, N; Schäfer, I; Radtke, M; Jacobi, A; Heigel, H; Augustin, M

2017-07-01

The field of dermatological comorbidity in psoriasis is only passively explored with contradictory results. Objective of this study was to further investigate the complex field of psoriasis and associated skin diseases by identifying skin comorbidity patterns in an extensive cohort of employees in Germany. Retrospective analysis of data deriving from occupational skin cancer screenings was conducted. From 2001 to 2014 German employees between 16 and 70 years from different branches underwent single whole-body screenings by trained dermatologists in their companies. All dermatological findings and need for treatment were documented. Point prevalence rates and their 95% confidence intervals were computed. Logistic regression analysis was performed to calculate odds ratios (OR) of single dermatological diseases to occur together with psoriasis controlled for age and sex. Data from 138,930 persons (56.5% male, mean age 43.2) were evaluated. Psoriasis point prevalence was 2.0%. Of those 20.6% had unmet treatment needs of their disease. Onychomycosis was the most frequent dermatological comorbidity with a prevalence of 7.8%. Regression analysis found rosacea (OR = 1.40, 95% CI 1.13-1.72) and telangiectasia (OR = 1.25, 95% CI 1.10-1.41) to be significantly associated with psoriasis. 17.2% of psoriasis patients had at least one further finding requiring treatment. The highest treatment needs were found for onychomycosis (3.4%), tinea pedis (3.1%), and verruca plantaris (1.0%). It can be concluded that persons with psoriasis are at increased risk to suffer from comorbid skin diseases, which should be considered in treatment regimens. Particular attention should be paid to fungal diseases of the feet.

Impact of Precision Medicine in Diverse Cancers: A Meta-Analysis of Phase II Clinical Trials

PubMed Central

Schwaederle, Maria; Zhao, Melissa; Lee, J. Jack; Eggermont, Alexander M.; Schilsky, Richard L.; Mendelsohn, John; Lazar, Vladimir; Kurzrock, Razelle

2015-01-01

Purpose The impact of a personalized cancer treatment strategy (ie, matching patients with drugs based on specific biomarkers) is still a matter of debate. Methods We reviewed phase II single-agent studies (570 studies; 32,149 patients) published between January 1, 2010, and December 31, 2012 (PubMed search). Response rate (RR), progression-free survival (PFS), and overall survival (OS) were compared for arms that used a personalized strategy versus those that did not. Results Multivariable analysis (both weighted multiple linear regression and random effects meta-regression) demonstrated that the personalized approach, compared with a nonpersonalized approach, consistently and independently correlated with higher median RR (31% v 10.5%, respectively; P < .001) and prolonged median PFS (5.9 v 2.7 months, respectively; P < .001) and OS (13.7 v 8.9 months, respectively; P < .001). Nonpersonalized targeted arms had poorer outcomes compared with either personalized targeted therapy or cytotoxics, with median RR of 4%, 30%, and 11.9%, respectively; median PFS of 2.6, 6.9, and 3.3 months, respectively (all P < .001); and median OS of 8.7, 15.9, and 9.4 months, respectively (all P < .05). Personalized arms using a genomic biomarker had higher median RR and prolonged median PFS and OS (all P ≤ .05) compared with personalized arms using a protein biomarker. A personalized strategy was associated with a lower treatment-related death rate than a nonpersonalized strategy (median, 1.5% v 2.3%, respectively; P < .001). Conclusion Comprehensive analysis of phase II, single-agent arms revealed that, across malignancies, a personalized strategy was an independent predictor of better outcomes and fewer toxic deaths. In addition, nonpersonalized targeted therapies were associated with significantly poorer outcomes than cytotoxic agents, which in turn were worse than personalized targeted therapy. PMID:26304871

"Singing in the Tube"--audiovisual assay of plant oil repellent activity against mosquitoes (Culex pipiens).

PubMed

Adams, Temitope F; Wongchai, Chatchawal; Chaidee, Anchalee; Pfeiffer, Wolfgang

2016-01-01

Plant essential oils have been suggested as a promising alternative to the established mosquito repellent DEET (N,N-diethyl-meta-toluamide). Searching for an assay with generally available equipment, we designed a new audiovisual assay of repellent activity against mosquitoes "Singing in the Tube," testing single mosquitoes in Drosophila cultivation tubes. Statistics with regression analysis should compensate for limitations of simple hardware. The assay was established with female Culex pipiens mosquitoes in 60 experiments, 120-h audio recording, and 2580 estimations of the distance between mosquito sitting position and the chemical. Correlations between parameters of sitting position, flight activity pattern, and flight tone spectrum were analyzed. Regression analysis of psycho-acoustic data of audio files (dB[A]) used a squared and modified sinus function determining wing beat frequency WBF ± SD (357 ± 47 Hz). Application of logistic regression defined the repelling velocity constant. The repelling velocity constant showed a decreasing order of efficiency of plant essential oils: rosemary (Rosmarinus officinalis), eucalyptus (Eucalyptus globulus), lavender (Lavandula angustifolia), citronella (Cymbopogon nardus), tea tree (Melaleuca alternifolia), clove (Syzygium aromaticum), lemon (Citrus limon), patchouli (Pogostemon cablin), DEET, cedar wood (Cedrus atlantica). In conclusion, we suggest (1) disease vector control (e.g., impregnation of bed nets) by eight plant essential oils with repelling velocity superior to DEET, (2) simple mosquito repellency testing in Drosophila cultivation tubes, (3) automated approaches and room surveillance by generally available audio equipment (dB[A]: ISO standard 226), and (4) quantification of repellent activity by parameters of the audiovisual assay defined by correlation and regression analyses.

Models for predicting the mass of lime fruits by some engineering properties.

PubMed

Miraei Ashtiani, Seyed-Hassan; Baradaran Motie, Jalal; Emadi, Bagher; Aghkhani, Mohammad-Hosein

2014-11-01

Grading fruits based on mass is important in packaging and reduces the waste, also increases the marketing value of agricultural produce. The aim of this study was mass modeling of two major cultivars of Iranian limes based on engineering attributes. Models were classified into three: 1-Single and multiple variable regressions of lime mass and dimensional characteristics. 2-Single and multiple variable regressions of lime mass and projected areas. 3-Single regression of lime mass based on its actual volume and calculated volume assumed as ellipsoid and prolate spheroid shapes. All properties considered in the current study were found to be statistically significant (ρ < 0.01). The results indicated that mass modeling of lime based on minor diameter and first projected area are the most appropriate models in the first and the second classifications, respectively. In third classification, the best model was obtained on the basis of the prolate spheroid volume. It was finally concluded that the suitable grading system of lime mass is based on prolate spheroid volume.

Technical note: Fu-Liou-Gu and Corti-Peter model performance evaluation for radiative retrievals from cirrus clouds

NASA Astrophysics Data System (ADS)

Lolli, Simone; Campbell, James R.; Lewis, Jasper R.; Gu, Yu; Welton, Ellsworth J.

2017-06-01

We compare, for the first time, the performance of a simplified atmospheric radiative transfer algorithm package, the Corti-Peter (CP) model, versus the more complex Fu-Liou-Gu (FLG) model, for resolving top-of-the-atmosphere radiative forcing characteristics from single-layer cirrus clouds obtained from the NASA Micro-Pulse Lidar Network database in 2010 and 2011 at Singapore and in Greenbelt, Maryland, USA, in 2012. Specifically, CP simplifies calculation of both clear-sky longwave and shortwave radiation through regression analysis applied to radiative calculations, which contributes significantly to differences between the two. The results of the intercomparison show that differences in annual net top-of-the-atmosphere (TOA) cloud radiative forcing can reach 65 %. This is particularly true when land surface temperatures are warmer than 288 K, where the CP regression analysis becomes less accurate. CP proves useful for first-order estimates of TOA cirrus cloud forcing, but may not be suitable for quantitative accuracy, including the absolute sign of cirrus cloud daytime TOA forcing that can readily oscillate around zero globally.

[The use of nucleolar morphological characteristics of birch seedlings for the assessment of environmental pollution].

PubMed

Karpova, S S; Kalaev, V N; Artiukov, V G; Trofimova, V A; OstashkovaL G, A D; Savko

2006-01-01

Micronucleus frequency in buccal mucosa from the oral cavity of children as well as nucleolar structural characteristics (surface area of single nucleoli as well as their number and type) in the root meristem of seed progeny of birch (Betula pendula Roth) were studied in some districts of Voronezh City and Voronezh Region (Novovoronezh Town, Zemlyansk Village). Similar trends of changes in cytogenetic parameters have been revealed for both subjects. Regression analysis allowed us to generate an equation relating the cytogenetic parameters of birch seed progeny (surface area of single nucleoli) and humans (frequency of micronuclei in buccal mucosa of children). This study can be considered as a result of cytogenetic monitoring of environmental pollution in some areas of Voronezh City and Voronezh Region.

Cardiac troponin I for the prediction of functional recovery and left ventricular remodelling following primary percutaneous coronary intervention for ST-elevation myocardial infarction.

PubMed

Hallén, Jonas; Jensen, Jesper K; Fagerland, Morten W; Jaffe, Allan S; Atar, Dan

2010-12-01

To investigate the ability of cardiac troponin I (cTnI) to predict functional recovery and left ventricular remodelling following primary percutaneous coronary intervention (pPCI) in ST-elevation myocardial infarction (STEMI). Post hoc study extending from randomised controlled trial. 132 patients with STEMI receiving pPCI. Left ventricular ejection fraction (LVEF), end-diastolic and end-systolic volume index (EDVI and ESVI) and changes in these parameters from day 5 to 4 months after the index event. Cardiac magnetic resonance examination performed at 5 days and 4 months for evaluation of LVEF, EDVI and ESVI. cTnI was sampled at 24 and 48 h. In linear regression models adjusted for early (5 days) assessment of LVEF, ESVI and EDVI, single-point cTnI at either 24 or 48 h were independent and strong predictors of changes in LVEF (p<0.01), EDVI (p<0.01) and ESVI (p<0.01) during the follow-up period. In a logistic regression analysis for prediction of an LVEF below 40% at 4 months, single-point cTnI significantly improved the prognostic strength of the model (area under the curve = 0.94, p<0.01) in comparison with the combination of clinical variables and LVEF at 5 days. Single-point sampling of cTnI after pPCI for STEMI provides important prognostic information on the time-dependent evolution of left ventricular function and volumes.

Regression of altitude-produced cardiac hypertrophy.

NASA Technical Reports Server (NTRS)

Sizemore, D. A.; Mcintyre, T. W.; Van Liere, E. J.; Wilson , M. F.

1973-01-01

The rate of regression of cardiac hypertrophy with time has been determined in adult male albino rats. The hypertrophy was induced by intermittent exposure to simulated high altitude. The percentage hypertrophy was much greater (46%) in the right ventricle than in the left (16%). The regression could be adequately fitted to a single exponential function with a half-time of 6.73 plus or minus 0.71 days (90% CI). There was no significant difference in the rates of regression for the two ventricles.

Across-Platform Imputation of DNA Methylation Levels Incorporating Nonlocal Information Using Penalized Functional Regression.

PubMed

Zhang, Guosheng; Huang, Kuan-Chieh; Xu, Zheng; Tzeng, Jung-Ying; Conneely, Karen N; Guan, Weihua; Kang, Jian; Li, Yun

2016-05-01

DNA methylation is a key epigenetic mark involved in both normal development and disease progression. Recent advances in high-throughput technologies have enabled genome-wide profiling of DNA methylation. However, DNA methylation profiling often employs different designs and platforms with varying resolution, which hinders joint analysis of methylation data from multiple platforms. In this study, we propose a penalized functional regression model to impute missing methylation data. By incorporating functional predictors, our model utilizes information from nonlocal probes to improve imputation quality. Here, we compared the performance of our functional model to linear regression and the best single probe surrogate in real data and via simulations. Specifically, we applied different imputation approaches to an acute myeloid leukemia dataset consisting of 194 samples and our method showed higher imputation accuracy, manifested, for example, by a 94% relative increase in information content and up to 86% more CpG sites passing post-imputation filtering. Our simulated association study further demonstrated that our method substantially improves the statistical power to identify trait-associated methylation loci. These findings indicate that the penalized functional regression model is a convenient and valuable imputation tool for methylation data, and it can boost statistical power in downstream epigenome-wide association study (EWAS). © 2016 WILEY PERIODICALS, INC.

Signal Analysis Algorithms for Optimized Fitting of Nonresonant Laser Induced Thermal Acoustics Damped Sinusoids

NASA Technical Reports Server (NTRS)

Balla, R. Jeffrey; Miller, Corey A.

2008-01-01

This study seeks a numerical algorithm which optimizes frequency precision for the damped sinusoids generated by the nonresonant LITA technique. It compares computed frequencies, frequency errors, and fit errors obtained using five primary signal analysis methods. Using variations on different algorithms within each primary method, results from 73 fits are presented. Best results are obtained using an AutoRegressive method. Compared to previous results using Prony s method, single shot waveform frequencies are reduced approx.0.4% and frequency errors are reduced by a factor of approx.20 at 303K to approx. 0.1%. We explore the advantages of high waveform sample rates and potential for measurements in low density gases.

Validation and comparison of two methods to assess human energy expenditure during free-living activities.

PubMed

Anastasopoulou, Panagiota; Tubic, Mirnes; Schmidt, Steffen; Neumann, Rainer; Woll, Alexander; Härtel, Sascha

2014-01-01

The measurement of activity energy expenditure (AEE) via accelerometry is the most commonly used objective method for assessing human daily physical activity and has gained increasing importance in the medical, sports and psychological science research in recent years. The purpose of this study was to determine which of the following procedures is more accurate to determine the energy cost during the most common everyday life activities; a single regression or an activity based approach. For this we used a device that utilizes single regression models (GT3X, ActiGraph Manufacturing Technology Inc., FL., USA) and a device using activity-dependent calculation models (move II, movisens GmbH, Karlsruhe, Germany). Nineteen adults (11 male, 8 female; 30.4±9.0 years) wore the activity monitors attached to the waist and a portable indirect calorimeter (IC) as reference measure for AEE while performing several typical daily activities. The accuracy of the two devices for estimating AEE was assessed as the mean differences between their output and the reference and evaluated using Bland-Altman analysis. The GT3X overestimated the AEE of walking (GT3X minus reference, 1.26 kcal/min), walking fast (1.72 kcal/min), walking up-/downhill (1.45 kcal/min) and walking upstairs (1.92 kcal/min) and underestimated the AEE of jogging (-1.30 kcal/min) and walking upstairs (-2.46 kcal/min). The errors for move II were smaller than those for GT3X for all activities. The move II overestimated AEE of walking (move II minus reference, 0.21 kcal/min), walking up-/downhill (0.06 kcal/min) and stair walking (upstairs: 0.13 kcal/min; downstairs: 0.29 kcal/min) and underestimated AEE of walking fast (-0.11 kcal/min) and jogging (-0.93 kcal/min). Our data suggest that the activity monitor using activity-dependent calculation models is more appropriate for predicting AEE in daily life than the activity monitor using a single regression model.

Analysis of radiological parameters associated with decreased fractional anisotropy values on diffusion tensor imaging in patients with lumbar spinal stenosis.

PubMed

Wang, Xiandi; Wang, Hongli; Sun, Chi; Zhou, Shuyi; Meng, Tao; Lv, Feizhou; Ma, Xiaosheng; Xia, Xinlei; Jiang, Jianyuan

2018-04-26

Previous studies have indicated that decreased fractional anisotropy (FA) values on diffusion tensor imaging (DTI) are well correlated with the symptoms of nerve root compression. The aim of our study is to determine primary radiological parameters associated with decreased FA values in patients with lumbar spinal stenosis involving single L5 nerve root. Patients confirmed with single L5 nerve root compression by transforaminal nerve root blocks were included in this study. FA values of L5 nerve roots on both symptomatic and asymptomatic side were obtained. Conventional radiological parameters, such as disc height, degenerative scoliosis, dural sac cross-sectional area (DSCSA), foraminal height (FH), hypertrophic facet joint degeneration (HFJD), sagittal rotation (SR), sedimentation sign, sagittal translation and traction spur were measured. Correlation and regression analyses were performed between the radiological parameters and FA values of the symptomatic L5 nerve roots. A predictive regression equation was established. Twenty-one patients were included in this study. FA values were significantly lower at the symptomatic side comparing to the asymptomatic side (0.263 ± 0.069 vs. 0.334 ± 0.080, P = 0.038). DSCSA, FH, HFJD, and SR were significantly correlated with the decreased FA values, with r = 0.518, 0.443, 0.472 and - 0.910, respectively (P < 0.05). DSCSA and SR were found to be the primary radiological parameters related to the decreased FA values, and the regression equation is FA = - 0.012 × SR + 0.002 × DSCSA. DSCSA and SR were primary contributors to decreased FA values in LSS patients involving single L5 nerve root, indicating that central canal decompression and segmental stability should be the first considerations in preoperative planning of these patients. These slides can be retrieved under Electronic Supplementary Material.

Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math.

PubMed

Raizada, Rajeev D S; Tsao, Feng-Ming; Liu, Huei-Mei; Holloway, Ian D; Ansari, Daniel; Kuhl, Patricia K

2010-05-15

A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain-behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain-behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain-behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects' behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math

PubMed Central

Raizada, Rajeev D.S.; Tsao, Feng-Ming; Liu, Huei-Mei; Holloway, Ian D.; Ansari, Daniel; Kuhl, Patricia K.

2010-01-01

A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain–behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain–behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain–behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects' behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct. PMID:20132896

Hepatitis B virus mutation may play a role in hepatocellular carcinoma recurrence: A systematic review and meta-regression analysis.

PubMed

Zhou, Hua-ying; Luo, Yue; Chen, Wen-dong; Gong, Guo-zhong

2015-06-01

A number of studies have confirmed that antiviral therapy with nucleotide analogs (NAs) can improve the prognosis of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) after curative therapy. However, what factors affected the prognosis of HBV-HCC after removal of the primary tumor and inhibition of HBV replication? A meta-regression analysis was conducted to explore the prognostic factor for this subgroup of patients. MEDLINE, EMBASE, Web of Science, and Cochrane library were searched from January 1995 to February 2014 for clinical trials evaluating the effect of NAs on the prognosis of HBV-HCC after curative therapy. Data were extracted for host, viral, and intervention information. Single-arm meta-analysis was performed to assess overall survival (OS) rates and HCC recurrence. Meta-regression analysis was carried out to explore risk factors for 1-year OS rate and HCC recurrence for HBV-HCC patients after curative therapy and antiviral therapy. Fourteen observational studies with 1284 patients met the inclusion criteria. Influential factors for prognosis of HCC were mainly baseline HBeAg positivity, cirrhotic stage, advanced Tumor-Node-Metastasis (TNM) stage, macrovascular invasion, and antiviral agent type. The 1-year OS rate decreased by more than four times (coefficient -4.45, P<0.001) and the 1-year HCC recurrence increased by more than one time (coefficient 1.20, P=0.003) when lamivudine was chosen for HCC after curative therapy, relative to entecavir for HCC. HBV mutation may play a role in HCC recurrence. Entecavir or tenofovir, a high genetic barrier to resistance, should be recommended for HBV-HCC patients. © 2015 The Authors. Journal of Gastroenterology and Hepatology published by Journal of Gastroenterology and Hepatology Foundation and Wiley Publishing Asia Pty Ltd.

Collaborative Chronic Care Models for Mental Health Conditions: Cumulative Meta-Analysis and Meta-Regression to Guide Future Research and Implementation

PubMed Central

Grogan-Kaylor, Andrew; Perron, Brian E.; Kilbourne, Amy M.; Woltmann, Emily; Bauer, Mark S.

2013-01-01

Objective Prior meta-analysis indicates that collaborative chronic care models (CCMs) improve mental and physical health outcomes for individuals with mental disorders. This study aimed to investigate the stability of evidence over time and identify patient and intervention factors associated with CCM effects in order to facilitate implementation and sustainability of CCMs in clinical practice. Method We reviewed 53 CCM trials that analyzed depression, mental quality of life (QOL), or physical QOL outcomes. Cumulative meta-analysis and meta-regression were supplemented by descriptive investigations across and within trials. Results Most trials targeted depression in the primary care setting, and cumulative meta-analysis indicated that effect sizes favoring CCM quickly achieved significance for depression outcomes, and more recently achieved significance for mental and physical QOL. Four of six CCM elements (patient self-management support, clinical information systems, system redesign, and provider decision support) were common among reviewed trials, while two elements (healthcare organization support and linkages to community resources) were rare. No single CCM element was statistically associated with the success of the model. Similarly, meta-regression did not identify specific factors associated with CCM effectiveness. Nonetheless, results within individual trials suggest that increased illness severity predicts CCM outcomes. Conclusions Significant CCM trials have been derived primarily from four original CCM elements. Nonetheless, implementing and sustaining this established model will require healthcare organization support. While CCMs have typically been tested as population-based interventions, evidence supports stepped care application to more severely ill individuals. Future priorities include developing implementation strategies to support adoption and sustainability of the model in clinical settings while maximizing fit of this multi-component framework to local contextual factors. PMID:23938600

Detection of gene-environment interactions in the presence of linkage disequilibrium and noise by using genetic risk scores with internal weights from elastic net regression.

PubMed

Hüls, Anke; Ickstadt, Katja; Schikowski, Tamara; Krämer, Ursula

2017-06-12

For the analysis of gene-environment (GxE) interactions commonly single nucleotide polymorphisms (SNPs) are used to characterize genetic susceptibility, an approach that mostly lacks power and has poor reproducibility. One promising approach to overcome this problem might be the use of weighted genetic risk scores (GRS), which are defined as weighted sums of risk alleles of gene variants. The gold-standard is to use external weights from published meta-analyses. In this study, we used internal weights from the marginal genetic effects of the SNPs estimated by a multivariate elastic net regression and thereby provided a method that can be used if there are no external weights available. We conducted a simulation study for the detection of GxE interactions and compared power and type I error of single SNPs analyses with Bonferroni correction and corresponding analysis with unweighted and our weighted GRS approach in scenarios with six risk SNPs and an increasing number of highly correlated (up to 210) and noise SNPs (up to 840). Applying weighted GRS increased the power enormously in comparison to the common single SNPs approach (e.g. 94.2% vs. 35.4%, respectively, to detect a weak interaction with an OR ≈ 1.04 for six uncorrelated risk SNPs and n = 700 with a well-controlled type I error). Furthermore, weighted GRS outperformed the unweighted GRS, in particular in the presence of SNPs without any effect on the phenotype (e.g. 90.1% vs. 43.9%, respectively, when 20 noise SNPs were added to the six risk SNPs). This outperforming of the weighted GRS was confirmed in a real data application on lung inflammation in the SALIA cohort (n = 402). However, in scenarios with a high number of noise SNPs (>200 vs. 6 risk SNPs), larger sample sizes are needed to avoid an increased type I error, whereas a high number of correlated SNPs can be handled even in small samples (e.g. n = 400). In conclusion, weighted GRS with weights from the marginal genetic effects of the SNPs estimated by a multivariate elastic net regression were shown to be a powerful tool to detect gene-environment interactions in scenarios of high Linkage disequilibrium and noise.

Prediction of random-regression coefficient for daily milk yield after 305 days in milk by using the regression-coefficient estimates from the first 305 days.

PubMed

Yamazaki, Takeshi; Takeda, Hisato; Hagiya, Koichi; Yamaguchi, Satoshi; Sasaki, Osamu

2018-03-13

Because lactation periods in dairy cows lengthen with increasing total milk production, it is important to predict individual productivities after 305 days in milk (DIM) to determine the optimal lactation period. We therefore examined whether the random regression (RR) coefficient from 306 to 450 DIM (M2) can be predicted from those during the first 305 DIM (M1) by using a random regression model. We analyzed test-day milk records from 85690 Holstein cows in their first lactations and 131727 cows in their later (second to fifth) lactations. Data in M1 and M2 were analyzed separately by using different single-trait RR animal models. We then performed a multiple regression analysis of the RR coefficients of M2 on those of M1 during the first and later lactations. The first-order Legendre polynomials were practical covariates of random regression for the milk yields of M2. All RR coefficients for the additive genetic (AG) effect and the intercept for the permanent environmental (PE) effect of M2 had moderate to strong correlations with the intercept for the AG effect of M1. The coefficients of determination for multiple regression of the combined intercepts for the AG and PE effects of M2 on the coefficients for the AG effect of M1 were moderate to high. The daily milk yields of M2 predicted by using the RR coefficients for the AG effect of M1 were highly correlated with those obtained by using the coefficients of M2. Milk production after 305 DIM can be predicted by using the RR coefficient estimates of the AG effect during the first 305 DIM.

A fuzzy adaptive network approach to parameter estimation in cases where independent variables come from an exponential distribution

NASA Astrophysics Data System (ADS)

Dalkilic, Turkan Erbay; Apaydin, Aysen

2009-11-01

In a regression analysis, it is assumed that the observations come from a single class in a data cluster and the simple functional relationship between the dependent and independent variables can be expressed using the general model; Y=f(X)+[epsilon]. However; a data cluster may consist of a combination of observations that have different distributions that are derived from different clusters. When faced with issues of estimating a regression model for fuzzy inputs that have been derived from different distributions, this regression model has been termed the [`]switching regression model' and it is expressed with . Here li indicates the class number of each independent variable and p is indicative of the number of independent variables [J.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transaction on Systems, Man and Cybernetics 23 (3) (1993) 665-685; M. Michel, Fuzzy clustering and switching regression models using ambiguity and distance rejects, Fuzzy Sets and Systems 122 (2001) 363-399; E.Q. Richard, A new approach to estimating switching regressions, Journal of the American Statistical Association 67 (338) (1972) 306-310]. In this study, adaptive networks have been used to construct a model that has been formed by gathering obtained models. There are methods that suggest the class numbers of independent variables heuristically. Alternatively, in defining the optimal class number of independent variables, the use of suggested validity criterion for fuzzy clustering has been aimed. In the case that independent variables have an exponential distribution, an algorithm has been suggested for defining the unknown parameter of the switching regression model and for obtaining the estimated values after obtaining an optimal membership function, which is suitable for exponential distribution.

Retention modelling of polychlorinated biphenyls in comprehensive two-dimensional gas chromatography.

PubMed

D'Archivio, Angelo Antonio; Incani, Angela; Ruggieri, Fabrizio

2011-01-01

In this paper, we use a quantitative structure-retention relationship (QSRR) method to predict the retention times of polychlorinated biphenyls (PCBs) in comprehensive two-dimensional gas chromatography (GC×GC). We analyse the GC×GC retention data taken from the literature by comparing predictive capability of different regression methods. The various models are generated using 70 out of 209 PCB congeners in the calibration stage, while their predictive performance is evaluated on the remaining 139 compounds. The two-dimensional chromatogram is initially estimated by separately modelling retention times of PCBs in the first and in the second column ((1) t (R) and (2) t (R), respectively). In particular, multilinear regression (MLR) combined with genetic algorithm (GA) variable selection is performed to extract two small subsets of predictors for (1) t (R) and (2) t (R) from a large set of theoretical molecular descriptors provided by the popular software Dragon, which after removal of highly correlated or almost constant variables consists of 237 structure-related quantities. Based on GA-MLR analysis, a four-dimensional and a five-dimensional relationship modelling (1) t (R) and (2) t (R), respectively, are identified. Single-response partial least square (PLS-1) regression is alternatively applied to independently model (1) t (R) and (2) t (R) without the need for preliminary GA variable selection. Further, we explore the possibility of predicting the two-dimensional chromatogram of PCBs in a single calibration procedure by using a two-response PLS (PLS-2) model or a feed-forward artificial neural network (ANN) with two output neurons. In the first case, regression is carried out on the full set of 237 descriptors, while the variables previously selected by GA-MLR are initially considered as ANN inputs and subjected to a sensitivity analysis to remove the redundant ones. Results show PLS-1 regression exhibits a noticeably better descriptive and predictive performance than the other investigated approaches. The observed values of determination coefficients for (1) t (R) and (2) t (R) in calibration (0.9999 and 0.9993, respectively) and prediction (0.9987 and 0.9793, respectively) provided by PLS-1 demonstrate that GC×GC behaviour of PCBs is properly modelled. In particular, the predicted two-dimensional GC×GC chromatogram of 139 PCBs not involved in the calibration stage closely resembles the experimental one. Based on the above lines of evidence, the proposed approach ensures accurate simulation of the whole GC×GC chromatogram of PCBs using experimental determination of only 1/3 retention data of representative congeners.

A reliable and cost effective approach for radiographic monitoring in nutritional rickets.

PubMed

Chatterjee, D; Gupta, V; Sharma, V; Sinha, B; Samanta, S

2014-04-01

Radiological scoring is particularly useful in rickets, where pre-treatment radiographical findings can reflect the disease severity and can be used to monitor the improvement. However, there is only a single radiographic scoring system for rickets developed by Thacher and, to the best of our knowledge, no study has evaluated radiographic changes in rickets based on this scoring system apart from the one done by Thacher himself. The main objective of this study is to compare and analyse the pre-treatment and post-treatment radiographic parameters in nutritional rickets with the help of Thacher's scoring technique. 176 patients with nutritional rickets were given a single intramuscular injection of vitamin D (600 000 IU) along with oral calcium (50 mg kg(-1)) and vitamin D (400 IU per day) until radiological resolution and followed for 1 year. Pre- and post-treatment radiological parameters were compared and analysed statistically based on Thacher's scoring system. Radiological resolution was complete by 6 months. Time for radiological resolution and initial radiological score were linearly associated on regression analysis. The distal ulna was the last to heal in most cases except when the initial score was 10, when distal femur was the last to heal. Thacher's scoring system can effectively monitor nutritional rickets. The formula derived through linear regression has prognostic significance. The distal femur is a better indicator in radiologically severe rickets and when resolution is delayed. Thacher's scoring is very useful for monitoring of rickets. The formula derived through linear regression can predict the expected time for radiological resolution.

Dual regression physiological modeling of resting-state EPI power spectra: Effects of healthy aging.

PubMed

Viessmann, Olivia; Möller, Harald E; Jezzard, Peter

2018-02-02

Aging and disease-related changes in the arteriovasculature have been linked to elevated levels of cardiac cycle-induced pulsatility in the cerebral microcirculation. Functional magnetic resonance imaging (fMRI), acquired fast enough to unalias the cardiac frequency contributions, can be used to study these physiological signals in the brain. Here, we propose an iterative dual regression analysis in the frequency domain to model single voxel power spectra of echo planar imaging (EPI) data using external recordings of the cardiac and respiratory cycles as input. We further show that a data-driven variant, without external physiological traces, produces comparable results. We use this framework to map and quantify cardiac and respiratory contributions in healthy aging. We found a significant increase in the spatial extent of cardiac modulated white matter voxels with age, whereas the overall strength of cardiac-related EPI power did not show an age effect. Copyright © 2018. Published by Elsevier Inc.

On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP.

PubMed

Winkler, Irene; Debener, Stefan; Müller, Klaus-Robert; Tangermann, Michael

2015-01-01

Standard artifact removal methods for electroencephalographic (EEG) signals are either based on Independent Component Analysis (ICA) or they regress out ocular activity measured at electrooculogram (EOG) channels. Successful ICA-based artifact reduction relies on suitable pre-processing. Here we systematically evaluate the effects of high-pass filtering at different frequencies. Offline analyses were based on event-related potential data from 21 participants performing a standard auditory oddball task and an automatic artifactual component classifier method (MARA). As a pre-processing step for ICA, high-pass filtering between 1-2 Hz consistently produced good results in terms of signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of `near-dipolar' ICA components. Relative to no artifact reduction, ICA-based artifact removal significantly improved SNR and classification accuracy. This was not the case for a regression-based approach to remove EOG artifacts.

Prediction of elemental creep. [steady state and cyclic data from regression analysis

NASA Technical Reports Server (NTRS)

Davis, J. W.; Rummler, D. R.

1975-01-01

Cyclic and steady-state creep tests were performed to provide data which were used to develop predictive equations. These equations, describing creep as a function of stress, temperature, and time, were developed through the use of a least squares regression analyses computer program for both the steady-state and cyclic data sets. Comparison of the data from the two types of tests, revealed that there was no significant difference between the cyclic and steady-state creep strains for the L-605 sheet under the experimental conditions investigated (for the same total time at load). Attempts to develop a single linear equation describing the combined steady-state and cyclic creep data resulted in standard errors of estimates higher than obtained for the individual data sets. A proposed approach to predict elemental creep in metals uses the cyclic creep equation and a computer program which applies strain and time hardening theories of creep accumulation.

Influence of salinity and temperature on acute toxicity of cadmium to Mysidopsis bahia molenock

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

Voyer, R.A.; Modica, G.

1990-01-01

Acute toxicity tests were conducted to compare estimates of toxicity, as modified by salinity and temperature, based on response surface techniques with those derived using conventional test methods, and to compare effect of a single episodic exposure to cadmium as a function of salinity with that of continuous exposure. Regression analysis indicated that mortality following continuous 96-hr exposure is related to linear and quadratic effects of salinity and cadmium at 20 C, and to the linear and quadratic effects of cadmium only at 25C. LC50s decreased with increases in temperature and decreases in salinity. Based on the regression model developed,more » 96-hr LC50s ranged from 15.5 to 28.0 micro Cd/L at 10 and 30% salinities, respectively, at 25C; and from 47 to 85 microgram Cd/L at these salinities at 20C.« less

Mapping Regional Impervious Surface Distribution from Night Time Light: The Variability across Global Cities

NASA Astrophysics Data System (ADS)

Lin, M.; Yang, Z.; Park, H.; Qian, S.; Chen, J.; Fan, P.

2017-12-01

Impervious surface area (ISA) has become an important indicator for studying urban environments, but mapping ISA at the regional or global scale is still challenging due to the complexity of impervious surface features. The Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light data is (NTL) and Resolution Imaging Spectroradiometer (MODIS) are the major remote sensing data source for regional ISA mapping. A single regression relationship between fractional ISA and NTL or various index derived based on NTL and MODIS vegetation index (NDVI) data was established in many previous studies for regional ISA mapping. However, due to the varying geographical, climatic, and socio-economic characteristics of different cities, the same regression relationship may vary significantly across different cities in the same region in terms of both fitting performance (i.e. R2) and the rate of change (Slope). In this study, we examined the regression relationship between fractional ISA and Vegetation Adjusted Nighttime light Urban Index (VANUI) for 120 randomly selected cities around the world with a multilevel regression model. We found that indeed there is substantial variability of both the R2 (0.68±0.29) and slopes (0.64±0.40) among individual regressions, which suggests that multilevel/hierarchical models are needed for accuracy improvement of future regional ISA mapping .Further analysis also let us find the this substantial variability are affected by climate conditions, socio-economic status, and urban spatial structures. However, all these effects are nonlinear rather than linear, thus could not modeled explicitly in multilevel linear regression models.

Alternative methods for CYP2D6 phenotyping: comparison of dextromethorphan metabolic ratios from AUC, single point plasma, and urine.

PubMed

Chen, Rui; Wang, Haotian; Shi, Jun; Hu, Pei

2016-05-01

CYP2D6 is a high polymorphic enzyme. Determining its phenotype before CYP2D6 substrate treatment can avoid dose-dependent adverse events or therapeutic failures. Alternative phenotyping methods of CYP2D6 were compared to aluate the appropriate and precise time points for phenotyping after single-dose and ultiple-dose of 30-mg controlled-release (CR) dextromethorphan (DM) and to explore the antimodes for potential sampling methods. This was an open-label, single and multiple-dose study. 21 subjects were assigned to receive a single dose of CR DM 30 mg orally, followed by a 3-day washout period prior to oral administration of CR DM 30 mg every 12 hours for 6 days. Metabolic ratios (MRs) from AUC∞ after single dosing and from AUC0-12h at steady state were taken as the gold standard. The correlations of metabolic ratios of DM to dextrorphan (MRDM/DX) values based on different phenotyping methods were assessed. Linear regression formulas were derived to calculate the antimodes for potential sample methods. In the single-dose part of the study statistically significant correlations were found between MRDM/DX from AUC∞ and from serial plasma points from 1 to 30 hours or from urine (all p-values < 0.001). In the multiple-dose part, statistically significant correlations were found between MRDM/DX from AUC0-12h on day 6 and MRDM/DX from serial plasma points from 0 to 36 hours after the last dosing (all p-values < 0.001). Based on reported urinary antimode and linear regression analysis, the antimodes of AUC and plasma points were derived to profile the trend of antimodes as the drug concentrations changed. MRDM/DX from plasma points had good correlations with MRDM/DX from AUC. Plasma points from 1 to 30 hours after single dose of 30-mg CR DM and any plasma point at steady state after multiple doses of CR DM could potentially be used for phenotyping of CYP2D6.

The use of segmented regression in analysing interrupted time series studies: an example in pre-hospital ambulance care.

PubMed

Taljaard, Monica; McKenzie, Joanne E; Ramsay, Craig R; Grimshaw, Jeremy M

2014-06-19

An interrupted time series design is a powerful quasi-experimental approach for evaluating effects of interventions introduced at a specific point in time. To utilize the strength of this design, a modification to standard regression analysis, such as segmented regression, is required. In segmented regression analysis, the change in intercept and/or slope from pre- to post-intervention is estimated and used to test causal hypotheses about the intervention. We illustrate segmented regression using data from a previously published study that evaluated the effectiveness of a collaborative intervention to improve quality in pre-hospital ambulance care for acute myocardial infarction (AMI) and stroke. In the original analysis, a standard regression model was used with time as a continuous variable. We contrast the results from this standard regression analysis with those from segmented regression analysis. We discuss the limitations of the former and advantages of the latter, as well as the challenges of using segmented regression in analysing complex quality improvement interventions. Based on the estimated change in intercept and slope from pre- to post-intervention using segmented regression, we found insufficient evidence of a statistically significant effect on quality of care for stroke, although potential clinically important effects for AMI cannot be ruled out. Segmented regression analysis is the recommended approach for analysing data from an interrupted time series study. Several modifications to the basic segmented regression analysis approach are available to deal with challenges arising in the evaluation of complex quality improvement interventions.

Association between norepinephrine transporter gene (SLC6A2) polymorphisms and suicide in patients with major depressive disorder.

PubMed

Kim, Yong-Ku; Hwang, Jung-A; Lee, Heon-Jeong; Yoon, Ho-Kyoung; Ko, Young-Hoon; Lee, Bun-Hee; Jung, Han-Yong; Hahn, Sang-Woo; Na, Kyoung-Sae

2014-04-01

Although several studies have investigated possible associations between norepinephrine neurotransmitter transporter gene (SLC6A2) polymorphisms and depression, few studies have examined associations between SLC6A2 polymorphisms and suicide. Three single-nucleotide polymorphisms (rs2242446, rs28386840, and rs5569) were measured in 550 patients: 201 with major depressive disorder (MDD) and suicide attempt/s, 160 with MDD without suicide attempts, and 189 healthy controls. Analysis of single-nucleotide polymorphisms (SNPs) and haplotype was conducted for the three groups. Subsequently, multivariate logistic regression analysis adjusting for age and gender was conducted to identify independent influences of each SNP. A possible association between suicide lethality and SLC6A2 polymorphisms was also investigated. In the genotype and allele frequency analysis, there were significant differences in rs28386840 between suicidal MDD patients and healthy controls. In the haplotype analysis, TAA (rs2242446-rs28386840-rs5569, from left to right) was associated with suicide attempts in MDD, although the significance (p=0.043) disappeared after Bonferroni correction. There were no relationships between lethality scores and SLC6A2 polymorphisms in suicidal MDD. Modest sample size and a single type of neurotransmitter analyzed (norepinephrine) are the primary limitations. Our results suggest that SLC6A2 polymorphisms were associated with suicide risk in patients with MDD. Future studies are warranted to elucidate possible mechanisms by which SLC6A2 polymorphisms influence suicide risk. Copyright © 2014 Elsevier B.V. All rights reserved.

Standardized Regression Coefficients as Indices of Effect Sizes in Meta-Analysis

ERIC Educational Resources Information Center

Kim, Rae Seon

2011-01-01

When conducting a meta-analysis, it is common to find many collected studies that report regression analyses, because multiple regression analysis is widely used in many fields. Meta-analysis uses effect sizes drawn from individual studies as a means of synthesizing a collection of results. However, indices of effect size from regression analyses…

General Framework for Meta-analysis of Rare Variants in Sequencing Association Studies

PubMed Central

Lee, Seunggeun; Teslovich, Tanya M.; Boehnke, Michael; Lin, Xihong

2013-01-01

We propose a general statistical framework for meta-analysis of gene- or region-based multimarker rare variant association tests in sequencing association studies. In genome-wide association studies, single-marker meta-analysis has been widely used to increase statistical power by combining results via regression coefficients and standard errors from different studies. In analysis of rare variants in sequencing studies, region-based multimarker tests are often used to increase power. We propose meta-analysis methods for commonly used gene- or region-based rare variants tests, such as burden tests and variance component tests. Because estimation of regression coefficients of individual rare variants is often unstable or not feasible, the proposed method avoids this difficulty by calculating score statistics instead that only require fitting the null model for each study and then aggregating these score statistics across studies. Our proposed meta-analysis rare variant association tests are conducted based on study-specific summary statistics, specifically score statistics for each variant and between-variant covariance-type (linkage disequilibrium) relationship statistics for each gene or region. The proposed methods are able to incorporate different levels of heterogeneity of genetic effects across studies and are applicable to meta-analysis of multiple ancestry groups. We show that the proposed methods are essentially as powerful as joint analysis by directly pooling individual level genotype data. We conduct extensive simulations to evaluate the performance of our methods by varying levels of heterogeneity across studies, and we apply the proposed methods to meta-analysis of rare variant effects in a multicohort study of the genetics of blood lipid levels. PMID:23768515

Combining synthetic controls and interrupted time series analysis to improve causal inference in program evaluation.

PubMed

Linden, Ariel

2018-04-01

Interrupted time series analysis (ITSA) is an evaluation methodology in which a single treatment unit's outcome is studied over time and the intervention is expected to "interrupt" the level and/or trend of the outcome. The internal validity is strengthened considerably when the treated unit is contrasted with a comparable control group. In this paper, we introduce a robust evaluation framework that combines the synthetic controls method (SYNTH) to generate a comparable control group and ITSA regression to assess covariate balance and estimate treatment effects. We evaluate the effect of California's Proposition 99 for reducing cigarette sales, by comparing California to other states not exposed to smoking reduction initiatives. SYNTH is used to reweight nontreated units to make them comparable to the treated unit. These weights are then used in ITSA regression models to assess covariate balance and estimate treatment effects. Covariate balance was achieved for all but one covariate. While California experienced a significant decrease in the annual trend of cigarette sales after Proposition 99, there was no statistically significant treatment effect when compared to synthetic controls. The advantage of using this framework over regression alone is that it ensures that a comparable control group is generated. Additionally, it offers a common set of statistical measures familiar to investigators, the capability for assessing covariate balance, and enhancement of the evaluation with a comprehensive set of postestimation measures. Therefore, this robust framework should be considered as a primary approach for evaluating treatment effects in multiple group time series analysis. © 2018 John Wiley & Sons, Ltd.

Marital status as a predictor of survival in patients with human papilloma virus-positive oropharyngeal cancer.

PubMed

Rubin, Samuel J; Kirke, Diana N; Ezzat, Waleed H; Truong, Minh T; Salama, Andrew R; Jalisi, Scharukh

Determine whether marital status is a significant predictor of survival in human papillomavirus-positive oropharyngeal cancer. A single center retrospective study included patients diagnosed with human papilloma virus-positive oropharyngeal cancer at Boston Medical Center between January 1, 2010 and December 30, 2015, and initiated treatment with curative intent at Boston Medical Center. Demographic data and tumor-related variables were recorded. Univariate analysis was performed using a two-sample t-test, chi-squared test, Fisher's exact test, and Kaplan Meier curves with a log rank test. Multivariate survival analysis was performed using a Cox regression model. A total of 65 patients were included in the study with 24 patients described as married and 41 patients described as single. There was no significant difference in most demographic variables or tumor related variables between the two study groups, except single patients were significantly more likely to have government insurance (p=0.0431). Furthermore, there was no significant difference in 3-year overall survival between married patients and single patients (married=91.67% vs single=87.80%; p=0.6532) or 3-year progression free survival (married=79.17% vs single=85.37%; p=0.8136). After adjusting for confounders including age, sex, race, insurance type, smoking status, treatment, and AJCC combined pathologic stage, marital status was not a significant predictor of survival [HR=0.903; 95% CI (0.126,6.489); p=0.9192]. Although previous literature has demonstrated that married patients with head and neck cancer have a survival benefit compared to single patients with head and neck cancer, we were unable to demonstrate the same survival benefit in a cohort of patients with human papilloma virus-positive oropharyngeal cancer. Copyright © 2017 Elsevier Inc. All rights reserved.

Quadriceps force and anterior tibial force occur obviously later than vertical ground reaction force: a simulation study.

PubMed

Ueno, Ryo; Ishida, Tomoya; Yamanaka, Masanori; Taniguchi, Shohei; Ikuta, Ryohei; Samukawa, Mina; Saito, Hiroshi; Tohyama, Harukazu

2017-11-18

Although it is well known that quadriceps force generates anterior tibial force, it has been unclear whether quadriceps force causes great anterior tibial force during the early phase of a landing task. The purpose of the present study was to examine whether the quadriceps force induced great anterior tibial force during the early phase of a landing task. Fourteen young, healthy, female subjects performed a single-leg landing task. Muscle force and anterior tibial force were estimated from motion capture data and synchronized force data from the force plate. One-way repeated measures analysis of variance and the post hoc Bonferroni test were conducted to compare the peak time of the vertical ground reaction force, quadriceps force and anterior tibial force during the single-leg landing. In addition, we examined the contribution of vertical and posterior ground reaction force, knee flexion angle and moment to peak quadriceps force using multiple linear regression. The peak times of the estimated quadriceps force (96.0 ± 23.0 ms) and anterior tibial force (111.9 ± 18.9 ms) were significantly later than that of the vertical ground reaction force (63.5 ± 6.8 ms) during the single-leg landing. The peak quadriceps force was positively correlated with the peak anterior tibial force (R = 0.953, P < 0.001). Multiple linear regression analysis showed that the peak knee flexion moment contributed significantly to the peak quadriceps force (R 2  = 0.778, P < 0.001). The peak times of the quadriceps force and the anterior tibial force were obviously later than that of the vertical ground reaction force for the female athletes during successful single-leg landings. Studies have reported that the peak time of the vertical ground reaction force was close to the time of anterior cruciate ligament (ACL) disruption in ACL injury cases. It is possible that early contraction of the quadriceps during landing might induce ACL disruption as a result of excessive anterior tibial force in unanticipated situations in ACL injury cases.

Is It Attachment Style or Socio-Demography: Singlehood in a Representative Sample.

PubMed

Petrowski, Katja; Schurig, Susan; Schmutzer, Gabriele; Brähler, Elmar; Stöbel-Richter, Yve

2015-01-01

Since the percentage of single adults is steadily increasing, the reasons for this development have become a matter of growing interest. Hereby, an individual's attachment style may have a connection to the partnership status. In the following analysis, attachment style, gender, age, education, and income were compared in regard to the partnership status. Furthermore, an analysis of variance was computed to compare the attachment style within different groups. In 2012, a sample of 1,676 representative participants was used. The participants were aged 18 to 60 (M = 41.0, SD = 12.3); 54% of the sample were female, and 40% were single. Attachment-related attitudes were assessed with the German version of the adult attachment scale (AAS). Single adult males did not show a more anxious attachment style than single adult females or females in relationships. Younger, i.e., 18 to 30 years old, paired individuals showed greater attachment anxiety than single individuals, whereby single individuals between the ages of 31 to 45 showed greater attachment anxiety than individuals in relationships. In addition, single individuals more frequently had obtained their high school diploma in contrast to individuals in relationships. Concerning attachment style, the individuals who had not completed their high school diploma showed less faith in others independent of singlehood or being in a relationship. Concerning age, older single individuals, i.e., 46 to 60 years, felt less comfortable in respect to closeness and showed less faith in others compared to paired individuals. Logistic regression showed that individuals were not single if they did not mind depending on others, showed high attachment anxiety, were older, and had lower education. An income below € 2000/month was linked to a nearly 13-fold increase of likelihood of being single. In sum, the attachment style had a differential age-dependent association to singlehood versus being in a relationship. Education played also a role, exclusively concerning faith in others.

Some difficulties and inconsistencies when using habit strength and reasoned action variables in models of metered household water conservation.

PubMed

Jorgensen, Bradley S; Martin, John F; Pearce, Meryl; Willis, Eileen

2013-01-30

Research employing household water consumption data has sought to test models of water demand and conservation using variables from attitude theory. A significant, albeit unrecognised, challenge has been that attitude models describe individual-level motivations while consumption data is recorded at the household level thereby creating inconsistency between units of theory and measurement. This study employs structural equation modelling and moderated regression techniques to addresses the level of analysis problem, and tests hypotheses by isolating effects on water conservation in single-person households. Furthermore, the results question the explanatory utility of habit strength, perceived behavioural control, and intentions for understanding metered water conservation in single-person households. For example, evidence that intentions predict water conservation or that they interact with habit strength in single-person households was contrary to theoretical expectations. On the other hand, habit strength, self-reports of past water conservation, and perceived behavioural control were good predictors of intentions to conserve water. Copyright © 2012 Elsevier Ltd. All rights reserved.

The influence of mindfulness, self-compassion, psychological flexibility, and posttraumatic stress disorder on disability and quality of life over time in war veterans.

PubMed

Meyer, Eric C; Frankfurt, Sheila B; Kimbrel, Nathan A; DeBeer, Bryann B; Gulliver, Suzy B; Morrisette, Sandra B

2018-07-01

Posttraumatic stress disorder (PTSD) strongly predicts greater disability and lower quality of life (QOL). Mindfulness-based and other third-wave behavior therapy interventions improve well-being by enhancing mindfulness, self-compassion, and psychological flexibility. We hypothesized that these mechanisms of therapeutic change would comprise a single latent factor that would predict disability and QOL after accounting for PTSD symptom severity. Iraq and Afghanistan war veterans (N = 117) completed a study of predictors of successful reintegration. Principal axis factor analysis tested whether mindfulness, self-compassion, and psychological flexibility comprised a single latent factor. Hierarchical regression tested whether this factor predicted disability and QOL 1 year later. Mindfulness, self-compassion, and psychological flexibility comprised a single factor that predicted disability and QOL after accounting for PTSD symptom severity. PTSD symptoms remained a significant predictor of disability but not QOL. Targeting these mechanisms may help veterans achieve functional recovery, even in the presence of PTSD symptoms. © 2018 Wiley Periodicals, Inc.

Psychological distress among employed fathers: associations with family structure, work quality, and the work-family interface.

PubMed

Janzen, Bonnie L; Kelly, Ivan W

2012-07-01

The aim of this study was to compare levels of psychological distress in employed single fathers relative to partnered fathers and to explore the role of psychosocial job quality, work-family conflict, and work-family facilitation as explanations for differences in distress. The data were collected from a cross-sectional telephone survey conducted in a Canadian city. Participants were 486 employed fathers with children living in the household. In addition to experiencing higher levels of psychological distress than partnered fathers (p = .057), single fathers reported greater work-family conflict, poorer work quality, and lower family-to-work facilitation. Adjusting for the strain-based work-family conflict variables in the regression analysis resulted in the largest reduction to the association between partner status and psychological distress. Future research employing a longitudinal design and subject to lower selection biases is required to tease out the interrelationship between these exposures and to point to the most appropriate policies to support employed single fathers.

Distribution and Determinants of 90-Day Payments for Multilevel Posterior Lumbar Fusion: A Medicare Analysis.

PubMed

Jain, Nikhil; Phillips, Frank M; Khan, Safdar N

2018-04-01

A retrospective, economic analysis. The objective of this article is to analyze the distribution of 90-day payments, sources of variation, and reimbursement for complications and readmissions for primary ≥3-level posterior lumbar fusion (PLF) from Medicare data. A secondary objective was to identify risk factors for complications. Bundled payments represent a single payment system to cover all costs associated with a single episode of care, typically over 90 days. The dollar amount spent on different health service providers and the variation in payments for ≥3-level PLF have not been analyzed from a bundled perspective. Administrative claims data were used to study 90-day Medicare (2005-2012) reimbursements for primary ≥3-level PLF for deformity and degenerative conditions of the lumbar spine. Distribution of payments, sources of variation, and reimbursements for managing complications were studied using linear regression models. Risk factors for complications were studied by stepwise multiple-variable logistic regression analysis. Hospital payments comprised 73.8% share of total 90-day payment. Adjusted analysis identified several factors for variation in index hospital payments. The average 90-day Medicare payment for all multilevel PLFs without complications was $35,878 per patient. The additional average cost of treating complications with/without revision surgery within 90 days period ranged from $17,284 to $68,963. A 90-day bundle for ≥3-level PLF with readmission ranges from $88,648 (3 levels) to $117,215 (8+ levels). Rates and risk factors for complications were also identified. The average 90-day payment per patient from Medicare was $35,878 with several factors such as levels of surgery, comorbidities, and development of complications influencing the cost. The study also identifies the risks and costs associated with complications and readmissions and emphasize the significant effect these would have on bundled payments (additional burden of up to 192% the cost of an average uncomplicated procedure over 90 days). Level 3.

The influence of the design matrix on treatment effect estimates in the quantitative analyses of single-subject experimental design research.

PubMed

Moeyaert, Mariola; Ugille, Maaike; Ferron, John M; Beretvas, S Natasha; Van den Noortgate, Wim

2014-09-01

The quantitative methods for analyzing single-subject experimental data have expanded during the last decade, including the use of regression models to statistically analyze the data, but still a lot of questions remain. One question is how to specify predictors in a regression model to account for the specifics of the design and estimate the effect size of interest. These quantitative effect sizes are used in retrospective analyses and allow synthesis of single-subject experimental study results which is informative for evidence-based decision making, research and theory building, and policy discussions. We discuss different design matrices that can be used for the most common single-subject experimental designs (SSEDs), namely, the multiple-baseline designs, reversal designs, and alternating treatment designs, and provide empirical illustrations. The purpose of this article is to guide single-subject experimental data analysts interested in analyzing and meta-analyzing SSED data. © The Author(s) 2014.

Efficient inference for genetic association studies with multiple outcomes.

PubMed

Ruffieux, Helene; Davison, Anthony C; Hager, Jorg; Irincheeva, Irina

2017-10-01

Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single clinical outcome on many genetic variants one by one, but there is an increasing demand for joint analysis of many molecular outcomes and genetic variants in order to unravel functional interactions. Unfortunately, most existing approaches to joint modeling are either too simplistic to be powerful or are impracticable for computational reasons. Inspired by Richardson and others (2010, Bayesian Statistics 9), we consider a sparse multivariate regression model that allows simultaneous selection of predictors and associated responses. As Markov chain Monte Carlo (MCMC) inference on such models can be prohibitively slow when the number of genetic variants exceeds a few thousand, we propose a variational inference approach which produces posterior information very close to that of MCMC inference, at a much reduced computational cost. Extensive numerical experiments show that our approach outperforms popular variable selection methods and tailored Bayesian procedures, dealing within hours with problems involving hundreds of thousands of genetic variants and tens to hundreds of clinical or molecular outcomes. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Very-short-term wind power prediction by a hybrid model with single- and multi-step approaches

NASA Astrophysics Data System (ADS)

Mohammed, E.; Wang, S.; Yu, J.

2017-05-01

Very-short-term wind power prediction (VSTWPP) has played an essential role for the operation of electric power systems. This paper aims at improving and applying a hybrid method of VSTWPP based on historical data. The hybrid method is combined by multiple linear regressions and least square (MLR&LS), which is intended for reducing prediction errors. The predicted values are obtained through two sub-processes:1) transform the time-series data of actual wind power into the power ratio, and then predict the power ratio;2) use the predicted power ratio to predict the wind power. Besides, the proposed method can include two prediction approaches: single-step prediction (SSP) and multi-step prediction (MSP). WPP is tested comparatively by auto-regressive moving average (ARMA) model from the predicted values and errors. The validity of the proposed hybrid method is confirmed in terms of error analysis by using probability density function (PDF), mean absolute percent error (MAPE) and means square error (MSE). Meanwhile, comparison of the correlation coefficients between the actual values and the predicted values for different prediction times and window has confirmed that MSP approach by using the hybrid model is the most accurate while comparing to SSP approach and ARMA. The MLR&LS is accurate and promising for solving problems in WPP.

Medication adherence among patients in a chronic disease clinic.

PubMed

Tourkmani, Ayla M; Al Khashan, Hisham I; Albabtain, Monirah A; Al Harbi, Turki J; Al Qahatani, Hala B; Bakhiet, Ahmed H

2012-12-01

To assess motivation and knowledge domains of medication adherence intention, and to determine their predictors in an ambulatory setting. We conducted a cross-sectional survey study among patients attending a chronic disease clinic at the Family and Community Medicine Department, Prince Sultan Military Medical City, Riyadh, Kingdom of Saudi Arabia between June and September 2010. Adherence intention was assessed using Modified Morisky Scale. Predictors of low motivation and/or knowledge were determined using logistic regression models. A total of 347 patients were interviewed during the study duration. Most patients (75.5%) had 2 or more chronic diseases with an average of 6.3 +/- 2.3 medications, and 6.5 +/- 2.9 pills per prescription. The frequency of adherence intention was low (4.6%), variable (37.2%), and high (58.2%). In multivariate logistic regression analysis, younger age and having asthma were significantly associated with low motivation, while male gender, single status, and not having hypertension were significantly associated with low knowledge. Single status was the only independent predictor of low adherence intention. In a population with multiple chronic diseases and high illiteracy rate, more than 40% had low/variable intention to adhere to prescribed medications. Identifying predictors of this group may help in providing group-specific interventional programs.

Investigation of Genetic Variants Associated with Alzheimer Disease in Parkinson Disease Cognition.

PubMed

Barrett, Matthew J; Koeppel, Alexander F; Flanigan, Joseph L; Turner, Stephen D; Worrall, Bradford B

2016-01-01

Meta-analysis of genome-wide association studies have implicated multiple single nucleotide polymorphisms (SNPs) and associated genes with Alzheimer disease. The role of these SNPs in cognitive impairment in Parkinson disease (PD) remains incompletely evaluated. The objective of this study was to test alleles associated with risk of Alzheimer disease for association with cognitive impairment in Parkinson disease (PD). Two datasets with PD subjects accessed through the NIH database of Genotypes and Phenotypes contained both single nucleotide polymorphism (SNP) arrays and mini-mental state exam (MMSE) scores. Genetic data underwent rigorous quality control and we selected SNPs for genes associated with AD other than APOE. We constructed logistic regression and ordinal regression models, adjusted for sex, age at MMSE, and duration of PD, to assess the association between selected SNPs and MMSE score. In one dataset, PICALM rs3851179 was associated with cognitive impairment (MMSE <  24) in PD subjects > 70 years old (OR = 2.3; adjusted p-value = 0.017; n = 250) but not in PD subjects ≤ 70 years old. Our finding suggests that PICALM rs3851179 could contribute to cognitive impairment in older patients with PD. It is important that future studies consider the interaction of age and genetic risk factors in the development of cognitive impairment in PD.

Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

ERIC Educational Resources Information Center

Azen, Razia; Traxel, Nicole

2009-01-01

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…

Single Parenthood and Children's Reading Performance in Asia

ERIC Educational Resources Information Center

Park, Hyunjoon

2007-01-01

Using the data from Program for International Student Assessment, I examine the gap in reading performance between 15-year-old students in single-parent and intact families in 5 Asian countries in comparison to the United States. The ordinary least square regression analyses show negligible disadvantages of students with a single parent in Hong…

Adherence in single-parent households in a long-term asthma clinical trial.

PubMed

Spicher, Mary; Bollers, Nancy; Chinn, Tamara; Hall, Anita; Plunkett, Anne; Rodgers, Denise; Sundström, D A; Wilson, Laura

2012-01-01

Adherence of participants in a long-term clinical trial is necessary to assure validity of findings. This article examines adherence differences between single-parent and two-parent families in the Childhood Asthma Management Program (CAMP). Adherence was defined as the percentage of completed daily diary cards and scheduled study visits during the course of the trial. Logistic regression and ordinal logistic regression analyses were used. Children from single-parent families had a lower percentage of completed diary cards (72% vs. 84%) than two-parent families. Single-parent families were also more likely to reschedule visits (62% vs. 45%) and miss more clinic visits (23% vs. 17%) than two-parent families. Suggestions are given for study coordinators to assist participants in completing a long-term clinical trial. Many suggestions may be adapted for nurses in inpatient or outpatient settings for assisting parents of patients with chronic diseases.

Novel point estimation from a semiparametric ratio estimator (SPRE): long-term health outcomes from short-term linear data, with application to weight loss in obesity.

PubMed

Weissman-Miller, Deborah

2013-11-02

Point estimation is particularly important in predicting weight loss in individuals or small groups. In this analysis, a new health response function is based on a model of human response over time to estimate long-term health outcomes from a change point in short-term linear regression. This important estimation capability is addressed for small groups and single-subject designs in pilot studies for clinical trials, medical and therapeutic clinical practice. These estimations are based on a change point given by parameters derived from short-term participant data in ordinary least squares (OLS) regression. The development of the change point in initial OLS data and the point estimations are given in a new semiparametric ratio estimator (SPRE) model. The new response function is taken as a ratio of two-parameter Weibull distributions times a prior outcome value that steps estimated outcomes forward in time, where the shape and scale parameters are estimated at the change point. The Weibull distributions used in this ratio are derived from a Kelvin model in mechanics taken here to represent human beings. A distinct feature of the SPRE model in this article is that initial treatment response for a small group or a single subject is reflected in long-term response to treatment. This model is applied to weight loss in obesity in a secondary analysis of data from a classic weight loss study, which has been selected due to the dramatic increase in obesity in the United States over the past 20 years. A very small relative error of estimated to test data is shown for obesity treatment with the weight loss medication phentermine or placebo for the test dataset. An application of SPRE in clinical medicine or occupational therapy is to estimate long-term weight loss for a single subject or a small group near the beginning of treatment.

Surgery of primary tumour has survival benefit in metastatic breast cancer with single-organ metastasis, especially bone.

PubMed

Rhu, Jinsoo; Lee, Se Kyung; Kil, Won Ho; Lee, Jeong Eon; Nam, Seok Jin

2015-04-01

Surgery for the primary breast tumour is usually not recommended in metastatic breast cancer (MBC); however, some reports have suggested a benefit of locoregional treatment. We designed this study to evaluate the efficacy of locoregional surgery in MBC. Data for patients diagnosed with MBC at Samsung Medical Center between 1995 and 2011 were retrospectively collected. We compared the survival benefit of all treatment modalities using Cox regression analysis. Subgroup analyses based on number of metastases were performed to delineate the indication for each treatment. Among 262 patients, 40 (15.3%) underwent surgery. Other treatments included chemotherapy (n = 213, 81.3%), radiotherapy (n = 138, 52.7%), hormone therapy (n = 118, 45.0%) and HER2/neu receptor (HER2)-targeted therapy (n = 37, 14.1%). Cox regression analysis showed that surgery (hazard ratios (HR) = 0.51, P < 0.01), hormone therapy (HR = 0.31, P < 0.01) and HER2-targeted therapy (HR = 0.33, P < 0.01) were associated with improved survival, whereas presence of three or more metastatic organs (HR = 1.62, P = 0.03) was associated with poor survival. In patients with metastasis to a single organ, surgery (HR = 0.43, P < 0.01), chemotherapy (HR = 0.62, P = 0.05), hormone therapy (HR = 0.39, P < 0.01) and HER2-targeted therapy (HR = 0.39, P = 0.02) had a survival benefit. Furthermore, for patients with bone-only metastasis, surgery (HR = 0.37, P = 0.02), chemotherapy (HR = 0.42, P < 0.01), hormone therapy (HR = 0.22, P < 0.01) and HER2-targeted therapy (HR = 0.09, P = 0.02) showed a survival benefit. However, only hormone therapy and HER2-targeted therapy had a survival benefit in MBC with metastasis to multiple organs. Surgical control of the primary breast tumour should be considered as a locoregional therapy in combination with systemic therapy in MBC with metastasis to a single organ, especially bone-only metastasis.

Multilevel Effects of Wealth on Women's Contraceptive Use in Mozambique

PubMed Central

Dias, José G.; de Oliveira, Isabel Tiago

2015-01-01

Objective This paper analyzes the impact of wealth on the use of contraception in Mozambique unmixing the contextual effects due to community wealth from the individual effects associated with the women's situation within the community of residence. Methods Data from the 2011 Mozambican Demographic and Health Survey on women who are married or living together are analyzed for the entire country and also for the rural and urban areas separately. We used single level and multilevel probit regression models. Findings A single level probit regression reveals that region, religion, age, previous fertility, education, and wealth impact contraceptive behavior. The multilevel analysis shows that average community wealth and the women’s relative socioeconomic position within the community have significant positive effects on the use of modern contraceptives. The multilevel framework proved to be necessary in rural settings but not relevant in urban areas. Moreover, the contextual effects due to community wealth are greater in rural than in urban areas and this feature is associated with the higher socioeconomic heterogeneity within the richest communities. Conclusion This analysis highlights the need for the studies on contraceptive behavior to specifically address the individual and contextual effects arising from the poverty-wealth dimension in rural and urban areas separately. The inclusion in a particular community of residence is not relevant in urban areas, but it is an important feature in rural areas. Although the women's individual position within the community of residence has a similar effect on contraceptive adoption in rural and urban settings, the impact of community wealth is greater in rural areas and smaller in urban areas. PMID:25786228

Validity and reliability of dental age estimation of teeth root translucency based on digital luminance determination.

PubMed

Ramsthaler, Frank; Kettner, Mattias; Verhoff, Marcel A

2014-01-01

In forensic anthropological casework, estimating age-at-death is key to profiling unknown skeletal remains. The aim of this study was to examine the reliability of a new, simple, fast, and inexpensive digital odontological method for age-at-death estimation. The method is based on the original Lamendin method, which is a widely used technique in the repertoire of odontological aging methods in forensic anthropology. We examined 129 single root teeth employing a digital camera and imaging software for the measurement of the luminance of the teeth's translucent root zone. Variability in luminance detection was evaluated using statistical technical error of measurement analysis. The method revealed stable values largely unrelated to observer experience, whereas requisite formulas proved to be camera-specific and should therefore be generated for an individual recording setting based on samples of known chronological age. Multiple regression analysis showed a highly significant influence of the coefficients of the variables "arithmetic mean" and "standard deviation" of luminance for the regression formula. For the use of this primer multivariate equation for age-at-death estimation in casework, a standard error of the estimate of 6.51 years was calculated. Step-by-step reduction of the number of embedded variables to linear regression analysis employing the best contributor "arithmetic mean" of luminance yielded a regression equation with a standard error of 6.72 years (p < 0.001). The results of this study not only support the premise of root translucency as an age-related phenomenon, but also demonstrate that translucency reflects a number of other influencing factors in addition to age. This new digital measuring technique of the zone of dental root luminance can broaden the array of methods available for estimating chronological age, and furthermore facilitate measurement and age classification due to its low dependence on observer experience.

Free-breathing Sparse Sampling Cine MR Imaging with Iterative Reconstruction for the Assessment of Left Ventricular Function and Mass at 3.0 T.

PubMed

Sudarski, Sonja; Henzler, Thomas; Haubenreisser, Holger; Dösch, Christina; Zenge, Michael O; Schmidt, Michaela; Nadar, Mariappan S; Borggrefe, Martin; Schoenberg, Stefan O; Papavassiliu, Theano

2017-01-01

Purpose To prospectively evaluate the accuracy of left ventricle (LV) analysis with a two-dimensional real-time cine true fast imaging with steady-state precession (trueFISP) magnetic resonance (MR) imaging sequence featuring sparse data sampling with iterative reconstruction (SSIR) performed with and without breath-hold (BH) commands at 3.0 T. Materials and Methods Ten control subjects (mean age, 35 years; range, 25-56 years) and 60 patients scheduled to undergo a routine cardiac examination that included LV analysis (mean age, 58 years; range, 20-86 years) underwent a fully sampled segmented multiple BH cine sequence (standard of reference) and a prototype undersampled SSIR sequence performed during a single BH and during free breathing (non-BH imaging). Quantitative analysis of LV function and mass was performed. Linear regression, Bland-Altman analysis, and paired t testing were performed. Results Similar to the results in control subjects, analysis of the 60 patients showed excellent correlation with the standard of reference for single-BH SSIR (r = 0.93-0.99) and non-BH SSIR (r = 0.92-0.98) for LV ejection fraction (EF), volume, and mass (P < .0001 for all). Irrespective of breath holding, LV end-diastolic mass was overestimated with SSIR (standard of reference: 163.9 g ± 58.9, single-BH SSIR: 178.5 g ± 62.0 [P < .0001], non-BH SSIR: 175.3 g ± 63.7 [P < .0001]); the other parameters were not significantly different (EF: 49.3% ± 11.9 with standard of reference, 48.8% ± 11.8 with single-BH SSIR, 48.8% ± 11 with non-BH SSIR; P = .03 and P = .12, respectively). Bland-Altman analysis showed similar measurement errors for single-BH SSIR and non-BH SSIR when compared with standard of reference measurements for EF, volume, and mass. Conclusion Assessment of LV function with SSIR at 3.0 T is noninferior to the standard of reference irrespective of BH commands. LV mass, however, is overestimated with SSIR. © RSNA, 2016 Online supplemental material is available for this article.

Genetic Contributions to The Association Between Adult Height and Head and Neck Cancer: A Mendelian Randomization Analysis.

PubMed

Pastorino, Roberta; Puggina, Anna; Carreras-Torres, Robert; Lagiou, Pagona; Holcátová, Ivana; Richiardi, Lorenzo; Kjaerheim, Kristina; Agudo, Antonio; Castellsagué, Xavier; Macfarlane, Tatiana V; Barzan, Luigi; Canova, Cristina; Thakker, Nalin S; Conway, David I; Znaor, Ariana; Healy, Claire M; Ahrens, Wolfgang; Zaridze, David; Szeszenia-Dabrowska, Neonilia; Lissowska, Jolanta; Fabianova, Eleonora; Mates, Ioan Nicolae; Bencko, Vladimir; Foretova, Lenka; Janout, Vladimir; Brennan, Paul; Gaborieau, Valérie; McKay, James D; Boccia, Stefania

2018-03-14

With the aim to dissect the effect of adult height on head and neck cancer (HNC), we use the Mendelian randomization (MR) approach to test the association between genetic instruments for height and the risk of HNC. 599 single nucleotide polymorphisms (SNPs) were identified as genetic instruments for height, accounting for 16% of the phenotypic variation. Genetic data concerning HNC cases and controls were obtained from a genome-wide association study. Summary statistics for genetic association were used in complementary MR approaches: the weighted genetic risk score (GRS) and the inverse-variance weighted (IVW). MR-Egger regression was used for sensitivity analysis and pleiotropy evaluation. From the GRS analysis, one standard deviation (SD) higher height (6.9 cm; due to genetic predisposition across 599 SNPs) raised the risk for HNC (Odds ratio (OR), 1.14; 95% Confidence Interval (95%CI), 0.99-1.32). The association analyses with potential confounders revealed that the GRS was associated with tobacco smoking (OR = 0.80, 95% CI (0.69-0.93)). MR-Egger regression did not provide evidence of overall directional pleiotropy. Our study indicates that height is potentially associated with HNC risk. However, the reported risk could be underestimated since, at the genetic level, height emerged to be inversely associated with smoking.

To Control False Positives in Gene-Gene Interaction Analysis: Two Novel Conditional Entropy-Based Approaches

PubMed Central

Lin, Meihua; Li, Haoli; Zhao, Xiaolei; Qin, Jiheng

2013-01-01

Genome-wide analysis of gene-gene interactions has been recognized as a powerful avenue to identify the missing genetic components that can not be detected by using current single-point association analysis. Recently, several model-free methods (e.g. the commonly used information based metrics and several logistic regression-based metrics) were developed for detecting non-linear dependence between genetic loci, but they are potentially at the risk of inflated false positive error, in particular when the main effects at one or both loci are salient. In this study, we proposed two conditional entropy-based metrics to challenge this limitation. Extensive simulations demonstrated that the two proposed metrics, provided the disease is rare, could maintain consistently correct false positive rate. In the scenarios for a common disease, our proposed metrics achieved better or comparable control of false positive error, compared to four previously proposed model-free metrics. In terms of power, our methods outperformed several competing metrics in a range of common disease models. Furthermore, in real data analyses, both metrics succeeded in detecting interactions and were competitive with the originally reported results or the logistic regression approaches. In conclusion, the proposed conditional entropy-based metrics are promising as alternatives to current model-based approaches for detecting genuine epistatic effects. PMID:24339984

Analysis of association of clinical aspects and IL1B tagSNPs with severe preeclampsia.

PubMed

Leme Galvão, Larissa Paes; Menezes, Filipe Emanuel; Mendonca, Caio; Barreto, Ikaro; Alvim-Pereira, Claudia; Alvim-Pereira, Fabiano; Gurgel, Ricardo

2016-01-01

This study investigates the association between IL1B genotypes using a tag SNP (single polymorphism) approach, maternal and environmental factors in Brazilian women with severe preeclampsia. A case-control study with a total of 456 patients (169 preeclamptic women and 287 controls) was conducted in the two reference maternity hospitals of Sergipe state, Northeast Brazil. A questionnaire was administered and DNA was extracted to genotype the population for four tag SNPs of the IL1Beta: rs 1143643, rs 1143633, rs 1143634 and rs 1143630. Haplotype association analysis and p-values were calculated using the THESIAS test. Odds ratio (OR) estimation, confidence interval (CI) and multivariate logistic regression were performed. High pregestational body mass index (pre-BMI), first gestation, cesarean section, more than six medical visits, low level of consciousness on admission and TC and TT genotype in rs1143630 of IL1Beta showed association with the preeclamptic group in univariate analysis. After multivariate logistic regression pre-BMI, first gestation and low level of consciousness on admission remained associated. We identified an association between clinical variables and preeclampsia. Univariate analysis suggested that inflammatory process-related genes, such as IL1B, may be involved and should be targeted in further studies. The identification of the genetic background involved in preeclampsia host response modulation is mandatory in order to understand the preeclampsia process.

Old and New Ideas for Data Screening and Assumption Testing for Exploratory and Confirmatory Factor Analysis

PubMed Central

Flora, David B.; LaBrish, Cathy; Chalmers, R. Philip

2011-01-01

We provide a basic review of the data screening and assumption testing issues relevant to exploratory and confirmatory factor analysis along with practical advice for conducting analyses that are sensitive to these concerns. Historically, factor analysis was developed for explaining the relationships among many continuous test scores, which led to the expression of the common factor model as a multivariate linear regression model with observed, continuous variables serving as dependent variables, and unobserved factors as the independent, explanatory variables. Thus, we begin our paper with a review of the assumptions for the common factor model and data screening issues as they pertain to the factor analysis of continuous observed variables. In particular, we describe how principles from regression diagnostics also apply to factor analysis. Next, because modern applications of factor analysis frequently involve the analysis of the individual items from a single test or questionnaire, an important focus of this paper is the factor analysis of items. Although the traditional linear factor model is well-suited to the analysis of continuously distributed variables, commonly used item types, including Likert-type items, almost always produce dichotomous or ordered categorical variables. We describe how relationships among such items are often not well described by product-moment correlations, which has clear ramifications for the traditional linear factor analysis. An alternative, non-linear factor analysis using polychoric correlations has become more readily available to applied researchers and thus more popular. Consequently, we also review the assumptions and data-screening issues involved in this method. Throughout the paper, we demonstrate these procedures using an historic data set of nine cognitive ability variables. PMID:22403561

Assessing the Multidimensional Relationship Between Medication Beliefs and Adherence in Older Adults With Hypertension Using Polynomial Regression.

PubMed

Dillon, Paul; Phillips, L Alison; Gallagher, Paul; Smith, Susan M; Stewart, Derek; Cousins, Gráinne

2018-02-05

The Necessity-Concerns Framework (NCF) is a multidimensional theory describing the relationship between patients' positive and negative evaluations of their medication which interplay to influence adherence. Most studies evaluating the NCF have failed to account for the multidimensional nature of the theory, placing the separate dimensions of medication "necessity beliefs" and "concerns" onto a single dimension (e.g., the Beliefs about Medicines Questionnaire-difference score model). To assess the multidimensional effect of patient medication beliefs (concerns and necessity beliefs) on medication adherence using polynomial regression with response surface analysis. Community-dwelling older adults >65 years (n = 1,211) presenting their own prescription for antihypertensive medication to 106 community pharmacies in the Republic of Ireland rated their concerns and necessity beliefs to antihypertensive medications at baseline and their adherence to antihypertensive medication at 12 months via structured telephone interview. Confirmatory polynomial regression found the difference-score model to be inaccurate; subsequent exploratory analysis identified a quadratic model to be the best-fitting polynomial model. Adherence was lowest among those with strong medication concerns and weak necessity beliefs, and adherence was greatest for those with weak concerns and strong necessity beliefs (slope β = -0.77, p<.001; curvature β = -0.26, p = .004). However, novel nonreciprocal effects were also observed; patients with simultaneously high concerns and necessity beliefs had lower adherence than those with simultaneously low concerns and necessity beliefs (slope β = -0.36, p = .004; curvature β = -0.25, p = .003). The difference-score model fails to account for the potential nonreciprocal effects. Results extend evidence supporting the use of polynomial regression to assess the multidimensional effect of medication beliefs on adherence.

Regression of a vaginal leiomyoma after ovariohysterectomy in a dog: a case report.

PubMed

Sathya, Suresh; Linn, Kathleen

2014-01-01

An 11 yr old female mixed-breed Siberian husky was presented with a history of sanguineous vaginal discharge, swelling of the perineal area, decreased appetite, and lethargy. A single, large vaginal leiomyoma and multiple mammary tumors were diagnosed. Mastectomy and ovariohysterectomy were performed. The vaginal leiomyoma regressed completely after ovariohysterectomy. This is the first reported case of spontaneous regression of a vaginal leiomyoma after ovariohysterectomy in a dog.

A Constrained Linear Estimator for Multiple Regression

ERIC Educational Resources Information Center

Davis-Stober, Clintin P.; Dana, Jason; Budescu, David V.

2010-01-01

"Improper linear models" (see Dawes, Am. Psychol. 34:571-582, "1979"), such as equal weighting, have garnered interest as alternatives to standard regression models. We analyze the general circumstances under which these models perform well by recasting a class of "improper" linear models as "proper" statistical models with a single predictor. We…

Regression: The Apple Does Not Fall Far From the Tree.

PubMed

Vetter, Thomas R; Schober, Patrick

2018-05-15

Researchers and clinicians are frequently interested in either: (1) assessing whether there is a relationship or association between 2 or more variables and quantifying this association; or (2) determining whether 1 or more variables can predict another variable. The strength of such an association is mainly described by the correlation. However, regression analysis and regression models can be used not only to identify whether there is a significant relationship or association between variables but also to generate estimations of such a predictive relationship between variables. This basic statistical tutorial discusses the fundamental concepts and techniques related to the most common types of regression analysis and modeling, including simple linear regression, multiple regression, logistic regression, ordinal regression, and Poisson regression, as well as the common yet often underrecognized phenomenon of regression toward the mean. The various types of regression analysis are powerful statistical techniques, which when appropriately applied, can allow for the valid interpretation of complex, multifactorial data. Regression analysis and models can assess whether there is a relationship or association between 2 or more observed variables and estimate the strength of this association, as well as determine whether 1 or more variables can predict another variable. Regression is thus being applied more commonly in anesthesia, perioperative, critical care, and pain research. However, it is crucial to note that regression can identify plausible risk factors; it does not prove causation (a definitive cause and effect relationship). The results of a regression analysis instead identify independent (predictor) variable(s) associated with the dependent (outcome) variable. As with other statistical methods, applying regression requires that certain assumptions be met, which can be tested with specific diagnostics.

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach.

PubMed

Chowdhury, Nilotpal; Sapru, Shantanu

2015-01-01

Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis. The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets. Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate - adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA). Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed. To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research.

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach

PubMed Central

Chowdhury, Nilotpal; Sapru, Shantanu

2015-01-01

Introduction Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis. Aim The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets. Methods Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate – adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA). Results Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed. Conclusion To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research. PMID:26080057

Predicting out-of-office blood pressure level using repeated measurements in the clinic: an observational cohort study

PubMed Central

Sheppard, James P.; Holder, Roger; Nichols, Linda; Bray, Emma; Hobbs, F.D. Richard; Mant, Jonathan; Little, Paul; Williams, Bryan; Greenfield, Sheila; McManus, Richard J.

2014-01-01

Objectives: Identification of people with lower (white-coat effect) or higher (masked effect) blood pressure at home compared to the clinic usually requires ambulatory or home monitoring. This study assessed whether changes in SBP with repeated measurement at a single clinic predict subsequent differences between clinic and home measurements. Methods: This study used an observational cohort design and included 220 individuals aged 35–84 years, receiving treatment for hypertension, but whose SBP was not controlled. The characteristics of change in SBP over six clinic readings were defined as the SBP drop, the slope and the quadratic coefficient using polynomial regression modelling. The predictive abilities of these characteristics for lower or higher home SBP readings were investigated with logistic regression and repeated operating characteristic analysis. Results: The single clinic SBP drop was predictive of the white-coat effect with a sensitivity of 90%, specificity of 50%, positive predictive value of 56% and negative predictive value of 88%. Predictive values for the masked effect and those of the slope and quadratic coefficient were slightly lower, but when the slope and quadratic variables were combined, the sensitivity, specificity, positive and negative predictive values for the masked effect were improved to 91, 48, 24 and 97%, respectively. Conclusion: Characteristics obtainable from multiple SBP measurements in a single clinic in patients with treated hypertension appear to reasonably predict those unlikely to have a large white-coat or masked effect, potentially allowing better targeting of out-of-office monitoring in routine clinical practice. PMID:25144295

Trait Mindfulness as a Limiting Factor for Residual Depressive Symptoms: An Explorative Study Using Quantile Regression

PubMed Central

Radford, Sholto; Eames, Catrin; Brennan, Kate; Lambert, Gwladys; Crane, Catherine; Williams, J. Mark G.; Duggan, Danielle S.; Barnhofer, Thorsten

2014-01-01

Mindfulness has been suggested to be an important protective factor for emotional health. However, this effect might vary with regard to context. This study applied a novel statistical approach, quantile regression, in order to investigate the relation between trait mindfulness and residual depressive symptoms in individuals with a history of recurrent depression, while taking into account symptom severity and number of episodes as contextual factors. Rather than fitting to a single indicator of central tendency, quantile regression allows exploration of relations across the entire range of the response variable. Analysis of self-report data from 274 participants with a history of three or more previous episodes of depression showed that relatively higher levels of mindfulness were associated with relatively lower levels of residual depressive symptoms. This relationship was most pronounced near the upper end of the response distribution and moderated by the number of previous episodes of depression at the higher quantiles. The findings suggest that with lower levels of mindfulness, residual symptoms are less constrained and more likely to be influenced by other factors. Further, the limiting effect of mindfulness on residual symptoms is most salient in those with higher numbers of episodes. PMID:24988072

A Wireless Electronic Nose System Using a Fe2O3 Gas Sensing Array and Least Squares Support Vector Regression

PubMed Central

Song, Kai; Wang, Qi; Liu, Qi; Zhang, Hongquan; Cheng, Yingguo

2011-01-01

This paper describes the design and implementation of a wireless electronic nose (WEN) system which can online detect the combustible gases methane and hydrogen (CH4/H2) and estimate their concentrations, either singly or in mixtures. The system is composed of two wireless sensor nodes—a slave node and a master node. The former comprises a Fe2O3 gas sensing array for the combustible gas detection, a digital signal processor (DSP) system for real-time sampling and processing the sensor array data and a wireless transceiver unit (WTU) by which the detection results can be transmitted to the master node connected with a computer. A type of Fe2O3 gas sensor insensitive to humidity is developed for resistance to environmental influences. A threshold-based least square support vector regression (LS-SVR)estimator is implemented on a DSP for classification and concentration measurements. Experimental results confirm that LS-SVR produces higher accuracy compared with artificial neural networks (ANNs) and a faster convergence rate than the standard support vector regression (SVR). The designed WEN system effectively achieves gas mixture analysis in a real-time process. PMID:22346587

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

PubMed Central

2011-01-01

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

Determining Cutoff Point of Ensemble Trees Based on Sample Size in Predicting Clinical Dose with DNA Microarray Data.

PubMed

Yılmaz Isıkhan, Selen; Karabulut, Erdem; Alpar, Celal Reha

2016-01-01

Background/Aim . Evaluating the success of dose prediction based on genetic or clinical data has substantially advanced recently. The aim of this study is to predict various clinical dose values from DNA gene expression datasets using data mining techniques. Materials and Methods . Eleven real gene expression datasets containing dose values were included. First, important genes for dose prediction were selected using iterative sure independence screening. Then, the performances of regression trees (RTs), support vector regression (SVR), RT bagging, SVR bagging, and RT boosting were examined. Results . The results demonstrated that a regression-based feature selection method substantially reduced the number of irrelevant genes from raw datasets. Overall, the best prediction performance in nine of 11 datasets was achieved using SVR; the second most accurate performance was provided using a gradient-boosting machine (GBM). Conclusion . Analysis of various dose values based on microarray gene expression data identified common genes found in our study and the referenced studies. According to our findings, SVR and GBM can be good predictors of dose-gene datasets. Another result of the study was to identify the sample size of n = 25 as a cutoff point for RT bagging to outperform a single RT.

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

PubMed

Keithley, Richard B; Wightman, R Mark

2011-06-07

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

Solid-phase microextraction fiber development for sampling and analysis of volatile organohalogen compounds in air.

PubMed

Attari, Seyed Ghavameddin; Bahrami, Abdolrahman; Shahna, Farshid Ghorbani; Heidari, Mahmoud

2014-01-01

A green, environmental friendly and sensitive method for determination of volatile organohalogen compounds was described in this paper. The method is based on a homemade sol-gel single-walled carbon nanotube/silica composite coated solid-phase microextraction to develop for sampling and analysis of Carbon tetrachloride, Benzotrichloride, Chloromethyl methyl ether and Trichloroethylene in air. Application of this method was investigated under different laboratory conditions. Predetermined concentrations of each analytes were prepared in a home-made standard chamber and the influences of experimental parameters such as temperature, humidity, extraction time, storage time, desorption temperature, desorption time and the sorbent performance were investigated. Under optimal conditions, the use of single-walled carbon nanotube/silica composite fiber showed good performance, high sensitive and fast sampling of volatile organohalogen compounds from air. For linearity test the regression correlation coefficient was more than 98% for analyte of interest and linear dynamic range for the proposed fiber and the applied Gas Chromatography-Flame Ionization Detector technique was from 1 to 100 ngmL(-1). Method detection limits ranged between 0.09 to 0.2 ngmL(-1) and method quantification limits were between 0.25 and 0.7 ngmL(-1). Single-walled carbon nanotube/silica composite fiber was highly reproducible, relative standard deviations were between 4.3 to 11.7 percent.

Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms.

PubMed

Roetker, Nicholas S; Page, C David; Yonker, James A; Chang, Vicky; Roan, Carol L; Herd, Pamela; Hauser, Taissa S; Hauser, Robert M; Atwood, Craig S

2013-10-01

We examined depression within a multidimensional framework consisting of genetic, environmental, and sociobehavioral factors and, using machine learning algorithms, explored interactions among these factors that might better explain the etiology of depressive symptoms. We measured current depressive symptoms using the Center for Epidemiologic Studies Depression Scale (n = 6378 participants in the Wisconsin Longitudinal Study). Genetic factors were 78 single nucleotide polymorphisms (SNPs); environmental factors-13 stressful life events (SLEs), plus a composite proportion of SLEs index; and sociobehavioral factors-18 personality, intelligence, and other health or behavioral measures. We performed traditional SNP associations via logistic regression likelihood ratio testing and explored interactions with support vector machines and Bayesian networks. After correction for multiple testing, we found no significant single genotypic associations with depressive symptoms. Machine learning algorithms showed no evidence of interactions. Naïve Bayes produced the best models in both subsets and included only environmental and sociobehavioral factors. We found no single or interactive associations with genetic factors and depressive symptoms. Various environmental and sociobehavioral factors were more predictive of depressive symptoms, yet their impacts were independent of one another. A genome-wide analysis of genetic alterations using machine learning methodologies will provide a framework for identifying genetic-environmental-sociobehavioral interactions in depressive symptoms.

Validity analysis on merged and averaged data using within and between analysis: focus on effect of qualitative social capital on self-rated health.

PubMed

Shin, Sang Soo; Shin, Young-Jeon

2016-01-01

With an increasing number of studies highlighting regional social capital (SC) as a determinant of health, many studies are using multi-level analysis with merged and averaged scores of community residents' survey responses calculated from community SC data. Sufficient examination is required to validate if the merged and averaged data can represent the community. Therefore, this study analyzes the validity of the selected indicators and their applicability in multi-level analysis. Within and between analysis (WABA) was performed after creating community variables using merged and averaged data of community residents' responses from the 2013 Community Health Survey in Korea, using subjective self-rated health assessment as a dependent variable. Further analysis was performed following the model suggested by WABA result. Both E-test results (1) and WABA results (2) revealed that single-level analysis needs to be performed using qualitative SC variable with cluster mean centering. Through single-level multivariate regression analysis, qualitative SC with cluster mean centering showed positive effect on self-rated health (0.054, p<0.001), although there was no substantial difference in comparison to analysis using SC variables without cluster mean centering or multi-level analysis. As modification in qualitative SC was larger within the community than between communities, we validate that relational analysis of individual self-rated health can be performed within the group, using cluster mean centering. Other tests besides the WABA can be performed in the future to confirm the validity of using community variables and their applicability in multi-level analysis.

Gender difference in utilization willingness of institutional care among the single seniors: evidence from rural Shandong, China.

PubMed

Qian, Yangyang; Chu, Jie; Ge, Dandan; Zhang, Li; Sun, Long; Zhou, Chengchao

2017-05-12

Institutional care has become an urgent issue in rural China. Rural single seniors, compared with their counterparts, have lower income and are more vulnerable. Gender is also a significant factor determining long-term institutional care. This study is designed to examine the gender difference towards utilization willingness of institutional care among rural single seniors. A total of 505 rural single seniors were included in the analysis. Binary logistic regression model was used to examine the gender difference towards utilization willingness for institutional care, and also to identify the determinants of the utilization willingness for institutional care among rural single male and female seniors. Our study found that about 5.7% rural single seniors had willingness for institutional care in Shandong, China. Single females were found to be less willing for institutional care than single males in rural areas (OR = 0.19; 95 CI 0.06-0.57). It's also found that psychological stress was associated with institutionalization willingness in both single males (P = 0.045) and single females (P = 0.013) in rural China. The rural single seniors who lived alone were found to be more willing for institutional care both in males (P = 0.032) and females (P = 0.002) compared with those who lived with children or others. This study found that there was a gender difference towards utilization willingness for institutional care among single seniors in rural China. Factors including psychological stress and living arrangements were determinants of institutionalization willingness both in single males and females. Targeted policies should be made for rural single seniors of different gender.

Resolving and quantifying overlapped chromatographic bands by transmutation

PubMed

Malinowski

2000-09-15

A new chemometric technique called "transmutation" is developed for the purpose of sharpening overlapped chromatographic bands in order to quantify the components. The "transmutation function" is created from the chromatogram of the pure component of interest, obtained from the same instrument, operating under the same experimental conditions used to record the unresolved chromatogram of the sample mixture. The method is used to quantify mixtures containing toluene, ethylbenzene, m-xylene, naphthalene, and biphenyl from unresolved chromatograms previously reported. The results are compared to those obtained using window factor analysis, rank annihilation factor analysis, and matrix regression analysis. Unlike the latter methods, the transmutation method is not restricted to two-dimensional arrays of data, such as those obtained from HPLC/DAD, but is also applicable to chromatograms obtained from single detector experiments. Limitations of the method are discussed.

Applied Multiple Linear Regression: A General Research Strategy

ERIC Educational Resources Information Center

Smith, Brandon B.

1969-01-01

Illustrates some of the basic concepts and procedures for using regression analysis in experimental design, analysis of variance, analysis of covariance, and curvilinear regression. Applications to evaluation of instruction and vocational education programs are illustrated. (GR)

Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution.

PubMed

Koch, Cosima; Posch, Andreas E; Goicoechea, Héctor C; Herwig, Christoph; Lendl, Bernhard

2014-01-07

This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution - alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L(-1) for Penicillin V and 0.32 g L(-1) for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L(-1) for Penicillin V and 0.15 g L(-1) for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Optimizing separate phase light hydrocarbon recovery from contaminated unconfined aquifers

NASA Astrophysics Data System (ADS)

Cooper, Grant S.; Peralta, Richard C.; Kaluarachchi, Jagath J.

A modeling approach is presented that optimizes separate phase recovery of light non-aqueous phase liquids (LNAPL) for a single dual-extraction well in a homogeneous, isotropic unconfined aquifer. A simulation/regression/optimization (S/R/O) model is developed to predict, analyze, and optimize the oil recovery process. The approach combines detailed simulation, nonlinear regression, and optimization. The S/R/O model utilizes nonlinear regression equations describing system response to time-varying water pumping and oil skimming. Regression equations are developed for residual oil volume and free oil volume. The S/R/O model determines optimized time-varying (stepwise) pumping rates which minimize residual oil volume and maximize free oil recovery while causing free oil volume to decrease a specified amount. This S/R/O modeling approach implicitly immobilizes the free product plume by reversing the water table gradient while achieving containment. Application to a simple representative problem illustrates the S/R/O model utility for problem analysis and remediation design. When compared with the best steady pumping strategies, the optimal stepwise pumping strategy improves free oil recovery by 11.5% and reduces the amount of residual oil left in the system due to pumping by 15%. The S/R/O model approach offers promise for enhancing the design of free phase LNAPL recovery systems and to help in making cost-effective operation and management decisions for hydrogeologists, engineers, and regulators.

Regression estimators for generic health-related quality of life and quality-adjusted life years.

PubMed

Basu, Anirban; Manca, Andrea

2012-01-01

To develop regression models for outcomes with truncated supports, such as health-related quality of life (HRQoL) data, and account for features typical of such data such as a skewed distribution, spikes at 1 or 0, and heteroskedasticity. Regression estimators based on features of the Beta distribution. First, both a single equation and a 2-part model are presented, along with estimation algorithms based on maximum-likelihood, quasi-likelihood, and Bayesian Markov-chain Monte Carlo methods. A novel Bayesian quasi-likelihood estimator is proposed. Second, a simulation exercise is presented to assess the performance of the proposed estimators against ordinary least squares (OLS) regression for a variety of HRQoL distributions that are encountered in practice. Finally, the performance of the proposed estimators is assessed by using them to quantify the treatment effect on QALYs in the EVALUATE hysterectomy trial. Overall model fit is studied using several goodness-of-fit tests such as Pearson's correlation test, link and reset tests, and a modified Hosmer-Lemeshow test. The simulation results indicate that the proposed methods are more robust in estimating covariate effects than OLS, especially when the effects are large or the HRQoL distribution has a large spike at 1. Quasi-likelihood techniques are more robust than maximum likelihood estimators. When applied to the EVALUATE trial, all but the maximum likelihood estimators produce unbiased estimates of the treatment effect. One and 2-part Beta regression models provide flexible approaches to regress the outcomes with truncated supports, such as HRQoL, on covariates, after accounting for many idiosyncratic features of the outcomes distribution. This work will provide applied researchers with a practical set of tools to model outcomes in cost-effectiveness analysis.

Cumulative sum analysis for experiences of a single-session retrograde intrarenal stone surgery and analysis of predictors for stone-free status.

PubMed

Cho, Sung Yong; Choo, Min Soo; Jung, Jae Hyun; Jeong, Chang Wook; Oh, Sohee; Lee, Seung Bae; Son, Hwancheol; Jeong, Hyeon

2014-01-01

This study investigated the learning curve of a single-session retrograde intrarenal surgery (RIRS) in patients with mid-sized stones. Competence and trainee proficiency for RIRS was assessed using cumulative sum analysis (CUSUM). The study design and the use of patients' information stored in the hospital database were approved by the Institutional Review Board of our institution. A retrospective review was performed for 100 patients who underwent a single-session RIRS. Patients were included if the main stone had a maximal diameter between 10 and 30 mm. The presence of a residual stone was checked on postoperative day 1 and at one-month follow-up visit. Fragmentation efficacy was calculated "removed stone volume (mm(3)) divided by operative time (min)". CUSUM analysis was used for monitoring change in fragmentation efficacy, and we tested whether or not acceptable surgical outcomes were achieved. The mean age was 54.7±14.8 years. Serum creatinine level did not change significantly. Estimated GFR and hemoglobin were within normal limits postoperatively. The CUSUM curve tended to be flat until the 25th case and showed a rising pattern but declined again until the 56th case. After that point, the fragmentation efficacy reached a plateau. The acceptable level of fragmentation efficacy was 25 ml/min. Multivariate logistic regression analyses showed that stone-free rate was significantly lower for cases with multiple stones than those with a single stone (OR = 0.147, CI 0.032 - 0.674, P value  = 0.005) and for cases with higher number of sites (OR = 0.676, CI 0.517 - 0.882, P value  = 0.004). The statistical analysis of RIRS learning experience revealed that 56 cases were required for reaching a plateau in the learning curve. The number of stones and the number of sites were significant predictors for stone-free status.

A flexible count data regression model for risk analysis.

PubMed

Guikema, Seth D; Coffelt, Jeremy P; Goffelt, Jeremy P

2008-02-01

In many cases, risk and reliability analyses involve estimating the probabilities of discrete events such as hardware failures and occurrences of disease or death. There is often additional information in the form of explanatory variables that can be used to help estimate the likelihood of different numbers of events in the future through the use of an appropriate regression model, such as a generalized linear model. However, existing generalized linear models (GLM) are limited in their ability to handle the types of variance structures often encountered in using count data in risk and reliability analysis. In particular, standard models cannot handle both underdispersed data (variance less than the mean) and overdispersed data (variance greater than the mean) in a single coherent modeling framework. This article presents a new GLM based on a reformulation of the Conway-Maxwell Poisson (COM) distribution that is useful for both underdispersed and overdispersed count data and demonstrates this model by applying it to the assessment of electric power system reliability. The results show that the proposed COM GLM can provide as good of fits to data as the commonly used existing models for overdispered data sets while outperforming these commonly used models for underdispersed data sets.

The Role of Genetic Factors in the Outbreak Mechanism of Dental Caries.

PubMed

Shimomura-Kuroki, Junko; Nashida, Tomoko; Miyagawa, Yukio; Sekimoto, Tsuneo

The aim of the present study was to investigate the relationships between cariogenic bacterial infection and single nucleotide polymorphisms (SNPs) in candidate genes associated with dental caries, and to explore the factors related to caries in children. Children aged 3 to 11 years were selected. Detection of cariogenic bacteria (Streptococcus mutans, Streptococcus oralis, Streptococcus sobrinus and Lactobacillus) from the plaque of each patient, and SNP analyses of five candidate genes (MBL2, TAS2R38, GLUT2, MMP13 and CA6) were performed using DNA isolated from buccal mucosal cells. The dental caries experience in primary and permanent teeth was determined using the decayed, missing and filled teeth (DMFT) index, and the effects of the observed factors on the DMFT value were analyzed by multiple regression analysis. The results of the multiple regression analysis showed that the DMFT value significantly increased in the presence of S. mutans or S. sobrinus (p < 0.001), while the dmft/DMFT value decreased in the presence of nucleobase C in MBL2 (p < 0.05). These results suggest that the MBL2 gene is related to the pathogenesis of dental caries.

Factors affecting the dissipation of pharmaceuticals in freshwater sediments.

PubMed

Al-Khazrajy, Omar S A; Bergström, Ed; Boxall, Alistair B A

2018-03-01

Degradation is one of the key processes governing the impact of pharmaceuticals in the aquatic environment. Most studies on the degradation of pharmaceuticals have focused on soil and sludge, with fewer exploring persistence in aquatic sediments. We investigated the dissipation of 6 pharmaceuticals from different therapeutic classes in a range of sediment types. Dissipation of each pharmaceutical was found to follow first-order exponential decay. Half-lives in the sediments ranged from 9.5 (atenolol) to 78.8 (amitriptyline) d. Under sterile conditions, the persistence of pharmaceuticals was considerably longer. Stepwise multiple linear regression analysis was performed to explore the relationships between half-lives of the pharmaceuticals, sediment physicochemical properties, and sorption coefficients for the compounds. Sediment clay, silt, and organic carbon content and microbial activity were the predominant factors related to the degradation rates of diltiazem, cimetidine, and ranitidine. Regression analysis failed to highlight a key property which may be responsible for observed differences in the degradation of the other pharmaceuticals. The present results suggest that the degradation rate of pharmaceuticals in sediments is determined by different factors and processes and does not exclusively depend on a single sediment parameter. Environ Toxicol Chem 2018;37:829-838. © 2017 SETAC. © 2017 SETAC.

Accuracy of Multi-echo Magnitude-based MRI (M-MRI) for Estimation of Hepatic Proton Density Fat Fraction (PDFF) in Children

PubMed Central

Zand, Kevin A.; Shah, Amol; Heba, Elhamy; Wolfson, Tanya; Hamilton, Gavin; Lam, Jessica; Chen, Joshua; Hooker, Jonathan C.; Gamst, Anthony C.; Middleton, Michael S.; Schwimmer, Jeffrey B.; Sirlin, Claude B.

2015-01-01

Purpose To assess accuracy of magnitude-based magnetic resonance imaging (M-MRI) in children to estimate hepatic proton density fat fraction (PDFF) using two to six echoes, with magnetic resonance spectroscopy (MRS)-measured PDFF as a reference standard. Materials and Methods This was an IRB-approved, HIPAA-compliant, single-center, cross-sectional, retrospective analysis of data collected prospectively between 2008 and 2013 in children with known or suspected non-alcoholic fatty liver disease (NAFLD). Two hundred and eighty-six children (8 – 20 [mean 14.2 ± 2.5] yrs; 182 boys) underwent same-day MRS and M-MRI. Unenhanced two-dimensional axial spoiled gradient-recalled-echo images at six echo times were obtained at 3T after a single low-flip-angle (10°) excitation with ≥ 120-ms recovery time. Hepatic PDFF was estimated using the first two, three, four, five, and all six echoes. For each number of echoes, accuracy of M-MRI to estimate PDFF was assessed by linear regression with MRS-PDFF as reference standard. Accuracy metrics were regression intercept, slope, average bias, and R2. Results MRS-PDFF ranged from 0.2 – 40.4% (mean 13.1 ± 9.8%). Using three to six echoes, regression intercept, slope, and average bias were 0.46 – 0.96%, 0.99 – 1.01, and 0.57 – 0.89%, respectively. Using two echoes, these values were 2.98%, 0.97, and 2.72%, respectively. R2 ranged 0.98 – 0.99 for all methods. Conclusion Using three to six echoes, M-MRI has high accuracy for hepatic PDFF estimation in children. PMID:25847512

A reliable and cost effective approach for radiographic monitoring in nutritional rickets

PubMed Central

Gupta, V; Sharma, V; Sinha, B; Samanta, S

2014-01-01

Objective: Radiological scoring is particularly useful in rickets, where pre-treatment radiographical findings can reflect the disease severity and can be used to monitor the improvement. However, there is only a single radiographic scoring system for rickets developed by Thacher and, to the best of our knowledge, no study has evaluated radiographic changes in rickets based on this scoring system apart from the one done by Thacher himself. The main objective of this study is to compare and analyse the pre-treatment and post-treatment radiographic parameters in nutritional rickets with the help of Thacher's scoring technique. Methods: 176 patients with nutritional rickets were given a single intramuscular injection of vitamin D (600 000 IU) along with oral calcium (50 mg kg−1) and vitamin D (400 IU per day) until radiological resolution and followed for 1 year. Pre- and post-treatment radiological parameters were compared and analysed statistically based on Thacher's scoring system. Results: Radiological resolution was complete by 6 months. Time for radiological resolution and initial radiological score were linearly associated on regression analysis. The distal ulna was the last to heal in most cases except when the initial score was 10, when distal femur was the last to heal. Conclusion: Thacher's scoring system can effectively monitor nutritional rickets. The formula derived through linear regression has prognostic significance. Advances in knowledge: The distal femur is a better indicator in radiologically severe rickets and when resolution is delayed. Thacher's scoring is very useful for monitoring of rickets. The formula derived through linear regression can predict the expected time for radiological resolution. PMID:24593231

Accuracy of multiecho magnitude-based MRI (M-MRI) for estimation of hepatic proton density fat fraction (PDFF) in children.

PubMed

Zand, Kevin A; Shah, Amol; Heba, Elhamy; Wolfson, Tanya; Hamilton, Gavin; Lam, Jessica; Chen, Joshua; Hooker, Jonathan C; Gamst, Anthony C; Middleton, Michael S; Schwimmer, Jeffrey B; Sirlin, Claude B

2015-11-01

To assess accuracy of magnitude-based magnetic resonance imaging (M-MRI) in children to estimate hepatic proton density fat fraction (PDFF) using two to six echoes, with magnetic resonance spectroscopy (MRS) -measured PDFF as a reference standard. This was an IRB-approved, HIPAA-compliant, single-center, cross-sectional, retrospective analysis of data collected prospectively between 2008 and 2013 in children with known or suspected nonalcoholic fatty liver disease (NAFLD). Two hundred eighty-six children (8-20 [mean 14.2 ± 2.5] years; 182 boys) underwent same-day MRS and M-MRI. Unenhanced two-dimensional axial spoiled gradient-recalled-echo images at six echo times were obtained at 3T after a single low-flip-angle (10°) excitation with ≥ 120-ms recovery time. Hepatic PDFF was estimated using the first two, three, four, five, and all six echoes. For each number of echoes, accuracy of M-MRI to estimate PDFF was assessed by linear regression with MRS-PDFF as reference standard. Accuracy metrics were regression intercept, slope, average bias, and R(2) . MRS-PDFF ranged from 0.2-40.4% (mean 13.1 ± 9.8%). Using three to six echoes, regression intercept, slope, and average bias were 0.46-0.96%, 0.99-1.01, and 0.57-0.89%, respectively. Using two echoes, these values were 2.98%, 0.97, and 2.72%, respectively. R(2) ranged 0.98-0.99 for all methods. Using three to six echoes, M-MRI has high accuracy for hepatic PDFF estimation in children. © 2015 Wiley Periodicals, Inc.

External Tank Liquid Hydrogen (LH2) Prepress Regression Analysis Independent Review Technical Consultation Report

NASA Technical Reports Server (NTRS)

Parsons, Vickie s.

2009-01-01

The request to conduct an independent review of regression models, developed for determining the expected Launch Commit Criteria (LCC) External Tank (ET)-04 cycle count for the Space Shuttle ET tanking process, was submitted to the NASA Engineering and Safety Center NESC on September 20, 2005. The NESC team performed an independent review of regression models documented in Prepress Regression Analysis, Tom Clark and Angela Krenn, 10/27/05. This consultation consisted of a peer review by statistical experts of the proposed regression models provided in the Prepress Regression Analysis. This document is the consultation's final report.

Determination of sedimentation coefficients for small peptides.

PubMed Central

Schuck, P; MacPhee, C E; Howlett, G J

1998-01-01

Direct fitting of sedimentation velocity data with numerical solutions of the Lamm equations has been exploited to obtain sedimentation coefficients for single solutes under conditions where solvent and solution plateaus are either not available or are transient. The calculated evolution was initialized with the first experimental scan and nonlinear regression was employed to obtain best-fit values for the sedimentation and diffusion coefficients. General properties of the Lamm equations as data analysis tools were examined. This method was applied to study a set of small peptides containing amphipathic heptad repeats with the general structure Ac-YS-(AKEAAKE)nGAR-NH2, n = 2, 3, or 4. Sedimentation velocity analysis indicated single sedimenting species with sedimentation coefficients (s(20,w) values) of 0.37, 0.45, and 0.52 S, respectively, in good agreement with sedimentation coefficients predicted by hydrodynamic theory. The described approach can be applied to synthetic boundary and conventional loading experiments, and can be extended to analyze sedimentation data for both large and small macromolecules in order to define shape, heterogeneity, and state of association. PMID:9449347

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

PubMed

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

2015-02-01

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

(13)C NMR substituent-induced chemical shifts in 4-(substituted phenyl)-3-phenyl-1,2,4-oxadiazol-5(4H)-ones (thiones).

PubMed

Kara, Yesim Saniye

2015-01-01

In the present, study mostly novel ten 4-(substituted phenyl)-3-phenyl-1,2,4-oxadiazol-5(4H)-ones and ten 4-(substituted phenyl)-3-phenyl-1,2,4-oxadiazol-5(4H)-thiones were synthesized. These oxadiazole derivatives were characterized by IR, (1)H NMR, (13)C NMR and elemental analyses. Their (13)C NMR spectra were measured in Deuterochloroform (CDCl3). The correlation analysis for the substituent-induced chemical shift (SCS) with Hammett substituent constants (σ), Brown Okamoto substituent constants (σ(+), σ(-)), inductive substituent constants (σI) and different of resonance substituent constants (σR, σR(o)) were performed using SSP (single substituent parameter), DSP (dual substituent parameter) and DSP-NLR (dual substituent parameter-non-linear resonance) methods, as well as single and multiple regression analysis. Negative ρ values were found for all correlations (reverse substituent effect). The results of all statistical analyses, (13)C NMR chemical shift of CN, CO and CS carbon of oxadiazole rings have shown satisfactory correlation. Copyright © 2015 Elsevier B.V. All rights reserved.

Development and Validation of a Cervical Cancer Screening Self-Efficacy Scale for Low-Income Mexican American Women

PubMed Central

Fernández, Maria E.; Diamond, Pamela M.; Rakowski, William; Gonzales, Alicia; Tortolero-Luna, Guillermo; Williams, Janet; Morales-Campos, Daisy Y.

2011-01-01

While self-efficacy (SE), a construct from Social Cognitive Theory, has been shown to influence other screening behaviors, few measures currently exist for measuring Pap test SE. This paper describes the development and psychometric testing of such a measure for Mexican-American women. Data from two separate samples of Mexican-American women 50 years or older, obtained as part of a study to develop and evaluate a breast and cervical cancer screening educational program, were used in the current study. Exploratory factor analysis indicated a single factor solution and all item loadings were > .73. Confirmatory analysis confirmed a single factor structure with all standardized loadings greater than .40 as hypothesized. The eight item SE scale demonstrated high internal consistency (Cronbach's alpha = .95). As hypothesized, SE was correlated with knowledge, prior experience, and screening intention. Logistic regression supported the theoretical relationship that women with higher SE were more likely to have had a recent Pap test. Findings showed a significant increase in SE following the intervention, indicating the measure has good sensitivity to change over time. PMID:19258484

Efficient strategies for leave-one-out cross validation for genomic best linear unbiased prediction.

PubMed

Cheng, Hao; Garrick, Dorian J; Fernando, Rohan L

2017-01-01

A random multiple-regression model that simultaneously fit all allele substitution effects for additive markers or haplotypes as uncorrelated random effects was proposed for Best Linear Unbiased Prediction, using whole-genome data. Leave-one-out cross validation can be used to quantify the predictive ability of a statistical model. Naive application of Leave-one-out cross validation is computationally intensive because the training and validation analyses need to be repeated n times, once for each observation. Efficient Leave-one-out cross validation strategies are presented here, requiring little more effort than a single analysis. Efficient Leave-one-out cross validation strategies is 786 times faster than the naive application for a simulated dataset with 1,000 observations and 10,000 markers and 99 times faster with 1,000 observations and 100 markers. These efficiencies relative to the naive approach using the same model will increase with increases in the number of observations. Efficient Leave-one-out cross validation strategies are presented here, requiring little more effort than a single analysis.

Predictive Utility of Marketed Volumetric Software Tools in Subjects at Risk for Alzheimer's: Do Regions Outside the Hippocampus Matter?

PubMed Central

Tanpitukpongse, Teerath P.; Mazurowski, Maciej A.; Ikhena, John; Petrella, Jeffrey R.

2016-01-01

Background and Purpose To assess prognostic efficacy of individual versus combined regional volumetrics in two commercially-available brain volumetric software packages for predicting conversion of patients with mild cognitive impairment to Alzheimer's disease. Materials and Methods Data was obtained through the Alzheimer's Disease Neuroimaging Initiative. 192 subjects (mean age 74.8 years, 39% female) diagnosed with mild cognitive impairment at baseline were studied. All had T1WI MRI sequences at baseline and 3-year clinical follow-up. Analysis was performed with NeuroQuant® and Neuroreader™. Receiver operating characteristic curves assessing the prognostic efficacy of each software package were generated using a univariable approach employing individual regional brain volumes, as well as two multivariable approaches (multiple regression and random forest), combining multiple volumes. Results On univariable analysis of 11 NeuroQuant® and 11 Neuroreader™ regional volumes, hippocampal volume had the highest area under the curve for both software packages (0.69 NeuroQuant®, 0.68 Neuroreader™), and was not significantly different (p > 0.05) between packages. Multivariable analysis did not increase the area under the curve for either package (0.63 logistic regression, 0.60 random forest NeuroQuant®; 0.65 logistic regression, 0.62 random forest Neuroreader™). Conclusion Of the multiple regional volume measures available in FDA-cleared brain volumetric software packages, hippocampal volume remains the best single predictor of conversion of mild cognitive impairment to Alzheimer's disease at 3-year follow-up. Combining volumetrics did not add additional prognostic efficacy. Therefore, future prognostic studies in MCI, combining such tools with demographic and other biomarker measures, are justified in using hippocampal volume as the only volumetric biomarker. PMID:28057634

Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components.

PubMed

Riccardi, M; Mele, G; Pulvento, C; Lavini, A; d'Andria, R; Jacobsen, S-E

2014-06-01

Leaf chlorophyll content provides valuable information about physiological status of plants; it is directly linked to photosynthetic potential and primary production. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination. This measurement is expensive, laborious, and time consuming. Over the years alternative methods, rapid and non-destructive, have been explored. The aim of this work was to evaluate the applicability of a fast and non-invasive field method for estimation of chlorophyll content in quinoa and amaranth leaves based on RGB components analysis of digital images acquired with a standard SLR camera. Digital images of leaves from different genotypes of quinoa and amaranth were acquired directly in the field. Mean values of each RGB component were evaluated via image analysis software and correlated to leaf chlorophyll provided by standard laboratory procedure. Single and multiple regression models using RGB color components as independent variables have been tested and validated. The performance of the proposed method was compared to that of the widely used non-destructive SPAD method. Sensitivity of the best regression models for different genotypes of quinoa and amaranth was also checked. Color data acquisition of the leaves in the field with a digital camera was quick, more effective, and lower cost than SPAD. The proposed RGB models provided better correlation (highest R (2)) and prediction (lowest RMSEP) of the true value of foliar chlorophyll content and had a lower amount of noise in the whole range of chlorophyll studied compared with SPAD and other leaf image processing based models when applied to quinoa and amaranth.

Depressive Symptoms in College Women: Examining the Cumulative Effect of Childhood and Adulthood Domestic Violence.

PubMed

Al-Modallal, Hanan

2016-10-01

The purpose of this study was to examine the cumulative effect of childhood and adulthood violence on depressive symptoms in a sample of Jordanian college women. Snowball sampling technique was used to recruit the participants. The participants were heterosexual college-aged women between the ages of 18 and 25. The participants were asked about their experiences of childhood violence (including physical violence, sexual violence, psychological violence, and witnessing parental violence), partner violence (including physical partner violence and sexual partner violence), experiences of depressive symptoms, and about other demographic and familial factors as possible predictors for their complaints of depressive symptoms. Multiple linear regression analysis was implemented to identify demographic- and violence-related predictors of their complainants of depressive symptoms. Logistic regression analysis was further performed to identify possible type(s) of violence associated with the increased risk of depressive symptoms. The prevalence of depressive symptoms in this sample was 47.4%. For the violence experience, witnessing parental violence was the most common during childhood, experienced by 40 (41.2%) women, and physical partner violence was the most common in adulthood, experienced by 35 (36.1%) women. Results of logistic regression analysis indicated that experiencing two types of violence (regardless of the time of occurrence) was significant in predicting depressive symptoms (odds ratio [OR] = 3.45, p < .05). Among college women's demographic characteristics, marital status (single vs. engaged), mothers' level of education, income, and smoking were significant in predicting depressive symptoms. Assessment of physical violence and depressive symptoms including the cumulative impact of longer periods of violence on depressive symptoms is recommended to be explored in future studies. © The Author(s) 2015.

Cell cycle-related genes as modifiers of age of onset of colorectal cancer in Lynch syndrome: a large-scale study in non-Hispanic white patients.

PubMed

Chen, Jinyun; Pande, Mala; Huang, Yu-Jing; Wei, Chongjuan; Amos, Christopher I; Talseth-Palmer, Bente A; Meldrum, Cliff J; Chen, Wei V; Gorlov, Ivan P; Lynch, Patrick M; Scott, Rodney J; Frazier, Marsha L

2013-02-01

Heterogeneity in age of onset of colorectal cancer in individuals with mutations in DNA mismatch repair genes (Lynch syndrome) suggests the influence of other lifestyle and genetic modifiers. We hypothesized that genes regulating the cell cycle influence the observed heterogeneity as cell cycle-related genes respond to DNA damage by arresting the cell cycle to provide time for repair and induce transcription of genes that facilitate repair. We examined the association of 1456 single nucleotide polymorphisms (SNPs) in 128 cell cycle-related genes and 31 DNA repair-related genes in 485 non-Hispanic white participants with Lynch syndrome to determine whether there are SNPs associated with age of onset of colorectal cancer. Genotyping was performed on an Illumina GoldenGate platform, and data were analyzed using Kaplan-Meier survival analysis, Cox regression analysis and classification and regression tree (CART) methods. Ten SNPs were independently significant in a multivariable Cox proportional hazards regression model after correcting for multiple comparisons (P < 5 × 10(-4)). Furthermore, risk modeling using CART analysis defined combinations of genotypes for these SNPs with which subjects could be classified into low-risk, moderate-risk and high-risk groups that had median ages of colorectal cancer onset of 63, 50 and 42 years, respectively. The age-associated risk of colorectal cancer in the high-risk group was more than four times the risk in the low-risk group (hazard ratio = 4.67, 95% CI = 3.16-6.92). The additional genetic markers identified may help in refining risk groups for more tailored screening and follow-up of non-Hispanic white patients with Lynch syndrome.

Cell cycle–related genes as modifiers of age of onset of colorectal cancer in Lynch syndrome: a large-scale study in non-Hispanic white patients

PubMed Central

Chen, Jinyun; Pande, Mala

2013-01-01

Heterogeneity in age of onset of colorectal cancer in individuals with mutations in DNA mismatch repair genes (Lynch syndrome) suggests the influence of other lifestyle and genetic modifiers. We hypothesized that genes regulating the cell cycle influence the observed heterogeneity as cell cycle–related genes respond to DNA damage by arresting the cell cycle to provide time for repair and induce transcription of genes that facilitate repair. We examined the association of 1456 single nucleotide polymorphisms (SNPs) in 128 cell cycle–related genes and 31 DNA repair–related genes in 485 non-Hispanic white participants with Lynch syndrome to determine whether there are SNPs associated with age of onset of colorectal cancer. Genotyping was performed on an Illumina GoldenGate platform, and data were analyzed using Kaplan–Meier survival analysis, Cox regression analysis and classification and regression tree (CART) methods. Ten SNPs were independently significant in a multivariable Cox proportional hazards regression model after correcting for multiple comparisons (P < 5×10–4). Furthermore, risk modeling using CART analysis defined combinations of genotypes for these SNPs with which subjects could be classified into low-risk, moderate-risk and high-risk groups that had median ages of colorectal cancer onset of 63, 50 and 42 years, respectively. The age-associated risk of colorectal cancer in the high-risk group was more than four times the risk in the low-risk group (hazard ratio = 4.67, 95% CI = 3.16–6.92). The additional genetic markers identified may help in refining risk groups for more tailored screening and follow-up of non-Hispanic white patients with Lynch syndrome. PMID:23125224

Regularized rare variant enrichment analysis for case-control exome sequencing data.

PubMed

Larson, Nicholas B; Schaid, Daniel J

2014-02-01

Rare variants have recently garnered an immense amount of attention in genetic association analysis. However, unlike methods traditionally used for single marker analysis in GWAS, rare variant analysis often requires some method of aggregation, since single marker approaches are poorly powered for typical sequencing study sample sizes. Advancements in sequencing technologies have rendered next-generation sequencing platforms a realistic alternative to traditional genotyping arrays. Exome sequencing in particular not only provides base-level resolution of genetic coding regions, but also a natural paradigm for aggregation via genes and exons. Here, we propose the use of penalized regression in combination with variant aggregation measures to identify rare variant enrichment in exome sequencing data. In contrast to marginal gene-level testing, we simultaneously evaluate the effects of rare variants in multiple genes, focusing on gene-based least absolute shrinkage and selection operator (LASSO) and exon-based sparse group LASSO models. By using gene membership as a grouping variable, the sparse group LASSO can be used as a gene-centric analysis of rare variants while also providing a penalized approach toward identifying specific regions of interest. We apply extensive simulations to evaluate the performance of these approaches with respect to specificity and sensitivity, comparing these results to multiple competing marginal testing methods. Finally, we discuss our findings and outline future research. © 2013 WILEY PERIODICALS, INC.

Comparison of low-dose spinal anesthesia and single-shot femoral block combination with conventional dose spinal anesthesia in outpatient arthroscopic meniscus repair.

PubMed

Turhan, K S Cakar; Akmese, R; Ozkan, F; Okten, F F

2015-04-01

In the current prospective, randomized study, we aimed to compare the effects of low dose selective spinal anesthesia with 5 mg of hyperbaric bupivacaine and single-shot femoral nerve block combination with conventional dose selective spinal anesthesia in terms of intraoperative anesthesia characteristics, block recovery characteristics, and postoperative analgesic consumption. After obtaining institutional Ethics Committee approval, 52 ASA I-II patients aged 25-65, undergoing arthroscopic meniscus repair were randomly assigned to Group S (conventional dose selective spinal anesthesia with 10 mg bupivacaine) and Group FS (low-dose selective spinal anesthesia with 5mg bupivacaine +single-shot femoral block with 0.25% bupivacaine). Primary endpoints were time to reach T12 sensory block level, L2 regression, and complete motor block regression. Secondary endpoints were maximum sensory block level (MSBL); time to reach MSBL, time to first urination, time to first analgesic consumption and pain severity at the time of first mobilization. Demographic characteristics were similar in both groups (p > 0.05). MSBL and time to reach T12 sensory level were similar in both groups (p > 0.05). Time to reach L2 regression, complete motor block regression, and time to first micturition were significantly shorter; time to first analgesic consumption was significantly longer; and total analgesic consumption and severity of pain at time of first mobilization were significantly lower in Group FS (p < 0.05). The findings of the current study suggest that addition of single-shot femoral block to low dose spinal anesthesia could be an alternative to conventional dose spinal anesthesia in outpatient arthroscopic meniscus repair. NCT02322372.

Multi-Locus Candidate Gene Analyses of Lipid Levels in a Pediatric Turkish Cohort: Lessons Learned on LPL, CETP, LIPC, ABCA1, and SHBG

PubMed Central

Eren, Fatih; Agirbasli, Deniz; White, Marquitta J.; Williams, Scott M

2013-01-01

Abstract Cardiovascular risk factors and atherosclerosis precursors were examined in 365 Turkish children and adolescents. Study participants were recruited at five different state schools. We tested single and multi-locus effects of six polymorphisms from five candidate genes, chosen based on prior known association with lipid levels in adults, for association with low (≤10th percentile) high density lipoprotein cholesterol (HDL-C) and high (≥90th percentile) triglycerides (TG), and the related continuous outcomes. We observed an association between CETP variant rs708272 and low HDL-C (allelic p=0.020, genotypic p=0.046), which was supported by an independent analysis, PRAT (PRAT control p=0.027). Sex-stratified logistic regression analysis showed that the B2 allele of rs708272 decreased odds of being in the lower tenth percentile of HDL-C measurements (OR=0.36, p=0.02) in girls; this direction of effect was also seen in boys but was not significant (OR=0.64, p=0.21). Logistic regression analysis also revealed that the T allele of rs6257 (SHBG) decreased odds of being in the top tenth percentile of TG measurements in boys (OR=0.43, p=0.03). Analysis of lipid levels as a continuous trait revealed a significant association between rs708272 (CETP) and LDL-C levels in males (p=0.02) with the B2B2 genotype group having the lowest mean LDL-C; the same direction of effect was also seen in females (p=0.05). An effect was also seen between rs708272 and HDL-C levels in girls (p=0.01), with the B2B2 genotype having the highest mean HDL-C levels. Multi-locus analysis, using quantitative multifactor dimensionality reduction (qMDR) identified the previously mentioned CETP variant as the best single locus model, and overall model, for predicting HDL-C levels in children. This study provides evidence for association between CETP and low HDL-C phenotype in children, but the results appear to be weaker in children than previous results in adults and may also be subject to gender effects. PMID:23988150

Joint analysis of longitudinal feed intake and single recorded production traits in pigs using a novel Horizontal model.

PubMed

Shirali, M; Strathe, A B; Mark, T; Nielsen, B; Jensen, J

2017-03-01

A novel Horizontal model is presented for multitrait analysis of longitudinal traits through random regression analysis combined with single recorded traits. Weekly ADFI on test for Danish Duroc, Landrace, and Yorkshire boars were available from the national test station and were collected from 30 to 100 kg BW. Single recorded production traits of ADG from birth to 30 kg BW (ADG30), ADG from 30 to 100 kg BW (ADG100), and lean meat percentage (LMP) were available from breeding herds or the national test station. The Horizontal model combined random regression analysis of feed intake (FI) with single recorded traits of ADG100, LMP, and ADG30. In the Horizontal model, the FI data were horizontally structured with FI on each week as a "trait." The additive genetic and litter effects were modeled to be common across different FI records by reducing the rank of the covariance matrices using second- and first-order Legendre polynomials of age on test, respectively. The fixed effect and random residual variance were estimated for each weekly FI trait. Residual feed intake (RFI) was derived from the conditional distribution of FI given the breeding values of ADG100 and LMP. The heritability of FI varied by week on test in Duroc (0.12 to 0.19), Landrace (0.13 to 0.22), and Yorkshire (0.21 to 0.23). The heritability of RFI was lowest and highest in wk 6 (0.03) and 10 (0.10), respectively, in Duroc and wk 7 (0.04 and 0.02) and 1 (0.09 and 0.20), respectively, in Landrace and Yorkshire. The proportion of FI genetic variance explained by RFI ranged from 20 to 75% in Duroc, from 19 to 75% in Landrace, and from 11 to 91% in Yorkshire. Average daily gain from 30 to 100 kg BW and ADG30 heritabilities were moderate in Duroc (0.24 and 0.22, respectively), Landrace (0.34 and 0.25, respectively), and Yorkshire (0.34 and 0.22, respectively). Lean meat percentage heritability was moderate in Duroc (0.37) and large in Landrace (0.62) and Yorkshire (0.60). The genetic correlation of FI with ADG100 increased by week on test followed by a 32% decrease from wk 7 in Duroc and a 7% decrease in dam line breeds. Defining RFI as genetically independent of production traits leads to consistent and easy interpretable breeding values. The genetic parameters of traits in the feed efficiency complex and their dynamics over the test period showed breed differences that could be related to the fatness and growth potential of the breeds. The Horizontal model can be used to simultaneously analyze repeated and single recorded traits through proper modeling of the environmental variances and covariances.

Educational Attainment of 25 Year Old Norwegians According to Birth Order and Gender

ERIC Educational Resources Information Center

Kristensen, Petter; Bjerkedal, Tor

2010-01-01

This register-based longitudinal study of 392 969 Norwegians examined associations between birth order, gender and educational attainment at age 25 years within families (fixed effects regression) and between families (ordinary OLS regression). Data were retrieved from national registers for births of mothers with single births only and a first…

Relationship between hip and core strength and frontal plane alignment during a single leg squat.

PubMed

Stickler, Laurie; Finley, Margaret; Gulgin, Heather

2015-02-01

The purpose of this study was to examine the relationship between frontal plane kinematics of the single leg squat and strength of the trunk and hip in females. Forty healthy females participated in this study. An isometric "make" test using a dynamometer was used to assess peak force normalized to body weight for hip abduction, hip extension, hip external rotation, and a sidelying plank test. Two-dimensional software was used to analyze the frontal plane projection angle (FPPA) and pelvic angle during a single leg squat to 60°. All 4 strength factors were significantly correlated with the FPPA, ranging from r = 0.396 to r = 0.466. During multiple regression analysis, hip abduction strength was the greatest predictor of the variation in FPPA at r(2) = 0.22, p = 0.002. Thus, hip abduction strength accounted for 22% of the variation in the FPPA during the single leg squat. The only strength factor demonstrating a significant correlation with the pelvic angle was hip extension strength (r = 0.550, p < 0.001). Clinicians should consider the role of the hip abductors, hip external rotators, hip extensors and core musculature on the impact on the FPPA during a single squat, with focus on the hip abductors. Copyright © 2014 Elsevier Ltd. All rights reserved.

Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram

PubMed Central

Kim, Jongin; Park, Hyeong-jun

2016-01-01

The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a feature selection algorithm based on the sparse regression model. These features were classified using a support vector machine with a radial basis function kernel, an extreme learning machine, and two variants of an extreme learning machine with different kernels. Because each single trial consisted of thirty segments, our algorithm decided the label of the single trial by selecting the most frequent output among the outputs of the thirty segments. As a result, we observed that the extreme learning machine and its variants achieved better classification rates than the support vector machine with a radial basis function kernel and linear discrimination analysis. Thus, our results suggested that EEG responses to imagined speech could be successfully classified in a single trial using an extreme learning machine with a radial basis function and linear kernel. This study with classification of imagined speech might contribute to the development of silent speech BCI systems. PMID:28097128

Testing a single regression coefficient in high dimensional linear models

PubMed Central

Zhong, Ping-Shou; Li, Runze; Wang, Hansheng; Tsai, Chih-Ling

2017-01-01

In linear regression models with high dimensional data, the classical z-test (or t-test) for testing the significance of each single regression coefficient is no longer applicable. This is mainly because the number of covariates exceeds the sample size. In this paper, we propose a simple and novel alternative by introducing the Correlated Predictors Screening (CPS) method to control for predictors that are highly correlated with the target covariate. Accordingly, the classical ordinary least squares approach can be employed to estimate the regression coefficient associated with the target covariate. In addition, we demonstrate that the resulting estimator is consistent and asymptotically normal even if the random errors are heteroscedastic. This enables us to apply the z-test to assess the significance of each covariate. Based on the p-value obtained from testing the significance of each covariate, we further conduct multiple hypothesis testing by controlling the false discovery rate at the nominal level. Then, we show that the multiple hypothesis testing achieves consistent model selection. Simulation studies and empirical examples are presented to illustrate the finite sample performance and the usefulness of the proposed method, respectively. PMID:28663668

Testing a single regression coefficient in high dimensional linear models.

PubMed

Lan, Wei; Zhong, Ping-Shou; Li, Runze; Wang, Hansheng; Tsai, Chih-Ling

2016-11-01

In linear regression models with high dimensional data, the classical z -test (or t -test) for testing the significance of each single regression coefficient is no longer applicable. This is mainly because the number of covariates exceeds the sample size. In this paper, we propose a simple and novel alternative by introducing the Correlated Predictors Screening (CPS) method to control for predictors that are highly correlated with the target covariate. Accordingly, the classical ordinary least squares approach can be employed to estimate the regression coefficient associated with the target covariate. In addition, we demonstrate that the resulting estimator is consistent and asymptotically normal even if the random errors are heteroscedastic. This enables us to apply the z -test to assess the significance of each covariate. Based on the p -value obtained from testing the significance of each covariate, we further conduct multiple hypothesis testing by controlling the false discovery rate at the nominal level. Then, we show that the multiple hypothesis testing achieves consistent model selection. Simulation studies and empirical examples are presented to illustrate the finite sample performance and the usefulness of the proposed method, respectively.

Preoperative predictors of increased hospital costs in elective anterior cervical fusions: a single-institution analysis of 1,082 patients.

PubMed

Minhas, Shobhit V; Chow, Ian; Jenkins, Tyler J; Dhingra, Brian; Patel, Alpesh A

2015-05-01

The frequency of anterior cervical fusion (ACF) surgery and total hospital costs in spine surgery have substantially increased in the last several years. To determine which patient comorbidities are associated with increased total hospital costs after elective one- or two-level ACFs. Retrospective cohort analysis. Individuals who have undergone elective one- or two-level ACFs at our single institution. The total number of patients amounted to 1,082. Total hospital costs during single admission. Multivariate linear regression models were used to analyze independent effects of preoperative patient characteristics on total hospital costs. Univariate analysis was used to examine association of these characteristics on operative time, length of hospital stay (LOS), and complications. Age, obesity, and diabetes were independently associated with increased average hospital costs of $1,404 (95% confidence interval [CI], $857-$1,951; p<.001), $681 (95% CI, $285-$1,076; p=.001), and $1,877 (95% CI, $726-$3,072; p=.001), respectively. Age was associated with increased LOS (p<.001) and complications (p<.001) but not operative time (p=.431). Diabetes was associated with increased LOS (p<.001) and complications (p=.042) but not operative time (p=.234). Obesity was not associated with increased LOS (p=.164), complications (p=.890), or operative time (p=.067). This study highlights the patient comorbidities associated with increased hospital costs after one- or two-level ACFs and the potential drivers of these costs. Copyright © 2015 Elsevier Inc. All rights reserved.

Maintenance Operations in Mission Oriented Protective Posture Level IV (MOPPIV)

DTIC Science & Technology

1987-10-01

Repair FADAC Printed Circuit Board ............. 6 3. Data Analysis Techniques ............................. 6 a. Multiple Linear Regression... ANALYSIS /DISCUSSION ............................... 12 1. Exa-ple of Regression Analysis ..................... 12 S2. Regression results for all tasks...6 * TABLE 9. Task Grouping for Analysis ........................ 7 "TABXLE 10. Remove/Replace H60A3 Power Pack................. 8 TABLE

Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context.

PubMed

Martinez, Josue G; Carroll, Raymond J; Müller, Samuel; Sampson, Joshua N; Chatterjee, Nilanjan

2011-11-01

When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso.

An Experimental Study in Determining Energy Expenditure from Treadmill Walking using Hip-Worn Inertial Sensors

PubMed Central

Vathsangam, Harshvardhan; Emken, Adar; Schroeder, E. Todd; Spruijt-Metz, Donna; Sukhatme, Gaurav S.

2011-01-01

This paper describes an experimental study in estimating energy expenditure from treadmill walking using a single hip-mounted triaxial inertial sensor comprised of a triaxial accelerometer and a triaxial gyroscope. Typical physical activity characterization using accelerometer generated counts suffers from two drawbacks - imprecison (due to proprietary counts) and incompleteness (due to incomplete movement description). We address these problems in the context of steady state walking by directly estimating energy expenditure with data from a hip-mounted inertial sensor. We represent the cyclic nature of walking with a Fourier transform of sensor streams and show how one can map this representation to energy expenditure (as measured by V O2 consumption, mL/min) using three regression techniques - Least Squares Regression (LSR), Bayesian Linear Regression (BLR) and Gaussian Process Regression (GPR). We perform a comparative analysis of the accuracy of sensor streams in predicting energy expenditure (measured by RMS prediction accuracy). Triaxial information is more accurate than uniaxial information. LSR based approaches are prone to outlier sensitivity and overfitting. Gyroscopic information showed equivalent if not better prediction accuracy as compared to accelerometers. Combining accelerometer and gyroscopic information provided better accuracy than using either sensor alone. We also analyze the best algorithmic approach among linear and nonlinear methods as measured by RMS prediction accuracy and run time. Nonlinear regression methods showed better prediction accuracy but required an order of magnitude of run time. This paper emphasizes the role of probabilistic techniques in conjunction with joint modeling of triaxial accelerations and rotational rates to improve energy expenditure prediction for steady-state treadmill walking. PMID:21690001

Creep-Rupture Data Analysis - Engineering Application of Regression Techniques. Ph.D. Thesis - North Carolina State Univ.

NASA Technical Reports Server (NTRS)

Rummler, D. R.

1976-01-01

The results are presented of investigations to apply regression techniques to the development of methodology for creep-rupture data analysis. Regression analysis techniques are applied to the explicit description of the creep behavior of materials for space shuttle thermal protection systems. A regression analysis technique is compared with five parametric methods for analyzing three simulated and twenty real data sets, and a computer program for the evaluation of creep-rupture data is presented.

A Fast, Locally Adaptive, Interactive Retrieval Algorithm for the Analysis of DIAL Measurements

NASA Astrophysics Data System (ADS)

Samarov, D. V.; Rogers, R.; Hair, J. W.; Douglass, K. O.; Plusquellic, D.

2010-12-01

Differential absorption light detection and ranging (DIAL) is a laser-based tool which is used for remote, range-resolved measurement of particular gases in the atmosphere, such as carbon-dioxide and methane. In many instances it is of interest to study how these gases are distributed over a region such as a landfill, factory, or farm. While a single DIAL measurement only tells us about the distribution of a gas along a single path, a sequence of consecutive measurements provides us with information on how that gas is distributed over a region, making DIAL a natural choice for such studies. DIAL measurements present a number of interesting challenges; first, in order to convert the raw data to concentration it is necessary to estimate the derivative along the path of the measurement. Second, as the distribution of gases across a region can be highly heterogeneous it is important that the spatial nature of the measurements be taken into account. Finally, since it is common for the set of collected measurements to be quite large it is important for the method to be computationally efficient. Existing work based on Local Polynomial Regression (LPR) has been developed which addresses the first two issues, but the issue of computational speed remains an open problem. In addition to the latter, another desirable property is to allow user input into the algorithm. In this talk we present a novel method based on LPR which utilizes a variant of the RODEO algorithm to provide a fast, locally adaptive and interactive approach to the analysis of DIAL measurements. This methodology is motivated by and applied to several simulated examples and a study out of NASA Langley Research Center (LaRC) looking at the estimation of aerosol extinction in the atmosphere. A comparison study of our method against several other algorithms is also presented. References Chaudhuri, P., Marron, J.S., Scale-space view of curve estimation, Annals of Statistics 28 (2000) 408-428. Duong, T., Cowling, A., Koch, I., Wand, M.P., Feature significance for multivariate kernel density estimation, Computational Statistics and Data Analysis 52 (2008) 4225-4242. Godtliebsen, F., Marron, J.S., Chaudhuri, P., Statistical Significance of features in digital images, Image and Vision Computing 22 (2004) 1093-1104. Lafferty, J., Wasserman, L., RODEO: Sparse, Greedy Nonparametric Regression, Annals of Statistics 36 (2008) 28-63. Lindstrom, T., Holst, U., Weibring, P., Analysis of lidar fields using local polynomial regression, Environmetrics 16 (2005) 619-634

Resting-state functional magnetic resonance imaging: the impact of regression analysis.

PubMed

Yeh, Chia-Jung; Tseng, Yu-Sheng; Lin, Yi-Ru; Tsai, Shang-Yueh; Huang, Teng-Yi

2015-01-01

To investigate the impact of regression methods on resting-state functional magnetic resonance imaging (rsfMRI). During rsfMRI preprocessing, regression analysis is considered effective for reducing the interference of physiological noise on the signal time course. However, it is unclear whether the regression method benefits rsfMRI analysis. Twenty volunteers (10 men and 10 women; aged 23.4 ± 1.5 years) participated in the experiments. We used node analysis and functional connectivity mapping to assess the brain default mode network by using five combinations of regression methods. The results show that regressing the global mean plays a major role in the preprocessing steps. When a global regression method is applied, the values of functional connectivity are significantly lower (P ≤ .01) than those calculated without a global regression. This step increases inter-subject variation and produces anticorrelated brain areas. rsfMRI data processed using regression should be interpreted carefully. The significance of the anticorrelated brain areas produced by global signal removal is unclear. Copyright © 2014 by the American Society of Neuroimaging.

The role of environmental heterogeneity in meta-analysis of gene-environment interactions with quantitative traits.

PubMed

Li, Shi; Mukherjee, Bhramar; Taylor, Jeremy M G; Rice, Kenneth M; Wen, Xiaoquan; Rice, John D; Stringham, Heather M; Boehnke, Michael

2014-07-01

With challenges in data harmonization and environmental heterogeneity across various data sources, meta-analysis of gene-environment interaction studies can often involve subtle statistical issues. In this paper, we study the effect of environmental covariate heterogeneity (within and between cohorts) on two approaches for fixed-effect meta-analysis: the standard inverse-variance weighted meta-analysis and a meta-regression approach. Akin to the results in Simmonds and Higgins (), we obtain analytic efficiency results for both methods under certain assumptions. The relative efficiency of the two methods depends on the ratio of within versus between cohort variability of the environmental covariate. We propose to use an adaptively weighted estimator (AWE), between meta-analysis and meta-regression, for the interaction parameter. The AWE retains full efficiency of the joint analysis using individual level data under certain natural assumptions. Lin and Zeng (2010a, b) showed that a multivariate inverse-variance weighted estimator retains full efficiency as joint analysis using individual level data, if the estimates with full covariance matrices for all the common parameters are pooled across all studies. We show consistency of our work with Lin and Zeng (2010a, b). Without sacrificing much efficiency, the AWE uses only univariate summary statistics from each study, and bypasses issues with sharing individual level data or full covariance matrices across studies. We compare the performance of the methods both analytically and numerically. The methods are illustrated through meta-analysis of interaction between Single Nucleotide Polymorphisms in FTO gene and body mass index on high-density lipoprotein cholesterol data from a set of eight studies of type 2 diabetes. © 2014 WILEY PERIODICALS, INC.

Single vs. multiple sets of resistance exercise for muscle hypertrophy: a meta-analysis.

PubMed

Krieger, James W

2010-04-01

Previous meta-analyses have compared the effects of single to multiple sets on strength, but analyses on muscle hypertrophy are lacking. The purpose of this study was to use multilevel meta-regression to compare the effects of single and multiple sets per exercise on muscle hypertrophy. The analysis comprised 55 effect sizes (ESs), nested within 19 treatment groups and 8 studies. Multiple sets were associated with a larger ES than a single set (difference = 0.10 +/- 0.04; confidence interval [CI]: 0.02, 0.19; p = 0.016). In a dose-response model, there was a trend for 2-3 sets per exercise to be associated with a greater ES than 1 set (difference = 0.09 +/- 0.05; CI: -0.02, 0.20; p = 0.09), and a trend for 4-6 sets per exercise to be associated with a greater ES than 1 set (difference = 0.20 +/- 0.11; CI: -0.04, 0.43; p = 0.096). Both of these trends were significant when considering permutation test p values (p < 0.01). There was no significant difference between 2-3 sets per exercise and 4-6 sets per exercise (difference = 0.10 +/- 0.10; CI: -0.09, 0.30; p = 0.29). There was a tendency for increasing ESs for an increasing number of sets (0.24 for 1 set, 0.34 for 2-3 sets, and 0.44 for 4-6 sets). Sensitivity analysis revealed no highly influential studies that affected the magnitude of the observed differences, but one study did slightly influence the level of significance and CI width. No evidence of publication bias was observed. In conclusion, multiple sets are associated with 40% greater hypertrophy-related ESs than 1 set, in both trained and untrained subjects.

Exome Array Analysis of Nuclear Lens Opacity.

PubMed

Loomis, Stephanie J; Klein, Alison P; Lee, Kristine E; Chen, Fei; Bomotti, Samantha; Truitt, Barbara; Iyengar, Sudha K; Klein, Ronald; Klein, Barbara E K; Duggal, Priya

2018-06-01

Nuclear cataract is the most common subtype of age-related cataract, the leading cause of blindness worldwide. It results from advanced nuclear sclerosis, or opacity in the center of the optic lens, and is affected by both genetic and environmental risk factors, including smoking. We sought to understand the genetic factors associated with nuclear sclerosis through interrogation of rare and low frequency coding variants using exome array data. We analyzed Illumina Human Exome Array data for 1,488 participants of European ancestry in the Beaver Dam Eye Study who were without cataract surgery for association with nuclear sclerosis grade, controlling for age and sex. We performed single-variant regression analysis for 32,138 variants with minor allele frequency (MAF) ≥0.003. In addition, gene-based analysis of 11,844 genes containing at least two variants with MAF < 0.05 was performed using a gene-based unified burden and non-burden sequence kernel association test (SKAT-O). Additionally, both single-variant and gene-based analyses were analyzed stratified by smoking status. No single-variant test was statistically significant after Bonferroni correction (p < 1.6 × 10 -6 ; top single nucleotide polymorphism (SNP): rs144458991, p = 2.83 × 10 -5 ). Gene-based tests were suggestively associated with the gene RNF149 overall (p = 8.29 × 10 -6 ) and among never smokers (N = 790, p = 2.67 × 10 -6 ). This study did not find a significant genetic association with nuclear sclerosis, the possible association with the RNF149 gene highlights a potential candidate gene for future studies that aim to understand the genetic architecture of nuclear sclerosis.

Peripheral neuropathy predicts nuclear gene defect in patients with mitochondrial ophthalmoplegia.

PubMed

Horga, Alejandro; Pitceathly, Robert D S; Blake, Julian C; Woodward, Catherine E; Zapater, Pedro; Fratter, Carl; Mudanohwo, Ese E; Plant, Gordon T; Houlden, Henry; Sweeney, Mary G; Hanna, Michael G; Reilly, Mary M

2014-12-01

Progressive external ophthalmoplegia is a common clinical feature in mitochondrial disease caused by nuclear DNA defects and single, large-scale mitochondrial DNA deletions and is less frequently associated with point mutations of mitochondrial DNA. Peripheral neuropathy is also a frequent manifestation of mitochondrial disease, although its prevalence and characteristics varies considerably among the different syndromes and genetic aetiologies. Based on clinical observations, we systematically investigated whether the presence of peripheral neuropathy could predict the underlying genetic defect in patients with progressive external ophthalmoplegia. We analysed detailed demographic, clinical and neurophysiological data from 116 patients with genetically-defined mitochondrial disease and progressive external ophthalmoplegia. Seventy-eight patients (67%) had a single mitochondrial DNA deletion, 12 (10%) had a point mutation of mitochondrial DNA and 26 (22%) had mutations in either POLG, C10orf2 or RRM2B, or had multiple mitochondrial DNA deletions in muscle without an identified nuclear gene defect. Seventy-seven patients had neurophysiological studies; of these, 16 patients (21%) had a large-fibre peripheral neuropathy. The prevalence of peripheral neuropathy was significantly lower in patients with a single mitochondrial DNA deletion (2%) as compared to those with a point mutation of mitochondrial DNA or with a nuclear DNA defect (44% and 52%, respectively; P<0.001). Univariate analyses revealed significant differences in the distribution of other clinical features between genotypes, including age at disease onset, gender, family history, progressive external ophthalmoplegia at clinical presentation, hearing loss, pigmentary retinopathy and extrapyramidal features. However, binomial logistic regression analysis identified peripheral neuropathy as the only independent predictor associated with a nuclear DNA defect (P=0.002; odds ratio 8.43, 95% confidence interval 2.24-31.76). Multinomial logistic regression analysis identified peripheral neuropathy, family history and hearing loss as significant predictors of the genotype, and the same three variables showed the highest performance in genotype classification in a decision tree analysis. Of these variables, peripheral neuropathy had the highest specificity (91%), negative predictive value (83%) and positive likelihood ratio (5.87) for the diagnosis of a nuclear DNA defect. These results indicate that peripheral neuropathy is a rare finding in patients with single mitochondrial DNA deletions but that it is highly predictive of an underlying nuclear DNA defect. This observation may facilitate the development of diagnostic algorithms. We suggest that nuclear gene testing may enable a more rapid diagnosis and avoid muscle biopsy in patients with progressive external ophthalmoplegia and peripheral neuropathy. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain.

Peripheral neuropathy predicts nuclear gene defect in patients with mitochondrial ophthalmoplegia

PubMed Central

Pitceathly, Robert D. S.; Blake, Julian C.; Woodward, Catherine E.; Zapater, Pedro; Fratter, Carl; Mudanohwo, Ese E.; Plant, Gordon T.; Houlden, Henry; Sweeney, Mary G.; Hanna, Michael G.; Reilly, Mary M.

2014-01-01

Progressive external ophthalmoplegia is a common clinical feature in mitochondrial disease caused by nuclear DNA defects and single, large-scale mitochondrial DNA deletions and is less frequently associated with point mutations of mitochondrial DNA. Peripheral neuropathy is also a frequent manifestation of mitochondrial disease, although its prevalence and characteristics varies considerably among the different syndromes and genetic aetiologies. Based on clinical observations, we systematically investigated whether the presence of peripheral neuropathy could predict the underlying genetic defect in patients with progressive external ophthalmoplegia. We analysed detailed demographic, clinical and neurophysiological data from 116 patients with genetically-defined mitochondrial disease and progressive external ophthalmoplegia. Seventy-eight patients (67%) had a single mitochondrial DNA deletion, 12 (10%) had a point mutation of mitochondrial DNA and 26 (22%) had mutations in either POLG, C10orf2 or RRM2B, or had multiple mitochondrial DNA deletions in muscle without an identified nuclear gene defect. Seventy-seven patients had neurophysiological studies; of these, 16 patients (21%) had a large-fibre peripheral neuropathy. The prevalence of peripheral neuropathy was significantly lower in patients with a single mitochondrial DNA deletion (2%) as compared to those with a point mutation of mitochondrial DNA or with a nuclear DNA defect (44% and 52%, respectively; P < 0.001). Univariate analyses revealed significant differences in the distribution of other clinical features between genotypes, including age at disease onset, gender, family history, progressive external ophthalmoplegia at clinical presentation, hearing loss, pigmentary retinopathy and extrapyramidal features. However, binomial logistic regression analysis identified peripheral neuropathy as the only independent predictor associated with a nuclear DNA defect (P = 0.002; odds ratio 8.43, 95% confidence interval 2.24–31.76). Multinomial logistic regression analysis identified peripheral neuropathy, family history and hearing loss as significant predictors of the genotype, and the same three variables showed the highest performance in genotype classification in a decision tree analysis. Of these variables, peripheral neuropathy had the highest specificity (91%), negative predictive value (83%) and positive likelihood ratio (5.87) for the diagnosis of a nuclear DNA defect. These results indicate that peripheral neuropathy is a rare finding in patients with single mitochondrial DNA deletions but that it is highly predictive of an underlying nuclear DNA defect. This observation may facilitate the development of diagnostic algorithms. We suggest that nuclear gene testing may enable a more rapid diagnosis and avoid muscle biopsy in patients with progressive external ophthalmoplegia and peripheral neuropathy. PMID:25281868

Single administration of intra-articular bupivacaine in arthroscopic knee surgery: a systematic review and meta-analysis.

PubMed

Sun, Qi-Bin; Liu, Shi-Dong; Meng, Qin-Jun; Qu, Hua-Zheng; Zhang, Zheng

2015-02-10

Single administration of intra-articular (IA) bupivacaine for pain relief after arthroscopic knee surgery is effective, but its active duration and dose-response relationship is unclear. We conducted this meta-analysis to summarize all published randomized controlled trials (RCTs), thus providing the most recent information on the safety and efficacy of single-administration IA bupivacaine for pain relief after arthroscopic knee surgery, and to determine whether a dose-response relationship exists. A systematic electronic literature search (through April 2014) was conducted to identify those RCTs that addressed the safety and efficacy of a single administration of IA bupivacaine for pain management after arthroscopic knee surgery. Subgroup analysis was conducted to determine changes in visual analog scale (VAS) scores at seven postoperative time points. Meta-regression and subgroup analyses were carried out to assess the effects of various treatment factors on efficacy and to evaluate the dose-response relationship of bupivacaine. Weighted mean differences or relative risks were calculated and pooled using a random-effects model. Twenty-eight trials involving 1,560 patients who underwent arthroscopic knee surgery met the inclusion criteria. The trials were subject to medium risk of bias. VAS scores at 2, 4, 6, 12, and 24 h postoperatively were significantly lower, the number of patients requiring supplementary analgesia was smaller, and the time to first request for analgesia was longer in the IA bupivacaine group than in the placebo group. The analgesic effect of single-administration IA bupivacaine may be associated with the effect of concomitant administration of epinephrine and concentration of bupivacaine, and no dose-response relationship was identified. No significant difference in side effects was detected between groups. Current evidence shows that the use of single-administration IA bupivacaine is effective for postoperative pain management in patients undergoing arthroscopic knee surgery, with satisfactory short-term safety. Low-dose administration of IA bupivacaine 0.5% combined with epinephrine adjuvant in clinical practice should be performed. Additional high-quality RCTs with longer follow-up periods are required to examine the safety of single-administration IA bupivacaine.

Linear regression analysis: part 14 of a series on evaluation of scientific publications.

PubMed

Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

2010-11-01

Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

An improved multiple linear regression and data analysis computer program package

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

Randomised clinical trial: study of escalating doses of NRL001 given in rectal suppositories of different weights.

PubMed

Bell, D; Pediconi, C; Jacobs, A

2014-03-01

The application of α-adrenoceptor agonists can improve faecal incontinence symptoms. The aim of this study was to investigate the pharmacokinetic and systemic effects of NRL001 administered as different strengths in 1 or 2 g suppositories. This randomised, double-blind, placebo controlled study included 48 healthy subjects. Group 1 consisted of two cohorts of 12 subjects administered either four single doses of 1 or 2 g rectal suppository with either 5, 7.5 or 10 mg NRL001, or matching placebo. Group 2 consisted of two cohorts of 12 subjects administered either four single doses of 1 or 2 g rectal suppository with either 10, 12.5 or 15 mg NRL001, or matching placebo. Doses were given in an escalating manner with placebo at a random position within the sequence. Tmax was at ~4.5 h post-dose for all NRL001 doses. Median AUC0-tz , AUC0-∞ and Cmax increased with increasing dose for both suppository sizes. The estimate of ratios of geometric means comparing 2 g with 1 g suppository, and regression analysis for dose proportionality, was close to 1 for the variables AUC0-tz , AUC0-∞ and Cmax (P > 0.05). For both suppository sizes, 20-min mean pulse rate was significantly decreased compared with placebo with all doses (P < 0.05). Blood pressure decreased overall. There were 144 adverse events (AEs) and no serious AEs reported during the study. All AEs were mild in severity. The regression analysis concluded that the doses were dose proportional. Colorectal Disease © 2014 The Association of Coloproctology of Great Britain and Ireland.

Effects of Demographic Factors, Body Mass Index, Alcohol Drinking and Smoking Habits on Irritable Bowel Syndrome: A Case Control Study

PubMed Central

Farzaneh, N; Ghobaklou, M; Moghimi-Dehkordi, B; Naderi, N; Fadai, F

2013-01-01

Background: Irritable Bowel Syndrome (IBS) is a common functional gastrointestinal disorder. Aims: To identify demographic factors in patients with IBS. Subjects and Methods: One-hundred and fifty three IBS patients seen at Taleghani Hospital Gastroenterology Clinic and met the Rome III criteria and 163 peoples who did not meet IBS criteria were consecutively enrolled. Both groups were asked to complete a self-rating questionnaire containing information, which included questions about age, sex, monthly income, education level, marital status, height, weight, alcohol drinking and smoking habits. Student's t-test, Pearson's Chi-square and logistic regression were used to statistical analysis. Results: The mean (SD) age for IBS patients 36.3 (13.5) years and 33.1 (9.9) years in non-IBS group (P < 0.001). Frequency of IBS defined by Rome III criteria was higher in females and younger individuals. Univariate analysis showed that IBS in males was associated with a lower monthly income and educational level and in females younger age, single, lower monthly income and educational level, body mass index (BMI), and unemployment status. Multivariate logistic regression identified a low level of education in males (Odds ratio [OR] = 3.6, 95% Confidence interval [CI]: 1.4-9.6) and in females, lower education level (OR = 2.4, 95% CI: 1.1-5.2), lower BMI (OR = 0.94, 95% CI: 0.89-0.99), unemployed (OR = 0.31, 95% CI: 0.11-0.85) and smoking (OR = 6.2, 95% CI: 1.03-37.2). Conclusion: We identified demographic factors in IBS patients. Being single and having a lower educational level, income, lower BMI and being unemployed were the most important factors associated with IBS, particularly in females. PMID:24116320

Menstrual cycle phase and single tablet antiretroviral medication adherence in women with HIV.

PubMed

Hessol, Nancy A; Holman, Susan; Minkoff, Howard; Cohen, Mardge H; Golub, Elizabeth T; Kassaye, Seble; Karim, Roksana; Sosanya, Oluwakemi; Shaheen, Christopher; Merhi, Zaher

2016-01-01

Suboptimal adherence to antiretroviral (ARV) therapy among HIV-infected individuals is associated with increased risk of progression to AIDS and the development of HIV resistance to ARV medications. To examine whether the luteal phase of the menstrual cycle is independently associated with suboptimal adherence to single tablet regimen (STR) ARV medication, data were analyzed from a multicenter cohort study of HIV-infected women who reported regular menstrual cycles and were taking an STR. In a cross-sectional analysis, suboptimal adherence to an STR among women in their follicular phase was compared with suboptimal adherence among women in their luteal phase. In two-way crossover analyses, whereby the same woman was assessed for STR medication adherence in both her follicular and luteal phases, the estimated exact conditional odds of non-adherence to an STR was measured. In adjusted logistic regression analysis of the cross-sectional data (N=327), women with ≤12 years of education were more than three times more likely to have suboptimal adherence (OR=3.6, p=.04) compared to those with >12 years of education. Additionally, women with Center for Epidemiological Studies Depression Scale (CES-D) scores ≥23 were 2.5-times more likely to have suboptimal adherence (OR=2.6, p=.02) compared to those with CES-D scores <23. In conditional logistic regression analyses of the crossover data (N=184), having childcare responsibilities was associated with greater odds of ≤95% adherence. Menstrual cycle phase was not associated with STR adherence in either the cross-sectional or crossover analyses. The lack of association between phase of the menstrual cycle and adherence to an STR in HIV-infected women means attention can be given to other more important risk factors for suboptimal adherence, such as depression, level of education, and childcare responsibilities.

Differences in Routine Laboratory Ordering Between a Teaching Service and a Hospitalist Service at a Single Academic Medical Center.

PubMed

Ellenbogen, Michael I; Ma, Madeleine; Christensen, Nicholas P; Lee, Jungwha; O'Leary, Kevin J

2017-01-01

Studies have shown that the overutilization of laboratory tests ("labs") for hospitalized patients is common and can cause adverse health outcomes. Our objective was to compare the ordering tendencies for routine complete blood counts (CBC) and chemistry panels by internal medicine residents and hospitalists. This observational study included a survey of medicine residents and hospitalists and a retrospective analysis of labs ordering data. The retrospective data analysis comprised patients admitted to either the teaching service or nonteaching hospitalist service at a single hospital during 2014. The survey asked residents and hospitalists about their practices and preferences on labs ordering. The frequency and timing of one-time and daily CBC and basic chemistry panel ordering for teaching service and hospitalist patients were obtained from our data warehouse. The average number of CBCs per patient per day and chemistry panels per patient per day was calculated for both services and multivariate regression was performed to control for patient characteristics. Forty-four of 120 (37%) residents and 41 of 53 (77%) hospitalists responded to the survey. Forty-four (100%) residents reported ordering a daily CBC and chemistry panel rather than one-time labs at patient admission compared with 22 (54%) hospitalists ( P < 0.001). For CBCs, teaching service patients averaged 1.72/day and hospitalist service patients averaged 1.43/day ( P < 0.001). For basic chemistry panels, teaching service patients averaged 1.96/day and hospitalist service patients averaged 1.78/day ( P < 0.001). Results were similar in multivariate regression models adjusting for patient characteristics. Residents' self-reported and actual use of CBCs and chemistry panels is significantly higher than that of hospitalists in the same hospital. Our results reveal an opportunity for greater supervision and improved instruction of cost-conscious ordering practices.

Change of sleep quality from pre- to 3 years post-solid organ transplantation: The Swiss Transplant Cohort Study

PubMed Central

Denhaerynck, Kris; Huynh-Do, Uyen; Binet, Isabelle; Hadaya, Karine; De Geest, Sabina

2017-01-01

Background Poor sleep quality (SQ) is common after solid organ transplantation; however, very little is known about its natural history. We assessed the changes in SQ from pre- to 3 years post-transplant in adult heart, kidney, liver and lung recipients included in the prospective nation-wide Swiss Transplant Cohort Study. We explored associations with selected variables in patients suffering persistent poor SQ compared to those with good or variable SQ. Methods Adult single organ transplant recipients enrolled in the Swiss Transplant Cohort Study with pre-transplant and at least 3 post-transplant SQ assessment data were included. SQ was self-reported pre-transplant (at listing), then at 6, 12, 24 and 36 months post-transplant. A single SQ item was used to identify poor (0–5) and good sleepers (6–10). Between organ groups, SQ was compared via logistic regression analysis with generalized estimating equations. Within the group reporting persistently poor SQ, we used logistic regression or Kaplan-Meier analysis as appropriate to check for differences in global quality of life and survival. Results In a sample of 1173 transplant patients (age: 52.1±13.2 years; 65% males; 66% kidney, 17% liver, 10% lung, 7% heart) transplanted between 2008 and 2012, pre- transplant poor SQ was highest in liver (50%) and heart (49%) recipients. Overall, poor SQ decreased significantly from pre-transplant (38%) to 24 months post-transplant (26%) and remained stable at 3 years (29%). Patients reporting persistently poor SQ had significantly more depressive symptomatology and lower global quality of life. Conclusion Because self-reported poor SQ is related to poorer global quality of life, these results emphasize the need for further studies to find suitable treatment options for poor SQ in transplant recipients. PMID:29020112

Comparison between imported versus domestic drug-eluting stents in China: A large single-center data.

PubMed

Liu, Ru; Gao, Zhan; Chen, Jue; Gao, Lijian; Song, Lei; Qiao, Shubin; Yang, Yuejin; Gao, Runlin; Xu, Bo; Yuan, Jinqing

2017-08-01

In recent years, most drug-eluting stents (DESs) were domestically produced in China, but how domestic DESs perform compared to imported DESs was still unknown. A total of 9011 consecutive cases with DESs implantation in a single center throughout 2013 were prospectively collected. Two-year clinical outcomes were evaluated between patients implanted with imported and domestic DESs. During 2-year follow-up, the rates of all-cause death, cardiac death, myocardial infarction, stroke, and stent thrombosis were not significantly different between two groups. However, the rate of revascularization was significantly higher in domestic DES group, shown as higher rates of overall revascularization, target vessel revascularization (TVR), and target lesion revascularization (TLR) (9.7% vs 6.4%, P < 0.001; 5.6% vs 3.2%, P < 0.001; 4.5% vs 2.2%, P < 0.001, respectively). Accordingly, major adverse cardiac events (MACE) rate was significantly higher in domestic DES group (12.1% vs 8.5%, P < 0.001). Multivariable Cox regression analysis indicated that domestic DES was an independent risk factor of MACE (HR [95%CI]: 1.22 [1.05-1.41]), overall revascularization (HR [95%CI]: 1.29 [1.09-1.53]), TVR (HR [95%CI]: 1.54 [1.22-1.94]), and TLR (HR [95%CI]: 1.85 [1.41-2.42]). After propensity score matching, the rates of overall revascularization, TVR, and TLR were still significantly higher in domestic DES group, and domestic DES was still predictive of overall revascularization, TVR, and TLR in multivariate Cox regression analysis. Domestic DESs showed the same safety as imported DESs in this real-world cohort. But, patients implanted with domestic DESs had a higher risk of revascularization than imported DESs. © 2017, Wiley Periodicals, Inc.

The Neck Disability Index (NDI) and its correlation with quality of life and mental health measures among patients with single-level cervical disc disease scheduled for surgery.

PubMed

Sundseth, J; Kolstad, F; Johnsen, L G; Pripp, A H; Nygaard, O P; Andresen, H; Fredriksli, O A; Myrseth, E; Züchner, M; Zwart, J A

2015-10-01

The Neck Disability Index (NDI) is widely used as a self-rated disability score in patients with cervical radiculopathy. The purpose of this study was to evaluate whether the NDI score correlated with other assessments of quality of life and mental health in a specific group of patients with single-level cervical disc disease and corresponding radiculopathy. One hundred thirty-six patients were included in a prospective, randomized controlled clinical multicenter study on one-level anterior cervical discectomy with arthroplasty (ACDA) versus one-level anterior cervical discectomy with fusion (ACDF). The preoperative data were obtained at hospital admission 1 to 3 days prior to surgery. The NDI score was used as the dependent variable and correlation as well as regression analyses were conducted to assess the relationship with the short form-36, EuroQol-5Dimension-3 level and Hospital Anxiety and Depression Scale. The mean age at inclusion was 44.1 years (SD ±7.0, range 26-59 years), of which 46.3 % were male. Mean NDI score was 48.6 (SD = 12.3, minimum 30 and maximum 88). Simple linear regression analysis demonstrated a significant correlation between NDI and the EuroQol-5Dimension-3 level [R = -0.64, 95 % confidence interval (CI) -30.1- -19.8, p < 0.001] and to a lesser extent between NDI and the short form-36 physical component summary [R = -0.49, 95 % CI (-1.10- -0.58), p < 0.001] and the short form-36 mental component summary [R = -0.25, 95 % CI (-0.47- -0-09), p = 0.004]. Regarding NDI and the Hospital Anxiety and Depression Scale, a significant correlation for depression was found [R = 0.26, 95 % CI (0.21-1.73), p = 0.01]. Multiple linear regression analysis showed a statistically significant and the strongest correlation between NDI and the independent variables in the following order: EuroQol-5Dimension-3 level [R = -0.64, 95 % CI (-23.5- -7.9), p <0.001], short form-36 physical component summary [R = -0.41, 95 % CI (-0.93- -0.23), p = 0.001] and short form-36 mental component summary [R = -0.36, 95 % CI (-0.53- -0.15), p = 0.001]. The results from the present study show that the NDI correlated significantly with a different quality of life and mental health measures among patients with single-level cervical disc disease and corresponding radiculopathy.

Retargeted Least Squares Regression Algorithm.

PubMed

Zhang, Xu-Yao; Wang, Lingfeng; Xiang, Shiming; Liu, Cheng-Lin

2015-09-01

This brief presents a framework of retargeted least squares regression (ReLSR) for multicategory classification. The core idea is to directly learn the regression targets from data other than using the traditional zero-one matrix as regression targets. The learned target matrix can guarantee a large margin constraint for the requirement of correct classification for each data point. Compared with the traditional least squares regression (LSR) and a recently proposed discriminative LSR models, ReLSR is much more accurate in measuring the classification error of the regression model. Furthermore, ReLSR is a single and compact model, hence there is no need to train two-class (binary) machines that are independent of each other. The convex optimization problem of ReLSR is solved elegantly and efficiently with an alternating procedure including regression and retargeting as substeps. The experimental evaluation over a range of databases identifies the validity of our method.

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

PubMed

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

2015-02-01

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

Portugal and Angola: similarities and differences in Toxoplasma gondii seroprevalence and risk factors in pregnant women.

PubMed

Lobo, M L; Patrocinio, G; Sevivas, T; DE Sousa, B; Matos, O

2017-01-01

In this study we determined the presence of IgM/IgG antibodies to Toxoplasma gondii in sera of 155 and 300 pregnant women from Lisbon (Portugal) and Luanda (Angola), respectively, and evaluated the potential risk factors associated with this infection. DNA detection was performed by PCR assays targeting T. gondii regions (RE/B1). Overall, 21·9% (10·9% IgG, 10·9% IgG/IgM) of the Lisbon women and 27·3% (23·7%, IgG, 2% IgM, 1·7% IgG/IgM) of the Luanda women had antibodies to T. gondii. Single variable and binary logistic regression analyses were conducted. Based on the latter, contacts with cats (family/friends), and having more than two births were identified as risk factors for Toxoplasma infection in Lisbon women. In Luanda, the risk factors for T. gondii infection suggested by the single variable analysis (outdoor contact with cats and consumption of pasteurized milk/dairy products) were not confirmed by binary logistic regression. This study shows original data from Angola, and updated data from Portugal in the study of infection by T. gondii in pregnant women, indicating that the prevalence of anti-Toxoplasma antibodies is high enough to alert the government health authorities and implement appropriate measures to control this infection.

A comparison of disordered eating attitudes and behaviors among Filipino and American college students.

PubMed

Madanat, H N; Hawks, S R; Novilla, M L

2006-09-01

The purpose of this study was to compare the levels of eating disordered attitudes and behaviors among college students in the United States and the Philippines. A convenience-based cross-sectional survey. General education classes in one college in Manila and another in the Western US. 340 college students. A paper-pencil survey was given to the students in the classroom consisting of Eating Attitudes Test (EAT-26) and demographic variables. Eating disordered attitudes and behaviors (scoring 20 or more on EAT-26) was the dependent variable, while gender and country of residence were the two main independent variables. Frequency distributions, chi-squares, and logistic regression analysis were employed to summarize and analyze the data. Filipino students were 10.9 times (p-value <0.0001) more likely to have eating disordered attitudes and behaviors than their American counterparts controlling for the demographics collected. This relationship remained significant when regression models were done for each gender separately. In addition, married students and more specifically married female students were more likely to have eating disordered attitudes and behaviors than single students or single females. Arguments are made as to why higher levels of eating disordered attitudes and behaviors are observed among Filipino college students. These results provide important information about the levels of eating disordered attitudes and behaviors in the Philippines and may be useful for developing future education programs.

Incidence and risk factors of axial symptoms after cervical disc arthroplasty: a minimum 5-year follow-up study.

PubMed

Chen, Jing; Li, Jia; Qiu, Gang; Wei, Jingchao; Qiu, Yanfen; An, Yonghui; Shen, Yong

2016-09-20

The purpose of this study was to investigate whether uncovertebral joint ossification was a risk factor for axial symptoms (AS) after cervical disc arthroplasty (CDA). This retrospective study included 52 consecutive patients who underwent CDA for single-level cervical disc disease. To examine possible risk factors for AS after CDA, univariate and multivariate logistic regression analyses were conducted to compare data from the patients with and without AS (the AS and no-AS groups, respectively). Among the 52 patients examined, AS were observed in 24 patients (46.2 %), including a stiff neck (n = 11), neck pain and dullness (n = 10), and shoulder pain (n = 3). Uncovertebral joint ossification was detected in 22 (42.3 %) patients, including 17 patients in the AS group and 5 patients in the no-AS group. Clinical outcome improved during the follow-up period for the AS group. According to multivariate logistic regression analysis, uncovertebral joint ossification, cervical kyphosis, and range of motion (ROM) at the index level were identified as significant risk factors for AS after CDA. Satisfactory clinical outcomes were observed following CDA for the treatment of single-level cervical disc disease in the present cohort. In addition, uncovertebral joint ossification, cervical kyphosis, and ROM at the index level were found to affect the incidence of AS after CDA.

Type I error rates of rare single nucleotide variants are inflated in tests of association with non-normally distributed traits using simple linear regression methods.

PubMed

Schwantes-An, Tae-Hwi; Sung, Heejong; Sabourin, Jeremy A; Justice, Cristina M; Sorant, Alexa J M; Wilson, Alexander F

2016-01-01

In this study, the effects of (a) the minor allele frequency of the single nucleotide variant (SNV), (b) the degree of departure from normality of the trait, and (c) the position of the SNVs on type I error rates were investigated in the Genetic Analysis Workshop (GAW) 19 whole exome sequence data. To test the distribution of the type I error rate, 5 simulated traits were considered: standard normal and gamma distributed traits; 2 transformed versions of the gamma trait (log 10 and rank-based inverse normal transformations); and trait Q1 provided by GAW 19. Each trait was tested with 313,340 SNVs. Tests of association were performed with simple linear regression and average type I error rates were determined for minor allele frequency classes. Rare SNVs (minor allele frequency < 0.05) showed inflated type I error rates for non-normally distributed traits that increased as the minor allele frequency decreased. The inflation of average type I error rates increased as the significance threshold decreased. Normally distributed traits did not show inflated type I error rates with respect to the minor allele frequency for rare SNVs. There was no consistent effect of transformation on the uniformity of the distribution of the location of SNVs with a type I error.

Evaluating the Applicability of Phi Coefficient in Indicating Habitat Preferences of Forest Soil Fauna Based on a Single Field Study in Subtropical China.

PubMed

Cui, Yang; Wang, Silong; Yan, Shaokui

2016-01-01

Phi coefficient directly depends on the frequencies of occurrence of organisms and has been widely used in vegetation ecology to analyse the associations of organisms with site groups, providing a characterization of ecological preference, but its application in soil ecology remains rare. Based on a single field experiment, this study assessed the applicability of phi coefficient in indicating the habitat preferences of soil fauna, through comparing phi coefficient-induced results with those of ordination methods in charactering soil fauna-habitat(factors) relationships. Eight different habitats of soil fauna were implemented by reciprocal transfer of defaunated soil cores between two types of subtropical forests. Canonical correlation analysis (CCorA) showed that ecological patterns of fauna-habitat relationships and inter-fauna taxa relationships expressed, respectively, by phi coefficients and predicted abundances calculated from partial redundancy analysis (RDA), were extremely similar, and a highly significant relationship between the two datasets was observed (Pillai's trace statistic = 1.998, P = 0.007). In addition, highly positive correlations between phi coefficients and predicted abundances for Acari, Collembola, Nematode and Hemiptera were observed using linear regression analysis. Quantitative relationships between habitat preferences and soil chemical variables were also obtained by linear regression, which were analogous to the results displayed in a partial RDA biplot. Our results suggest that phi coefficient could be applicable on a local scale in evaluating habitat preferences of soil fauna at coarse taxonomic levels, and that the phi coefficient-induced information, such as ecological preferences and the associated quantitative relationships with habitat factors, will be largely complementary to the results of ordination methods. The application of phi coefficient in soil ecology may extend our knowledge about habitat preferences and distribution-abundance relationships, which will benefit the understanding of biodistributions and variations in community compositions in the soil. Similar studies in other places and scales apart from our local site will be need for further evaluation of phi coefficient.

Evaluating the Applicability of Phi Coefficient in Indicating Habitat Preferences of Forest Soil Fauna Based on a Single Field Study in Subtropical China

PubMed Central

Cui, Yang; Wang, Silong; Yan, Shaokui

2016-01-01

Phi coefficient directly depends on the frequencies of occurrence of organisms and has been widely used in vegetation ecology to analyse the associations of organisms with site groups, providing a characterization of ecological preference, but its application in soil ecology remains rare. Based on a single field experiment, this study assessed the applicability of phi coefficient in indicating the habitat preferences of soil fauna, through comparing phi coefficient-induced results with those of ordination methods in charactering soil fauna-habitat(factors) relationships. Eight different habitats of soil fauna were implemented by reciprocal transfer of defaunated soil cores between two types of subtropical forests. Canonical correlation analysis (CCorA) showed that ecological patterns of fauna-habitat relationships and inter-fauna taxa relationships expressed, respectively, by phi coefficients and predicted abundances calculated from partial redundancy analysis (RDA), were extremely similar, and a highly significant relationship between the two datasets was observed (Pillai's trace statistic = 1.998, P = 0.007). In addition, highly positive correlations between phi coefficients and predicted abundances for Acari, Collembola, Nematode and Hemiptera were observed using linear regression analysis. Quantitative relationships between habitat preferences and soil chemical variables were also obtained by linear regression, which were analogous to the results displayed in a partial RDA biplot. Our results suggest that phi coefficient could be applicable on a local scale in evaluating habitat preferences of soil fauna at coarse taxonomic levels, and that the phi coefficient-induced information, such as ecological preferences and the associated quantitative relationships with habitat factors, will be largely complementary to the results of ordination methods. The application of phi coefficient in soil ecology may extend our knowledge about habitat preferences and distribution-abundance relationships, which will benefit the understanding of biodistributions and variations in community compositions in the soil. Similar studies in other places and scales apart from our local site will be need for further evaluation of phi coefficient. PMID:26930593

Limited sampling strategy for determining metformin area under the plasma concentration–time curve

PubMed Central

Santoro, Ana Beatriz; Stage, Tore Bjerregaard; Struchiner, Claudio José; Christensen, Mette Marie Hougaard; Brosen, Kim

2016-01-01

Aim The aim was to develop and validate limited sampling strategy (LSS) models to predict the area under the plasma concentration–time curve (AUC) for metformin. Methods Metformin plasma concentrations (n = 627) at 0–24 h after a single 500 mg dose were used for LSS development, based on all subsets linear regression analysis. The LSS‐derived AUC(0,24 h) was compared with the parameter ‘best estimate’ obtained by non‐compartmental analysis using all plasma concentration data points. Correlation between the LSS‐derived and the best estimated AUC(0,24 h) (r 2), bias and precision of the LSS estimates were quantified. The LSS models were validated in independent cohorts. Results A two‐point (3 h and 10 h) regression equation with no intercept estimated accurately the individual AUC(0,24 h) in the development cohort: r 2 = 0.927, bias (mean, 95% CI) –0.5, −2.7–1.8% and precision 6.3, 4.9–7.7%. The accuracy of the two point LSS model was verified in study cohorts of individuals receiving single 500 or 1000 mg (r 2 = –0.933–0.934) or seven 1000 mg daily doses (r 2 = 0.918), as well as using data from 16 published studies covering a wide range of metformin doses, demographics, clinical and experimental conditions (r 2 = 0.976). The LSS model reproduced previously reported results for effects of polymorphisms in OCT2 and MATE1 genes on AUC(0,24 h) and renal clearance of metformin. Conclusions The two point LSS algorithm may be used to assess the systemic exposure to metformin under diverse conditions, with reduced costs of sampling and analysis, and saving time for both subjects and investigators. PMID:27324407

Exact Analysis of Squared Cross-Validity Coefficient in Predictive Regression Models

ERIC Educational Resources Information Center

Shieh, Gwowen

2009-01-01

In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference…

Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

USDA-ARS?s Scientific Manuscript database

Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...

The N400 as a snapshot of interactive processing: evidence from regression analyses of orthographic neighbor and lexical associate effects

PubMed Central

Laszlo, Sarah; Federmeier, Kara D.

2010-01-01

Linking print with meaning tends to be divided into subprocesses, such as recognition of an input's lexical entry and subsequent access of semantics. However, recent results suggest that the set of semantic features activated by an input is broader than implied by a view wherein access serially follows recognition. EEG was collected from participants who viewed items varying in number and frequency of both orthographic neighbors and lexical associates. Regression analysis of single item ERPs replicated past findings, showing that N400 amplitudes are greater for items with more neighbors, and further revealed that N400 amplitudes increase for items with more lexical associates and with higher frequency neighbors or associates. Together, the data suggest that in the N400 time window semantic features of items broadly related to inputs are active, consistent with models in which semantic access takes place in parallel with stimulus recognition. PMID:20624252

Using an innovative multiple regression procedure in a cancer population (Part II): fever, depressive affect, and mobility problems clarify an influential symptom pair (pain-fatigue/weakness) and cluster (pain-fatigue/weakness-sleep problems).

PubMed

Francoeur, Richard B

2015-01-01

Most patients with advanced cancer experience symptom pairs or clusters among pain, fatigue, and insomnia. However, only combinations where symptoms are mutually influential hold potential for identifying patient subgroups at greater risk, and in some contexts, interventions with "cross-over" (multisymptom) effects. Improved methods to detect and interpret interactions among symptoms, signs, or biomarkers are needed to reveal these influential pairs and clusters. I recently created sequential residual centering (SRC) to reduce multicollinearity in moderated regression, which enhances sensitivity to detect these interactions. I applied SRC to moderated regressions of single-item symptoms that interact to predict outcomes from 268 palliative radiation outpatients. I investigated: 1) the hypothesis that the interaction, pain × fatigue/weakness × sleep problems, predicts depressive affect only when fever presents, and 2) an exploratory analysis, when fever is absent, that the interaction, pain × fatigue/weakness × sleep problems × depressive affect, predicts mobility problems. In the fever context, three-way interactions (and derivative terms) of the four symptoms (pain, fatigue/weakness, fever, sleep problems) are tested individually and simultaneously; in the non-fever context, a single four-way interaction (and derivative terms) is tested. Fever interacts separately with fatigue/weakness and sleep problems; these comoderators each magnify the pain-depressive affect relationship along the upper or full range of pain values. In non-fever contexts, fatigue/weakness, sleep problems, and depressive affect comagnify the relationship between pain and mobility problems. Different mechanisms contribute to the pain × fatigue/weakness × sleep problems interaction, but all depend on the presence of fever, a sign/biomarker/symptom of proinflammatory sickness behavior. In non-fever contexts, depressive affect is no longer an outcome representing malaise from the physical symptoms of sickness, but becomes a fourth symptom of the interaction. In outpatient subgroups at heightened risk, single interventions could potentially relieve multiple symptoms when fever accompanies sickness malaise and in non-fever contexts with mobility problems. SRC strengthens insights into symptom pairs/clusters.

Unpredictability of fighter pilots' g duration tolerance by anthropometric and physiological characteristics.

PubMed

Park, Myunghwan; Yoo, Seunghoon; Seol, Hyeongju; Kim, Cheonyoung; Hong, Youngseok

2015-04-01

While the factors affecting fighter pilots' G level tolerance have been widely accepted, the factors affecting fighter pilots' G duration tolerance have not been well understood. Thirty-eight subjects wearing anti-G suits were exposed to sustained high G forces using a centrifuge. The subjects exerted AGSM and decelerated the centrifuge when they reached the point of loss of peripheral vision. The G profile consisted of a +2.3 G onset rate, +7.3 G single plateau, and -1.6 G offset rate. Each subject's G tolerance time was recorded and the relationship between the tolerance time and the subject's anthropometric and physiological factors were analyzed. The mean tolerance time of the 38 subjects was 31.6 s, and the min and max tolerance times were 20 s and 58 s, respectively. The correlation analysis indicated that none of the factors had statistically significant correlations with the subjects' G duration tolerance. Stepwise multiple regression analysis showed that G duration tolerance was not dependent on any personal factors of the subjects. After the values of personal factors were simplified into 0 or 1, the t-test analysis showed that subjects' heights were inversely correlated with G duration tolerance at a statistically significant level. However, a logistic regression analysis suggested that the effect of the height factor to a pilot's G duration tolerance was too weak to be used as a predictor of a pilot's G tolerance. Fighter pilots' G duration tolerance could not be predicted by pilots' anthropometric and physiological factors.

Estimating Causal Effects of Education Interventions Using a Two-Rating Regression Discontinuity Design: Lessons from a Simulation Study

ERIC Educational Resources Information Center

Porter, Kristin E.; Reardon, Sean F.; Unlu, Fatih; Bloom, Howard S.; Robinson-Cimpian, Joseph P.

2014-01-01

A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the "surface" method, the "frontier" method, the "binding-score" method, and…

Dose-related cerebellar abnormality in rats with prenatal exposure to X-irradiation by magnetic resonance imaging volumetric analysis.

PubMed

Sawada, Kazuhiko; Saito, Shigeyoshi; Horiuchi-Hirose, Miwa; Mori, Yuki; Yoshioka, Yoshichika; Murase, Kenya

2013-09-01

Cerebellar abnormalities in 4-week-old rats with a single whole body X-irradiation at a dose of 0.5, 1.0, or 1.5 Gy on embryonic day (ED) 15 were examined by magnetic resonance imaging (MRI) volumetry. A 3D T2 W-MRI anatomical sequence with high-spatial resolution at 11.7-tesla was acquired from the fixed rat heads. By MRI volumetry, whole cerebellar volumes decreased dose-dependently. Multiple linear regression analysis revealed that the cortical volume (standardized β=0.901; P<0.001) was a major explanatory variable for the whole cerebellar volume, whereas both volumes of the white matter and deep cerebellar nuclei also decreased depending on the X-irradiation dose. The present MRI volumetric analysis revealed a dose-related cerebellar cortical hypoplasia by prenatal exposure to X-irradiation on E15. © 2013 The Authors. Congenital Anomalies © 2013 Japanese Teratology Society.

A 3-Year Study of Predictive Factors for Positive and Negative Appendicectomies.

PubMed

Chang, Dwayne T S; Maluda, Melissa; Lee, Lisa; Premaratne, Chandrasiri; Khamhing, Srisongham

2018-03-06

Early and accurate identification or exclusion of acute appendicitis is the key to avoid the morbidity of delayed treatment for true appendicitis or unnecessary appendicectomy, respectively. We aim (i) to identify potential predictive factors for positive and negative appendicectomies; and (ii) to analyse the use of ultrasound scans (US) and computed tomography (CT) scans for acute appendicitis. All appendicectomies that took place at our hospital from the 1st of January 2013 to the 31st of December 2015 were retrospectively recorded. Test results of potential predictive factors of acute appendicitis were recorded. Statistical analysis was performed using Fisher exact test, logistic regression analysis, sensitivity, specificity, and positive and negative predictive values calculation. 208 patients were included in this study. 184 patients had histologically proven acute appendicitis. The other 24 patients had either nonappendicitis pathology or normal appendix. Logistic regression analysis showed statistically significant associations between appendicitis and white cell count, neutrophil count, C-reactive protein, and bilirubin. Neutrophil count was the test with the highest sensitivity and negative predictive values, whereas bilirubin was the test with the highest specificity and positive predictive values (PPV). US and CT scans had high sensitivity and PPV for diagnosing appendicitis. No single test was sufficient to diagnose or exclude acute appendicitis by itself. Combining tests with high sensitivity (abnormal neutrophil count, and US and CT scans) and high specificity (raised bilirubin) may predict acute appendicitis more accurately.

The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques

NASA Astrophysics Data System (ADS)

Deng, Chengbin; Wu, Changshan

2013-12-01

Urban impervious surface information is essential for urban and environmental applications at the regional/national scales. As a popular image processing technique, spectral mixture analysis (SMA) has rarely been applied to coarse-resolution imagery due to the difficulty of deriving endmember spectra using traditional endmember selection methods, particularly within heterogeneous urban environments. To address this problem, we derived endmember signatures through a least squares solution (LSS) technique with known abundances of sample pixels, and integrated these endmember signatures into SMA for mapping large-scale impervious surface fraction. In addition, with the same sample set, we carried out objective comparative analyses among SMA (i.e. fully constrained and unconstrained SMA) and machine learning (i.e. Cubist regression tree and Random Forests) techniques. Analysis of results suggests three major conclusions. First, with the extrapolated endmember spectra from stratified random training samples, the SMA approaches performed relatively well, as indicated by small MAE values. Second, Random Forests yields more reliable results than Cubist regression tree, and its accuracy is improved with increased sample sizes. Finally, comparative analyses suggest a tentative guide for selecting an optimal approach for large-scale fractional imperviousness estimation: unconstrained SMA might be a favorable option with a small number of samples, while Random Forests might be preferred if a large number of samples are available.

A Quasi-Experiment To Study the Impact of Vancomycin Area under the Concentration-Time Curve-Guided Dosing on Vancomycin-Associated Nephrotoxicity

PubMed Central

Finch, Natalie A.; Zasowski, Evan J.; Murray, Kyle P.; Mynatt, Ryan P.; Zhao, Jing J.; Yost, Raymond; Pogue, Jason M.

2017-01-01

ABSTRACT Evidence suggests that maintenance of vancomycin trough concentrations at between 15 and 20 mg/liter, as currently recommended, is frequently unnecessary to achieve the daily area under the concentration-time curve (AUC24) target of ≥400 mg · h/liter. Many patients with trough concentrations in this range have AUC24 values in excess of the therapeutic threshold and within the exposure range associated with nephrotoxicity. On the basis of this, the Detroit Medical Center switched from trough concentration-guided dosing to AUC-guided dosing to minimize potentially unnecessary vancomycin exposure. The primary objective of this analysis was to assess the impact of this intervention on vancomycin-associated nephrotoxicity in a single-center, retrospective quasi-experiment of hospitalized adult patients receiving intravenous vancomycin from 2014 to 2015. The primary analysis compared the incidence of nephrotoxicity between patients monitored by assessment of the AUC24 and those monitored by assessment of the trough concentration. Multivariable logistic and Cox proportional hazards regression examined the independent association between the monitoring strategy and nephrotoxicity. Secondary analysis compared vancomycin exposures (total daily dose, AUC, and trough concentrations) between monitoring strategies. Overall, 1,280 patients were included in the analysis. After adjusting for severity of illness, comorbidity, duration of vancomycin therapy, and concomitant receipt of nephrotoxins, AUC-guided dosing was independently associated with lower nephrotoxicity by both logistic regression (odds ratio, 0.52; 95% confidence interval [CI], 0.34 to 0.80; P = 0.003) and Cox proportional hazards regression (hazard ratio, 0.53; 95% CI, 0.35 to 0.78; P = 0.002). AUC-guided dosing was associated with lower total daily vancomycin doses, AUC values, and trough concentrations. Vancomycin AUC-guided dosing was associated with reduced nephrotoxicity, which appeared to be a result of reduced vancomycin exposure. PMID:28923869

A cephalometric analysis of Class II dentate subjects to establish a formula to determine the occlusal plane in Class II edentate subjects: A neo adjunct.

PubMed

Sinha, Nikita; Reddy, K Mahendranadh; Gupta, Nidhi; Shastry, Y M

2017-01-01

Occlusal plane (OP) differs considerably in participants with skeletal Class I and Class II participants. In this study, cephalometrics has been used to help in the determination of orientation of the OP utilizing the nonresorbable bony anatomic landmarks in skeletal Class II participants and an attempt has been made to predict and examine the OP in individuals with skeletal class II jaw relationship. One hundred dentulous participants with skeletal Class II malocclusion who came to the hospital for correcting their jaw relationship participated in the study. Their right lateral cephalogram was taken using standardized procedures, and all the tracings were manually done by a single trained examiner. The cephalograms which were taken for the diagnostic purpose were utilized for the study, and the patient was not exposed to any unnecessary radiation. The numerical values obtained from the cephalograms were subjected to statistical analysis. Pearson's correlation of <0.001 was considered significant, and a linear regression analysis was performed to determine a formula which would help in the determination of orientation of the OP in Class II edentulous participants. Pearson's correlation coefficient and linear regression analysis were performed, and a high correlation was found between A2 and (A2 + B2)/(B2 + C2) with " r " value of 0.5. A medium correlation was found between D2 and (D2 + E2)/(E2 + F2) with " r " value of 0.42. The formula obtained for posterior reference frame through linear regression equation was y = 0.018* × +0.459 and the formula obtained for anterior reference frame was y1 = 0.011* × 1 + 0.497. It was hypothesized that by substituting these formulae in the cephalogram obtained from the Class II edentate individual, the OP can be obtained and verified. It was concluded that cephalometrics can be useful in examining the orientation of OP in skeletal Class II participants.

Genetic correlations among body condition score, yield, and fertility in first-parity cows estimated by random regression models.

PubMed

Veerkamp, R F; Koenen, E P; De Jong, G

2001-10-01

Twenty type classifiers scored body condition (BCS) of 91,738 first-parity cows from 601 sires and 5518 maternal grandsires. Fertility data during first lactation were extracted for 177,220 cows, of which 67,278 also had a BCS observation, and first-lactation 305-d milk, fat, and protein yields were added for 180,631 cows. Heritabilities and genetic correlations were estimated using a sire-maternal grandsire model. Heritability of BCS was 0.38. Heritabilities for fertility traits were low (0.01 to 0.07), but genetic standard deviations were substantial, 9 d for days to first service and calving interval, 0.25 for number of services, and 5% for first-service conception. Phenotypic correlations between fertility and yield or BCS were small (-0.15 to 0.20). Genetic correlations between yield and all fertility traits were unfavorable (0.37 to 0.74). Genetic correlations with BCS were between -0.4 and -0.6 for calving interval and days to first service. Random regression analysis (RR) showed that correlations changed with days in milk for BCS. Little agreement was found between variances and correlations from RR, and analysis including a single month (mo 1 to 10) of data for BCS, especially during early and late lactation. However, this was due to excluding data from the conventional analysis, rather than due to the polynomials used. RR and a conventional five-traits model where BCS in mo 1, 4, 7, and 10 was treated as a separate traits (plus yield or fertility) gave similar results. Thus a parsimonious random regression model gave more realistic estimates for the (co)variances than a series of bivariate analysis on subsets of the data for BCS. A higher genetic merit for yield has unfavorable effects on fertility, but the genetic correlation suggests that BCS (at some stages of lactation) might help to alleviate the unfavorable effect of selection for higher yield on fertility.

A Quasi-Experiment To Study the Impact of Vancomycin Area under the Concentration-Time Curve-Guided Dosing on Vancomycin-Associated Nephrotoxicity.

PubMed

Finch, Natalie A; Zasowski, Evan J; Murray, Kyle P; Mynatt, Ryan P; Zhao, Jing J; Yost, Raymond; Pogue, Jason M; Rybak, Michael J

2017-12-01

Evidence suggests that maintenance of vancomycin trough concentrations at between 15 and 20 mg/liter, as currently recommended, is frequently unnecessary to achieve the daily area under the concentration-time curve (AUC 24 ) target of ≥400 mg · h/liter. Many patients with trough concentrations in this range have AUC 24 values in excess of the therapeutic threshold and within the exposure range associated with nephrotoxicity. On the basis of this, the Detroit Medical Center switched from trough concentration-guided dosing to AUC-guided dosing to minimize potentially unnecessary vancomycin exposure. The primary objective of this analysis was to assess the impact of this intervention on vancomycin-associated nephrotoxicity in a single-center, retrospective quasi-experiment of hospitalized adult patients receiving intravenous vancomycin from 2014 to 2015. The primary analysis compared the incidence of nephrotoxicity between patients monitored by assessment of the AUC 24 and those monitored by assessment of the trough concentration. Multivariable logistic and Cox proportional hazards regression examined the independent association between the monitoring strategy and nephrotoxicity. Secondary analysis compared vancomycin exposures (total daily dose, AUC, and trough concentrations) between monitoring strategies. Overall, 1,280 patients were included in the analysis. After adjusting for severity of illness, comorbidity, duration of vancomycin therapy, and concomitant receipt of nephrotoxins, AUC-guided dosing was independently associated with lower nephrotoxicity by both logistic regression (odds ratio, 0.52; 95% confidence interval [CI], 0.34 to 0.80; P = 0.003) and Cox proportional hazards regression (hazard ratio, 0.53; 95% CI, 0.35 to 0.78; P = 0.002). AUC-guided dosing was associated with lower total daily vancomycin doses, AUC values, and trough concentrations. Vancomycin AUC-guided dosing was associated with reduced nephrotoxicity, which appeared to be a result of reduced vancomycin exposure. Copyright © 2017 American Society for Microbiology.

Regression Analysis and the Sociological Imagination

ERIC Educational Resources Information Center

De Maio, Fernando

2014-01-01

Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.

SCOPA and META-SCOPA: software for the analysis and aggregation of genome-wide association studies of multiple correlated phenotypes.

PubMed

Mägi, Reedik; Suleimanov, Yury V; Clarke, Geraldine M; Kaakinen, Marika; Fischer, Krista; Prokopenko, Inga; Morris, Andrew P

2017-01-11

Genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits. We have developed the SCOPA software to enable GWAS analysis of multiple correlated phenotypes. The software implements "reverse regression" methodology, which treats the genotype of an individual at a SNP as the outcome and the phenotypes as predictors in a general linear model. SCOPA can be applied to quantitative traits and categorical phenotypes, and can accommodate imputed genotypes under a dosage model. The accompanying META-SCOPA software enables meta-analysis of association summary statistics from SCOPA across GWAS. Application of SCOPA to two GWAS of high-and low-density lipoprotein cholesterol, triglycerides and body mass index, and subsequent meta-analysis with META-SCOPA, highlighted stronger association signals than univariate phenotype analysis at established lipid and obesity loci. The META-SCOPA meta-analysis also revealed a novel signal of association at genome-wide significance for triglycerides mapping to GPC5 (lead SNP rs71427535, p = 1.1x10 -8 ), which has not been reported in previous large-scale GWAS of lipid traits. The SCOPA and META-SCOPA software enable discovery and dissection of multiple phenotype association signals through implementation of a powerful reverse regression approach.

Factors related to clinical pregnancy after vitrified-warmed embryo transfer: a retrospective and multivariate logistic regression analysis of 2313 transfer cycles.

PubMed

Shi, Wenhao; Zhang, Silin; Zhao, Wanqiu; Xia, Xue; Wang, Min; Wang, Hui; Bai, Haiyan; Shi, Juanzi

2013-07-01

What factors does multivariate logistic regression show to be significantly associated with the likelihood of clinical pregnancy in vitrified-warmed embryo transfer (VET) cycles? Assisted hatching (AH) and if the reason to freeze embryos was to avoid the risk of ovarian hyperstimulation syndrome (OHSS) were significantly positively associated with a greater likelihood of clinical pregnancy. Single factor analysis has shown AH, number of embryos transferred and the reason of freezing for OHSS to be positively and damaged blastomere to be negatively significantly associated with the chance of clinical pregnancy after VET. It remains unclear what factors would be significant after multivariate analysis. The study was a retrospective analysis of 2313 VET cycles from 1481 patients performed between January 2008 and April 2012. A multivariate logistic regression analysis was performed to identify the factors to affect clinical pregnancy outcome of VET. There were 22 candidate variables selected based on clinical experiences and the literature. With the thresholds of α entry = α removal= 0.05 for both variable entry and variable removal, eight variables were chosen to contribute the multivariable model by the bootstrap stepwise variable selection algorithm (n = 1000). Eight variables were age at controlled ovarian hyperstimulation (COH), reason for freezing, AH, endometrial thickness, damaged blastomere, number of embryos transferred, number of good-quality embryos, and blood presence on transfer catheter. A descriptive comparison of the relative importance was accomplished by the proportion of explained variation (PEV). Among the reasons for freezing, the OHSS group showed a higher OR than the surplus embryo group when compared with other reasons for VET groups (OHSS versus Other, OR: 2.145; CI: 1.4-3.286; Surplus embryos versus Other, OR: 1.152; CI: 0.761-1.743) and high PEV (marginal 2.77%, P = 0.2911; partial 1.68%; CI of area under receptor operator characteristic curve (ROC): 0.5576-0.6000). AH also showed a high OR (OR: 2.105, CI: 1.554-2.85) and high PEV (marginal 1.97%; partial 1.02%; CI of area under ROC: 0.5344-0.5647). The number of good-quality embryos showed the highest marginal PEV and partial PEV (marginal 3.91%, partial 2.28%; CI of area under ROC: 0.5886-0.6343). This was a retrospective multivariate analysis of the data obtained in 5 years from a single IVF center. Repeated cycles in the same woman were treated as independent observations, which could introduce bias. Results are based on clinical pregnancy and not live births. Prospective analysis of a larger data set from a multicenter study based on live births is necessary to confirm the findings. Paying attention to the quality of embryos, the number of good embryos, AH and the reasons for freezing that are associated with clinical pregnancy after VET will assist the improvement of success rates.

Improved score statistics for meta-analysis in single-variant and gene-level association studies.

PubMed

Yang, Jingjing; Chen, Sai; Abecasis, Gonçalo

2018-06-01

Meta-analysis is now an essential tool for genetic association studies, allowing them to combine large studies and greatly accelerating the pace of genetic discovery. Although the standard meta-analysis methods perform equivalently as the more cumbersome joint analysis under ideal settings, they result in substantial power loss under unbalanced settings with various case-control ratios. Here, we investigate the power loss problem by the standard meta-analysis methods for unbalanced studies, and further propose novel meta-analysis methods performing equivalently to the joint analysis under both balanced and unbalanced settings. We derive improved meta-score-statistics that can accurately approximate the joint-score-statistics with combined individual-level data, for both linear and logistic regression models, with and without covariates. In addition, we propose a novel approach to adjust for population stratification by correcting for known population structures through minor allele frequencies. In the simulated gene-level association studies under unbalanced settings, our method recovered up to 85% power loss caused by the standard methods. We further showed the power gain of our methods in gene-level tests with 26 unbalanced studies of age-related macular degeneration . In addition, we took the meta-analysis of three unbalanced studies of type 2 diabetes as an example to discuss the challenges of meta-analyzing multi-ethnic samples. In summary, our improved meta-score-statistics with corrections for population stratification can be used to construct both single-variant and gene-level association studies, providing a useful framework for ensuring well-powered, convenient, cross-study analyses. © 2018 WILEY PERIODICALS, INC.

Prosthetic restoration in the single-tooth gap: patient preferences and analysis of the WTP index.

PubMed

Augusti, Davide; Augusti, Gabriele; Re, Dino

2014-11-01

The objective of this study was to evaluate the preference of a patients' population, according to the index of willingness to pay (WTP), against two treatments to restore a single-tooth gap: the implant-supported crown (ISC) and the 3-unit fixed partial denture prosthesis (FPDP) on natural teeth. Willingness to pay values were recorded on 107 subjects by asking the WTP from a starting bid of €2000 modifiable through monetary increases or decreases (€100). Data were collected through an individually delivered questionnaire. The characteristics of the population and choices made, the median values and WTP associations with socio-demographic parameters (Mann-Whitney and Kruskal-Wallis tests), correlations between variables (chi-square test in contingency tables) and significant parameters for predicting WTP values obtained in a multiple linear regression model were revealed. The 64% of patients expressed a preference for ISC, while the remaining 36% of the population chose the FPDP. The current therapeutic choice and those carried out in the past were generally in agreement (>70% of cases, P = 0.0001); a relationship was discovered between the anterior and posterior area to the same method of rehabilitation (101 of 107 cases, 94.4%). The WTP median values for ISC were of €3000 and of €2500 in the anterior and posterior areas, respectively. The smallest amount of money has been allocated for FPDP in posterior region (median of €1500). The "importance of oral care" for the patient was a significant predictor, in the regression model analysis, for the estimation of both anterior (P = 0.0003) and posterior (P < 0.0001) WTP values. The "previous therapy" variable reached and was just close to significance in anterior (P = 0.0367) and posterior (P = 0.0511) analyses, respectively. Within the limitations of this study, most of the population (64%) surveyed indicated the ISC as a therapeutic solution for the replacement of a single missing tooth, showing a higher WTP index in the anterior area. Among investigated socio-demographic variables, the importance assigned by the patient to oral care appeared to influence WTP values of the rehabilitation, regardless the location of the single gap in the mouth. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Can single empirical algorithms accurately predict inland shallow water quality status from high resolution, multi-sensor, multi-temporal satellite data?

NASA Astrophysics Data System (ADS)

Theologou, I.; Patelaki, M.; Karantzalos, K.

2015-04-01

Assessing and monitoring water quality status through timely, cost effective and accurate manner is of fundamental importance for numerous environmental management and policy making purposes. Therefore, there is a current need for validated methodologies which can effectively exploit, in an unsupervised way, the enormous amount of earth observation imaging datasets from various high-resolution satellite multispectral sensors. To this end, many research efforts are based on building concrete relationships and empirical algorithms from concurrent satellite and in-situ data collection campaigns. We have experimented with Landsat 7 and Landsat 8 multi-temporal satellite data, coupled with hyperspectral data from a field spectroradiometer and in-situ ground truth data with several physico-chemical and other key monitoring indicators. All available datasets, covering a 4 years period, in our case study Lake Karla in Greece, were processed and fused under a quantitative evaluation framework. The performed comprehensive analysis posed certain questions regarding the applicability of single empirical models across multi-temporal, multi-sensor datasets towards the accurate prediction of key water quality indicators for shallow inland systems. Single linear regression models didn't establish concrete relations across multi-temporal, multi-sensor observations. Moreover, the shallower parts of the inland system followed, in accordance with the literature, different regression patterns. Landsat 7 and 8 resulted in quite promising results indicating that from the recreation of the lake and onward consistent per-sensor, per-depth prediction models can be successfully established. The highest rates were for chl-a (r2=89.80%), dissolved oxygen (r2=88.53%), conductivity (r2=88.18%), ammonium (r2=87.2%) and pH (r2=86.35%), while the total phosphorus (r2=70.55%) and nitrates (r2=55.50%) resulted in lower correlation rates.

Identifying significant gene‐environment interactions using a combination of screening testing and hierarchical false discovery rate control

PubMed Central

Shen, Li; Saykin, Andrew J.; Williams, Scott M.; Moore, Jason H.

2016-01-01

ABSTRACT Although gene‐environment (G× E) interactions play an important role in many biological systems, detecting these interactions within genome‐wide data can be challenging due to the loss in statistical power incurred by multiple hypothesis correction. To address the challenge of poor power and the limitations of existing multistage methods, we recently developed a screening‐testing approach for G× E interaction detection that combines elastic net penalized regression with joint estimation to support a single omnibus test for the presence of G× E interactions. In our original work on this technique, however, we did not assess type I error control or power and evaluated the method using just a single, small bladder cancer data set. In this paper, we extend the original method in two important directions and provide a more rigorous performance evaluation. First, we introduce a hierarchical false discovery rate approach to formally assess the significance of individual G× E interactions. Second, to support the analysis of truly genome‐wide data sets, we incorporate a score statistic‐based prescreening step to reduce the number of single nucleotide polymorphisms prior to fitting the first stage penalized regression model. To assess the statistical properties of our method, we compare the type I error rate and statistical power of our approach with competing techniques using both simple simulation designs as well as designs based on real disease architectures. Finally, we demonstrate the ability of our approach to identify biologically plausible SNP‐education interactions relative to Alzheimer's disease status using genome‐wide association study data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). PMID:27578615

Logic regression and its extensions.

PubMed

Schwender, Holger; Ruczinski, Ingo

2010-01-01

Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

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

Temporally-Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer’s Disease

PubMed Central

Jie, Biao; Liu, Mingxia; Liu, Jun

2016-01-01

Sparse learning has been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). However, most existing sparse learning-based studies only adopt cross-sectional analysis methods, where the sparse model is learned using data from a single time-point. Actually, multiple time-points of data are often available in brain imaging applications, which can be used in some longitudinal analysis methods to better uncover the disease progression patterns. Accordingly, in this paper we propose a novel temporally-constrained group sparse learning method aiming for longitudinal analysis with multiple time-points of data. Specifically, we learn a sparse linear regression model by using the imaging data from multiple time-points, where a group regularization term is first employed to group the weights for the same brain region across different time-points together. Furthermore, to reflect the smooth changes between data derived from adjacent time-points, we incorporate two smoothness regularization terms into the objective function, i.e., one fused smoothness term which requires that the differences between two successive weight vectors from adjacent time-points should be small, and another output smoothness term which requires the differences between outputs of two successive models from adjacent time-points should also be small. We develop an efficient optimization algorithm to solve the proposed objective function. Experimental results on ADNI database demonstrate that, compared with conventional sparse learning-based methods, our proposed method can achieve improved regression performance and also help in discovering disease-related biomarkers. PMID:27093313

75 FR 47284 - Secretary's Priorities for Discretionary Grant Programs

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-05

... the most currently available data. Interrupted time series design \\4\\ means a type of quasi... findings. \\4\\ A single subject or single case design is an adaptation of an interrupted time series design...), interrupted time series designs (as defined in this notice), or regression discontinuity designs (as defined...

A Nonlinear Regression Model Estimating Single Source Concentrations of Primary and Secondarily Formed 2.5

EPA Science Inventory

Various approaches and tools exist to estimate local and regional PM_2.5 impacts from a single emissions source, ranging from simple screening techniques to Gaussian based dispersion models and complex grid-based Eulerian photochemical transport models. These approache...

Lower eccentric hamstring strength and single leg hop for distance predict hamstring injury in PETE students.

PubMed

Goossens, L; Witvrouw, E; Vanden Bossche, L; De Clercq, D

2015-01-01

Hamstring injuries have not been under research in physical education teacher education (PETE) students so far. Within the frame of the development of an injury prevention program, for this study we conducted an analysis of modifiable risk factors for hamstring injuries in PETE students. Hamstring injuries of 102 freshmen bachelor PETE students were registered prospectively during one academic year. Eighty-one students completed maximum muscle strength tests of hip extensors, hamstrings, quadriceps (isometric) and hamstrings (eccentric) at the start of the academic year. Sixty-nine of the latter completed a single leg hop for distance (SLHD). Risk factors for hamstring injuries were statistically detected using logistic regression. Sixteen hamstring injuries (0.16 injuries/student/academic year; 0.46 injuries/1000 h) occurred to 10 participants. Eight cases were included in the risk factor analysis. Lower eccentric hamstring strength (odds ratio (ODD) = 0.977; p = 0.043), higher isometric/eccentric hamstring strength ratio (ODD = 970.500; p = 0.019) and lower score on the SLHD (ODD = 0.884; p = 0.005) were significant risk factors for hamstring injury. A combination of eccentric hamstring strength test and SLHD could give a good risk analysis of hamstring injuries in PETE students. This might offer great perspectives for easily applicable screening in a clinical setting.

Estimation of test-day model (co)variance components across breeds using New Zealand dairy cattle data.

PubMed

Vanderick, S; Harris, B L; Pryce, J E; Gengler, N

2009-03-01

In New Zealand, a large proportion of cows are currently crossbreds, mostly Holstein-Friesians (HF) x Jersey (JE). The genetic evaluation system for milk yields is considering the same additive genetic effects for all breeds. The objective was to model different additive effects according to parental breeds to obtain first estimates of correlations among breed-specific effects and to study the usefulness of this type of random regression test-day model. Estimates of (co)variance components for purebred HF and JE cattle in purebred herds were computed by using a single-breed model. This analysis showed differences between the 2 breeds, with a greater variability in the HF breed. (Co)variance components for purebred HF and JE and crossbred HF x JE cattle were then estimated by using a complete multibreed model in which computations of complete across-breed (co)variances were simplified by correlating only eigenvectors for HF and JE random regressions of the same order as obtained from the single-breed analysis. Parameter estimates differed more strongly than expected between the single-breed and multibreed analyses, especially for JE. This could be due to differences between animals and management in purebred and non-purebred herds. In addition, the model used only partially accounted for heterosis. The multibreed analysis showed additive genetic differences between the HF and JE breeds, expressed as genetic correlations of additive effects in both breeds, especially in linear and quadratic Legendre polynomials (respectively, 0.807 and 0.604). The differences were small for overall milk production (0.926). Results showed that permanent environmental lactation curves were highly correlated across breeds; however, intraherd lactation curves were also affected by the breed-environment interaction. This result may indicate the existence of breed-specific competition effects that vary through the different lactation stages. In conclusion, a multibreed model similar to the one presented could optimally use the environmental and genetic parameters and provide breed-dependent additive breeding values. This model could also be a useful tool to evaluate crossbred dairy cattle populations like those in New Zealand. However, a routine evaluation would still require the development of an improved methodology. It would also be computationally very challenging because of the simultaneous presence of a large number of breeds.

Data mining: Potential applications in research on nutrition and health.

PubMed

Batterham, Marijka; Neale, Elizabeth; Martin, Allison; Tapsell, Linda

2017-02-01

Data mining enables further insights from nutrition-related research, but caution is required. The aim of this analysis was to demonstrate and compare the utility of data mining methods in classifying a categorical outcome derived from a nutrition-related intervention. Baseline data (23 variables, 8 categorical) on participants (n = 295) in an intervention trial were used to classify participants in terms of meeting the criteria of achieving 10 000 steps per day. Results from classification and regression trees (CARTs), random forests, adaptive boosting, logistic regression, support vector machines and neural networks were compared using area under the curve (AUC) and error assessments. The CART produced the best model when considering the AUC (0.703), overall error (18%) and within class error (28%). Logistic regression also performed reasonably well compared to the other models (AUC 0.675, overall error 23%, within class error 36%). All the methods gave different rankings of variables' importance. CART found that body fat, quality of life using the SF-12 Physical Component Summary (PCS) and the cholesterol: HDL ratio were the most important predictors of meeting the 10 000 steps criteria, while logistic regression showed the SF-12PCS, glucose levels and level of education to be the most significant predictors (P ≤ 0.01). Differing outcomes suggest caution is required with a single data mining method, particularly in a dataset with nonlinear relationships and outliers and when exploring relationships that were not the primary outcomes of the research. © 2017 Dietitians Association of Australia.

A novel variational Bayes multiple locus Z-statistic for genome-wide association studies with Bayesian model averaging

PubMed Central

Logsdon, Benjamin A.; Carty, Cara L.; Reiner, Alexander P.; Dai, James Y.; Kooperberg, Charles

2012-01-01

Motivation: For many complex traits, including height, the majority of variants identified by genome-wide association studies (GWAS) have small effects, leaving a significant proportion of the heritable variation unexplained. Although many penalized multiple regression methodologies have been proposed to increase the power to detect associations for complex genetic architectures, they generally lack mechanisms for false-positive control and diagnostics for model over-fitting. Our methodology is the first penalized multiple regression approach that explicitly controls Type I error rates and provide model over-fitting diagnostics through a novel normally distributed statistic defined for every marker within the GWAS, based on results from a variational Bayes spike regression algorithm. Results: We compare the performance of our method to the lasso and single marker analysis on simulated data and demonstrate that our approach has superior performance in terms of power and Type I error control. In addition, using the Women's Health Initiative (WHI) SNP Health Association Resource (SHARe) GWAS of African-Americans, we show that our method has power to detect additional novel associations with body height. These findings replicate by reaching a stringent cutoff of marginal association in a larger cohort. Availability: An R-package, including an implementation of our variational Bayes spike regression (vBsr) algorithm, is available at http://kooperberg.fhcrc.org/soft.html. Contact: blogsdon@fhcrc.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22563072

Regression Analysis: Legal Applications in Institutional Research

ERIC Educational Resources Information Center

Frizell, Julie A.; Shippen, Benjamin S., Jr.; Luna, Andrew L.

2008-01-01

This article reviews multiple regression analysis, describes how its results should be interpreted, and instructs institutional researchers on how to conduct such analyses using an example focused on faculty pay equity between men and women. The use of multiple regression analysis will be presented as a method with which to compare salaries of…

RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,

DTIC Science & Technology

This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)

Improvement of motor function in early Parkinson disease by safinamide.

PubMed

Stocchi, F; Arnold, G; Onofrj, M; Kwiecinski, H; Szczudlik, A; Thomas, A; Bonuccelli, U; Van Dijk, A; Cattaneo, C; Sala, P; Fariello, R G

2004-08-24

A median safinamide (SAF) dose of 70 mg/day (range 40 to 90 mg/day) increased the percentage of parkinsonian patients improving their motor scores by > or =30% from baseline (responders) after 3 months from 21.4% (placebo) to 37.5% (p < 0.05, calculated by logistic regression analysis). In a subgroup of 101 patients under stable treatment with a single dopamine agonist, addition of SAF magnified the response (47.1% responders, mean 4.7-point motor score decrease; p > or = 0.05). These results suggest that doses of SAF exerting ion channel block and glutamate release inhibition add to its symptomatic effect and warrant exploration of higher doses.

Development and test of different methods to improve the description and NO{sub x} emissions in staged combustion

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

Brink, A.; Kilpinen, P.; Hupa, M.

1996-01-01

Two methods to improve the modeling of NO{sub x} emissions in numerical flow simulation of combustion are investigated. The models used are a reduced mechanism for nitrogen chemistry in methane combustion and a new model based on regression analysis of perfectly stirred reactor simulations using detailed comprehensive reaction kinetics. The applicability of the methods to numerical flow simulation of practical furnaces, especially in the near burner region, is tested against experimental data from a pulverized coal fired single burner furnace. The results are also compared to those obtained using a commonly used description for the overall reaction rate of NO.

Real time gamma-ray signature identifier

DOEpatents

Rowland, Mark [Alamo, CA; Gosnell, Tom B [Moraga, CA; Ham, Cheryl [Livermore, CA; Perkins, Dwight [Livermore, CA; Wong, James [Dublin, CA

2012-05-15

A real time gamma-ray signature/source identification method and system using principal components analysis (PCA) for transforming and substantially reducing one or more comprehensive spectral libraries of nuclear materials types and configurations into a corresponding concise representation/signature(s) representing and indexing each individual predetermined spectrum in principal component (PC) space, wherein an unknown gamma-ray signature may be compared against the representative signature to find a match or at least characterize the unknown signature from among all the entries in the library with a single regression or simple projection into the PC space, so as to substantially reduce processing time and computing resources and enable real-time characterization and/or identification.

Statistical considerations in the development of injury risk functions.

PubMed

McMurry, Timothy L; Poplin, Gerald S

2015-01-01

We address 4 frequently misunderstood and important statistical ideas in the construction of injury risk functions. These include the similarities of survival analysis and logistic regression, the correct scale on which to construct pointwise confidence intervals for injury risk, the ability to discern which form of injury risk function is optimal, and the handling of repeated tests on the same subject. The statistical models are explored through simulation and examination of the underlying mathematics. We provide recommendations for the statistically valid construction and correct interpretation of single-predictor injury risk functions. This article aims to provide useful and understandable statistical guidance to improve the practice in constructing injury risk functions.

Family environment and its relation to adolescent personality factors.

PubMed

Forman, S G; Forman, B D

1981-04-01

Investigated the relationship between family social climate characteristics and adolescent personality functioning. The High School Personality Questionnaire (HSPQ) was administered to 80 high school students. These students and their parents also completed the Family Environment Scale (FES). Results of a stepwise multiple regression analysis indicated that one or more HSPQ scales had significant associations with each FES scale. Significant variance in child behavior was attributed to family social system functioning; however, no single family variable accounted for a major portion of the variance to the exclusion of other factors. It was concluded that child behavior varies with total system functioning, more than with separate system factors.

Real-time soil sensing based on fiber optics and spectroscopy

NASA Astrophysics Data System (ADS)

Li, Minzan

2005-08-01

Using NIR spectroscopic techniques, correlation analysis and regression analysis for soil parameter estimation was conducted with raw soil samples collected in a cornfield and a forage field. Soil parameters analyzed were soil moisture, soil organic matter, nitrate nitrogen, soil electrical conductivity and pH. Results showed that all soil parameters could be evaluated by NIR spectral reflectance. For soil moisture, a linear regression model was available at low moisture contents below 30 % db, while an exponential model can be used in a wide range of moisture content up to 100 % db. Nitrate nitrogen estimation required a multi-spectral exponential model and electrical conductivity could be evaluated by a single spectral regression. According to the result above mentioned, a real time soil sensor system based on fiber optics and spectroscopy was developed. The sensor system was composed of a soil subsoiler with four optical fiber probes, a spectrometer, and a control unit. Two optical fiber probes were used for illumination and the other two optical fiber probes for collecting soil reflectance from visible to NIR wavebands at depths around 30 cm. The spectrometer was used to obtain the spectra of reflected lights. The control unit consisted of a data logging device, a personal computer, and a pulse generator. The experiment showed that clear photo-spectral reflectance was obtained from the underground soil. The soil reflectance was equal to that obtained by the desktop spectrophotometer in laboratory tests. Using the spectral reflectance, the soil parameters, such as soil moisture, pH, EC and SOM, were evaluated.

Assessment of Genetic and Nongenetic Interactions for the Prediction of Depressive Symptomatology: An Analysis of the Wisconsin Longitudinal Study Using Machine Learning Algorithms

PubMed Central

Roetker, Nicholas S.; Yonker, James A.; Chang, Vicky; Roan, Carol L.; Herd, Pamela; Hauser, Taissa S.; Hauser, Robert M.

2013-01-01

Objectives. We examined depression within a multidimensional framework consisting of genetic, environmental, and sociobehavioral factors and, using machine learning algorithms, explored interactions among these factors that might better explain the etiology of depressive symptoms. Methods. We measured current depressive symptoms using the Center for Epidemiologic Studies Depression Scale (n = 6378 participants in the Wisconsin Longitudinal Study). Genetic factors were 78 single nucleotide polymorphisms (SNPs); environmental factors—13 stressful life events (SLEs), plus a composite proportion of SLEs index; and sociobehavioral factors—18 personality, intelligence, and other health or behavioral measures. We performed traditional SNP associations via logistic regression likelihood ratio testing and explored interactions with support vector machines and Bayesian networks. Results. After correction for multiple testing, we found no significant single genotypic associations with depressive symptoms. Machine learning algorithms showed no evidence of interactions. Naïve Bayes produced the best models in both subsets and included only environmental and sociobehavioral factors. Conclusions. We found no single or interactive associations with genetic factors and depressive symptoms. Various environmental and sociobehavioral factors were more predictive of depressive symptoms, yet their impacts were independent of one another. A genome-wide analysis of genetic alterations using machine learning methodologies will provide a framework for identifying genetic–environmental–sociobehavioral interactions in depressive symptoms. PMID:23927508

[Comparison of application of Cochran-Armitage trend test and linear regression analysis for rate trend analysis in epidemiology study].

PubMed

Wang, D Z; Wang, C; Shen, C F; Zhang, Y; Zhang, H; Song, G D; Xue, X D; Xu, Z L; Zhang, S; Jiang, G H

2017-05-10

We described the time trend of acute myocardial infarction (AMI) from 1999 to 2013 in Tianjin incidence rate with Cochran-Armitage trend (CAT) test and linear regression analysis, and the results were compared. Based on actual population, CAT test had much stronger statistical power than linear regression analysis for both overall incidence trend and age specific incidence trend (Cochran-Armitage trend P value

A primer for biomedical scientists on how to execute model II linear regression analysis.

PubMed

Ludbrook, John

2012-04-01

1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

Water quality parameter measurement using spectral signatures

NASA Technical Reports Server (NTRS)

White, P. E.

1973-01-01

Regression analysis is applied to the problem of measuring water quality parameters from remote sensing spectral signature data. The equations necessary to perform regression analysis are presented and methods of testing the strength and reliability of a regression are described. An efficient algorithm for selecting an optimal subset of the independent variables available for a regression is also presented.

Development and implementation of a regression model for predicting recreational water quality in the Cuyahoga River, Cuyahoga Valley National Park, Ohio 2009-11

USGS Publications Warehouse

Brady, Amie M.G.; Plona, Meg B.

2012-01-01

The Cuyahoga River within Cuyahoga Valley National Park (CVNP) is at times impaired for recreational use due to elevated concentrations of Escherichia coli (E. coli), a fecal-indicator bacterium. During the recreational seasons of mid-May through September during 2009–11, samples were collected 4 days per week and analyzed for E. coli concentrations at two sites within CVNP. Other water-quality and environ-mental data, including turbidity, rainfall, and streamflow, were measured and (or) tabulated for analysis. Regression models developed to predict recreational water quality in the river were implemented during the recreational seasons of 2009–11 for one site within CVNP–Jaite. For the 2009 and 2010 seasons, the regression models were better at predicting exceedances of Ohio's single-sample standard for primary-contact recreation compared to the traditional method of using the previous day's E. coli concentration. During 2009, the regression model was based on data collected during 2005 through 2008, excluding available 2004 data. The resulting model for 2009 did not perform as well as expected (based on the calibration data set) and tended to overestimate concentrations (correct responses at 69 percent). During 2010, the regression model was based on data collected during 2004 through 2009, including all of the available data. The 2010 model performed well, correctly predicting 89 percent of the samples above or below the single-sample standard, even though the predictions tended to be lower than actual sample concentrations. During 2011, the regression model was based on data collected during 2004 through 2010 and tended to overestimate concentrations. The 2011 model did not perform as well as the traditional method or as expected, based on the calibration dataset (correct responses at 56 percent). At a second site—Lock 29, approximately 5 river miles upstream from Jaite, a regression model based on data collected at the site during the recreational seasons of 2008–10 also did not perform as well as the traditional method or as well as expected (correct responses at 60 percent). Above normal precipitation in the region and a delayed start to the 2011 sampling season (sampling began mid-June) may have affected how well the 2011 models performed. With these new data, however, updated regression models may be better able to predict recreational water quality conditions due to the increased amount of diverse water quality conditions included in the calibration data. Daily recreational water-quality predictions for Jaite were made available on the Ohio Nowcast Web site at www.ohionowcast.info. Other public outreach included signage at trailheads in the park, articles in the park's quarterly-published schedule of events and volunteer newsletters. A U.S. Geological Survey Fact Sheet was also published to bring attention to water-quality issues in the park.

Missing heritability in the tails of quantitative traits? A simulation study on the impact of slightly altered true genetic models.

PubMed

Pütter, Carolin; Pechlivanis, Sonali; Nöthen, Markus M; Jöckel, Karl-Heinz; Wichmann, Heinz-Erich; Scherag, André

2011-01-01

Genome-wide association studies have identified robust associations between single nucleotide polymorphisms and complex traits. As the proportion of phenotypic variance explained is still limited for most of the traits, larger and larger meta-analyses are being conducted to detect additional associations. Here we investigate the impact of the study design and the underlying assumption about the true genetic effect in a bimodal mixture situation on the power to detect associations. We performed simulations of quantitative phenotypes analysed by standard linear regression and dichotomized case-control data sets from the extremes of the quantitative trait analysed by standard logistic regression. Using linear regression, markers with an effect in the extremes of the traits were almost undetectable, whereas analysing extremes by case-control design had superior power even for much smaller sample sizes. Two real data examples are provided to support our theoretical findings and to explore our mixture and parameter assumption. Our findings support the idea to re-analyse the available meta-analysis data sets to detect new loci in the extremes. Moreover, our investigation offers an explanation for discrepant findings when analysing quantitative traits in the general population and in the extremes. Copyright © 2011 S. Karger AG, Basel.

Hospital electronic medical record enterprise application strategies: do they matter?

PubMed

Fareed, Naleef; Ozcan, Yasar A; DeShazo, Jonathan P

2012-01-01

Successful implementations and the ability to reap the benefits of electronic medical record (EMR) systems may be correlated with the type of enterprise application strategy that an administrator chooses when acquiring an EMR system. Moreover, identifying the most optimal enterprise application strategy is a task that may have important linkages with hospital performance. This study explored whether hospitals that have adopted differential EMR enterprise application strategies concomitantly differ in their overall efficiency. Specifically, the study examined whether hospitals with a single-vendor strategy had a higher likelihood of being efficient than those with a best-of-breed strategy and whether hospitals with a best-of-suite strategy had a higher probability of being efficient than those with best-of-breed or single-vendor strategies. A conceptual framework was used to formulate testable hypotheses. A retrospective cross-sectional approach using data envelopment analysis was used to obtain efficiency scores of hospitals by EMR enterprise application strategy. A Tobit regression analysis was then used to determine the probability of a hospital being inefficient as related to its EMR enterprise application strategy, while moderating for the hospital's EMR "implementation status" and controlling for hospital and market characteristics. The data envelopment analysis of hospitals suggested that only 32 hospitals were efficient in the study's sample of 2,171 hospitals. The results from the post hoc analysis showed partial support for the hypothesis that hospitals with a best-of-suite strategy were more likely to be efficient than those with a single-vendor strategy. This study underscores the importance of understanding the differences between the three strategies discussed in this article. On the basis of the findings, hospital administrators should consider the efficiency associations that a specific strategy may have compared with another prior to moving toward an enterprise application strategy.

Influence of cerebral white matter lesions on the activities of daily living of older patients with mild stroke.

PubMed

Yamashita, Yutaka; Wada, Ikuo; Horiba, Mitsuya; Sahashi, Kento

2016-08-01

Neurological symptom severity is a prognostic factor for post-stroke activities of daily living (ADL). Recently, it has been reported that white matter lesions indicate poor functional prognosis in patients with stroke. The present study investigated the influence of white matter lesions on the ADL of older patients with stroke who have mild neurological symptoms. We investigated ADL at discharge in 44 patients with stroke (men, n = 27; women, n = 17; mean age 78 years [range 71-85 years]) aged ≥65 years with National Institutes of Health Stroke Scale scores of ≤5 (cerebral infarction, n = 37; cerebral hemorrhage, n = 7). We used single correlation analysis and multiple regression analysis to investigate factors that correlated with ADL at discharge. ADL at discharge was also evaluated on the basis of white matter lesion severity (Fazekas classification, grades 0-3). Single correlation analysis showed that age (r = -0.36, P = 0.016), male sex (r = 0.362, P = 0.016), neurological symptom severity (r = -0.361, P = 0.016), ADL on starting rehabilitation (r = 0.685, P < 0.001) and white matter lesion severity (r = -0.361, P = 0.016) significantly correlated with ADL at discharge. Multiple regression analysis showed that ADL on starting rehabilitation (β = 0.519, t = 4.723, P < 0.001) and white matter lesion severity (β = -0.309, t = -3.057, P < 0.01) were statistically significant prognostic factors for ADL at discharge. ADL at discharge score was significantly lower in the group with high white matter lesion severity (Fazekas, grade 2) than in the other two groups (Fazekas, grade 0, P < 0.01; Fazekas, grade 1, P < 0.05). Severe white matter lesions are a prognostic factor for poor ADL at discharge in older patients with stroke who have mild neurological symptoms. Geriatr Gerontol Int 2016; 16: 942-947. © 2015 Japan Geriatrics Society.

Ovarian stimulation length, number of follicles higher than 17 mm and estradiol on the day of human chorionic gonadotropin administration are risk factors for multiple pregnancy in intrauterine insemination

PubMed Central

MELO, MARCO A.B.; SIMÓN, CARLOS; REMOHÍ, JOSÉ; PELLICER, ANTONIO; MESEGUER, MARCOS

2007-01-01

Aim:  The aim of the present study was to identify the risk factors, their prognostic value on multiple pregnancies (MP) prediction and their thresholds in women undergoing controlled ovarian hyperstimulation (COH) with follicle stimulating hormone (FSH) and intrauterine insemination (IUI). Methods:  A case‐control study was carried out by identifying in our database all the pregnancies reached by donor and conjugal IUI (DIUI and CIUI, respectively), and compared cycle features, patients’ characteristics and sperm analysis results between women achieving single pregnancy (SP) versus MP. The number of gestational sacs, follicular sizes and estradiol levels on the human chorionic gonadotropin (hCG) administration day, COH length and semen parameters were obtained from each cycle and compared. Student's t‐tests for mean comparisons, receiver–operator curve (ROC) analysis to determine the predictive value of each parameter on MP achievement and multiple regression analysis to determine single parameter influence were carried out. Results:  Women with MP in IUI stimulated cycles reached the adequate size of the dominant follicle (17 mm) significantly earlier than those achieving SP. Also, the mean follicles number, and estradiol levels on the hCG day were higher in the CIUI and DIUI MP group. Nevertheless, only ROC curve analysis revealed good prognostic value for estradiol and follicles higher than 17 mm. Multiple regression analysis confirmed these results. No feature of the basic sperm analysis, either in the ejaculate or in the prepared sample, was different or predictive of MP. When using donor sperm, different thresholds of follicle number, stimulation length and estradiol in the prediction of MP were noted, in comparison with CIUI. Conclusions:  MP in stimulated IUI cycles are closely associated to stimulation length, number of developed follicles higher than 17 mm on the day of hCG administration and estradiol levels. Also, estradiol has a good predictive value over MP in IUI stimulated cycles. The establishment of clinical thresholds will certainly help in the management of these couples to avoid undesired multiple pregnancies by canceling cycles or converting them into in vitro fertilization procedures. (Reprod Med Biol 2007; 6: 19–26) PMID:29699262

Predictors of condom use and refusal among the population of Free State province in South Africa

PubMed Central

2012-01-01

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

Estimating Causal Effects of Education Interventions Using a Two-Rating Regression Discontinuity Design: Lessons from a Simulation Study and an Application

ERIC Educational Resources Information Center

Porter, Kristin E.; Reardon, Sean F.; Unlu, Fatih; Bloom, Howard S.; Cimpian, Joseph R.

2017-01-01

A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the "surface" method, the "frontier" method, the "binding-score" method, and…

Avoiding and Correcting Bias in Score-Based Latent Variable Regression with Discrete Manifest Items

ERIC Educational Resources Information Center

Lu, Irene R. R.; Thomas, D. Roland

2008-01-01

This article considers models involving a single structural equation with latent explanatory and/or latent dependent variables where discrete items are used to measure the latent variables. Our primary focus is the use of scores as proxies for the latent variables and carrying out ordinary least squares (OLS) regression on such scores to estimate…

A Generalized Logistic Regression Procedure to Detect Differential Item Functioning among Multiple Groups

ERIC Educational Resources Information Center

Magis, David; Raiche, Gilles; Beland, Sebastien; Gerard, Paul

2011-01-01

We present an extension of the logistic regression procedure to identify dichotomous differential item functioning (DIF) in the presence of more than two groups of respondents. Starting from the usual framework of a single focal group, we propose a general approach to estimate the item response functions in each group and to test for the presence…

Evaluation and application of regional turbidity-sediment regression models in Virginia

USGS Publications Warehouse

Hyer, Kenneth; Jastram, John D.; Moyer, Douglas; Webber, James S.; Chanat, Jeffrey G.

2015-01-01

Conventional thinking has long held that turbidity-sediment surrogate-regression equations are site specific and that regression equations developed at a single monitoring station should not be applied to another station; however, few studies have evaluated this issue in a rigorous manner. If robust regional turbidity-sediment models can be developed successfully, their applications could greatly expand the usage of these methods. Suspended sediment load estimation could occur as soon as flow and turbidity monitoring commence at a site, suspended sediment sampling frequencies for various projects potentially could be reduced, and special-project applications (sediment monitoring following dam removal, for example) could be significantly enhanced. The objective of this effort was to investigate the turbidity-suspended sediment concentration (SSC) relations at all available USGS monitoring sites within Virginia to determine whether meaningful turbidity-sediment regression models can be developed by combining the data from multiple monitoring stations into a single model, known as a “regional” model. Following the development of the regional model, additional objectives included a comparison of predicted SSCs between the regional model and commonly used site-specific models, as well as an evaluation of why specific monitoring stations did not fit the regional model.

A collaborative comparison of objective structured clinical examination (OSCE) standard setting methods at Australian medical schools.

PubMed

Malau-Aduli, Bunmi Sherifat; Teague, Peta-Ann; D'Souza, Karen; Heal, Clare; Turner, Richard; Garne, David L; van der Vleuten, Cees

2017-12-01

A key issue underpinning the usefulness of the OSCE assessment to medical education is standard setting, but the majority of standard-setting methods remain challenging for performance assessment because they produce varying passing marks. Several studies have compared standard-setting methods; however, most of these studies are limited by their experimental scope, or use data on examinee performance at a single OSCE station or from a single medical school. This collaborative study between 10 Australian medical schools investigated the effect of standard-setting methods on OSCE cut scores and failure rates. This research used 5256 examinee scores from seven shared OSCE stations to calculate cut scores and failure rates using two different compromise standard-setting methods, namely the Borderline Regression and Cohen's methods. The results of this study indicate that Cohen's method yields similar outcomes to the Borderline Regression method, particularly for large examinee cohort sizes. However, with lower examinee numbers on a station, the Borderline Regression method resulted in higher cut scores and larger difference margins in the failure rates. Cohen's method yields similar outcomes as the Borderline Regression method and its application for benchmarking purposes and in resource-limited settings is justifiable, particularly with large examinee numbers.

Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

2012-05-01

Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Handling nonnormality and variance heterogeneity for quantitative sublethal toxicity tests.

PubMed

Ritz, Christian; Van der Vliet, Leana

2009-09-01

The advantages of using regression-based techniques to derive endpoints from environmental toxicity data are clear, and slowly, this superior analytical technique is gaining acceptance. As use of regression-based analysis becomes more widespread, some of the associated nuances and potential problems come into sharper focus. Looking at data sets that cover a broad spectrum of standard test species, we noticed that some model fits to data failed to meet two key assumptions-variance homogeneity and normality-that are necessary for correct statistical analysis via regression-based techniques. Failure to meet these assumptions often is caused by reduced variance at the concentrations showing severe adverse effects. Although commonly used with linear regression analysis, transformation of the response variable only is not appropriate when fitting data using nonlinear regression techniques. Through analysis of sample data sets, including Lemna minor, Eisenia andrei (terrestrial earthworm), and algae, we show that both the so-called Box-Cox transformation and use of the Poisson distribution can help to correct variance heterogeneity and nonnormality and so allow nonlinear regression analysis to be implemented. Both the Box-Cox transformation and the Poisson distribution can be readily implemented into existing protocols for statistical analysis. By correcting for nonnormality and variance heterogeneity, these two statistical tools can be used to encourage the transition to regression-based analysis and the depreciation of less-desirable and less-flexible analytical techniques, such as linear interpolation.

The development and validation of the Physical Appearance Comparison Scale-Revised (PACS-R).

PubMed

Schaefer, Lauren M; Thompson, J Kevin

2014-04-01

The Physical Appearance Comparison Scale (PACS; Thompson, Heinberg, & Tantleff, 1991) was revised to assess appearance comparisons relevant to women and men in a wide variety of contexts. The revised scale (Physical Appearance Comparison Scale-Revised, PACS-R) was administered to 1176 college females. In Study 1, exploratory factor analysis and parallel analysis using one half of the sample suggested a single factor structure for the PACS-R. Study 2 utilized the remaining half of the sample to conduct confirmatory factor analysis, item analysis, and to examine the convergent validity of the scale. These analyses resulted in an 11-item measure that demonstrated excellent internal consistency and convergent validity with measures of body satisfaction, eating pathology, sociocultural influences on appearance, and self-esteem. Regression analyses demonstrated the utility of the PACS-R in predicting body satisfaction and eating pathology. Overall, results indicate that the PACS-R is a reliable and valid tool for assessing appearance comparison tendencies in women. Copyright © 2014. Published by Elsevier Ltd.

Mixed oxidizer hybrid propulsion system optimization under uncertainty using applied response surface methodology and Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Whitehead, James Joshua

The analysis documented herein provides an integrated approach for the conduct of optimization under uncertainty (OUU) using Monte Carlo Simulation (MCS) techniques coupled with response surface-based methods for characterization of mixture-dependent variables. This novel methodology provides an innovative means of conducting optimization studies under uncertainty in propulsion system design. Analytic inputs are based upon empirical regression rate information obtained from design of experiments (DOE) mixture studies utilizing a mixed oxidizer hybrid rocket concept. Hybrid fuel regression rate was selected as the target response variable for optimization under uncertainty, with maximization of regression rate chosen as the driving objective. Characteristic operational conditions and propellant mixture compositions from experimental efforts conducted during previous foundational work were combined with elemental uncertainty estimates as input variables. Response surfaces for mixture-dependent variables and their associated uncertainty levels were developed using quadratic response equations incorporating single and two-factor interactions. These analysis inputs, response surface equations and associated uncertainty contributions were applied to a probabilistic MCS to develop dispersed regression rates as a function of operational and mixture input conditions within design space. Illustrative case scenarios were developed and assessed using this analytic approach including fully and partially constrained operational condition sets over all of design mixture space. In addition, optimization sets were performed across an operationally representative region in operational space and across all investigated mixture combinations. These scenarios were selected as representative examples relevant to propulsion system optimization, particularly for hybrid and solid rocket platforms. Ternary diagrams, including contour and surface plots, were developed and utilized to aid in visualization. The concept of Expanded-Durov diagrams was also adopted and adapted to this study to aid in visualization of uncertainty bounds. Regions of maximum regression rate and associated uncertainties were determined for each set of case scenarios. Application of response surface methodology coupled with probabilistic-based MCS allowed for flexible and comprehensive interrogation of mixture and operating design space during optimization cases. Analyses were also conducted to assess sensitivity of uncertainty to variations in key elemental uncertainty estimates. The methodology developed during this research provides an innovative optimization tool for future propulsion design efforts.

Influential factors of red-light running at signalized intersection and prediction using a rare events logistic regression model.

PubMed

Ren, Yilong; Wang, Yunpeng; Wu, Xinkai; Yu, Guizhen; Ding, Chuan

2016-10-01

Red light running (RLR) has become a major safety concern at signalized intersection. To prevent RLR related crashes, it is critical to identify the factors that significantly impact the drivers' behaviors of RLR, and to predict potential RLR in real time. In this research, 9-month's RLR events extracted from high-resolution traffic data collected by loop detectors from three signalized intersections were applied to identify the factors that significantly affect RLR behaviors. The data analysis indicated that occupancy time, time gap, used yellow time, time left to yellow start, whether the preceding vehicle runs through the intersection during yellow, and whether there is a vehicle passing through the intersection on the adjacent lane were significantly factors for RLR behaviors. Furthermore, due to the rare events nature of RLR, a modified rare events logistic regression model was developed for RLR prediction. The rare events logistic regression method has been applied in many fields for rare events studies and shows impressive performance, but so far none of previous research has applied this method to study RLR. The results showed that the rare events logistic regression model performed significantly better than the standard logistic regression model. More importantly, the proposed RLR prediction method is purely based on loop detector data collected from a single advance loop detector located 400 feet away from stop-bar. This brings great potential for future field applications of the proposed method since loops have been widely implemented in many intersections and can collect data in real time. This research is expected to contribute to the improvement of intersection safety significantly. Copyright © 2016 Elsevier Ltd. All rights reserved.

Potential pitfalls when denoising resting state fMRI data using nuisance regression.

PubMed

Bright, Molly G; Tench, Christopher R; Murphy, Kevin

2017-07-01

In resting state fMRI, it is necessary to remove signal variance associated with noise sources, leaving cleaned fMRI time-series that more accurately reflect the underlying intrinsic brain fluctuations of interest. This is commonly achieved through nuisance regression, in which the fit is calculated of a noise model of head motion and physiological processes to the fMRI data in a General Linear Model, and the "cleaned" residuals of this fit are used in further analysis. We examine the statistical assumptions and requirements of the General Linear Model, and whether these are met during nuisance regression of resting state fMRI data. Using toy examples and real data we show how pre-whitening, temporal filtering and temporal shifting of regressors impact model fit. Based on our own observations, existing literature, and statistical theory, we make the following recommendations when employing nuisance regression: pre-whitening should be applied to achieve valid statistical inference of the noise model fit parameters; temporal filtering should be incorporated into the noise model to best account for changes in degrees of freedom; temporal shifting of regressors, although merited, should be achieved via optimisation and validation of a single temporal shift. We encourage all readers to make simple, practical changes to their fMRI denoising pipeline, and to regularly assess the appropriateness of the noise model used. By negotiating the potential pitfalls described in this paper, and by clearly reporting the details of nuisance regression in future manuscripts, we hope that the field will achieve more accurate and precise noise models for cleaning the resting state fMRI time-series. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units

PubMed Central

Kam-Thong, Tony; Czamara, Darina; Tsuda, Koji; Borgwardt, Karsten; Lewis, Cathryn M; Erhardt-Lehmann, Angelika; Hemmer, Bernhard; Rieckmann, Peter; Daake, Markus; Weber, Frank; Wolf, Christiane; Ziegler, Andreas; Pütz, Benno; Holsboer, Florian; Schölkopf, Bernhard; Müller-Myhsok, Bertram

2011-01-01

Detection of epistatic interaction between loci has been postulated to provide a more in-depth understanding of the complex biological and biochemical pathways underlying human diseases. Studying the interaction between two loci is the natural progression following traditional and well-established single locus analysis. However, the added costs and time duration required for the computation involved have thus far deterred researchers from pursuing a genome-wide analysis of epistasis. In this paper, we propose a method allowing such analysis to be conducted very rapidly. The method, dubbed EPIBLASTER, is applicable to case–control studies and consists of a two-step process in which the difference in Pearson's correlation coefficients is computed between controls and cases across all possible SNP pairs as an indication of significant interaction warranting further analysis. For the subset of interactions deemed potentially significant, a second-stage analysis is performed using the likelihood ratio test from the logistic regression to obtain the P-value for the estimated coefficients of the individual effects and the interaction term. The algorithm is implemented using the parallel computational capability of commercially available graphical processing units to greatly reduce the computation time involved. In the current setup and example data sets (211 cases, 222 controls, 299468 SNPs; and 601 cases, 825 controls, 291095 SNPs), this coefficient evaluation stage can be completed in roughly 1 day. Our method allows for exhaustive and rapid detection of significant SNP pair interactions without imposing significant marginal effects of the single loci involved in the pair. PMID:21150885

Which tinnitus-related aspects are relevant for quality of life and depression: results from a large international multicentre sample

PubMed Central

2014-01-01

Background The aim of the present study was to investigate, which aspects of tinnitus are most relevant for impairment of quality of life. For this purpose we analysed how responses to the Tinnitus Handicap Inventory (THI) and to the question “How much of a problem is your tinnitus at present” correlate with the different aspects of quality of life and depression. Methods 1274 patients of the Tinnitus Research Initiative database were eligible for analysis. The Tinnitus Research Initiative database is composed of eight study centres from five countries. We assessed to which extent the Tinnitus Handicap Inventory (THI) and its subscales and single items as well as the tinnitus severity correlate with Beck Depression Inventory (BDI) score and different domains of the short version of the WHO-Quality of Life questionnaire (WHO-QoL Bref) by means of simple and multiple linear regression models. Results The THI explained considerable portions of the variance of the WHO-QoL Physical Health (R2 = 0.39) and Psychological Health (R2 = 0.40) and the BDI (R2 = 0.46). Furthermore, multiple linear regression models which included each THI item separately explained an additional 5% of the variance compared to the THI total score. The items feeling confused from tinnitus, the trouble of falling asleep at night, the interference with job or household responsibilities, getting upset from tinnitus, and the feeling of being depressed were those with the highest influence on quality of life and depression. The single question with regard to tinnitus severity explained 18%, 16%, and 20% of the variance of Physical Health, Psychological Health, and BDI respectively. Conclusions In the context of a cross-sectional correlation analysis, our findings confirmed the strong and consistent relationships between self-reported tinnitus burden and both quality of life, and depression. The single question “How much of a problem is your tinnitus” reflects tinnitus-related impairment in quality of life and can thus be recommended for use in clinical routine. PMID:24422941

Which tinnitus-related aspects are relevant for quality of life and depression: results from a large international multicentre sample.

PubMed

Zeman, Florian; Koller, Michael; Langguth, Berthold; Landgrebe, Michael

2014-01-14

The aim of the present study was to investigate, which aspects of tinnitus are most relevant for impairment of quality of life. For this purpose we analysed how responses to the Tinnitus Handicap Inventory (THI) and to the question "How much of a problem is your tinnitus at present" correlate with the different aspects of quality of life and depression. 1274 patients of the Tinnitus Research Initiative database were eligible for analysis. The Tinnitus Research Initiative database is composed of eight study centres from five countries. We assessed to which extent the Tinnitus Handicap Inventory (THI) and its subscales and single items as well as the tinnitus severity correlate with Beck Depression Inventory (BDI) score and different domains of the short version of the WHO-Quality of Life questionnaire (WHO-QoL Bref) by means of simple and multiple linear regression models. The THI explained considerable portions of the variance of the WHO-QoL Physical Health (R2 = 0.39) and Psychological Health (R2 = 0.40) and the BDI (R2 = 0.46). Furthermore, multiple linear regression models which included each THI item separately explained an additional 5% of the variance compared to the THI total score. The items feeling confused from tinnitus, the trouble of falling asleep at night, the interference with job or household responsibilities, getting upset from tinnitus, and the feeling of being depressed were those with the highest influence on quality of life and depression. The single question with regard to tinnitus severity explained 18%, 16%, and 20% of the variance of Physical Health, Psychological Health, and BDI respectively. In the context of a cross-sectional correlation analysis, our findings confirmed the strong and consistent relationships between self-reported tinnitus burden and both quality of life, and depression. The single question "How much of a problem is your tinnitus" reflects tinnitus-related impairment in quality of life and can thus be recommended for use in clinical routine.

Validation of a single-stage fixed-rate step test for the prediction of maximal oxygen uptake in healthy adults.

PubMed

Hansen, Dominique; Jacobs, Nele; Thijs, Herbert; Dendale, Paul; Claes, Neree

2016-09-01

Healthcare professionals with limited access to ergospirometry remain in need of valid and simple submaximal exercise tests to predict maximal oxygen uptake (VO2max ). Despite previous validation studies concerning fixed-rate step tests, accurate equations for the estimation of VO2max remain to be formulated from a large sample of healthy adults between age 18-75 years (n > 100). The aim of this study was to develop a valid equation to estimate VO2max from a fixed-rate step test in a larger sample of healthy adults. A maximal ergospirometry test, with assessment of cardiopulmonary parameters and VO2max , and a 5-min fixed-rate single-stage step test were executed in 112 healthy adults (age 18-75 years). During the step test and subsequent recovery, heart rate was monitored continuously. By linear regression analysis, an equation to predict VO2max from the step test was formulated. This equation was assessed for level of agreement by displaying Bland-Altman plots and calculation of intraclass correlations with measured VO2max . Validity further was assessed by employing a Jackknife procedure. The linear regression analysis generated the following equation to predict VO2max (l min(-1) ) from the step test: 0·054(BMI)+0·612(gender)+3·359(body height in m)+0·019(fitness index)-0·012(HRmax)-0·011(age)-3·475. This equation explained 78% of the variance in measured VO2max (F = 66·15, P<0·001). The level of agreement and intraclass correlation was high (ICC = 0·94, P<0·001) between measured and predicted VO2max . From this study, a valid fixed-rate single-stage step test equation has been developed to estimate VO2max in healthy adults. This tool could be employed by healthcare professionals with limited access to ergospirometry. © 2015 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

Prevalence and Predisposing Factors for Depressive Status in Chinese Patients with Obstructive Sleep Apnoea: A Large-Sample Survey.

PubMed

Dai, Yaozhang; Li, Xuewu; Zhang, Xin; Wang, Sihua; Sang, Jianzhong; Tian, Xiufen; Cao, Hua

2016-01-01

Recently, there are few studies reporting on depressive status and obstructive sleep apnoea (OSA) in China. A large-sample survey was to be performed to explore the prevalence of depressive status and related factors in Chinese patients with OSA. From among a randomly-selected group of OSA patients, 1,327 met inclusion criteria. After screening with the Symptom Checklist 90 (SCL-90) and Self-Rating Depression Scale (SDS), patients were assigned to OSA without depressive status (control group, n = 698) and OSA with depressive status (n = 629) groups. Using chi-squared testing, the correlation analyses between the depressive status and OSA patient demographic and clinical variables were tested. Then depression-related risk factors in OSA patients were analysed using stepwise linear regression analysis. The effects of family and social factors on depressive status in OSA patients were investigated using Mann-Whitney U (one of nonparametric test). The prevalence of depressive status was 47.4% in OSA patients. Depressive status was significantly associated with female gender, single status, Family Burden Scale of Disease (FBS), Family APGAR Index (APGAR), apnoea-hypopnea index (AHI), and Perceived Social Support Scale (PSSS). Stepwise linear regression analysis further indicated that single status, hypoxemia, APGAR, AHI, PSSS, AHI, and FBS were all risk factors for depressive status in OSA patients. The total of the FBS score and three of its sub-factors scores (family daily activities, family relationships and mental health of family members) were higher, and the total of the APGAR score and two of its sub-factors scores (adaptability and affection) were lower in OSA with depressive status compared with the control group. Besides, the total score for the PSSS and scores for its two sub-factors (family support and social support) were all lower in OSA patients with depressive status than those of the control group. Depressive status has high comorbid rate in Chinese OSA patients and is significantly associated with single status, apnoea-hypopnea index, hypoxemia, family and social supports.

Prevalence and Predisposing Factors for Depressive Status in Chinese Patients with Obstructive Sleep Apnoea: A Large-Sample Survey

PubMed Central

Dai, Yaozhang; Li, Xuewu; Zhang, Xin; Wang, Sihua; Sang, Jianzhong; Tian, Xiufen; Cao, Hua

2016-01-01

Background and Objective Recently, there are few studies reporting on depressive status and obstructive sleep apnoea (OSA) in China. A large-sample survey was to be performed to explore the prevalence of depressive status and related factors in Chinese patients with OSA. Methods From among a randomly-selected group of OSA patients, 1,327 met inclusion criteria. After screening with the Symptom Checklist 90 (SCL-90) and Self-Rating Depression Scale (SDS), patients were assigned to OSA without depressive status (control group, n = 698) and OSA with depressive status (n = 629) groups. Using chi-squared testing, the correlation analyses between the depressive status and OSA patient demographic and clinical variables were tested. Then depression-related risk factors in OSA patients were analysed using stepwise linear regression analysis. The effects of family and social factors on depressive status in OSA patients were investigated using Mann-Whitney U (one of nonparametric test). Results The prevalence of depressive status was 47.4% in OSA patients. Depressive status was significantly associated with female gender, single status, Family Burden Scale of Disease (FBS), Family APGAR Index (APGAR), apnoea-hypopnea index (AHI), and Perceived Social Support Scale (PSSS). Stepwise linear regression analysis further indicated that single status, hypoxemia, APGAR, AHI, PSSS, AHI, and FBS were all risk factors for depressive status in OSA patients. The total of the FBS score and three of its sub-factors scores (family daily activities, family relationships and mental health of family members) were higher, and the total of the APGAR score and two of its sub-factors scores (adaptability and affection) were lower in OSA with depressive status compared with the control group. Besides, the total score for the PSSS and scores for its two sub-factors (family support and social support) were all lower in OSA patients with depressive status than those of the control group. Conclusions Depressive status has high comorbid rate in Chinese OSA patients and is significantly associated with single status, apnoea-hypopnea index, hypoxemia, family and social supports. PMID:26934192

The value of the injury severity score in pediatric trauma: Time for a new definition of severe injury?

PubMed

Brown, Joshua B; Gestring, Mark L; Leeper, Christine M; Sperry, Jason L; Peitzman, Andrew B; Billiar, Timothy R; Gaines, Barbara A

2017-06-01

The Injury Severity Score (ISS) is the most commonly used injury scoring system in trauma research and benchmarking. An ISS greater than 15 conventionally defines severe injury; however, no studies evaluate whether ISS performs similarly between adults and children. Our objective was to evaluate ISS and Abbreviated Injury Scale (AIS) to predict mortality and define optimal thresholds of severe injury in pediatric trauma. Patients from the Pennsylvania trauma registry 2000-2013 were included. Children were defined as younger than 16 years. Logistic regression predicted mortality from ISS for children and adults. The optimal ISS cutoff for mortality that maximized diagnostic characteristics was determined in children. Regression also evaluated the association between mortality and maximum AIS in each body region, controlling for age, mechanism, and nonaccidental trauma. Analysis was performed in single and multisystem injuries. Sensitivity analyses with alternative outcomes were performed. Included were 352,127 adults and 50,579 children. Children had similar predicted mortality at ISS of 25 as adults at ISS of 15 (5%). The optimal ISS cutoff in children was ISS greater than 25 and had a positive predictive value of 19% and negative predictive value of 99% compared to a positive predictive value of 7% and negative predictive value of 99% for ISS greater than 15 to predict mortality. In single-system-injured children, mortality was associated with head (odds ratio, 4.80; 95% confidence interval, 2.61-8.84; p < 0.01) and chest AIS (odds ratio, 3.55; 95% confidence interval, 1.81-6.97; p < 0.01), but not abdomen, face, neck, spine, or extremity AIS (p > 0.05). For multisystem injury, all body region AIS scores were associated with mortality except extremities. Sensitivity analysis demonstrated ISS greater than 23 to predict need for full trauma activation, and ISS greater than 26 to predict impaired functional independence were optimal thresholds. An ISS greater than 25 may be a more appropriate definition of severe injury in children. Pattern of injury is important, as only head and chest injury drive mortality in single-system-injured children. These findings should be considered in benchmarking and performance improvement efforts. Epidemiologic study, level III.

Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis

ERIC Educational Resources Information Center

Williams, Ryan T.

2012-01-01

Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use…

A Quality Assessment Tool for Non-Specialist Users of Regression Analysis

ERIC Educational Resources Information Center

Argyrous, George

2015-01-01

This paper illustrates the use of a quality assessment tool for regression analysis. It is designed for non-specialist "consumers" of evidence, such as policy makers. The tool provides a series of questions such consumers of evidence can ask to interrogate regression analysis, and is illustrated with reference to a recent study published…

A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy.

PubMed

Park, Ji Hyun; Kim, Hyeon-Young; Lee, Hanna; Yun, Eun Kyoung

2015-12-01

This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Visual grading characteristics and ordinal regression analysis during optimisation of CT head examinations.

PubMed

Zarb, Francis; McEntee, Mark F; Rainford, Louise

2015-06-01

To evaluate visual grading characteristics (VGC) and ordinal regression analysis during head CT optimisation as a potential alternative to visual grading assessment (VGA), traditionally employed to score anatomical visualisation. Patient images (n = 66) were obtained using current and optimised imaging protocols from two CT suites: a 16-slice scanner at the national Maltese centre for trauma and a 64-slice scanner in a private centre. Local resident radiologists (n = 6) performed VGA followed by VGC and ordinal regression analysis. VGC alone indicated that optimised protocols had similar image quality as current protocols. Ordinal logistic regression analysis provided an in-depth evaluation, criterion by criterion allowing the selective implementation of the protocols. The local radiology review panel supported the implementation of optimised protocols for brain CT examinations (including trauma) in one centre, achieving radiation dose reductions ranging from 24 % to 36 %. In the second centre a 29 % reduction in radiation dose was achieved for follow-up cases. The combined use of VGC and ordinal logistic regression analysis led to clinical decisions being taken on the implementation of the optimised protocols. This improved method of image quality analysis provided the evidence to support imaging protocol optimisation, resulting in significant radiation dose savings. • There is need for scientifically based image quality evaluation during CT optimisation. • VGC and ordinal regression analysis in combination led to better informed clinical decisions. • VGC and ordinal regression analysis led to dose reductions without compromising diagnostic efficacy.

Viability estimation of pepper seeds using time-resolved photothermal signal characterization

NASA Astrophysics Data System (ADS)

Kim, Ghiseok; Kim, Geon-Hee; Lohumi, Santosh; Kang, Jum-Soon; Cho, Byoung-Kwan

2014-11-01

We used infrared thermal signal measurement system and photothermal signal and image reconstruction techniques for viability estimation of pepper seeds. Photothermal signals from healthy and aged seeds were measured for seven periods (24, 48, 72, 96, 120, 144, and 168 h) using an infrared camera and analyzed by a regression method. The photothermal signals were regressed using a two-term exponential decay curve with two amplitudes and two time variables (lifetime) as regression coefficients. The regression coefficients of the fitted curve showed significant differences for each seed groups, depending on the aging times. In addition, the viability of a single seed was estimated by imaging of its regression coefficient, which was reconstructed from the measured photothermal signals. The time-resolved photothermal characteristics, along with the regression coefficient images, can be used to discriminate the aged or dead pepper seeds from the healthy seeds.

REGRESSION ANALYSIS OF SEA-SURFACE-TEMPERATURE PATTERNS FOR THE NORTH PACIFIC OCEAN.

DTIC Science & Technology

SEA WATER, *SURFACE TEMPERATURE, *OCEANOGRAPHIC DATA, PACIFIC OCEAN, REGRESSION ANALYSIS , STATISTICAL ANALYSIS, UNDERWATER EQUIPMENT, DETECTION, UNDERWATER COMMUNICATIONS, DISTRIBUTION, THERMAL PROPERTIES, COMPUTERS.

Prognostic factors of single-visit endodontic and restorative treatment under general anaesthesia for special needs patients.

PubMed

Chang, J; Kim, H-Y

2017-02-01

The aim of this study was to evaluate the longevity of teeth with single-visit endodontic and restorative treatment under general anaesthesia (GA) for special needs patients and to investigate factors associated with survival and success. Data were collected from 381 teeth in 203 patients [mean (s.d.) age = 27·0 (14·1)]. All endodontic and restorative procedures were performed during a single GA session except for cementation of crowns in the cases requiring crown restoration (38%). A total of 267 teeth (70·6%) were followed-up for 6-81 months [mean (s.d.): 32·7 (20·0)]. Patients and teeth with and without follow-up were compared. Kaplan-Meier analysis with generalised Wilcoxon test was used to compare the mean survival and success period. Cox proportion hazard regression model was applied for multivariate analysis. At the end of the observation period, 10 teeth had a crown fracture (5-year survival rate = 89·8%), and an additional 10 teeth had primary or secondary caries (5-year success rate = 86·4%). Risk factors associated with survival were age (>40), non-parental caregiver, cooperation level and periodontal disease. A soft diet was an additional risk factor against the success of teeth. Single-visit endodontic and restorative treatment under GA showed favourable outcomes, suggesting a promising treatment option for special needs patients. Patient- and dental-specific circumstances need to be carefully considered to enhance the longevity of reconstructed teeth. © 2016 John Wiley & Sons Ltd.

Variation in the ciliary neurotrophic factor gene and muscle strength in older Caucasian women.

PubMed

Arking, Dan E; Fallin, Daniele M; Fried, Linda P; Li, Tao; Beamer, Brock A; Xue, Qian Li; Chakravarti, Aravinda; Walston, Jeremy

2006-05-01

To determine whether genetic variants in the ciliary neurotrophic factor (CNTF) gene are associated with muscle strength in older women. Cross-sectional analysis of baseline data from the Women's Health and Aging Studies I (1992) and II (1994), complementary population-based studies. Twelve contiguous ZIP code areas in Baltimore, Maryland. Three hundred sixty-three Caucasian, community-dwelling women aged 70 to 79. Participants were genotyped at the CNTF locus for eight single nucleotide polymorphisms (SNPs), including the null allele rs1800169. The dependent variables were grip strength and the frailty syndrome, identified as presence of three or more of five frailty indicators (weakness, slowness, weight loss, low physical activity, exhaustion). In addition to genotypes, independent variables of body mass index (BMI) and osteoarthritis of the hands were included. Using multivariate linear regression, single SNP analysis identified five SNPs significantly associated with grip strength (P<.05), after adjusting for age, BMI, and osteoarthritis. Haplotype analysis was performed, and a single haplotype associated with grip strength was identified (P<.01). The rs1800169 null allele fully explained the association between this haplotype and grip strength under a recessive model, with individuals homozygous for the null allele exhibiting a 3.80-kg lower (95% confidence interval=1.01-6.58) grip strength. No association was seen between the CNTF null allele and frailty. Individuals homozygous for the CNTF null allele had significantly lower grip strength but did not exhibit overt frailty. Larger prospective studies are needed to confirm this finding and extend it to additional populations.

Utility of respiratory ward-based NIV in acidotic hypercapnic respiratory failure.

PubMed

Dave, Chirag; Turner, Alice; Thomas, Ajit; Beauchamp, Ben; Chakraborty, Biman; Ali, Asad; Mukherjee, Rahul; Banerjee, Dev

2014-11-01

We sought to elicit predictors of in-hospital mortality for first and subsequent admissions with acidotic hypercapnic respiratory failure (AHRF) in a cohort of chronic obstructive pulmonary disease patients who have undergone ward-based non-invasive ventilation (NIV), and identify features associated with long-term survival. Analysis of prospectively collected data at a single centre on patients undergoing NIV for AHRF between 2004 and 2009. Predictors of in-hospital mortality and intubation were sought by logistic regression and predictors of long-term survival by Cox regression. Initial pH exhibited a threshold effect for in-hospital mortality at pH 7.15. This relationship remained in patients undergoing their first episode of AHRF. In both first and subsequent admissions, a pH threshold of 7.25 at 4 h was associated with better prognosis (P = 0.02 and P = 0.04 respectively). In second or subsequent episodes of AHRF, mortality was lower and predicted only by age (P = 0.002) on multivariate analysis. NIV could be used on medical wards for patients with pH 7.16 or greater on their first admission, although more conservative values should continue to be used for those with a second or subsequent episodes of AHRF. © 2014 Asian Pacific Society of Respirology.

Exploring visuospatial abilities and their contribution to constructional abilities and nonverbal intelligence.

PubMed

Trojano, Luigi; Siciliano, Mattia; Cristinzio, Chiara; Grossi, Dario

2018-01-01

The present study aimed at exploring relationships among the visuospatial tasks included in the Battery for Visuospatial Abilities (BVA), and at assessing the relative contribution of different facets of visuospatial processing on tests tapping constructional abilities and nonverbal abstract reasoning. One hundred forty-four healthy subjects with a normal score on Mini Mental State Examination completed the BVA plus Raven's Coloured Progressive Matrices and Constructional Apraxia test. We used Principal Axis Factoring and Parallel Analysis to investigate relationships among the BVA visuospatial tasks, and performed regression analyses to assess the visuospatial contribution to constructional abilities and nonverbal abstract reasoning. Principal Axis Factoring and Parallel Analysis revealed two eigenvalues exceeding 1, accounting for about 60% of the variance. A 2-factor model provided the best fit. Factor 1 included sub-tests exploring "complex" visuospatial skills, whereas Factor 2 included two subtests tapping "simple" visuospatial skills. Regression analyses revealed that both Factor 1 and Factor 2 significantly affected performance on Raven's Coloured Progressive Matrices, whereas only the Factor 1 affected performance on Constructional Apraxia test. Our results supported functional segregation proposed by De Renzi, suggesting clinical caution to utilize a single test to assess visuospatial domain, and qualified the visuospatial contribution in drawing and non-verbal intelligence test.

Replacing zoledronic acid with denosumab is a risk factor for developing osteonecrosis of the jaw.

PubMed

Higuchi, Tomoko; Soga, Yoshihiko; Muro, Misato; Kajizono, Makoto; Kitamura, Yoshihisa; Sendo, Toshiaki; Sasaki, Akira

2018-06-01

Intravenous zoledronic acid (ZA) is often replaced with subcutaneous denosumab in patients with bone metastatic cancer. Despite their different pharmacologic mechanisms of action, both denosumab and ZA are effective in bone metastasis but cause osteonecrosis of the jaw (ONJ) as a side effect. ZA persists in the body almost indefinitely, whereas denosumab does not persist for long periods. This study evaluated the risks of developing ONJ when replacing ZA with denosumab. In total, 161 Japanese patients administered ZA for bone metastatic cancer were enrolled in this single-center, retrospective, observational study. The risk of developing ONJ was evaluated by logistic regression analysis using the following factors: age, gender, cancer type, angiogenesis inhibitors, steroids, and replacement of ZA with denosumab. Seventeen patients (10.6%) developed ONJ. Multiple regression analysis indicated a significant difference in rate of ONJ associated with replacement of ZA with denosumab (odds ratio = 3.81; 95% confidence interval 1.04-13.97; P = .043). Replacing ZA with denosumab is a risk factor for the development of ONJ. Both binding of bisphosphonate to bone and receptor activator of nuclear factor-κ B ligand inhibition could additively increase the risk of ONJ. We bring the replacement of ZA with denosumab to the attention of clinical oncologists. Copyright © 2018 Elsevier Inc. All rights reserved.

Predictive Factors of Atelectasis Following Endoscopic Resection.

PubMed

Choe, Jung Wan; Jung, Sung Woo; Song, Jong Kyu; Shim, Euddeum; Choo, Ji Yung; Kim, Seung Young; Hyun, Jong Jin; Koo, Ja Seol; Yim, Hyung Joon; Lee, Sang Woo

2016-01-01

Atelectasis is one of the pulmonary complications associated with anesthesia. Little is known about atelectasis following endoscopic procedures under deep sedation. This study evaluated the frequency, risk factors, and clinical course of atelectasis after endoscopic resection. A total of 349 patients who underwent endoscopic resection of the upper gastrointestinal tract at a single academic tertiary referral center from March 2010 to October 2013 were enrolled. Baseline characteristics and clinical data were retrospectively reviewed from medical records. To identify atelectasis, we compared the chest radiography taken before and after the endoscopic procedure. Among the 349 patients, 68 (19.5 %) had newly developed atelectasis following endoscopic resection. In univariate logistic regression analysis, atelectasis correlated significantly with high body mass index, smoking, diabetes mellitus, procedure duration, size of lesion, and total amount of propofol. In multiple logistic regression analysis, body mass index, procedure duration, and total propofol amount were risk factors for atelectasis following endoscopic procedures. Of the 68 patients with atelectasis, nine patients developed fever, and six patients displayed pneumonic infiltration. The others had no symptoms related to atelectasis. The incidence of radiographic atelectasis following endoscopic resection was nearly 20 %. Obesity, procedural time, and amount of propofol were the significant risk factors for atelectasis following endoscopic procedure. Most cases of the atelectasis resolved spontaneously with no sequelae.

The process and utility of classification and regression tree methodology in nursing research

PubMed Central

Kuhn, Lisa; Page, Karen; Ward, John; Worrall-Carter, Linda

2014-01-01

Aim This paper presents a discussion of classification and regression tree analysis and its utility in nursing research. Background Classification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams. Design Discussion paper. Data sources English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984–2013. Discussion Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered. Implications for Nursing Research Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method. Conclusion Classification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions. PMID:24237048

The process and utility of classification and regression tree methodology in nursing research.

PubMed

Kuhn, Lisa; Page, Karen; Ward, John; Worrall-Carter, Linda

2014-06-01

This paper presents a discussion of classification and regression tree analysis and its utility in nursing research. Classification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams. Discussion paper. English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984-2013. Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered. Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method. Classification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions. © 2013 The Authors. Journal of Advanced Nursing Published by John Wiley & Sons Ltd.

Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context

PubMed Central

Martinez, Josue G.; Carroll, Raymond J.; Müller, Samuel; Sampson, Joshua N.; Chatterjee, Nilanjan

2012-01-01

When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso. PMID:22347720

Advantages of the net benefit regression framework for economic evaluations of interventions in the workplace: a case study of the cost-effectiveness of a collaborative mental health care program for people receiving short-term disability benefits for psychiatric disorders.

PubMed

Hoch, Jeffrey S; Dewa, Carolyn S

2014-04-01

Economic evaluations commonly accompany trials of new treatments or interventions; however, regression methods and their corresponding advantages for the analysis of cost-effectiveness data are not well known. To illustrate regression-based economic evaluation, we present a case study investigating the cost-effectiveness of a collaborative mental health care program for people receiving short-term disability benefits for psychiatric disorders. We implement net benefit regression to illustrate its strengths and limitations. Net benefit regression offers a simple option for cost-effectiveness analyses of person-level data. By placing economic evaluation in a regression framework, regression-based techniques can facilitate the analysis and provide simple solutions to commonly encountered challenges. Economic evaluations of person-level data (eg, from a clinical trial) should use net benefit regression to facilitate analysis and enhance results.

78 FR 16808 - Connect America Fund; High-Cost Universal Service Support

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-19

... to use one regression to generate a single cap on total loop costs for each study area. A single cap.... * * * A preferable, and simpler, approach would be to develop one conditional quantile model for aggregate.... Total universal service support for such carriers was approaching $2 billion annually--more than 40...

Spatial analysis of paediatric swimming pool submersions by housing type.

PubMed

Shenoi, Rohit P; Levine, Ned; Jones, Jennifer L; Frost, Mary H; Koerner, Christine E; Fraser, John J

2015-08-01

Drowning is a major cause of unintentional childhood death. The relationship between childhood swimming pool submersions, neighbourhood sociodemographics, housing type and swimming pool location was examined in Harris County, Texas. Childhood pool submersion incidents were examined for spatial clustering using the Nearest Neighbor Hierarchical Cluster (Nnh) algorithm. To relate submersions to predictive factors, an Markov Chain Monte Carlo (MCMC) Poisson-Lognormal-Conditional Autoregressive (CAR) spatial regression model was tested at the census tract level. There were 260 submersions; 49 were fatal. Forty-two per cent occurred at single-family residences and 36% at multifamily residential buildings. The risk of a submersion was 2.7 times higher for a child at a multifamily than a single-family residence and 28 times more likely in a multifamily swimming pool than a single family pool. However, multifamily submersions were clustered because of the concentration of such buildings with pools. Spatial clustering did not occur in single-family residences. At the tract level, submersions in single-family and multifamily residences were best predicted by the number of pools by housing type and the number of children aged 0-17 by housing type. Paediatric swimming pool submersions in multifamily buildings are spatially clustered. The likelihood of submersions is higher for children who live in multifamily buildings with pools than those who live in single-family homes with pools. 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.

CADDIS Volume 4. Data Analysis: Basic Analyses

EPA Pesticide Factsheets

Use of statistical tests to determine if an observation is outside the normal range of expected values. Details of CART, regression analysis, use of quantile regression analysis, CART in causal analysis, simplifying or pruning resulting trees.

Combined Effects of Prenatal Exposures to Environmental Chemicals on Birth Weight.

PubMed

Govarts, Eva; Remy, Sylvie; Bruckers, Liesbeth; Den Hond, Elly; Sioen, Isabelle; Nelen, Vera; Baeyens, Willy; Nawrot, Tim S; Loots, Ilse; Van Larebeke, Nick; Schoeters, Greet

2016-05-12

Prenatal chemical exposure has been frequently associated with reduced fetal growth by single pollutant regression models although inconsistent results have been obtained. Our study estimated the effects of exposure to single pollutants and mixtures on birth weight in 248 mother-child pairs. Arsenic, copper, lead, manganese and thallium were measured in cord blood, cadmium in maternal blood, methylmercury in maternal hair, and five organochlorines, two perfluorinated compounds and diethylhexyl phthalate metabolites in cord plasma. Daily exposure to particulate matter was modeled and averaged over the duration of gestation. In single pollutant models, arsenic was significantly associated with reduced birth weight. The effect estimate increased when including cadmium, and mono-(2-ethyl-5-carboxypentyl) phthalate (MECPP) co-exposure. Combining exposures by principal component analysis generated an exposure factor loaded by cadmium and arsenic that was associated with reduced birth weight. MECPP induced gender specific effects. In girls, the effect estimate was doubled with co-exposure of thallium, PFOS, lead, cadmium, manganese, and mercury, while in boys, the mixture of MECPP with cadmium showed the strongest association with birth weight. In conclusion, birth weight was consistently inversely associated with exposure to pollutant mixtures. Chemicals not showing significant associations at single pollutant level contributed to stronger effects when analyzed as mixtures.

Regimen durability in HIV-infected children and adolescents initiating first-line antiretroviral therapy in a large public sector HIV cohort in South Africa.

PubMed

Bonawitz, Rachael; Brennan, Alana T; Long, Lawrence; Heeren, Timothy; Maskew, Mhairi; Sanne, Ian; Fox, Matthew P

2018-06-01

In April 2010, tenofovir and abacavir replaced stavudine in public sector first-line antiretroviral therapy (ART) for children under 20 years old in South Africa. The association of both abacavir and tenofovir with fewer side effects and toxicities compared to stavudine could translate to increased durability of tenofovir or abacavir-based regimens. We evaluated changes over time in regimen durability for paediatric patients 3-19 years of age at eight public sector clinics in Johannesburg, South Africa. Cohort analysis of treatment-naïve, non-pregnant paediatric patients from 3 to 19 years old initiated on ART between April 2004 and December 2013. First-line ART regimens before April 2010 consisted of stavudine or zidovudine with lamivudine and either efavirenz or nevirapine. Tenofovir and/or abacavir was substituted for stavudine after April 2010 in first-line ART. We evaluated the frequency and type of single-drug substitutions, treatment interruptions and switches to second-line therapy. Fine and Gray competing risk regression models were used to evaluate the association of antiretroviral drug type with single-drug substitutions, treatment interruptions and second-line switches in the first 24 months on treatment. Three hundred and ninety-eight (15.3%) single-drug substitutions, 187 (7.2%) treatment interruptions and 86 (3.3%) switches to second-line therapy occurred among 2602 paediatric patients over 24-months on ART. Overall, the rate of single-drug substitutions started to increase in 2009, peaked in 2011 at 25% and then declined to 10% in 2013, well after the integration of tenofovir into paediatric regimens; no patients over the age of 3 were initiated on abacavir for first-line therapy. Competing risk regression models showed patients on zidovudine or stavudine had upwards of a fivefold increase in single-drug substitution vs. patients initiated on tenofovir in the first 24 months on ART. Older adolescents also had a two- to threefold increase in treatment interruptions and switches to second-line therapy compared to younger patients in the first 24 months on ART. The decline in single-drug substitutions is associated with the introduction of tenofovir. Tenofovir use could improve regimen durability and treatment outcomes in resource-limited settings. © 2018 John Wiley & Sons Ltd.

Super-resolution method for face recognition using nonlinear mappings on coherent features.

PubMed

Huang, Hua; He, Huiting

2011-01-01

Low-resolution (LR) of face images significantly decreases the performance of face recognition. To address this problem, we present a super-resolution method that uses nonlinear mappings to infer coherent features that favor higher recognition of the nearest neighbor (NN) classifiers for recognition of single LR face image. Canonical correlation analysis is applied to establish the coherent subspaces between the principal component analysis (PCA) based features of high-resolution (HR) and LR face images. Then, a nonlinear mapping between HR/LR features can be built by radial basis functions (RBFs) with lower regression errors in the coherent feature space than in the PCA feature space. Thus, we can compute super-resolved coherent features corresponding to an input LR image according to the trained RBF model efficiently and accurately. And, face identity can be obtained by feeding these super-resolved features to a simple NN classifier. Extensive experiments on the Facial Recognition Technology, University of Manchester Institute of Science and Technology, and Olivetti Research Laboratory databases show that the proposed method outperforms the state-of-the-art face recognition algorithms for single LR image in terms of both recognition rate and robustness to facial variations of pose and expression.

Population heterogeneity in the salience of multiple risk factors for adolescent delinquency.

PubMed

Lanza, Stephanie T; Cooper, Brittany R; Bray, Bethany C

2014-03-01

To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors. We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered. Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents. Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Detection of Bi-Directionality in Strain-Gage Balance Calibration Data

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert

2012-01-01

An indicator variable was developed for both visualization and detection of bi-directionality in wind tunnel strain-gage balance calibration data. First, the calculation of the indicator variable is explained in detail. Then, a criterion is discussed that may be used to decide which gage outputs of a balance have bi- directional behavior. The result of this analysis could be used, for example, to justify the selection of certain absolute value or other even function terms in the regression model of gage outputs whenever the Iterative Method is chosen for the balance calibration data analysis. Calibration data of NASA s MK40 Task balance is analyzed to illustrate both the calculation of the indicator variable and the application of the proposed criterion. Finally, bi directionality characteristics of typical multi piece, hybrid, single piece, and semispan balances are determined and discussed.

Nonlinear estimation of parameters in biphasic Arrhenius plots.

PubMed

Puterman, M L; Hrboticky, N; Innis, S M

1988-05-01

This paper presents a formal procedure for the statistical analysis of data on the thermotropic behavior of membrane-bound enzymes generated using the Arrhenius equation and compares the analysis to several alternatives. Data is modeled by a bent hyperbola. Nonlinear regression is used to obtain estimates and standard errors of the intersection of line segments, defined as the transition temperature, and slopes, defined as energies of activation of the enzyme reaction. The methodology allows formal tests of the adequacy of a biphasic model rather than either a single straight line or a curvilinear model. Examples on data concerning the thermotropic behavior of pig brain synaptosomal acetylcholinesterase are given. The data support the biphasic temperature dependence of this enzyme. The methodology represents a formal procedure for statistical validation of any biphasic data and allows for calculation of all line parameters with estimates of precision.

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.

A Note on the Relationship between the Number of Indicators and Their Reliability in Detecting Regression Coefficients in Latent Regression Analysis

ERIC Educational Resources Information Center

Dolan, Conor V.; Wicherts, Jelte M.; Molenaar, Peter C. M.

2004-01-01

We consider the question of how variation in the number and reliability of indicators affects the power to reject the hypothesis that the regression coefficients are zero in latent linear regression analysis. We show that power remains constant as long as the coefficient of determination remains unchanged. Any increase in the number of indicators…

[Correlation analysis of cement leakage with volume ratio of intravertebral bone cement to vertebral body and vertebral body wall incompetence in percutaneous vertebroplasty for osteoporotic vertebral compression fractures].

PubMed

Liang, De; Ye, Linqiang; Jiang, Xiaobing; Huang, Weiquan; Yao, Zhensong; Tang, Yongchao; Zhang, Shuncong; Jin, Daxiang

2014-11-01

To investigate the risk factors of cement leakage in percutaneous vertebroplasty (PVP) for osteoporotic vertebral compression fracture (OVCF). Between March 2011 and March 2012, 98 patients with single level OVCF were treated by PVP, and the clinical data were analyzed retrospectively. There were 13 males and 85 females, with a mean age of 77.2 years (range, 54-95 years). The mean disease duration was 43 days (range, 15-120 days), and the mean T score of bone mineral density (BMD) was -3.8 (range, -6.7- -2.5). Bilateral transpedicular approach was used in all the patients. The patients were divided into cement leakage group and no cement leakage group by occurrence of cement leakage based on postoperative CT. Single factor analysis was used to analyze the difference between 2 groups in T score of BMD, operative level, preoperative anterior compression degree of operative vertebrae, preoperative middle compression degree of operative vertebrae, preoperative sagittal Cobb angle of operative vertebrae, preoperative vertebral body wall incompetence, cement volume, and volume ratio of intravertebral bone cement to vertebral body. All relevant factors were introduced to logistic regression analysis to analyze the risk factors of cement leakage. All procedures were performed successfully. The mean operation time was 40 minutes (range, 30-50 minutes), and the mean volume ratio of intravertebral bone cement to vertebral body was 24.88% (range, 7.84%-38.99%). Back pain was alleviated significantly in all the patients postoperatively. All patients were followed up with a mean time of 8 months (range, 6-12 months). Cement leakage occurred in 49 patients. Single factor analysis showed that there were significant differences in the volume ratio of intravertebral bone cement to vertebral body and preoperative vertebral body wall incompetence between 2 groups (P < 0.05), while no significant difference in T score of BMD, operative level, preoperative anterior compression degree of operative vertebrae, preoperative middle compression degree of operative vertebrae, preoperative sagittal Cobb angle of operative vertebrae, and cement volume (P > 0.05). The logistic regression analysis showed that the volume ratio of intravertebral bone cement to vertebral body (P < 0.05) and vertebral body wall incompetence (P < 0.05) were the risk factors for occurrence of cement leakage. The volume ratio of intravertebral bone cement to vertebral body and vertebral body wall incompetence are risk factors of cement leakage in PVP for OVCF. Cement leakage is easy to occur in operative level with vertebral body wall incompetence and high volume ratio of intravertebral bone cement to vertebral body.

Trend Estimation and Regression Analysis in Climatological Time Series: An Application of Structural Time Series Models and the Kalman Filter.

NASA Astrophysics Data System (ADS)

Visser, H.; Molenaar, J.

1995-05-01

The detection of trends in climatological data has become central to the discussion on climate change due to the enhanced greenhouse effect. To prove detection, a method is needed (i) to make inferences on significant rises or declines in trends, (ii) to take into account natural variability in climate series, and (iii) to compare output from GCMs with the trends in observed climate data. To meet these requirements, flexible mathematical tools are needed. A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. The stochastic trend component is described using the class of ARIMA models. The regression component is assumed to be linear. However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. The mathematical technique used to estimate this trend-regression model is the Kaiman filter. The main features of the filter are discussed.Examples of trend estimation are given using annual mean temperatures at a single station in the Netherlands (1706-1990) and annual mean temperatures at Northern Hemisphere land stations (1851-1990). The inclusion of explanatory variables is shown by regressing the latter temperature series on four variables: Southern Oscillation index (SOI), volcanic dust index (VDI), sunspot numbers (SSN), and a simulated temperature signal, induced by increasing greenhouse gases (GHG). In all analyses, the influence of SSN on global temperatures is found to be negligible. The correlations between temperatures and SOI and VDI appear to be negative. For SOI, this correlation is significant, but for VDI it is not, probably because of a lack of volcanic eruptions during the sample period. The relation between temperatures and GHG is positive, which is in agreement with the hypothesis of a warming climate because of increasing levels of greenhouse gases. The prediction performance of the model is rather poor, and possible explanations are discussed.

Case-control study on the associations between lifestyle-behavioral risk factors and phlegm-wetness constitution.

PubMed

Zhu, Yanbo; Wang, Qi; Dai, Zhaoyu; Origasa, Hideki; Di, Jie; Wang, Yangyang; Lin, Lin; Fan, Chunpok

2014-06-01

To explore the relationships between different lifestyle-behavioral factors and phlegm-wetness type of Traditional Chinese Medicine constitution, so as to provide health management strategies for phlegm-wetness constitution. A case-control study was conducted with the cases selected from the database of Chinese constitution survey in 9 provinces or municipalities of China. 1380 cases met the diagnostic criteria of phlegm-wetness type were taken as the case group, and 1380 cases were randomly selected from gentleness type as the control group. Using Chi-square test to compare the differences of lifestyle-behavior composition in each group; single factor and multiple logistic regression analysis were used to compare the relationships of lifestyle-behavioral factors and phlegm-wetness type. There were statistically significant differences between phlegm-wetness type group and gentleness type group in lifestyle behaviors (dietary habits, tobacco and liquor consumptions, exercise habits, sleeping habits). The results of single factor logistic regression analysis demonstrated that the risk of phlegm-wetness constitution decreased significantly in light diet (odds ratio, OR = 0.68); The risk factors of phlegm-wetness type were fatty food intake (OR = 2.36), sleeping early and getting up late (OR = 1.87), tobacco smoking (OR = 1.83), barbecued food intake (OR = 1.68), alcohol drinking (OR = 1.63), salty food intake (OR = 1.44), sleeping erratically (OR = 1.43), less physical activities (OR = 1.42), sweet food intake (OR = 1.29), sleeping and getting up late (OR = 1.26), and pungent food intake (OR = 1.21), respectively. Regardless of the interaction among lifestyle-behavioral factors, the results of the multiple logistic regression analysis revealed that the risk factors of phlegm-wetness type were sleeping early and getting up late (OR = 1.94), fatty food intake (OR = 1.80), tobacco smoking (OR = 1.50), sleeping erratically (OR = 1.50), barbecued food intake (OR = 1.40), sleeping and getting up late (OR = 1.40), less physical activities (OR = 1.31), sleeping late and getting up early (OR = 1.27), and sweet food intake (OR = 1.27, respectively, and the risk of phlegm-wetness type still decreased significantly in light food intake (OR = 0.79). Light diet can decrease the risk of being phlegm-wetness constitution, and bad lifestyle behaviors such as sleeping early and getting up late, sleeping erratically, fatty food, barbecued food or sweet food intake, tobacco and liquor consumptions, and less physical activities can increase the risks of becoming phlegm-wetness constitution.

Patient phenotypes associated with outcomes after aneurysmal subarachnoid hemorrhage: a principal component analysis.

PubMed

Ibrahim, George M; Morgan, Benjamin R; Macdonald, R Loch

2014-03-01

Predictors of outcome after aneurysmal subarachnoid hemorrhage have been determined previously through hypothesis-driven methods that often exclude putative covariates and require a priori knowledge of potential confounders. Here, we apply a data-driven approach, principal component analysis, to identify baseline patient phenotypes that may predict neurological outcomes. Principal component analysis was performed on 120 subjects enrolled in a prospective randomized trial of clazosentan for the prevention of angiographic vasospasm. Correlation matrices were created using a combination of Pearson, polyserial, and polychoric regressions among 46 variables. Scores of significant components (with eigenvalues>1) were included in multivariate logistic regression models with incidence of severe angiographic vasospasm, delayed ischemic neurological deficit, and long-term outcome as outcomes of interest. Sixteen significant principal components accounting for 74.6% of the variance were identified. A single component dominated by the patients' initial hemodynamic status, World Federation of Neurosurgical Societies score, neurological injury, and initial neutrophil/leukocyte counts was significantly associated with poor outcome. Two additional components were associated with angiographic vasospasm, of which one was also associated with delayed ischemic neurological deficit. The first was dominated by the aneurysm-securing procedure, subarachnoid clot clearance, and intracerebral hemorrhage, whereas the second had high contributions from markers of anemia and albumin levels. Principal component analysis, a data-driven approach, identified patient phenotypes that are associated with worse neurological outcomes. Such data reduction methods may provide a better approximation of unique patient phenotypes and may inform clinical care as well as patient recruitment into clinical trials. http://www.clinicaltrials.gov. Unique identifier: NCT00111085.

Analysis of Radiation Effects in Digital Subtraction Angiography of Intracranial Artery Stenosis.

PubMed

Guo, Chaoqun; Shi, Xiaolei; Ding, Xianhui; Zhou, Zhiming

2018-04-21

Intracranial artery stenosis (IAS) is the most common cause for acute cerebral accidents. Digital subtraction angiography (DSA) is the gold standard to detect IAS and usually brings excess radiation exposure to examinees and examiners. The artery pathology might influence the interventional procedure, causing prolonged radiation effects. However, no studies on the association between IAS pathology and operational parameters are available. A retrospective analysis was conducted on 93 patients with first-ever stroke/transient ischemic attack, who received DSA examination within 3 months from onset in this single center. Comparison of baseline characteristics was determined by 2-tailed Student's t-test or the chi-square test between subjects with and without IAS. A binary logistic regression analysis was performed to determine the association between IAS pathology and the items with a P value <0.05 in Student's t-test or chi-square test. There were 93 candidates (42 with IAS and 51 without IAS) in this study. The 2 groups shared no significance of the baseline characteristics (P > 0.05). We found a significantly higher total time, higher kerma area product, greater total dose, and greater DSA dose in the IAS group than in those without IAS (P < 0.05). A binary logistic regression analysis indicated the significant association between total time and IAS pathology (P < 0.05) but no significance in kerma area product, radiation dose, and DSA dose (P > 0.05). IAS pathology would indicate a prolonged total time of DSA procedure in clinical practice. However, the radiation effects would not change with pathologic changes. Copyright © 2018 Elsevier Inc. All rights reserved.

Single-agent Taxane Versus Taxane-containing Combination Chemotherapy as Salvage Therapy for Advanced Urothelial Carcinoma.

PubMed

Sonpavde, Guru; Pond, Gregory R; Choueiri, Toni K; Mullane, Stephanie; Niegisch, Guenter; Albers, Peter; Necchi, Andrea; Di Lorenzo, Giuseppe; Buonerba, Carlo; Rozzi, Antonio; Matsumoto, Kazumasa; Lee, Jae-Lyun; Kitamura, Hiroshi; Kume, Haruki; Bellmunt, Joaquim

2016-04-01

Single-agent taxanes are commonly used as salvage systemic therapy for patients with advanced urothelial carcinoma (UC). To study the impact of combination chemotherapy delivering a taxane plus other chemotherapeutic agents compared with single-agent taxane as salvage therapy. Individual patient-level data from phase 2 trials of salvage systemic therapy were used. Trials evaluating either single agents (paclitaxel or docetaxel) or combination chemotherapy (taxane plus one other chemotherapeutic agent or more) following prior platinum-based therapy were used. Information regarding the known major baseline prognostic factors was required: time from prior chemotherapy, hemoglobin, performance status, albumin, and liver metastasis status. Cox proportional hazards regression was used to evaluate the association of prognostic factors and combination versus single-agent chemotherapy with overall survival (OS). Data were available from eight trials including 370 patients; two trials (n=109) evaluated single-agent chemotherapy with docetaxel (n=72) and cremophor-free paclitaxel (n=37), and six trials (n=261) evaluated combination chemotherapy with gemcitabine-paclitaxel (two trials, with n=99 and n=24), paclitaxel-cyclophosphamide (n=32), paclitaxel-ifosfamide-nedaplatin (n=45), docetaxel-ifosfamide-cisplatin (n=26), and paclitaxel-epirubicin (n=35). On multivariable analysis after adjustment for baseline prognostic factors, combination chemotherapy was independently and significantly associated with improved OS (hazard ratio: 0.60; 95% confidence interval, 0.45-0.82; p=0.001). The retrospective design of this analysis and the trial-eligible population were inherent limitations. Patients enrolled in trials of combination chemotherapy exhibited improved OS compared with patients enrolled in trials of single-agent chemotherapy as salvage therapy for advanced UC. Prospective randomized trials are required to validate a potential role for rational and tolerable combination chemotherapeutic regimens for the salvage therapy of advanced UC. This retrospective study suggests that a combination of chemotherapy agents may extend survival compared with single-agent chemotherapy in selected patients with metastatic urothelial cancer progressing after prior chemotherapy. Copyright © 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.

A comparative analysis of conventional and pretargeted radioimmunotherapy of B-cell lymphomas by targeting CD20, CD22, and HLA-DR singly and in combinations

PubMed Central

Orgun, Nural; Hamlin, Donald K.; Wilbur, D. Scott; Gooley, Theodore A.; Gopal, Ajay K.; Park, Steven I.; Green, Damian J.; Lin, Yukang; Press, Oliver W.

2009-01-01

Relapsed B-cell lymphomas are currently incurable with conventional chemotherapy and radiation treatments. Radiolabeled antibodies directed against B-cell surface antigens have emerged as effective and safe therapies for relapsed lymphomas. We therefore investigated the potential utility of both directly radiolabeled 1F5 (anti-CD20), HD39 (anti-CD22), and Lym-1 (anti-DR) antibodies (Abs) and of pretargeted radioimmunotherapy (RIT) using Ab-streptavidin (SA) conjugates, followed by an N-acetylgalactosamine dendrimeric clearing agent and radiometal-labeled DOTA-biotin, for treatment of lymphomas in mouse models using Ramos, Raji, and FL-18 human lymphoma xenografts. This study demonstrates the marked superiority of pretargeted RIT for each of the antigenic targets with more complete tumor regressions and longer mouse survival compared with conventional one-step RIT. The Ab-SA conjugate yielding the best tumor regression and progression-free survival after pretargeted RIT varied depending upon the lymphoma cell line used, with 1F5 Ab-SA and Lym-1 Ab-SA conjugates yielding the most promising results overall. Contrary to expectations, the best rates of mouse survival were obtained using optimal single Ab-SA conjugates rather than combinations of conjugates targeting different antigens. We hypothesize that clinical implementation of pretargeted RIT methods will provide a meaningful prolongation of survival for patients with relapsed lymphomas compared with currently available treatment strategies. PMID:19124831

Association of Depressed Mood With Herpes Simplex Virus-2 Immunoglobulin-G Levels in Pregnancy.

PubMed

Hsu, Pao-Chu; Yolken, Robert H; Postolache, Teodor T; Beckie, Theresa M; Munro, Cindy L; Groer, Maureen W

2016-10-01

Depressed mood is common in pregnancy, is associated with stress, and could result in immune suppression that may lead to latent herpes viral reactivation. This study investigated whether depressed mood is associated with higher herpes viral IgG levels in pregnant women. Complete cross-sectional data from 247 pregnant women were available for this substudy. The data included demographics, scores on the Perceived Stress Scale and Profile of Mood States (POMS), and a panel of serum IgG levels for human herpesviruses. Only the herpes simplex virus type 2 (HSV-2) (genital herpes) IgG level was associated with Perceived Stress Scale and POMS-Depression/Dejection (POMS-D) score. Hierarchical multiple regression analysis was used to examine the association of POMS-D with herpesviral IgG levels adjusting for demographic variables. In the final model, African American race (β = .251, p < .001), older age (β = .199, p = .002), single marital status (β = -.304, p < .001), and depressed mood (β = .122, p = .04) were associated with HSV-2 IgG levels. In logistic regression, the strongest correlates of HSV IgG positivity were single marital status, followed by POMS-D scores and African American race. Genital herpes is a concern in pregnancy. Antibody titers may indicate asymptomatic viral shedding, viral reactivation, or primary viral infection. Antibody levels may be higher because of the immune changes during pregnancy and potential immune effects of depressed mood causing reactivation of latent HSV-2.

Moderation analysis using a two-level regression model.

PubMed

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

Noninvasive detection of hepatic lipidosis in dairy cows with calibrated ultrasonographic image analysis.

PubMed

Starke, A; Haudum, A; Weijers, G; Herzog, K; Wohlsein, P; Beyerbach, M; de Korte, C L; Thijssen, J M; Rehage, J

2010-07-01

The aim was to test the accuracy of calibrated digital analysis of ultrasonographic hepatic images for diagnosing fatty liver in dairy cows. Digital analysis was performed by means of a novel method, computer-aided ultrasound diagnosis (CAUS), previously published by the authors. This method implies a set of pre- and postprocessing steps to normalize and correct the transcutaneous ultrasonographic images. Transcutaneous hepatic ultrasonography was performed before surgical correction on 151 German Holstein dairy cows (mean +/- standard error of the means; body weight: 571+/-7 kg; age: 4.9+/-0.2 yr; DIM: 35+/-5) with left-sided abomasal displacement. Concentration of triacylglycerol (TAG) was biochemically determined in liver samples collected via biopsy and values were considered the gold standard to which ultrasound estimates were compared. According to histopathologic examination of biopsies, none of the cows suffered from hepatic disorders other than hepatic lipidosis. Hepatic TAG concentrations ranged from 4.6 to 292.4 mg/g of liver fresh weight (FW). High correlations were found between the hepatic TAG and mean echo level (r=0.59) and residual attenuation (ResAtt; r=0.80) obtained in ultrasonographic imaging. High correlation existed between ResAtt and mean echo level (r=0.76). The 151 studied cows were split randomly into a training set of 76 cows and a test set of 75 cows. Based on the data from the training set, ResAtt was statistically selected by means of stepwise multiple regression analysis for hepatic TAG prediction (R(2)=0.69). Then, using the predicted TAG data of the test set, receiver operating characteristic analysis was performed to summarize the accuracy and predictive potential of the differentiation between various measured hepatic TAG values, based on TAG predicted from the regression formula. The area under the curve values of the receiver operating characteristic based on the regression equation were 0.94 (<50 vs. >or=50mg of TAG/g of FW), 0.83 (<100 vs. >or=100mg of TAG/g of FW), and 0.97 (<50 vs. >or=100mg of TAG/g of FW). The CAUS methodology and software for digitally analyzing liver ultrasonographic images is considered feasible for noninvasive screening of fatty liver in dairy herd health programs. Using the single parameter linear regression equation might be ideal for practical applications. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Multiple Correlation versus Multiple Regression.

ERIC Educational Resources Information Center

Huberty, Carl J.

2003-01-01

Describes differences between multiple correlation analysis (MCA) and multiple regression analysis (MRA), showing how these approaches involve different research questions and study designs, different inferential approaches, different analysis strategies, and different reported information. (SLD)

Functional Relationships and Regression Analysis.

ERIC Educational Resources Information Center

Preece, Peter F. W.

1978-01-01

Using a degenerate multivariate normal model for the distribution of organismic variables, the form of least-squares regression analysis required to estimate a linear functional relationship between variables is derived. It is suggested that the two conventional regression lines may be considered to describe functional, not merely statistical,…

Isolating and Examining Sources of Suppression and Multicollinearity in Multiple Linear Regression

ERIC Educational Resources Information Center

Beckstead, Jason W.

2012-01-01

The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic…

General Nature of Multicollinearity in Multiple Regression Analysis.

ERIC Educational Resources Information Center

Liu, Richard

1981-01-01

Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)

Logistic Regression: Concept and Application

ERIC Educational Resources Information Center

Cokluk, Omay

2010-01-01

The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…

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.

Modifications of haematology analyzers to improve cell counting and leukocyte differentiating in cerebrospinal fluid controls of the Joint German Society for Clinical Chemistry and Laboratory Medicine.

PubMed

Kleine, Tilmann O; Nebe, C Thomas; Löwer, Christa; Lehmitz, Reinhard; Kruse, Rolf; Geilenkeuser, Wolf-Jochen; Dorn-Beineke, Alexandra

2009-08-01

Flow cytometry (FCM) is used with haematology analyzers (HAs) to count cells and differentiate leukocytes in cerebrospinal fluid (CSF). To evaluate the FCM techniques of HAs, 10 external DGKL trials with CSF controls were carried out in 2004 to 2008. Eight single platform HAs with and without CSF equipment were evaluated with living blood leukocytes and erythrocytes in CSF like DGKL controls: Coulter (LH750,755), Abbott CD3200, CD3500, CD3700, CD4000, Sapphire, ADVIA 120(R) CSF assay, and Sysmex XE-2100(R). Results were compared with visual counting of native cells in Fuchs-Rosenthal chamber, unstained, and absolute values of leukocyte differentiation, assayed by dual platform analysis with immune-FCM (FACSCalibur, CD45, CD14) and the chamber counts. Reference values X were compared with HA values Y by statistical evaluation with Passing/Bablock (P/B) linear regression analysis to reveal conformity of both methods. The HAs, studied, produced no valid results with DGKL CSF controls, because P/B regression revealed no conformity with the reference values due to:-blank problems with impedance analysis,-leukocyte loss with preanalytical erythrocyte lysis procedures, especially of monocytes,-inaccurate results with ADVIA cell sphering and cell differentiation with algorithms and enzyme activities (e.g., peroxidase). HA techniques have to be improved, e.g., using no erythrocyte lysis and CSF adequate techniques, to examine CSF samples precise and accurate. Copyright 2009 International Society for Advancement of Cytometry.

Rapid and safe learning of robotic gastrectomy for gastric cancer: multidimensional analysis in a comparison with laparoscopic gastrectomy.

PubMed

Kim, H-I; Park, M S; Song, K J; Woo, Y; Hyung, W J

2014-10-01

The learning curve of robotic gastrectomy has not yet been evaluated in comparison with the laparoscopic approach. We compared the learning curves of robotic gastrectomy and laparoscopic gastrectomy based on operation time and surgical success. We analyzed 172 robotic and 481 laparoscopic distal gastrectomies performed by single surgeon from May 2003 to April 2009. The operation time was analyzed using a moving average and non-linear regression analysis. Surgical success was evaluated by a cumulative sum plot with a target failure rate of 10%. Surgical failure was defined as laparoscopic or open conversion, insufficient lymph node harvest for staging, resection margin involvement, postoperative morbidity, and mortality. Moving average and non-linear regression analyses indicated stable state for operation time at 95 and 121 cases in robotic gastrectomy, and 270 and 262 cases in laparoscopic gastrectomy, respectively. The cumulative sum plot identified no cut-off point for surgical success in robotic gastrectomy and 80 cases in laparoscopic gastrectomy. Excluding the initial 148 laparoscopic gastrectomies that were performed before the first robotic gastrectomy, the two groups showed similar number of cases to reach steady state in operation time, and showed no cut-off point in analysis of surgical success. The experience of laparoscopic surgery could affect the learning process of robotic gastrectomy. An experienced laparoscopic surgeon requires fewer cases of robotic gastrectomy to reach steady state. Moreover, the surgical outcomes of robotic gastrectomy were satisfactory. Copyright © 2013 Elsevier Ltd. All rights reserved.

Factors Predicting a Good Symptomatic Outcome After Prostate Artery Embolisation (PAE).

PubMed

Maclean, D; Harris, M; Drake, T; Maher, B; Modi, S; Dyer, J; Somani, B; Hacking, N; Bryant, T

2018-02-26

As prostate artery embolisation (PAE) becomes an established treatment for benign prostatic obstruction, factors predicting good symptomatic outcome remain unclear. Pre-embolisation prostate size as a predictor is controversial with a handful of papers coming to conflicting conclusions. We aimed to investigate if an association existed in our patient cohort between prostate size and clinical benefit, in addition to evaluating percentage volume reduction as a predictor of symptomatic outcome following PAE. Prospective follow-up of 86 PAE patients at a single institution between June 2012 and January 2016 was conducted (mean age 64.9 years, range 54-80 years). Multiple linear regression analysis was performed to assess strength of association between clinical improvement (change in IPSS) and other variables, of any statistical correlation, through Pearson's bivariate analysis. No major procedural complications were identified and clinical success was achieved in 72.1% (n = 62) at 12 months. Initial prostate size and percentage reduction were found to have a significant association with clinical improvement. Multiple linear regression analysis (r 2  = 0.48) demonstrated that percentage volume reduction at 3 months (r = 0.68, p < 0.001) had the strongest correlation with good symptomatic improvement at 12 months after adjusting for confounding factors. Both the initial prostate size and percentage volume reduction at 3 months predict good symptomatic outcome at 12 months. These findings therefore aid patient selection and counselling to achieve optimal outcomes for men undergoing prostate artery embolisation.

Evaluating EIV, OLS, and SEM Estimators of Group Slope Differences in the Presence of Measurement Error: The Single-Indicator Case

ERIC Educational Resources Information Center

Culpepper, Steven Andrew

2012-01-01

Measurement error significantly biases interaction effects and distorts researchers' inferences regarding interactive hypotheses. This article focuses on the single-indicator case and shows how to accurately estimate group slope differences by disattenuating interaction effects with errors-in-variables (EIV) regression. New analytic findings were…

Using Generalized Additive Models to Analyze Single-Case Designs

ERIC Educational Resources Information Center

Shadish, William; Sullivan, Kristynn

2013-01-01

Many analyses for single-case designs (SCDs)--including nearly all the effect size indicators-- currently assume no trend in the data. Regression and multilevel models allow for trend, but usually test only linear trend and have no principled way of knowing if higher order trends should be represented in the model. This paper shows how Generalized…

Testing the Efficacy of a Scholarship Program for Single Parent, Post-Freshmen, Full Time Undergraduates

ERIC Educational Resources Information Center

Carpenter, Dick M., II; Kaka, Sarah J.; Tygret, Jennifer A.; Cathcart, Katy

2018-01-01

This study examines the efficacy of a scholarship program designed to assist single parent, post-freshmen, full time undergraduate students and predictors of success among a sample of said students, where success is defined as progress toward completion, academic achievement, and degree completion. Results of fixed effects regression and…

Substituting values for censored data from Texas, USA, reservoirs inflated and obscured trends in analyses commonly used for water quality target development.

PubMed

Grantz, Erin; Haggard, Brian; Scott, J Thad

2018-06-12

We calculated four median datasets (chlorophyll a, Chl a; total phosphorus, TP; and transparency) using multiple approaches to handling censored observations, including substituting fractions of the quantification limit (QL; dataset 1 = 1QL, dataset 2 = 0.5QL) and statistical methods for censored datasets (datasets 3-4) for approximately 100 Texas, USA reservoirs. Trend analyses of differences between dataset 1 and 3 medians indicated percent difference increased linearly above thresholds in percent censored data (%Cen). This relationship was extrapolated to estimate medians for site-parameter combinations with %Cen > 80%, which were combined with dataset 3 as dataset 4. Changepoint analysis of Chl a- and transparency-TP relationships indicated threshold differences up to 50% between datasets. Recursive analysis identified secondary thresholds in dataset 4. Threshold differences show that information introduced via substitution or missing due to limitations of statistical methods biased values, underestimated error, and inflated the strength of TP thresholds identified in datasets 1-3. Analysis of covariance identified differences in linear regression models relating transparency-TP between datasets 1, 2, and the more statistically robust datasets 3-4. Study findings identify high-risk scenarios for biased analytical outcomes when using substitution. These include high probability of median overestimation when %Cen > 50-60% for a single QL, or when %Cen is as low 16% for multiple QL's. Changepoint analysis was uniquely vulnerable to substitution effects when using medians from sites with %Cen > 50%. Linear regression analysis was less sensitive to substitution and missing data effects, but differences in model parameters for transparency cannot be discounted and could be magnified by log-transformation of the variables.