Beyond logistic regression: structural equations modelling for binary variables and its application to investigating unobserved confounders.

PubMed

Kupek, Emil

2006-03-15

Structural equation modelling (SEM) has been increasingly used in medical statistics for solving a system of related regression equations. However, a great obstacle for its wider use has been its difficulty in handling categorical variables within the framework of generalised linear models. A large data set with a known structure among two related outcomes and three independent variables was generated to investigate the use of Yule's transformation of odds ratio (OR) into Q-metric by (OR-1)/(OR+1) to approximate Pearson's correlation coefficients between binary variables whose covariance structure can be further analysed by SEM. Percent of correctly classified events and non-events was compared with the classification obtained by logistic regression. The performance of SEM based on Q-metric was also checked on a small (N = 100) random sample of the data generated and on a real data set. SEM successfully recovered the generated model structure. SEM of real data suggested a significant influence of a latent confounding variable which would have not been detectable by standard logistic regression. SEM classification performance was broadly similar to that of the logistic regression. The analysis of binary data can be greatly enhanced by Yule's transformation of odds ratios into estimated correlation matrix that can be further analysed by SEM. The interpretation of results is aided by expressing them as odds ratios which are the most frequently used measure of effect in medical statistics.

Flexible link functions in nonparametric binary regression with Gaussian process priors.

PubMed

Li, Dan; Wang, Xia; Lin, Lizhen; Dey, Dipak K

2016-09-01

In many scientific fields, it is a common practice to collect a sequence of 0-1 binary responses from a subject across time, space, or a collection of covariates. Researchers are interested in finding out how the expected binary outcome is related to covariates, and aim at better prediction in the future 0-1 outcomes. Gaussian processes have been widely used to model nonlinear systems; in particular to model the latent structure in a binary regression model allowing nonlinear functional relationship between covariates and the expectation of binary outcomes. A critical issue in modeling binary response data is the appropriate choice of link functions. Commonly adopted link functions such as probit or logit links have fixed skewness and lack the flexibility to allow the data to determine the degree of the skewness. To address this limitation, we propose a flexible binary regression model which combines a generalized extreme value link function with a Gaussian process prior on the latent structure. Bayesian computation is employed in model estimation. Posterior consistency of the resulting posterior distribution is demonstrated. The flexibility and gains of the proposed model are illustrated through detailed simulation studies and two real data examples. Empirical results show that the proposed model outperforms a set of alternative models, which only have either a Gaussian process prior on the latent regression function or a Dirichlet prior on the link function. © 2015, The International Biometric Society.

Flexible Link Functions in Nonparametric Binary Regression with Gaussian Process Priors

PubMed Central

Li, Dan; Lin, Lizhen; Dey, Dipak K.

2015-01-01

Summary In many scientific fields, it is a common practice to collect a sequence of 0-1 binary responses from a subject across time, space, or a collection of covariates. Researchers are interested in finding out how the expected binary outcome is related to covariates, and aim at better prediction in the future 0-1 outcomes. Gaussian processes have been widely used to model nonlinear systems; in particular to model the latent structure in a binary regression model allowing nonlinear functional relationship between covariates and the expectation of binary outcomes. A critical issue in modeling binary response data is the appropriate choice of link functions. Commonly adopted link functions such as probit or logit links have fixed skewness and lack the flexibility to allow the data to determine the degree of the skewness. To address this limitation, we propose a flexible binary regression model which combines a generalized extreme value link function with a Gaussian process prior on the latent structure. Bayesian computation is employed in model estimation. Posterior consistency of the resulting posterior distribution is demonstrated. The flexibility and gains of the proposed model are illustrated through detailed simulation studies and two real data examples. Empirical results show that the proposed model outperforms a set of alternative models, which only have either a Gaussian process prior on the latent regression function or a Dirichlet prior on the link function. PMID:26686333

Logistic Regression and Path Analysis Method to Analyze Factors influencing Students’ Achievement

NASA Astrophysics Data System (ADS)

Noeryanti, N.; Suryowati, K.; Setyawan, Y.; Aulia, R. R.

2018-04-01

Students' academic achievement cannot be separated from the influence of two factors namely internal and external factors. The first factors of the student (internal factors) consist of intelligence (X1), health (X2), interest (X3), and motivation of students (X4). The external factors consist of family environment (X5), school environment (X6), and society environment (X7). The objects of this research are eighth grade students of the school year 2016/2017 at SMPN 1 Jiwan Madiun sampled by using simple random sampling. Primary data are obtained by distributing questionnaires. The method used in this study is binary logistic regression analysis that aims to identify internal and external factors that affect student’s achievement and how the trends of them. Path Analysis was used to determine the factors that influence directly, indirectly or totally on student’s achievement. Based on the results of binary logistic regression, variables that affect student’s achievement are interest and motivation. And based on the results obtained by path analysis, factors that have a direct impact on student’s achievement are students’ interest (59%) and students’ motivation (27%). While the factors that have indirect influences on students’ achievement, are family environment (97%) and school environment (37).

An Attempt at Quantifying Factors that Affect Efficiency in the Management of Solid Waste Produced by Commercial Businesses in the City of Tshwane, South Africa

PubMed Central

Worku, Yohannes; Muchie, Mammo

2012-01-01

Objective. The objective was to investigate factors that affect the efficient management of solid waste produced by commercial businesses operating in the city of Pretoria, South Africa. Methods. Data was gathered from 1,034 businesses. Efficiency in solid waste management was assessed by using a structural time-based model designed for evaluating efficiency as a function of the length of time required to manage waste. Data analysis was performed using statistical procedures such as frequency tables, Pearson's chi-square tests of association, and binary logistic regression analysis. Odds ratios estimated from logistic regression analysis were used for identifying key factors that affect efficiency in the proper disposal of waste. Results. The study showed that 857 of the 1,034 businesses selected for the study (83%) were found to be efficient enough with regards to the proper collection and disposal of solid waste. Based on odds ratios estimated from binary logistic regression analysis, efficiency in the proper management of solid waste was significantly influenced by 4 predictor variables. These 4 influential predictor variables are lack of adherence to waste management regulations, wrong perception, failure to provide customers with enough trash cans, and operation of businesses by employed managers, in a decreasing order of importance. PMID:23209483

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.

Performance and separation occurrence of binary probit regression estimator using maximum likelihood method and Firths approach under different sample size

NASA Astrophysics Data System (ADS)

Lusiana, Evellin Dewi

2017-12-01

The parameters of binary probit regression model are commonly estimated by using Maximum Likelihood Estimation (MLE) method. However, MLE method has limitation if the binary data contains separation. Separation is the condition where there are one or several independent variables that exactly grouped the categories in binary response. It will result the estimators of MLE method become non-convergent, so that they cannot be used in modeling. One of the effort to resolve the separation is using Firths approach instead. This research has two aims. First, to identify the chance of separation occurrence in binary probit regression model between MLE method and Firths approach. Second, to compare the performance of binary probit regression model estimator that obtained by MLE method and Firths approach using RMSE criteria. Those are performed using simulation method and under different sample size. The results showed that the chance of separation occurrence in MLE method for small sample size is higher than Firths approach. On the other hand, for larger sample size, the probability decreased and relatively identic between MLE method and Firths approach. Meanwhile, Firths estimators have smaller RMSE than MLEs especially for smaller sample sizes. But for larger sample sizes, the RMSEs are not much different. It means that Firths estimators outperformed MLE estimator.

Use of generalized ordered logistic regression for the analysis of multidrug resistance data.

PubMed

Agga, Getahun E; Scott, H Morgan

2015-10-01

Statistical analysis of antimicrobial resistance data largely focuses on individual antimicrobial's binary outcome (susceptible or resistant). However, bacteria are becoming increasingly multidrug resistant (MDR). Statistical analysis of MDR data is mostly descriptive often with tabular or graphical presentations. Here we report the applicability of generalized ordinal logistic regression model for the analysis of MDR data. A total of 1,152 Escherichia coli, isolated from the feces of weaned pigs experimentally supplemented with chlortetracycline (CTC) and copper, were tested for susceptibilities against 15 antimicrobials and were binary classified into resistant or susceptible. The 15 antimicrobial agents tested were grouped into eight different antimicrobial classes. We defined MDR as the number of antimicrobial classes to which E. coli isolates were resistant ranging from 0 to 8. Proportionality of the odds assumption of the ordinal logistic regression model was violated only for the effect of treatment period (pre-treatment, during-treatment and post-treatment); but not for the effect of CTC or copper supplementation. Subsequently, a partially constrained generalized ordinal logistic model was built that allows for the effect of treatment period to vary while constraining the effects of treatment (CTC and copper supplementation) to be constant across the levels of MDR classes. Copper (Proportional Odds Ratio [Prop OR]=1.03; 95% CI=0.73-1.47) and CTC (Prop OR=1.1; 95% CI=0.78-1.56) supplementation were not significantly associated with the level of MDR adjusted for the effect of treatment period. MDR generally declined over the trial period. In conclusion, generalized ordered logistic regression can be used for the analysis of ordinal data such as MDR data when the proportionality assumptions for ordered logistic regression are violated. Published by Elsevier B.V.

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

PubMed

Tang, Yongqiang

2018-04-30

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

Predicting Social Trust with Binary Logistic Regression

ERIC Educational Resources Information Center

Adwere-Boamah, Joseph; Hufstedler, Shirley

2015-01-01

This study used binary logistic regression to predict social trust with five demographic variables from a national sample of adult individuals who participated in The General Social Survey (GSS) in 2012. The five predictor variables were respondents' highest degree earned, race, sex, general happiness and the importance of personally assisting…

Constraints on the Dynamical Environments of Supermassive Black-Hole Binaries Using Pulsar-Timing Arrays.

PubMed

Taylor, Stephen R; Simon, Joseph; Sampson, Laura

2017-05-05

We introduce a technique for gravitational-wave analysis, where Gaussian process regression is used to emulate the strain spectrum of a stochastic background by training on population-synthesis simulations. This leads to direct Bayesian inference on astrophysical parameters. For pulsar timing arrays specifically, we interpolate over the parameter space of supermassive black-hole binary environments, including three-body stellar scattering, and evolving orbital eccentricity. We illustrate our approach on mock data, and assess the prospects for inference with data similar to the NANOGrav 9-yr data release.

A study of the kinetics and isotherms for Cr(VI) adsorption in a binary mixture of Cr(VI)-Ni(II) using hierarchical porous carbon obtained from pig bone.

PubMed

Li, Chengxian; Huang, Zhe; Huang, Bicheng; Liu, Changfeng; Li, Chengming; Huang, Yaqin

2014-01-01

Cr(VI) adsorption in a binary mixture Cr(VI)-Ni(II) using the hierarchical porous carbon prepared from pig bone (HPC) was investigated. The various factors affecting adsorption of Cr(VI) ions from aqueous solutions such as initial concentration, pH, temperature and contact time were analyzed. The results showed excellent efficiency of Cr(VI) adsorption by HPC. The kinetics and isotherms for Cr(VI) adsorption from a binary mixture Cr(VI)-Ni(II) by HPC were studied. The adsorption equilibrium described by the Langmuir isotherm model is better than that described by the Freundlich isotherm model for the binary mixture in this study. The maximum adsorption capacity was reliably found to be as high as 192.68 mg/g in the binary mixture at pH 2. On fitting the experimental data to both pseudo-first- and second-order equations, the regression analysis of the second-order equation gave a better R² value.

A local equation for differential diagnosis of β-thalassemia trait and iron deficiency anemia by logistic regression analysis in Southeast Iran.

PubMed

Sargolzaie, Narjes; Miri-Moghaddam, Ebrahim

2014-01-01

The most common differential diagnosis of β-thalassemia (β-thal) trait is iron deficiency anemia. Several red blood cell equations were introduced during different studies for differential diagnosis between β-thal trait and iron deficiency anemia. Due to genetic variations in different regions, these equations cannot be useful in all population. The aim of this study was to determine a native equation with high accuracy for differential diagnosis of β-thal trait and iron deficiency anemia for the Sistan and Baluchestan population by logistic regression analysis. We selected 77 iron deficiency anemia and 100 β-thal trait cases. We used binary logistic regression analysis and determined best equations for probability prediction of β-thal trait against iron deficiency anemia in our population. We compared diagnostic values and receiver operative characteristic (ROC) curve related to this equation and another 10 published equations in discriminating β-thal trait and iron deficiency anemia. The binary logistic regression analysis determined the best equation for best probability prediction of β-thal trait against iron deficiency anemia with area under curve (AUC) 0.998. Based on ROC curves and AUC, Green & King, England & Frazer, and then Sirdah indices, respectively, had the most accuracy after our equation. We suggest that to get the best equation and cut-off in each region, one needs to evaluate specific information of each region, specifically in areas where populations are homogeneous, to provide a specific formula for differentiating between β-thal trait and iron deficiency anemia.

A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network

PubMed Central

Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

2012-01-01

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins. PMID:27418910

A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network.

PubMed

Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

2012-01-01

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins.

Introduction to the use of regression models in epidemiology.

PubMed

Bender, Ralf

2009-01-01

Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

Modelling of binary logistic regression for obesity among secondary students in a rural area of Kedah

NASA Astrophysics Data System (ADS)

Kamaruddin, Ainur Amira; Ali, Zalila; Noor, Norlida Mohd.; Baharum, Adam; Ahmad, Wan Muhamad Amir W.

2014-07-01

Logistic regression analysis examines the influence of various factors on a dichotomous outcome by estimating the probability of the event's occurrence. Logistic regression, also called a logit model, is a statistical procedure used to model dichotomous outcomes. In the logit model the log odds of the dichotomous outcome is modeled as a linear combination of the predictor variables. The log odds ratio in logistic regression provides a description of the probabilistic relationship of the variables and the outcome. In conducting logistic regression, selection procedures are used in selecting important predictor variables, diagnostics are used to check that assumptions are valid which include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers and a test statistic is calculated to determine the aptness of the model. This study used the binary logistic regression model to investigate overweight and obesity among rural secondary school students on the basis of their demographics profile, medical history, diet and lifestyle. The results indicate that overweight and obesity of students are influenced by obesity in family and the interaction between a student's ethnicity and routine meals intake. The odds of a student being overweight and obese are higher for a student having a family history of obesity and for a non-Malay student who frequently takes routine meals as compared to a Malay student.

Face Alignment via Regressing Local Binary Features.

PubMed

Ren, Shaoqing; Cao, Xudong; Wei, Yichen; Sun, Jian

2016-03-01

This paper presents a highly efficient and accurate regression approach for face alignment. Our approach has two novel components: 1) a set of local binary features and 2) a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. This approach achieves the state-of-the-art results when tested on the most challenging benchmarks to date. Furthermore, because extracting and regressing local binary features are computationally very cheap, our system is much faster than previous methods. It achieves over 3000 frames per second (FPS) on a desktop or 300 FPS on a mobile phone for locating a few dozens of landmarks. We also study a key issue that is important but has received little attention in the previous research, which is the face detector used to initialize alignment. We investigate several face detectors and perform quantitative evaluation on how they affect alignment accuracy. We find that an alignment friendly detector can further greatly boost the accuracy of our alignment method, reducing the error up to 16% relatively. To facilitate practical usage of face detection/alignment methods, we also propose a convenient metric to measure how good a detector is for alignment initialization.

Binary logistic regression modelling: Measuring the probability of relapse cases among drug addict

NASA Astrophysics Data System (ADS)

Ismail, Mohd Tahir; Alias, Siti Nor Shadila

2014-07-01

For many years Malaysia faced the drug addiction issues. The most serious case is relapse phenomenon among treated drug addict (drug addict who have under gone the rehabilitation programme at Narcotic Addiction Rehabilitation Centre, PUSPEN). Thus, the main objective of this study is to find the most significant factor that contributes to relapse to happen. The binary logistic regression analysis was employed to model the relationship between independent variables (predictors) and dependent variable. The dependent variable is the status of the drug addict either relapse, (Yes coded as 1) or not, (No coded as 0). Meanwhile the predictors involved are age, age at first taking drug, family history, education level, family crisis, community support and self motivation. The total of the sample is 200 which the data are provided by AADK (National Antidrug Agency). The finding of the study revealed that age and self motivation are statistically significant towards the relapse cases..

Regression analysis for solving diagnosis problem of children's health

NASA Astrophysics Data System (ADS)

Cherkashina, Yu A.; Gerget, O. M.

2016-04-01

The paper includes results of scientific researches. These researches are devoted to the application of statistical techniques, namely, regression analysis, to assess the health status of children in the neonatal period based on medical data (hemostatic parameters, parameters of blood tests, the gestational age, vascular-endothelial growth factor) measured at 3-5 days of children's life. In this paper a detailed description of the studied medical data is given. A binary logistic regression procedure is discussed in the paper. Basic results of the research are presented. A classification table of predicted values and factual observed values is shown, the overall percentage of correct recognition is determined. Regression equation coefficients are calculated, the general regression equation is written based on them. Based on the results of logistic regression, ROC analysis was performed, sensitivity and specificity of the model are calculated and ROC curves are constructed. These mathematical techniques allow carrying out diagnostics of health of children providing a high quality of recognition. The results make a significant contribution to the development of evidence-based medicine and have a high practical importance in the professional activity of the author.

Odds Ratio, Delta, ETS Classification, and Standardization Measures of DIF Magnitude for Binary Logistic Regression

ERIC Educational Resources Information Center

Monahan, Patrick O.; McHorney, Colleen A.; Stump, Timothy E.; Perkins, Anthony J.

2007-01-01

Previous methodological and applied studies that used binary logistic regression (LR) for detection of differential item functioning (DIF) in dichotomously scored items either did not report an effect size or did not employ several useful measures of DIF magnitude derived from the LR model. Equations are provided for these effect size indices.…

Locally Weighted Score Estimation for Quantile Classification in Binary Regression Models

PubMed Central

Rice, John D.; Taylor, Jeremy M. G.

2016-01-01

One common use of binary response regression methods is classification based on an arbitrary probability threshold dictated by the particular application. Since this is given to us a priori, it is sensible to incorporate the threshold into our estimation procedure. Specifically, for the linear logistic model, we solve a set of locally weighted score equations, using a kernel-like weight function centered at the threshold. The bandwidth for the weight function is selected by cross validation of a novel hybrid loss function that combines classification error and a continuous measure of divergence between observed and fitted values; other possible cross-validation functions based on more common binary classification metrics are also examined. This work has much in common with robust estimation, but diers from previous approaches in this area in its focus on prediction, specifically classification into high- and low-risk groups. Simulation results are given showing the reduction in error rates that can be obtained with this method when compared with maximum likelihood estimation, especially under certain forms of model misspecification. Analysis of a melanoma data set is presented to illustrate the use of the method in practice. PMID:28018492

Impact of Perceived Risk and Friend Influence on Alcohol and Marijuana Use among Students

ERIC Educational Resources Information Center

Merianos, Ashley L.; Rosen, Brittany L.; Montgomery, LaTrice; Barry, Adam E.; Smith, Matthew Lee

2017-01-01

We performed a secondary analysis of Adolescent Health Risk Behavior Survey data (N=937), examining associations between lifetime alcohol and marijuana use with intrapersonal (i.e., risk perceptions) and interpersonal (e.g., peer approval and behavior) factors. Multinomial and binary logistic regression analyses contend students reporting lifetime…

Headspace quantification of pure and aqueous solutions of binary mixtures of key volatile organic compounds in Swiss cheeses using selected ion flow tube mass spectrometry.

PubMed

Castada, Hardy Z; Wick, Cheryl; Harper, W James; Barringer, Sheryl

2015-01-15

Twelve volatile organic compounds (VOCs) have recently been identified as key compounds in Swiss cheese with split defects. It is important to know how these VOCs interact in binary mixtures and if their behavior changes with concentration in binary mixtures. Selected ion flow tube mass spectrometry (SIFT-MS) was used for the headspace analysis of VOCs commonly found in Swiss cheeses. Headspace (H/S) sampling and quantification checks using SIFT-MS and further linear regression analyses were carried out on twelve selected aqueous solutions of VOCs. Five binary mixtures of standard solutions of VOCs were also prepared and the H/S profile of each mixture was analyzed. A very good fit of linearity for the twelve VOCs (95% confidence level) confirms direct proportionality between the H/S and the aqueous concentration of the standard solutions. Henry's Law coefficients were calculated with a high degree of confidence. SIFT-MS analysis of five binary mixtures showed that the more polar compounds reduced the H/S concentration of the less polar compounds, while the addition of a less polar compound increased the H/S concentration of the more polar compound. In the binary experiment, it was shown that the behavior of a compound in the headspace can be significantly affected by the presence of another compound. Thus, the matrix effect plays a significant role in the behavior of molecules in a mixed solution. Copyright © 2014 John Wiley & Sons, Ltd.

Introduction to methodology of dose-response meta-analysis for binary outcome: With application on software.

PubMed

Zhang, Chao; Jia, Pengli; Yu, Liu; Xu, Chang

2018-05-01

Dose-response meta-analysis (DRMA) is widely applied to investigate the dose-specific relationship between independent and dependent variables. Such methods have been in use for over 30 years and are increasingly employed in healthcare and clinical decision-making. In this article, we give an overview of the methodology used in DRMA. We summarize the commonly used regression model and the pooled method in DRMA. We also use an example to illustrate how to employ a DRMA by these methods. Five regression models, linear regression, piecewise regression, natural polynomial regression, fractional polynomial regression, and restricted cubic spline regression, were illustrated in this article to fit the dose-response relationship. And two types of pooling approaches, that is, one-stage approach and two-stage approach are illustrated to pool the dose-response relationship across studies. The example showed similar results among these models. Several dose-response meta-analysis methods can be used for investigating the relationship between exposure level and the risk of an outcome. However the methodology of DRMA still needs to be improved. © 2018 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.

Modelling Status Food Security Households Disease Sufferers Pulmonary Tuberculosis Uses the Method Regression Logistics Binary

NASA Astrophysics Data System (ADS)

Wulandari, S. P.; Salamah, M.; Rositawati, A. F. D.

2018-04-01

Food security is the condition where the food fulfilment is managed well for the country till the individual. Indonesia is one of the country which has the commitment to create the food security becomes main priority. However, the food necessity becomes common thing means that it doesn’t care about nutrient standard and the health condition of family member, so in the fulfilment of food necessity also has to consider the disease suffered by the family member, one of them is pulmonary tuberculosa. From that reasons, this research is conducted to know the factors which influence on household food security status which suffered from pulmonary tuberculosis in the coastal area of Surabaya by using binary logistic regression method. The analysis result by using binary logistic regression shows that the variables wife latest education, house density and spacious house ventilation significantly affect on household food security status which suffered from pulmonary tuberculosis in the coastal area of Surabaya, where the wife education level is University/equivalent, the house density is eligible or 8 m2/person and spacious house ventilation 10% of the floor area has the opportunity to become food secure households amounted to 0.911089. While the chance of becoming food insecure households amounted to 0.088911. The model household food security status which suffered from pulmonary tuberculosis in the coastal area of Surabaya has been conformable, and the overall percentages of those classifications are at 71.8%.

Identifying predictive features in drug response using machine learning: opportunities and challenges.

PubMed

Vidyasagar, Mathukumalli

2015-01-01

This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

Generalized Partial Least Squares Approach for Nominal Multinomial Logit Regression Models with a Functional Covariate

ERIC Educational Resources Information Center

Albaqshi, Amani Mohammed H.

2017-01-01

Functional Data Analysis (FDA) has attracted substantial attention for the last two decades. Within FDA, classifying curves into two or more categories is consistently of interest to scientists, but multi-class prediction within FDA is challenged in that most classification tools have been limited to binary response applications. The functional…

Low-Level Stratus Prediction Using Binary Statistical Regression: A Progress Report Using Moffett Field Data.

DTIC Science & Technology

1983-12-01

analysis; such work is not reported here. It seems pos- sible that a robust principle component analysis may he informa- tive (see Gnanadesikan (1977...Statistics in Atmospheric Sciences, American Meteorological Soc., Boston, Mass. (1979) pp. 46-48. a Gnanadesikan , R., Methods for Statistical Data...North Carolina Chapel Hill, NC 20742 Dr. R. Gnanadesikan Bell Telephone Lab Murray Hill, NJ 07733 -%.. *5%a: *1 *15 I ,, - . . , ,, ... . . . . . . NO

A simple measure of cognitive reserve is relevant for cognitive performance in MS patients.

PubMed

Della Corte, Marida; Santangelo, Gabriella; Bisecco, Alvino; Sacco, Rosaria; Siciliano, Mattia; d'Ambrosio, Alessandro; Docimo, Renato; Cuomo, Teresa; Lavorgna, Luigi; Bonavita, Simona; Tedeschi, Gioacchino; Gallo, Antonio

2018-05-04

Cognitive reserve (CR) contributes to preserve cognition despite brain damage. This theory has been applied to multiple sclerosis (MS) to explain the partial relationship between cognition and MRI markers of brain pathology. Our aim was to determine the relationship between two measures of CR and cognition in MS. One hundred and forty-seven MS patients were enrolled. Cognition was assessed using the Rao's Brief Repeatable Battery and the Stroop Test. CR was measured as the vocabulary subtest of the WAIS-R score (VOC) and the number of years of formal education (EDU). Regression analysis included raw score data on each neuropsychological (NP) test as dependent variables and demographic/clinical parameters, VOC, and EDU as independent predictors. A binary logistic regression analysis including clinical/CR parameters as covariates and absence/presence of cognitive deficits as dependent variables was performed too. VOC, but not EDU, was strongly correlated with performances at all ten NP tests. EDU was correlated with executive performances. The binary logistic regression showed that only the Expanded Disability Status Scale (EDSS) and VOC were independently correlated with the presence/absence of CD. The lower the VOC and/or the higher the EDSS, the higher the frequency of CD. In conclusion, our study supports the relevance of CR in subtending cognitive performances and the presence of CD in MS patients.

A comparative study of the use of powder X-ray diffraction, Raman and near infrared spectroscopy for quantification of binary polymorphic mixtures of piracetam.

PubMed

Croker, Denise M; Hennigan, Michelle C; Maher, Anthony; Hu, Yun; Ryder, Alan G; Hodnett, Benjamin K

2012-04-07

Diffraction and spectroscopic methods were evaluated for quantitative analysis of binary powder mixtures of FII(6.403) and FIII(6.525) piracetam. The two polymorphs of piracetam could be distinguished using powder X-ray diffraction (PXRD), Raman and near-infrared (NIR) spectroscopy. The results demonstrated that Raman and NIR spectroscopy are most suitable for quantitative analysis of this polymorphic mixture. When the spectra are treated with the combination of multiplicative scatter correction (MSC) and second derivative data pretreatments, the partial least squared (PLS) regression model gave a root mean square error of calibration (RMSEC) of 0.94 and 0.99%, respectively. FIII(6.525) demonstrated some preferred orientation in PXRD analysis, making PXRD the least preferred method of quantification. Copyright © 2012 Elsevier B.V. All rights reserved.

RBSURFpred: Modeling protein accessible surface area in real and binary space using regularized and optimized regression.

PubMed

Tarafder, Sumit; Toukir Ahmed, Md; Iqbal, Sumaiya; Tamjidul Hoque, Md; Sohel Rahman, M

2018-03-14

Accessible surface area (ASA) of a protein residue is an effective feature for protein structure prediction, binding region identification, fold recognition problems etc. Improving the prediction of ASA by the application of effective feature variables is a challenging but explorable task to consider, specially in the field of machine learning. Among the existing predictors of ASA, REGAd 3 p is a highly accurate ASA predictor which is based on regularized exact regression with polynomial kernel of degree 3. In this work, we present a new predictor RBSURFpred, which extends REGAd 3 p on several dimensions by incorporating 58 physicochemical, evolutionary and structural properties into 9-tuple peptides via Chou's general PseAAC, which allowed us to obtain higher accuracies in predicting both real-valued and binary ASA. We have compared RBSURFpred for both real and binary space predictions with state-of-the-art predictors, such as REGAd 3 p and SPIDER2. We also have carried out a rigorous analysis of the performance of RBSURFpred in terms of different amino acids and their properties, and also with biologically relevant case-studies. The performance of RBSURFpred establishes itself as a useful tool for the community. Copyright © 2018 Elsevier Ltd. All rights reserved.

Are math readiness and personality predictive of first-year retention in engineering?

PubMed

Moses, Laurie; Hall, Cathy; Wuensch, Karl; De Urquidi, Karen; Kauffmann, Paul; Swart, William; Duncan, Steve; Dixon, Gene

2011-01-01

On the basis of J. G. Borkowski, L. K. Chan, and N. Muthukrishna's model of academic success (2000), the present authors hypothesized that freshman retention in an engineering program would be related to not only basic aptitude but also affective factors. Participants were 129 college freshmen with engineering as their stated major. Aptitude was measured by SAT verbal and math scores, high school grade-point average (GPA), and an assessment of calculus readiness. Affective factors were assessed by the NEO-Five Factor Inventory (FFI; P. I. Costa & R. R. McCrae, 2007), and the Nowicki-Duke Locus of Control (LOC) scale (S. Nowicki & M. Duke, 1974). A binary logistic regression analysis found that calculus readiness and high school GPA were predictive of retention. Scores on the Neuroticism and Openness subscales from the NEO-FFI and LOC were correlated with retention status, but Openness was the only affective factor with a significant unique effect in the binary logistic regression. Results of the study lend modest support to Borkowski's model.

Experiences That Predict Early Career Teacher Commitment to and Retention in High-Poverty Urban Schools

ERIC Educational Resources Information Center

Whipp, Joan L.; Geronime, Lara

2017-01-01

Correlation analysis was used to analyze what experiences before and during teacher preparation for 72 graduates of an urban teacher education program were associated with urban commitment, first job location, and retention in urban schools for 3 or more years. Binary logistic regression was then used to analyze whether urban K-12 schooling,…

Predicting the occurrence of wildfires with binary structured additive regression models.

PubMed

Ríos-Pena, Laura; Kneib, Thomas; Cadarso-Suárez, Carmen; Marey-Pérez, Manuel

2017-02-01

Wildfires are one of the main environmental problems facing societies today, and in the case of Galicia (north-west Spain), they are the main cause of forest destruction. This paper used binary structured additive regression (STAR) for modelling the occurrence of wildfires in Galicia. Binary STAR models are a recent contribution to the classical logistic regression and binary generalized additive models. Their main advantage lies in their flexibility for modelling non-linear effects, while simultaneously incorporating spatial and temporal variables directly, thereby making it possible to reveal possible relationships among the variables considered. The results showed that the occurrence of wildfires depends on many covariates which display variable behaviour across space and time, and which largely determine the likelihood of ignition of a fire. The joint possibility of working on spatial scales with a resolution of 1 × 1 km cells and mapping predictions in a colour range makes STAR models a useful tool for plotting and predicting wildfire occurrence. Lastly, it will facilitate the development of fire behaviour models, which can be invaluable when it comes to drawing up fire-prevention and firefighting plans. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multivariate meta-analysis using individual participant data

PubMed Central

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

2016-01-01

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

Advanced spectrophotometric chemometric methods for resolving the binary mixture of doxylamine succinate and pyridoxine hydrochloride.

PubMed

Katsarov, Plamen; Gergov, Georgi; Alin, Aylin; Pilicheva, Bissera; Al-Degs, Yahya; Simeonov, Vasil; Kassarova, Margarita

2018-03-01

The prediction power of partial least squares (PLS) and multivariate curve resolution-alternating least squares (MCR-ALS) methods have been studied for simultaneous quantitative analysis of the binary drug combination - doxylamine succinate and pyridoxine hydrochloride. Analysis of first-order UV overlapped spectra was performed using different PLS models - classical PLS1 and PLS2 as well as partial robust M-regression (PRM). These linear models were compared to MCR-ALS with equality and correlation constraints (MCR-ALS-CC). All techniques operated within the full spectral region and extracted maximum information for the drugs analysed. The developed chemometric methods were validated on external sample sets and were applied to the analyses of pharmaceutical formulations. The obtained statistical parameters were satisfactory for calibration and validation sets. All developed methods can be successfully applied for simultaneous spectrophotometric determination of doxylamine and pyridoxine both in laboratory-prepared mixtures and commercial dosage forms.

Unitary Response Regression Models

ERIC Educational Resources Information Center

Lipovetsky, S.

2007-01-01

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

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

PubMed

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

2017-07-01

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

Deletion Diagnostics for Alternating Logistic Regressions

PubMed Central

Preisser, John S.; By, Kunthel; Perin, Jamie; Qaqish, Bahjat F.

2013-01-01

Deletion diagnostics are introduced for the regression analysis of clustered binary outcomes estimated with alternating logistic regressions, an implementation of generalized estimating equations (GEE) that estimates regression coefficients in a marginal mean model and in a model for the intracluster association given by the log odds ratio. The diagnostics are developed within an estimating equations framework that recasts the estimating functions for association parameters based upon conditional residuals into equivalent functions based upon marginal residuals. Extensions of earlier work on GEE diagnostics follow directly, including computational formulae for one-step deletion diagnostics that measure the influence of a cluster of observations on the estimated regression parameters and on the overall marginal mean or association model fit. The diagnostic formulae are evaluated with simulations studies and with an application concerning an assessment of factors associated with health maintenance visits in primary care medical practices. The application and the simulations demonstrate that the proposed cluster-deletion diagnostics for alternating logistic regressions are good approximations of their exact fully iterated counterparts. PMID:22777960

A new method for constructing networks from binary data

NASA Astrophysics Data System (ADS)

van Borkulo, Claudia D.; Borsboom, Denny; Epskamp, Sacha; Blanken, Tessa F.; Boschloo, Lynn; Schoevers, Robert A.; Waldorp, Lourens J.

2014-08-01

Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. Possible extensions of the model are discussed.

Binary Logistic Regression Analysis in Assessment and Identifying Factors That Influence Students' Academic Achievement: The Case of College of Natural and Computational Science, Wolaita Sodo University, Ethiopia

ERIC Educational Resources Information Center

Zewude, Bereket Tessema; Ashine, Kidus Meskele

2016-01-01

An attempt has been made to assess and identify the major variables that influence student academic achievement at college of natural and computational science of Wolaita Sodo University in Ethiopia. Study time, peer influence, securing first choice of department, arranging study time outside class, amount of money received from family, good life…

Social Impact of Stigma Regarding Tuberculosis Hindering Adherence to Treatment: A Cross Sectional Study Involving Tuberculosis Patients in Rajshahi City, Bangladesh.

PubMed

Chowdhury, Md Rocky Khan; Rahman, Md Shafiur; Mondal, Md Nazrul Islam; Sayem, Abu; Billah, Baki

2015-01-01

Stigma, considered a social disease, is more apparent in developing societies which are driven by various social affairs, and influences adherence to treatment. The aim of the present study was to examine levels of social stigma related to tuberculosis (TB) in sociodemographic context and identify the effects of sociodemographic factors on stigma. The study sample consisted of 372 TB patients. Data were collected using stratified sampling with simple random sampling techniques. T tests, chi-square tests, and binary logistic regression analysis were performed to examine correlations between stigma and sociodemographic variables. Approximately 85.9% of patients had experienced stigma. The most frequent indicator of the stigma experienced by patients involved problems taking part in social programs (79.5%). Mean levels of stigma were significantly higher in women (55.5%), illiterate individuals (60.8%), and villagers (60.8%) relative to those of other groups. Chi-square tests revealed that education, monthly family income, and type of patient (pulmonary and extrapulmonary) were significantly associated with stigma. Binary logistic regression analysis demonstrated that stigma was influenced by sex, education, and type of patient. Stigma is one of the most important barriers to treatment adherence. Therefore, in interventions that aim to reduce stigma, strong collaboration between various institutions is essential.

Automated particle identification through regression analysis of size, shape and colour

NASA Astrophysics Data System (ADS)

Rodriguez Luna, J. C.; Cooper, J. M.; Neale, S. L.

2016-04-01

Rapid point of care diagnostic tests and tests to provide therapeutic information are now available for a range of specific conditions from the measurement of blood glucose levels for diabetes to card agglutination tests for parasitic infections. Due to a lack of specificity these test are often then backed up by more conventional lab based diagnostic methods for example a card agglutination test may be carried out for a suspected parasitic infection in the field and if positive a blood sample can then be sent to a lab for confirmation. The eventual diagnosis is often achieved by microscopic examination of the sample. In this paper we propose a computerized vision system for aiding in the diagnostic process; this system used a novel particle recognition algorithm to improve specificity and speed during the diagnostic process. We will show the detection and classification of different types of cells in a diluted blood sample using regression analysis of their size, shape and colour. The first step is to define the objects to be tracked by a Gaussian Mixture Model for background subtraction and binary opening and closing for noise suppression. After subtracting the objects of interest from the background the next challenge is to predict if a given object belongs to a certain category or not. This is a classification problem, and the output of the algorithm is a Boolean value (true/false). As such the computer program should be able to "predict" with reasonable level of confidence if a given particle belongs to the kind we are looking for or not. We show the use of a binary logistic regression analysis with three continuous predictors: size, shape and color histogram. The results suggest this variables could be very useful in a logistic regression equation as they proved to have a relatively high predictive value on their own.

A comparison of multiple imputation methods for incomplete longitudinal binary data.

PubMed

Yamaguchi, Yusuke; Misumi, Toshihiro; Maruo, Kazushi

2018-01-01

Longitudinal binary data are commonly encountered in clinical trials. Multiple imputation is an approach for getting a valid estimation of treatment effects under an assumption of missing at random mechanism. Although there are a variety of multiple imputation methods for the longitudinal binary data, a limited number of researches have reported on relative performances of the methods. Moreover, when focusing on the treatment effect throughout a period that has often been used in clinical evaluations of specific disease areas, no definite investigations comparing the methods have been available. We conducted an extensive simulation study to examine comparative performances of six multiple imputation methods available in the SAS MI procedure for longitudinal binary data, where two endpoints of responder rates at a specified time point and throughout a period were assessed. The simulation study suggested that results from naive approaches of a single imputation with non-responders and a complete case analysis could be very sensitive against missing data. The multiple imputation methods using a monotone method and a full conditional specification with a logistic regression imputation model were recommended for obtaining unbiased and robust estimations of the treatment effect. The methods were illustrated with data from a mental health research.

A Comparison of Methods for Nonparametric Estimation of Item Characteristic Curves for Binary Items

ERIC Educational Resources Information Center

Lee, Young-Sun

2007-01-01

This study compares the performance of three nonparametric item characteristic curve (ICC) estimation procedures: isotonic regression, smoothed isotonic regression, and kernel smoothing. Smoothed isotonic regression, employed along with an appropriate kernel function, provides better estimates and also satisfies the assumption of strict…

A computer tool for a minimax criterion in binary response and heteroscedastic simple linear regression models.

PubMed

Casero-Alonso, V; López-Fidalgo, J; Torsney, B

2017-01-01

Binary response models are used in many real applications. For these models the Fisher information matrix (FIM) is proportional to the FIM of a weighted simple linear regression model. The same is also true when the weight function has a finite integral. Thus, optimal designs for one binary model are also optimal for the corresponding weighted linear regression model. The main objective of this paper is to provide a tool for the construction of MV-optimal designs, minimizing the maximum of the variances of the estimates, for a general design space. MV-optimality is a potentially difficult criterion because of its nondifferentiability at equal variance designs. A methodology for obtaining MV-optimal designs where the design space is a compact interval [a, b] will be given for several standard weight functions. The methodology will allow us to build a user-friendly computer tool based on Mathematica to compute MV-optimal designs. Some illustrative examples will show a representation of MV-optimal designs in the Euclidean plane, taking a and b as the axes. The applet will be explained using two relevant models. In the first one the case of a weighted linear regression model is considered, where the weight function is directly chosen from a typical family. In the second example a binary response model is assumed, where the probability of the outcome is given by a typical probability distribution. Practitioners can use the provided applet to identify the solution and to know the exact support points and design weights. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Cluster randomised crossover trials with binary data and unbalanced cluster sizes: application to studies of near-universal interventions in intensive care.

PubMed

Forbes, Andrew B; Akram, Muhammad; Pilcher, David; Cooper, Jamie; Bellomo, Rinaldo

2015-02-01

Cluster randomised crossover trials have been utilised in recent years in the health and social sciences. Methods for analysis have been proposed; however, for binary outcomes, these have received little assessment of their appropriateness. In addition, methods for determination of sample size are currently limited to balanced cluster sizes both between clusters and between periods within clusters. This article aims to extend this work to unbalanced situations and to evaluate the properties of a variety of methods for analysis of binary data, with a particular focus on the setting of potential trials of near-universal interventions in intensive care to reduce in-hospital mortality. We derive a formula for sample size estimation for unbalanced cluster sizes, and apply it to the intensive care setting to demonstrate the utility of the cluster crossover design. We conduct a numerical simulation of the design in the intensive care setting and for more general configurations, and we assess the performance of three cluster summary estimators and an individual-data estimator based on binomial-identity-link regression. For settings similar to the intensive care scenario involving large cluster sizes and small intra-cluster correlations, the sample size formulae developed and analysis methods investigated are found to be appropriate, with the unweighted cluster summary method performing well relative to the more optimal but more complex inverse-variance weighted method. More generally, we find that the unweighted and cluster-size-weighted summary methods perform well, with the relative efficiency of each largely determined systematically from the study design parameters. Performance of individual-data regression is adequate with small cluster sizes but becomes inefficient for large, unbalanced cluster sizes. When outcome prevalences are 6% or less and the within-cluster-within-period correlation is 0.05 or larger, all methods display sub-nominal confidence interval coverage, with the less prevalent the outcome the worse the coverage. As with all simulation studies, conclusions are limited to the configurations studied. We confined attention to detecting intervention effects on an absolute risk scale using marginal models and did not explore properties of binary random effects models. Cluster crossover designs with binary outcomes can be analysed using simple cluster summary methods, and sample size in unbalanced cluster size settings can be determined using relatively straightforward formulae. However, caution needs to be applied in situations with low prevalence outcomes and moderate to high intra-cluster correlations. © The Author(s) 2014.

Multivariate meta-analysis using individual participant data.

PubMed

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

2015-06-01

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

Classifying machinery condition using oil samples and binary logistic regression

NASA Astrophysics Data System (ADS)

Phillips, J.; Cripps, E.; Lau, John W.; Hodkiewicz, M. R.

2015-08-01

The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.

PARAMETRIC AND NON PARAMETRIC (MARS: MULTIVARIATE ADDITIVE REGRESSION SPLINES) LOGISTIC REGRESSIONS FOR PREDICTION OF A DICHOTOMOUS RESPONSE VARIABLE WITH AN EXAMPLE FOR PRESENCE/ABSENCE OF AMPHIBIANS

EPA Science Inventory

The purpose of this report is to provide a reference manual that could be used by investigators for making informed use of logistic regression using two methods (standard logistic regression and MARS). The details for analyses of relationships between a dependent binary response ...

Analyzing Student Learning Outcomes: Usefulness of Logistic and Cox Regression Models. IR Applications, Volume 5

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2005-01-01

Logistic and Cox regression methods are practical tools used to model the relationships between certain student learning outcomes and their relevant explanatory variables. The logistic regression model fits an S-shaped curve into a binary outcome with data points of zero and one. The Cox regression model allows investigators to study the duration…

Self-declared stock ownership and association with positive trial outcome in randomized controlled trials with binary outcomes published in general medical journals: a cross-sectional study.

PubMed

Falk Delgado, Alberto; Falk Delgado, Anna

2017-07-26

Describe the prevalence and types of conflicts of interest (COI) in published randomized controlled trials (RCTs) in general medical journals with a binary primary outcome and assess the association between conflicts of interest and favorable outcome. Parallel-group RCTs with a binary primary outcome published in three general medical journals during 2013-2015 were identified. COI type, funding source, and outcome were extracted. Binomial logistic regression model was performed to assess association between COI and funding source with outcome. A total of 509 consecutive parallel-group RCTs were included in the study. COI was reported in 74% in mixed funded RCTs and in 99% in for-profit funded RCTs. Stock ownership was reported in none of the non-profit RCTs, in 7% of mixed funded RCTs, and in 50% of for-profit funded RCTs. Mixed-funded RCTs had employees from the funding company in 11% and for-profit RCTs in 76%. Multivariable logistic regression revealed that stock ownership in the funding company among any of the authors was associated with a favorable outcome (odds ratio = 3.53; 95% confidence interval = 1.59-7.86; p < 0.01). COI in for-profit funded RCTs is extensive, because the factors related to COI are not fully independent, a multivariable analysis should be cautiously interpreted. However, after multivariable adjustment only stock ownership from the funding company among authors is associated with a favorable outcome.

Determination of riverbank erosion probability using Locally Weighted Logistic Regression

NASA Astrophysics Data System (ADS)

Ioannidou, Elena; Flori, Aikaterini; Varouchakis, Emmanouil A.; Giannakis, Georgios; Vozinaki, Anthi Eirini K.; Karatzas, George P.; Nikolaidis, Nikolaos

2015-04-01

Riverbank erosion is a natural geomorphologic process that affects the fluvial environment. The most important issue concerning riverbank erosion is the identification of the vulnerable locations. An alternative to the usual hydrodynamic models to predict vulnerable locations is to quantify the probability of erosion occurrence. This can be achieved by identifying the underlying relations between riverbank erosion and the geomorphological or hydrological variables that prevent or stimulate erosion. Thus, riverbank erosion can be determined by a regression model using independent variables that are considered to affect the erosion process. The impact of such variables may vary spatially, therefore, a non-stationary regression model is preferred instead of a stationary equivalent. Locally Weighted Regression (LWR) is proposed as a suitable choice. This method can be extended to predict the binary presence or absence of erosion based on a series of independent local variables by using the logistic regression model. It is referred to as Locally Weighted Logistic Regression (LWLR). Logistic regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable (e.g. binary response) based on one or more predictor variables. The method can be combined with LWR to assign weights to local independent variables of the dependent one. LWR allows model parameters to vary over space in order to reflect spatial heterogeneity. The probabilities of the possible outcomes are modelled as a function of the independent variables using a logistic function. Logistic regression measures the relationship between a categorical dependent variable and, usually, one or several continuous independent variables by converting the dependent variable to probability scores. Then, a logistic regression is formed, which predicts success or failure of a given binary variable (e.g. erosion presence or absence) for any value of the independent variables. The erosion occurrence probability can be calculated in conjunction with the model deviance regarding the independent variables tested. The most straightforward measure for goodness of fit is the G statistic. It is a simple and effective way to study and evaluate the Logistic Regression model efficiency and the reliability of each independent variable. The developed statistical model is applied to the Koiliaris River Basin on the island of Crete, Greece. Two datasets of river bank slope, river cross-section width and indications of erosion were available for the analysis (12 and 8 locations). Two different types of spatial dependence functions, exponential and tricubic, were examined to determine the local spatial dependence of the independent variables at the measurement locations. The results show a significant improvement when the tricubic function is applied as the erosion probability is accurately predicted at all eight validation locations. Results for the model deviance show that cross-section width is more important than bank slope in the estimation of erosion probability along the Koiliaris riverbanks. The proposed statistical model is a useful tool that quantifies the erosion probability along the riverbanks and can be used to assist managing erosion and flooding events. Acknowledgements This work is part of an on-going THALES project (CYBERSENSORS - High Frequency Monitoring System for Integrated Water Resources Management of Rivers). The project has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES. Investing in knowledge society through the European Social Fund.

Is parenting style a predictor of suicide attempts in a representative sample of adolescents?

PubMed

Donath, Carolin; Graessel, Elmar; Baier, Dirk; Bleich, Stefan; Hillemacher, Thomas

2014-04-26

Suicidal ideation and suicide attempts are serious but not rare conditions in adolescents. However, there are several research and practical suicide-prevention initiatives that discuss the possibility of preventing serious self-harm. Profound knowledge about risk and protective factors is therefore necessary. The aim of this study is a) to clarify the role of parenting behavior and parenting styles in adolescents' suicide attempts and b) to identify other statistically significant and clinically relevant risk and protective factors for suicide attempts in a representative sample of German adolescents. In the years 2007/2008, a representative written survey of N = 44,610 students in the 9th grade of different school types in Germany was conducted. In this survey, the lifetime prevalence of suicide attempts was investigated as well as potential predictors including parenting behavior. A three-step statistical analysis was carried out: I) As basic model, the association between parenting and suicide attempts was explored via binary logistic regression controlled for age and sex. II) The predictive values of 13 additional potential risk/protective factors were analyzed with single binary logistic regression analyses for each predictor alone. Non-significant predictors were excluded in Step III. III) In a multivariate binary logistic regression analysis, all significant predictor variables from Step II and the parenting styles were included after testing for multicollinearity. Three parental variables showed a relevant association with suicide attempts in adolescents - (all protective): mother's warmth and father's warmth in childhood and mother's control in adolescence (Step I). In the full model (Step III), Authoritative parenting (protective: OR: .79) and Rejecting-Neglecting parenting (risk: OR: 1.63) were identified as significant predictors (p < .001) for suicidal attempts. Seven further variables were interpreted to be statistically significant and clinically relevant: ADHD, female sex, smoking, Binge Drinking, absenteeism/truancy, migration background, and parental separation events. Parenting style does matter. While children of Authoritative parents profit, children of Rejecting-Neglecting parents are put at risk - as we were able to show for suicide attempts in adolescence. Some of the identified risk factors contribute new knowledge and potential areas of intervention for special groups such as migrants or children diagnosed with ADHD.

Socioeconomic and Demographic Disparities in Knowledge of Reproductive Healthcare among Female University Students in Bangladesh

PubMed Central

Islam Mondal, Md. Nazrul; Nasir Ullah, Md. Monzur Morshad; Khan, Md. Nuruzzaman; Islam, Mohammad Zamirul; Islam, Md. Nurul; Moni, Sabiha Yasmin; Hoque, Md. Nazrul; Rahman, Md. Mashiur

2015-01-01

Background: Reproductive health (RH) is a critical component of women’s health and overall well-being around the world, especially in developing countries. We examine the factors that determine knowledge of RH care among female university students in Bangladesh. Methods: Data on 300 female students were collected from Rajshahi University, Bangladesh through a structured questionnaire using purposive sampling technique. The data were used for univariate analysis, to carry out the description of the variables; bivariate analysis was used to examine the associations between the variables; and finally, multivariate analysis (binary logistic regression model) was used to examine and fit the model and interpret the parameter estimates, especially in terms of odds ratios. Results: The results revealed that more than one-third (34.3%) respondents do not have sufficient knowledge of RH care. The χ2-test identified the significant (p < 0.05) associations between respondents’ knowledge of RH care with respondents’ age, education, family type, watching television; and knowledge about pregnancy, family planning, and contraceptive use. Finally, the binary logistic regression model identified respondents’ age, education, family type; and knowledge about family planning, and contraceptive use as the significant (p < 0.05) predictors of RH care. Conclusions and Global Health Implications: Knowledge of RH care among female university students was found unsatisfactory. Government and concerned organizations should promote and strengthen various health education programs to focus on RH care especially for the female university students in Bangladesh. PMID:27622005

Factors associated with trait anger level of juvenile offenders in Hubei province: A binary logistic regression analysis.

PubMed

Tang, Li-Na; Ye, Xiao-Zhou; Yan, Qiu-Ge; Chang, Hong-Juan; Ma, Yu-Qiao; Liu, De-Bin; Li, Zhi-Gen; Yu, Yi-Zhen

2017-02-01

The risk factors of high trait anger of juvenile offenders were explored through questionnaire study in a youth correctional facility of Hubei province, China. A total of 1090 juvenile offenders in Hubei province were investigated by self-compiled social-demographic questionnaire, Childhood Trauma Questionnaire (CTQ), and State-Trait Anger Expression Inventory-II (STAXI-II). The risk factors were analyzed by chi-square tests, correlation analysis, and binary logistic regression analysis with SPSS 19.0. A total of 1082 copies of valid questionnaires were collected. High trait anger group (n=316) was defined as those who scored in the upper 27th percentile of STAXI-II trait anger scale (TAS), and the rest were defined as low trait anger group (n=766). The risk factors associated with high level of trait anger included: childhood emotional abuse, childhood sexual abuse, step family, frequent drug abuse, and frequent internet using (P<0.05 or P<0.01). Birth sequence, number of sibling, ranking in the family, identity of the main care-taker, the education level of care-taker, educational style of care-taker, family income, relationship between parents, social atmosphere of local area, frequent drinking, and frequent smoking did not predict to high level of trait anger (P>0.05). It was suggested that traumatic experience in childhood and unhealthy life style may significantly increase the level of trait anger in adulthood. The risk factors of high trait anger and their effects should be taken into consideration seriously.

Depression and incident dementia. An 8-year population-based prospective study.

PubMed

Luppa, Melanie; Luck, Tobias; Ritschel, Franziska; Angermeyer, Matthias C; Villringer, Arno; Riedel-Heller, Steffi G

2013-01-01

The aim of the study was to investigate the impact of depression (categorical diagnosis; major depression, MD) and depressive symptoms (dimensional diagnosis and symptom patterns) on incident dementia in the German general population. Within the Leipzig Longitudinal Study of the Aged (LEILA 75+), a representative sample of 1,265 individuals aged 75 years and older were interviewed every 1.5 years over 8 years (mean observation time 4.3 years; mean number of visits 4.2). Cox proportional hazards and binary logistic regressions were used to estimate the effect of baseline depression and depressive symptoms on incident dementia. The incidence of dementia was 48 per 1,000 person-years (95% confidence interval (CI) 45-51). Depressive symptoms (Hazard ratio HR 1.03, 95% CI 1.01-1.05), and in particular mood-related symptoms (HR 1.08, 95% CI 1.03-1.14), showed a significant impact on the incidence of dementia only in univariate analysis, but not after adjustment for cognitive and functional impairment. MD showed only a significant impact on incidence of dementia in Cox proportional hazards regression, but not in binary logistic regression models. The present study using different diagnostic measures of depression on future dementia found no clear significant associations of depression and incident dementia. Further in-depth investigation would help to understand the nature of depression in the context of incident dementia.

Psychological trauma symptoms and Type 2 diabetes prevalence, glucose control, and treatment modality among American Indians in the Strong Heart Family Study

PubMed Central

Jacob, Michelle M.; Gonzales, Kelly L.; Calhoun, Darren; Beals, Janette; Muller, Clemma Jacobsen; Goldberg, Jack; Nelson, Lonnie; Welty, Thomas K.; Howard, Barbara V.

2013-01-01

Aims The aims of this paper are to examine the relationship between psychological trauma symptoms and Type 2 diabetes prevalence, glucose control, and treatment modality among 3,776 American Indians in Phase V of the Strong Heart Family Study. Methods This cross-sectional analysis measured psychological trauma symptoms using the National Anxiety Disorder Screening Day instrument, diabetes by American Diabetes Association criteria, and treatment modality by four categories: no medication, oral medication only, insulin only, or both oral medication and insulin. We used binary logistic regression to evaluate the association between psychological trauma symptoms and diabetes prevalence. We used ordinary least squares regression to evaluate the association between psychological trauma symptoms and glucose control. We used binary logistic regression to model the association of psychological trauma symptoms with treatment modality. Results Neither diabetes prevalence (22-31%; p = 0.19) nor control (8.0-8.6; p = 0.25) varied significantly by psychological trauma symptoms categories. However, diabetes treatment modality was associated with psychological trauma symptoms categories, as people with greater burden used either no medication, or both oral and insulin medications (odds ratio = 3.1, p < 0.001). Conclusions The positive relationship between treatment modality and psychological trauma symptoms suggests future research investigate patient and provider treatment decision making. PMID:24051029

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.

Methods for calculating confidence and credible intervals for the residual between-study variance in random effects meta-regression models

PubMed Central

2014-01-01

Background Meta-regression is becoming increasingly used to model study level covariate effects. However this type of statistical analysis presents many difficulties and challenges. Here two methods for calculating confidence intervals for the magnitude of the residual between-study variance in random effects meta-regression models are developed. A further suggestion for calculating credible intervals using informative prior distributions for the residual between-study variance is presented. Methods Two recently proposed and, under the assumptions of the random effects model, exact methods for constructing confidence intervals for the between-study variance in random effects meta-analyses are extended to the meta-regression setting. The use of Generalised Cochran heterogeneity statistics is extended to the meta-regression setting and a Newton-Raphson procedure is developed to implement the Q profile method for meta-analysis and meta-regression. WinBUGS is used to implement informative priors for the residual between-study variance in the context of Bayesian meta-regressions. Results Results are obtained for two contrasting examples, where the first example involves a binary covariate and the second involves a continuous covariate. Intervals for the residual between-study variance are wide for both examples. Conclusions Statistical methods, and R computer software, are available to compute exact confidence intervals for the residual between-study variance under the random effects model for meta-regression. These frequentist methods are almost as easily implemented as their established counterparts for meta-analysis. Bayesian meta-regressions are also easily performed by analysts who are comfortable using WinBUGS. Estimates of the residual between-study variance in random effects meta-regressions should be routinely reported and accompanied by some measure of their uncertainty. Confidence and/or credible intervals are well-suited to this purpose. PMID:25196829

Noise exposure-response relationships established from repeated binary observations: Modeling approaches and applications.

PubMed

Schäffer, Beat; Pieren, Reto; Mendolia, Franco; Basner, Mathias; Brink, Mark

2017-05-01

Noise exposure-response relationships are used to estimate the effects of noise on individuals or a population. Such relationships may be derived from independent or repeated binary observations, and modeled by different statistical methods. Depending on the method by which they were established, their application in population risk assessment or estimation of individual responses may yield different results, i.e., predict "weaker" or "stronger" effects. As far as the present body of literature on noise effect studies is concerned, however, the underlying statistical methodology to establish exposure-response relationships has not always been paid sufficient attention. This paper gives an overview on two statistical approaches (subject-specific and population-averaged logistic regression analysis) to establish noise exposure-response relationships from repeated binary observations, and their appropriate applications. The considerations are illustrated with data from three noise effect studies, estimating also the magnitude of differences in results when applying exposure-response relationships derived from the two statistical approaches. Depending on the underlying data set and the probability range of the binary variable it covers, the two approaches yield similar to very different results. The adequate choice of a specific statistical approach and its application in subsequent studies, both depending on the research question, are therefore crucial.

A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

PubMed

Karabatsos, George

2017-02-01

Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected functionals and values of covariates. The software is illustrated through the BNP regression analysis of real data.

Spectroscopic and DFT study of solvent effects on the electronic absorption spectra of sulfamethoxazole in neat and binary solvent mixtures

NASA Astrophysics Data System (ADS)

Almandoz, M. C.; Sancho, M. I.; Blanco, S. E.

2014-01-01

The solvatochromic behavior of sulfamethoxazole (SMX) was investigated using UV-vis spectroscopy and DFT methods in neat and binary solvent mixtures. The spectral shifts of this solute were correlated with the Kamlet and Taft parameters (α, β and π*). Multiple lineal regression analysis indicates that both specific hydrogen-bond interaction and non specific dipolar interaction play an important role in the position of the absorption maxima in neat solvents. The simulated absorption spectra using TD-DFT methods were in good agreement with the experimental ones. Binary mixtures consist of cyclohexane (Cy)-ethanol (EtOH), acetonitrile (ACN)-dimethylsulfoxide (DMSO), ACN-dimethylformamide (DMF), and aqueous mixtures containing as co-solvents DMSO, ACN, EtOH and MeOH. Index of preferential solvation was calculated as a function of solvent composition and non-ideal characteristics are observed in all binary mixtures. In ACN-DMSO and ACN-DMF mixtures, the results show that the solvents with higher polarity and hydrogen bond donor ability interact preferentially with the solute. In binary mixtures containing water, the SMX molecules are solvated by the organic co-solvent (DMSO or EtOH) over the whole composition range. Synergistic effect is observed in the case of ACN-H2O and MeOH-H2O, indicating that at certain concentrations solvents interact to form association complexes, which should be more polar than the individual solvents of the mixture.

Reduction from cost-sensitive ordinal ranking to weighted binary classification.

PubMed

Lin, Hsuan-Tien; Li, Ling

2012-05-01

We present a reduction framework from ordinal ranking to binary classification. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm, and constructing a ranker from the binary classifier. Based on the framework, we show that a weighted 0/1 loss of the binary classifier upper-bounds the mislabeling cost of the ranker, both error-wise and regret-wise. Our framework allows not only the design of good ordinal ranking algorithms based on well-tuned binary classification approaches, but also the derivation of new generalization bounds for ordinal ranking from known bounds for binary classification. In addition, our framework unifies many existing ordinal ranking algorithms, such as perceptron ranking and support vector ordinal regression. When compared empirically on benchmark data sets, some of our newly designed algorithms enjoy advantages in terms of both training speed and generalization performance over existing algorithms. In addition, the newly designed algorithms lead to better cost-sensitive ordinal ranking performance, as well as improved listwise ranking performance.

Sequence analysis to assess labour market participation following vocational rehabilitation: an observational study among patients sick-listed with low back pain from a randomised clinical trial in Denmark

PubMed Central

Lindholdt, Louise; Labriola, Merete; Nielsen, Claus Vinther; Horsbøl, Trine Allerslev; Lund, Thomas

2017-01-01

Introduction The return-to-work (RTW) process after long-term sickness absence is often complex and long and implies multiple shifts between different labour market states for the absentee. Standard methods for examining RTW research typically rely on the analysis of one outcome measure at a time, which will not capture the many possible states and transitions the absentee can go through. The purpose of this study was to explore the potential added value of sequence analysis in supplement to standard regression analysis of a multidisciplinary RTW intervention among patients with low back pain (LBP). Methods The study population consisted of 160 patients randomly allocated to either a hospital-based brief or a multidisciplinary intervention. Data on labour market participation following intervention were obtained from a national register and analysed in two ways: as a binary outcome expressed as active or passive relief at a 1-year follow-up and as four different categories for labour market participation. Logistic regression and sequence analysis were performed. Results The logistic regression analysis showed no difference in labour market participation for patients in the two groups after 1 year. Applying sequence analysis showed differences in subsequent labour market participation after 2 years after baseline in favour of the brief intervention group versus the multidisciplinary intervention group. Conclusion The study indicated that sequence analysis could provide added analytical value as a supplement to traditional regression analysis in prospective studies of RTW among patients with LBP. PMID:28729315

Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey)

NASA Astrophysics Data System (ADS)

Ozdemir, Adnan

2011-07-01

SummaryThe purpose of this study is to produce a groundwater spring potential map of the Sultan Mountains in central Turkey, based on a logistic regression method within a Geographic Information System (GIS) environment. Using field surveys, the locations of the springs (440 springs) were determined in the study area. In this study, 17 spring-related factors were used in the analysis: geology, relative permeability, land use/land cover, precipitation, elevation, slope, aspect, total curvature, plan curvature, profile curvature, wetness index, stream power index, sediment transport capacity index, distance to drainage, distance to fault, drainage density, and fault density map. The coefficients of the predictor variables were estimated using binary logistic regression analysis and were used to calculate the groundwater spring potential for the entire study area. The accuracy of the final spring potential map was evaluated based on the observed springs. The accuracy of the model was evaluated by calculating the relative operating characteristics. The area value of the relative operating characteristic curve model was found to be 0.82. These results indicate that the model is a good estimator of the spring potential in the study area. The spring potential map shows that the areas of very low, low, moderate and high groundwater spring potential classes are 105.586 km 2 (28.99%), 74.271 km 2 (19.906%), 101.203 km 2 (27.14%), and 90.05 km 2 (24.671%), respectively. The interpretations of the potential map showed that stream power index, relative permeability of lithologies, geology, elevation, aspect, wetness index, plan curvature, and drainage density play major roles in spring occurrence and distribution in the Sultan Mountains. The logistic regression approach has not yet been used to delineate groundwater potential zones. In this study, the logistic regression method was used to locate potential zones for groundwater springs in the Sultan Mountains. The evolved model was found to be in strong agreement with the available groundwater spring test data. Hence, this method can be used routinely in groundwater exploration under favourable conditions.

The crux of the method: assumptions in ordinary least squares and logistic regression.

PubMed

Long, Rebecca G

2008-10-01

Logistic regression has increasingly become the tool of choice when analyzing data with a binary dependent variable. While resources relating to the technique are widely available, clear discussions of why logistic regression should be used in place of ordinary least squares regression are difficult to find. The current paper compares and contrasts the assumptions of ordinary least squares with those of logistic regression and explains why logistic regression's looser assumptions make it adept at handling violations of the more important assumptions in ordinary least squares.

Estimating the Probability of Rare Events Occurring Using a Local Model Averaging.

PubMed

Chen, Jin-Hua; Chen, Chun-Shu; Huang, Meng-Fan; Lin, Hung-Chih

2016-10-01

In statistical applications, logistic regression is a popular method for analyzing binary data accompanied by explanatory variables. But when one of the two outcomes is rare, the estimation of model parameters has been shown to be severely biased and hence estimating the probability of rare events occurring based on a logistic regression model would be inaccurate. In this article, we focus on estimating the probability of rare events occurring based on logistic regression models. Instead of selecting a best model, we propose a local model averaging procedure based on a data perturbation technique applied to different information criteria to obtain different probability estimates of rare events occurring. Then an approximately unbiased estimator of Kullback-Leibler loss is used to choose the best one among them. We design complete simulations to show the effectiveness of our approach. For illustration, a necrotizing enterocolitis (NEC) data set is analyzed. © 2016 Society for Risk Analysis.

Using the theory of planned behavior to predict the use of incentive spirometry among cardiac surgery patients in Taiwan.

PubMed

Tung, Heng-Hsin; Jan, Ming-Shan; Huang, Chiu-Mieh; Shih, Chun-Che; Chang, Chung-Yi; Liau, Cheu-Ye

2011-01-01

The use of incentive spirometry (IS) is reported to prevent and treat postoperative pulmonary complications. This study sought to use the theory of planned behavior to predict the use of IS in this population. The study used a prospective design, with convenience sampling, to recruit a total of 116 postcardiac-surgery patients from 2 medical centers in Taipei, Taiwan, from November 2008 to May 2009. Data were collected through 2 instruments: a demographic questionnaire, and an IS questionnaire. Descriptive analysis, independent t test, one-way analysis of variance, binary regression, and liner regression were used to analyze the data. Perceived behavioral control, but not intention, was a predictor of the use of IS. Our findings provide partial support for the utility of the theory of planned behavior in explaining the use of IS behavior for cardiac surgery patients. Copyright © 2011. Published by Mosby, Inc.

The Prevalence of Antenatal Depression and its Related Factors in Chinese Pregnant Women who Present with Obstetrical Complications.

PubMed

Zhao, Ying; Kane, Irene; Mao, Liping; Shi, Shenxun; Wang, Jing; Lin, Qiping; Luo, Jianfeng

2016-06-01

The psychological status of Chinese pregnant women who present with obstetrical complications is concerning to Chinese health professionals. This study aimed to investigate the prevalence of antenatal depression and analyzed related risk factors in a population of high-risk Chinese women. A large sample size, cross-sectional study. A total of 842 pregnant women with complications completed the Chinese version of the Postpartum Depression Screen Scale (PDSS) in this cross-sectional study. t-Test, ANOVA and Binary logistic regression tests were used in data analysis of antenatal depression and risk factors. The prevalence of major or minor depression in high-risk Chinese pregnant women during antenatal period was 8.3% and 28.9%, respectively. Independent-sample t-test and two-way analysis of variance (ANOVA) indicated significant differences in age, education, occupation and the number of complications (P<0.05). Binary logistic regression analysis indicated a significant negative association between depression and education (P<0.01) with lower educational level (OR: 0.590; 95% CI: 0.424-0.820) associated with a higher risk for depression. A significant positive association was observed between depression and age (P<0.05) with higher age (OR: 1.338; 95% CI: 1.008-1.774) correlated with a higher risk for depression. Women who experienced obstetric complications presented with higher PDSS depression scores. Screening for antenatal depression in high-risk pregnant women to promote early detection of depression and reduce health risks for universal health promotion is recommended. Copyright © 2015 Elsevier Inc. All rights reserved.

Nurses’ knowledge on phlebotomy in tertiary hospitals in China: a cross-sectional multicentric survey

PubMed Central

Cai, Qian; Zhou, Yunxian; Yang, Dangan

2017-01-01

Introduction In China, phlebotomy practice is mostly executed by nurses instead of phlebotomists. Our hypothesis was that these nurses may lack of knowledge on phlebotomy, especially factors influencing quality of blood samples. This study aims to assess the overall nurses’ knowledge on phlebotomy to provide reference for improving blood sampling practice in China. Materials and methods A survey was conducted involving nurses from 4 regions and 13 hospitals in China. A phlebotomy knowledge questionnaire was designed based on the Clinical and Laboratory Standards Institute H3-A6 guidelines, combining with the situations in China. Descriptive analysis and binary logistic regression analysis were used to analyze the knowledge level and its influencing factors. Results A total of 3400 questionnaires were distributed and 3077 valid questionnaires were returned, with an effective return rate of 90.5%. The correct rates of patient identification, hand sanitization, patient assessment, tube mixing time, needle disposing location and tube labelling were greater than 90%. However, the correct rates of order of draw (15.5%), definition of an inversion (22.5%), time to release tourniquet (18.5%) and time to change tube (28.5%) were relatively low. Binary logistic regression analysis showed that the correct rates of the aforementioned four questions were mainly related to the regional distribution of the hospitals (P < 0.001). Conclusions The knowledge level on phlebotomy among Chinese nurses was found unsatisfactory in some areas. An education program on phlebotomy should be developed for Chinese nurses to improve the consistency among different regions and to enhance nurse’s knowledge level on phlebotomy. PMID:29187796

Genome-wide regression and prediction with the BGLR statistical package.

PubMed

Pérez, Paulino; de los Campos, Gustavo

2014-10-01

Many modern genomic data analyses require implementing regressions where the number of parameters (p, e.g., the number of marker effects) exceeds sample size (n). Implementing these large-p-with-small-n regressions poses several statistical and computational challenges, some of which can be confronted using Bayesian methods. This approach allows integrating various parametric and nonparametric shrinkage and variable selection procedures in a unified and consistent manner. The BGLR R-package implements a large collection of Bayesian regression models, including parametric variable selection and shrinkage methods and semiparametric procedures (Bayesian reproducing kernel Hilbert spaces regressions, RKHS). The software was originally developed for genomic applications; however, the methods implemented are useful for many nongenomic applications as well. The response can be continuous (censored or not) or categorical (either binary or ordinal). The algorithm is based on a Gibbs sampler with scalar updates and the implementation takes advantage of efficient compiled C and Fortran routines. In this article we describe the methods implemented in BGLR, present examples of the use of the package, and discuss practical issues emerging in real-data analysis. Copyright © 2014 by the Genetics Society of America.

Contributions of sociodemographic factors to criminal behavior

PubMed Central

Mundia, Lawrence; Matzin, Rohani; Mahalle, Salwa; Hamid, Malai Hayati; Osman, Ratna Suriani

2016-01-01

We explored the extent to which prisoner sociodemographic variables (age, education, marital status, employment, and whether their parents were married or not) influenced offending in 64 randomly selected Brunei inmates, comprising both sexes. A quantitative field survey design ideal for the type of participants used in a prison context was employed to investigate the problem. Hierarchical multiple regression analysis with backward elimination identified prisoner marital status and age groups as significantly related to offending. Furthermore, hierarchical multinomial logistic regression analysis with backward elimination indicated that prisoners’ age, primary level education, marital status, employment status, and parental marital status as significantly related to stealing offenses with high odds ratios. All 29 nonrecidivists were false negatives and predicted to reoffend upon release. Similarly, all 33 recidivists were projected to reoffend after release. Hierarchical binary logistic regression analysis revealed age groups (24–29 years and 30–35 years), employed prisoner, and primary level education as variables with high likelihood trends for reoffending. The results suggested that prisoner interventions (educational, counseling, and psychotherapy) in Brunei should treat not only antisocial personality, psychopathy, and mental health problems but also sociodemographic factors. The study generated offending patterns, trends, and norms that may inform subsequent investigations on Brunei prisoners. PMID:27382342

Kernel analysis of partial least squares (PLS) regression models.

PubMed

Shinzawa, Hideyuki; Ritthiruangdej, Pitiporn; Ozaki, Yukihiro

2011-05-01

An analytical technique based on kernel matrix representation is demonstrated to provide further chemically meaningful insight into partial least squares (PLS) regression models. The kernel matrix condenses essential information about scores derived from PLS or principal component analysis (PCA). Thus, it becomes possible to establish the proper interpretation of the scores. A PLS model for the total nitrogen (TN) content in multiple Thai fish sauces is built with a set of near-infrared (NIR) transmittance spectra of the fish sauce samples. The kernel analysis of the scores effectively reveals that the variation of the spectral feature induced by the change in protein content is substantially associated with the total water content and the protein hydration. Kernel analysis is also carried out on a set of time-dependent infrared (IR) spectra representing transient evaporation of ethanol from a binary mixture solution of ethanol and oleic acid. A PLS model to predict the elapsed time is built with the IR spectra and the kernel matrix is derived from the scores. The detailed analysis of the kernel matrix provides penetrating insight into the interaction between the ethanol and the oleic acid.

Acculturation and healthy lifestyle habits among Hispanics in United States-Mexico border communities.

PubMed

Ghaddar, Suad; Brown, Cynthia J; Pagán, José A; Díaz, Violeta

2010-09-01

To explore the relationship between acculturation and healthy lifestyle habits in the largely Hispanic populations living in underserved communities in the United States of America along the U.S.-Mexico border. A cross-sectional study was conducted from April 2006 to June 2008 using survey data from the Alliance for a Healthy Border, a program designed to reduce health disparities in the U.S.-Mexico border region by funding nutrition and physical activity education programs at 12 federally qualified community health centers in Arizona, California, New Mexico, and Texas. The survey included questions on acculturation, diet, exercise, and demographic factors and was completed by 2,381 Alliance program participants, of whom 95.3% were Hispanic and 45.4% were under the U.S. poverty level for 2007. Chi-square (χ2) and Student's t tests were used for bivariate comparisons between acculturation and dietary and physical activity measures. Linear regression and binary logistic regression were used to control for factors associated with nutrition and exercise. Based on univariate tests and confirmed by regression analysis controlling for sociodemographic and health variables, less acculturated survey respondents reported a significantly higher frequency of fruit and vegetable consumption and healthier dietary habits than those who were more acculturated. Adjusted binary logistic regression confirmed that individuals with low language acculturation were less likely to engage in physical activity than those with moderate to high acculturation (odds ratio 0.75, 95% confidence interval 0.59-0.95). Findings confirmed an association between acculturation and healthy lifestyle habits and supported the hypothesis that acculturation in border community populations tends to decrease the practice of some healthy dietary habits while increasing exposure to and awareness of the importance of other healthy behaviors.

Two-Part and Related Regression Models for Longitudinal Data

PubMed Central

Farewell, V.T.; Long, D.L.; Tom, B.D.M.; Yiu, S.; Su, L.

2017-01-01

Statistical models that involve a two-part mixture distribution are applicable in a variety of situations. Frequently, the two parts are a model for the binary response variable and a model for the outcome variable that is conditioned on the binary response. Two common examples are zero-inflated or hurdle models for count data and two-part models for semicontinuous data. Recently, there has been particular interest in the use of these models for the analysis of repeated measures of an outcome variable over time. The aim of this review is to consider motivations for the use of such models in this context and to highlight the central issues that arise with their use. We examine two-part models for semicontinuous and zero-heavy count data, and we also consider models for count data with a two-part random effects distribution. PMID:28890906

Do classic blood biomarkers of JSLE identify active lupus nephritis? Evidence from the UK JSLE Cohort Study.

PubMed

Smith, E M D; Jorgensen, A L; Beresford, M W

2017-10-01

Background Lupus nephritis (LN) affects up to 80% of juvenile-onset systemic lupus erythematosus (JSLE) patients. The value of commonly available biomarkers, such as anti-dsDNA antibodies, complement (C3/C4), ESR and full blood count parameters in the identification of active LN remains uncertain. Methods Participants from the UK JSLE Cohort Study, aged <16 years at diagnosis, were categorized as having active or inactive LN according to the renal domain of the British Isles Lupus Assessment Group score. Classic biomarkers: anti-dsDNA, C3, C4, ESR, CRP, haemoglobin, total white cells, neutrophils, lymphocytes, platelets and immunoglobulins were assessed for their ability to identify active LN using binary logistic regression modeling, with stepAIC function applied to select a final model. Receiver-operating curve analysis was used to assess diagnostic accuracy. Results A total of 370 patients were recruited; 191 (52%) had active LN and 179 (48%) had inactive LN. Binary logistic regression modeling demonstrated a combination of ESR, C3, white cell count, neutrophils, lymphocytes and IgG to be best for the identification of active LN (area under the curve 0.724). Conclusions At best, combining common classic blood biomarkers of lupus activity using multivariate analysis provides a 'fair' ability to identify active LN. Urine biomarkers were not included in these analyses. These results add to the concern that classic blood biomarkers are limited in monitoring discrete JSLE manifestations such as LN.

The rate of adherence to urate-lowering therapy and associated factors in Chinese gout patients: a cross-sectional study.

PubMed

Yin, Rulan; Cao, Haixia; Fu, Ting; Zhang, Qiuxiang; Zhang, Lijuan; Li, Liren; Gu, Zhifeng

2017-07-01

The aim of this study was to assess adherence rate and predictors of non-adherence with urate-lowering therapy (ULT) in Chinese gout patients. A cross-sectional study was administered to 125 gout patients using the Compliance Questionnaire on Rheumatology (CQR) for adherence to ULT. Patients were asked to complete the Treatment Satisfaction Questionnaire for Medication version II, Health Assessment Questionnaire, Confidence in Gout Treatment Questionnaire, Gout Knowledge Questionnaire, Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and 36-Item Short Form Health Survey. Data were analyzed by independent sample t test, rank sum test, Chi-square analysis as well as binary stepwise logistic regression modeling. The data showed that the rate of adherence (CQR ≥80%) to ULT was 9.6% in our investigated gout patients. Adherence was associated with functional capacity, gout-related knowledge, satisfaction with medication, confidence in gout treatment and mental components summary. Multivariable analysis of binary stepwise logistic regression identified gout-related knowledge and satisfaction of effectiveness with medication was the independent risk factors of medication non-adherence. Patients unaware of gout-related knowledge, or with low satisfaction of effectiveness with medication, were more likely not to adhere to ULT. Non-adherence to ULT among gout patients is exceedingly common, particularly in patients unaware of gout-related knowledge, or with low satisfaction of effectiveness with medication. These findings could help medical personnel develop useful interventions to improve gout patients' medication adherence.

Financial performance monitoring of the technical efficiency of critical access hospitals: a data envelopment analysis and logistic regression modeling approach.

PubMed

Wilson, Asa B; Kerr, Bernard J; Bastian, Nathaniel D; Fulton, Lawrence V

2012-01-01

From 1980 to 1999, rural designated hospitals closed at a disproportionally high rate. In response to this emergent threat to healthcare access in rural settings, the Balanced Budget Act of 1997 made provisions for the creation of a new rural hospital--the critical access hospital (CAH). The conversion to CAH and the associated cost-based reimbursement scheme significantly slowed the closure rate of rural hospitals. This work investigates which methods can ensure the long-term viability of small hospitals. This article uses a two-step design to focus on a hypothesized relationship between technical efficiency of CAHs and a recently developed set of financial monitors for these entities. The goal is to identify the financial performance measures associated with efficiency. The first step uses data envelopment analysis (DEA) to differentiate efficient from inefficient facilities within a data set of 183 CAHs. Determining DEA efficiency is an a priori categorization of hospitals in the data set as efficient or inefficient. In the second step, DEA efficiency is the categorical dependent variable (efficient = 0, inefficient = 1) in the subsequent binary logistic regression (LR) model. A set of six financial monitors selected from the array of 20 measures were the LR independent variables. We use a binary LR to test the null hypothesis that recently developed CAH financial indicators had no predictive value for categorizing a CAH as efficient or inefficient, (i.e., there is no relationship between DEA efficiency and fiscal performance).

Spatial properties of snow cover in the Upper Merced River Basin: implications for a distributed snow measurement network

NASA Astrophysics Data System (ADS)

Bouffon, T.; Rice, R.; Bales, R.

2006-12-01

The spatial distributions of snow water equivalent (SWE) and snow depth within a 1, 4, and 16 km2 grid element around two automated snow pillows in a forested and open- forested region of the Upper Merced River Basin (2,800 km2) of Yosemite National Park were characterized using field observations and analyzed using binary regression trees. Snow surveys occurred at the forested site during the accumulation and ablation seasons, while at the open-forest site a survey was performed only during the accumulation season. An average of 130 snow depth and 7 snow density measurements were made on each survey, within the 4 km2 grid. Snow depth was distributed using binary regression trees and geostatistical methods using the physiographic parameters (e.g. elevation, slope, vegetation, aspect). Results in the forest region indicate that the snow pillow overestimated average SWE within the 1, 4, and 16 km2 areas by 34 percent during ablation, but during accumulation the snow pillow provides a good estimate of the modeled mean SWE grid value, however it is suspected that the snow pillow was underestimating SWE. However, at the open forest site, during accumulation, the snow pillow was 28 percent greater than the mean modeled grid element. In addition, the binary regression trees indicate that the independent variables of vegetation, slope, and aspect are the most influential parameters of snow depth distribution. The binary regression tree and multivariate linear regression models explain about 60 percent of the initial variance for snow depth and 80 percent for density, respectively. This short-term study provides motivation and direction for the installation of a distributed snow measurement network to fill the information gap in basin-wide SWE and snow depth measurements. Guided by these results, a distributed snow measurement network was installed in the Fall 2006 at Gin Flat in the Upper Merced River Basin with the specific objective of measuring accumulation and ablation across topographic variables with the aim of providing guidance for future larger scale observation network designs.

HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL SPARSE BINARY REGRESSION

PubMed Central

Mukherjee, Rajarshi; Pillai, Natesh S.; Lin, Xihong

2015-01-01

In this paper, we study the detection boundary for minimax hypothesis testing in the context of high-dimensional, sparse binary regression models. Motivated by genetic sequencing association studies for rare variant effects, we investigate the complexity of the hypothesis testing problem when the design matrix is sparse. We observe a new phenomenon in the behavior of detection boundary which does not occur in the case of Gaussian linear regression. We derive the detection boundary as a function of two components: a design matrix sparsity index and signal strength, each of which is a function of the sparsity of the alternative. For any alternative, if the design matrix sparsity index is too high, any test is asymptotically powerless irrespective of the magnitude of signal strength. For binary design matrices with the sparsity index that is not too high, our results are parallel to those in the Gaussian case. In this context, we derive detection boundaries for both dense and sparse regimes. For the dense regime, we show that the generalized likelihood ratio is rate optimal; for the sparse regime, we propose an extended Higher Criticism Test and show it is rate optimal and sharp. We illustrate the finite sample properties of the theoretical results using simulation studies. PMID:26246645

Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed

USGS Publications Warehouse

Balk, Benjamin; Elder, Kelly

2000-01-01

We model the spatial distribution of snow across a mountain basin using an approach that combines binary decision tree and geostatistical techniques. In April 1997 and 1998, intensive snow surveys were conducted in the 6.9‐km2 Loch Vale watershed (LVWS), Rocky Mountain National Park, Colorado. Binary decision trees were used to model the large‐scale variations in snow depth, while the small‐scale variations were modeled through kriging interpolation methods. Binary decision trees related depth to the physically based independent variables of net solar radiation, elevation, slope, and vegetation cover type. These decision tree models explained 54–65% of the observed variance in the depth measurements. The tree‐based modeled depths were then subtracted from the measured depths, and the resulting residuals were spatially distributed across LVWS through kriging techniques. The kriged estimates of the residuals were added to the tree‐based modeled depths to produce a combined depth model. The combined depth estimates explained 60–85% of the variance in the measured depths. Snow densities were mapped across LVWS using regression analysis. Snow‐covered area was determined from high‐resolution aerial photographs. Combining the modeled depths and densities with a snow cover map produced estimates of the spatial distribution of snow water equivalence (SWE). This modeling approach offers improvement over previous methods of estimating SWE distribution in mountain basins.

Multicomponent ionic liquid CMC prediction.

PubMed

Kłosowska-Chomiczewska, I E; Artichowicz, W; Preiss, U; Jungnickel, C

2017-09-27

We created a model to predict CMC of ILs based on 704 experimental values published in 43 publications since 2000. Our model was able to predict CMC of variety of ILs in binary or ternary system in a presence of salt or alcohol. The molecular volume of IL (V m ), solvent-accessible surface (Ŝ), solvation enthalpy (Δ solv G ∞ ), concentration of salt (C s ) or alcohol (C a ) and their molecular volumes (V ms and V ma , respectively) were chosen as descriptors, and Kernel Support Vector Machine (KSVM) and Evolutionary Algorithm (EA) as regression methodologies to create the models. Data was split into training and validation set (80/20) and subjected to bootstrap aggregation. KSVM provided better fit with average R 2 of 0.843, and MSE of 0.608, whereas EA resulted in R 2 of 0.794 and MSE of 0.973. From the sensitivity analysis it was shown that V m and Ŝ have the highest impact on ILs micellization in both binary and ternary systems, however surprisingly in the presence of alcohol the V m becomes insignificant/irrelevant. Micelle stabilizing or destabilizing influence of the descriptors depends upon the additives. Previous attempts at modelling the CMC of ILs was generally limited to small number of ILs in simplified (binary) systems. We however showed successful prediction of the CMC over a range of different systems (binary and ternary).

Missing Data in Alcohol Clinical Trials with Binary Outcomes

PubMed Central

Hallgren, Kevin A.; Witkiewitz, Katie; Kranzler, Henry R.; Falk, Daniel E.; Litten, Raye Z.; O’Malley, Stephanie S.; Anton, Raymond F.

2017-01-01

Background Missing data are common in alcohol clinical trials for both continuous and binary endpoints. Approaches to handle missing data have been explored for continuous outcomes, yet no studies have compared missing data approaches for binary outcomes (e.g., abstinence, no heavy drinking days). The present study compares approaches to modeling binary outcomes with missing data in the COMBINE study. Method We included participants in the COMBINE Study who had complete drinking data during treatment and who were assigned to active medication or placebo conditions (N=1146). Using simulation methods, missing data were introduced under common scenarios with varying sample sizes and amounts of missing data. Logistic regression was used to estimate the effect of naltrexone (vs. placebo) in predicting any drinking and any heavy drinking outcomes at the end of treatment using four analytic approaches: complete case analysis (CCA), last observation carried forward (LOCF), the worst-case scenario of missing equals any drinking or heavy drinking (WCS), and multiple imputation (MI). In separate analyses, these approaches were compared when drinking data were manually deleted for those participants who discontinued treatment but continued to provide drinking data. Results WCS produced the greatest amount of bias in treatment effect estimates. MI usually yielded less biased estimates than WCS and CCA in the simulated data, and performed considerably better than LOCF when estimating treatment effects among individuals who discontinued treatment. Conclusions Missing data can introduce bias in treatment effect estimates in alcohol clinical trials. Researchers should utilize modern missing data methods, including MI, and avoid WCS and CCA when analyzing binary alcohol clinical trial outcomes. PMID:27254113

Missing Data in Alcohol Clinical Trials with Binary Outcomes.

PubMed

Hallgren, Kevin A; Witkiewitz, Katie; Kranzler, Henry R; Falk, Daniel E; Litten, Raye Z; O'Malley, Stephanie S; Anton, Raymond F

2016-07-01

Missing data are common in alcohol clinical trials for both continuous and binary end points. Approaches to handle missing data have been explored for continuous outcomes, yet no studies have compared missing data approaches for binary outcomes (e.g., abstinence, no heavy drinking days). This study compares approaches to modeling binary outcomes with missing data in the COMBINE study. We included participants in the COMBINE study who had complete drinking data during treatment and who were assigned to active medication or placebo conditions (N = 1,146). Using simulation methods, missing data were introduced under common scenarios with varying sample sizes and amounts of missing data. Logistic regression was used to estimate the effect of naltrexone (vs. placebo) in predicting any drinking and any heavy drinking outcomes at the end of treatment using 4 analytic approaches: complete case analysis (CCA), last observation carried forward (LOCF), the worst case scenario (WCS) of missing equals any drinking or heavy drinking, and multiple imputation (MI). In separate analyses, these approaches were compared when drinking data were manually deleted for those participants who discontinued treatment but continued to provide drinking data. WCS produced the greatest amount of bias in treatment effect estimates. MI usually yielded less biased estimates than WCS and CCA in the simulated data and performed considerably better than LOCF when estimating treatment effects among individuals who discontinued treatment. Missing data can introduce bias in treatment effect estimates in alcohol clinical trials. Researchers should utilize modern missing data methods, including MI, and avoid WCS and CCA when analyzing binary alcohol clinical trial outcomes. Copyright © 2016 by the Research Society on Alcoholism.

Is parenting style a predictor of suicide attempts in a representative sample of adolescents?

PubMed Central

2014-01-01

Background Suicidal ideation and suicide attempts are serious but not rare conditions in adolescents. However, there are several research and practical suicide-prevention initiatives that discuss the possibility of preventing serious self-harm. Profound knowledge about risk and protective factors is therefore necessary. The aim of this study is a) to clarify the role of parenting behavior and parenting styles in adolescents’ suicide attempts and b) to identify other statistically significant and clinically relevant risk and protective factors for suicide attempts in a representative sample of German adolescents. Methods In the years 2007/2008, a representative written survey of N = 44,610 students in the 9th grade of different school types in Germany was conducted. In this survey, the lifetime prevalence of suicide attempts was investigated as well as potential predictors including parenting behavior. A three-step statistical analysis was carried out: I) As basic model, the association between parenting and suicide attempts was explored via binary logistic regression controlled for age and sex. II) The predictive values of 13 additional potential risk/protective factors were analyzed with single binary logistic regression analyses for each predictor alone. Non-significant predictors were excluded in Step III. III) In a multivariate binary logistic regression analysis, all significant predictor variables from Step II and the parenting styles were included after testing for multicollinearity. Results Three parental variables showed a relevant association with suicide attempts in adolescents – (all protective): mother’s warmth and father’s warmth in childhood and mother’s control in adolescence (Step I). In the full model (Step III), Authoritative parenting (protective: OR: .79) and Rejecting-Neglecting parenting (risk: OR: 1.63) were identified as significant predictors (p < .001) for suicidal attempts. Seven further variables were interpreted to be statistically significant and clinically relevant: ADHD, female sex, smoking, Binge Drinking, absenteeism/truancy, migration background, and parental separation events. Conclusions Parenting style does matter. While children of Authoritative parents profit, children of Rejecting-Neglecting parents are put at risk – as we were able to show for suicide attempts in adolescence. Some of the identified risk factors contribute new knowledge and potential areas of intervention for special groups such as migrants or children diagnosed with ADHD. PMID:24766881

Logits and Tigers and Bears, Oh My! A Brief Look at the Simple Math of Logistic Regression and How It Can Improve Dissemination of Results

ERIC Educational Resources Information Center

Osborne, Jason W.

2012-01-01

Logistic regression is slowly gaining acceptance in the social sciences, and fills an important niche in the researcher's toolkit: being able to predict important outcomes that are not continuous in nature. While OLS regression is a valuable tool, it cannot routinely be used to predict outcomes that are binary or categorical in nature. These…

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

PubMed

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

2015-05-12

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

Sequence analysis to assess labour market participation following vocational rehabilitation: an observational study among patients sick-listed with low back pain from a randomised clinical trial in Denmark.

PubMed

Lindholdt, Louise; Labriola, Merete; Nielsen, Claus Vinther; Horsbøl, Trine Allerslev; Lund, Thomas

2017-07-20

The return-to-work (RTW) process after long-term sickness absence is often complex and long and implies multiple shifts between different labour market states for the absentee. Standard methods for examining RTW research typically rely on the analysis of one outcome measure at a time, which will not capture the many possible states and transitions the absentee can go through. The purpose of this study was to explore the potential added value of sequence analysis in supplement to standard regression analysis of a multidisciplinary RTW intervention among patients with low back pain (LBP). The study population consisted of 160 patients randomly allocated to either a hospital-based brief or a multidisciplinary intervention. Data on labour market participation following intervention were obtained from a national register and analysed in two ways: as a binary outcome expressed as active or passive relief at a 1-year follow-up and as four different categories for labour market participation. Logistic regression and sequence analysis were performed. The logistic regression analysis showed no difference in labour market participation for patients in the two groups after 1 year. Applying sequence analysis showed differences in subsequent labour market participation after 2 years after baseline in favour of the brief intervention group versus the multidisciplinary intervention group. The study indicated that sequence analysis could provide added analytical value as a supplement to traditional regression analysis in prospective studies of RTW among patients with LBP. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

America's Democracy Colleges: The Civic Engagement of Community College Students

ERIC Educational Resources Information Center

Angeli Newell, Mallory

2014-01-01

This study explored the civic engagement of current two- and four-year students to explore whether differences exist between the groups and what may explain the differences. Using binary logistic regression and Ordinary Least Squares regression it was found that community-based engagement was lower for two- than four-year students, though…

The Effect of Latent Binary Variables on the Uncertainty of the Prediction of a Dichotomous Outcome Using Logistic Regression Based Propensity Score Matching.

PubMed

Szekér, Szabolcs; Vathy-Fogarassy, Ágnes

2018-01-01

Logistic regression based propensity score matching is a widely used method in case-control studies to select the individuals of the control group. This method creates a suitable control group if all factors affecting the output variable are known. However, if relevant latent variables exist as well, which are not taken into account during the calculations, the quality of the control group is uncertain. In this paper, we present a statistics-based research in which we try to determine the relationship between the accuracy of the logistic regression model and the uncertainty of the dependent variable of the control group defined by propensity score matching. Our analyses show that there is a linear correlation between the fit of the logistic regression model and the uncertainty of the output variable. In certain cases, a latent binary explanatory variable can result in a relative error of up to 70% in the prediction of the outcome variable. The observed phenomenon calls the attention of analysts to an important point, which must be taken into account when deducting conclusions.

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

A FORTRAN technique for correlating a circular environmental variable with a linear physiological variable in the sugar maple.

PubMed

Pease, J M; Morselli, M F

1987-01-01

This paper deals with a computer program adapted to a statistical method for analyzing an unlimited quantity of binary recorded data of an independent circular variable (e.g. wind direction), and a linear variable (e.g. maple sap flow volume). Circular variables cannot be statistically analyzed with linear methods, unless they have been transformed. The program calculates a critical quantity, the acrophase angle (PHI, phi o). The technique is adapted from original mathematics [1] and is written in Fortran 77 for easier conversion between computer networks. Correlation analysis can be performed following the program or regression which, because of the circular nature of the independent variable, becomes periodic regression. The technique was tested on a file of approximately 4050 data pairs.

Diagnostic accuracy of serum antibodies to human papillomavirus type 16 early antigens in the detection of human papillomavirus-related oropharyngeal cancer.

PubMed

Dahlstrom, Kristina R; Anderson, Karen S; Field, Matthew S; Chowell, Diego; Ning, Jing; Li, Nan; Wei, Qingyi; Li, Guojun; Sturgis, Erich M

2017-12-15

Because of the current epidemic of human papillomavirus (HPV)-related oropharyngeal cancer (OPC), a screening strategy is urgently needed. The presence of serum antibodies to HPV-16 early (E) antigens is associated with an increased risk for OPC. The purpose of this study was to evaluate the diagnostic accuracy of antibodies to a panel of HPV-16 E antigens in screening for OPC. This case-control study included 378 patients with OPC, 153 patients with nonoropharyngeal head and neck cancer (non-OPC), and 782 healthy control subjects. The tumor HPV status was determined with p16 immunohistochemistry and HPV in situ hybridization. HPV-16 E antibody levels in serum were identified with an enzyme-linked immunosorbent assay. A trained binary logistic regression model based on the combination of all E antigens was predefined and applied to the data set. The sensitivity and specificity of the assay for distinguishing HPV-related OPC from controls were calculated. Logistic regression analysis was used to calculate odds ratios with 95% confidence intervals for the association of head and neck cancer with the antibody status. Of the 378 patients with OPC, 348 had p16-positive OPC. HPV-16 E antibody levels were significantly higher among patients with p16-positive OPC but not among patients with non-OPC or among controls. Serology showed high sensitivity and specificity for HPV-related OPC (binary classifier: 83% sensitivity and 99% specificity for p16-positive OPC). A trained binary classification algorithm that incorporates information about multiple E antibodies has high sensitivity and specificity and may be advantageous for risk stratification in future screening trials. Cancer 2017;123:4886-94. © 2017 American Cancer Society. © 2017 American Cancer Society.

Optimizing methods for linking cinematic features to fMRI data.

PubMed

Kauttonen, Janne; Hlushchuk, Yevhen; Tikka, Pia

2015-04-15

One of the challenges of naturalistic neurosciences using movie-viewing experiments is how to interpret observed brain activations in relation to the multiplicity of time-locked stimulus features. As previous studies have shown less inter-subject synchronization across viewers of random video footage than story-driven films, new methods need to be developed for analysis of less story-driven contents. To optimize the linkage between our fMRI data collected during viewing of a deliberately non-narrative silent film 'At Land' by Maya Deren (1944) and its annotated content, we combined the method of elastic-net regularization with the model-driven linear regression and the well-established data-driven independent component analysis (ICA) and inter-subject correlation (ISC) methods. In the linear regression analysis, both IC and region-of-interest (ROI) time-series were fitted with time-series of a total of 36 binary-valued and one real-valued tactile annotation of film features. The elastic-net regularization and cross-validation were applied in the ordinary least-squares linear regression in order to avoid over-fitting due to the multicollinearity of regressors, the results were compared against both the partial least-squares (PLS) regression and the un-regularized full-model regression. Non-parametric permutation testing scheme was applied to evaluate the statistical significance of regression. We found statistically significant correlation between the annotation model and 9 ICs out of 40 ICs. Regression analysis was also repeated for a large set of cubic ROIs covering the grey matter. Both IC- and ROI-based regression analyses revealed activations in parietal and occipital regions, with additional smaller clusters in the frontal lobe. Furthermore, we found elastic-net based regression more sensitive than PLS and un-regularized regression since it detected a larger number of significant ICs and ROIs. Along with the ISC ranking methods, our regression analysis proved a feasible method for ordering the ICs based on their functional relevance to the annotated cinematic features. The novelty of our method is - in comparison to the hypothesis-driven manual pre-selection and observation of some individual regressors biased by choice - in applying data-driven approach to all content features simultaneously. We found especially the combination of regularized regression and ICA useful when analyzing fMRI data obtained using non-narrative movie stimulus with a large set of complex and correlated features. Copyright © 2015. Published by Elsevier Inc.

Sensitivity subgroup analysis based on single-center vs. multi-center trial status when interpreting meta-analyses pooled estimates: the logical way forward.

PubMed

Alexander, Paul E; Bonner, Ashley J; Agarwal, Arnav; Li, Shelly-Anne; Hariharan, Abishek; Izhar, Zain; Bhatnagar, Neera; Alba, Carolina; Akl, Elie A; Fei, Yutong; Guyatt, Gordon H; Beyene, Joseph

2016-06-01

Prior studies regarding whether single-center trial estimates are larger than multi-center are equivocal. We examined the extent to which single-center trials yield systematically larger effects than multi-center trials. We searched the 119 core clinical journals and the Cochrane Database of Systematic Reviews for meta-analyses (MAs) of randomized controlled trials (RCTs) published during 2012. In this meta-epidemiologic study, for binary variables, we computed the pooled ratio of ORs (RORs), and for continuous outcomes mean difference in standardized mean differences (SMDs), we conducted weighted random-effects meta-regression and random-effects MA modeling. Our primary analyses were restricted to MAs that included at least five RCTs and in which at least 25% of the studies used each of single trial center (SC) and more trial center (MC) designs. We identified 81 MAs for the odds ratio (OR) and 43 for the SMD outcome measures. Based on our analytic plan, our primary analysis (core) is based on 25 MAs/241 RCTs (binary outcome) and 18 MAs/173 RCTs (continuous outcome). Based on the core analysis, we found no difference in magnitude of effect between SC and MC for binary outcomes [RORs: 1.02; 95% confidence interval (CI): 0.83, 1.24; I(2) 20.2%]. Effect sizes were systematically larger for SC than MC for the continuous outcome measure (mean difference in SMDs: -0.13; 95% CI: -0.21, -0.05; I(2) 0%). Our results do not support prior findings of larger effects in SC than MC trials addressing binary outcomes but show a very similar small increase in effect in SC than MC trials addressing continuous outcomes. Authors of systematic reviews would be wise to include all trials irrespective of SC vs. MC design and address SC vs. MC status as a possible explanation of heterogeneity (and consider sensitivity analyses). Copyright © 2015 Elsevier Inc. All rights reserved.

Socio-Economic, Demographic and Lifestyle Determinants of Overweight and Obesity among Adults of Northeast India.

PubMed

Rengma, Melody Seb; Sen, Jaydip; Mondal, Nitish

2015-07-01

Overweight and obesity are the accumulation of high body adiposity, which can have detrimental health effects and contribute to the development of numerous preventable non-communicable diseases. This study aims to evaluate the effect of socio-economic, demographic and lifestyle factors on the prevalence of overweight and obesity among adults belonging to the Rengma-Naga population of North-east India. This cross-sectional study was conducted among 826 Rengma-Naga individuals (males: 422; females: 404) aged 20-49 years from the Karbi Anglong District of Assam, using a two-stage stratified random sampling. The socio-economic, demographic and lifestyle variables were recorded using structured schedules. Height and weight were recorded and the Body Mass Index (BMI) was calculated using standard procedures and equation. The WHO (2000) cut-off points were utilized to assess the prevalence of overweight (BMI ≥23.00-24.99 kg/m(2)) and obesity (BMI ≥25.00 kg/m(2)). The data were analysed using ANOVA, chi-square analysis and binary logistic regression analysis using SPSS (version 17.0). The prevalence of overweight and obesity were 32.57% (males: 39.34%; females: 25.50%) and 10.77% (males: 9.95%; females: 11.63%), respectively. The binary logistic regression analysis showed that age groups (e.g., 40-49 years), education (≥9(th) standard), part-time occupation and monthly income (≥Rs.10000) were significantly associated with overweight and obesity (p<0.05). Age, education occupation and income appear to have higher associations with overweight and obesity among adults. Suitable healthcare strategies and intervention programmes are needed for combating such prevalence in population.

London Measure of Unplanned Pregnancy: guidance for its use as an outcome measure

PubMed Central

Hall, Jennifer A; Barrett, Geraldine; Copas, Andrew; Stephenson, Judith

2017-01-01

Background The London Measure of Unplanned Pregnancy (LMUP) is a psychometrically validated measure of the degree of intention of a current or recent pregnancy. The LMUP is increasingly being used worldwide, and can be used to evaluate family planning or preconception care programs. However, beyond recommending the use of the full LMUP scale, there is no published guidance on how to use the LMUP as an outcome measure. Ordinal logistic regression has been recommended informally, but studies published to date have all used binary logistic regression and dichotomized the scale at different cut points. There is thus a need for evidence-based guidance to provide a standardized methodology for multivariate analysis and to enable comparison of results. This paper makes recommendations for the regression method for analysis of the LMUP as an outcome measure. Materials and methods Data collected from 4,244 pregnant women in Malawi were used to compare five regression methods: linear, logistic with two cut points, and ordinal logistic with either the full or grouped LMUP score. The recommendations were then tested on the original UK LMUP data. Results There were small but no important differences in the findings across the regression models. Logistic regression resulted in the largest loss of information, and assumptions were violated for the linear and ordinal logistic regression. Consequently, robust standard errors were used for linear regression and a partial proportional odds ordinal logistic regression model attempted. The latter could only be fitted for grouped LMUP score. Conclusion We recommend the linear regression model with robust standard errors to make full use of the LMUP score when analyzed as an outcome measure. Ordinal logistic regression could be considered, but a partial proportional odds model with grouped LMUP score may be required. Logistic regression is the least-favored option, due to the loss of information. For logistic regression, the cut point for un/planned pregnancy should be between nine and ten. These recommendations will standardize the analysis of LMUP data and enhance comparability of results across studies. PMID:28435343

Three-way analysis of the UPLC-PDA dataset for the multicomponent quantitation of hydrochlorothiazide and olmesartan medoxomil in tablets by parallel factor analysis and three-way partial least squares.

PubMed

Dinç, Erdal; Ertekin, Zehra Ceren

2016-01-01

An application of parallel factor analysis (PARAFAC) and three-way partial least squares (3W-PLS1) regression models to ultra-performance liquid chromatography-photodiode array detection (UPLC-PDA) data with co-eluted peaks in the same wavelength and time regions was described for the multicomponent quantitation of hydrochlorothiazide (HCT) and olmesartan medoxomil (OLM) in tablets. Three-way dataset of HCT and OLM in their binary mixtures containing telmisartan (IS) as an internal standard was recorded with a UPLC-PDA instrument. Firstly, the PARAFAC algorithm was applied for the decomposition of three-way UPLC-PDA data into the chromatographic, spectral and concentration profiles to quantify the concerned compounds. Secondly, 3W-PLS1 approach was subjected to the decomposition of a tensor consisting of three-way UPLC-PDA data into a set of triads to build 3W-PLS1 regression for the analysis of the same compounds in samples. For the proposed three-way analysis methods in the regression and prediction steps, the applicability and validity of PARAFAC and 3W-PLS1 models were checked by analyzing the synthetic mixture samples, inter-day and intra-day samples, and standard addition samples containing HCT and OLM. Two different three-way analysis methods, PARAFAC and 3W-PLS1, were successfully applied to the quantitative estimation of the solid dosage form containing HCT and OLM. Regression and prediction results provided from three-way analysis were compared with those obtained by traditional UPLC method. Copyright © 2015 Elsevier B.V. All rights reserved.

Logistic regression for circular data

NASA Astrophysics Data System (ADS)

Al-Daffaie, Kadhem; Khan, Shahjahan

2017-05-01

This paper considers the relationship between a binary response and a circular predictor. It develops the logistic regression model by employing the linear-circular regression approach. The maximum likelihood method is used to estimate the parameters. The Newton-Raphson numerical method is used to find the estimated values of the parameters. A data set from weather records of Toowoomba city is analysed by the proposed methods. Moreover, a simulation study is considered. The R software is used for all computations and simulations.

Industry Perspective of Pediatric Drug Development in the United States: Involvement of the European Union Countries.

PubMed

Onishi, Taku; Tsukamoto, Katsura; Matsumaru, Naoki; Waki, Takashi

2018-01-01

Efforts to promote the development of pediatric pharmacotherapy include regulatory frameworks and close collaboration between the US Food and Drug Administration and the European Medicines Agency. We characterized the current status of pediatric clinical trials conducted in the United States by the pharmaceutical industry, focusing on the involvement of the European Union member countries, to clarify the industry perspective. Data on US pediatric clinical trials were obtained from ClinicalTrials.gov . Binary regression analysis was performed to identify what factors influence the likelihood of involvement of European Union countries. A total of 633 US pediatric clinical trials that met inclusion criteria were extracted and surveyed. Of these, 206 (32.5%) involved a European Union country site(s). The results of binary regression analysis indicated that attribution of industry, phase, disease area, and age of pediatric participants influenced the likelihood of the involvement of European Union countries in US pediatric clinical trials. Relatively complicated or large pediatric clinical trials, such as phase II and III trials and those that included a broad age range of participants, had a significantly greater likelihood of the involvement of European Union countries ( P < .05). Our results suggest that (1) the pharmaceutical industry utilizes regulatory frameworks in making business decisions regarding pediatric clinical trials, (2) disease area affects the involvement of European Union countries, and (3) feasibility of clinical trials is mainly concerned by pharmaceutical industry for pediatric drug development. Additional incentives for high marketability may further motivate pharmaceutical industry to develop pediatric drugs.

Gender differences in depressive symptom profiles and patterns of psychotropic drug usage in Asian patients with depression: Findings from the Research on Asian Psychotropic Prescription Patterns for Antidepressants study.

PubMed

Park, Seon-Cheol; Lee, Min-Soo; Shinfuku, Naotaka; Sartorius, Norman; Park, Yong Chon

2015-09-01

The purpose of this study was to investigate whether there were gender-specific depressive symptom profiles or gender-specific patterns of psychotropic agent usage in Asian patients with depression. Clinical data from the Research on Asian Psychotropic Prescription Patterns for Antidepressant study (1171 depressed patients) were used to determine gender differences by analysis of covariates for continuous variables and by logistic regression analysis for discrete variables. In addition, a binary logistic regression model was fitted to identify independent clinical correlates of the gender-specific pattern on psychotropic drug usage. Men were more likely than women to have loss of interest (adjusted odds ratio = 1.379, p = 0.009), fatigue (adjusted odds ratio = 1.298, p = 0.033) and concurrent substance abuse (adjusted odds ratio = 3.793, p = 0.008), but gender differences in other symptom profiles and clinical features were not significant. Men were also more likely than women to be prescribed adjunctive therapy with a second-generation antipsychotic (adjusted odds ratio = 1.320, p = 0.044). However, men were less likely than women to have suicidal thoughts/acts (adjusted odds ratio = 0.724, p = 0.028). Binary logistic regression models revealed that lower age (odds ratio = 0.986, p = 0.027) and current hospitalization (odds ratio = 3.348, p < 0.0001) were independent clinical correlates of use of second-generation antipsychotics as adjunctive therapy for treating depressed Asian men. Unique gender-specific symptom profiles and gender-specific patterns of psychotropic drug usage can be identified in Asian patients with depression. Hence, ethnic and cultural influences on the gender preponderance of depression should be considered in the clinical psychiatry of Asian patients. © The Royal Australian and New Zealand College of Psychiatrists 2015.

Comparison of statistical tests for association between rare variants and binary traits.

PubMed

Bacanu, Silviu-Alin; Nelson, Matthew R; Whittaker, John C

2012-01-01

Genome-wide association studies have found thousands of common genetic variants associated with a wide variety of diseases and other complex traits. However, a large portion of the predicted genetic contribution to many traits remains unknown. One plausible explanation is that some of the missing variation is due to the effects of rare variants. Nonetheless, the statistical analysis of rare variants is challenging. A commonly used method is to contrast, within the same region (gene), the frequency of minor alleles at rare variants between cases and controls. However, this strategy is most useful under the assumption that the tested variants have similar effects. We previously proposed a method that can accommodate heterogeneous effects in the analysis of quantitative traits. Here we extend this method to include binary traits that can accommodate covariates. We use simulations for a variety of causal and covariate impact scenarios to compare the performance of the proposed method to standard logistic regression, C-alpha, SKAT, and EREC. We found that i) logistic regression methods perform well when the heterogeneity of the effects is not extreme and ii) SKAT and EREC have good performance under all tested scenarios but they can be computationally intensive. Consequently, it would be more computationally desirable to use a two-step strategy by (i) selecting promising genes by faster methods and ii) analyzing selected genes using SKAT/EREC. To select promising genes one can use (1) regression methods when effect heterogeneity is assumed to be low and the covariates explain a non-negligible part of trait variability, (2) C-alpha when heterogeneity is assumed to be large and covariates explain a small fraction of trait's variability and (3) the proposed trend and heterogeneity test when the heterogeneity is assumed to be non-trivial and the covariates explain a large fraction of trait variability.

National health insurance subscription and maternal healthcare utilisation across mothers' wealth status in Ghana.

PubMed

Ameyaw, Edward Kwabena; Kofinti, Raymond Elikplim; Appiah, Francis

2017-12-01

This study is against the backdrop that despite the forty-nine percent decline in Maternal Mortality Rate in Ghana, the situation still remains high averaging 319 per 100,000 live births between 2011 and 2015. To examine the relationship between National Health Insurance and maternal healthcare utilisation across three main wealth quintiles (Poor, Middle and Rich). The study employed data from the 2014 Ghana Demographic and Health Survey. Both descriptive analysis and binary logistic regression were conducted. Descriptively, rich women had high antenatal attendance and health facility deliveries represented by 96.5% and 95.6% respectively. However, the binary logistic regression results revealed that poor women owning NHIS are 7% (CI = 1.76-2.87) more likely to make at least four antenatal care visits compared to women in the middle wealth quintile (5%, CI = 2.12-4.76) and rich women (2%, CI = 1.14-4.14). Similarly, poor women who owned the NHIS are 14% (CI = 1.42-2.13) likely to deliver in health facility than women in the middle and rich wealth quintile. The study has vindicated the claim that NHIS Scheme is pro-poor in Ghana. The Ministry of Health should target women in the rural area to be enrolled on the NHIS to improve maternal healthcare utilisation since poverty is principally a rural phenomenon in Ghana.

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.

Prevalence and Extrinsic Risk Factors for Dental Erosion in Adolescents.

PubMed

Mafla, Ana C; Cerón-Bastidas, Ximena A; Munoz-Ceballos, Maria E; Vallejo-Bravo, Diana C; Fajardo-Santacruz, Maria C

This manuscript examined the prevalence and extrinsic risk factors for dental erosion (DE) in early and middle adolescents in Pasto, Colombia. Dental erosion was evaluated in a random sample of 384 individuals aged 10-15 years attending three primary and high schools in this cross-sectional study. Clinical dental assessment for DE was done using O'Sullivan index. Data on general sociodemographic variables and extrinsic risks factors were obtained. Descriptive and univariate binary logistic regression analyses were performed. Dental erosion was observed in 57.3% of individuals. The univariate binary logistic regression analysis showed that frequency of drinking natural fruit juices (OR 2.670, 95% CI 1.346 - 5.295, P=0.004) and their pH (OR 2.303, 95% CI 1.292 - 4.107, P=0.004) were more associated with the odd of DE in early adolescence. However, a high SES (OR 10.360, 95% CI 3.700 - 29.010, P<0.001) and frequency of snacks with artificial lemon taste (OR 3.659, 95% CI 1.506 - 8.891, P=0.003) were highly associated with the risk of DE in middle adolescence. The results suggest that DE is a prevalent condition in adolescents living in a city in southern Colombia. The transition from early to middle adolescence implies new bio-psychosocial changes, which increase the risk for DE.

Risk estimation using probability machines

PubMed Central

2014-01-01

Background Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. Results We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. Conclusions The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from. PMID:24581306

Risk estimation using probability machines.

PubMed

Dasgupta, Abhijit; Szymczak, Silke; Moore, Jason H; Bailey-Wilson, Joan E; Malley, James D

2014-03-01

Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a "risk machine", will share properties from the statistical machine that it is derived from.

Human Epidermal Growth Factor Receptor 2 Expression in Unresectable Gastric Cancers: Relationship with CT Characteristics.

PubMed

Lee, Jeong Sub; Kim, Se Hyung; Im, Seock-Ah; Kim, Min A; Han, Joon Koo

2017-01-01

To retrospectively analyze the qualitative CT features that correlate with human epidermal growth factor receptor 2 (HER2)-expression in pathologically-proven gastric cancers. A total of 181 patients with pathologically-proven unresectable gastric cancers with HER2-expression (HER2-positive [n = 32] and negative [n = 149]) were included. CT features of primary gastric and metastatic tumors were reviewed. The prevalence of each CT finding was compared in both groups. Thereafter, binary logistic regression determined the most significant differential CT features. Clinical outcomes were compared using Kaplan-Meier method. HER2-postive cancers showed lower clinical T stage (21.9% vs. 8.1%; p = 0.015), hyperattenuation on portal phase (62.5% vs. 30.9%; p = 0.003), and was more frequently metastasized to the liver (62.5% vs. 32.2%; p = 0.001), than HER2-negative cancers. On binary regression analysis, hyperattenuation of the tumor (odds ratio [OR], 4.68; p < 0.001) and hepatic metastasis (OR, 4.43; p = 0.001) were significant independent factors that predict HER2-positive cancers. Median survival of HER2-positive cancers (13.7 months) was significantly longer than HER2-negative cancers (9.6 months) ( p = 0.035). HER2-positive gastric cancers show less-advanced T stage, hyperattenuation on the portal phase, and frequently metastasize to the liver, as compared to HER2-negative cancers.

The intermediate endpoint effect in logistic and probit regression

PubMed Central

MacKinnon, DP; Lockwood, CM; Brown, CH; Wang, W; Hoffman, JM

2010-01-01

Background An intermediate endpoint is hypothesized to be in the middle of the causal sequence relating an independent variable to a dependent variable. The intermediate variable is also called a surrogate or mediating variable and the corresponding effect is called the mediated, surrogate endpoint, or intermediate endpoint effect. Clinical studies are often designed to change an intermediate or surrogate endpoint and through this intermediate change influence the ultimate endpoint. In many intermediate endpoint clinical studies the dependent variable is binary, and logistic or probit regression is used. Purpose The purpose of this study is to describe a limitation of a widely used approach to assessing intermediate endpoint effects and to propose an alternative method, based on products of coefficients, that yields more accurate results. Methods The intermediate endpoint model for a binary outcome is described for a true binary outcome and for a dichotomization of a latent continuous outcome. Plots of true values and a simulation study are used to evaluate the different methods. Results Distorted estimates of the intermediate endpoint effect and incorrect conclusions can result from the application of widely used methods to assess the intermediate endpoint effect. The same problem occurs for the proportion of an effect explained by an intermediate endpoint, which has been suggested as a useful measure for identifying intermediate endpoints. A solution to this problem is given based on the relationship between latent variable modeling and logistic or probit regression. Limitations More complicated intermediate variable models are not addressed in the study, although the methods described in the article can be extended to these more complicated models. Conclusions Researchers are encouraged to use an intermediate endpoint method based on the product of regression coefficients. A common method based on difference in coefficient methods can lead to distorted conclusions regarding the intermediate effect. PMID:17942466

Analysis and Thermodynamic Prediction of Hydrogen Solution in Solid and Liquid Multicomponent Aluminum Alloys

NASA Astrophysics Data System (ADS)

Anyalebechi, P. N.

Reported experimentally determined values of hydrogen solubility in liquid and solid Al-H and Al-H-X (where X = Cu, Si, Zn, Mg, Li, Fe or Ti) systems have been critically reviewed and analyzed in terms of Wagner's interaction parameter. An attempt has been made to use Wagner's interaction parameter and statistic linear regression models derived from reported hydrogen solubility limits for binary aluminum alloys to predict the hydrogen solubility limits in liquid and solid (commercial) multicomponent aluminum alloys. Reasons for the observed poor agreement between the predicted and experimentally determined hydrogen solubility limits are discussed.

Completing the Remedial Sequence and College-Level Credit-Bearing Math: Comparing Binary, Cumulative, and Continuation Ratio Logistic Regression Models

ERIC Educational Resources Information Center

Davidson, J. Cody

2016-01-01

Mathematics is the most common subject area of remedial need and the majority of remedial math students never pass a college-level credit-bearing math class. The majorities of studies that investigate this phenomenon are conducted at community colleges and use some type of regression model; however, none have used a continuation ratio model. The…

Comparative decision models for anticipating shortage of food grain production in India

NASA Astrophysics Data System (ADS)

Chattopadhyay, Manojit; Mitra, Subrata Kumar

2018-01-01

This paper attempts to predict food shortages in advance from the analysis of rainfall during the monsoon months along with other inputs used for crop production, such as land used for cereal production, percentage of area covered under irrigation and fertiliser use. We used six binary classification data mining models viz., logistic regression, Multilayer Perceptron, kernel lab-Support Vector Machines, linear discriminant analysis, quadratic discriminant analysis and k-Nearest Neighbors Network, and found that linear discriminant analysis and kernel lab-Support Vector Machines are equally suitable for predicting per capita food shortage with 89.69 % accuracy in overall prediction and 92.06 % accuracy in predicting food shortage ( true negative rate). Advance information of food shortage can help policy makers to take remedial measures in order to prevent devastating consequences arising out of food non-availability.

An appraisal of convergence failures in the application of logistic regression model in published manuscripts.

PubMed

Yusuf, O B; Bamgboye, E A; Afolabi, R F; Shodimu, M A

2014-09-01

Logistic regression model is widely used in health research for description and predictive purposes. Unfortunately, most researchers are sometimes not aware that the underlying principles of the techniques have failed when the algorithm for maximum likelihood does not converge. Young researchers particularly postgraduate students may not know why separation problem whether quasi or complete occurs, how to identify it and how to fix it. This study was designed to critically evaluate convergence issues in articles that employed logistic regression analysis published in an African Journal of Medicine and medical sciences between 2004 and 2013. Problems of quasi or complete separation were described and were illustrated with the National Demographic and Health Survey dataset. A critical evaluation of articles that employed logistic regression was conducted. A total of 581 articles was reviewed, of which 40 (6.9%) used binary logistic regression. Twenty-four (60.0%) stated the use of logistic regression model in the methodology while none of the articles assessed model fit. Only 3 (12.5%) properly described the procedures. Of the 40 that used the logistic regression model, the problem of convergence occurred in 6 (15.0%) of the articles. Logistic regression tends to be poorly reported in studies published between 2004 and 2013. Our findings showed that the procedure may not be well understood by researchers since very few described the process in their reports and may be totally unaware of the problem of convergence or how to deal with it.

[A strategy for assessing environmental influence on airway allergy using a regression binary tree-based method].

PubMed

Yoshioka, Fumi; Azuma, Emiko; Nakajima, Takae; Hashimoto, Masafumi; Toyoshima, Kyoichiro; Komachi, Yoshio

2004-08-01

To clarify the living environment factors that increase the risk of allergic sensitization to house dust mites, we applied a regression binary tree-based method (CART, Classification & Regression Trees) to an epidemiological study on airway allergy. The utility of the tree map in personal sanitary guidance for preventing allergic sensitization was examined with respect to feasibility and validity. A questionnaire was given to 386 healthy adult women, asking them about their individual living environments. Also, blood samples were collected to measure Dermatophagoides pteronyssinus (Dp)-specific IgE, the presence/absence of Dp-sensitization being expressed as positive/negative. The questionnaire consisted of nine items on (1) home ventilation by keeping windows open, (2) personal or family smoking habits, (3) use of air conditioners in hot weather, (4) type of flooring (tatami/wooden/carpet) in the living room, (5) visible mold proliferation in the kitchen, (6) type of housing (concrete/wooden), (7) residential area (heavy or light traffic area) (8) heating system (use of unventilated combustion appliances), and (9) frequency of cleaning (every day or less often). There also were queries on the past history of airway allergic diseases, such as bronchial asthma and allergic rhinitis. CART and a multivariate logistic regression analysis (MLRA) were performed. The subjects were first classified into two groups, with and without a history of airway allergic diseases (Groups WPH and WOPH). In each group, the involvement of living environment factors in Dp-sensitization was examined using CART and MLRA. In the MLRA study, individual living environment factors showed promotional or suppressive effects on Dp-sensitization with differences between the two groups. With respect to the CART results, the two groups were first split by the factor that had the most significant odds ratio for MLRA. In Group WPH, which had a Dp-sensitization risk of 19.5%, the first split was by the factor of visible mold proliferation in the kitchen into the factor-present group with a risk value of 45.5% and the factor-absent group with 13.5%. The mold proliferation group was split with reference to frequent cleaning, and the risk rose to 75% in the factor-absent group and to 100% when family smoking habits were reported. Group WOPH (the risk: 10.8%) was first split into two groups according to the use of air conditioners in hot weather for more than 6 hours a day or less, which showed risk values of 16.7% and 6.9%, respectively. The risk of the group that intensively used air conditioners fell to 8.3% with tatami as flooring in the living room, and, if others, rose to 20.8%. The risk of the factor-lacking group fell to 4.0% without wooden flooring. CART analysis enables us to express complex relationships between living environment factors and Dp-sensitization simply by a binary regression tree, pointing to preventive strategies that can be flexibly changed according to the individual living environments of the subjects.

Clinicians' adherence to clinical practice guidelines for cardiac function monitoring during antipsychotic treatment: a retrospective report on 434 patients with severe mental illness.

PubMed

Manchia, Mirko; Firinu, Giorgio; Carpiniello, Bernardo; Pinna, Federica

2017-03-31

Severe mental illness (SMI) has considerable excess morbidity and mortality, a proportion of which is explained by cardiovascular diseases, caused in part by antipsychotic (AP) induced QT-related arrhythmias and sudden death by Torsade de Point (TdP). The implementation of evidence-based recommendations for cardiac function monitoring might reduce the incidence of these AP-related adverse events. To investigate clinicians' adherence to cardiac function monitoring before and after starting AP, we performed a retrospective assessment of 434 AP-treated SMI patients longitudinally followed-up for 5 years at an academic community mental health center. We classified antipsychotics according to their risk of inducing QT-related arrhythmias and TdP (Center for Research on Therapeutics, University of Arizona). We used univariate tests and multinomial or binary logistic regression model for data analysis. Univariate and multinomial regression analysis showed that psychiatrists were more likely to perform pre-treatment electrocardiogram (ECG) and electrolyte testing with AP carrying higher cardiovascular risk, but not on the basis of AP pharmacological class. Univariate and binomial regression analysis showed that cardiac function parameters (ECG and electrolyte balance) were more frequently monitored during treatment with second generation AP than with first generation AP. Our data show the presence of weaknesses in the cardiac function monitoring of AP-treated SMI patients, and might guide future interventions to tackle them.

Construction of a pathological risk model of occult lymph node metastases for prognostication by semi-automated image analysis of tumor budding in early-stage oral squamous cell carcinoma

PubMed Central

Pedersen, Nicklas Juel; Jensen, David Hebbelstrup; Lelkaitis, Giedrius; Kiss, Katalin; Charabi, Birgitte; Specht, Lena; von Buchwald, Christian

2017-01-01

It is challenging to identify at diagnosis those patients with early oral squamous cell carcinoma (OSCC), who have a poor prognosis and those that have a high risk of harboring occult lymph node metastases. The aim of this study was to develop a standardized and objective digital scoring method to evaluate the predictive value of tumor budding. We developed a semi-automated image-analysis algorithm, Digital Tumor Bud Count (DTBC), to evaluate tumor budding. The algorithm was tested in 222 consecutive patients with early-stage OSCC and major endpoints were overall (OS) and progression free survival (PFS). We subsequently constructed and cross-validated a binary logistic regression model and evaluated its clinical utility by decision curve analysis. A high DTBC was an independent predictor of both poor OS and PFS in a multivariate Cox regression model. The logistic regression model was able to identify patients with occult lymph node metastases with an area under the curve (AUC) of 0.83 (95% CI: 0.78–0.89, P <0.001) and a 10-fold cross-validated AUC of 0.79. Compared to other known histopathological risk factors, the DTBC had a higher diagnostic accuracy. The proposed, novel risk model could be used as a guide to identify patients who would benefit from an up-front neck dissection. PMID:28212555

Binary Decision Trees for Preoperative Periapical Cyst Screening Using Cone-beam Computed Tomography.

PubMed

Pitcher, Brandon; Alaqla, Ali; Noujeim, Marcel; Wealleans, James A; Kotsakis, Georgios; Chrepa, Vanessa

2017-03-01

Cone-beam computed tomographic (CBCT) analysis allows for 3-dimensional assessment of periradicular lesions and may facilitate preoperative periapical cyst screening. The purpose of this study was to develop and assess the predictive validity of a cyst screening method based on CBCT volumetric analysis alone or combined with designated radiologic criteria. Three independent examiners evaluated 118 presurgical CBCT scans from cases that underwent apicoectomies and had an accompanying gold standard histopathological diagnosis of either a cyst or granuloma. Lesion volume, density, and specific radiologic characteristics were assessed using specialized software. Logistic regression models with histopathological diagnosis as the dependent variable were constructed for cyst prediction, and receiver operating characteristic curves were used to assess the predictive validity of the models. A conditional inference binary decision tree based on a recursive partitioning algorithm was constructed to facilitate preoperative screening. Interobserver agreement was excellent for volume and density, but it varied from poor to good for the radiologic criteria. Volume and root displacement were strong predictors for cyst screening in all analyses. The binary decision tree classifier determined that if the volume of the lesion was >247 mm 3 , there was 80% probability of a cyst. If volume was <247 mm 3 and root displacement was present, cyst probability was 60% (78% accuracy). The good accuracy and high specificity of the decision tree classifier renders it a useful preoperative cyst screening tool that can aid in clinical decision making but not a substitute for definitive histopathological diagnosis after biopsy. Confirmatory studies are required to validate the present findings. Published by Elsevier Inc.

Analyzing prospective teachers' images of scientists using positive, negative and stereotypical images of scientists

NASA Astrophysics Data System (ADS)

Subramaniam, Karthigeyan; Esprívalo Harrell, Pamela; Wojnowski, David

2013-04-01

Background and purpose : This study details the use of a conceptual framework to analyze prospective teachers' images of scientists to reveal their context-specific conceptions of scientists. The conceptual framework consists of context-specific conceptions related to positive, stereotypical and negative images of scientists as detailed in the literature on the images, role and work of scientists. Sample, design and method : One hundred and ninety-six drawings of scientists, generated by prospective teachers, were analyzed using the Draw-A-Scientist-Test Checklist (DAST-C), a binary linear regression and the conceptual framework. Results : The results of the binary linear regression analysis revealed a statistically significant difference for two DAST-C elements: ethnicity differences with regard to drawing a scientist who was Caucasian and gender differences for indications of danger. Analysis using the conceptual framework helped to categorize the same drawings into positive, stereotypical, negative and composite images of a scientist. Conclusions : The conceptual framework revealed that drawings were focused on the physical appearance of the scientist, and to a lesser extent on the equipment, location and science-related practices that provided the context of a scientist's role and work. Implications for teacher educators include the need to understand that there is a need to provide tools, like the conceptual framework used in this study, to help prospective teachers to confront and engage with their multidimensional perspectives of scientists in light of the current trends on perceiving and valuing scientists. In addition, teacher educators need to use the conceptual framework, which yields qualitative perspectives about drawings, together with the DAST-C, which yields quantitative measure for drawings, to help prospective teachers to gain a holistic outlook on their drawings of scientists.

Personality traits and coping styles explain anxiety in lung cancer patients to a greater extent than other factors.

PubMed

Shimizu, Ken; Nakaya, Naoki; Saito-Nakaya, Kumi; Akechi, Tatsuo; Ogawa, Asao; Fujisawa, Daisuke; Sone, Toshimasa; Yoshiuchi, Kazuhiro; Goto, Koichi; Iwasaki, Motoki; Tsugane, Shoichiro; Uchitomi, Yosuke

2015-05-01

Although various factors thought to be correlated with anxiety in cancer patients, relative importance of each factors were unknown. We tested our hypothesis that personality traits and coping styles explain anxiety in lung cancer patients to a greater extent than other factors. A total of 1334 consecutively recruited lung cancer patients were selected, and data on cancer-related variables, demographic characteristics, health behaviors, physical symptoms and psychological factors consisting of personality traits and coping styles were obtained. The participants were divided into groups with or without a significant anxiety using the Hospital Anxiety and Depression Scale-Anxiety, and a binary logistic regression analysis was used to identify factors correlated with significant anxiety using a multivariate model. Among the recruited patients, 440 (33.0%) had significant anxiety. The binary logistic regression analysis revealed a coefficient of determination (overall R(2)) of 39.0%, and the explanation for psychological factors was much higher (30.7%) than those for cancer-related variables (1.1%), demographic characteristics (2.1%), health behaviors (0.8%) and physical symptoms (4.3%). Four specific factors remained significant in a multivariate model. A neurotic personality trait, a coping style of helplessness/hopelessness, and a female sex were positively correlated with significant anxiety, while a coping style of fatalism was negatively correlated. Our hypothesis was supported, and anxiety was strongly linked with personality trait and coping style. As a clinical implication, the use of screening instruments to identify these factors and intervention for psychological crisis may be needed. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The effect of migration on social capital and depression among older adults in China.

PubMed

Li, Qiuju; Zhou, Xudong; Ma, Sha; Jiang, Minmin; Li, Lu

2017-12-01

An estimated 9 million elderly people accompanied their adult children to urban areas in China, raising concerns about their social capital and mental health following re-location. The aim of this study was to examine the effect of migration on social capital and depression among this population. Multistage stratified cluster sampling was applied to recruit the migrant and urban elderly in Hangzhou from May to August, 2013. Data were collected from face-to-face interviews by trained college students using a standardized questionnaire. Social capital measurements included cognitive (generalized trust and reciprocity) and structure (support from individual and social contact) aspects. Depression was measured by Geriatric Depression Scale-30 (GDS-30). Chi-square tests and binary logistic regression models were used for analysis. A total of 1248 migrant elderly and 1322 urban elderly were eligible for analysis. After adjusting for a range of confounder factors, binary logistic regression models revealed that migrant elderly reported significantly lower levels of generalized trust [OR = 1.34, 95% CI (1.10-1.64)], reciprocity [OR = 1.55, 95% CI (1.29-1.87)], support from individual [OR = 1.96, 95% CI (1.61-2.38)] and social contact [OR = 3.27, 95% CI (2.70-3.97)]. In the full adjusted model, migrant elderly were more likely to be mentally unhealthy [OR = 1.85, 95% CI (1.44-2.36)] compared with urban elderly. Migrant elderly suffered from a lower mental health status and social capital than their urban counterparts in the emigrating city. Attention should focus on improving the social capital and mental health of this growing population.

In-hospital mortality after pre-treatment with antiplatelet agents or oral anticoagulants and hematoma evacuation of intracerebral hematomas.

PubMed

Stein, Marco; Misselwitz, Björn; Hamann, Gerhard F; Kolodziej, Malgorzata; Reinges, Marcus H T; Uhl, Eberhard

2016-04-01

Pre-treatment with antiplatelet agents is described to be a risk factor for mortality after spontaneous intracerebral hemorrhage (ICH). However, the impact of antithrombotic agents on mortality in patients who undergo hematoma evacuation compared to conservatively treated patients with ICH remains controversial. This analysis is based on a prospective registry for quality assurance in stroke care in the State of Hesse, Germany. Patients' data were collected between January 2008 and December 2012. Only patients with the diagnosis of spontaneous ICH were included (International Classification of Diseases 10th Revision codes I61.0-I61.9). Predictors of in-hospital mortality were determined by univariate analysis. Predictors with P<0.1 were included in a binary logistic regression model. The binary logistic regression model was adjusted for age, initial Glasgow Coma Score (GCS), the presence of intraventricular hemorrhage (IVH), and pre-ICH disability prior to ictus. In 8,421 patients with spontaneous ICH, pre-treatment with oral anticoagulants or antiplatelet agents was documented in 16.3% and 25.1%, respectively. Overall in-hospital mortality was 23.2%. In-hospital mortality was decreased in operatively treated patients compared to conservatively treated patients (11.6% versus 24.0%; P<0.001). Patients with antiplatelet pre-treatment had a significantly higher risk of death during the hospital stay after hematoma evacuation (odds ratio [OR]: 2.5; 95% confidence interval [CI]: 1.24-4.97; P=0.010) compared to patients without antiplatelet pre-treatment treatment (OR: 0.9; 95% CI: 0.79-1.09; P=0.376). In conclusion a higher rate of in-hospital mortality after pre-treatment with antiplatelet agents in combination with hematoma evacuation after spontaneous ICH was observed in the presented cohort. Copyright © 2015 Elsevier Ltd. All rights reserved.

Multidetector CT features of pulmonary focal ground-glass opacity: differences between benign and malignant

PubMed Central

Fan, L; Liu, S-Y; Li, Q-C; Yu, H; Xiao, X-S

2012-01-01

Objective To evaluate different features between benign and malignant pulmonary focal ground-glass opacity (fGGO) on multidetector CT (MDCT). Methods 82 pathologically or clinically confirmed fGGOs were retrospectively analysed with regard to demographic data, lesion size and location, attenuation value and MDCT features including shape, margin, interface, internal characteristics and adjacent structure. Differences between benign and malignant fGGOs were analysed using a χ2 test, Fisher's exact test or Mann–Whitney U-test. Morphological characteristics were analysed by binary logistic regression analysis to estimate the likelihood of malignancy. Results There were 21 benign and 61 malignant lesions. No statistical differences were found between benign and malignant fGGOs in terms of demographic data, size, location and attenuation value. The frequency of lobulation (p=0.000), spiculation (p=0.008), spine-like process (p=0.004), well-defined but coarse interface (p=0.000), bronchus cut-off (p=0.003), other air-containing space (p=0.000), pleural indentation (p=0.000) and vascular convergence (p=0.006) was significantly higher in malignant fGGOs than that in benign fGGOs. Binary logistic regression analysis showed that lobulation, interface and pleural indentation were important indicators for malignant diagnosis of fGGO, with the corresponding odds ratios of 8.122, 3.139 and 9.076, respectively. In addition, a well-defined but coarse interface was the most important indicator of malignancy among all interface types. With all three important indicators considered, the diagnostic sensitivity, specificity and accuracy were 93.4%, 66.7% and 86.6%, respectively. Conclusion An fGGO with lobulation, a well-defined but coarse interface and pleural indentation gives a greater than average likelihood of being malignant. PMID:22128130

Prevalence of kidney stones in China: an ultrasonography based cross-sectional study.

PubMed

Zeng, Guohua; Mai, Zanlin; Xia, Shujie; Wang, Zhiping; Zhang, Keqin; Wang, Li; Long, Yongfu; Ma, Jinxiang; Li, Yi; Wan, Show P; Wu, Wenqi; Liu, Yongda; Cui, Zelin; Zhao, Zhijian; Qin, Jing; Zeng, Tao; Liu, Yang; Duan, Xiaolu; Mai, Xin; Yang, Zhou; Kong, Zhenzhen; Zhang, Tao; Cai, Chao; Shao, Yi; Yue, Zhongjin; Li, Shujing; Ding, Jiandong; Tang, Shan; Ye, Zhangqun

2017-07-01

To investigate the prevalence and associated factors of kidney stones among adults in China. A nationwide cross-sectional survey was conducted among individuals aged ≥18 years across China, from May 2013 to July 2014. Participants underwent urinary tract ultrasonographic examinations, completed pre-designed and standardised questionnaires, and provided blood and urine samples for analysis. Kidney stones were defined as particles of ≥4 mm. Prevalence was defined as the proportion of participants with kidney stones and binary logistic regression was used to estimate the associated factors. A total of 12 570 individuals (45.2% men) with a mean (sd, range) age of 48.8 (15.3, 18-96) years were selected and invited to participate in the study. In all, 9310 (40.7% men) participants completed the investigation, with a response rate of 74.1%. The prevalence of kidney stones was 6.4% [95% confidence interval (CI) 5.9, 6.9], and the age- and sex-adjusted prevalence was 5.8% (95% CI 5.3, 6.3; 6.5% in men and 5.1% in women). Binary logistic regression analysis showed that male gender, rural residency, age, family history of urinary stones, concurrent diabetes mellitus and hyperuricaemia, increased consumption of meat, and excessive sweating were all statistically significantly associated with a greater risk of kidney stones. By contrast, consumption of more tea, legumes, and fermented vinegar was statistically significantly associated with a lesser risk of kidney stone formation. Kidney stones are common among Chinese adults, with about one in 17 adults affected currently. Some Chinese dietary habits may lower the risk of kidney stone formation. © 2017 The Authors BJU International © 2017 BJU International Published by John Wiley & Sons Ltd.

Assessment of renal function and electrolytes in patients with thyroid dysfunction in Addis Ababa, Ethiopia: a cross sectional study.

PubMed

Abebe, Nardos; Kebede, Tedla; Wolde, Mistire

2016-01-01

Studies demonstrated that abnormal thyroid functions may result in decreased or increased kidney size, kidney weight, and affect renal functions. In this regard, studies on the association of abnormal thyroid functions and renal function tests are scarcely found in Ethiopia. To assess renal function and electrolytes in patients with thyroid dysfunction, in Addis Ababa, Ethiopia. Cross sectional study was conducted from March 21/2015-May 27/2015 at Arsho Advanced Medical Laboratory. During the study period, 71 patients with thyroid dysfunction were eligible, and socio demographic data collected by structured questionnaire. Then blood sample was collected for thyroid function tests, renal function and blood electrolyte analysis. The collected data was analyzed by SPSS version 20. ANOVA and binary logistic regression were employed to evaluate the mean deference and associations of thyroid hormone with renal function and electrolyte balances. Among the renal function tests, serum uric acid, and creatinine mean values were significantly decreased in hyperthyroid patients; whereas, eGFR mean value was significantly increased in hyperthyroid study patients (P<0.05). Meanwhile, from the electrolyte measurements made, only the mean serum sodium value was significantly increased in hyperthyroid study participants. Binary logistic regression analysis on the association of thyroid dysfunction with electrolyte balance and renal function tests indicated that serum sodium, creatinine, eGFR values and hyperthyroidism have a statistical significant association at AOR 95% CI of 0.141(0.033-0.593, P=0.008); 16.236(3.481-75.739, P=0.001), and 13.797(3.261-58.67, P=0.001) respectively. The current study reveals, thyroid abnormalities may lead to renal function alterations and also may disturb electrolyte balance. Knowledge of this significant association has worthwhile value for clinicians, to manage their patients' optimally.

Clinical outcomes and predictors of response to photodynamic therapy in symptomatic circumscribed choroidal hemangioma: A retrospective case series

PubMed Central

Ho, Yeen-Fey; Chao, Anne; Chen, Kuan-Jen; Wang, Nan-Kai; Liu, Laura; Chen, Yen-Po; Hwang, Yih-Shiou; Wu, Wei-Chi; Lai, Chi-Chun; Chen, Tun-Lu

2018-01-01

Background To investigate the treatment outcomes and predictors of response to photodynamic therapy (PDT) in patients with symptomatic circumscribed hemangioma (CCH). Methods This retrospective case series examined 20 patients with symptomatic CCH (10 submacular CCHs and10 juxtapapillary CCHs) who underwent standard PDT (wavelength: 662 nm; light dose: 50J/cm2; exposure time: 83 sec) with verteporfin (6mg/m2), either as monotherapy (n = 9) or in association with other treatments (n = 11), of which 7 received intravitreal injections (IVI) of anti-vascular endothelial growth factor (anti-VEGF). A post-PDT improvement of at least two lines in best-corrected visual acuity (BCVA) was the primary outcome measure. Predictors of response were investigated with binary logistic regression analysis. Results Seventeen (85%) patients received one PDT session, and three patients (15%) underwent PDT at least twice. Ten patients (50%) achieved the primary outcome of a post-PDT BCVA improvement of at least two lines. Macular atrophy and recalcitrant cystoid macular edema in 2 patients. Binary logistic regression analysis revealed that younger age (< 50 years) (P = 0.033), pre-PDT BCVA of ≧20/200 (P = 0.013), exudative retinal detachment resolved within one month after PDT (P = 0.007), and a thinner post-PDT tumor thickness (P = 0.015) were associated with the achievement of a post-PDT BCVA improvement. Additional treatments to PDT including IVI anti-VEGF did not appear to improve visual and anatomical outcomes. Conclusions Symptomatic CCHs respond generally well to PDT. Patients with younger age (< 50 years), pretreatment BCVA≥ 20/200, and thinner foveal edema are most likely to benefit from this approach. PMID:29851977

[Relationship between family functioning and lifestyle in school-age adolescents].

PubMed

Lima-Serrano, Marta; Guerra-Martín, María Dolores; Lima-Rodríguez, Joaquín Salvador

Risk behaviors in adolescents can lead to serious disorders, therefore the objectives of this work are to characterize the lifestyles of teenagers about substance use, sex, and road safety, and to meet socio-demographic factors associated with these. A cross-sectional, descriptive and correlational study was conducted with 204 school-age-children from 12 to 17 years, in 2013. They were given a validated questionnaire about sociodemographic, family functioning, and lifestyles such as substance abuse, sexual intercourse and road safety. A descriptive and multivariate analysis was performed by using multiple linear regression in the case of quantitative dependent variables, and binary logistic regression models in the case of binary categories. Data analysis was based on SPSS 20.0, with a significance level of p<0.05. 32.4% of students had smoked, and 61.3% had drunk alcohol. 26% of adolescent between 14-17 years had sexual intercourse; the average age of the first sexual intercourse was 14.9 years. 85.2% used condoms. 94.6% respected traffic signs, 77.5% used to wear a seat belt and 81.9% a helmet. Family functioning, as protective factor, was the variable more frequently associated to risk behaviour: smoking (OR=7.06, p=.000), alcohol drinking (OR=3.97, p=.008), sexual intercourse (OR=3.67, p=.041), and road safety (β=1.82, p=.000). According the results, age, gender and family functioning are the main factors associated with the adoption of risk behaviors. This information is important for the development of public health policies, for instance health promotion at schools. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

Investigating the discrimination potential of linear and nonlinear spectral multivariate calibrations for analysis of phenolic compounds in their binary and ternary mixtures and calculation pKa values.

PubMed

Rasouli, Zolaikha; Ghavami, Raouf

2016-08-05

Vanillin (VA), vanillic acid (VAI) and syringaldehyde (SIA) are important food additives as flavor enhancers. The current study for the first time is devote to the application of partial least square (PLS-1), partial robust M-regression (PRM) and feed forward neural networks (FFNNs) as linear and nonlinear chemometric methods for the simultaneous detection of binary and ternary mixtures of VA, VAI and SIA using data extracted directly from UV-spectra with overlapped peaks of individual analytes. Under the optimum experimental conditions, for each compound a linear calibration was obtained in the concentration range of 0.61-20.99 [LOD=0.12], 0.67-23.19 [LOD=0.13] and 0.73-25.12 [LOD=0.15] μgmL(-1) for VA, VAI and SIA, respectively. Four calibration sets of standard samples were designed by combination of a full and fractional factorial designs with the use of the seven and three levels for each factor for binary and ternary mixtures, respectively. The results of this study reveal that both the methods of PLS-1 and PRM are similar in terms of predict ability each binary mixtures. The resolution of ternary mixture has been accomplished by FFNNs. Multivariate curve resolution-alternating least squares (MCR-ALS) was applied for the description of spectra from the acid-base titration systems each individual compound, i.e. the resolution of the complex overlapping spectra as well as to interpret the extracted spectral and concentration profiles of any pure chemical species identified. Evolving factor analysis (EFA) and singular value decomposition (SVD) were used to distinguish the number of chemical species. Subsequently, their corresponding dissociation constants were derived. Finally, FFNNs has been used to detection active compounds in real and spiked water samples. Copyright © 2016 Elsevier B.V. All rights reserved.

Investigating the discrimination potential of linear and nonlinear spectral multivariate calibrations for analysis of phenolic compounds in their binary and ternary mixtures and calculation pKa values

NASA Astrophysics Data System (ADS)

Rasouli, Zolaikha; Ghavami, Raouf

2016-08-01

Vanillin (VA), vanillic acid (VAI) and syringaldehyde (SIA) are important food additives as flavor enhancers. The current study for the first time is devote to the application of partial least square (PLS-1), partial robust M-regression (PRM) and feed forward neural networks (FFNNs) as linear and nonlinear chemometric methods for the simultaneous detection of binary and ternary mixtures of VA, VAI and SIA using data extracted directly from UV-spectra with overlapped peaks of individual analytes. Under the optimum experimental conditions, for each compound a linear calibration was obtained in the concentration range of 0.61-20.99 [LOD = 0.12], 0.67-23.19 [LOD = 0.13] and 0.73-25.12 [LOD = 0.15] μg mL- 1 for VA, VAI and SIA, respectively. Four calibration sets of standard samples were designed by combination of a full and fractional factorial designs with the use of the seven and three levels for each factor for binary and ternary mixtures, respectively. The results of this study reveal that both the methods of PLS-1 and PRM are similar in terms of predict ability each binary mixtures. The resolution of ternary mixture has been accomplished by FFNNs. Multivariate curve resolution-alternating least squares (MCR-ALS) was applied for the description of spectra from the acid-base titration systems each individual compound, i.e. the resolution of the complex overlapping spectra as well as to interpret the extracted spectral and concentration profiles of any pure chemical species identified. Evolving factor analysis (EFA) and singular value decomposition (SVD) were used to distinguish the number of chemical species. Subsequently, their corresponding dissociation constants were derived. Finally, FFNNs has been used to detection active compounds in real and spiked water samples.

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

NASA Astrophysics Data System (ADS)

Naguib, Ibrahim A.; Darwish, Hany W.

2012-02-01

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

Bayesian Analysis of High Dimensional Classification

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Subhadeep; Liang, Faming

2009-12-01

Modern data mining and bioinformatics have presented an important playground for statistical learning techniques, where the number of input variables is possibly much larger than the sample size of the training data. In supervised learning, logistic regression or probit regression can be used to model a binary output and form perceptron classification rules based on Bayesian inference. In these cases , there is a lot of interest in searching for sparse model in High Dimensional regression(/classification) setup. we first discuss two common challenges for analyzing high dimensional data. The first one is the curse of dimensionality. The complexity of many existing algorithms scale exponentially with the dimensionality of the space and by virtue of that algorithms soon become computationally intractable and therefore inapplicable in many real applications. secondly, multicollinearities among the predictors which severely slowdown the algorithm. In order to make Bayesian analysis operational in high dimension we propose a novel 'Hierarchical stochastic approximation monte carlo algorithm' (HSAMC), which overcomes the curse of dimensionality, multicollinearity of predictors in high dimension and also it possesses the self-adjusting mechanism to avoid the local minima separated by high energy barriers. Models and methods are illustrated by simulation inspired from from the feild of genomics. Numerical results indicate that HSAMC can work as a general model selection sampler in high dimensional complex model space.

Exhaled volatile organic compounds in individuals with a history of high altitude pulmonary edema and varying hypoxia-induced responses.

PubMed

Figueroa, Jennifer A; Mansoor, Jim K; Allen, Roblee P; Davis, Cristina E; Walby, William F; Aksenov, Alexander A; Zhao, Weixiang; Lewis, William R; Schelegle, Edward S

2015-04-20

With ascent to altitude, certain individuals are susceptible to high altitude pulmonary edema (HAPE), which in turn can cause disability and even death. The ability to identify individuals at risk of HAPE prior to ascent is poor. The present study examined the profile of volatile organic compounds (VOC) in exhaled breath condensate (EBC) and pulmonary artery systolic pressures (PASP) before and after exposure to normobaric hypoxia (12% O2) in healthy males with and without a history of HAPE (Hx HAPE, n = 5; Control, n = 11). In addition, hypoxic ventilatory response (HVR), and PASP response to normoxic exercise were also measured. Auto-regression/partial least square regression of whole gas chromatography/mass spectrometry (GC/MS) data and binary logistic regression (BLR) of individual GC peaks and physiologic parameters resulted in models that separate individual subjects into their groups with variable success. The result of BLR analysis highlights HVR, PASP response to hypoxia and the amount of benzyl alcohol and dimethylbenzaldehyde dimethyl in expired breath as markers of HAPE history. These findings indicate the utility of EBC VOC analysis to discriminate between individuals with and without a history of HAPE and identified potential novel biomarkers that correlated with physiological responses to hypoxia.

Use of antidementia drugs in frontotemporal lobar degeneration.

PubMed

López-Pousa, Secundino; Calvó-Perxas, Laia; Lejarreta, Saioa; Cullell, Marta; Meléndez, Rosa; Hernández, Erélido; Bisbe, Josep; Perkal, Héctor; Manzano, Anna; Roig, Anna Maria; Turró-Garriga, Oriol; Vilalta-Franch, Joan; Garre-Olmo, Josep

2012-06-01

Clinical evidence indicates that acetylcholinesterase inhibitors (AChEIs) are not efficacious to treat frontotemporal lobar degeneration (FTLD). The British Association for Psychopharmacology recommends avoiding the use of AChEI and memantine in patients with FTLD. Cross-sectional design using 1092 cases with Alzheimer's disease (AD) and 64 cases with FTLD registered by the Registry of Dementias of Girona. Bivariate analyses were performed, and binary logistic regressions were used to detect variables associated with antidementia drugs consumption. The AChEIs were consumed by 57.6% and 42.2% of the patients with AD and FTLD, respectively. Memantine was used by 17.2% and 10.9% of patients with AD and FTLD, respectively. Binary logistic regressions yielded no associations with antidementia drugs consumption. There is a discrepancy regarding clinical practice and the recommendations based upon clinical evidence. The increased central nervous system drug use detected in FTLD requires multicentric studies aiming at finding the best means to treat these patients.

Multiplexed analysis combining distinctly-sized CdTe-MPA quantum dots and chemometrics for multiple mutually interfering analyte determination.

PubMed

Bittar, Dayana B; Ribeiro, David S M; Páscoa, Ricardo N M J; Soares, José X; Rodrigues, S Sofia M; Castro, Rafael C; Pezza, Leonardo; Pezza, Helena R; Santos, João L M

2017-11-01

Semiconductor quantum dots (QDs) have demonstrated a great potential as fluorescent probes for heavy metals monitoring. However, their great reactivity, whose tunability could be difficult to attain, could impair selectivity yielding analytical results with poor accuracy. In this work, the combination in the same analysis of multiple QDs, each with a particular ability to interact with the analyte, assured a multi-point detection that was not only exploited for a more precise analyte discrimination but also for the simultaneous discrimination of multiple mutually interfering species, in the same sample. Three different MPA-CdTe QDs (2.5, 3.0 and 3.8nm) with a good size distribution, confirmed by the FWHM values of 48.6, 55.4 and 80.8nm, respectively, were used. Principal component analysis (PCA) and partial least squares regression (PLS) were used for fluorescence data analysis. Mixtures of two MPA-CdTe QDs, emitting at different wavelength namely 549/566, 549/634 and 566/634nm were assayed. The 549/634nm emitting QDs mixture provided the best results for the discrimination of distinct ions on binary and ternary mixtures. The obtained RMSECV and R 2 CV values for the binary mixture were good, namely, from 0.01 to 0.08mgL -1 and from 0.74 to 0.89, respectively. Regarding the ternary mixture the RMSECV and R 2 CV values were good for Hg(II) (0.06 and 0.73mgL -1 , respectively) and Pb(II) (0.08 and 0.87mg L -1 , respectively) and acceptable for Cu(II) (0.02 and 0.51mgL -1 , respectively). In conclusion, the obtained results showed that the developed approach is capable of resolve binary and ternary mixtures of Pb (II), Hg (II) and Cu (II), providing accurate information about lead (II) and mercury (II) concentration and signaling the occurrence of Cu (II). Copyright © 2017 Elsevier B.V. All rights reserved.

Seeing Double Old and New: Observations and Lightcurve Analysis at the Palmer Divide Observatory of Six Binary Asteroids

NASA Astrophysics Data System (ADS)

Warner, Brian D.

2013-04-01

Results of the analysis of lightcurves of six binary asteroids obtained at the Palmer Divide Observatory are reported. Of the six, three were previously known to be binary: 9069 Hovland, (26471) 2000 AS152, and 1994 XD. The remaining three are new confirmed or probable binary discoveries made at PDO: 2047 Smetana, (5646) 1990 TR, and (52316) 1992 BD.

The special case of the 2 × 2 table: asymptotic unconditional McNemar test can be used to estimate sample size even for analysis based on GEE.

PubMed

Borkhoff, Cornelia M; Johnston, Patrick R; Stephens, Derek; Atenafu, Eshetu

2015-07-01

Aligning the method used to estimate sample size with the planned analytic method ensures the sample size needed to achieve the planned power. When using generalized estimating equations (GEE) to analyze a paired binary primary outcome with no covariates, many use an exact McNemar test to calculate sample size. We reviewed the approaches to sample size estimation for paired binary data and compared the sample size estimates on the same numerical examples. We used the hypothesized sample proportions for the 2 × 2 table to calculate the correlation between the marginal proportions to estimate sample size based on GEE. We solved the inside proportions based on the correlation and the marginal proportions to estimate sample size based on exact McNemar, asymptotic unconditional McNemar, and asymptotic conditional McNemar. The asymptotic unconditional McNemar test is a good approximation of GEE method by Pan. The exact McNemar is too conservative and yields unnecessarily large sample size estimates than all other methods. In the special case of a 2 × 2 table, even when a GEE approach to binary logistic regression is the planned analytic method, the asymptotic unconditional McNemar test can be used to estimate sample size. We do not recommend using an exact McNemar test. Copyright © 2015 Elsevier Inc. All rights reserved.

Equilibrium isotherm and kinetic studies for the simultaneous removal of phenol and cyanide by use of S. odorifera (MTCC 5700) immobilized on coconut shell activated carbon

NASA Astrophysics Data System (ADS)

Singh, Neetu; Balomajumder, Chandrajit

2017-10-01

In this study, simultaneous removal of phenol and cyanide by a microorganism S. odorifera (MTCC 5700) immobilized onto coconut shell activated carbon surface (CSAC) was studied in batch reactor from mono and binary component aqueous solution. Activated carbon was derived from coconut shell by chemical activation method. Ferric chloride (Fecl3), used as surface modification agents was applied to biomass. Optimum biosorption conditions were obtained as a function of biosorbent dosage, pH, temperature, contact time and initial phenol and cyanide concentration. To define the equilibrium isotherms, experimental data were analyzed by five mono component isotherm and six binary component isotherm models. The higher uptake capacity of phenol and cyanide onto CSAC biosorbent surface was 450.02 and 2.58 mg/g, respectively. Nonlinear regression analysis was used for determining the best fit model on the basis of error functions and also for calculating the parameters involved in kinetic and isotherm models. The kinetic study results revealed that Fractal-like mixed first second order model and Brouser-Weron-Sototlongo models for phenol and cyanide were capable to offer accurate explanation of biosorption kinetic. According to the experimental data results, CSAC with immobilization of bacterium S. odorifera (MTCC 5700) seems to be an alternative and effective biosorbent for the elimination of phenol and cyanide from binary component aqueous solution.

Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression

NASA Astrophysics Data System (ADS)

Martínez-Fernández, J.; Chuvieco, E.; Koutsias, N.

2013-02-01

Humans are responsible for most forest fires in Europe, but anthropogenic factors behind these events are still poorly understood. We tried to identify the driving factors of human-caused fire occurrence in Spain by applying two different statistical approaches. Firstly, assuming stationary processes for the whole country, we created models based on multiple linear regression and binary logistic regression to find factors associated with fire density and fire presence, respectively. Secondly, we used geographically weighted regression (GWR) to better understand and explore the local and regional variations of those factors behind human-caused fire occurrence. The number of human-caused fires occurring within a 25-yr period (1983-2007) was computed for each of the 7638 Spanish mainland municipalities, creating a binary variable (fire/no fire) to develop logistic models, and a continuous variable (fire density) to build standard linear regression models. A total of 383 657 fires were registered in the study dataset. The binary logistic model, which estimates the probability of having/not having a fire, successfully classified 76.4% of the total observations, while the ordinary least squares (OLS) regression model explained 53% of the variation of the fire density patterns (adjusted R2 = 0.53). Both approaches confirmed, in addition to forest and climatic variables, the importance of variables related with agrarian activities, land abandonment, rural population exodus and developmental processes as underlying factors of fire occurrence. For the GWR approach, the explanatory power of the GW linear model for fire density using an adaptive bandwidth increased from 53% to 67%, while for the GW logistic model the correctly classified observations improved only slightly, from 76.4% to 78.4%, but significantly according to the corrected Akaike Information Criterion (AICc), from 3451.19 to 3321.19. The results from GWR indicated a significant spatial variation in the local parameter estimates for all the variables and an important reduction of the autocorrelation in the residuals of the GW linear model. Despite the fitting improvement of local models, GW regression, more than an alternative to "global" or traditional regression modelling, seems to be a valuable complement to explore the non-stationary relationships between the response variable and the explanatory variables. The synergy of global and local modelling provides insights into fire management and policy and helps further our understanding of the fire problem over large areas while at the same time recognizing its local character.

Hierarchical faunal filters: An approach to assessing effects of habitat and nonnative species on native fishes

USGS Publications Warehouse

Quist, M.C.; Rahel, F.J.; Hubert, W.A.

2005-01-01

Understanding factors related to the occurrence of species across multiple spatial and temporal scales is critical to the conservation and management of native fishes, especially for those species at the edge of their natural distribution. We used the concept of hierarchical faunal filters to provide a framework for investigating the influence of habitat characteristics and normative piscivores on the occurrence of 10 native fishes in streams of the North Platte River watershed in Wyoming. Three faunal filters were developed for each species: (i) large-scale biogeographic, (ii) local abiotic, and (iii) biotic. The large-scale biogeographic filter, composed of elevation and stream-size thresholds, was used to determine the boundaries within which each species might be expected to occur. Then, a local abiotic filter (i.e., habitat associations), developed using binary logistic-regression analysis, estimated the probability of occurrence of each species from features such as maximum depth, substrate composition, submergent aquatic vegetation, woody debris, and channel morphology (e.g., amount of pool habitat). Lastly, a biotic faunal filter was developed using binary logistic regression to estimate the probability of occurrence of each species relative to the abundance of nonnative piscivores in a reach. Conceptualising fish assemblages within a framework of hierarchical faunal filters is simple and logical, helps direct conservation and management activities, and provides important information on the ecology of fishes in the western Great Plains of North America. ?? Blackwell Munksgaard, 2004.

The influence of endometriosis-related symptoms on work life and work ability: a study of Danish endometriosis patients in employment.

PubMed

Hansen, Karina E; Kesmodel, Ulrik S; Baldursson, Einar B; Schultz, Rikke; Forman, Axel

2013-07-01

Little is known about the implications of endometriosis on women's work life. This study aimed at examining the relation between endometriosis-related symptoms and work ability in employed women with endometriosis. In a cohort study, 610 patients with diagnosed endometriosis and 751 reference women completed an electronic survey based on the Endometriosis Health Profile 30-questionnaire and the Work Ability Index (short form). Percentages were reported for all data. Binary and multivariate logistic regression analyses were used to assess risk factors for low work ability. The level of statistical significance was set at p<0.025 in all analyses. In binary analyses a diagnosis of endometriosis was associated with more sick days, work disturbances due to symptoms, lower work ability and a wide number of other implications on work life in employed women. Moreover, a higher pain level and degree of symptoms were associated with low work ability. Full regression analysis indicated that tiredness, frequent pain, a higher daily pain level, a higher number of sick days and feeling depressed at work were associated with low work ability. A long delay from symptom onset to diagnosis was associated with low work ability. These data indicate a severe impact of endometriosis on the work ability of employed women with endometriosis and add to the evidence that this disease represents a significant socio-economic burden. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Neuronal autoantibodies in mesial temporal lobe epilepsy with hippocampal sclerosis.

PubMed

Vanli-Yavuz, Ebru Nur; Erdag, Ece; Tuzun, Erdem; Ekizoglu, Esme; Baysal-Kirac, Leyla; Ulusoy, Canan; Peach, Sian; Gundogdu, Gokcen; Sencer, Serra; Sencer, Altay; Kucukali, Cem Ismail; Bebek, Nerses; Gurses, Candan; Gokyigit, Aysen; Baykan, Betul

2016-07-01

Our aim was to investigate the prevalence of neuronal autoantibodies (NAbs) in a large consecutive series with mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS) and to elucidate the clinical and laboratory clues for detection of NAbs in this prototype of frequent, drug-resistant epilepsy syndrome. Consecutive patients diagnosed with MTLE fulfilling the MRI criteria for HS were enrolled. The sera of patients and various control groups (80 subjects) were tested for eight NAbs after ethical approval and signed consents. Brain tissues obtained from surgical specimens were also investigated by immunohistochemical analysis for the presence of inflammatory infiltrates. The features of seropositive versus seronegative groups were compared and binary logistic regression analysis was performed to explore the differentiating variables. We found antibodies against antigens, contactin-associated protein-like 2 in 11 patients, uncharacterised voltage-gated potassium channel (VGKC)-complex antigens in four patients, glycine receptor (GLY-R) in 5 patients, N-methyl-d-aspartate receptor in 4 patients and γ-aminobutyric acid receptor A in 1 patient of 111 patients with MTLE-HS and none of the control subjects. The history of status epilepticus, diagnosis of psychosis and positron emission tomography or single-photon emission CT findings in temporal plus extratemporal regions were found significantly more frequently in the seropositive group. Binary logistic regression analysis disclosed that status epilepticus, psychosis and cognitive dysfunction were statistically significant variables to differentiate between the VGKC-complex subgroup versus seronegative group. This first systematic screening study of various NAbs showed 22.5% seropositivity belonging mostly to VGKC-complex antibodies in a large consecutive series of patients with MTLE-HS. Our results indicated a VGKC-complex autoimmunity-related subgroup in the syndrome of MTLE-HS. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Cerebrospinal fluid ferritin and albumin index: potential candidates for scoring system to differentiate between bacterial and viral meningitis in children.

PubMed

Jebamalar, Angelin A; Prabhat; Balakrishnapillai, Agiesh K; Parmeswaran, Narayanan; Dhiman, Pooja; Rajendiran, Soundravally

2016-07-01

To evaluate the diagnostic role of cerebrospinal fluid (CSF) ferritin and albumin index (AI = CSF albumin/serum albumin × 1000) in differentiating acute bacterial meningitis (ABM) from acute viral meningitis (AVM) in children. The study included 42 cases each of ABM and AVM in pediatric age group. Receiver operating characteristic (ROC) analysis was carried out for CSF ferritin and AI. Binary logistic regression was also done. CSF ferritin and AI were found significantly higher in ABM compared to AVM. Model obtained using AI and CSF ferritin along with conventional criteria is better than existing models.

ON MODEL SELECTION STRATEGIES TO IDENTIFY GENES UNDERLYING BINARY TRAITS USING GENOME-WIDE ASSOCIATION DATA.

PubMed

Wu, Zheyang; Zhao, Hongyu

2012-01-01

For more fruitful discoveries of genetic variants associated with diseases in genome-wide association studies, it is important to know whether joint analysis of multiple markers is more powerful than the commonly used single-marker analysis, especially in the presence of gene-gene interactions. This article provides a statistical framework to rigorously address this question through analytical power calculations for common model search strategies to detect binary trait loci: marginal search, exhaustive search, forward search, and two-stage screening search. Our approach incorporates linkage disequilibrium, random genotypes, and correlations among score test statistics of logistic regressions. We derive analytical results under two power definitions: the power of finding all the associated markers and the power of finding at least one associated marker. We also consider two types of error controls: the discovery number control and the Bonferroni type I error rate control. After demonstrating the accuracy of our analytical results by simulations, we apply them to consider a broad genetic model space to investigate the relative performances of different model search strategies. Our analytical study provides rapid computation as well as insights into the statistical mechanism of capturing genetic signals under different genetic models including gene-gene interactions. Even though we focus on genetic association analysis, our results on the power of model selection procedures are clearly very general and applicable to other studies.

ON MODEL SELECTION STRATEGIES TO IDENTIFY GENES UNDERLYING BINARY TRAITS USING GENOME-WIDE ASSOCIATION DATA

PubMed Central

Wu, Zheyang; Zhao, Hongyu

2013-01-01

For more fruitful discoveries of genetic variants associated with diseases in genome-wide association studies, it is important to know whether joint analysis of multiple markers is more powerful than the commonly used single-marker analysis, especially in the presence of gene-gene interactions. This article provides a statistical framework to rigorously address this question through analytical power calculations for common model search strategies to detect binary trait loci: marginal search, exhaustive search, forward search, and two-stage screening search. Our approach incorporates linkage disequilibrium, random genotypes, and correlations among score test statistics of logistic regressions. We derive analytical results under two power definitions: the power of finding all the associated markers and the power of finding at least one associated marker. We also consider two types of error controls: the discovery number control and the Bonferroni type I error rate control. After demonstrating the accuracy of our analytical results by simulations, we apply them to consider a broad genetic model space to investigate the relative performances of different model search strategies. Our analytical study provides rapid computation as well as insights into the statistical mechanism of capturing genetic signals under different genetic models including gene-gene interactions. Even though we focus on genetic association analysis, our results on the power of model selection procedures are clearly very general and applicable to other studies. PMID:23956610

SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications

PubMed Central

Karim, Ahmad; Salleh, Rosli; Khan, Muhammad Khurram

2016-01-01

Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks’ back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps’ detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies. PMID:26978523

SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications.

PubMed

Karim, Ahmad; Salleh, Rosli; Khan, Muhammad Khurram

2016-01-01

Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks' back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies.

Three quantitative approaches to the diagnosis of abdominal pain in children: practical applications of decision theory.

PubMed

Klein, M D; Rabbani, A B; Rood, K D; Durham, T; Rosenberg, N M; Bahr, M J; Thomas, R L; Langenburg, S E; Kuhns, L R

2001-09-01

The authors compared 3 quantitative methods for assisting clinicians in the differential diagnosis of abdominal pain in children, where the most common important endpoint is whether the patient has appendicitis. Pretest probability in different age and sex groups were determined to perform Bayesian analysis, binary logistic regression was used to determine which variables were statistically significantly likely to contribute to a diagnosis, and recursive partitioning was used to build decision trees with quantitative endpoints. The records of all children (1,208) seen at a large urban emergency department (ED) with a chief complaint of abdominal pain were immediately reviewed retrospectively (24 to 72 hours after the encounter). Attempts were made to contact all the patients' families to determine an accurate final diagnosis. A total of 1,008 (83%) families were contacted. Data were analyzed by calculation of the posttest probability, recursive partitioning, and binary logistic regression. In all groups the most common diagnosis was abdominal pain (ICD-9 Code 789). After this, however, the order of the most common final diagnoses for abdominal pain varied significantly. The entire group had a pretest probability of appendicitis of 0.06. This varied with age and sex from 0.02 in boys 2 to 5 years old to 0.16 in boys older than 12 years. In boys age 5 to 12, recursive partitioning and binary logistic regression agreed on guarding and anorexia as important variables. Guarding and tenderness were important in girls age 5 to 12. In boys age greater than 12, both agreed on guarding and anorexia. Using sensitivities and specificities from the literature, computed tomography improved the posttest probability for the group from.06 to.33; ultrasound improved it from.06 to.48; and barium enema improved it from.06 to.58. Knowing the pretest probabilities in a specific population allows the physician to evaluate the likely diagnoses first. Other quantitative methods can help judge how much importance a certain criterion should have in the decision making and how much a particular test is likely to influence the probability of a correct diagnosis. It now should be possible to make these sophisticated quantitative methods readily available to clinicians via the computer. Copyright 2001 by W.B. Saunders Company.

Phenotypes of comorbidity in OSAS patients: combining categorical principal component analysis with cluster analysis.

PubMed

Vavougios, George D; George D, George; Pastaka, Chaido; Zarogiannis, Sotirios G; Gourgoulianis, Konstantinos I

2016-02-01

Phenotyping obstructive sleep apnea syndrome's comorbidity has been attempted for the first time only recently. The aim of our study was to determine phenotypes of comorbidity in obstructive sleep apnea syndrome patients employing a data-driven approach. Data from 1472 consecutive patient records were recovered from our hospital's database. Categorical principal component analysis and two-step clustering were employed to detect distinct clusters in the data. Univariate comparisons between clusters included one-way analysis of variance with Bonferroni correction and chi-square tests. Predictors of pairwise cluster membership were determined via a binary logistic regression model. The analyses revealed six distinct clusters: A, 'healthy, reporting sleeping related symptoms'; B, 'mild obstructive sleep apnea syndrome without significant comorbidities'; C1: 'moderate obstructive sleep apnea syndrome, obesity, without significant comorbidities'; C2: 'moderate obstructive sleep apnea syndrome with severe comorbidity, obesity and the exclusive inclusion of stroke'; D1: 'severe obstructive sleep apnea syndrome and obesity without comorbidity and a 33.8% prevalence of hypertension'; and D2: 'severe obstructive sleep apnea syndrome with severe comorbidities, along with the highest Epworth Sleepiness Scale score and highest body mass index'. Clusters differed significantly in apnea-hypopnea index, oxygen desaturation index; arousal index; age, body mass index, minimum oxygen saturation and daytime oxygen saturation (one-way analysis of variance P < 0.0001). Binary logistic regression indicated that older age, greater body mass index, lower daytime oxygen saturation and hypertension were associated independently with an increased risk of belonging in a comorbid cluster. Six distinct phenotypes of obstructive sleep apnea syndrome and its comorbidities were identified. Mapping the heterogeneity of the obstructive sleep apnea syndrome may help the early identification of at-risk groups. Finally, determining predictors of comorbidity for the moderate and severe strata of these phenotypes implies a need to take these factors into account when considering obstructive sleep apnea syndrome treatment options. © 2015 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.

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

Waveform model for an eccentric binary black hole based on the effective-one-body-numerical-relativity formalism

NASA Astrophysics Data System (ADS)

Cao, Zhoujian; Han, Wen-Biao

2017-08-01

Binary black hole systems are among the most important sources for gravitational wave detection. They are also good objects for theoretical research for general relativity. A gravitational waveform template is important to data analysis. An effective-one-body-numerical-relativity (EOBNR) model has played an essential role in the LIGO data analysis. For future space-based gravitational wave detection, many binary systems will admit a somewhat orbit eccentricity. At the same time, the eccentric binary is also an interesting topic for theoretical study in general relativity. In this paper, we construct the first eccentric binary waveform model based on an effective-one-body-numerical-relativity framework. Our basic assumption in the model construction is that the involved eccentricity is small. We have compared our eccentric EOBNR model to the circular one used in the LIGO data analysis. We have also tested our eccentric EOBNR model against another recently proposed eccentric binary waveform model; against numerical relativity simulation results; and against perturbation approximation results for extreme mass ratio binary systems. Compared to numerical relativity simulations with an eccentricity as large as about 0.2, the overlap factor for our eccentric EOBNR model is better than 0.98 for all tested cases, including spinless binary and spinning binary, equal mass binary, and unequal mass binary. Hopefully, our eccentric model can be the starting point to develop a faithful template for future space-based gravitational wave detectors.

Benign-malignant mass classification in mammogram using edge weighted local texture features

NASA Astrophysics Data System (ADS)

Rabidas, Rinku; Midya, Abhishek; Sadhu, Anup; Chakraborty, Jayasree

2016-03-01

This paper introduces novel Discriminative Robust Local Binary Pattern (DRLBP) and Discriminative Robust Local Ternary Pattern (DRLTP) for the classification of mammographic masses as benign or malignant. Mass is one of the common, however, challenging evidence of breast cancer in mammography and diagnosis of masses is a difficult task. Since DRLBP and DRLTP overcome the drawbacks of Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) by discriminating a brighter object against the dark background and vice-versa, in addition to the preservation of the edge information along with the texture information, several edge-preserving texture features are extracted, in this study, from DRLBP and DRLTP. Finally, a Fisher Linear Discriminant Analysis method is incorporated with discriminating features, selected by stepwise logistic regression method, for the classification of benign and malignant masses. The performance characteristics of DRLBP and DRLTP features are evaluated using a ten-fold cross-validation technique with 58 masses from the mini-MIAS database, and the best result is observed with DRLBP having an area under the receiver operating characteristic curve of 0.982.

Mediation Analysis with Multiple Mediators

PubMed Central

VanderWeele, T.J.; Vansteelandt, S.

2014-01-01

Recent advances in the causal inference literature on mediation have extended traditional approaches to direct and indirect effects to settings that allow for interactions and non-linearities. In this paper, these approaches from causal inference are further extended to settings in which multiple mediators may be of interest. Two analytic approaches, one based on regression and one based on weighting are proposed to estimate the effect mediated through multiple mediators and the effects through other pathways. The approaches proposed here accommodate exposure-mediator interactions and, to a certain extent, mediator-mediator interactions as well. The methods handle binary or continuous mediators and binary, continuous or count outcomes. When the mediators affect one another, the strategy of trying to assess direct and indirect effects one mediator at a time will in general fail; the approach given in this paper can still be used. A characterization is moreover given as to when the sum of the mediated effects for multiple mediators considered separately will be equal to the mediated effect of all of the mediators considered jointly. The approach proposed in this paper is robust to unmeasured common causes of two or more mediators. PMID:25580377

Mediation Analysis with Multiple Mediators.

PubMed

VanderWeele, T J; Vansteelandt, S

2014-01-01

Recent advances in the causal inference literature on mediation have extended traditional approaches to direct and indirect effects to settings that allow for interactions and non-linearities. In this paper, these approaches from causal inference are further extended to settings in which multiple mediators may be of interest. Two analytic approaches, one based on regression and one based on weighting are proposed to estimate the effect mediated through multiple mediators and the effects through other pathways. The approaches proposed here accommodate exposure-mediator interactions and, to a certain extent, mediator-mediator interactions as well. The methods handle binary or continuous mediators and binary, continuous or count outcomes. When the mediators affect one another, the strategy of trying to assess direct and indirect effects one mediator at a time will in general fail; the approach given in this paper can still be used. A characterization is moreover given as to when the sum of the mediated effects for multiple mediators considered separately will be equal to the mediated effect of all of the mediators considered jointly. The approach proposed in this paper is robust to unmeasured common causes of two or more mediators.

Improvement of Binary Analysis Components in Automated Malware Analysis Framework

DTIC Science & Technology

2017-02-21

analyze malicious software (malware) with minimum human interaction. The system autonomously analyze malware samples by analyzing malware binary program...AFRL-AFOSR-JP-TR-2017-0018 Improvement of Binary Analysis Components in Automated Malware Analysis Framework Keiji Takeda KEIO UNIVERSITY Final...currently valid OMB control number . PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION. 1. REPORT DATE (DD-MM-YYYY)      21-02-2017 2. REPORT

Studies of Horst's Procedure for Binary Data Analysis.

ERIC Educational Resources Information Center

Gray, William M.; Hofmann, Richard J.

Most responses to educational and psychological test items may be represented in binary form. However, such dichotomously scored items present special problems when an analysis of correlational interrelationships among the items is attempted. Two general methods of analyzing binary data are proposed by Horst to partial out the effects of…

Work related complaints of neck, shoulder and arm among computer office workers: a cross-sectional evaluation of prevalence and risk factors in a developing country.

PubMed

Ranasinghe, Priyanga; Perera, Yashasvi S; Lamabadusuriya, Dilusha A; Kulatunga, Supun; Jayawardana, Naveen; Rajapakse, Senaka; Katulanda, Prasad

2011-08-04

Complaints of arms, neck and shoulders (CANS) is common among computer office workers. We evaluated an aetiological model with physical/psychosocial risk-factors. We invited 2,500 computer office workers for the study. Data on prevalence and risk-factors of CANS were collected by validated Maastricht-Upper-extremity-Questionnaire. Workstations were evaluated by Occupational Safety and Health Administration (OSHA) Visual-Display-Terminal workstation-checklist. Participants' knowledge and awareness was evaluated by a set of expert-validated questions. A binary logistic regression analysis investigated relationships/correlations between risk-factors and symptoms. Sample size was 2,210. Mean age 30.8 ± 8.1 years, 50.8% were males. The 1-year prevalence of CANS was 56.9%, commonest region of complaint was forearm/hand (42.6%), followed by neck (36.7%) and shoulder/arm (32.0%). In those with CANS, 22.7% had taken treatment from a health care professional, only in 1.1% seeking medical advice an occupation-related injury had been suspected/diagnosed. In addition 9.3% reported CANS-related absenteeism from work, while 15.4% reported CANS causing disruption of normal activities. A majority of evaluated workstations in all participants (88.4%,) and in those with CANS (91.9%) had OSHA non-compliant workstations. In the binary logistic regression analyses female gender, daily computer usage, incorrect body posture, bad work-habits, work overload, poor social support and poor ergonomic knowledge were associated with CANS and its' severity In a multiple logistic regression analysis controlling for age, gender and duration of occupation, incorrect body posture, bad work-habits and daily computer usage were significant independent predictors of CANS. The prevalence of work-related CANS among computer office workers in Sri Lanka, a developing, South Asian country is high and comparable to prevalence in developed countries. Work-related physical factors, psychosocial factors and lack of awareness were all important associations of CANS and effective preventive strategies need to address all three areas.

Classification of Dust Days by Satellite Remotely Sensed Aerosol Products

NASA Technical Reports Server (NTRS)

Sorek-Hammer, M.; Cohen, A.; Levy, Robert C.; Ziv, B.; Broday, D. M.

2013-01-01

Considerable progress in satellite remote sensing (SRS) of dust particles has been seen in the last decade. From an environmental health perspective, such an event detection, after linking it to ground particulate matter (PM) concentrations, can proxy acute exposure to respirable particles of certain properties (i.e. size, composition, and toxicity). Being affected considerably by atmospheric dust, previous studies in the Eastern Mediterranean, and in Israel in particular, have focused on mechanistic and synoptic prediction, classification, and characterization of dust events. In particular, a scheme for identifying dust days (DD) in Israel based on ground PM10 (particulate matter of size smaller than 10 nm) measurements has been suggested, which has been validated by compositional analysis. This scheme requires information regarding ground PM10 levels, which is naturally limited in places with sparse ground-monitoring coverage. In such cases, SRS may be an efficient and cost-effective alternative to ground measurements. This work demonstrates a new model for identifying DD and non-DD (NDD) over Israel based on an integration of aerosol products from different satellite platforms (Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI)). Analysis of ground-monitoring data from 2007 to 2008 in southern Israel revealed 67 DD, with more than 88 percent occurring during winter and spring. A Classification and Regression Tree (CART) model that was applied to a database containing ground monitoring (the dependent variable) and SRS aerosol product (the independent variables) records revealed an optimal set of binary variables for the identification of DD. These variables are combinations of the following primary variables: the calendar month, ground-level relative humidity (RH), the aerosol optical depth (AOD) from MODIS, and the aerosol absorbing index (AAI) from OMI. A logistic regression that uses these variables, coded as binary variables, demonstrated 93.2 percent correct classifications of DD and NDD. Evaluation of the combined CART-logistic regression scheme in an adjacent geographical region (Gush Dan) demonstrated good results. Using SRS aerosol products for DD and NDD, identification may enable us to distinguish between health, ecological, and environmental effects that result from exposure to these distinct particle populations.

Relationship between serum bilirubin concentrations and diabetic nephropathy in Shanghai Han's patients with type 1 diabetes mellitus.

PubMed

Li, Xu; Zhang, Lei; Chen, Haibing; Guo, Kaifeng; Yu, Haoyong; Zhou, Jian; Li, Ming; Li, Qing; Li, Lianxi; Yin, Jun; Liu, Fang; Bao, Yuqian; Han, Junfeng; Jia, Weiping

2017-03-31

Recent studies highlight a negative association between total bilirubin concentrations and albuminuria in patients with type 2 diabetes mellitus. Our study evaluated the relationship between bilirubin concentrations and the prevalence of diabetic nephropathy (DN) in Chinese patients with type 1 diabetes mellitus (T1DM). A total of 258 patients with T1DM were recruited and bilirubin concentrations were compared between patients with or without diabetic nephropathy. Multiple stepwise regression analysis was used to examine the relationship between bilirubin concentrations and 24 h urinary microalbumin. Binary logistic regression analysis was performed to assess independent risk factors for diabetic nephropathy. Participants were divided into four groups according to the quartile of total bilirubin concentrations (Q1, 0.20-0.60; Q2, 0.60-0.80; Q3, 0.80-1.00; Q4, 1.00-1.90 mg/dL) and the chi-square test was used to compare the prevalence of DN in patients with T1DM. The median bilirubin level was 0.56 (interquartile: 0.43-0.68 mg/dL) in the DN group, significantly lower than in the non-DN group (0.70 [interquartile: 0.58-0.89 mg/dL], P < 0.001). Spearman's correlational analysis showed bilirubin concentrations were inversely correlated with 24 h urinary microalbumin (r = -0.13, P < 0.05) and multiple stepwise regression analysis showed bilirubin concentrations were independently associated with 24 h urinary microalbumin. In logistic regression analysis, bilirubin concentrations were significantly inversely associated with nephropathy. In addition, in stratified analysis, from the first to the fourth quartile group, increased bilirubin concentrations were associated with decreased prevalence of DN from 21.90% to 2.00%. High bilirubin concentrations are independently and negatively associated with albuminuria and the prevalence of DN in patients with T1DM.

The relationship between hemoglobin level and the type 1 diabetic nephropathy in Anhui Han's patients.

PubMed

Jiang, Jun; Lei, Lan; Zhou, Xiaowan; Li, Peng; Wei, Ren

2018-02-20

Recent studies have shown that low hemoglobin (Hb) level promote the progression of chronic kidney disease. This study assessed the relationship between Hb level and type 1 diabetic nephropathy (DN) in Anhui Han's patients. There were a total of 236 patients diagnosed with type 1 diabetes mellitus and (T1DM) seen between January 2014 and December 2016 in our centre. Hemoglobin levels in patients with DN were compared with those without DN. The relationship between Hb level and the urinary albumin-creatinine ratio (ACR) was examined by Spearman's correlational analysis and multiple stepwise regression analysis. The binary logistic multivariate regression analysis was performed to analyze the correlated factors for type 1 DN, calculate the Odds Ratio (OR) and 95%confidence interval (CI). The predicting value of Hb level for DN was evaluated by area under receiver operation characteristic curve (AUROC) for discrimination and Hosmer-Lemeshow goodness-of-fit test for calibration. The average Hb levels in the DN group (116.1 ± 20.8 g/L) were significantly lower than the non-DN group (131.9 ± 14.4 g/L) , P < 0.001. Hb levels were independently correlated with the urinary ACR in multiple stepwise regression analysis. The logistic multivariate regression analysis showed that the Hb level (OR: 0.936, 95% CI: 0.910 to 0.963, P < 0.001) was inversely correlated with DN in patients with T1DM. In sub-analysis, low Hb level (Hb < 120g/L in female, Hb < 130g/L in male) was still negatively associated with DN in patients with T1DM. The AUROC was 0.721 (95% CI: 0.655 to 0.787) in assessing the discrimination of the Hb level for DN. The value of P was 0.593 in Hosmer-Lemeshow goodness-of-fit test. In Anhui Han's patients with T1DM, the Hb level is inversely correlated with urinary ACR and DN. This article is protected by copyright. All rights reserved.

Binary dislocation junction formation and strength in hexagonal close-packed crystals

DOE PAGES

Wu, Chi -Chin; Aubry, Sylvie; Arsenlis, Athanasios; ...

2015-12-17

This work examines binary dislocation interactions, junction formation and junction strengths in hexagonal close-packed ( hcp ) crystals. Through a line-tension model and dislocation dynamics (DD) simulations, the interaction and dissociation of different sets of binary junctions are investigated involving one dislocation on the (011¯0) prismatic plane and a second dislocation on one of the following planes: (0001) basal, (11¯00) prismatic, (11¯01) primary pyramidal, or (2¯112) secondary pyramidal. Varying pairs of Burgers vectors are chosen from among the common types the basal type < a > 1/3 < 112¯0 >, prismatic type < c > <0001>, and pyramidal type 1/3 < 112¯3¯ >. For binary interaction due to dislocation intersection, both the analytical results and DD-simulations indicate a relationship between symmetry of interaction maps and the relative magnitude of the Burgers vectors that constitute the junction. Using analytical formulae, a simple regressive model is also developed to represent the junction yield surface. The equation is treated as a degenerated super elliptical equation to quantify the aspect ratio and tilting angle. Lastly, the results provide analytical insights on binary dislocation interactions that may occur in general hcp metals.« less

Body Image Satisfaction as a Physical Activity Indicator in University Students.

PubMed

Ramos-Jiménez, Arnulfo; Hernández-Torres, Rosa P; Urquidez-Romero, René; Wall-Medrano, Abraham; Villalobos-Molina, Rafael

2017-09-01

We examined the association of body image satisfaction (BIS) with physical activity (PA) in university athletes and non-athletes from northern Mexico. In a non-probability cross-sectional study, 294 participants (51% male, 41% athletes; 18-35 years old) completed 2 self-administered questionnaires to evaluate BIS and PA. We categorized somatotypes (endomorphy-mesomorphy-ectomorphy) by international standardized anthropometry. Data analysis included the Mann-Whitney U test, χ2test, Kendall's Tau-b correlation, binary logistic regression analysis, and receiver operating characteristic (ROC) curves. Self-perceived sports abilities and desirable body shape predicted 30% of sports participation in students, whereas an endomorphic shape (<5.4 units) and being male predicted 15.4% of sports participation. BIS was a reliable indicator of sports participation among these university students.

Statistical analysis of subjective preferences for video enhancement

NASA Astrophysics Data System (ADS)

Woods, Russell L.; Satgunam, PremNandhini; Bronstad, P. Matthew; Peli, Eli

2010-02-01

Measuring preferences for moving video quality is harder than for static images due to the fleeting and variable nature of moving video. Subjective preferences for image quality can be tested by observers indicating their preference for one image over another. Such pairwise comparisons can be analyzed using Thurstone scaling (Farrell, 1999). Thurstone (1927) scaling is widely used in applied psychology, marketing, food tasting and advertising research. Thurstone analysis constructs an arbitrary perceptual scale for the items that are compared (e.g. enhancement levels). However, Thurstone scaling does not determine the statistical significance of the differences between items on that perceptual scale. Recent papers have provided inferential statistical methods that produce an outcome similar to Thurstone scaling (Lipovetsky and Conklin, 2004). Here, we demonstrate that binary logistic regression can analyze preferences for enhanced video.

Adolescent sexual victimization: a prospective study on risk factors for first time sexual assault.

PubMed

Bramsen, Rikke Holm; Lasgaard, Mathias; Koss, Mary P; Elklit, Ask; Banner, Jytte

2012-09-01

The present study set out to investigate predictors of first time adolescent peer-on-peer sexual victimization (APSV) among 238 female Grade 9 students from 30 schools in Denmark. A prospective research design was utilized to examine the relationship among five potential predictors as measured at baseline and first time APSV during a 6-month period. Data analysis was a binary logistic regression analysis. Number of sexual partners and displaying sexual risk behaviors significantly predicted subsequent first time peer-on-peer sexual victimization, whereas a history of child sexual abuse, early sexual onset and failing to signal sexual boundaries did not. The present study identifies specific risk factors for first time sexual victimization that are potentially changeable. Thus, the results may inform prevention initiatives targeting initial experiences of APSV.

Impact of high lipoprotein(a) levels on in-stent restenosis and long-term clinical outcomes of angina pectoris patients undergoing percutaneous coronary intervention with drug-eluting stents in Asian population.

PubMed

Park, Sang-Ho; Rha, Seung-Woon; Choi, Byoung-Geol; Park, Ji-Young; Jeon, Ung; Seo, Hong-Seog; Kim, Eung-Ju; Na, Jin-Oh; Choi, Cheol-Ung; Kim, Jin-Won; Lim, Hong-Euy; Park, Chang-Gyu; Oh, Dong-Joo

2015-06-01

Lipoprotein(a) (Lp(a)) is known to be associated with cardiovascular complications and atherothrombotic properties in general populations. However, it has not been examined whether Lp(a) levels are able to predict adverse cardiovascular outcomes in patients undergoing percutaneous coronary intervention (PCI) with drug-eluting stents (DES). A total of 595 consecutive patients with angina pectoris who underwent elective PCI with DES were enrolled from 2004 to 2010. The patients were divided into two groups according to the levels of Lp(a): Lp(a) < 50 mg/dL (n = 485 patients), and Lp(a) ≥ 50 mg/dL (n = 111 patients). The 6-9-month angiographic outcomes and 3-year cumulative major clinical outcomes were compared between the two groups. Binary restenosis occurred in 26 of 133 lesions (19.8%) in the high Lp(a) group and 43 of 550 lesions (7.9%) in the low Lp(a) group (P = 0.001). In multivariate analysis, the reference vessel diameter, low density lipoprotein cholesterol, total lesion length, and Lp(a) ≥ 50 mg/dL were predictors of binary restenosis. In the Cox proportional hazards regression analysis, Lp(a) > 50 mg/dL was significantly associated with the 3-year adverse clinical outcomes including any myocardial infarction, revascularization (target lesion revascularization (TLR) and target vessel revascularization (TVR)), TLR-major adverse cardiac events (MACEs), TVR-MACE, and All-MACEs. In our study, high Lp(a) level ≥ 50 mg/dL in angina pectoris patients undergoing elective PCI with DES was significantly associated with binary restenosis and 3-year adverse clinical outcomes in an Asian population. © 2015 Wiley Publishing Asia Pty Ltd.

Clustering and Dimensionality Reduction to Discover Interesting Patterns in Binary Data

NASA Astrophysics Data System (ADS)

Palumbo, Francesco; D'Enza, Alfonso Iodice

The attention towards binary data coding increased consistently in the last decade due to several reasons. The analysis of binary data characterizes several fields of application, such as market basket analysis, DNA microarray data, image mining, text mining and web-clickstream mining. The paper illustrates two different approaches exploiting a profitable combination of clustering and dimensionality reduction for the identification of non-trivial association structures in binary data. An application in the Association Rules framework supports the theory with the empirical evidence.

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.

Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases.

PubMed

Wendling, T; Jung, K; Callahan, A; Schuler, A; Shah, N H; Gallego, B

2018-06-03

There is growing interest in using routinely collected data from health care databases to study the safety and effectiveness of therapies in "real-world" conditions, as it can provide complementary evidence to that of randomized controlled trials. Causal inference from health care databases is challenging because the data are typically noisy, high dimensional, and most importantly, observational. It requires methods that can estimate heterogeneous treatment effects while controlling for confounding in high dimensions. Bayesian additive regression trees, causal forests, causal boosting, and causal multivariate adaptive regression splines are off-the-shelf methods that have shown good performance for estimation of heterogeneous treatment effects in observational studies of continuous outcomes. However, it is not clear how these methods would perform in health care database studies where outcomes are often binary and rare and data structures are complex. In this study, we evaluate these methods in simulation studies that recapitulate key characteristics of comparative effectiveness studies. We focus on the conditional average effect of a binary treatment on a binary outcome using the conditional risk difference as an estimand. To emulate health care database studies, we propose a simulation design where real covariate and treatment assignment data are used and only outcomes are simulated based on nonparametric models of the real outcomes. We apply this design to 4 published observational studies that used records from 2 major health care databases in the United States. Our results suggest that Bayesian additive regression trees and causal boosting consistently provide low bias in conditional risk difference estimates in the context of health care database studies. Copyright © 2018 John Wiley & Sons, Ltd.

Risk of Recurrence in Operated Parasagittal Meningiomas: A Logistic Binary Regression Model.

PubMed

Escribano Mesa, José Alberto; Alonso Morillejo, Enrique; Parrón Carreño, Tesifón; Huete Allut, Antonio; Narro Donate, José María; Méndez Román, Paddy; Contreras Jiménez, Ascensión; Pedrero García, Francisco; Masegosa González, José

2018-02-01

Parasagittal meningiomas arise from the arachnoid cells of the angle formed between the superior sagittal sinus (SSS) and the brain convexity. In this retrospective study, we focused on factors that predict early recurrence and recurrence times. We reviewed 125 patients with parasagittal meningiomas operated from 1985 to 2014. We studied the following variables: age, sex, location, laterality, histology, surgeons, invasion of the SSS, Simpson removal grade, follow-up time, angiography, embolization, radiotherapy, recurrence and recurrence time, reoperation, neurologic deficit, degree of dependency, and patient status at the end of follow-up. Patients ranged in age from 26 to 81 years (mean 57.86 years; median 60 years). There were 44 men (35.2%) and 81 women (64.8%). There were 57 patients with neurologic deficits (45.2%). The most common presenting symptom was motor deficit. World Health Organization grade I tumors were identified in 104 patients (84.6%), and the majority were the meningothelial type. Recurrence was detected in 34 cases. Time of recurrence was 9 to 336 months (mean: 84.4 months; median: 79.5 months). Male sex was identified as an independent risk for recurrence with relative risk 2.7 (95% confidence interval 1.21-6.15), P = 0.014. Kaplan-Meier curves for recurrence had statistically significant differences depending on sex, age, histologic type, and World Health Organization histologic grade. A binary logistic regression was made with the Hosmer-Lemeshow test with P > 0.05; sex, tumor size, and histologic type were used in this model. Male sex is an independent risk factor for recurrence that, associated with other factors such tumor size and histologic type, explains 74.5% of all cases in a binary regression model. Copyright © 2017 Elsevier Inc. All rights reserved.

Do Concomitant Cranium and Axis Injuries Predict Worse Outcome? A Trauma Database Quantitative Analysis

PubMed Central

Chittiboina, Prashant; Banerjee, Anirban Deep; Nanda, Anil

2011-01-01

We performed a trauma database analysis to identify the effect of concomitant cranial injuries on outcome in patients with fractures of the axis. We identified patients with axis fractures over a 14-year period. A binary outcome measure was used. Univariate and multiple logistic regression analysis were performed. There were 259 cases with axis fractures. Closed head injury was noted in 57% and skull base trauma in 14%. Death occurred in 17 cases (6%). Seventy-two percent had good outcome. Presence of abnormal computed tomography head findings, skull base fractures, and visceral injury was significantly associated with poor outcome. Skull base injury in association with fractures of the axis is a significant independent predictor of worse outcomes, irrespective of the severity of the head injury. We propose that presence of concomitant cranial and upper vertebral injuries require careful evaluation in view of the associated poor prognosis. PMID:22470268

Malnutrition risk predicts recovery of full oral intake among older adult stroke patients undergoing enteral nutrition: Secondary analysis of a multicentre survey (the APPLE study).

PubMed

Nishioka, Shinta; Okamoto, Takatsugu; Takayama, Masako; Urushihara, Maki; Watanabe, Misuzu; Kiriya, Yumiko; Shintani, Keiko; Nakagomi, Hiromi; Kageyama, Noriko

2017-08-01

Whether malnutrition risk correlates with recovery of swallowing function of convalescent stroke patients is unknown. This study was conducted to clarify whether malnutrition risks predict achievement of full oral intake in convalescent stroke patients undergoing enteral nutrition. We conducted a secondary analysis of 466 convalescent stroke patients, aged 65 years or over, who were undergoing enteral nutrition. Patients were extracted from the "Algorithm for Post-stroke Patients to improve oral intake Level; APPLE" study database compiled at the Kaifukuki (convalescent) rehabilitation wards. Malnutrition risk was determined by the Geriatric Nutritional Risk Index as follows: severe (<82), moderate (82 to <92), mild (92 to <98), and no malnutrition risks (≥98). Swallowing function was assessed by Fujishima's swallowing grade (FSG) on admission and discharge. The primary outcome was achievement of full oral intake, indicated by FSG ≥ 7. Binary logistic regression analysis was performed to identify predictive factors, including malnutrition risk, for achieving full oral intake. Estimated hazard risk was computed by Cox's hazard model. Of the 466 individuals, 264 were ultimately included in this study. Participants with severe malnutrition risk showed a significantly lower proportion of achievement of full oral intake than lower severity groups (P = 0.001). After adjusting for potential confounders, binary logistic regression analysis showed that patients with severe malnutrition risk were less likely to achieve full oral intake (adjusted odds ratio: 0.232, 95% confidence interval [95% CI]: 0.047-1.141). Cox's proportional hazard model revealed that severe malnutrition risk was an independent predictor of full oral intake (adjusted hazard ratio: 0.374, 95% CI: 0.166-0.842). Compared to patients who did not achieve full oral intake, patients who achieved full oral intake had significantly higher energy intake, but there was no difference in protein intake and weight change. Severe malnutrition risk independently predicts the achievement of full oral intake in convalescent stroke patients undergoing enteral nutrition. Copyright © 2016 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Low triiodothyronine: A new facet of inflammation in acute ischemic stroke.

PubMed

Ma, Lili; Zhu, Dongliang; Jiang, Ying; Liu, Yingying; Ma, Xiaomeng; Liu, Mei; Chen, Xiaohong

2016-07-01

Patients with acute ischemic stroke (AIS) frequently experience low free triiodothyronine (fT3) concentrations. Inflammation is recognized as a key contributor to the pathophysiology of stroke. Previous studies, however, did not simultaneously evaluate fT3 and inflammation biomarkers in AIS patients. Markers of inflammation, including serum concentrations of C-reactive protein (CRP) and albumin, and fT3 were assessed retrospectively in 117 patients. Stroke severity was measured on the National Institutes of Health Stroke Scale (NIHSS). Regression analyses were performed to adjust for confounders. Serum fT3 concentrations were significantly lower in moderate AIS patients than those in mild AIS patients (P<0.001). fT3 concentration also positively correlated with serum albumin concentration (r=0.358, P<0.001) and negatively correlated with log10CRP concentration (r=-0.341, P<0.001), NIHSS score (r=-0.384, P<0.001). Multiple regression analysis showed that CRP, albumin concentrations and NIHSS score were independently correlated with fT3 concentration. Binary logistic regression analysis showed that fT3 concentration was an independent factor correlated with NIHSS score, the area under the receiver operating characteristic curve was 0.712 (95% CI, 0.618-0.805). Low fT3 concentrations may be involved in the pathogenic pathway linking inflammation to stroke severity in AIS patients. Copyright © 2016 Elsevier B.V. All rights reserved.

[Willingness of Patients with Obesity to Use New Media in Rehabilitation Aftercare].

PubMed

Dorow, M; Löbner, M; Stein, J; Kind, P; Markert, J; Keller, J; Weidauer, E; Riedel-Heller, S G

2017-06-01

Digital media offer new possibilities in rehabilitation aftercare. This study investigates the rehabilitants' willingness to use new media (sms, internet, social networks) in rehabilitation aftercare and factors that are associated with the willingness to use media-based aftercare. 92 rehabilitants (patients with obesity) filled in a questionnaire on the willingness to use new media in rehabilitation aftercare. In order to identify influencing factors, binary logistic regression models were calculated. 3 quarters of the rehabilitants (76.1%) reported that they would be willing to use new media in rehabilitation aftercare. The binary logistic regression model yielded two factors that were associated with the willingness to use media-based aftercare: the possession of a smartphone and the willingness to receive telephone counseling for aftercare. The majority of the rehabilitants was willing to use new media in rehabilitation aftercare. The reasons for refusal of media-based aftercare need to be examined more closely. © Georg Thieme Verlag KG Stuttgart · New York.

Statistical power to detect violation of the proportional hazards assumption when using the Cox regression model.

PubMed

Austin, Peter C

2018-01-01

The use of the Cox proportional hazards regression model is widespread. A key assumption of the model is that of proportional hazards. Analysts frequently test the validity of this assumption using statistical significance testing. However, the statistical power of such assessments is frequently unknown. We used Monte Carlo simulations to estimate the statistical power of two different methods for detecting violations of this assumption. When the covariate was binary, we found that a model-based method had greater power than a method based on cumulative sums of martingale residuals. Furthermore, the parametric nature of the distribution of event times had an impact on power when the covariate was binary. Statistical power to detect a strong violation of the proportional hazards assumption was low to moderate even when the number of observed events was high. In many data sets, power to detect a violation of this assumption is likely to be low to modest.

Statistical power to detect violation of the proportional hazards assumption when using the Cox regression model

PubMed Central

Austin, Peter C.

2017-01-01

The use of the Cox proportional hazards regression model is widespread. A key assumption of the model is that of proportional hazards. Analysts frequently test the validity of this assumption using statistical significance testing. However, the statistical power of such assessments is frequently unknown. We used Monte Carlo simulations to estimate the statistical power of two different methods for detecting violations of this assumption. When the covariate was binary, we found that a model-based method had greater power than a method based on cumulative sums of martingale residuals. Furthermore, the parametric nature of the distribution of event times had an impact on power when the covariate was binary. Statistical power to detect a strong violation of the proportional hazards assumption was low to moderate even when the number of observed events was high. In many data sets, power to detect a violation of this assumption is likely to be low to modest. PMID:29321694

Bayesian multivariate hierarchical transformation models for ROC analysis.

PubMed

O'Malley, A James; Zou, Kelly H

2006-02-15

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

Bayesian multivariate hierarchical transformation models for ROC analysis

PubMed Central

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

2006-01-01

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

Exploring definitions of financial abuse in elderly Korean immigrants: the contribution of traditional cultural values.

PubMed

Lee, Hee Yun; Lee, Sang E; Eaton, Charissa K

2012-10-01

The purpose of this study is to explore the cultural definitions of financial abuse from the perspective of 124 elderly Korean immigrants and to examine the role of traditional cultural values in their definitions by using a mixed methods approach. The qualitative analysis generated four themes relevant to definition of financial abuse. A binary logistic regression indicated that those with stronger cultural adherence to traditional values had higher odds of providing culture-based definitions of financial abuse. Education is needed for health professionals, social service providers, and adult protective workers to increase their understanding of culture-specific experiences of financial abuse among ethnic minority elders.

The influence of intrinsic and extrinsic job values on turnover intention among continuing care assistants in Nova Scotia.

PubMed

Dill, Donna M; Keefe, Janice M; McGrath, Daniel S

2012-01-01

This article examines the influence that intrinsic and extrinsic job values have on the turnover intention of continuing care assistants (CCAs) who work either in home care or facility-based care in Nova Scotia (n = 188). Factor analysis of job values identified three latent job values structures: "compensation and commitment," "flexibility and opportunity," and "positive work relationships." Using binary logistic regression, we examined the predictive utility of these factors on two indices of turnover intention. Regression results indicate that, in general, job values constructs did not significantly predict turnover intention when controlling for demographics and job characteristics. However, a trend was found for the "positive work relationships" factor in predicting consideration of changing employers. In addition, CCAs who work in facility-based care were significantly more likely to have considered leaving their current employer. With projected increases in the demand for these workers in both home and continuing care, more attention is needed to identify and address factors to reduce turnover intention.

Efficient logistic regression designs under an imperfect population identifier.

PubMed

Albert, Paul S; Liu, Aiyi; Nansel, Tonja

2014-03-01

Motivated by actual study designs, this article considers efficient logistic regression designs where the population is identified with a binary test that is subject to diagnostic error. We consider the case where the imperfect test is obtained on all participants, while the gold standard test is measured on a small chosen subsample. Under maximum-likelihood estimation, we evaluate the optimal design in terms of sample selection as well as verification. We show that there may be substantial efficiency gains by choosing a small percentage of individuals who test negative on the imperfect test for inclusion in the sample (e.g., verifying 90% test-positive cases). We also show that a two-stage design may be a good practical alternative to a fixed design in some situations. Under optimal and nearly optimal designs, we compare maximum-likelihood and semi-parametric efficient estimators under correct and misspecified models with simulations. The methodology is illustrated with an analysis from a diabetes behavioral intervention trial. © 2013, The International Biometric Society.

A global goodness-of-fit statistic for Cox regression models.

PubMed

Parzen, M; Lipsitz, S R

1999-06-01

In this paper, a global goodness-of-fit test statistic for a Cox regression model, which has an approximate chi-squared distribution when the model has been correctly specified, is proposed. Our goodness-of-fit statistic is global and has power to detect if interactions or higher order powers of covariates in the model are needed. The proposed statistic is similar to the Hosmer and Lemeshow (1980, Communications in Statistics A10, 1043-1069) goodness-of-fit statistic for binary data as well as Schoenfeld's (1980, Biometrika 67, 145-153) statistic for the Cox model. The methods are illustrated using data from a Mayo Clinic trial in primary billiary cirrhosis of the liver (Fleming and Harrington, 1991, Counting Processes and Survival Analysis), in which the outcome is the time until liver transplantation or death. The are 17 possible covariates. Two Cox proportional hazards models are fit to the data, and the proposed goodness-of-fit statistic is applied to the fitted models.

Stochastic model search with binary outcomes for genome-wide association studies.

PubMed

Russu, Alberto; Malovini, Alberto; Puca, Annibale A; Bellazzi, Riccardo

2012-06-01

The spread of case-control genome-wide association studies (GWASs) has stimulated the development of new variable selection methods and predictive models. We introduce a novel Bayesian model search algorithm, Binary Outcome Stochastic Search (BOSS), which addresses the model selection problem when the number of predictors far exceeds the number of binary responses. Our method is based on a latent variable model that links the observed outcomes to the underlying genetic variables. A Markov Chain Monte Carlo approach is used for model search and to evaluate the posterior probability of each predictor. BOSS is compared with three established methods (stepwise regression, logistic lasso, and elastic net) in a simulated benchmark. Two real case studies are also investigated: a GWAS on the genetic bases of longevity, and the type 2 diabetes study from the Wellcome Trust Case Control Consortium. Simulations show that BOSS achieves higher precisions than the reference methods while preserving good recall rates. In both experimental studies, BOSS successfully detects genetic polymorphisms previously reported to be associated with the analyzed phenotypes. BOSS outperforms the other methods in terms of F-measure on simulated data. In the two real studies, BOSS successfully detects biologically relevant features, some of which are missed by univariate analysis and the three reference techniques. The proposed algorithm is an advance in the methodology for model selection with a large number of features. Our simulated and experimental results showed that BOSS proves effective in detecting relevant markers while providing a parsimonious model.

Serum osteocalcin is significantly related to indices of obesity and lipid profile in Malaysian men.

PubMed

Chin, Kok-Yong; Ima-Nirwana, Soelaiman; Mohamed, Isa Naina; Ahmad, Fairus; Ramli, Elvy Suhana Mohd; Aminuddin, Amilia; Ngah, Wan Zurinah Wan

2014-01-01

Recent studies revealed a possible reciprocal relationship between the skeletal system and obesity and lipid metabolism, mediated by osteocalcin, an osteoblast-specific protein. This study aimed to validate the relationship between serum osteocalcin and indices of obesity and lipid parameters in a group of Malaysian men. A total of 373 men from the Malaysian Aging Male Study were included in the analysis. Data on subjects' demography, body mass index (BMI), body fat (BF) mass, waist circumference (WC), serum osteocalcin and fasting lipid levels were collected. Bioelectrical impendence (BIA) method was used to estimate BF. Multiple linear and binary logistic regression analyses were performed to analyze the association between serum osteocalcin and the aforementioned variables, with adjustment for age, ethnicity and BMI. Multiple regression results indicated that weight, BMI, BF mass, BF %, WC were significantly and negatively associated with serum osteocalcin (p < 0.001). There was a significant positive association between serum osteocalcin and high density lipoprotein (HDL) cholesterol (p = 0.032). Binary logistic results indicated that subjects with low serum osteocalcin level were more likely to be associated with high BMI (obese and overweight), high BF%, high WC and low HDL cholesterol (p < 0.05). Subjects with high osteocalcin level also demonstrated high total cholesterol level (p < 0.05) but this association was probably driven by high HDL level. These variables were not associated with serum C-terminal of telopeptide crosslinks in the subjects (p > 0.05). Serum osteocalcin is associated with indices of obesity and HDL level in men. These relationships should be validated by a longitudinal study, with comprehensive hormone profile testing.

Quality management and job related factors predicting satisfaction of dental clinic staff in Estonia.

PubMed

Merisalu, Eda; Männik, Georg; Põlluste, Kaja

2014-01-01

The aim of the study was to explore the role of managerial style, work environment factors and burnout in determining job satisfaction during the implementation of quality improvement activities in a dental clinic. Quantitative research was carried out using a prestructured anonymous questionnaire to survey 302 respondents in Kaarli Dental Clinic, Estonia. Dental clinic staff assessed job satisfaction, managerial style, work stress and burnout levels through the implementation period of ISO 9000 quality management system in 2003 and annually during 2006-2009. Binary logistic regression was used to explain the impact of satisfaction with management and work organisation, knowledge about managerial activities, work environment and psychosocial stress and burnout on job satisfaction. The response rate limits were between 60% and 89.6%. Job satisfaction increased significantly from 2003 to 2006 and the percentage of very satisfied staff increased from 17 to 38 (p<0.01) over this period. In 2007, the proportion of very satisfied people dropped to 21% before increasing again in 2008-2009 (from 24% to 35%). Binary logistic regression analysis resulted in a model that included five groups of factors: managerial support, information about results achieved and progress to goals, work organisation and working environment, as well as factors related to career, security and planning. The average scores of emotional exhaustion showed significant decrease, correlating negatively with job satisfaction (p<0.05). The implementation of quality improvement activities in the Kaarli Dental Clinic has improved the work environment by decreasing burnout symptoms and increased job satisfaction in staff.

Relationship between postoperative lung atelectasis and position of the endotracheal tube in pediatric living-donor liver transplantation.

PubMed

Lee, H-Y; Lu, C-H; Lu, H-F; Chen, C-L; Wang, C-H; Cheng, K-W; Wu, S-C; Jawan, B; Huang, C-J

2012-05-01

The aims of current study were: 1) to evaluate the incidence of lung atelectasis; and 2) to investigate whether or not the position of the endotracheal (ET) tube is associated with this complication. The medical records and chest roentgenograms of 183 pediatric patients who underwent living-donor liver transplantation were retrospectively reviewed and analyzed. Patients without atelectasis were grouped in group I (GI) and those with atelectasis in group II (GII). The patients' characteristics and ET tube level between groups were compared with unpaired Student's t test. Multiple binary logistic regressions were also performed to identify the important risk factors associated with lung atelectasis. Right upper lung (RUL) atelectsis could be found in ET tube at any level from T1 to T5, with incidence rates of 12.7%, 15.2%, 26.3%, 6.7%, and 100% for T1, T2, T3, T4, and T5, respectively. The incidence of atelectasis is 16.6%, and all of the atelectasis occurred in the RUL. No significant difference between groups was observed in the patients' characteristics, except for the amount of preoperative ascites. The likelihood of this risk factor could not be confirmed by multivariate binary logistic regression analysis. The incidence of lung atelectasis in our study was 16.6%, which all occurred in the RUL. No predictive risk factor from the patients' characteristics could be found, and no correlation between the level of the ET tube and the occurrence of RUL atelectasis could be observed. Copyright © 2012 Elsevier Inc. All rights reserved.

[Analysis of binary classification repeated measurement data with GEE and GLMMs using SPSS software].

PubMed

An, Shengli; Zhang, Yanhong; Chen, Zheng

2012-12-01

To analyze binary classification repeated measurement data with generalized estimating equations (GEE) and generalized linear mixed models (GLMMs) using SPSS19.0. GEE and GLMMs models were tested using binary classification repeated measurement data sample using SPSS19.0. Compared with SAS, SPSS19.0 allowed convenient analysis of categorical repeated measurement data using GEE and GLMMs.

Toxocara infection in the United States: the relevance of poverty, geography and demography as risk factors, and implications for estimating county prevalence.

PubMed

Congdon, Peter; Lloyd, Patsy

2011-02-01

To estimate Toxocara infection rates by age, gender and ethnicity for US counties using data from the National Health and Nutrition Examination Survey (NHANES). After initial analysis to account for missing data, a binary regression model is applied to obtain relative risks of Toxocara infection for 20,396 survey subjects. The regression incorporates interplay between demographic attributes (age, ethnicity and gender), family poverty and geographic context (region, metropolitan status). Prevalence estimates for counties are then made, distinguishing between subpopulations in poverty and not in poverty. Even after allowing for elevated infection risk associated with poverty, seropositivity is elevated among Black non-Hispanics and other ethnic groups. There are also distinct effects of region. When regression results are translated into county prevalence estimates, the main influences on variation in county rates are percentages of non-Hispanic Blacks and county poverty. For targeting prevention it is important to assess implications of national survey data for small area prevalence. Using data from NHANES, the study confirms that both individual level risk factors and geographic contextual factors affect chances of Toxocara infection.

Comparison of robustness to outliers between robust poisson models and log-binomial models when estimating relative risks for common binary outcomes: a simulation study.

PubMed

Chen, Wansu; Shi, Jiaxiao; Qian, Lei; Azen, Stanley P

2014-06-26

To estimate relative risks or risk ratios for common binary outcomes, the most popular model-based methods are the robust (also known as modified) Poisson and the log-binomial regression. Of the two methods, it is believed that the log-binomial regression yields more efficient estimators because it is maximum likelihood based, while the robust Poisson model may be less affected by outliers. Evidence to support the robustness of robust Poisson models in comparison with log-binomial models is very limited. In this study a simulation was conducted to evaluate the performance of the two methods in several scenarios where outliers existed. The findings indicate that for data coming from a population where the relationship between the outcome and the covariate was in a simple form (e.g. log-linear), the two models yielded comparable biases and mean square errors. However, if the true relationship contained a higher order term, the robust Poisson models consistently outperformed the log-binomial models even when the level of contamination is low. The robust Poisson models are more robust (or less sensitive) to outliers compared to the log-binomial models when estimating relative risks or risk ratios for common binary outcomes. Users should be aware of the limitations when choosing appropriate models to estimate relative risks or risk ratios.

Sensory impairments of the lower limb after stroke: a pooled analysis of individual patient data.

PubMed

Tyson, Sarah F; Crow, J Lesley; Connell, Louise; Winward, Charlotte; Hillier, Susan

2013-01-01

To obtain more generalizable information on the frequency and factors influencing sensory impairment after stroke and their relationship to mobility and function. A pooled analysis of individual data of stroke survivors (N = 459); mean (SD) age = 67.2 (14.8) years, 54% male, mean (SD) time since stroke = 22.33 (63.1) days, 50% left-sided weakness. Where different measurement tools were used, data were recorded. Descriptive statistics described frequency of sensory impairments, kappa coefficients investigated relationships between sensory modalities, binary logistic regression explored the factors influencing sensory impairments, and linear regression assessed the impact of sensory impairments on activity limitations. Most patients' sensation was intact (55%), and individual sensory modalities were highly associated (κ = 0.60, P < .001). Weakness and neglect influenced sensory impairment (P < .001), but demographics, stroke pathology, and spasticity did not. Sensation influenced independence in activities of daily living, mobility, and balance but less strongly than weakness. Pooled individual data analysis showed sensation of the lower limb is grossly preserved in most stroke survivors but, when present, it affects function. Sensory modalities are highly interrelated; interventions that treat the motor system during functional tasks may be as effective at treating the sensory system as sensory retraining alone.

Research on the Orbital Period of Massive Binaries

NASA Astrophysics Data System (ADS)

Zhao, E.; Qain, S.

2011-12-01

Massive binary is the kind of binary, whose spectral type is earlier than B5. Research on massive binary plays an important role in the mass and angular momentum transfer or loss between the components, and the evolution of binary. Some massive binaries are observed and analyzed, including O-type binary LY Aur, B-type contact binary RZ Pyx and B-type semi-detached binary AI Cru. It is found that all of their periods have a long-term increasing, which indicates that the system is undergoing a Case A slow mass transfer stage on the nuclear time-scale of the secondary. Moreover, analysis show a cyclic change of orbital period, which can be explained by the light-travel effect time of the third body.

MARVELS Radial Velocity Solutions to Seven Kepler Eclipsing Binaries

NASA Astrophysics Data System (ADS)

Heslar, Michael Francis; Thomas, Neil B.; Ge, Jian; Ma, Bo; Herczeg, Alec; Reyes, Alan; SDSS-III MARVELS Team

2016-01-01

Eclipsing binaries serve momentous purposes to improve the basis of understanding aspects of stellar astrophysics, such as the accurate calculation of the physical parameters of stars and the enigmatic mass-radius relationship of M and K dwarfs. We report the investigation results of 7 eclipsing binary candidates, initially identified by the Kepler mission, overlapped with the radial velocity observations from the SDSS-III Multi-Object APO Radial-Velocity Exoplanet Large-Area Survey (MARVELS). The RV extractions and spectroscopic solutions of these eclipsing binaries were generated by the University of Florida's 1D data pipeline with a median RV precision of ~60-100 m/s, which was utilized for the DR12 data release. We performed the cross-reference fitting of the MARVELS RV data and the Kepler photometric fluxes obtained from the Kepler Eclipsing Binary Catalog (V2) and modelled the 7 eclipsing binaries in the BinaryMaker3 and PHOEBE programs. This analysis accurately determined the absolute physical and orbital parameters of each binary. Most of the companion stars were determined to have masses of K and M dwarf stars (0.3-0.8 M⊙), and allowed for an investigation into the mass-radius relationship of M and K dwarfs. Among the cases are KIC 9163796, a 122.2 day period "heartbeat star", a recently-discovered class of eccentric binaries known for tidal distortions and pulsations, with a high eccentricity (e~0.75) and KIC 11244501, a 0.29 day period, contact binary with a double-lined spectrum and mass ratio (q~0.45). We also report on the possible reclassification of 2 Kepler eclipsing binary candidates as background eclipsing binaries based on the analysis of the flux measurements, flux ratios of the spectroscopic and photometric solutions, the differences in the FOVs, the image processing of Kepler, and RV and spectral analysis of MARVELS.

Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models

PubMed Central

Chen, Han; Wang, Chaolong; Conomos, Matthew P.; Stilp, Adrienne M.; Li, Zilin; Sofer, Tamar; Szpiro, Adam A.; Chen, Wei; Brehm, John M.; Celedón, Juan C.; Redline, Susan; Papanicolaou, George J.; Thornton, Timothy A.; Laurie, Cathy C.; Rice, Kenneth; Lin, Xihong

2016-01-01

Linear mixed models (LMMs) are widely used in genome-wide association studies (GWASs) to account for population structure and relatedness, for both continuous and binary traits. Motivated by the failure of LMMs to control type I errors in a GWAS of asthma, a binary trait, we show that LMMs are generally inappropriate for analyzing binary traits when population stratification leads to violation of the LMM’s constant-residual variance assumption. To overcome this problem, we develop a computationally efficient logistic mixed model approach for genome-wide analysis of binary traits, the generalized linear mixed model association test (GMMAT). This approach fits a logistic mixed model once per GWAS and performs score tests under the null hypothesis of no association between a binary trait and individual genetic variants. We show in simulation studies and real data analysis that GMMAT effectively controls for population structure and relatedness when analyzing binary traits in a wide variety of study designs. PMID:27018471

Increased brain white matter axial diffusivity associated with fatigue, pain and hyperalgesia in Gulf War illness.

PubMed

Rayhan, Rakib U; Stevens, Benson W; Timbol, Christian R; Adewuyi, Oluwatoyin; Walitt, Brian; VanMeter, John W; Baraniuk, James N

2013-01-01

Gulf War exposures in 1990 and 1991 have caused 25% to 30% of deployed personnel to develop a syndrome of chronic fatigue, pain, hyperalgesia, cognitive and affective dysfunction. Gulf War veterans (n = 31) and sedentary veteran and civilian controls (n = 20) completed fMRI scans for diffusion tensor imaging. A combination of dolorimetry, subjective reports of pain and fatigue were correlated to white matter diffusivity properties to identify tracts associated with symptom constructs. Gulf War Illness subjects had significantly correlated fatigue, pain, hyperalgesia, and increased axial diffusivity in the right inferior fronto-occipital fasciculus. ROC generated thresholds and subsequent binary regression analysis predicted CMI classification based upon axial diffusivity in the right inferior fronto-occipital fasciculus. These correlates were absent for controls in dichotomous regression analysis. The right inferior fronto-occipital fasciculus may be a potential biomarker for Gulf War Illness. This tract links cortical regions involved in fatigue, pain, emotional and reward processing, and the right ventral attention network in cognition. The axonal neuropathological mechanism(s) explaining increased axial diffusivity may account for the most prominent symptoms of Gulf War Illness.

Risk Factors for Problem Gambling in California: Demographics, Comorbidities and Gambling Participation.

PubMed

Volberg, Rachel A; McNamara, Lauren M; Carris, Kari L

2018-06-01

While population surveys have been carried out in numerous jurisdictions internationally, little has been done to assess the relative strength of different risk factors that may contribute to the development of problem gambling. This is an important preparatory step for future research on the etiology of problem gambling. Using data from the 2006 California Problem Gambling Prevalence Survey, a telephone survey of adult California residents that used the NODS to assess respondents for gambling problems, binary logistic regression analysis was used to identify demographic characteristics, health-related behaviors, and gambling participation variables that statistically predicted the odds of being a problem or pathological gambler. In a separate approach, linear regression analysis was used to assess the impact of changes in these variables on the severity of the disorder. In both of the final models, the greatest statistical predictor of problem gambling status was past year Internet gambling. Furthermore, the unique finding of a significant interaction between physical or mental disability, Internet gambling, and problem gambling highlights the importance of exploring the interactions between different forms of gambling, the experience of mental and physical health issues, and the development of problem gambling using a longitudinal lens.

A cross-sectional assessment of health-related quality of life in Chinese patients with chronic hepatitis c virus infection with EQ-5D.

PubMed

Huang, Rui; Rao, Huiying; Shang, Jia; Chen, Hong; Li, Jun; Xie, Qing; Gao, Zhiliang; Wang, Lei; Wei, Jia; Jiang, Jianning; Sun, Jian; Jiang, Jiaji; Wei, Lai

2018-06-15

Hepatitis C virus (HCV) infection is one of the most common liver infections, with a decrement in HRQoL of HCV patients. This study aims to assess Health-related quality of life (HRQoL) in Chinese patients with chronic HCV infection, and to identify significant predictors of the HRQoL in these patients of China. In this cross-sectional observational study, treatment-naïve Han ethnic adults with chronic HCV infection were enrolled. Adopting European Quality of Life scale (EQ-5D) and EuroQOL visual analogue scale (EQ-VAS) were used to qualify HRQoL. Results were reported in descriptive analyses to describe sociodemographic and clinical characteristics. Multiple linear regression analysis was applied to investigate the associations of these variables with HRQoL. Binary logistic regression analysis was performed to identify associations of these variables with HRQoL by dimensions of EQ-5D. Nine hundred ninety-seven patients were enrolled in the study [median age 46.0 (37.0, 56.0) years; male 54.8%]. Mean EQ-5D index and EQ-VAS score were 0.780 ± 0.083 and 77.2 ± 14.8. Multiple Linear regression analysis showed that income (< 2000 RMB, β = - 0.134; 2000-4999 RMB, β = - 0.085), moderate or severe symptoms of discomfort (more than one symptoms, β = - 0.090), disease profile (cirrhosis, β = - 0.114), hyperlipidemia (β = - 0.065) and depression (β = - 0.065) were independently associated with EQ-5D index. Residence (the west, β = 0.087), income (< 2000 RMB, β = - 0.129; 2000-4999 RMB, β = - 0.052), moderate or severe symptoms of discomfort (more than one symptoms, β = - 0.091), disease profile and depression (β = - 0.316) were the influencing factors on EQ-VAS. Binary logistic regression indicated that disease profile and clinical depression were the major influencing factors on all five dimensions of EQ-5D. In this cross-sectional assessment of HCV patients in China, we indicated HRQoL of Chinese HCV patients. Significant negative associations between HRQoL and sociodemographic and clinical factors such as moderate or severe symptoms of discomfort, disease profile and depression emerged. We have to focus on optimally managing care of HCV patients and improving their HRQoL. ClinicalTrials.gov identifier NCT01293279. Date of registration: February 10, 2011.

Application of two-dimensional binary fingerprinting methods for the design of selective Tankyrase I inhibitors.

PubMed

Muddukrishna, B S; Pai, Vasudev; Lobo, Richard; Pai, Aravinda

2017-11-22

In the present study, five important binary fingerprinting techniques were used to model novel flavones for the selective inhibition of Tankyrase I. From the fingerprints used: the fingerprint atom pairs resulted in a statistically significant 2D QSAR model using a kernel-based partial least square regression method. This model indicates that the presence of electron-donating groups positively contributes to activity, whereas the presence of electron withdrawing groups negatively contributes to activity. This model could be used to develop more potent as well as selective analogues for the inhibition of Tankyrase I. Schematic representation of 2D QSAR work flow.

Malware detection and analysis

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

Chiang, Ken; Lloyd, Levi; Crussell, Jonathan

Embodiments of the invention describe systems and methods for malicious software detection and analysis. A binary executable comprising obfuscated malware on a host device may be received, and incident data indicating a time when the binary executable was received and identifying processes operating on the host device may be recorded. The binary executable is analyzed via a scalable plurality of execution environments, including one or more non-virtual execution environments and one or more virtual execution environments, to generate runtime data and deobfuscation data attributable to the binary executable. At least some of the runtime data and deobfuscation data attributable tomore » the binary executable is stored in a shared database, while at least some of the incident data is stored in a private, non-shared database.« less

Modification of the Sandwich Estimator in Generalized Estimating Equations with Correlated Binary Outcomes in Rare Event and Small Sample Settings

PubMed Central

Rogers, Paul; Stoner, Julie

2016-01-01

Regression models for correlated binary outcomes are commonly fit using a Generalized Estimating Equations (GEE) methodology. GEE uses the Liang and Zeger sandwich estimator to produce unbiased standard error estimators for regression coefficients in large sample settings even when the covariance structure is misspecified. The sandwich estimator performs optimally in balanced designs when the number of participants is large, and there are few repeated measurements. The sandwich estimator is not without drawbacks; its asymptotic properties do not hold in small sample settings. In these situations, the sandwich estimator is biased downwards, underestimating the variances. In this project, a modified form for the sandwich estimator is proposed to correct this deficiency. The performance of this new sandwich estimator is compared to the traditional Liang and Zeger estimator as well as alternative forms proposed by Morel, Pan and Mancl and DeRouen. The performance of each estimator was assessed with 95% coverage probabilities for the regression coefficient estimators using simulated data under various combinations of sample sizes and outcome prevalence values with an Independence (IND), Autoregressive (AR) and Compound Symmetry (CS) correlation structure. This research is motivated by investigations involving rare-event outcomes in aviation data. PMID:26998504

The Use of Linear Instrumental Variables Methods in Health Services Research and Health Economics: A Cautionary Note

PubMed Central

Terza, Joseph V; Bradford, W David; Dismuke, Clara E

2008-01-01

Objective To investigate potential bias in the use of the conventional linear instrumental variables (IV) method for the estimation of causal effects in inherently nonlinear regression settings. Data Sources Smoking Supplement to the 1979 National Health Interview Survey, National Longitudinal Alcohol Epidemiologic Survey, and simulated data. Study Design Potential bias from the use of the linear IV method in nonlinear models is assessed via simulation studies and real world data analyses in two commonly encountered regression setting: (1) models with a nonnegative outcome (e.g., a count) and a continuous endogenous regressor; and (2) models with a binary outcome and a binary endogenous regressor. Principle Findings The simulation analyses show that substantial bias in the estimation of causal effects can result from applying the conventional IV method in inherently nonlinear regression settings. Moreover, the bias is not attenuated as the sample size increases. This point is further illustrated in the survey data analyses in which IV-based estimates of the relevant causal effects diverge substantially from those obtained with appropriate nonlinear estimation methods. Conclusions We offer this research as a cautionary note to those who would opt for the use of linear specifications in inherently nonlinear settings involving endogeneity. PMID:18546544

Prediction of cold and heat patterns using anthropometric measures based on machine learning.

PubMed

Lee, Bum Ju; Lee, Jae Chul; Nam, Jiho; Kim, Jong Yeol

2018-01-01

To examine the association of body shape with cold and heat patterns, to determine which anthropometric measure is the best indicator for discriminating between the two patterns, and to investigate whether using a combination of measures can improve the predictive power to diagnose these patterns. Based on a total of 4,859 subjects (3,000 women and 1,859 men), statistical analyses using binary logistic regression were performed to assess the significance of the difference and the predictive power of each anthropometric measure, and binary logistic regression and Naive Bayes with the variable selection technique were used to assess the improvement in the predictive power of the patterns using the combined measures. In women, the strongest indicators for determining the cold and heat patterns among anthropometric measures were body mass index (BMI) and rib circumference; in men, the best indicator was BMI. In experiments using a combination of measures, the values of the area under the receiver operating characteristic curve in women were 0.776 by Naive Bayes and 0.772 by logistic regression, and the values in men were 0.788 by Naive Bayes and 0.779 by logistic regression. Individuals with a higher BMI have a tendency toward a heat pattern in both women and men. The use of a combination of anthropometric measures can slightly improve the diagnostic accuracy. Our findings can provide fundamental information for the diagnosis of cold and heat patterns based on body shape for personalized medicine.

Association of Brain-Derived Neurotrophic Factor and Vitamin D with Depression and Obesity: A Population-Based Study.

PubMed

Goltz, Annemarie; Janowitz, Deborah; Hannemann, Anke; Nauck, Matthias; Hoffmann, Johanna; Seyfart, Tom; Völzke, Henry; Terock, Jan; Grabe, Hans Jörgen

2018-06-19

Depression and obesity are widespread and closely linked. Brain-derived neurotrophic factor (BDNF) and vitamin D are both assumed to be associated with depression and obesity. Little is known about the interplay between vitamin D and BDNF. We explored the putative associations and interactions between serum BDNF and vitamin D levels with depressive symptoms and abdominal obesity in a large population-based cohort. Data were obtained from the population-based Study of Health in Pomerania (SHIP)-Trend (n = 3,926). The associations of serum BDNF and vitamin D levels with depressive symptoms (measured using the Patient Health Questionnaire) were assessed with binary and multinomial logistic regression models. The associations of serum BDNF and vitamin D levels with obesity (measured by the waist-to-hip ratio [WHR]) were assessed with binary logistic and linear regression models with restricted cubic splines. Logistic regression models revealed inverse associations of vitamin D with depression (OR = 0.966; 95% CI 0.951-0.981) and obesity (OR = 0.976; 95% CI 0.967-0.985). No linear association of serum BDNF with depression or obesity was found. However, linear regression models revealed a U-shaped association of BDNF with WHR (p < 0.001). Vitamin D was inversely associated with depression and obesity. BDNF was associated with abdominal obesity, but not with depression. At the population level, our results support the relevant roles of vitamin D and BDNF in mental and physical health-related outcomes. © 2018 S. Karger AG, Basel.

Hard state neutron star and black hole X-ray binaries in the radio:X-ray luminosity plane

NASA Astrophysics Data System (ADS)

Gallo, Elena; Degenaar, Nathalie; van den Eijnden, Jakob

2018-07-01

Motivated by the large body of literature around the phenomenological properties of accreting black hole (BH) and neutron star (NS) X-ray binaries in the radio:X-ray luminosity plane, we carry out a comparative regression analysis on 36 BHs and 41 NSs in hard X-ray states, with data over 7 dex in X-ray luminosity for both. The BHs follow a radio to X-ray (logarithmic) luminosity relation with slope β = 0.59 ± 0.02, consistent with the NSs' slope (β =0.44^{+0.05}_{-0.04}) within 2.5σ. The best-fitting intercept for the BHs significantly exceeds that for the NSs, cementing BHs as more radio loud, by a factor ˜22. This discrepancy cannot be fully accounted for by the mass or bolometric correction gap, or by the NS boundary layer contribution to the X-rays, and is likely to reflect physical differences in the accretion flow efficiency, or the jet powering mechanism. Once importance sampling is implemented to account for the different luminosity distributions, the slopes of the non-pulsating and pulsating NS subsamples are formally inconsistent (>3σ), unless the transitional millisecond pulsars (whose incoherent radio emission mechanism is not firmly established) are excluded from the analysis. We confirm the lack of a robust partitioning of the BH data set into separate luminosity tracks.

Hard state neutron star and black hole X-ray binaries in the radio:X-ray luminosity plane

NASA Astrophysics Data System (ADS)

Gallo, Elena; Degenaar, Nathalie; van den Eijnden, Jakob

2018-05-01

Motivated by the large body of literature around the phenomenological properties of accreting black hole (BH) and neutron star (NS) X-ray binaries in the radio:X-ray luminosity plane, we carry out a comparative regression analysis on 36 BHs and 41 NSs in hard X-ray states, with data over 7 dex in X-ray luminosity for both. The BHs follow a radio to X-ray (logarithmic) luminosity relation with slope β = 0.59 ± 0.02, consistent with the NSs' slope (β =0.44^{+0.05}_{-0.04}) within 2.5σ. The best-fitting intercept for the BHs significantly exceeds that for the NSs, cementing BHs as more radio loud, by a factor ˜22. This discrepancy can not be fully accounted for by the mass or bolometric correction gap, nor by the NS boundary layer contribution to the X-rays, and is likely to reflect physical differences in the accretion flow efficiency, or the jet powering mechanism. Once importance sampling is implemented to account for the different luminosity distributions, the slopes of the non-pulsating and pulsating NS subsamples are formally inconsistent (>3σ), unless the transitional millisecond pulsars (whose incoherent radio emission mechanism is not firmly established) are excluded from the analysis. We confirm the lack of a robust partitioning of the BH data set into separate luminosity tracks.

Baseline adjustments for binary data in repeated cross-sectional cluster randomized trials.

PubMed

Nixon, R M; Thompson, S G

2003-09-15

Analysis of covariance models, which adjust for a baseline covariate, are often used to compare treatment groups in a controlled trial in which individuals are randomized. Such analysis adjusts for any baseline imbalance and usually increases the precision of the treatment effect estimate. We assess the value of such adjustments in the context of a cluster randomized trial with repeated cross-sectional design and a binary outcome. In such a design, a new sample of individuals is taken from the clusters at each measurement occasion, so that baseline adjustment has to be at the cluster level. Logistic regression models are used to analyse the data, with cluster level random effects to allow for different outcome probabilities in each cluster. We compare the estimated treatment effect and its precision in models that incorporate a covariate measuring the cluster level probabilities at baseline and those that do not. In two data sets, taken from a cluster randomized trial in the treatment of menorrhagia, the value of baseline adjustment is only evident when the number of subjects per cluster is large. We assess the generalizability of these findings by undertaking a simulation study, and find that increased precision of the treatment effect requires both large cluster sizes and substantial heterogeneity between clusters at baseline, but baseline imbalance arising by chance in a randomized study can always be effectively adjusted for. Copyright 2003 John Wiley & Sons, Ltd.

Is the cluster environment quenching the Seyfert activity in elliptical and spiral galaxies?

NASA Astrophysics Data System (ADS)

de Souza, R. S.; Dantas, M. L. L.; Krone-Martins, A.; Cameron, E.; Coelho, P.; Hattab, M. W.; de Val-Borro, M.; Hilbe, J. M.; Elliott, J.; Hagen, A.; COIN Collaboration

2016-09-01

We developed a hierarchical Bayesian model (HBM) to investigate how the presence of Seyfert activity relates to their environment, herein represented by the galaxy cluster mass, M200, and the normalized cluster centric distance, r/r200. We achieved this by constructing an unbiased sample of galaxies from the Sloan Digital Sky Survey, with morphological classifications provided by the Galaxy Zoo Project. A propensity score matching approach is introduced to control the effects of confounding variables: stellar mass, galaxy colour, and star formation rate. The connection between Seyfert-activity and environmental properties in the de-biased sample is modelled within an HBM framework using the so-called logistic regression technique, suitable for the analysis of binary data (e.g. whether or not a galaxy hosts an AGN). Unlike standard ordinary least square fitting methods, our methodology naturally allows modelling the probability of Seyfert-AGN activity in galaxies on their natural scale, I.e. as a binary variable. Furthermore, we demonstrate how an HBM can incorporate information of each particular galaxy morphological type in an unified framework. In elliptical galaxies our analysis indicates a strong correlation of Seyfert-AGN activity with r/r200, and a weaker correlation with the mass of the host cluster. In spiral galaxies these trends do not appear, suggesting that the link between Seyfert activity and the properties of spiral galaxies are independent of the environment.

Non-specific low back pain: occupational or lifestyle consequences?

PubMed

Stričević, Jadranka; Papež, Breda Jesenšek

2015-12-01

Nursing occupation was identified as a risk occupation for the development of low back pain (LBP). The aim of our study was to find out how much occupational factors influence the development of LBP in hospital nursing personnel. Non-experimental approach with a cross-sectional survey and statistical analysis. Nine hundred questionnaires were distributed among nursing personnel, 663 were returned and 659 (73.2 %) were considered for the analysis. Univariate and multivariate statistics for LBP risk was calculated by the binary logistic regression. The χ(2), influence factor, 95 % confidence interval and P value were calculated. Multivariate binary logistic regression was calculated by the Wald method to omit insignificant variables. Not performing exercises represented the highest risk for the development of LBP (OR 2.8, 95 % CI 1.7-4.4; p < 0.001). The second and third ranked risk factors were frequent manual lifting > 10 kg (OR 2.4, 95 % CI 1.5-3.8; p < 0.001) and duration of employment ≥ 19 years (OR 2.4, 95 % CI 1.6-3.7; p < 0.001). The fourth ranked risk factor was better physical condition by frequent recreation and sports, which reduced the risk for the development of LBP (OR 0.4, 95 % CI 0.3-0.7; p = 0.001). Work with the computer ≥ 2 h per day as last significant risk factor also reduced the risk for the development of LBP (OR 0.6, 95 % CI 0.4-0.1; p = 0.049). Risk factors for LBP established in our study (exercises, duration of employment, frequent manual lifting, recreation and sports and work with the computer) are not specifically linked to the working environment of the nursing personnel. Rather than focusing on mechanical causes and direct workload in the development of non-specific LBP, the complex approach to LBP including genetics, psychosocial environment, lifestyle and quality of life is coming more to the fore.

Knowledge Attıtudes and Behavıors About Organ Donatıon Among Fırst- and Sıxth-class Medıcal Students: A Study From Turkey.

PubMed

Naçar, M; Çetinkaya, F; Baykan, Z; Elmalı, F

2015-01-01

The aim of this study is to determine the knowledge, attitude, and behaviors of Erciyes University School of Medicine students regarding organ donation. This descriptive study was conducted in 2014 on Erciyes University School of Medicine first- and sixth-grade students via questionnaire. It was to be conducted on all 490 students; in total, 464 students were enrolled-304 from first grade and 160 from sixth grade. Data were analyzed using descriptive statistics, χ(2) test, and binary logistic regression analysis. The mean age was 20.9 ± 2.8 years and it was found that 48.9% were male, 65.5% were in first grade; 50.0% of the students who participated in the study were considering donating their organs and this rate is 45.4% in the first grade and 58.8% at sixth grade. Those who donated their organs were 3.4% in the entire group and were 1.6% and 6.9% consequently in first and sixth grades. Those who are; at the sixth grade, female gender, those who feel themselves responsible for the donation of society, who think organ donation is appropriate in terms of religion and conversations within family about organ donations significantly want organ donation more statistically. However, grade and gender had no effect on wishing donating organs according to binary logistic regression analysis. The rate of feeling themselves responsible from the donation in society was 73.9% and finding organ donation appropriate in terms of religion was 75.6% and there wasn't significant difference between first and sixth grades. Although there are increases in many variables about this issue at sixth grade, students are unable to gain sufficient attitude and behavior about organ donation. Training can be planned during medical educations in terms of gaining attitudes and behaviors about the issue. Copyright © 2015 Elsevier Inc. All rights reserved.

Association of funding and conclusions in randomized drug trials: a reflection of treatment effect or adverse events?

PubMed

Als-Nielsen, Bodil; Chen, Wendong; Gluud, Christian; Kjaergard, Lise L

2003-08-20

Previous studies indicate that industry-sponsored trials tend to draw proindustry conclusions. To explore whether the association between funding and conclusions in randomized drug trials reflects treatment effects or adverse events. Observational study of 370 randomized drug trials included in meta-analyses from Cochrane reviews selected from the Cochrane Library, May 2001. From a random sample of 167 Cochrane reviews, 25 contained eligible meta-analyses (assessed a binary outcome; pooled at least 5 full-paper trials of which at least 1 reported adequate and 1 reported inadequate allocation concealment). The primary binary outcome from each meta-analysis was considered the primary outcome for all trials included in each meta-analysis. The association between funding and conclusions was analyzed by logistic regression with adjustment for treatment effect, adverse events, and additional confounding factors (methodological quality, control intervention, sample size, publication year, and place of publication). Conclusions in trials, classified into whether the experimental drug was recommended as the treatment of choice or not. The experimental drug was recommended as treatment of choice in 16% of trials funded by nonprofit organizations, 30% of trials not reporting funding, 35% of trials funded by both nonprofit and for-profit organizations, and 51% of trials funded by for-profit organizations (P<.001; chi2 test). Logistic regression analyses indicated that funding, treatment effect, and double blinding were the only significant predictors of conclusions. Adjusted analyses showed that trials funded by for-profit organizations were significantly more likely to recommend the experimental drug as treatment of choice (odds ratio, 5.3; 95% confidence interval, 2.0-14.4) compared with trials funded by nonprofit organizations. This association did not appear to reflect treatment effect or adverse events. Conclusions in trials funded by for-profit organizations may be more positive due to biased interpretation of trial results. Readers should carefully evaluate whether conclusions in randomized trials are supported by data.

Low uric acid level increases the risk of infectious mononucleosis and this effect is more pronounced in women

PubMed Central

Zhang, Li; Zhou, Pingping; Meng, Zhaowei; Gong, Lu; Pang, Chongjie; Li, Xue; Jia, Qiang; Tan, Jian; Liu, Na; Hu, Tianpeng; Zhang, Qing; Jia, Qiyu; Song, Kun

2017-01-01

Infectious mononucleosis (IM) due to Epstein-Barr virus infection is common. Uric acid (UA) is an important endogenous antioxidant. To the best of our knowledge, the association between UA and IM has not been comprehensively investigated to date. The aim of the present study was to investigate this association in Chinese patients. A total of 95 patients (47 men and 48 women) with IM were recruited, along with 95 healthy controls. Clinical data were classified by patient sex. Receiver operating characteristic (ROC) curve analysis was adopted to determine the cut-off values of UA for IM diagnosis and prediction. Crude and adjusted odds ratios (ORs) of UA for IM were analyzed by binary logistic regression. The UA levels were significantly lower in IM patients compared with those in controls. In addition, UA levels in men were significantly higher compared with those in women. The ROC curve demonstrated good diagnostic and predictive values of UA for IM in both sexes. The UA cut-off values were 326.00 and 243.50 µmol/l for diagnosing IM in men and women, respectively, with a diagnostic accuracy of 76.596 and 80.208%, respectively. Binary logistic regression analysis revealed a significant risk of IM in the low UA quartiles in both sexes. Following adjustments, the ORs even increased. Women with low UA levels appeared to be more susceptible to IM. For example, the crude ORs in quartile 1 were 24.000 and 52.500 for men and women, respectively, and the respective adjusted ORs were 31.437 and 301.746 (all P<0.01). To the best of our knowledge, the present study is the first to demonstrate the inverse association between UA and IM, suggesting a progressive decrease of antioxidant reserve in IM. Moreover, low UA was suggestive of IM, particularly in women. PMID:29285370

Knowledge, attitude, and practices related to cervical cancer among adult women: A hospital-based cross-sectional study.

PubMed

Bansal, Agam B; Pakhare, Abhijit P; Kapoor, Neelkamal; Mehrotra, Ragini; Kokane, Arun Mahadeo

2015-01-01

Cervical cancer is the most common cancer among Indian women of reproductive age. Unfortunately, despite the evidence of methods for prevention, most of the women remain unscreened. The reported barriers to screening include unawareness of risk factors, symptoms and prevention; stigma and misconceptions about gynecological diseases and lack of national cervical cancer screening guidelines and policies. This study attempts to assess the knowledge, attitude, and practices related to cervical cancer and its screening among women of reproductive age (15-45 years). A facility-based cross-sectional study was done on 400 females of reproductive age who presented to out-patient-department of All India Institute of Medical Sciences Bhopal. Structured questionnaire consisting 20 knowledge items and 7-items for attitude and history of pap smear for practices were administered by one of the investigators after informed consent. Data were entered and analyzed using Epi-Info version 7. Qualitative variables were summarized as counts and percentages while quantitative variables as mean and standard deviation. Predictors of better knowledge, attitude, and practices were identified by binary logistic regression analysis. A total of 442 women were approached for interview of which 400 responded of which two-third (65.5%) had heard of cervical cancer. At least one symptom and one risk factor were known to 35.25% and 39.75% participants. Only 34.5% participants had heard, and 9.5% actually underwent screening test, however, 76.25% of the participants expressed a favorable attitude for screening. Binary logistic regression analysis revealed that education age and income were independent predictors of better knowledge. Education level influences attitude toward screening and actual practice depends on age, income, and marital status. This study shows that despite the fact that women had suboptimal level of knowledge regarding cervical cancer, their attitude is favorable for screening. However, uptake is low in actual practice. Strategic communication targeting eligible women may increase the uptake of screening.

Predicting the Risk of Breakthrough Urinary Tract Infections: Primary Vesicoureteral Reflux.

PubMed

Hidas, Guy; Billimek, John; Nam, Alexander; Soltani, Tandis; Kelly, Maryellen S; Selby, Blake; Dorgalli, Crystal; Wehbi, Elias; McAleer, Irene; McLorie, Gordon; Greenfield, Sheldon; Kaplan, Sherrie H; Khoury, Antoine E

2015-11-01

We constructed a risk prediction instrument stratifying patients with primary vesicoureteral reflux into groups according to their 2-year probability of breakthrough urinary tract infection. Demographic and clinical information was retrospectively collected in children diagnosed with primary vesicoureteral reflux and followed for 2 years. Bivariate and binary logistic regression analyses were performed to identify factors associated with breakthrough urinary tract infection. The final regression model was used to compute an estimation of the 2-year probability of breakthrough urinary tract infection for each subject. Accuracy of the binary classifier for breakthrough urinary tract infection was evaluated using receiver operator curve analysis. Three distinct risk groups were identified. The model was then validated in a prospective cohort. A total of 252 bivariate analyses showed that high grade (IV or V) vesicoureteral reflux (OR 9.4, 95% CI 3.8-23.5, p <0.001), presentation after urinary tract infection (OR 5.3, 95% CI 1.1-24.7, p = 0.034) and female gender (OR 2.6, 95% CI 0.097-7.11, p <0.054) were important risk factors for breakthrough urinary tract infection. Subgroup analysis revealed bladder and bowel dysfunction was a significant risk factor more pronounced in low grade (I to III) vesicoureteral reflux (OR 2.8, p = 0.018). The estimation model was applied for prospective validation, which demonstrated predicted vs actual 2-year breakthrough urinary tract infection rates of 19% vs 21%. Stratifying the patients into 3 risk groups based on parameters in the risk model showed 2-year risk for breakthrough urinary tract infection was 8.6%, 26.0% and 62.5% in the low, intermediate and high risk groups, respectively. This proposed risk stratification and probability model allows prediction of 2-year risk of patient breakthrough urinary tract infection to better inform parents of possible outcomes and treatment strategies. Copyright © 2015 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Predictive Risk Factors in the Development of Intraoperative Hyperkalemia in Adult Living Donor Liver Transplantation.

PubMed

Juang, S-E; Huang, C-E; Chen, C-L; Wang, C-H; Huang, C-J; Cheng, K-W; Wu, S-C; Shih, T-H; Yang, S-C; Wong, Z-W; Jawan, B; Lee, Y-E

2016-05-01

Hyperkalemia, defined as a serum potassium level higher than 5 mEq/L, is common in the liver transplantation setting. Severe hyperkalemia may induce fatal cardiac arrhythmias; therefore, it should be monitored and treated accordingly. The aim of the current retrospective study is to evaluate and indentify the predictive risk factors of hyperkalemia during living-donor liver transplantation (LDLT). Four hundred eighty-seven adult LDLT patients were included in the study. Intraoperative serum potassium levels were monitored at least five times during LDLT; patients with a potassium level higher than 5 mEq/L were included in group 1, and the others with normokalemia in group 2. Patients' categorical characteristics and intraoperative numeric variables with a P value <.1 were selected into a multiple binary logistic regression model. In multivariate analysis, a P value of <.05 is regarded as a risk factor in the development of hyperkalemia. Fifty-one of 487 (10.4%) patients had hyperkalemia with a serum potassium level higher than 5.0 mEq/L during LDLT. Predictive factors with P < .1 in univariate analysis (Table 1), such as anesthesia time, preoperative albumin level, Model for End-stage Liver Disease score, preoperative bilirubin level, amount of blood loss, red blood cell (RBC) and fresh frozen plasma transfused, 5% albumin administered, hemoglobin at the end of surgery, and the amount of furosemide used, were further analyzed by multivariate binary regression. Results show that the anesthesia time, preoperative serum albumin level, and RBC count are determinant risk factors in the development of the hyperkalemia in our LDLT serials. Prolonged anesthesia time, preoperative serum albumin level, and intraoperative RBC transfusion are three determinant factors in the development of intraoperative hyperkalemia, and close monitoring of serum potassium levels in patients with abovementioned risk factors are recommended. Copyright © 2016 Elsevier Inc. All rights reserved.

Progressive Occlusion of Small Saccular Aneurysms Incompletely Occluded After Stent-Assisted Coil Embolization : Analysis of Related Factors and Long-Term Outcomes.

PubMed

Lim, Jeong Wook; Lee, Jeongjun; Cho, Young Dae

2017-08-08

Incompletely occluded aneurysms after coil embolization are subject to recanalization but occasionally progress to a totally occluded state. Deployed stents may actually promote thrombosis of coiled aneurysms. We evaluated outcomes of small aneurysms (<10 mm) wherein saccular filling with contrast medium was evident after stent-assisted coiling, assessing factors implicated in subsequent progressive occlusion. Between September 2012 and June 2016, a total of 463 intracranial aneurysms were treated by stent-assisted coil embolization. Of these, 132 small saccular aneurysms displayed saccular filling with contrast medium in the immediate aftermath of coiling. Progressive thrombosis was defined as complete aneurysmal occlusion at the 6‑month follow-up point. Rates of progressive occlusion and factors predisposing to this were analyzed via binary logistic regression. In 101 (76.5%) of the 132 intracranial aneurysms, complete occlusion was observed in follow-up imaging studies at 6 months. Binary logistic regression analysis indicated that progressive occlusion was linked to smaller neck diameter (odds ratio [OR] = 1.533; p = 0.003), hyperlipidemia (OR = 3.329; p = 0.036) and stent type (p = 0.031). The LVIS stent is especially susceptible to progressive thrombosis, more so than Neuroform (OR = 0.098; p = 0.008) or Enterprise (OR = 0.317; p = 0.098) stents. In 57 instances of progressive thrombosis, followed for ≥12 months (mean 25.0 ± 10.7 months), 56 (98.2%) were stable, with minor recanalization noted once (1.8%) and no major recanalization. Aneurysms associated with smaller diameter necks, hyperlipidemic states and LVIS stent deployment may be inclined to possible thrombosis, if occlusion immediately after stent-assisted coil embolization is incomplete. In such instances, excellent long-term durability is anticipated.

Chronic obstructive pulmonary disease and coronary disease: COPDCoRi, a simple and effective algorithm for predicting the risk of coronary artery disease in COPD patients.

PubMed

Cazzola, Mario; Calzetta, Luigino; Matera, Maria Gabriella; Muscoli, Saverio; Rogliani, Paola; Romeo, Francesco

2015-08-01

Chronic obstructive pulmonary disease (COPD) is often associated with cardiovascular artery disease (CAD), representing a potential and independent risk factor for cardiovascular morbidity. Therefore, the aim of this study was to identify an algorithm for predicting the risk of CAD in COPD patients. We analyzed data of patients afferent to the Cardiology ward and the Respiratory Diseases outpatient clinic of Tor Vergata University (2010-2012, 1596 records). The study population was clustered as training population (COPD patients undergoing coronary arteriography), control population (non-COPD patients undergoing coronary arteriography), test population (COPD patients whose records reported information on the coronary status). The predicting model was built via causal relationship between variables, stepwise binary logistic regression and Hosmer-Lemeshow analysis. The algorithm was validated via split-sample validation method and receiver operating characteristics (ROC) curve analysis. The diagnostic accuracy was assessed. In training population the variables gender (men/women OR: 1.7, 95%CI: 1.237-2.5, P < 0.05), dyslipidemia (OR: 1.8, 95%CI: 1.2-2.5, P < 0.01) and smoking habit (OR: 1.5, 95%CI: 1.2-1.9, P < 0.001) were significantly associated with CAD in COPD patients, whereas in control population also age and diabetes were correlated. The stepwise binary logistic regressions permitted to build a well fitting predictive model for training population but not for control population. The predictive algorithm shown a diagnostic accuracy of 81.5% (95%CI: 77.78-84.71) and an AUC of 0.81 (95%CI: 0.78-0.85) for the validation set. The proposed algorithm is effective for predicting the risk of CAD in COPD patients via a rapid, inexpensive and non-invasive approach. Copyright © 2015 Elsevier Ltd. All rights reserved.

Association of the leptin-to-adiponectin ratio with metabolic syndrome in a sub-Saharan African population.

PubMed

Ayina, Clarisse Noël A; Endomba, Francky Teddy A; Mandengue, Samuel Honoré; Noubiap, Jean Jacques N; Ngoa, Laurent Serge Etoundi; Boudou, Philippe; Gautier, Jean-François; Mbanya, Jean Claude; Sobngwi, Eugene

2017-01-01

Worldwide there is an increased prevalence of metabolic syndrome mainly due to life-style modifications, and Africans are not saved of this situation. Many markers have been studied to predict the risk of this syndrome but the most used are leptin and adiponectin. Data on these metabolic markers are scare in Africa and this study aimed to assess the association between the leptin-to-adiponectin ratio (LAR) with metabolic syndrome in a Cameroonian population. This was a cross-sectional study that included 476 adults among a general population of Cameroon. Data collected concerned the body mass index, waist circumference, systolic blood pressure, diastolic blood pressure, fasting blood glucose, plasma lipids, adiponectin, leptin, insulin and homeostasis model for assessment of insulin resistance (HOMA-IR). To assess correlations we used Spearman's analyses and association of the studied variables with metabolic syndrome were done using binary logistic regression analysis. The leptin to adiponectin ratio was significantly and positively correlated with the body mass index (r = 0.669, p < 0.0001), waist circumference (r = 0.595, p < 0.0001), triglycerides (r = 0.190, p = 0.001), insulin levels (r = 0.333, p < 0.0001) and HOMA-IR (r = 0.306, p < 0.0001). Binary logistic regression analysis revealed that leptin, adiponectin and LAR were significantly associated with metabolic syndrome with respective unadjusted OR of 1.429, 0.468 and 1.502. After adjustment, for age and sex, the associations remained significative; LAR was also found to be significantly associated with metabolic syndrome (OR = 1.573, p value =0.000) as well as lower levels of adiponectin (OR = 0.359, p value =0.000) and higher levels of leptin (OR = 1.469, p value =0.001). This study revealed that LAR is significantly associated with metabolic syndrome in sub-Saharan African population, independently to age and sex.

Spectroscopy of hot subdwarf binaries

NASA Astrophysics Data System (ADS)

Kreuzer, Simon; Irrgang, Andreas; Heber, Ulrich

2018-06-01

We present a status report of our spectroscopic analysis of subdwarf binaries consisting of a subdwarf and a F/G/K-type main-sequence companion. These systems selected from SDSS photometry show significant excess in the (infra-)red which can not be explained by interstellar reddening. Inspection of SDSS spectra revealed that most of them are composite spectrum sdB binaries. Once their spectra are disentangled, a detailed spectral analysis can be carried out. It reveals Teff, log g and the metal abundance of each individual star. The cool companion is of particular interest, because its spectrum reveals the original chemical composition of the binary.

Predictors of Employment Outcomes for State-Federal Vocational Rehabilitation Consumers with HIV/AIDS

ERIC Educational Resources Information Center

Jung, Youngoh; Schaller, James; Bellini, James

2010-01-01

In this study, the authors investigated the effects of demographic, medical, and vocational rehabilitation service variables on employment outcomes of persons living with HIV/AIDS. Binary logistic regression analyses were conducted to determine predictors of employment outcomes using two groups drawn from Rehabilitation Services Administration…

Dynamics of Volunteering in Older Europeans

ERIC Educational Resources Information Center

Hank, Karsten; Erlinghagen, Marcel

2010-01-01

Purpose: To investigate the dynamics of volunteering in the population aged 50 years or older across 11 Continental European countries. Design and Methods: Using longitudinal data from the first 2 waves of the Survey of Health, Ageing and Retirement in Europe, we run multivariate regressions on a set of binary-dependent variables indicating…

Who Benefits from Tuition Discounts at Public Universities?

ERIC Educational Resources Information Center

Hillman, Nicholas W.

2010-01-01

This article uses data from the 2004 National Postsecondary Student Aid Study to provide insight about the range of tuition discounting practices at public institutions. Specifically, it examines the characteristics of students who receive tuition discounts from public four-year colleges and universities. A binary logistic regression is applied to…

An Examination of Master's Student Retention & Completion

ERIC Educational Resources Information Center

Barry, Melissa; Mathies, Charles

2011-01-01

This study was conducted at a research-extensive public university in the southeastern United States. It examined the retention and completion of master's degree students across numerous disciplines. Results were derived from a series of descriptive statistics, T-tests, and a series of binary logistic regression models. The findings from binary…

Graduate Unemployment in South Africa: Social Inequality Reproduced

ERIC Educational Resources Information Center

Baldry, Kim

2016-01-01

In this study, I examine the influence of demographic and educational characteristics of South African graduates on their employment/unemployment status. A sample of 1175 respondents who graduated between 2006 and 2012 completed an online survey. Using binary logistic regression, the strongest determinants of unemployment were the graduates' race,…

Commitment of Licensed Social Workers to Aging Practice

ERIC Educational Resources Information Center

Simons, Kelsey; Bonifas, Robin; Gammonley, Denise

2011-01-01

This study sought to identify client, professional, and employment characteristics that enhance licensed social workers' commitment to aging practice. A series of binary logistic regressions were performed using data from 181 licensed, full-time social workers who reported aging as their primary specialty area as part of the 2004 NASW's national…

Comparison of two gas chromatograph models and analysis of binary data

NASA Technical Reports Server (NTRS)

Keba, P. S.; Woodrow, P. T.

1972-01-01

The overall objective of the gas chromatograph system studies is to generate fundamental design criteria and techniques to be used in the optimum design of the system. The particular tasks currently being undertaken are the comparison of two mathematical models of the chromatograph and the analysis of binary system data. The predictions of two mathematical models, an equilibrium absorption model and a non-equilibrium absorption model exhibit the same weaknesses in their inability to predict chromatogram spreading for certain systems. The analysis of binary data using the equilibrium absorption model confirms that, for the systems considered, superposition of predicted single component behaviors is a first order representation of actual binary data. Composition effects produce non-idealities which limit the rigorous validity of superposition.

High statistical heterogeneity is more frequent in meta-analysis of continuous than binary outcomes.

PubMed

Alba, Ana C; Alexander, Paul E; Chang, Joanne; MacIsaac, John; DeFry, Samantha; Guyatt, Gordon H

2016-02-01

We compared the distribution of heterogeneity in meta-analyses of binary and continuous outcomes. We searched citations in MEDLINE and Cochrane databases for meta-analyses of randomized trials published in 2012 that reported a measure of heterogeneity of either binary or continuous outcomes. Two reviewers independently performed eligibility screening and data abstraction. We evaluated the distribution of I(2) in meta-analyses of binary and continuous outcomes and explored hypotheses explaining the difference in distributions. After full-text screening, we selected 671 meta-analyses evaluating 557 binary and 352 continuous outcomes. Heterogeneity as assessed by I(2) proved higher in continuous than in binary outcomes: the proportion of continuous and binary outcomes reporting an I(2) of 0% was 34% vs. 52%, respectively, and reporting an I(2) of 60-100% was 39% vs. 14%. In continuous but not binary outcomes, I(2) increased with larger number of studies included in a meta-analysis. Increased precision and sample size do not explain the larger I(2) found in meta-analyses of continuous outcomes with a larger number of studies. Meta-analyses evaluating continuous outcomes showed substantially higher I(2) than meta-analyses of binary outcomes. Results suggest differing standards for interpreting I(2) in continuous vs. binary outcomes may be appropriate. Copyright © 2016 Elsevier Inc. All rights reserved.

A survival tree method for the analysis of discrete event times in clinical and epidemiological studies.

PubMed

Schmid, Matthias; Küchenhoff, Helmut; Hoerauf, Achim; Tutz, Gerhard

2016-02-28

Survival trees are a popular alternative to parametric survival modeling when there are interactions between the predictor variables or when the aim is to stratify patients into prognostic subgroups. A limitation of classical survival tree methodology is that most algorithms for tree construction are designed for continuous outcome variables. Hence, classical methods might not be appropriate if failure time data are measured on a discrete time scale (as is often the case in longitudinal studies where data are collected, e.g., quarterly or yearly). To address this issue, we develop a method for discrete survival tree construction. The proposed technique is based on the result that the likelihood of a discrete survival model is equivalent to the likelihood of a regression model for binary outcome data. Hence, we modify tree construction methods for binary outcomes such that they result in optimized partitions for the estimation of discrete hazard functions. By applying the proposed method to data from a randomized trial in patients with filarial lymphedema, we demonstrate how discrete survival trees can be used to identify clinically relevant patient groups with similar survival behavior. Copyright © 2015 John Wiley & Sons, Ltd.

Risk Factors of Subacute Thrombosis After Intracranial Stenting for Symptomatic Intracranial Arterial Stenosis.

PubMed

Sun, Xuan; Tong, Xu; Lo, Wai Ting; Mo, Dapeng; Gao, Feng; Ma, Ning; Wang, Bo; Miao, Zhongrong

2017-03-01

We aimed to explore the risk factors of subacute thrombosis (SAT) after intracranial stenting for patients with symptomatic intracranial arterial stenosis. From January to December 2013, all symptomatic intracranial arterial stenosis patients who underwent intracranial stenting in Beijing Tiantan Hospital were prospectively registered into this study. Baseline clinical features and operative data were compared in patients who developed SAT with those who did not. Binary logistic regression model was used to determine the risk factors associated with SAT. Of the 221 patients enrolled, 9 (4.1%) cases had SAT 2 to 8 days after stenting. Binary logistic analysis showed that SAT was related with tandem stenting (odds ratio [OR], 11.278; 95% confidence interval [CI], 2.422-52.519) and antiplatelet resistance (aspirin resistance: OR, 6.267; 95% CI, 1.574-24.952; clopidogrel resistance: OR, 15.526; 95% CI, 3.105-77.626; aspirin and clopidogrel resistance: OR, 12.246; 95% CI, 2.932-51.147; and aspirin or clopidogrel resistance: OR, 11.340; 95% CI, 2.282-56.344). Tandem stenting and antiplatelet resistance might contribute to the development of SAT after intracranial stenting in patients with symptomatic intracranial arterial stenosis. © 2017 American Heart Association, Inc.

Heterogeneous Suppression of Sequential Effects in Random Sequence Generation, but Not in Operant Learning.

PubMed

Shteingart, Hanan; Loewenstein, Yonatan

2016-01-01

There is a long history of experiments in which participants are instructed to generate a long sequence of binary random numbers. The scope of this line of research has shifted over the years from identifying the basic psychological principles and/or the heuristics that lead to deviations from randomness, to one of predicting future choices. In this paper, we used generalized linear regression and the framework of Reinforcement Learning in order to address both points. In particular, we used logistic regression analysis in order to characterize the temporal sequence of participants' choices. Surprisingly, a population analysis indicated that the contribution of the most recent trial has only a weak effect on behavior, compared to more preceding trials, a result that seems irreconcilable with standard sequential effects that decay monotonously with the delay. However, when considering each participant separately, we found that the magnitudes of the sequential effect are a monotonous decreasing function of the delay, yet these individual sequential effects are largely averaged out in a population analysis because of heterogeneity. The substantial behavioral heterogeneity in this task is further demonstrated quantitatively by considering the predictive power of the model. We show that a heterogeneous model of sequential dependencies captures the structure available in random sequence generation. Finally, we show that the results of the logistic regression analysis can be interpreted in the framework of reinforcement learning, allowing us to compare the sequential effects in the random sequence generation task to those in an operant learning task. We show that in contrast to the random sequence generation task, sequential effects in operant learning are far more homogenous across the population. These results suggest that in the random sequence generation task, different participants adopt different cognitive strategies to suppress sequential dependencies when generating the "random" sequences.

Childhood diarrheal morbidity and sanitation predictors in a nomadic community.

PubMed

Bitew, Bikes Destaw; Woldu, Wondwoson; Gizaw, Zemichael

2017-10-06

Diarrhea remains a leading killer of young children on the globe despite the availability of simple and effective solutions to prevent and control it. The disease is more prevalent among under - five children (U5C) in the developing world due to lack of sanitation. A child dies every 15 s from diarrheal disease caused largely by poor sanitation. Nearly 90% of diarrheal disease is attributed to inadequate sanitation. Even though, the health burden of diarrheal disease is widely recognized at global level, its prevalence and sanitation predictors among a nomadic population of Ethiopia are not researched. This study was therefore designed to assess the prevalence of childhood diarrheal disease and sanitation predictors among a nomadic people in Hadaleala district, Afar region, Northeast Ethiopia. A community based cross-sectional study design was carried out to investigate diarrheal disease among U5C. A total of 704 households who had U5C were included in this study and the study subjects were recruited by a multistage cluster sampling technique. Data were collected using a structured questionnaire and an observational checklist. All the mothers of U5C found in the selected clusters were interviewed. Furthermore, the living environment was observed. Univariable binary logistic regression analysis was used to choose variables for the multivariable binary logistic regression analysis on the basis of p- value less than 0.2. Finally, multivariable binary logistic regression analysis was used to identify variables associated with childhood diarrhea disease on the basis of adjusted odds ratio (AOR) with 95% confidence interval (CI) and p < 0.05. The two weeks period prevalence of diarrheal disease among U5C in Hadaleala district was 26.1% (95% CI: 22.9 - 29.3%). Childhood diarrheal disease was statistically associated with unprotected drinking water sources [AOR = 2.449, 95% CI = (1.264, 4.744)], inadequate drinking water service level [AOR = 1.535, 95% CI = (1.004, 2.346)], drinking water sources not protected from animal contact [AOR = 4.403, 95% CI = (2.424, 7.999)], un-availability of any type of latrine [AOR = 2.278, 95% CI = (1.045, 4.965)], presence of human excreta in the compound [AOR = 11.391, 95% CI = (2.100, 61.787)], not washing hand after visiting toilet [AOR = 16.511, 95% CI = (3.304, 82.509)], and live in one living room [AOR = 5.827, 95% CI = (3.208, 10.581)]. Childhood diarrheal disease was the common public health problem in Hadaleala district. Compared with the national and regional prevalence of childhood diarrhea, higher prevalence of diarrhea among U5C was reported. Types of drinking water sources, households whose water sources are shared with livestock, volume of daily water collected, availability of latrine, presence of faeces in the compound, hand washing after visiting the toilet and number of rooms were the sanitation predictors associated with childhood diarrhea. Therefore, enabling the community with safe and continuous supply of water and proper disposal of wastes including excreta is necessary with particular emphasis to the rural nomadic communities.

Intermediate and advanced topics in multilevel logistic regression analysis

PubMed Central

Merlo, Juan

2017-01-01

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher‐level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within‐cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population‐average effect of covariates measured at the subject and cluster level, in contrast to the within‐cluster or cluster‐specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster‐level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:28543517

Intermediate and advanced topics in multilevel logistic regression analysis.

PubMed

Austin, Peter C; Merlo, Juan

2017-09-10

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Evaluation of nature and extent of injuries during Dahihandi festival.

PubMed

Nemade, P; Wade, R; Patwardhan, A R; Kale, S

2012-01-01

Injuries related to the Hindu festival of Dahihandi where a human pyramid is formed and a pot of money kept at a height is broken, celebrated in the state of Maharashtra, have seen a significant rise in the past few years. The human pyramid formed is multi-layered and carries with it a high risk of injury including mortality. To evaluate the nature, extent and influencing factors of injuries related to Dahihandi festival. We present a retrospective analysis of patients who presented in a tertiary care center with injuries during the Dahihandi festival in the year 2010. 124 patients' records were evaluated for timing of injury, height of the Dahihandi pyramid, position of the patient in the multi-layered pyramid, mode of pyramid collapse and mechanism of an injury. A binary regression logistic analysis for risk factors was done at 5% significance level. Univariate and multi-variate binary logistic regression of the risk factors for occurrence of a major or minor injury was done using Minitab™ version 16.0 at 5% significance. Out of 139 patients presented to the center, 15 were not involved directly in the formation of pyramid, rest 124 were included in the analysis. A majority of the patients were above 15 years of age [110 (83.6%)]. 46 (37.1%) patients suffered major injuries. There were 39 fractures, 3 cases of chest wall trauma with 10 cases of head injuries and 1 death. More than half of the patients [78 (56.1%)] were injured after 1800 hours. 73 (58.9%) injured participants were part of the pyramid constructed to reach the Dahihandi placed at 30 feet or more above the ground. 72 (51.8%) participants were part of the middle layers of the pyramid. Fall of a participant from upstream layers on the body was the main mechanism of injury, and majority [101 (81.5%)] of the patients suffered injury during descent phase of the pyramid. There is a considerable risk of serious, life-threatening injuries inherent to human pyramid formation and descent in the Dahihandi festival. Safety guidelines are urgently needed to minimize risk and prevent loss of human life.

All Source Sensor Analysis

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

- PNNL, Harold Trease

2012-10-10

ASSA is a software application that processes binary data into summarized index tables that can be used to organize features contained within the data. ASSA's index tables can also be used to search for user specified features. ASSA is designed to organize and search for patterns in unstructured binary data streams or archives, such as video, images, audio, and network traffic. ASSA is basically a very general search engine used to search for any pattern in any binary data stream. It has uses in video analytics, image analysis, audio analysis, searching hard-drives, monitoring network traffic, etc.

Applications Of Binary Image Analysis Techniques

NASA Astrophysics Data System (ADS)

Tropf, H.; Enderle, E.; Kammerer, H. P.

1983-10-01

After discussing the conditions where binary image analysis techniques can be used, three new applications of the fast binary image analysis system S.A.M. (Sensorsystem for Automation and Measurement) are reported: (1) The human view direction is measured at TV frame rate while the subject's head is free movable. (2) Industrial parts hanging on a moving conveyor are classified prior to spray painting by robot. (3) In automotive wheel assembly, the eccentricity of the wheel is minimized by turning the tyre relative to the rim in order to balance the eccentricity of the components.

Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models.

PubMed

Chen, Han; Wang, Chaolong; Conomos, Matthew P; Stilp, Adrienne M; Li, Zilin; Sofer, Tamar; Szpiro, Adam A; Chen, Wei; Brehm, John M; Celedón, Juan C; Redline, Susan; Papanicolaou, George J; Thornton, Timothy A; Laurie, Cathy C; Rice, Kenneth; Lin, Xihong

2016-04-07

Linear mixed models (LMMs) are widely used in genome-wide association studies (GWASs) to account for population structure and relatedness, for both continuous and binary traits. Motivated by the failure of LMMs to control type I errors in a GWAS of asthma, a binary trait, we show that LMMs are generally inappropriate for analyzing binary traits when population stratification leads to violation of the LMM's constant-residual variance assumption. To overcome this problem, we develop a computationally efficient logistic mixed model approach for genome-wide analysis of binary traits, the generalized linear mixed model association test (GMMAT). This approach fits a logistic mixed model once per GWAS and performs score tests under the null hypothesis of no association between a binary trait and individual genetic variants. We show in simulation studies and real data analysis that GMMAT effectively controls for population structure and relatedness when analyzing binary traits in a wide variety of study designs. Copyright © 2016 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Multireader multicase reader studies with binary agreement data: simulation, analysis, validation, and sizing.

PubMed

Chen, Weijie; Wunderlich, Adam; Petrick, Nicholas; Gallas, Brandon D

2014-10-01

We treat multireader multicase (MRMC) reader studies for which a reader's diagnostic assessment is converted to binary agreement (1: agree with the truth state, 0: disagree with the truth state). We present a mathematical model for simulating binary MRMC data with a desired correlation structure across readers, cases, and two modalities, assuming the expected probability of agreement is equal for the two modalities ([Formula: see text]). This model can be used to validate the coverage probabilities of 95% confidence intervals (of [Formula: see text], [Formula: see text], or [Formula: see text] when [Formula: see text]), validate the type I error of a superiority hypothesis test, and size a noninferiority hypothesis test (which assumes [Formula: see text]). To illustrate the utility of our simulation model, we adapt the Obuchowski-Rockette-Hillis (ORH) method for the analysis of MRMC binary agreement data. Moreover, we use our simulation model to validate the ORH method for binary data and to illustrate sizing in a noninferiority setting. Our software package is publicly available on the Google code project hosting site for use in simulation, analysis, validation, and sizing of MRMC reader studies with binary agreement data.

Multireader multicase reader studies with binary agreement data: simulation, analysis, validation, and sizing

PubMed Central

Chen, Weijie; Wunderlich, Adam; Petrick, Nicholas; Gallas, Brandon D.

2014-01-01

Abstract. We treat multireader multicase (MRMC) reader studies for which a reader’s diagnostic assessment is converted to binary agreement (1: agree with the truth state, 0: disagree with the truth state). We present a mathematical model for simulating binary MRMC data with a desired correlation structure across readers, cases, and two modalities, assuming the expected probability of agreement is equal for the two modalities (P1=P2). This model can be used to validate the coverage probabilities of 95% confidence intervals (of P1, P2, or P1−P2 when P1−P2=0), validate the type I error of a superiority hypothesis test, and size a noninferiority hypothesis test (which assumes P1=P2). To illustrate the utility of our simulation model, we adapt the Obuchowski–Rockette–Hillis (ORH) method for the analysis of MRMC binary agreement data. Moreover, we use our simulation model to validate the ORH method for binary data and to illustrate sizing in a noninferiority setting. Our software package is publicly available on the Google code project hosting site for use in simulation, analysis, validation, and sizing of MRMC reader studies with binary agreement data. PMID:26158051

Characteristics of Illinois School Districts That Employ School Nurses.

PubMed

Searing, Lisabeth M; Guenette, Molly

2016-08-01

Research indicates that school nursing services are cost-effective, but the National Association of School Nurses estimates that 25% of schools do not have a school nurse (SN). The purpose of this study was to identify the characteristics of Illinois school districts that employed SNs. This was a secondary data analysis of Illinois School Report Card system data as well as data obtained from district websites regarding SNs. Employment of an SN was determined for 95% of the 862 existing districts. Binary logistic regression analysis found that district size was the largest significant predictor of employment of an SN. Other factors included the type of district and diversity of the teaching staff as well as the percentage of students receiving special education services or with limited English proficiency. These findings indicate where to focus advocacy and policy efforts to encourage employment of SNs. © The Author(s) 2015.

Statistical Analysis of Factors Affecting Child Mortality in Pakistan.

PubMed

Ahmed, Zoya; Kamal, Asifa; Kamal, Asma

2016-06-01

Child mortality is a composite indicator reflecting economic, social, environmental, healthcare services, and their delivery situation in a country. Globally, Pakistan has the third highest burden of fetal, maternal, and child mortality. Factors affecting child mortality in Pakistan are investigated by using Binary Logistic Regression Analysis. Region, education of mother, birth order, preceding birth interval (the period between the previous child birth and the index child birth), size of child at birth, and breastfeeding and family size were found to be significantly important with child mortality in Pakistan. Child mortality decreased as level of mother's education, preceding birth interval, size of child at birth, and family size increased. Child mortality was found to be significantly higher in Balochistan as compared to other regions. Child mortality was low for low birth orders. Child survival was significantly higher for children who were breastfed as compared to those who were not.

Attitudes towards Participation in Business Development Programmes: An Ethnic Comparison in Sweden

ERIC Educational Resources Information Center

Abbasian, Saeid; Yazdanfar, Darush

2015-01-01

Purpose: The aim of the study is to investigate whether there are any differences between the attitudes towards participation in development programmes of entrepreneurs who are immigrants and those who are native-born. Design/methodology/approach: Several statistical methods, including a binary logistic regression model, were used to analyse a…

2004 Carolyn Sherif Award Address: Heart Disease and Gender Inequity

ERIC Educational Resources Information Center

Travis, Cheryl Brown

2005-01-01

Individual patient records from the National Hospital Discharge Survey for 1988 and 1998 comprising approximately 10 million cases were the basis for a binary logistic regression model to predict coronary artery bypass graft. Patterns in 1988 and in 1998 indicated a dramatic and pernicious gender discrepancy in medical decisions involving bypass…

Factors Affecting Code Status in a University Hospital Intensive Care Unit

ERIC Educational Resources Information Center

Van Scoy, Lauren Jodi; Sherman, Michael

2013-01-01

The authors collected data on diagnosis, hospital course, and end-of-life preparedness in patients who died in the intensive care unit (ICU) with "full code" status (defined as receiving cardiopulmonary resuscitation), compared with those who didn't. Differences were analyzed using binary and stepwise logistic regression. They found no…

Comparison between students and residents on determinants of willingness to separate waste and waste separation behaviour in Zhengzhou, China.

PubMed

Dai, Xiaoping; Han, Yuping; Zhang, Xiaohong; Hu, Wei; Huang, Liangji; Duan, Wenpei; Li, Siyi; Liu, Xiaolu; Wang, Qian

2017-09-01

A better understanding of willingness to separate waste and waste separation behaviour can aid the design and improvement of waste management policies. Based on the intercept questionnaire survey data of undergraduate students and residents in Zhengzhou City of China, this article compared factors affecting the willingness and behaviour of students and residents to participate in waste separation using two binary logistic regression models. Improvement opportunities for waste separation were also discussed. Binary logistic regression results indicate that knowledge of and attitude to waste separation and acceptance of waste education significantly affect the willingness of undergraduate students to separate waste, and demographic factors, such as gender, age, education level, and income, significantly affect the willingness of residents to do so. Presence of waste-specific bins and attitude to waste separation are drivers of waste separation behaviour for both students and residents. Improved education about waste separation and facilities are effective to stimulate waste separation, and charging on unsorted waste may be an effective way to improve it in Zhengzhou.

Stochastic model search with binary outcomes for genome-wide association studies

PubMed Central

Malovini, Alberto; Puca, Annibale A; Bellazzi, Riccardo

2012-01-01

Objective The spread of case–control genome-wide association studies (GWASs) has stimulated the development of new variable selection methods and predictive models. We introduce a novel Bayesian model search algorithm, Binary Outcome Stochastic Search (BOSS), which addresses the model selection problem when the number of predictors far exceeds the number of binary responses. Materials and methods Our method is based on a latent variable model that links the observed outcomes to the underlying genetic variables. A Markov Chain Monte Carlo approach is used for model search and to evaluate the posterior probability of each predictor. Results BOSS is compared with three established methods (stepwise regression, logistic lasso, and elastic net) in a simulated benchmark. Two real case studies are also investigated: a GWAS on the genetic bases of longevity, and the type 2 diabetes study from the Wellcome Trust Case Control Consortium. Simulations show that BOSS achieves higher precisions than the reference methods while preserving good recall rates. In both experimental studies, BOSS successfully detects genetic polymorphisms previously reported to be associated with the analyzed phenotypes. Discussion BOSS outperforms the other methods in terms of F-measure on simulated data. In the two real studies, BOSS successfully detects biologically relevant features, some of which are missed by univariate analysis and the three reference techniques. Conclusion The proposed algorithm is an advance in the methodology for model selection with a large number of features. Our simulated and experimental results showed that BOSS proves effective in detecting relevant markers while providing a parsimonious model. PMID:22534080

KEPLER ECLIPSING BINARIES WITH STELLAR COMPANIONS

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

Gies, D. R.; Matson, R. A.; Guo, Z.

2015-12-15

Many short-period binary stars have distant orbiting companions that have played a role in driving the binary components into close separation. Indirect detection of a tertiary star is possible by measuring apparent changes in eclipse times of eclipsing binaries as the binary orbits the common center of mass. Here we present an analysis of the eclipse timings of 41 eclipsing binaries observed throughout the NASA Kepler mission of long duration and precise photometry. This subset of binaries is characterized by relatively deep and frequent eclipses of both stellar components. We present preliminary orbital elements for seven probable triple stars amongmore » this sample, and we discuss apparent period changes in seven additional eclipsing binaries that may be related to motion about a tertiary in a long period orbit. The results will be used in ongoing investigations of the spectra and light curves of these binaries for further evidence of the presence of third stars.« less

Technical and physical analysis of the 2014 FIFA World Cup Brazil: winners vs. losers.

PubMed

Rumpf, Michael C; Silva, Joao R; Hertzog, Maxime; Farooq, Abdulaziz; Nassis, George

2017-10-01

The purpose of the present study was to investigate the technical and physical performance parameters that distinguish between teams winning and losing matches in the 2014 FIFA World Cup Brazil. Data were derived from the FIFA website and from live-statistics provided during each game of the world cup. Twelve physical (such as total distance covered in meters (TD), TD in distinct locomotor categories: low-intensity running (LIR; <11 km/h), moderate-intensity running (MIR; 11 to 14 km/h) and high-intensity-running (HIR; >14 km/h)) and 21 technical parameters (total passes, short-, medium- and long-distance passes, total pass completion rate, dangerous attacks, attacking attempts, delivery in penalty area, ball possession, goals, goals from set-pieces, goals per shot on goal, defending saves, shots, shots on goal, shot accuracy, set-pieces, crosses, corners, clearances, yellow cards) were analyzed. Forty-two games in which a winner and consequently a loser were presented after 90 minutes of game time were investigated with independent t-tests. A binary-logistic regression was utilized to investigate whether the significant variables predicted success of the winning teams. The winning teams scored significantly (P<0.05) greater amount of goals, goals per set-pieces, goals per shots on goals, shots on goal and shot accuracy and received significantly lower yellow cards. The binary-logistic regression utilized showed that shot accuracy was the best predictor for success. The physical parameters did not differ between teams winning and losing a match. Technical performance related to goal scoring parameters play a decisive role in World Cup games. Furthermore, scoring efficacy from open-play as well as from set-pieces are crucial to win matches in a World Cup tournament. At this level, physical performance was not the factor to discriminate between winners and losers.

Expression of p53 Breast Cancer in Kurdish Women in the West of Iran: a Reverse Correlation with Lymph Node Metastasis.

PubMed

Payandeh, Mehrdad; Sadeghi, Masoud; Sadeghi, Edris; Madani, Seyed-Hamid

2016-01-01

In breast cancer (BC), it has been suggested that nuclear overexpression of p53 protein might be an indicator of poor prognosis. The aim of the current study was to evaluate the expression of p53 BC in Kurdish women from the West of Iran and its correlation with other clinicopathology figures. In the present retrospective study, 231 patients were investigated for estrogen receptor (ER) and progesterone receptor (PR) positivity, defined as ≥10% positive tumor cells with nuclear staining. A binary logistic regression model was selected using Akaike Information Criteria (AIC) in stepwise selection for determination of important factors. ER, PR, the human epidermal growth factor receptor 2 (HER2) and p53 were positive in 58.4%, 55.4%, 59.7% and 45% of cases, respectively. Ki67 index was divided into two groups: 54.5% had Ki67<20% and 45.5% had Ki67 ≥20%. Of 214 patients, 137(64%) had lymph node metastasis and of 186 patients, 122(65.6%) had vascular invasion. Binary logistic regression analysis showed that there was inverse significant correlation between lymph node metastasis (P=0.008, OR 0.120 and 95%CI 0.025-0.574), ER status (P=0.006, OR 0.080, 95%CI 0.014-0.477) and a direct correlation between HER2 (P=005, OR 3.047, 95%CI 1.407-6.599) with the expression of p53. As in a number of studies, expression of p53 had a inverse correlation with lymph node metastasis and ER status and also a direct correlation with HER2 status. Also, p53-positivity is more likely in triple negative BC compared to other subtypes.

Empirical study of seven data mining algorithms on different characteristics of datasets for biomedical classification applications.

PubMed

Zhang, Yiyan; Xin, Yi; Li, Qin; Ma, Jianshe; Li, Shuai; Lv, Xiaodan; Lv, Weiqi

2017-11-02

Various kinds of data mining algorithms are continuously raised with the development of related disciplines. The applicable scopes and their performances of these algorithms are different. Hence, finding a suitable algorithm for a dataset is becoming an important emphasis for biomedical researchers to solve practical problems promptly. In this paper, seven kinds of sophisticated active algorithms, namely, C4.5, support vector machine, AdaBoost, k-nearest neighbor, naïve Bayes, random forest, and logistic regression, were selected as the research objects. The seven algorithms were applied to the 12 top-click UCI public datasets with the task of classification, and their performances were compared through induction and analysis. The sample size, number of attributes, number of missing values, and the sample size of each class, correlation coefficients between variables, class entropy of task variable, and the ratio of the sample size of the largest class to the least class were calculated to character the 12 research datasets. The two ensemble algorithms reach high accuracy of classification on most datasets. Moreover, random forest performs better than AdaBoost on the unbalanced dataset of the multi-class task. Simple algorithms, such as the naïve Bayes and logistic regression model are suitable for a small dataset with high correlation between the task and other non-task attribute variables. K-nearest neighbor and C4.5 decision tree algorithms perform well on binary- and multi-class task datasets. Support vector machine is more adept on the balanced small dataset of the binary-class task. No algorithm can maintain the best performance in all datasets. The applicability of the seven data mining algorithms on the datasets with different characteristics was summarized to provide a reference for biomedical researchers or beginners in different fields.

Correlates of anal sex roles among Malay and Chinese MSM in Kuala Lumpur, Malaysia.

PubMed

Dangerfield, Derek T; Gravitt, Patti; Rompalo, Anne M; Tai, Raymond; Lim, Sin How

2016-03-01

Identifying roles for anal sex is an important issue for populations of MSM. We describe the prevalence of identifying as being 'top', 'bottom', 'versatile', or 'don't know/not applicable' among Malay and Chinese MSM in Kuala Lumpur, Malaysia, and behavioural outcomes according to these labels for sexual role identity. Data analysis was conducted on a survey administered during weekly outreach throughout Kuala Lumpur in 2012. Pearson's Chi square tests were used to compare demographic and behavioural characteristics of MSM who reported roles for anal sex. Binary logistic regression was used to explore the odds of behavioural outcomes among MSM who identified as 'bottom', 'versatile,' and 'don't know' compared to MSM who reported that 'top' was their sexual role. Labels for anal sex roles were significantly associated with condom use for last anal sex. Among MSM who used labels for anal sex roles, MSM who identified as 'bottom' had highest level of not using condoms for last anal sex (24.1%, p = .045). In binary logistic regression model, identifying as 'top' was significantly associated with reporting using a condom during last anal sex and reported consistent condom use for anal sex in the past six months (p = .039 and .017, respectively). With regard to sexual role identity, some MSM may be a part of a special subgroup of at-risk men to be targeted. Future research should evaluate the origins, meanings, and perceptions of these labels, and the developmental process of how these MSM identify with any of these categories. Research should also uncover condom use decision making with regard to these labels for sexual positioning. © The Author(s) 2016.

Simultaneous spectrophotometric determination of glimepiride and pioglitazone in binary mixture and combined dosage form using chemometric-assisted techniques

NASA Astrophysics Data System (ADS)

El-Zaher, Asmaa A.; Elkady, Ehab F.; Elwy, Hanan M.; Saleh, Mahmoud Abo El Makarim

2017-07-01

In the present work, pioglitazone and glimepiride, 2 widely used antidiabetics, were simultaneously determined by a chemometric-assisted UV-spectrophotometric method which was applied to a binary synthetic mixture and a pharmaceutical preparation containing both drugs. Three chemometric techniques - Concentration residual augmented classical least-squares (CRACLS), principal component regression (PCR), and partial least-squares (PLS) were implemented by using the synthetic mixtures containing the two drugs in acetonitrile. The absorbance data matrix corresponding to the concentration data matrix was obtained by the measurements of absorbencies in the range between 215 and 235 nm in the intervals with Δλ = 0.4 nm in their zero-order spectra. Then, calibration or regression was obtained by using the absorbance data matrix and concentration data matrix for the prediction of the unknown concentrations of pioglitazone and glimepiride in their mixtures. The described techniques have been validated by analyzing synthetic mixtures containing the two drugs showing good mean recovery values lying between 98 and 100%. In addition, accuracy and precision of the three methods have been assured by recovery values lying between 98 and 102% and R.S.D. % ˂0.6 for intra-day precision and ˂1.2 for inter-day precision. The proposed chemometric techniques were successfully applied to a pharmaceutical preparation containing a combination of pioglitazone and glimepiride in the ratio of 30: 4, showing good recovery values. Finally, statistical analysis was carried out to add a value to the verification of the proposed methods. It was carried out by an intrinsic comparison between the 3 chemometric techniques and by comparing values of present methods with those obtained by implementing reference pharmacopeial methods for each of pioglitazone and glimepiride.

Disposal of children's stools and its association with childhood diarrhea in India.

PubMed

Bawankule, Rahul; Singh, Abhishek; Kumar, Kaushalendra; Pedgaonkar, Sarang

2017-01-05

Children's stool disposal is often overlooked in sanitation programs of any country. Unsafe disposal of children's stool makes children susceptible to many diseases that transmit through faecal-oral route. Therefore, the study aims to examine the magnitude of unsafe disposal of children's stools in India, the factors associated with it and finally its association with childhood diarrhea. Data from the third round of the National Family Health Survey (NFHS-3) conducted in 2005-06 is used to carry out the analysis. The binary logistic regression model is used to examine the factors associated with unsafe disposal of children's stool. Binary logistic regression is also used to examine the association between unsafe disposal of children's stool and childhood diarrhea. Overall, stools of 79% of children in India were disposed of unsafely. The urban-rural gap in the unsafe disposal of children's stool was wide. Mother's illiteracy and lack of exposure to media, the age of the child, religion and caste/tribe of the household head, wealth index, access to toilet facility and urban-rural residence were statistically associated with unsafe disposal of stool. The odds of diarrhea in children whose stools were disposed of unsafely was estimated to be 11% higher (95% CI: 1.01-1.21) than that of children whose stools were disposed of safely. An increase in the unsafe disposal of children's stool in the community also increased the risk of diarrhea in children. We found significant statistical association between children's stool disposal and diarrhea. Therefore, gains in reduction of childhood diarrhea can be achieved in India through the complete elimination of unsafe disposal of children's stools. The sanitation programmes currently being run in India must also focus on safe disposal of children's stool.

Relationships between stressful life events and impaired fasting glucose among left-behind farmers in rural China.

PubMed

Liang, Han; Cheng, Jing; Shen, Xingrong; Chen, Penglai; Tong, Guixian; Chai, Jing; Li, Kaichun; Xie, Shaoyu; Shi, Yong; Wang, Debin; Sun, Yehuan

2015-02-01

This study aims at examining the effects of stressful life events on risk of impaired fasting glucose among left-behind farmers in rural China. The study collected data about stressful life events, family history of diabetes, lifestyle, demographics and minimum anthropometrics from left-behind famers aged 40-70 years. Calculated life event index was applied to assess the combined effects of stressful life events experienced by the left-behind farmers and its association with impaired fasting glucose was estimated using binary logistic regression models. The prevalence of abnormal fasting glucose was 61.4% by American Diabetes Association (ADA) standard and 32.4% by World Health Organization (WHO) standard. Binary logistic regression analysis revealed a coefficient of 0.033 (P<.001) by ADA standard or 0.028 (P<.001) by WHO standard between impaired fasting glucose and life event index. The overall odds ratios of impaired glucose for the second, third and fourth (highest) versus the first (lowest) quartile of life event index were 1.419 [95% CI=(1.173, 1.717)], 1.711 [95% CI=(1.413, 2.071)] and 1.957 [95% CI=(1.606, 2.385)] respectively by ADA standard. When more and more confounding factors were controlled for, these odds ratios remained statistically significant though decreased to a small extent. The left-behind farmers showed over two-fold prevalence rate of pre-diabetes than that of the nation's average and their risk of impaired fasting glucose was positively associated with stressful life events in a dose-dependent way. Both the population studied and their life events merit special attention. Copyright © 2014 Elsevier Inc. All rights reserved.

The median hazard ratio: a useful measure of variance and general contextual effects in multilevel survival analysis.

PubMed

Austin, Peter C; Wagner, Philippe; Merlo, Juan

2017-03-15

Multilevel data occurs frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models (MLRM). MLRM incorporate cluster-specific random effects which allow one to partition the total individual variance into between-cluster variation and between-individual variation. Statistically, MLRM account for the dependency of the data within clusters and provide correct estimates of uncertainty around regression coefficients. Substantively, the magnitude of the effect of clustering provides a measure of the General Contextual Effect (GCE). When outcomes are binary, the GCE can also be quantified by measures of heterogeneity like the Median Odds Ratio (MOR) calculated from a multilevel logistic regression model. Time-to-event outcomes within a multilevel structure occur commonly in epidemiological and medical research. However, the Median Hazard Ratio (MHR) that corresponds to the MOR in multilevel (i.e., 'frailty') Cox proportional hazards regression is rarely used. Analogously to the MOR, the MHR is the median relative change in the hazard of the occurrence of the outcome when comparing identical subjects from two randomly selected different clusters that are ordered by risk. We illustrate the application and interpretation of the MHR in a case study analyzing the hazard of mortality in patients hospitalized for acute myocardial infarction at hospitals in Ontario, Canada. We provide R code for computing the MHR. The MHR is a useful and intuitive measure for expressing cluster heterogeneity in the outcome and, thereby, estimating general contextual effects in multilevel survival analysis. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

The median hazard ratio: a useful measure of variance and general contextual effects in multilevel survival analysis

PubMed Central

Wagner, Philippe; Merlo, Juan

2016-01-01

Multilevel data occurs frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models (MLRM). MLRM incorporate cluster‐specific random effects which allow one to partition the total individual variance into between‐cluster variation and between‐individual variation. Statistically, MLRM account for the dependency of the data within clusters and provide correct estimates of uncertainty around regression coefficients. Substantively, the magnitude of the effect of clustering provides a measure of the General Contextual Effect (GCE). When outcomes are binary, the GCE can also be quantified by measures of heterogeneity like the Median Odds Ratio (MOR) calculated from a multilevel logistic regression model. Time‐to‐event outcomes within a multilevel structure occur commonly in epidemiological and medical research. However, the Median Hazard Ratio (MHR) that corresponds to the MOR in multilevel (i.e., ‘frailty’) Cox proportional hazards regression is rarely used. Analogously to the MOR, the MHR is the median relative change in the hazard of the occurrence of the outcome when comparing identical subjects from two randomly selected different clusters that are ordered by risk. We illustrate the application and interpretation of the MHR in a case study analyzing the hazard of mortality in patients hospitalized for acute myocardial infarction at hospitals in Ontario, Canada. We provide R code for computing the MHR. The MHR is a useful and intuitive measure for expressing cluster heterogeneity in the outcome and, thereby, estimating general contextual effects in multilevel survival analysis. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:27885709

Associations between national viral hepatitis policies/programmes and country-level socioeconomic factors: a sub-analysis of data from the 2013 WHO viral hepatitis policy report.

PubMed

Lazarus, Jeffrey V; Sperle, Ida; Safreed-Harmon, Kelly; Gore, Charles; Cebolla, Beatriz; Spina, Alexander

2017-07-26

As more countries worldwide develop national viral hepatitis strategies, it is important to ask whether context-specific factors affect their decision-making. This study aimed to determine whether country-level socioeconomic factors are associated with viral hepatitis programmes and policy responses across WHO Member States (MS). WHO MS focal points completed a questionnaire on national viral hepatitis policies. This secondary analysis of data reported in the 2013 Global Policy Report on the Prevention and Control of Viral Hepatitis in WHO Member States used logistic regression to examine associations between four survey questions and four socioeconomic factors: country income level, Human Development Index (HDI), health expenditure and physician density. This analysis included 119 MS. MS were more likely to have routine viral hepatitis surveillance and to have a national strategy and/or policy/guidelines for preventing infection in healthcare settings if they were in the higher binary categories for income level, HDI, health expenditure and physician density. In multivariable analyses, the only significant finding was a positive association between having routine surveillance and being in the higher binary HDI category (adjusted odds ratio 26; 95% confidence interval 2.0-340). Countries with differing socioeconomic status indicators did not appear to differ greatly regarding the existence of key national policies and programmes. A more nuanced understanding of the multifaceted interactions of socioeconomic factors, health policy, service delivery and health outcomes is needed to support country-level efforts to eliminate viral hepatitis.

Detection of melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model.

PubMed

Lim, Jongguk; Kim, Giyoung; Mo, Changyeun; Kim, Moon S; Chao, Kuanglin; Qin, Jianwei; Fu, Xiaping; Baek, Insuck; Cho, Byoung-Kwan

2016-05-01

Illegal use of nitrogen-rich melamine (C3H6N6) to boost perceived protein content of food products such as milk, infant formula, frozen yogurt, pet food, biscuits, and coffee drinks has caused serious food safety problems. Conventional methods to detect melamine in foods, such as Enzyme-linked immunosorbent assay (ELISA), High-performance liquid chromatography (HPLC), and Gas chromatography-mass spectrometry (GC-MS), are sensitive but they are time-consuming, expensive, and labor-intensive. In this research, near-infrared (NIR) hyperspectral imaging technique combined with regression coefficient of partial least squares regression (PLSR) model was used to detect melamine particles in milk powders easily and quickly. NIR hyperspectral reflectance imaging data in the spectral range of 990-1700nm were acquired from melamine-milk powder mixture samples prepared at various concentrations ranging from 0.02% to 1%. PLSR models were developed to correlate the spectral data (independent variables) with melamine concentration (dependent variables) in melamine-milk powder mixture samples. PLSR models applying various pretreatment methods were used to reconstruct the two-dimensional PLS images. PLS images were converted to the binary images to detect the suspected melamine pixels in milk powder. As the melamine concentration was increased, the numbers of suspected melamine pixels of binary images were also increased. These results suggested that NIR hyperspectral imaging technique and the PLSR model can be regarded as an effective tool to detect melamine particles in milk powders. Copyright © 2016 Elsevier B.V. All rights reserved.

Measures of clustering and heterogeneity in multilevel Poisson regression analyses of rates/count data

PubMed Central

Austin, Peter C.; Stryhn, Henrik; Leckie, George; Merlo, Juan

2017-01-01

Multilevel data occur frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models. These models incorporate cluster‐specific random effects that allow one to partition the total variation in the outcome into between‐cluster variation and between‐individual variation. The magnitude of the effect of clustering provides a measure of the general contextual effect. When outcomes are binary or time‐to‐event in nature, the general contextual effect can be quantified by measures of heterogeneity like the median odds ratio or the median hazard ratio, respectively, which can be calculated from a multilevel regression model. Outcomes that are integer counts denoting the number of times that an event occurred are common in epidemiological and medical research. The median (incidence) rate ratio in multilevel Poisson regression for counts that corresponds to the median odds ratio or median hazard ratio for binary or time‐to‐event outcomes respectively is relatively unknown and is rarely used. The median rate ratio is the median relative change in the rate of the occurrence of the event when comparing identical subjects from 2 randomly selected different clusters that are ordered by rate. We also describe how the variance partition coefficient, which denotes the proportion of the variation in the outcome that is attributable to between‐cluster differences, can be computed with count outcomes. We illustrate the application and interpretation of these measures in a case study analyzing the rate of hospital readmission in patients discharged from hospital with a diagnosis of heart failure. PMID:29114926

Quantitative Analysis of Driving Factors of Grassland Degradation: A Case Study in Xilin River Basin, Inner Mongolia

PubMed Central

Xie, Yichun; Sha, Zongyao

2012-01-01

Current literature suggests that grassland degradation occurs in areas with poor soil conditions or noticeable environmental changes and is often a result of overgrazing or human disturbances. However, these views are questioned in our analyses. Based on the analysis of satellite vegetation maps from 1984, 1998, and 2004 for the Xilin River Basin, Inner Mongolia, China, and binary logistic regression (BLR) analysis, we observe the following: (1) grassland degradation is positively correlated with the growth density of climax communities; (2) our findings do not support a common notion that a decrease of biological productivity is a direct indicator of grassland degradation; (3) a causal relationship between grazing intensity and grassland degradation was not found; (4) degradation severity increased steadily towards roads but showed different trends near human settlements. This study found complex relationships between vegetation degradation and various microhabitat conditions, for example, elevation, slope, aspect, and proximity to water. PMID:22619613

[Correspondence analysis of association between types of unintentional injuries and influential factors among rural rear pupils].

PubMed

Dou, Dongmei; Wang, Peixi

2015-07-01

To explore the association between types of unintentional injuries and influential factors among rural rear pupils. The multistage stratified sampling method was used to select the study participant and thus 594 rural pupils were sampled, 292 rear pupils were confirmed and measured with unintentional injuries and influential factors of rural rear pupils scale. Binary logistic regression analysis indicate that the risk facts related to unintentional injury were left-behind status (OR = 2.68, 95% CI 1.06-6.79), gender (OR = 5.12, 95% C2.68-9.79) and surrounding environment (OR = 3.44, 95% CI 1.37-8.70). Correspondence analysis showed living with father, middle personality and low age were related possibly with traffic accident injury. Living with grandparents, extrovert personality and elder pupils were related possibly with unintentional falls injury. Living with mother, introvert personality and middle-age pupils were related possibly with animmal injury. The personality, ages and guardian types of rural rear pupils are correlated with types of unintentional injuries.

Exsanguinated blood volume estimation using fractal analysis of digital images.

PubMed

Sant, Sonia P; Fairgrieve, Scott I

2012-05-01

The estimation of bloodstain volume using fractal analysis of digital images of passive blood stains is presented. Binary digital photos of bloodstains of known volumes (ranging from 1 to 7 mL), dispersed in a defined area, were subjected to image analysis using FracLac V. 2.0 for ImageJ. The box-counting method was used to generate a fractal dimension for each trial. A positive correlation between the generated fractal number and the volume of blood was found (R(2) = 0.99). Regression equations were produced to estimate the volume of blood in blind trials. An error rate ranging from 78% for 1 mL to 7% for 6 mL demonstrated that as the volume increases so does the accuracy of the volume estimation. This method used in the preliminary study proved that bloodstain patterns may be deconstructed into mathematical parameters, thus removing the subjective element inherent in other methods of volume estimation. © 2012 American Academy of Forensic Sciences.

Pineal Gland Calcification in Kurdistan: A Cross-Sectional Study of 480 Roentgenograms.

PubMed

Mohammed, Kahee A; Adjei Boakye, Eric; Ismail, Honer A; Geneus, Christian J; Tobo, Betelihem B; Buchanan, Paula M; Zelicoff, Alan P

2016-01-01

The goal of this study was to compare the incidence of Pineal Gland Calcification (PGC) by age group and gender among the populations living in the Kurdistan Region-Iraq. This prospective study examined skull X-rays of 480 patients between the ages of 3 and 89 years who sought care at a large teaching public hospital in Duhok, Iraq from June 2014 to November 2014. Descriptive statistics and a binary logistic regression were used for analysis. The overall incidence rate of PGC among the study population was 26.9% with the 51-60 age group and males having the highest incidence. PGC incidence increased after the first decade and remained steady until the age of 60. Thereafter the incidence began to decrease. Logistic regression analysis revealed that both age and gender significantly affected the risk of PGC. After adjusting for age, males were 1.94 (95% CI, 1.26-2.99) times more likely to have PGC compared to females. In addition, a one year increase in age increases the odds of developing PGC by 1.02 (95% CI, 1.01-1.03) units after controlling for the effects of gender. Our analysis demonstrated a close relationship between PGC and age and gender, supporting a link between the development of PGC and these factors. This study provides a basis for future researchers to further investigate the nature and mechanisms underlying pineal gland calcification.

Risk prediction for myocardial infarction via generalized functional regression models.

PubMed

Ieva, Francesca; Paganoni, Anna M

2016-08-01

In this paper, we propose a generalized functional linear regression model for a binary outcome indicating the presence/absence of a cardiac disease with multivariate functional data among the relevant predictors. In particular, the motivating aim is the analysis of electrocardiographic traces of patients whose pre-hospital electrocardiogram (ECG) has been sent to 118 Dispatch Center of Milan (the Italian free-toll number for emergencies) by life support personnel of the basic rescue units. The statistical analysis starts with a preprocessing of ECGs treated as multivariate functional data. The signals are reconstructed from noisy observations. The biological variability is then removed by a nonlinear registration procedure based on landmarks. Thus, in order to perform a data-driven dimensional reduction, a multivariate functional principal component analysis is carried out on the variance-covariance matrix of the reconstructed and registered ECGs and their first derivatives. We use the scores of the Principal Components decomposition as covariates in a generalized linear model to predict the presence of the disease in a new patient. Hence, a new semi-automatic diagnostic procedure is proposed to estimate the risk of infarction (in the case of interest, the probability of being affected by Left Bundle Brunch Block). The performance of this classification method is evaluated and compared with other methods proposed in literature. Finally, the robustness of the procedure is checked via leave-j-out techniques. © The Author(s) 2013.

Association between Serum Cystatin C and Diabetic Foot Ulceration in Patients with Type 2 Diabetes: A Cross-Sectional Study

PubMed Central

Zhao, Jie; Deng, Wuquan; Zhang, Yuping; Zheng, Yanling; Zhou, Lina; Boey, Johnson; Armstrong, David G.; Yang, Gangyi

2016-01-01

Serum cystatin C (CysC) has been identified as a possible potential biomarker in a variety of diabetic complications, including diabetic peripheral neuropathy and peripheral artery disease. We aimed to examine the association between CysC and diabetic foot ulceration (DFU) in patients with type 2 diabetes (T2D). 411 patients with T2D were enrolled in this cross-sectional study at a university hospital. Clinical manifestations and biochemical parameters were compared between DFU group and non-DFU group. The association between serum CysC and DFU was explored by binary logistic regression analysis. The cut point of CysC for DFU was also evaluated by receiver operating characteristic (ROC) curve. The prevalence of coronary artery disease, diabetic nephropathy (DN), and DFU dramatically increased with CysC (P < 0.01) in CysC quartiles. Multivariate logistic regression analysis indicated that the significant risk factors for DFU were serum CysC, coronary artery disease, hypertension, insulin use, the differences between supine and sitting TcPO2, and hypertension. ROC curve analysis revealed that the cut point of CysC for DFU was 0.735 mg/L. Serum CysC levels correlated with DFU and severity of tissue loss. Our study results indicated that serum CysC was associated with a high prevalence of DFU in Chinese T2D subjects. PMID:27668262

Bayesian propensity scores for high-dimensional causal inference: A comparison of drug-eluting to bare-metal coronary stents.

PubMed

Spertus, Jacob V; Normand, Sharon-Lise T

2018-04-23

High-dimensional data provide many potential confounders that may bolster the plausibility of the ignorability assumption in causal inference problems. Propensity score methods are powerful causal inference tools, which are popular in health care research and are particularly useful for high-dimensional data. Recent interest has surrounded a Bayesian treatment of propensity scores in order to flexibly model the treatment assignment mechanism and summarize posterior quantities while incorporating variance from the treatment model. We discuss methods for Bayesian propensity score analysis of binary treatments, focusing on modern methods for high-dimensional Bayesian regression and the propagation of uncertainty. We introduce a novel and simple estimator for the average treatment effect that capitalizes on conjugacy of the beta and binomial distributions. Through simulations, we show the utility of horseshoe priors and Bayesian additive regression trees paired with our new estimator, while demonstrating the importance of including variance from the treatment regression model. An application to cardiac stent data with almost 500 confounders and 9000 patients illustrates approaches and facilitates comparison with existing alternatives. As measured by a falsifiability endpoint, we improved confounder adjustment compared with past observational research of the same problem. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Photometric study of the eclipsing binary GR Bootis

NASA Astrophysics Data System (ADS)

Zhang, Z. L.; Zhang, Y. P.; Fu, J. N.; Xue, H. F.

2016-07-01

We present CCD photometry and low-resolution spectra of the eclipsing binary GR Boo. A new ephemeris is determined based on all the available times of the minimum light. The period analysis reveals that the orbital period is decreasing with a rate of dP / dt = - 2.05 ×10-10 d yr-1 . A photometric analysis for the obtained light curves is performed with the Wilson-Devinney Differential Correction program for the first time. The photometric solutions confirm the W UMa-type nature of the binary system. The mass ratio turns out to be q = 0.985 ± 0.001 . The evolutionary status and physical nature of the binary system are briefly discussed.

The impact of IUE on binary star studies

NASA Technical Reports Server (NTRS)

Plavec, M. J.

1981-01-01

The use of IUE observations in the investigation of binary stars is discussed. The results of data analysis of several classes of binary systems are briefly reviewed including zeta Aurigae and VV Cephei stars, mu Sagittarii, epsilon Aurigae, beta Lyrae and the W Serpentis stars, symbiotic stars, and the Algols.

Seroprevalence of human hydatidosis using ELISA method in qom province, central iran.

PubMed

Rakhshanpour, A; Harandi, M Fasihi; Moazezi, Ss; Rahimi, Mt; Mohebali, M; Mowlavi, Ghh; Babaei, Z; Ariaeipour, M; Heidari, Z; Rokni, Mb

2012-01-01

The objective of this study was to determine the prevalence of cystic echinococcosis (CE) in Qom Province, central Iran using ELISA test. Overall, 1564 serum samples (800 males and 764 females) were collected from selected subjects by randomized cluster sampling in 2011-2012. Sera were analyzed by ELISA test using AgB. Before sampling, a questionnaire was filled out for each case. Data were analyzed using Chi-square test and multivariate logistic regression for risk factors analysis. Seropositivity was 1.6% (25 cases). Males (2.2%) showed significantly more positivity than females (0.9%) (P= 0.03). There was no significant association between CE seropositivity and age group, occupation, and region. Age group of 30-60 years encompassed the highest rate of positivity. The seropositivity of CE was 2.1% and 1.2% for urban and rural cases respectively. Binary logistic regression showed that males were 2.5 times at higher risk for infection than females. Although seroprevalence of CE is relatively low in Qom Province, yet due to the importance of the disease, all preventive measures should be taken into consideration.

An All-Sky Search for Wide Binaries in the SUPERBLINK Proper Motion Catalog

NASA Astrophysics Data System (ADS)

Hartman, Zachary; Lepine, Sebastien

2017-01-01

We present initial results from an all-sky search for Common Proper Motion (CPM) binaries in the SUPERBLINK all-sky proper motion catalog of 2.8 million stars with proper motions greater than 40 mas/yr, which has been recently enhanced with data from the GAIA mission. We initially search the SUPERBLINK catalog for pairs of stars with angular separations up to 1 degree and proper motion difference less than 40 mas/yr. In order to determine which of these pairs are real binaries, we develop a Bayesian analysis to calculate probabilities of true companionship based on a combination of proper motion magnitude, angular separation, and proper motion differences. The analysis reveals that the SUPERBLINK catalog most likely contains ~40,000 genuine common proper motion binaries. We provide initial estimates of the distances and projected physical separations of these wide binaries.

Predicting the composition of red wine blends using an array of multicomponent Peptide-based sensors.

PubMed

Ghanem, Eman; Hopfer, Helene; Navarro, Andrea; Ritzer, Maxwell S; Mahmood, Lina; Fredell, Morgan; Cubley, Ashley; Bolen, Jessica; Fattah, Rabia; Teasdale, Katherine; Lieu, Linh; Chua, Tedmund; Marini, Federico; Heymann, Hildegarde; Anslyn, Eric V

2015-05-20

Differential sensing using synthetic receptors as mimics of the mammalian senses of taste and smell is a powerful approach for the analysis of complex mixtures. Herein, we report on the effectiveness of a cross-reactive, supramolecular, peptide-based sensing array in differentiating and predicting the composition of red wine blends. Fifteen blends of Cabernet Sauvignon, Merlot and Cabernet Franc, in addition to the mono varietals, were used in this investigation. Linear Discriminant Analysis (LDA) showed a clear differentiation of blends based on tannin concentration and composition where certain mono varietals like Cabernet Sauvignon seemed to contribute less to the overall characteristics of the blend. Partial Least Squares (PLS) Regression and cross validation were used to build a predictive model for the responses of the receptors to eleven binary blends and the three mono varietals. The optimized model was later used to predict the percentage of each mono varietal in an independent test set composted of four tri-blends with a 15% average error. A partial least square regression model using the mouth-feel and taste descriptive sensory attributes of the wine blends revealed a strong correlation of the receptors to perceived astringency, which is indicative of selective binding to polyphenols in wine.

The CD4/CD8 ratio is associated with coronary artery disease (CAD) in elderly Chinese patients.

PubMed

Gao, Pan; Rong, Hong-Hui; Lu, Ting; Tang, Gang; Si, Liang-Yi; Lederer, James A; Xiong, Wei

2017-01-01

The aim of this study was to investigate the relationship between number of circulating T cells and coronary artery disease (CAD) in an elderly Chinese population. A total of 295 elderly inpatients (age≥60) were included in this cross-sectional study. Their clinical and biochemical characteristics were recorded. Patients were divided to two groups: control patients and CAD patients. The risk factors of CAD were explored by binary logistic regression analysis. Compared with control patients, the ratio of CD4 to CD8 T cells was significantly increased in CAD patients. There was no difference in the number of CD3, CD4, and CD8 T cells between the two groups. Multiple logistic regression analysis showed that CAD was independently associated with age, gender, body mass index (BMI), systolic blood pressure (SBP), chronic heart failure (CHF) and the CD4/CD8 ratio. In addition, after adjusting for different clinical parameters (including gender, age, CHF, hypertension, arrhythmia, SBP, and BMI), the risk of CAD was significantly increased in patients with a CD4/CD8 ratio>1.5. There was a strong and independent association between the ratio of CD4/CD8 and CAD in elderly Chinese population. Copyright © 2016. Published by Elsevier B.V.

Has there been a change in the knowledge of GP registrars between 2011 and 2016 as measured by performance on common items in the Applied Knowledge Test?

PubMed

Neden, Catherine A; Parkin, Claire; Blow, Carol; Siriwardena, Aloysius Niroshan

2018-05-08

The aim of this study was to assess whether the absolute standard of candidates sitting the MRCGP Applied Knowledge Test (AKT) between 2011 and 2016 had changed. It is a descriptive study comparing the performance on marker questions of a reference group of UK graduates taking the AKT for the first time between 2011 and 2016. Using aggregated examination data, the performance of individual 'marker' questions was compared using Pearson's chi-squared tests and trend-line analysis. Binary logistic regression was used to analyse changes in performance over the study period. Changes in performance of individual marker questions using Pearson's chi-squared test showed statistically significant differences in 32 of the 49 questions included in the study. Trend line analysis showed a positive trend in 29 questions and a negative trend in the remaining 23. The magnitude of change was small. Logistic regression did not demonstrate any evidence for a change in the performance of the question set over the study period. However, candidates were more likely to get items on administration wrong compared with clinical medicine or research. There was no evidence of a change in performance of the question set as a whole.

Application of wavelet analysis to estimation of parameters of the gravitational-wave signal from a coalescing binary

NASA Astrophysics Data System (ADS)

Królak, Andrzej; Trzaskoma, Pawel

1996-05-01

Application of wavelet analysis to the estimation of parameters of the broad-band gravitational-wave signal emitted by a binary system is investigated. A method of instantaneous frequency extraction first proposed in this context by Innocent and Vinet is used. The gravitational-wave signal from a binary is investigated from the point of view of signal analysis theory and it is shown that such a signal is characterized by a large time - bandwidth product. This property enables the extraction of frequency modulation from the wavelet transform of the signal. The wavelet transform of the chirp signal from a binary is calculated analytically. Numerical simulations with the noisy chirp signal are performed. The gravitational-wave signal from a binary is taken in the quadrupole approximation and it is buried in noise corresponding to three different values of the signal-to-noise ratio and the wavelet method to extract the frequency modulation of the signal is applied. Then, from the frequency modulation, the chirp mass parameter of the binary is estimated. It is found that the chirp mass can be estimated to a good accuracy, typically of the order of (20/0264-9381/13/5/006/img5% where 0264-9381/13/5/006/img6 is the optimal signal-to-noise ratio. It is also shown that the post-Newtonian effects in the gravitational wave signal from a binary can be discriminated to a satisfactory accuracy.

Trainee-Associated Factors and Proficiency at Percutaneous Nephrolithotomy.

PubMed

Aghamir, Seyed Mohammad Kazem; Behtash, Negar; Hamidi, Morteza; Farahmand, Hasan; Salavati, Alborz; Mortaz Hejri, Sara

2017-07-01

Percutaneous nephrolithotomy (PNL) is a complicated procedure for urology trainees. This study was designed to investigate the effect of trainees' ages and previous experience, as well as the number of operated cases, on proficiency at PNL by using patient outcomes. A cross sectional observational study was designed during a five-year period. Trainees in PNL fellowship programs were included. At the end of the program, the trainees' performance in PNL was assessed regarding five competencies and scored 1-5. If the overall score was 4 or above, the trainee was considered as proficient. The trainees' age at the beginning of the program and the years passed from their residency graduation were asked and recorded. Also, the number of PNL cases operated by each trainee was obtained via their logbooks. The age, years passed from graduation, and number of operated cases were compared between two groups of proficient and non-proficient trainees. Univariate and multivariate binary logistic regression analysis was applied to estimate the effect of aforementioned variables on the occurrence of the proficiency. Forty-two trainees were included in the study. The mean and standard deviation for the overall score were 3.40 (out of 5) and 0.67, respectively. Eleven trainees (26.2%) recognized as proficient in performing PNL. Univariate regression analysis indicated that each of three variables (age, years passed from graduation and number of operated cases) had statistically significant effect on proficiency. However, the multivariate regression analysis revealed that just the number of cases had significant effect on achieving proficiency. Although it might be assumed that trainees' age negatively correlates with their scores, in fact, it is their amount of practice that makes a difference. A certain number of cases is required to be operated by a trainee in order to reach the desired competency in PNL.

Binary tree eigen solver in finite element analysis

NASA Technical Reports Server (NTRS)

Akl, F. A.; Janetzke, D. C.; Kiraly, L. J.

1993-01-01

This paper presents a transputer-based binary tree eigensolver for the solution of the generalized eigenproblem in linear elastic finite element analysis. The algorithm is based on the method of recursive doubling, which parallel implementation of a number of associative operations on an arbitrary set having N elements is of the order of o(log2N), compared to (N-1) steps if implemented sequentially. The hardware used in the implementation of the binary tree consists of 32 transputers. The algorithm is written in OCCAM which is a high-level language developed with the transputers to address parallel programming constructs and to provide the communications between processors. The algorithm can be replicated to match the size of the binary tree transputer network. Parallel and sequential finite element analysis programs have been developed to solve for the set of the least-order eigenpairs using the modified subspace method. The speed-up obtained for a typical analysis problem indicates close agreement with the theoretical prediction given by the method of recursive doubling.

Predictors of Gender Inequalities in the Rank of Full Professor

ERIC Educational Resources Information Center

Heijstra, Thamar; Bjarnason, Thoroddur; Rafnsdóttir, Gudbjörg Linda

2015-01-01

This article examines whether age, work-related, and family-related predictors explain differences in the academic advancement of women and men in Iceland. Survey data were analyzed by binary logistic regression. The findings put that women climb the academic career ladder at a slower pace than men. This finding puts one of the widely known…

South Texas Mexican American Use of Traditional Folk and Mainstream Alternative Therapies

ERIC Educational Resources Information Center

Martinez, Leslie N.

2009-01-01

A telephone survey was conducted with a large sample of Mexican Americans from border (n = 1,001) and nonborder (n = 1,030) regions in Texas. Patterns of traditional folk and mainstream complementary and alternative medicine (CAM) use were analyzed with two binary logistic regressions, using gender, self-rated health, confidence in medical…

Propensity of University Students in the Region of Antofagasta, Chile to Create Enterprise

ERIC Educational Resources Information Center

Romani, Gianni; Didonet, Simone; Contuliano, Sue-Hellen; Portilla, Rodrigo

2013-01-01

The authors aim to discuss the propensity or intention to create enterprise among university students in the region of Antofagasta, Chile, and to analyze the factors that influence the step from desire to intention. 681 students were surveyed. The data were analyzed by binary logistical regression. The results show that curriculum is among the…

Phasor Analysis of Binary Diffraction Gratings with Different Fill Factors

ERIC Educational Resources Information Center

Martinez, Antonio; Sanchez-Lopez, Ma del Mar; Moreno, Ignacio

2007-01-01

In this work, we present a simple analysis of binary diffraction gratings with different slit widths relative to the grating period. The analysis is based on a simple phasor technique directly derived from the Huygens principle. By introducing a slit phasor and a grating phasor, the intensity of the diffracted orders and the grating's resolving…

Analysis of 45-years of Eclipse Timings of the Hyades (K2 V+ DA) Eclipsing Binary V471 Tauri

NASA Astrophysics Data System (ADS)

Marchioni, Lucas; Guinan, Edward; Engle, Scott

2018-01-01

V471 Tau is an important detached 0.521-day eclipsing binary composed of a K2 V and a hot DA white dwarf star. This system resides in the Hyades star cluster located approximately 153 Ly from us. V471 Tau is considered to be the end-product of common-envelope binary star evolution and is currently a pre-CV system. V471 Tau serves as a valuable astrophysical laboratory for studying stellar evolution, white dwarfs, stellar magnetic dynamos, and possible detection of low mass companions using the Light Travel Time (LTT) Effects. Since its discovery as an eclipsing binary in 1970, photometry has been carried out and many eclipse timings have been determined. We have performed an analysis of the available photometric data available on V471 Tauri. The binary system has been the subject of analyses regarding the orbital period. From this analysis several have postulated the existence of a third body in the form of a brown dwarf that is causing periodic variations in the system’s apparent period. In this study we combine ground based data with photometry secured recently from the Kepler K2 mission. After detrending and phasing the available data, we are able to compare the changing period of the eclipsing binary system against predictions on the existence of this third body. The results of the analysis will be presented. This research is sponsored by grants from NASA and NSF for which we are very grateful.

Application of Pulsed-Field Gel Electrophoresis and Binary Typing as Tools in Veterinary Clinical Microbiology and Molecular Epidemiologic Analysis of Bovine and Human Staphylococcus aureus Isolates

PubMed Central

Zadoks, Ruth; van Leeuwen, Willem; Barkema, Herman; Sampimon, Otlis; Verbrugh, Henri; Schukken, Ynte Hein; van Belkum, Alex

2000-01-01

Thirty-eight bovine mammary Staphylococcus aureus isolates from diverse clinical, temporal, and geographical origins were genotyped by pulsed-field gel electrophoresis (PFGE) after SmaI digestion of prokaryotic DNA and by means of binary typing using 15 strain-specific DNA probes. Seven pulsed-field types and four subtypes were identified, as were 16 binary types. Concordant delineation of genetic relatedness was documented by both techniques, yet based on practical and epidemiological considerations, binary typing was the preferable method. Genotypes of bovine isolates were compared to 55 previously characterized human S. aureus isolates through cluster analysis of binary types. Genetic clusters containing strains of both human and bovine origin were found, but bacterial genotypes were predominantly associated with a single host species. Binary typing proved an excellent tool for comparison of S. aureus strains, including methicillin-resistant S. aureus, derived from different host species and from different databases. For 28 bovine S. aureus isolates, detailed clinical observations in vivo were compared to strain typing results in vitro. Associations were found between distinct genotypes and severity of disease, suggesting strain-specific bacterial virulence. Circumstantial evidence furthermore supports strain-specific routes of bacterial dissemination. We conclude that PFGE and binary typing can be successfully applied for genetic analysis of S. aureus isolates from bovine mammary secretions. Binary typing in particular is a robust and simple method and promises to become a powerful tool for strain characterization, for resolution of clonal relationships of bacteria within and between host species, and for identification of sources and transmission routes of bovine S. aureus. PMID:10790124

Error-analysis and comparison to analytical models of numerical waveforms produced by the NRAR Collaboration

NASA Astrophysics Data System (ADS)

Hinder, Ian; Buonanno, Alessandra; Boyle, Michael; Etienne, Zachariah B.; Healy, James; Johnson-McDaniel, Nathan K.; Nagar, Alessandro; Nakano, Hiroyuki; Pan, Yi; Pfeiffer, Harald P.; Pürrer, Michael; Reisswig, Christian; Scheel, Mark A.; Schnetter, Erik; Sperhake, Ulrich; Szilágyi, Bela; Tichy, Wolfgang; Wardell, Barry; Zenginoğlu, Anıl; Alic, Daniela; Bernuzzi, Sebastiano; Bode, Tanja; Brügmann, Bernd; Buchman, Luisa T.; Campanelli, Manuela; Chu, Tony; Damour, Thibault; Grigsby, Jason D.; Hannam, Mark; Haas, Roland; Hemberger, Daniel A.; Husa, Sascha; Kidder, Lawrence E.; Laguna, Pablo; London, Lionel; Lovelace, Geoffrey; Lousto, Carlos O.; Marronetti, Pedro; Matzner, Richard A.; Mösta, Philipp; Mroué, Abdul; Müller, Doreen; Mundim, Bruno C.; Nerozzi, Andrea; Paschalidis, Vasileios; Pollney, Denis; Reifenberger, George; Rezzolla, Luciano; Shapiro, Stuart L.; Shoemaker, Deirdre; Taracchini, Andrea; Taylor, Nicholas W.; Teukolsky, Saul A.; Thierfelder, Marcus; Witek, Helvi; Zlochower, Yosef

2013-01-01

The Numerical-Relativity-Analytical-Relativity (NRAR) collaboration is a joint effort between members of the numerical relativity, analytical relativity and gravitational-wave data analysis communities. The goal of the NRAR collaboration is to produce numerical-relativity simulations of compact binaries and use them to develop accurate analytical templates for the LIGO/Virgo Collaboration to use in detecting gravitational-wave signals and extracting astrophysical information from them. We describe the results of the first stage of the NRAR project, which focused on producing an initial set of numerical waveforms from binary black holes with moderate mass ratios and spins, as well as one non-spinning binary configuration which has a mass ratio of 10. All of the numerical waveforms are analysed in a uniform and consistent manner, with numerical errors evaluated using an analysis code created by members of the NRAR collaboration. We compare previously-calibrated, non-precessing analytical waveforms, notably the effective-one-body (EOB) and phenomenological template families, to the newly-produced numerical waveforms. We find that when the binary's total mass is ˜100-200M⊙, current EOB and phenomenological models of spinning, non-precessing binary waveforms have overlaps above 99% (for advanced LIGO) with all of the non-precessing-binary numerical waveforms with mass ratios ⩽4, when maximizing over binary parameters. This implies that the loss of event rate due to modelling error is below 3%. Moreover, the non-spinning EOB waveforms previously calibrated to five non-spinning waveforms with mass ratio smaller than 6 have overlaps above 99.7% with the numerical waveform with a mass ratio of 10, without even maximizing on the binary parameters.

Adjusting for multiple prognostic factors in the analysis of randomised trials

PubMed Central

2013-01-01

Background When multiple prognostic factors are adjusted for in the analysis of a randomised trial, it is unclear (1) whether it is necessary to account for each of the strata, formed by all combinations of the prognostic factors (stratified analysis), when randomisation has been balanced within each stratum (stratified randomisation), or whether adjusting for the main effects alone will suffice, and (2) the best method of adjustment in terms of type I error rate and power, irrespective of the randomisation method. Methods We used simulation to (1) determine if a stratified analysis is necessary after stratified randomisation, and (2) to compare different methods of adjustment in terms of power and type I error rate. We considered the following methods of analysis: adjusting for covariates in a regression model, adjusting for each stratum using either fixed or random effects, and Mantel-Haenszel or a stratified Cox model depending on outcome. Results Stratified analysis is required after stratified randomisation to maintain correct type I error rates when (a) there are strong interactions between prognostic factors, and (b) there are approximately equal number of patients in each stratum. However, simulations based on real trial data found that type I error rates were unaffected by the method of analysis (stratified vs unstratified), indicating these conditions were not met in real datasets. Comparison of different analysis methods found that with small sample sizes and a binary or time-to-event outcome, most analysis methods lead to either inflated type I error rates or a reduction in power; the lone exception was a stratified analysis using random effects for strata, which gave nominal type I error rates and adequate power. Conclusions It is unlikely that a stratified analysis is necessary after stratified randomisation except in extreme scenarios. Therefore, the method of analysis (accounting for the strata, or adjusting only for the covariates) will not generally need to depend on the method of randomisation used. Most methods of analysis work well with large sample sizes, however treating strata as random effects should be the analysis method of choice with binary or time-to-event outcomes and a small sample size. PMID:23898993

On the potential of models for location and scale for genome-wide DNA methylation data

PubMed Central

2014-01-01

Background With the help of epigenome-wide association studies (EWAS), increasing knowledge on the role of epigenetic mechanisms such as DNA methylation in disease processes is obtained. In addition, EWAS aid the understanding of behavioral and environmental effects on DNA methylation. In terms of statistical analysis, specific challenges arise from the characteristics of methylation data. First, methylation β-values represent proportions with skewed and heteroscedastic distributions. Thus, traditional modeling strategies assuming a normally distributed response might not be appropriate. Second, recent evidence suggests that not only mean differences but also variability in site-specific DNA methylation associates with diseases, including cancer. The purpose of this study was to compare different modeling strategies for methylation data in terms of model performance and performance of downstream hypothesis tests. Specifically, we used the generalized additive models for location, scale and shape (GAMLSS) framework to compare beta regression with Gaussian regression on raw, binary logit and arcsine square root transformed methylation data, with and without modeling a covariate effect on the scale parameter. Results Using simulated and real data from a large population-based study and an independent sample of cancer patients and healthy controls, we show that beta regression does not outperform competing strategies in terms of model performance. In addition, Gaussian models for location and scale showed an improved performance as compared to models for location only. The best performance was observed for the Gaussian model on binary logit transformed β-values, referred to as M-values. Our results further suggest that models for location and scale are specifically sensitive towards violations of the distribution assumption and towards outliers in the methylation data. Therefore, a resampling procedure is proposed as a mode of inference and shown to diminish type I error rate in practically relevant settings. We apply the proposed method in an EWAS of BMI and age and reveal strong associations of age with methylation variability that are validated in an independent sample. Conclusions Models for location and scale are promising tools for EWAS that may help to understand the influence of environmental factors and disease-related phenotypes on methylation variability and its role during disease development. PMID:24994026

Is There Evidence for Systematic Upcoding of ASA Physical Status Coincident with Payer Incentives? A Regression Discontinuity Analysis of the National Anesthesia Clinical Outcomes Registry.

PubMed

Schonberger, Robert B; Dutton, Richard P; Dai, Feng

2016-01-01

Modifications in physician billing patterns have been shown to occur in response to payer incentives, but the phenomenon remains largely unexplored in billing for anesthesia services. Within the field of anesthesiology, Medicare's policy not to provide additional reimbursement for higher ASA physical status scores contrasts with the practices of most private payers, and this pattern of reimbursement introduces a change in billing incentives once patients attain Medicare eligibility. We hypothesized that, coincident with the onset of widespread Medicare eligibility at age 65 years, a discontinuity in reported ASA physical status scores would be observed after controlling for the underlying trend of increasing ASA physical status scores with age. This phenomenon would manifest as a pattern of upcoding of ASA physical status scores for patients younger than 65 years that would become less common in patients age 65 years and older. Using data on age, sex, ASA physical status scores, and type of surgery from the National Anesthesia Clinical Outcomes Registry, we used a quasi-experimental regression discontinuity design to analyze whether there was evidence for a discontinuity in reported ASA physical status scores occurring at age 65 years for the nondeferrable anesthesia services accompanying hip, femur, or lower leg fracture repair. A total of 49,850 records were analyzed. In models designed to detect regression discontinuity at 65 years of age, neither the binary variable "age ≥ 65" nor the interaction term of age × age ≥ 65 was a statistically significant predictor of the outcome of ASA physical status score. The statistical inference was unchanged when ASA physical status scores were reclassified as a binary outcome (I-II vs III-V) and when different bandwidths around age 65 years were used. To test the validity of our study design for detecting regression discontinuity, simulations of the occurrence of deliberate upcoding of ASA physical status scores demonstrated the ability to detect deliberate upcoding occurring at rates exceeding 2% of eligible cases of patients younger than 65 years. We found no evidence for a significant discontinuity in the pattern of ASA physical status scores coincident with Medicare eligibility at age 65 years for the nondeferrable conditions of hip, femur, or lower leg fracture repair. Our data do not support the presence of fraudulent ASA physical status scoring among National Anesthesia Clinical Outcomes Registry contributors. If deliberate upcoding of ASA physical status scores is present in our data, the behavior is either too rare or too insensitive to the removal of payer incentives at age 65 years to be evident in the present analysis.

Prevalence and determinants of cardiovascular disease risk factors among the residents of urban community housing projects in Malaysia.

PubMed

Amiri, Mohammadreza; Majid, Hazreen Abdul; Hairi, FarizahMohd; Thangiah, Nithiah; Bulgiba, Awang; Su, Tin Tin

2014-01-01

The objectives are to assess the prevalence and determinants of cardiovascular disease (CVD) risk factors among the residents of Community Housing Projects in metropolitan Kuala Lumpur, Malaysia. By using simple random sampling, we selected and surveyed 833 households which comprised of 3,722 individuals. Out of the 2,360 adults, 50.5% participated in blood sampling and anthropometric measurement sessions. Uni and bivariate data analysis and multivariate binary logistic regression were applied to identify demographic and socioeconomic determinants of the existence of having at least one CVD risk factor. As a Result, while obesity (54.8%), hypercholesterolemia (51.5%), and hypertension (39.3%) were the most common CVD risk factors among the low-income respondents, smoking (16.3%), diabetes mellitus (7.8%) and alcohol consumption (1.4%) were the least prevalent. Finally, the results from the multivariate binary logistic model illustrated that compared to the Malays, the Indians were 41% less likely to have at least one of the CVD risk factors (OR = 0.59; 95% CI: 0.37 - 0.93). In Conclusion, the low-income individuals were at higher risk of developing CVDs. Prospective policies addressing preventive actions and increased awareness focusing on low-income communities are highly recommended and to consider age, gender, ethnic backgrounds, and occupation classes.

Formation enthalpies for transition metal alloys using machine learning

NASA Astrophysics Data System (ADS)

Ubaru, Shashanka; Miedlar, Agnieszka; Saad, Yousef; Chelikowsky, James R.

2017-06-01

The enthalpy of formation is an important thermodynamic property. Developing fast and accurate methods for its prediction is of practical interest in a variety of applications. Material informatics techniques based on machine learning have recently been introduced in the literature as an inexpensive means of exploiting materials data, and can be used to examine a variety of thermodynamics properties. We investigate the use of such machine learning tools for predicting the formation enthalpies of binary intermetallic compounds that contain at least one transition metal. We consider certain easily available properties of the constituting elements complemented by some basic properties of the compounds, to predict the formation enthalpies. We show how choosing these properties (input features) based on a literature study (using prior physics knowledge) seems to outperform machine learning based feature selection methods such as sensitivity analysis and LASSO (least absolute shrinkage and selection operator) based methods. A nonlinear kernel based support vector regression method is employed to perform the predictions. The predictive ability of our model is illustrated via several experiments on a dataset containing 648 binary alloys. We train and validate the model using the formation enthalpies calculated using a model by Miedema, which is a popular semiempirical model used for the prediction of formation enthalpies of metal alloys.

A comparison of adaptive sampling designs and binary spatial models: A simulation study using a census of Bromus inermis

USGS Publications Warehouse

Irvine, Kathryn M.; Thornton, Jamie; Backus, Vickie M.; Hohmann, Matthew G.; Lehnhoff, Erik A.; Maxwell, Bruce D.; Michels, Kurt; Rew, Lisa

2013-01-01

Commonly in environmental and ecological studies, species distribution data are recorded as presence or absence throughout a spatial domain of interest. Field based studies typically collect observations by sampling a subset of the spatial domain. We consider the effects of six different adaptive and two non-adaptive sampling designs and choice of three binary models on both predictions to unsampled locations and parameter estimation of the regression coefficients (species–environment relationships). Our simulation study is unique compared to others to date in that we virtually sample a true known spatial distribution of a nonindigenous plant species, Bromus inermis. The census of B. inermis provides a good example of a species distribution that is both sparsely (1.9 % prevalence) and patchily distributed. We find that modeling the spatial correlation using a random effect with an intrinsic Gaussian conditionally autoregressive prior distribution was equivalent or superior to Bayesian autologistic regression in terms of predicting to un-sampled areas when strip adaptive cluster sampling was used to survey B. inermis. However, inferences about the relationships between B. inermis presence and environmental predictors differed between the two spatial binary models. The strip adaptive cluster designs we investigate provided a significant advantage in terms of Markov chain Monte Carlo chain convergence when trying to model a sparsely distributed species across a large area. In general, there was little difference in the choice of neighborhood, although the adaptive king was preferred when transects were randomly placed throughout the spatial domain.

Categorical QSAR models for skin sensitization based on local lymph node assay measures and both ground and excited state 4D-fingerprint descriptors

NASA Astrophysics Data System (ADS)

Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Santos-Filho, Osvaldo A.; Esposito, Emilio X.; Hopfinger, Anton J.; Tseng, Yufeng J.

2008-06-01

In previous studies we have developed categorical QSAR models for predicting skin-sensitization potency based on 4D-fingerprint (4D-FP) descriptors and in vivo murine local lymph node assay (LLNA) measures. Only 4D-FP derived from the ground state (GMAX) structures of the molecules were used to build the QSAR models. In this study we have generated 4D-FP descriptors from the first excited state (EMAX) structures of the molecules. The GMAX, EMAX and the combined ground and excited state 4D-FP descriptors (GEMAX) were employed in building categorical QSAR models. Logistic regression (LR) and partial least square coupled logistic regression (PLS-CLR), found to be effective model building for the LLNA skin-sensitization measures in our previous studies, were used again in this study. This also permitted comparison of the prior ground state models to those involving first excited state 4D-FP descriptors. Three types of categorical QSAR models were constructed for each of the GMAX, EMAX and GEMAX datasets: a binary model (2-state), an ordinal model (3-state) and a binary-binary model (two-2-state). No significant differences exist among the LR 2-state model constructed for each of the three datasets. However, the PLS-CLR 3-state and 2-state models based on the EMAX and GEMAX datasets have higher predictivity than those constructed using only the GMAX dataset. These EMAX and GMAX categorical models are also more significant and predictive than corresponding models built in our previous QSAR studies of LLNA skin-sensitization measures.

More caregiving, less working: caregiving roles and gender difference.

PubMed

Lee, Yeonjung; Tang, Fengyan

2015-06-01

This study examined the relationship of caregiving roles to labor force participation using the nationally representative data from the Health and Retirement Study. The sample was composed of men and women aged 50 to 61 years (N = 5,119). Caregiving roles included caregiving for spouse, parents, and grandchildren; a summary of three caregiving roles was used to indicate multiple caregiving roles. Bivariate analysis using chi-square and t tests and binary logistic regression models were applied. Results show that women caregivers for parents and/or grandchildren were less likely to be in the labor force than non-caregivers and that caregiving responsibility was not related to labor force participation for the sample of men. Findings have implication for supporting family caregivers, especially women, to balance work and caregiving commitments. © The Author(s) 2013.

Binary versus non-binary information in real time series: empirical results and maximum-entropy matrix models

NASA Astrophysics Data System (ADS)

Almog, Assaf; Garlaschelli, Diego

2014-09-01

The dynamics of complex systems, from financial markets to the brain, can be monitored in terms of multiple time series of activity of the constituent units, such as stocks or neurons, respectively. While the main focus of time series analysis is on the magnitude of temporal increments, a significant piece of information is encoded into the binary projection (i.e. the sign) of such increments. In this paper we provide further evidence of this by showing strong nonlinear relations between binary and non-binary properties of financial time series. These relations are a novel quantification of the fact that extreme price increments occur more often when most stocks move in the same direction. We then introduce an information-theoretic approach to the analysis of the binary signature of single and multiple time series. Through the definition of maximum-entropy ensembles of binary matrices and their mapping to spin models in statistical physics, we quantify the information encoded into the simplest binary properties of real time series and identify the most informative property given a set of measurements. Our formalism is able to accurately replicate, and mathematically characterize, the observed binary/non-binary relations. We also obtain a phase diagram allowing us to identify, based only on the instantaneous aggregate return of a set of multiple time series, a regime where the so-called ‘market mode’ has an optimal interpretation in terms of collective (endogenous) effects, a regime where it is parsimoniously explained by pure noise, and a regime where it can be regarded as a combination of endogenous and exogenous factors. Our approach allows us to connect spin models, simple stochastic processes, and ensembles of time series inferred from partial information.

Error analysis of numerical gravitational waveforms from coalescing binary black holes

NASA Astrophysics Data System (ADS)

Fong, Heather; Chu, Tony; Kumar, Prayush; Pfeiffer, Harald; Boyle, Michael; Hemberger, Daniel; Kidder, Lawrence; Scheel, Mark; Szilagyi, Bela; SXS Collaboration

2016-03-01

The Advanced Laser Interferometer Gravitational-wave Observatory (Advanced LIGO) has finished a successful first observation run and will commence its second run this summer. Detection of compact object binaries utilizes matched-filtering, which requires a vast collection of highly accurate gravitational waveforms. This talk will present a set of about 100 new aligned-spin binary black hole simulations. I will discuss their properties, including a detailed error analysis, which demonstrates that the numerical waveforms are sufficiently accurate for gravitational wave detection purposes, as well as for parameter estimation purposes.

Prediction of future falls in a community dwelling older adult population using instrumented balance and gait analysis.

PubMed

Bauer, C M; Gröger, I; Rupprecht, R; Marcar, V L; Gaßmann, K G

2016-04-01

The role of instrumented balance and gait assessment when screening for prospective fallers is currently a topic of controversial discussion. This study analyzed the association between variables derived from static posturography, instrumented gait analysis and clinical assessments with the occurrence of prospective falls in a sample of community dwelling older people. In this study 84 older people were analyzed. Based on a prospective occurrence of falls, participants were categorized into fallers and non-fallers. Variables derived from clinical assessments, static posturography and instrumented gait analysis were evaluated with respect to the association with the occurrence of prospective falls using a forward stepwise, binary, logistic regression procedure. Fallers displayed a significantly shorter single support time during walking while counting backwards, increased mediolateral to anteroposterior sway amplitude ratio, increased fast mediolateral oscillations and a larger coefficient (Coeff) of sway direction during various static posturography tests. Previous falls were insignificantly associated with the occurrence of prospective falls. Variables derived from posturography and instrumented gait analysis showed significant associations with the occurrence of prospective falls in a sample of community dwelling older adults.

Spectral properties of binary asteroids

NASA Astrophysics Data System (ADS)

Pajuelo, Myriam; Birlan, Mirel; Carry, Benoît; DeMeo, Francesca E.; Binzel, Richard P.; Berthier, Jérôme

2018-04-01

We present the first attempt to characterize the distribution of taxonomic class among the population of binary asteroids (15% of all small asteroids). For that, an analysis of 0.8-2.5{μ m} near-infrared spectra obtained with the SpeX instrument on the NASA/IRTF is presented. Taxonomic class and meteorite analog is determined for each target, increasing the sample of binary asteroids with known taxonomy by 21%. Most binary systems are bound in the S-, X-, and C- classes, followed by Q and V-types. The rate of binary systems in each taxonomic class agrees within uncertainty with the background population of small near-Earth objects and inner main belt asteroids, but for the C-types which are under-represented among binaries.

Multivariate Models for Normal and Binary Responses in Intervention Studies

ERIC Educational Resources Information Center

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

2016-01-01

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

Marginal and Random Intercepts Models for Longitudinal Binary Data with Examples from Criminology

ERIC Educational Resources Information Center

Long, Jeffrey D.; Loeber, Rolf; Farrington, David P.

2009-01-01

Two models for the analysis of longitudinal binary data are discussed: the marginal model and the random intercepts model. In contrast to the linear mixed model (LMM), the two models for binary data are not subsumed under a single hierarchical model. The marginal model provides group-level information whereas the random intercepts model provides…

The mechanical properties of high speed GTAW weld and factors of nonlinear multiple regression model under external transverse magnetic field

NASA Astrophysics Data System (ADS)

Lu, Lin; Chang, Yunlong; Li, Yingmin; He, Youyou

2013-05-01

A transverse magnetic field was introduced to the arc plasma in the process of welding stainless steel tubes by high-speed Tungsten Inert Gas Arc Welding (TIG for short) without filler wire. The influence of external magnetic field on welding quality was investigated. 9 sets of parameters were designed by the means of orthogonal experiment. The welding joint tensile strength and form factor of weld were regarded as the main standards of welding quality. A binary quadratic nonlinear regression equation was established with the conditions of magnetic induction and flow rate of Ar gas. The residual standard deviation was calculated to adjust the accuracy of regression model. The results showed that, the regression model was correct and effective in calculating the tensile strength and aspect ratio of weld. Two 3D regression models were designed respectively, and then the impact law of magnetic induction on welding quality was researched.

Prevalence of abortion and stillbirth in a beef cattle system in Southeastern Mexico.

PubMed

Segura-Correa, José C; Segura-Correa, Victor M

2009-12-01

Prenatal mortality is an important cause of production losses in the livestock industry. This study estimates the prevalences of abortion and stillbirth in a beef cattle system and determines the significance of some risk factors, in the tropics of Mexico. Data were obtained from a Zebu cattle herd and their crosses with Bos taurus breeds, in Yucatan, Mexico. The logit of the probability of an abortion or stillbirth was modeled using binary logistic regression. The risk factors tested were: year of abortion (or calving), season of abortion (or calving), parity number and dam breed group. The effect of twins on stillbirth was tested using Fisher exact test. Of the 4175 calvings studied 49 were abortions (1.17%). Significant factors in the logistic regression analysis for abortions were season of abortion and parity number. The risk of abortion was lower in the dry seasons compared to the rainy and windy seasons (P = 0.009). The risk of abortion was higher in second parity cows followed by the third and first parity cows, as compared to older cows (P = 0.015). Of the 4126 births, 87 were stillbirths (2.11%). Significant factors in the logistic regression analysis for stillbirth were year of calving (P = 0.0001) and parity number (P < 0.001). The risk of stillbirth in first parity cows was 2.6 times that of old cows. Of the total births, 15 were twins (0.36%) of which 7 were born dead calves. Herd owners must focus on the significant risk factors under their control to reduce the prevalence of prenatal mortality.

Finding an appropriate equation to measure similarity between binary vectors: case studies on Indonesian and Japanese herbal medicines.

PubMed

Wijaya, Sony Hartono; Afendi, Farit Mochamad; Batubara, Irmanida; Darusman, Latifah K; Altaf-Ul-Amin, Md; Kanaya, Shigehiko

2016-12-07

The binary similarity and dissimilarity measures have critical roles in the processing of data consisting of binary vectors in various fields including bioinformatics and chemometrics. These metrics express the similarity and dissimilarity values between two binary vectors in terms of the positive matches, absence mismatches or negative matches. To our knowledge, there is no published work presenting a systematic way of finding an appropriate equation to measure binary similarity that performs well for certain data type or application. A proper method to select a suitable binary similarity or dissimilarity measure is needed to obtain better classification results. In this study, we proposed a novel approach to select binary similarity and dissimilarity measures. We collected 79 binary similarity and dissimilarity equations by extensive literature search and implemented those equations as an R package called bmeasures. We applied these metrics to quantify the similarity and dissimilarity between herbal medicine formulas belonging to the Indonesian Jamu and Japanese Kampo separately. We assessed the capability of binary equations to classify herbal medicine pairs into match and mismatch efficacies based on their similarity or dissimilarity coefficients using the Receiver Operating Characteristic (ROC) curve analysis. According to the area under the ROC curve results, we found Indonesian Jamu and Japanese Kampo datasets obtained different ranking of binary similarity and dissimilarity measures. Out of all the equations, the Forbes-2 similarity and the Variant of Correlation similarity measures are recommended for studying the relationship between Jamu formulas and Kampo formulas, respectively. The selection of binary similarity and dissimilarity measures for multivariate analysis is data dependent. The proposed method can be used to find the most suitable binary similarity and dissimilarity equation wisely for a particular data. Our finding suggests that all four types of matching quantities in the Operational Taxonomic Unit (OTU) table are important to calculate the similarity and dissimilarity coefficients between herbal medicine formulas. Also, the binary similarity and dissimilarity measures that include the negative match quantity d achieve better capability to separate herbal medicine pairs compared to equations that exclude d.

Development and Validation of Chemometric Spectrophotometric Methods for Simultaneous Determination of Simvastatin and Nicotinic Acid in Binary Combinations.

PubMed

Alahmad, Shoeb; Elfatatry, Hamed M; Mabrouk, Mokhtar M; Hammad, Sherin F; Mansour, Fotouh R

2018-01-01

The development and introduction of combined therapy represent a challenge for analysis due to severe overlapping of their UV spectra in case of spectroscopy or the requirement of a long tedious and high cost separation technique in case of chromatography. Quality control laboratories have to develop and validate suitable analytical procedures in order to assay such multi component preparations. New spectrophotometric methods for the simultaneous determination of simvastatin (SIM) and nicotinic acid (NIA) in binary combinations were developed. These methods are based on chemometric treatment of data, the applied chemometric techniques are multivariate methods including classical least squares (CLS), principal component regression (PCR) and partial least squares (PLS). In these techniques, the concentration data matrix were prepared by using the synthetic mixtures containing SIM and NIA dissolved in ethanol. The absorbance data matrix corresponding to the concentration data matrix was obtained by measuring the absorbance at 12 wavelengths in the range 216 - 240 nm at 2 nm intervals in the zero-order. The spectrophotometric procedures do not require any separation step. The accuracy, precision and the linearity ranges of the methods have been determined and validated by analyzing synthetic mixtures containing the studied drugs. Chemometric spectrophotometric methods have been developed in the present study for the simultaneous determination of simvastatin and nicotinic acid in their synthetic binary mixtures and in their mixtures with possible excipients present in tablet dosage form. The validation was performed successfully. The developed methods have been shown to be accurate, linear, precise, and so simple. The developed methods can be used routinely for the determination dosage form. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

The logistic model for predicting the non-gonoactive Aedes aegypti females.

PubMed

Reyes-Villanueva, Filiberto; Rodríguez-Pérez, Mario A

2004-01-01

To estimate, using logistic regression, the likelihood of occurrence of a non-gonoactive Aedes aegypti female, previously fed human blood, with relation to body size and collection method. This study was conducted in Monterrey, Mexico, between 1994 and 1996. Ten samplings of 60 mosquitoes of Ae. aegypti females were carried out in three dengue endemic areas: six of biting females, two of emerging mosquitoes, and two of indoor resting females. Gravid females, as well as those with blood in the gut were removed. Mosquitoes were taken to the laboratory and engorged on human blood. After 48 hours, ovaries were dissected to register whether they were gonoactive or non-gonoactive. Wing-length in mm was an indicator for body size. The logistic regression model was used to assess the likelihood of non-gonoactivity, as a binary variable, in relation to wing-length and collection method. Of the 600 females, 164 (27%) remained non-gonoactive, with a wing-length range of 1.9-3.2 mm, almost equal to that of all females (1.8-3.3 mm). The logistic regression model showed a significant likelihood of a female remaining non-gonoactive (Y=1). The collection method did not influence the binary response, but there was an inverse relationship between non-gonoactivity and wing-length. Dengue vector populations from Monterrey, Mexico display a wide-range body size. Logistic regression was a useful tool to estimate the likelihood for an engorged female to remain non-gonoactive. The necessity for a second blood meal is present in any female, but small mosquitoes are more likely to bite again within a 2-day interval, in order to attain egg maturation. The English version of this paper is available too at: http://www.insp.mx/salud/index.html.

Predicting High School Students' Interest in Majoring in a STEM Field: Insight into High School Students' Postsecondary Plans

ERIC Educational Resources Information Center

Lichtenberger, Eric; George-Jackson, Casey

2013-01-01

This study examined how various individual, family, and school level contextual factors impact the likelihood of planning to major in one of the science, technology, engineering, or mathematics (STEM) fields for high school students. A binary logistic regression model was developed to determine the extent to which each of the covariates helped to…

Community-Based Juvenile Reentry Services: The Effects of Service Dosage on Juvenile and Adult Recidivism

ERIC Educational Resources Information Center

Abrams, Laura S.; Terry, Diane; Franke, Todd M.

2011-01-01

In this study the authors examined the influence of length of participation in a community-based reentry program on the odds of reconviction in the juvenile and adult criminal justice systems. A structured telephone survey of reentry program alumni was conducted with 75 transition-age (18-25 year-old) young men. Binary logistic regression analysis…

Predicators of Success for Undergraduate Students Reinstated after an Academic Dismissal at a Small Midwest Private University Campus

ERIC Educational Resources Information Center

Meador, Ryan E.

2012-01-01

This study examined students who successfully applied for reinstatement after being academically dismissed for the first time in order to discover indicators of future success. This study examined 666 students' appeals filed at the DeVry University Kansas City campus between 2004 and 2009. Binary logistic regression was used to discover if a…

Won't You Be My Neighbor? Using an Ecological Approach to Examine the Impact of Community on Revictimization

ERIC Educational Resources Information Center

Obasaju, Mayowa A.; Palin, Frances L.; Jacobs, Carli; Anderson, Page; Kaslow, Nadine J.

2009-01-01

An ecological model is used to explore the moderating effects of community-level variables on the relation between childhood sexual, physical, and emotional abuse and adult intimate partner violence (IPV) within a sample of 98 African American women from low incomes. Results from hierarchical, binary logistics regressions analyses show that…

The cost of acquiring public hunting access on family forests lands

Treesearch

Michael A. Kilgore; Stephanie A. Snyder; Joesph M. Schertz; Steven J. Taff

2008-01-01

To address the issue of declining access to private forest land in the United States for hunting, over 1,000 Minnesota family forest owners were surveyed to estimate the cost of acquiring non-exclusive public hunting access rights. The results indicate landowner interest in selling access rights is extremely modest. Using binary logistic regression, the mean annual...

Exploring the Effects of Managerial Ownership on the Decision to Go Private: A Behavioral Agency Model Approach

ERIC Educational Resources Information Center

Valenti, Alix; Schneider, Marguerite

2012-01-01

This paper utilizes the behavioral agency model to investigate why many formerly public companies have been converted to privately held corporations. Using a matched pairs sample and categorical binary regression, and controlling for effects found in previous studies, we explore how the equity ownership of those entrusted to manage firms, the…

Understanding the Gap between Cognitive Abilities and Daily Living Skills in Adolescents with Autism Spectrum Disorders with Average Intelligence

ERIC Educational Resources Information Center

Duncan, Amie W.; Bishop, Somer L.

2015-01-01

Daily living skills standard scores on the Vineland Adaptive Behavior Scales-2nd edition were examined in 417 adolescents from the Simons Simplex Collection. All participants had at least average intelligence and a diagnosis of autism spectrum disorder. Descriptive statistics and binary logistic regressions were used to examine the prevalence and…

Sociodemographic Barriers to Early Detection of Autism: Screening and Evaluation Using the M-CHAT, M-CHAT-R, and Follow-Up

ERIC Educational Resources Information Center

Khowaja, Meena K.; Hazzard, Ann P.; Robins, Diana L.

2015-01-01

Parents (n = 11,845) completed the Modified Checklist for Autism in Toddlers (or its latest revision) at pediatric visits. Using sociodemographic predictors of maternal education and race, binary logistic regressions were utilized to examine differences in autism screening, diagnostic evaluation participation rates and outcomes, and reasons for…

The Effect of Participating in Indiana's Twenty-First Century Scholars Program on College Enrollments

ERIC Educational Resources Information Center

Toutkoushian, Robert K.; Hossler, Don; DesJardins, Stephen L.; McCall, Brian; Gonzalez Canche, Manuel S.

2015-01-01

Our study adds to prior work on Indiana's Twenty-first Century Scholars(TFCS) program by focusing on whether participating in--rather than completing--the program affects the likelihood of students going to college and where they initially enrolled. We first employ binary and multinomial logistic regression to obtain estimates of the impact of the…

Sample size adjustments for varying cluster sizes in cluster randomized trials with binary outcomes analyzed with second-order PQL mixed logistic regression.

PubMed

Candel, Math J J M; Van Breukelen, Gerard J P

2010-06-30

Adjustments of sample size formulas are given for varying cluster sizes in cluster randomized trials with a binary outcome when testing the treatment effect with mixed effects logistic regression using second-order penalized quasi-likelihood estimation (PQL). Starting from first-order marginal quasi-likelihood (MQL) estimation of the treatment effect, the asymptotic relative efficiency of unequal versus equal cluster sizes is derived. A Monte Carlo simulation study shows this asymptotic relative efficiency to be rather accurate for realistic sample sizes, when employing second-order PQL. An approximate, simpler formula is presented to estimate the efficiency loss due to varying cluster sizes when planning a trial. In many cases sampling 14 per cent more clusters is sufficient to repair the efficiency loss due to varying cluster sizes. Since current closed-form formulas for sample size calculation are based on first-order MQL, planning a trial also requires a conversion factor to obtain the variance of the second-order PQL estimator. In a second Monte Carlo study, this conversion factor turned out to be 1.25 at most. (c) 2010 John Wiley & Sons, Ltd.

A binary genetic programing model for teleconnection identification between global sea surface temperature and local maximum monthly rainfall events

NASA Astrophysics Data System (ADS)

Danandeh Mehr, Ali; Nourani, Vahid; Hrnjica, Bahrudin; Molajou, Amir

2017-12-01

The effectiveness of genetic programming (GP) for solving regression problems in hydrology has been recognized in recent studies. However, its capability to solve classification problems has not been sufficiently explored so far. This study develops and applies a novel classification-forecasting model, namely Binary GP (BGP), for teleconnection studies between sea surface temperature (SST) variations and maximum monthly rainfall (MMR) events. The BGP integrates certain types of data pre-processing and post-processing methods with conventional GP engine to enhance its ability to solve both regression and classification problems simultaneously. The model was trained and tested using SST series of Black Sea, Mediterranean Sea, and Red Sea as potential predictors as well as classified MMR events at two locations in Iran as predictand. Skill of the model was measured in regard to different rainfall thresholds and SST lags and compared to that of the hybrid decision tree-association rule (DTAR) model available in the literature. The results indicated that the proposed model can identify potential teleconnection signals of surrounding seas beneficial to long-term forecasting of the occurrence of the classified MMR events.

Predicting the "graduate on time (GOT)" of PhD students using binary logistics regression model

NASA Astrophysics Data System (ADS)

Shariff, S. Sarifah Radiah; Rodzi, Nur Atiqah Mohd; Rahman, Kahartini Abdul; Zahari, Siti Meriam; Deni, Sayang Mohd

2016-10-01

Malaysian government has recently set a new goal to produce 60,000 Malaysian PhD holders by the year 2023. As a Malaysia's largest institution of higher learning in terms of size and population which offers more than 500 academic programmes in a conducive and vibrant environment, UiTM has taken several initiatives to fill up the gap. Strategies to increase the numbers of graduates with PhD are a process that is challenging. In many occasions, many have already identified that the struggle to get into the target set is even more daunting, and that implementation is far too ideal. This has further being progressing slowly as the attrition rate increases. This study aims to apply the proposed models that incorporates several factors in predicting the number PhD students that will complete their PhD studies on time. Binary Logistic Regression model is proposed and used on the set of data to determine the number. The results show that only 6.8% of the 2014 PhD students are predicted to graduate on time and the results are compared wih the actual number for validation purpose.

The likelihood of achieving quantified road safety targets: a binary logistic regression model for possible factors.

PubMed

Sze, N N; Wong, S C; Lee, C Y

2014-12-01

In past several decades, many countries have set quantified road safety targets to motivate transport authorities to develop systematic road safety strategies and measures and facilitate the achievement of continuous road safety improvement. Studies have been conducted to evaluate the association between the setting of quantified road safety targets and road fatality reduction, in both the short and long run, by comparing road fatalities before and after the implementation of a quantified road safety target. However, not much work has been done to evaluate whether the quantified road safety targets are actually achieved. In this study, we used a binary logistic regression model to examine the factors - including vehicle ownership, fatality rate, and national income, in addition to level of ambition and duration of target - that contribute to a target's success. We analyzed 55 quantified road safety targets set by 29 countries from 1981 to 2009, and the results indicate that targets that are in progress and with lower level of ambitions had a higher likelihood of eventually being achieved. Moreover, possible interaction effects on the association between level of ambition and the likelihood of success are also revealed. Copyright © 2014 Elsevier Ltd. All rights reserved.

Stability of binaries. Part II: Rubble-pile binaries

NASA Astrophysics Data System (ADS)

Sharma, Ishan

2016-10-01

We consider the stability of the binary asteroids whose members are granular aggregates held together by self-gravity alone. A binary is said to be stable whenever both its members are orbitally and structurally stable to both orbital and structural perturbations. To this end, we extend the stability analysis of Sharma (Sharma [2015] Icarus, 258, 438-453), that is applicable to binaries with rigid members, to the case of binary systems with rubble members. We employ volume averaging (Sharma et al. [2009] Icarus, 200, 304-322), which was inspired by past work on elastic/fluid, rotating and gravitating ellipsoids. This technique has shown promise when applied to rubble-pile ellipsoids, but requires further work to settle some of its underlying assumptions. The stability test is finally applied to some suspected binary systems, viz., 216 Kleopatra, 624 Hektor and 90 Antiope. We also see that equilibrated binaries that are close to mobilizing their maximum friction can sustain only a narrow range of shapes and, generally, congruent shapes are preferred.

Eclipsing Binaries From the CSTAR Project at Dome A, Antarctica

NASA Astrophysics Data System (ADS)

Yang, Ming; Zhang, Hui; Wang, Songhu; Zhou, Ji-Lin; Zhou, Xu; Wang, Lingzhi; Wang, Lifan; Wittenmyer, R. A.; Liu, Hui-Gen; Meng, Zeyang; Ashley, M. C. B.; Storey, J. W. V.; Bayliss, D.; Tinney, Chris; Wang, Ying; Wu, Donghong; Liang, Ensi; Yu, Zhouyi; Fan, Zhou; Feng, Long-Long; Gong, Xuefei; Lawrence, J. S.; Liu, Qiang; Luong-Van, D. M.; Ma, Jun; Wu, Zhenyu; Yan, Jun; Yang, Huigen; Yang, Ji; Yuan, Xiangyan; Zhang, Tianmeng; Zhu, Zhenxi; Zou, Hu

2015-04-01

The Chinese Small Telescope ARray (CSTAR) has observed an area around the Celestial South Pole at Dome A since 2008. About 20,000 light curves in the i band were obtained during the observation season lasting from 2008 March to July. The photometric precision achieves about 4 mmag at i = 7.5 and 20 mmag at i = 12 within a 30 s exposure time. These light curves are analyzed using Lomb-Scargle, Phase Dispersion Minimization, and Box Least Squares methods to search for periodic signals. False positives may appear as a variable signature caused by contaminating stars and the observation mode of CSTAR. Therefore, the period and position of each variable candidate are checked to eliminate false positives. Eclipsing binaries are removed by visual inspection, frequency spectrum analysis, and a locally linear embedding technique. We identify 53 eclipsing binaries in the field of view of CSTAR, containing 24 detached binaries, 8 semi-detached binaries, 18 contact binaries, and 3 ellipsoidal variables. To derive the parameters of these binaries, we use the Eclipsing Binaries via Artificial Intelligence method. The primary and secondary eclipse timing variations (ETVs) for semi-detached and contact systems are analyzed. Correlated primary and secondary ETVs confirmed by false alarm tests may indicate an unseen perturbing companion. Through ETV analysis, we identify two triple systems (CSTAR J084612.64-883342.9 and CSTAR J220502.55-895206.7). The orbital parameters of the third body in CSTAR J220502.55-895206.7 are derived using a simple dynamical model.

Smooth Scalar-on-Image Regression via Spatial Bayesian Variable Selection

PubMed Central

Goldsmith, Jeff; Huang, Lei; Crainiceanu, Ciprian M.

2013-01-01

We develop scalar-on-image regression models when images are registered multidimensional manifolds. We propose a fast and scalable Bayes inferential procedure to estimate the image coefficient. The central idea is the combination of an Ising prior distribution, which controls a latent binary indicator map, and an intrinsic Gaussian Markov random field, which controls the smoothness of the nonzero coefficients. The model is fit using a single-site Gibbs sampler, which allows fitting within minutes for hundreds of subjects with predictor images containing thousands of locations. The code is simple and is provided in less than one page in the Appendix. We apply this method to a neuroimaging study where cognitive outcomes are regressed on measures of white matter microstructure at every voxel of the corpus callosum for hundreds of subjects. PMID:24729670

Influence of management of variables, sampling zones and land units on LR analysis for landslide spatial prevision

NASA Astrophysics Data System (ADS)

Greco, R.; Sorriso-Valvo, M.

2013-09-01

Several authors, according to different methodological approaches, have employed logistic Regression (LR), a multivariate statistical analysis adopted to assess the spatial probability of landslide, even though its fundamental principles have remained unaltered. This study aims at assessing the influence of some of these methodological approaches on the performance of LR, through a series of sensitivity analyses developed over a test area of about 300 km2 in Calabria (southern Italy). In particular, four types of sampling (1 - the whole study area; 2 - transects running parallel to the general slope direction of the study area with a total surface of about 1/3 of the whole study area; 3 - buffers surrounding the phenomena with a 1/1 ratio between the stable and the unstable area; 4 - buffers surrounding the phenomena with a 1/2 ratio between the stable and the unstable area), two variable coding modes (1 - grouped variables; 2 - binary variables), and two types of elementary land (1 - cells units; 2 - slope units) units have been tested. The obtained results must be considered as statistically relevant in all cases (Aroc values > 70%), thus confirming the soundness of the LR analysis which maintains high predictive capacities notwithstanding the features of input data. As for the area under investigation, the best performing methodological choices are the following: (i) transects produced the best results (0 < P(y) ≤ 93.4%; Aroc = 79.5%); (ii) as for sampling modalities, binary variables (0 < P(y) ≤ 98.3%; Aroc = 80.7%) provide better performance than ordinated variables; (iii) as for the choice of elementary land units, slope units (0 < P(y) ≤ 100%; Aroc = 84.2%) have obtained better results than cells matrix.

A stratification approach using logit-based models for confounder adjustment in the study of continuous outcomes.

PubMed

Tan, Chuen Seng; Støer, Nathalie C; Chen, Ying; Andersson, Marielle; Ning, Yilin; Wee, Hwee-Lin; Khoo, Eric Yin Hao; Tai, E-Shyong; Kao, Shih Ling; Reilly, Marie

2017-01-01

The control of confounding is an area of extensive epidemiological research, especially in the field of causal inference for observational studies. Matched cohort and case-control study designs are commonly implemented to control for confounding effects without specifying the functional form of the relationship between the outcome and confounders. This paper extends the commonly used regression models in matched designs for binary and survival outcomes (i.e. conditional logistic and stratified Cox proportional hazards) to studies of continuous outcomes through a novel interpretation and application of logit-based regression models from the econometrics and marketing research literature. We compare the performance of the maximum likelihood estimators using simulated data and propose a heuristic argument for obtaining the residuals for model diagnostics. We illustrate our proposed approach with two real data applications. Our simulation studies demonstrate that our stratification approach is robust to model misspecification and that the distribution of the estimated residuals provides a useful diagnostic when the strata are of moderate size. In our applications to real data, we demonstrate that parity and menopausal status are associated with percent mammographic density, and that the mean level and variability of inpatient blood glucose readings vary between medical and surgical wards within a national tertiary hospital. Our work highlights how the same class of regression models, available in most statistical software, can be used to adjust for confounding in the study of binary, time-to-event and continuous outcomes.

Spectral properties of binary asteroids

NASA Astrophysics Data System (ADS)

Pajuelo, Myriam; Birlan, Mirel; Carry, Benoît; DeMeo, Francesca E.; Binzel, Richard P.; Berthier, Jérôme

2018-07-01

We present the first attempt to characterize the distribution of taxonomic class among the population of binary asteroids (15 per cent of all small asteroids). For that, an analysis of 0.8-2.5 µm near-infrared spectra obtained with the SpeX instrument on the NASA/IRTF (Infrared Telescope Facility) is presented. Taxonomic class and meteorite analogue is determined for each target, increasing the sample of binary asteroids with known taxonomy by 21 per cent. Most binary systems are bound in the S, X, and C classes, followed by Q and V types. The rate of binary systems in each taxonomic class agrees within uncertainty with the background population of small near-Earth objects and inner main belt asteroids, but for the C types which are under-represented among binaries.

A photometric analysis of the neglected EW-type binary V336 TrA

NASA Astrophysics Data System (ADS)

Kriwattanawong, W.; Sarotsakulchai, T.; Maungkorn, S.; Reichart, D. E.; Haislip, J. B.; Kouprianov, V. V.; LaCluyze, A. P.; Moore, J. P.

2018-05-01

This study presents an analysis of photometric light curves and absolute parameters for the EW-type binary V336 TrA. VRI imaging observations were taken in 2013 by using the robotic telescopes PROMPT 4 and PROMPT 5 at Cerro Tololo Inter-American Observatory (CTIO), Chile. The observed light curves were fitted by using the Wilson-Devinney method. The results showed that V336 TrA is a W-type contact binary with a mass ratio of q = 1.396. The binary is a weak contact system with a fill-out factor of f = 15.69%. The system contains components with masses of 0.653 M⊙ and 0.912 M⊙ for the hotter and the cooler, respectively. The location of the secondary (less massive) component on the log M - log L diagram was found to be near the TAMS. The component has evolved to be oversize and overluminous. The orbital angular momentum of the binary was found to be log Jo = 51.61 cgs, less than all detached systems for same mass. The system has undergone angular momentum and/or mass loss, during the binary evolution from the detached to contact system.

Is fetal-type posterior cerebral artery a risk factor for intracranial aneurysm as analyzed by multislice CT angiography?

PubMed

He, Zhen; Wan, Yeda

2018-01-01

Fetal-type posterior cerebral artery (FTP) is a common anatomic variation that is closely associated with intracranial aneurysm. In the present study, multislice computed tomography angiography (CTA) was performed to assess whether FTP is a risk factor for intracranial aneurysm. CTA data of 364 consecutive cases of patients who were suspected with cerebrovascular disease or intracranial aneurysm of intracranial artery from 2013 to 2016 were reviewed and the incidence rates of FTP, other variations of the circle of Willis, intracranial aneurysm and FTP with intracranial aneurysm were evaluated. The χ 2 test was used to assess the influence of FTP and gender on the incidence rates of other variations of the circle of Willis, intracranial aneurysm and internal carotid artery-posterior communicating artery (ICA-PComA) aneurysm. Binary logistic regression analysis was performed to assess the associations of FTP and gender with intracranial aneurysm and ICA-PComA aneurysm. Compared with non-FTP patients, FTP cases exhibited significantly higher rates of other variations of the circle of Willis (χ 2 =80.173, P<0.001) and ICA-PComA aneurysm (χ 2 =4.437, P=0.035). Among patients with FTP and bilateral FTP, more female than male patients with intracranial aneurysm were identified. However, among all patients with intracranial aneurysm, no statistically significant differences in the prevalence of FTP (χ 2 =2.577, P=0.108) and bilateral FTP (χ 2 =2.199, P=0.159) between males and females were identified. Binary logistic regression analysis revealed that FTP and gender were risk factors for intracranial aneurysm and ICA-PComA aneurysm. A moderate association between FTP and ICA-PComA aneurysm (OR=2.762) were identified, although there was a weak association between FTP and intracranial aneurysm [odds ratio (OR)=1.365]. Furthermore, a strong association was identified between gender and intracranial aneurysm (OR=0.328), and a moderate association existed between gender and ICA-PComA aneurysm (OR=0.357). In conclusion, female gender is an independent risk factor for intracranial aneurysm, and FTP and female gender are independent risk factors for ICA-PComA aneurysm.

Is fetal-type posterior cerebral artery a risk factor for intracranial aneurysm as analyzed by multislice CT angiography?

PubMed Central

He, Zhen; Wan, Yeda

2018-01-01

Fetal-type posterior cerebral artery (FTP) is a common anatomic variation that is closely associated with intracranial aneurysm. In the present study, multislice computed tomography angiography (CTA) was performed to assess whether FTP is a risk factor for intracranial aneurysm. CTA data of 364 consecutive cases of patients who were suspected with cerebrovascular disease or intracranial aneurysm of intracranial artery from 2013 to 2016 were reviewed and the incidence rates of FTP, other variations of the circle of Willis, intracranial aneurysm and FTP with intracranial aneurysm were evaluated. The χ2 test was used to assess the influence of FTP and gender on the incidence rates of other variations of the circle of Willis, intracranial aneurysm and internal carotid artery-posterior communicating artery (ICA-PComA) aneurysm. Binary logistic regression analysis was performed to assess the associations of FTP and gender with intracranial aneurysm and ICA-PComA aneurysm. Compared with non-FTP patients, FTP cases exhibited significantly higher rates of other variations of the circle of Willis (χ2=80.173, P<0.001) and ICA-PComA aneurysm (χ2=4.437, P=0.035). Among patients with FTP and bilateral FTP, more female than male patients with intracranial aneurysm were identified. However, among all patients with intracranial aneurysm, no statistically significant differences in the prevalence of FTP (χ2=2.577, P=0.108) and bilateral FTP (χ2=2.199, P=0.159) between males and females were identified. Binary logistic regression analysis revealed that FTP and gender were risk factors for intracranial aneurysm and ICA-PComA aneurysm. A moderate association between FTP and ICA-PComA aneurysm (OR=2.762) were identified, although there was a weak association between FTP and intracranial aneurysm [odds ratio (OR)=1.365]. Furthermore, a strong association was identified between gender and intracranial aneurysm (OR=0.328), and a moderate association existed between gender and ICA-PComA aneurysm (OR=0.357). In conclusion, female gender is an independent risk factor for intracranial aneurysm, and FTP and female gender are independent risk factors for ICA-PComA aneurysm. PMID:29434687

Association of Genetic Polymorphisms of Renin–Angiotensin–Aldosterone System-Related Genes with Arterio-Venous Fistula Malfunction in Hemodialysis Patients

PubMed Central

Chen, Yu-Wei; Wu, Yu-Te; Lin, Jhin-Shyaun; Yang, Wu-Chang; Hsu, Yung-Ho; Lee, Kuo-Hua; Ou, Shou-Ming; Chen, Yung-Tai; Shih, Chia-Jen; Lee, Pui-Ching; Chan, Chia-Hao; Chung, Ming-Yi; Lin, Chih-Ching

2016-01-01

Hemodialysis (HD) is the most commonly-used renal replacement therapy for patients with end-stage renal disease worldwide. Arterio-venous fistula (AVF) is the vascular access of choice for HD patients with lowest risk of infection and thrombosis. In addition to environmental factors, genetic factors may also contribute to malfunction of AVF. Previous studies have demonstrated the effect of genotype polymorphisms of angiotensin converting enzyme on vascular access malfunction. We conducted a multicenter, cross-sectional study to evaluate the association between genetic polymorphisms of renin-angiotensin-aldosterone system and AVF malfunction. Totally, 577 patients were enrolled. Their mean age was 60 years old and 53% were male. HD patients with AVF malfunction had longer duration of HD (92.5 ± 68.1 vs. 61.2 ± 51.9 months, p < 0.001), lower prevalence of hypertension (44.8% vs. 55.3%, p = 0.025), right-sided (31.8% vs. 18.4%, p = 0.002) and upper arm AVF (26.6% vs. 9.7%, p < 0.001), and higher mean dynamic venous pressure (DVP) (147.8 ± 28.3 vs. 139.8 ± 30.0, p = 0.021). In subgroup analysis of different genders, location of AVF and DVP remained significant clinical risk factors of AVF malfunction in univariate and multivariate binary logistic regression in female HD patients. Among male HD patients, univariate binary logistic regression analysis revealed that right-side AVF and upper arm location are two important clinical risk factors. In addition, two single nucleotide polymorphisms (SNPs), rs275653 (Odds ratio 1.90, p = 0.038) and rs1492099 (Odds ratio 2.29, p = 0.017) of angiotensin II receptor 1 (AGTR1), were associated with increased risk of AVF malfunction. After adjustment for age and other clinical factors, minor allele-containing genotype polymorphisms (AA and CA) of rs1492099 still remained to be a significant risk factor of AVF malfunction (Odds ratio 3.63, p = 0.005). In conclusion, we demonstrated that rs1492099, a SNP of AGTR1 gene, could be a potential genetic risk factor of AVF malfunction in male HD patients. PMID:27240348

Formation and Evolution of X-ray Binaries

NASA Astrophysics Data System (ADS)

Fragkos, Anastasios

X-ray binaries - mass-transferring binary stellar systems with compact object accretors - are unique astrophysical laboratories. They carry information about many complex physical processes such as star formation, compact object formation, and evolution of interacting binaries. My thesis work involves the study of the formation and evolution of Galactic and extra-galacticX-ray binaries using both detailed and realistic simulation tools, and population synthesis techniques. I applied an innovative analysis method that allows the reconstruction of the full evolutionary history of known black hole X-ray binaries back to the time of compact object formation. This analysis takes into account all the available observationally determined properties of a system, and models in detail four of its evolutionary evolutionary phases: mass transfer through the ongoing X-ray phase, tidal evolution before the onset of Roche-lobe overflow, motion through the Galactic potential after the formation of the black hole, and binary orbital dynamics at the time of core collapse. Motivated by deep extra-galactic Chandra survey observations, I worked on population synthesis models of low-mass X-ray binaries in the two elliptical galaxies NGC3379 and NGC4278. These simulations were targeted at understanding the origin of the shape and normalization of the observed X-ray luminosity functions. In a follow up study, I proposed a physically motivated prescription for the modeling of transient neutron star low-mass X-ray binary properties, such as duty cycle, outburst duration and recurrence time. This prescription enabled the direct comparison of transient low-mass X-ray binary population synthesis models to the Chandra X-ray survey of the two ellipticals NGC3379 and NGC4278. Finally, I worked on population synthesismodels of black holeX-ray binaries in the MilkyWay. This work was motivated by recent developments in observational techniques for the measurement of black hole spin magnitudes in black hole X-ray binaries. The accuracy of these techniques depend on misalignment of the black hole spin with respect to the orbital angular momentum. In black hole X-ray binaries, this misalignment can occur during the supernova explosion that forms the compact object. In this study, I presented population synthesis models of Galactic black hole X-ray binaries, and examined the distribution of misalignment angles, and its dependence on the model parameters.

Novel spectrophotometric methods for simultaneous determination of timolol and dorzolamide in their binary mixture.

PubMed

Lotfy, Hayam Mahmoud; Hegazy, Maha A; Rezk, Mamdouh R; Omran, Yasmin Rostom

2014-05-21

Two smart and novel spectrophotometric methods namely; absorbance subtraction (AS) and amplitude modulation (AM) were developed and validated for the determination of a binary mixture of timolol maleate (TIM) and dorzolamide hydrochloride (DOR) in presence of benzalkonium chloride without prior separation, using unified regression equation. Additionally, simple, specific, accurate and precise spectrophotometric methods manipulating ratio spectra were developed and validated for simultaneous determination of the binary mixture namely; simultaneous ratio subtraction (SRS), ratio difference (RD), ratio subtraction (RS) coupled with extended ratio subtraction (EXRS), constant multiplication method (CM) and mean centering of ratio spectra (MCR). The proposed spectrophotometric procedures do not require any separation steps. Accuracy, precision and linearity ranges of the proposed methods were determined and the specificity was assessed by analyzing synthetic mixtures of both drugs. They were applied to their pharmaceutical formulation and the results obtained were statistically compared to that of a reported spectrophotometric method. The statistical comparison showed that there is no significant difference between the proposed methods and the reported one regarding both accuracy and precision. Copyright © 2014 Elsevier B.V. All rights reserved.

Analysis of Pulsating Components in the Eclipsing Binary Systems LT Herculis, RZ Microscopii, LY Puppis, V632 Scorpii, and V638 Scorpii

NASA Astrophysics Data System (ADS)

Streamer, M.; Bohlsen, T.; Ogmen, Y.

2016-06-01

Eclipsing binary stars are especially valuable for studies of stellar evolution. If pulsating components are also present then the stellar interior can be studied using asteroseismology techniques. We present photometric data and the analysis of the delta Scuti pulsations that we have discovered in five eclipsing binary systems. The systems are: LT Herculis, RZ Microscopii, LY Puppis, V632 Scorpii and V638 Scorpii. The dominant pulsation frequencies range between 13 - 29 cycles per day with semi-amplitudes of 4 - 20 millimagnitudes.

Phase relations in the system NaCl-KCl-H2O: IV. Differential thermal analysis of the sylvite liquidus in the KCl-H2O binary, the liquidus in the NaCl-KCl-H2O ternary, and the solidus in the NaCl-KCl binary to 2 kb pressure, and a summary of experimental data for thermodynamic-PTX analysis of solid-liquid equilibria at elevated P-T conditions

USGS Publications Warehouse

Chou, I.-Ming; Sterner, S.M.; Pitzer, Kenneth S.

1992-01-01

The sylvite liquidus in the binary system KCl-H2O and the liquidus in the ternary system NaCl-KCl-H2O were determined by using isobaric differential thermal analysis (DTA) cooling scans at pressures up to 2 kbars. Sylvite solubilities along the three-phase curve in the binary system KCl-H2O were obtained by the intersection of sylvite-liquidus isopleths with the three-phase curve in a P-T plot. These solubility data can be represented by the equation Wt.% KCl (??0.2) = 12.19 + 0.1557T - 5.4071 ?? 10-5 T2, where 400 ??? T ??? 770??C. These data are consistent with previous experimental observations. The solidus in the binary system NaCl-KCl was determined by using isobaric DTA heating scans at pressures up to 2 kbars. Using these liquidus and solidus data and other published information, a thermodynamic-PTX analysis of solid-liquid equilibria at high pressures and temperatures for the ternary system has been performed and is presented in an accompanying paper (Part V of this series). However, all experimental liquidus, solidus, and solvus data used in this analysis are summarized in this report (Part IV) and they are compared with the calculated values based on the analysis. ?? 1992.

Indicators of suboptimal performance embedded in the Wechsler Memory Scale-Fourth Edition (WMS-IV).

PubMed

Bouman, Zita; Hendriks, Marc P H; Schmand, Ben A; Kessels, Roy P C; Aldenkamp, Albert P

2016-01-01

Recognition and visual working memory tasks from the Wechsler Memory Scale-Fourth Edition (WMS-IV) have previously been documented as useful indicators for suboptimal performance. The present study examined the clinical utility of the Dutch version of the WMS-IV (WMS-IV-NL) for the identification of suboptimal performance using an analogue study design. The patient group consisted of 59 mixed-etiology patients; the experimental malingerers were 50 healthy individuals who were asked to simulate cognitive impairment as a result of a traumatic brain injury; the last group consisted of 50 healthy controls who were instructed to put forth full effort. Experimental malingerers performed significantly lower on all WMS-IV-NL tasks than did the patients and healthy controls. A binary logistic regression analysis was performed on the experimental malingerers and the patients. The first model contained the visual working memory subtests (Spatial Addition and Symbol Span) and the recognition tasks of the following subtests: Logical Memory, Verbal Paired Associates, Designs, Visual Reproduction. The results showed an overall classification rate of 78.4%, and only Spatial Addition explained a significant amount of variation (p < .001). Subsequent logistic regression analysis and receiver operating characteristic (ROC) analysis supported the discriminatory power of the subtest Spatial Addition. A scaled score cutoff of <4 produced 93% specificity and 52% sensitivity for detection of suboptimal performance. The WMS-IV-NL Spatial Addition subtest may provide clinically useful information for the detection of suboptimal performance.

Effective role of lady health workers in immunization of children in Pakistan.

PubMed

Afzal, Saira; Naeem, Azka; Shahid, Unaiza; Noor Syed, Wajiha; Khan, Urva; Misal Zaidi, Nayyar

2016-01-01

To determine the association of Lady Health Worker's role with immunization of children in Pakistan. Secondary analysis was conducted on data obtained from Pakistan's Demographic and Health Survey. Children who did not receive all doses of vaccines were considered incompletely immunized or vice versa. The association between determinants was assessed by simple and multivariable binary logistic regression. The mothers and fathers had a mean age of 32.7 (SD+8.6) years and 37.9 (SD +10.1) years, respectively. Age of mother greater than 35 (OR=0.93; 95% CI:0.70-1.25); born in Baluchistan (OR=3.47,95% CI:2.21-5.49); rural area dwellers (OR=2.04; 95% CI:1.65-2.51); female gender (OR=1.06; 95% CI:0.87-1.29); birth order (of last born child) greater than 7 (OR=2.21, 95% CI:1.60-3.06); delivered at home (OR=2.20, 95% CI:1.76-2.74); long distance to health care facility (OR=2.66, 95% CI:2.16-3.28); and no LHW visit in last 12 months (OR=1.91, CI:1.48-2.47) were significantly associated with incomplete immunization in bivariate analysis. In final model of multinomial regression analysis the absence of visit by LHW in last 12 months was the most significant factor when all risk factors were analyzed in last model. This study has concluded that visit of LHW in last 12 months was significantly associated with immunization.

The PyCBC search for binary black hole coalescences in Advanced LIGO's first observing run

NASA Astrophysics Data System (ADS)

Willis, Joshua; LIGO Scientific Collaboration

2017-01-01

Advanced LIGO's first observing run saw the first detections of binary black hole coalescences. We describe the PyCBC matched filter analysis, and the results of that search for binary systems with total mass up to 100 solar masses. This is a matched filter search for general-relativistic signals from binary black hole systems. Two signals, GW150914 and GW151226, were identified with very high significance, and a third possible signal, LVT151012, was found, though at much lower significance. Supported by NSF award PHY-1506254.

The respective roles of polar/nonpolar binary patterns and amino acid composition in protein regular secondary structures explored exhaustively using hydrophobic cluster analysis.

PubMed

Rebehmed, Joseph; Quintus, Flavien; Mornon, Jean-Paul; Callebaut, Isabelle

2016-05-01

Several studies have highlighted the leading role of the sequence periodicity of polar and nonpolar amino acids (binary patterns) in the formation of regular secondary structures (RSS). However, these were based on the analysis of only a few simple cases, with no direct mean to correlate binary patterns with the limits of RSS. Here, HCA-derived hydrophobic clusters (HC) which are conditioned binary patterns whose positions fit well those of RSS, were considered. All the HC types, defined by unique binary patterns, which were commonly observed in three-dimensional (3D) structures of globular domains, were analyzed. The 180 HC types with preferences for either α-helices or β-strands distinctly contain basic binary units typical of these RSS. Therefore a general trend supporting the "binary pattern preference" assumption was observed. HC for which observed RSS are in disagreement with their expected behavior (discordant HC) were also examined. They were separated in HC types with moderate preferences for RSS, having "weak" binary patterns and versatile RSS and HC types with high preferences for RSS, having "strong" binary patterns and then displaying nonpolar amino acids at the protein surface. It was shown that in both cases, discordant HC could be distinguished from concordant ones by well-differentiated amino acid compositions. The obtained results could, thus, help to complement the currently available methods for the accurate prediction of secondary structures in proteins from the only information of a single amino acid sequence. This can be especially useful for characterizing orphan sequences and for assisting protein engineering and design. © 2016 Wiley Periodicals, Inc.

Detecting nonsense for Chinese comments based on logistic regression

NASA Astrophysics Data System (ADS)

Zhuolin, Ren; Guang, Chen; Shu, Chen

2016-07-01

To understand cyber citizens' opinion accurately from Chinese news comments, the clear definition on nonsense is present, and a detection model based on logistic regression (LR) is proposed. The detection of nonsense can be treated as a binary-classification problem. Besides of traditional lexical features, we propose three kinds of features in terms of emotion, structure and relevance. By these features, we train an LR model and demonstrate its effect in understanding Chinese news comments. We find that each of proposed features can significantly promote the result. In our experiments, we achieve a prediction accuracy of 84.3% which improves the baseline 77.3% by 7%.

The relationship between problem gambling and mental and physical health correlates among a nationally representative sample of Canadian women.

PubMed

Afifi, Tracie O; Cox, Brian J; Martens, Patricia J; Sareen, Jitender; Enns, Murray W

2010-01-01

Gambling has become an increasingly common activity among women since the widespread growth of the gambling industry. Currently, our knowledge of the relationship between problem gambling among women and mental and physical correlates is limited. Therefore, important relationships between problem gambling and health and functioning, mental disorders, physical health conditions, and help-seeking behaviours among women were examined using a nationally representative Canadian sample. Data were from the nationally representative Canadian Community Health Survey Cycle 1.2 (CCHS 1.2; n = 10,056 women aged 15 years and older; data collected in 2002). The statistical analysis included binary logistic regression, multinomial logistic regression, and linear regression models. Past 12-month problem gambling was associated with a significantly higher probability of current lower general health, suicidal ideation and attempts, decreased psychological well-being, increased distress, depression, mania, panic attacks, social phobia, agoraphobia, alcohol dependence, any mental disorder, comorbidity of mental disorders, chronic bronchitis, fibromyalgia, migraine headaches, help-seeking from a professional, attending a self-help group, and calling a telephone help line (odds ratios ranged from 1.5 to 8.2). Problem gambling was associated with a broad range of negative health correlates among women. Problem gambling is an important public health concern. These findings can be used to inform healthy public policies on gambling.

Impact of low vision on employment.

PubMed

Mojon-Azzi, Stefania M; Sousa-Poza, Alfonso; Mojon, Daniel S

2010-01-01

We investigated the influence of self-reported corrected eyesight on several variables describing the perception by employees and self-employed persons of their employment. Our study was based on data from the Survey of Health, Ageing and Retirement in Europe (SHARE). SHARE is a multidisciplinary, cross-national database of microdata on health, socioeconomic status, social and family networks, collected on 31,115 individuals in 11 European countries and in Israel. With the help of ordered logistic regressions and binary logistic regressions, we analyzed the influence of perceived visual impairment--corrected by 19 covariates capturing socioeconomic and health-related factors--on 10 variables describing the respondents' employment situation. Based on data covering 10,340 working individuals, the results of the logistic and ordered regressions indicate that respondents with lower levels of self-reported general eyesight were significantly less satisfied with their jobs, felt they had less freedom to decide, less opportunity to develop new skills, less support in difficult situations, less recognition for their work, and an inadequate salary. Respondents with a lower eyesight level more frequently reported that they feared their health might limit their ability to work before regular retirement age and more often indicated that they were seeking early retirement. Analysis of this dataset from 12 countries demonstrates the strong impact of self-reported visual impairment on individual employment, and therefore on job satisfaction, productivity, and well-being. Copyright © 2010 S. Karger AG, Basel.

Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble-based methods?

PubMed Central

Austin, Peter C; Lee, Douglas S; Steyerberg, Ewout W; Tu, Jack V

2012-01-01

In biomedical research, the logistic regression model is the most commonly used method for predicting the probability of a binary outcome. While many clinical researchers have expressed an enthusiasm for regression trees, this method may have limited accuracy for predicting health outcomes. We aimed to evaluate the improvement that is achieved by using ensemble-based methods, including bootstrap aggregation (bagging) of regression trees, random forests, and boosted regression trees. We analyzed 30-day mortality in two large cohorts of patients hospitalized with either acute myocardial infarction (N = 16,230) or congestive heart failure (N = 15,848) in two distinct eras (1999–2001 and 2004–2005). We found that both the in-sample and out-of-sample prediction of ensemble methods offered substantial improvement in predicting cardiovascular mortality compared to conventional regression trees. However, conventional logistic regression models that incorporated restricted cubic smoothing splines had even better performance. We conclude that ensemble methods from the data mining and machine learning literature increase the predictive performance of regression trees, but may not lead to clear advantages over conventional logistic regression models for predicting short-term mortality in population-based samples of subjects with cardiovascular disease. PMID:22777999

Classification and regression tree (CART) analysis of endometrial carcinoma: Seeing the forest for the trees.

PubMed

Barlin, Joyce N; Zhou, Qin; St Clair, Caryn M; Iasonos, Alexia; Soslow, Robert A; Alektiar, Kaled M; Hensley, Martee L; Leitao, Mario M; Barakat, Richard R; Abu-Rustum, Nadeem R

2013-09-01

The objectives of the study are to evaluate which clinicopathologic factors influenced overall survival (OS) in endometrial carcinoma and to determine if the surgical effort to assess para-aortic (PA) lymph nodes (LNs) at initial staging surgery impacts OS. All patients diagnosed with endometrial cancer from 1/1993-12/2011 who had LNs excised were included. PALN assessment was defined by the identification of one or more PALNs on final pathology. A multivariate analysis was performed to assess the effect of PALNs on OS. A form of recursive partitioning called classification and regression tree (CART) analysis was implemented. Variables included: age, stage, tumor subtype, grade, myometrial invasion, total LNs removed, evaluation of PALNs, and adjuvant chemotherapy. The cohort included 1920 patients, with a median age of 62 years. The median number of LNs removed was 16 (range, 1-99). The removal of PALNs was not associated with OS (P=0.450). Using the CART hierarchically, stage I vs. stages II-IV and grades 1-2 vs. grade 3 emerged as predictors of OS. If the tree was allowed to grow, further branching was based on age and myometrial invasion. Total number of LNs removed and assessment of PALNs as defined in this study were not predictive of OS. This innovative CART analysis emphasized the importance of proper stage assignment and a binary grading system in impacting OS. Notably, the total number of LNs removed and specific evaluation of PALNs as defined in this study were not important predictors of OS. Copyright © 2013 Elsevier Inc. All rights reserved.

The Young Visual Binary Survey

NASA Astrophysics Data System (ADS)

Prato, Lisa; Avilez, Ian; Lindstrom, Kyle; Graham, Sean; Sullivan, Kendall; Biddle, Lauren; Skiff, Brian; Nofi, Larissa; Schaefer, Gail; Simon, Michal

2018-01-01

Differences in the stellar and circumstellar properties of the components of young binaries provide key information about star and disk formation and evolution processes. Because objects with separations of a few to a few hundred astronomical units share a common environment and composition, multiple systems allow us to control for some of the factors which play into star formation. We are completing analysis of a rich sample of about 100 pre-main sequence binaries and higher order multiples, primarily located in the Taurus and Ophiuchus star forming regions. This poster will highlight some of out recent, exciting results. All reduced spectra and the results of our analysis will be publicly available to the community at http://jumar.lowell.edu/BinaryStars/. Support for this research was provided in part by NSF award AST-1313399 and by NASA Keck KPDA funding.

Lean and Efficient Software: Whole-Program Optimization of Executables

DTIC Science & Technology

2015-09-30

libraries. Many levels of library interfaces—where some libraries are dynamically linked and some are provided in binary form only—significantly limit...software at build time. The opportunity: Our objective in this project is to substantially improve the performance, size, and robustness of binary ...executables by using static and dynamic binary program analysis techniques to perform whole-program optimization directly on compiled programs

First photometric study of two southern eclipsing binaries IS Tel and DW Aps

NASA Astrophysics Data System (ADS)

Özer, S.; Sürgit, D.; Erdem, A.; Öztürk, O.

2017-02-01

The paper presents the first photometric analysis of two southern eclipsing binary stars, IS Tel and DW Aps. Their V light curves from the All Sky Automated Survey were modelled by using Wilson-Devinney method. The final models give these two Algol-like binary stars as having detached configurations. Absolute parameters of the components of the systems were also estimated.

Operation Pied Piper: a geographical reappraisal of the impact of wartime evacuation on scarlet fever and diphtheria rates in England and Wales, 1939-1945.

PubMed

Smallman-Raynor, M R; Cliff, A D

2015-10-01

This paper examines the geographical impact of the British Government's wartime evacuation scheme on notified rates of two common acute childhood diseases (scarlet fever and diphtheria) in the 1470 local government districts of England and Wales, 1939-1945. Drawing on the notifications of communicable diseases collated by the General Register Office (GRO), we establish pre-war (baseline) disease rates for the 1470 districts. For the war years, techniques of binary logistic regression analysis are used to assess the associations between (a) above-baseline ('raised') disease rates in evacuation, neutral and reception districts and (b) the major phases of the evacuation scheme. The analysis demonstrates that the evacuation was temporally associated with distinct national and regional effects on notified levels of disease activity. These effects were most pronounced in the early years of the dispersal (1939-1941) and corresponded with initial levels of evacuation-related population change at the regional and district scales.

[Voluntary help in dementia - predictors for utilisation and expected quality from a family caregiver's point of view].

PubMed

Grässel, E; Luttenberger, K; Römer, H; Donath, C

2010-09-01

Although support services are considered cost-effective in the relief of care-giving family members of dementia patients, there has been little research to date on the predictors of use and quality expectations. These two questions are examined for the first time based on cross-sectional data of 404 care-giving family members, users and non-users of voluntary help services. Quantitative data are evaluated using binary logistical regression analysis, qualitative data using content analysis. The rating of how helpful the use of a voluntary help service is in the personal situation was found to be the only significant predictor of use. With respect to quality, it is most important that the persons giving support be punctual and well-trained. To increase the rate of use, care-giving family members must be convinced of the relevant benefits of using a voluntary help service. In addition, attention must be paid to the professional organization and training of voluntary helpers. Georg Thieme Verlag KG Stuttgart, New York.

Postsurgical complications in patients with renal tumours with venous thrombosis treated with surgery.

PubMed

Caño-Velasco, J; Herranz-Amo, F; Barbas-Bernardos, G; Mayor-de Castro, J; Aragón-Chamizo, J; Arnal-Chacón, G; Lledó García, E; Hernández-Fernández, C

2018-04-06

Surgery on renal tumours with venous thrombosis suffers a high rate of complications and non-negligible perioperative mortality. Our objective was to analyse the postoperative complications, their relationship with the level of the thrombus and its potential predisposing factors. A retrospective analysis was conducted of 101 patients with renal tumours with venous thrombosis operated on between 1988 and 2017. Two patients were excluded because of intraoperative pulmonary thromboembolism and exitus (2%). The postsurgical complications were classified according to Clavien-Dindo. To compare the qualitative variables, we employed the chi-squared test. We performed a multivariate analysis using binary logistic regression to identify the independent predictors. Some type of postsurgical complication occurred in 34 (34.3%) patients, 11 (11.1%) of which were severe (Clavien III-V). There were significant differences in the total complications (P=.003) and severe complications (Clavien≥III; P=.03) depending on the level of the tumour thrombus. Copyright © 2018 AEU. Publicado por Elsevier España, S.L.U. All rights reserved.

Parallel Analysis with Unidimensional Binary Data

ERIC Educational Resources Information Center

Weng, Li-Jen; Cheng, Chung-Ping

2005-01-01

The present simulation investigated the performance of parallel analysis for unidimensional binary data. Single-factor models with 8 and 20 indicators were examined, and sample size (50, 100, 200, 500, and 1,000), factor loading (.45, .70, and .90), response ratio on two categories (50/50, 60/40, 70/30, 80/20, and 90/10), and types of correlation…

Different spectrophotometric methods applied for the analysis of binary mixture of flucloxacillin and amoxicillin: A comparative study.

PubMed

Attia, Khalid A M; Nassar, Mohammed W I; El-Zeiny, Mohamed B; Serag, Ahmed

2016-05-15

Three different spectrophotometric methods were applied for the quantitative analysis of flucloxacillin and amoxicillin in their binary mixture, namely, ratio subtraction, absorbance subtraction and amplitude modulation. A comparative study was done listing the advantages and the disadvantages of each method. All the methods were validated according to the ICH guidelines and the obtained accuracy, precision and repeatability were found to be within the acceptable limits. The selectivity of the proposed methods was tested using laboratory prepared mixtures and assessed by applying the standard addition technique. So, they can be used for the routine analysis of flucloxacillin and amoxicillin in their binary mixtures. Copyright © 2016 Elsevier B.V. All rights reserved.

Using Logistic Regression To Predict the Probability of Debris Flows Occurring in Areas Recently Burned By Wildland Fires

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.

2003-01-01

Logistic regression was used to predict the probability of debris flows occurring in areas recently burned by wildland fires. Multiple logistic regression is conceptually similar to multiple linear regression because statistical relations between one dependent variable and several independent variables are evaluated. In logistic regression, however, the dependent variable is transformed to a binary variable (debris flow did or did not occur), and the actual probability of the debris flow occurring is statistically modeled. Data from 399 basins located within 15 wildland fires that burned during 2000-2002 in Colorado, Idaho, Montana, and New Mexico were evaluated. More than 35 independent variables describing the burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows were delineated from National Elevation Data using a Geographic Information System (GIS). (2) Data describing the burn severity, geology, land surface gradient, rainfall, and soil properties were determined for each basin. These data were then downloaded to a statistics software package for analysis using logistic regression. (3) Relations between the occurrence/non-occurrence of debris flows and burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated and several preliminary multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combination produced the most effective model. The multivariate model that best predicted the occurrence of debris flows was selected. (4) The multivariate logistic regression model was entered into a GIS, and a map showing the probability of debris flows was constructed. The most effective model incorporates the percentage of each basin with slope greater than 30 percent, percentage of land burned at medium and high burn severity in each basin, particle size sorting, average storm intensity (millimeters per hour), soil organic matter content, soil permeability, and soil drainage. The results of this study demonstrate that logistic regression is a valuable tool for predicting the probability of debris flows occurring in recently-burned landscapes.

Prevalence and predictors of thyroid functional abnormalities in newly diagnosed AL amyloidosis.

PubMed

Muchtar, E; Dean, D S; Dispenzieri, A; Dingli, D; Buadi, F K; Lacy, M Q; Hayman, S R; Kapoor, P; Leung, N; Russell, S; Lust, J A; Lin, Yi; Warsame, R; Gonsalves, W; Kourelis, T V; Go, R S; Chakraborty, R; Zeldenrust, S; Kyle, R A; Rajkumar, S Vincent; Kumar, S K; Gertz, M A

2017-06-01

Data on the effect of systemic immunoglobulin light chain amyloidosis (AL amyloidosis) on thyroid function are limited. To assess the prevalence of hypothyroidism in AL amyloidosis patients and determine its predictors. 1142 newly diagnosed AL amyloidosis patients were grouped based on the thyroid-stimulating hormone (TSH) measurement at diagnosis: hypothyroid group (TSH above upper normal reference; >5 mIU L -1 ; n = 217, 19% of study participants) and euthyroid group (n = 925, 81%). Predictors for hypothyroidism were assessed in a binary multivariate model. Survival between groups was compared using the log-rank test and a multivariate analysis. Patients with hypothyroidism were older, more likely to present with renal and hepatic involvement and had a higher light chain burden compared to patients in the euthyroid group. Higher proteinuria in patients with renal involvement and lower albumin in patients with hepatic involvement were associated with hypothyroidism. In a binary logistic regression model, age ≥65 years, female sex, renal involvement, hepatic involvement, kappa light chain restriction and amiodarone use were independently associated with hypothyroidism. Ninety-three per cent of patients in the hypothyroid group with free thyroxine measurement had normal values, consistent with subclinical hypothyroidism. Patients in the hypothyroid group had a shorter survival compared to patients in the euthyroid group (4-year survival 36% vs 43%; P = 0.008), a difference that was maintained in a multivariate analysis. A significant proportion of patients with AL amyloidosis present with hypothyroidism, predominantly subclinical, which carries a survival disadvantage. Routine assessment of TSH in these patients is warranted. © 2017 The Association for the Publication of the Journal of Internal Medicine.

Constraints on the Progenitor System of SN 2016gkg from a Comprehensive Statistical Analysis

NASA Astrophysics Data System (ADS)

Sravan, Niharika; Marchant, Pablo; Kalogera, Vassiliki; Margutti, Raffaella

2018-01-01

Type IIb supernovae (SNe) present a unique opportunity for understanding the progenitors of stripped-envelope SNe because the stellar progenitor of several SNe IIb have been identified in pre-explosion images. In this paper, we use Bayesian inference and a large grid of non-rotating solar-metallicity single and binary stellar models to derive the associated probability distributions of single and binary progenitors of the SN IIb 2016gkg using existing observational constraints. We find that potential binary star progenitors have smaller pre-SN hydrogen-envelope and helium-core masses than potential single-star progenitors typically by 0.1 M ⊙ and 2 M ⊙, respectively. We find that, a binary companion, if present, is a main-sequence or red-giant star. Apart from this, we do not find strong constraints on the nature of the companion star. We demonstrate that the range of progenitor helium-core mass inferred from observations could help improve constraints on the progenitor. We find that the probability that the progenitor of SN 2016gkg was a binary is 22% when we use constraints only on the progenitor luminosity and effective temperature. Imposing the range of pre-SN progenitor hydrogen-envelope mass and radius inferred from SN light curves, the probability that the progenitor is a binary increases to 44%. However, there is no clear preference for a binary progenitor. This is in contrast to binaries being the currently favored formation channel for SNe IIb. Our analysis demonstrates the importance of statistical inference methods to constrain progenitor channels.

Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods

Treesearch

Gretchen G. Moisen; Elizabeth A. Freeman; Jock A. Blackard; Tracey S. Frescino; Niklaus E. Zimmermann; Thomas C. Edwards

2006-01-01

Many efforts are underway to produce broad-scale forest attribute maps by modelling forest class and structure variables collected in forest inventories as functions of satellite-based and biophysical information. Typically, variants of classification and regression trees implemented in Rulequest's© See5 and Cubist (for binary and continuous responses,...

Modeling Student Performance in Mathematics Using Binary Logistic Regression at Selected Secondary Schools a Case Study of Mtwara Municipality and Ilemela District

ERIC Educational Resources Information Center

Mabula, Salyungu

2015-01-01

This study investigated the performance of secondary school students in Mathematics at the Selected Secondary Schools in Mtwara Municipality and Ilemela District by Absenteeism, Conduct, Type of School and Gender as explanatory Factors. The data used in the study was collected from documented records of 250 form three students with 1:1 gender…

Factors That Contributed to Gifted Students' Success on STEM Pathways: The Role of Race, Personal Interests, and Aspects of High School Experience

ERIC Educational Resources Information Center

Steenbergen-Hu, Saiying; Olszewski-Kubilius, Paula

2017-01-01

In this study, we conducted binary logistic regression on survey data collected from 244 past participants of a Talent Search program who attended regular high schools but supplemented their regular high school education with enriched or accelerated math and science learning activities. The participants completed an online survey 4 to 6 years…

Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

PubMed

Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

2017-01-01

Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

Socio-economic Correlates of Malnutrition among Married Women in Bangladesh.

PubMed

Mostafa Kamal, S M; Md Aynul, Islam

2010-12-01

This paper examines the prevalence and socio-economic correlates of malnutrition among ever married non-pregnant women of reproductive age of Bangladesh using a nationally representative weighted sample of 10,145. Body mass index was used to measure nutritional status. Both bivariate and multivariate statistical analyses were employed to assess the relationship between socio-economic characteristics and women's nutritional status. Overall, 28.5% of the women were found to be underweight. The fixed effect multivariate binary logistic regression analysis yielded significantly increased risk of underweight for the young, currently working, non-Muslim, rural residents, widowed, divorced or separated women. Significant wide variations of malnourishment prevailed in the administrative regions of the country. Wealth index and women's education were the most important determinants of underweight. The multivariate logistic regression analysis revealed that the risk of being underweight was almost seven times higher (OR=6.76, 95% CI=5.20-8.80) among women with no formal education as compared to those with higher education and the likelihood of underweight was significantly (p<0.001) 5.2 times (OR=5.23, 95% CI=4.51-6.07) in the poorest as compared to their richest counterparts. Poverty alleviation programmes should be strengthened targeting the poor. Effective policies, information and health education programmes for women are required to ensure adequate access to health services and for them to understand the components of a healthy diet.

Characterisation of crystalline-amorphous blends of sucrose with terahertz-pulsed spectroscopy: the development of a prediction technique for estimating the degree of crystallinity with partial least squares regression.

PubMed

Ermolina, I; Darkwah, J; Smith, G

2014-04-01

The control of the amorphous and crystalline states of drugs and excipients is important in many instances of product formulation, manufacture, and packaging, such as the formulation of certain (freeze-dried) fast melt tablets. This study examines the use of terahertz-pulsed spectroscopy (TPS) coupled with two different data analytical methods as an off-line tool (in the first instance) for assessing the degree of crystallinity in a binary mixture of amorphous and polycrystalline sucrose. The terahertz spectrum of sucrose was recorded in the wave number range between 3 and 100 cm(-1) for both the pure crystalline form and for a mixture of the crystalline and amorphous (freeze-dried) form. The THz spectra of crystalline sucrose showed distinct absorption bands at ∼48, ∼55, and ∼60 cm(-1) while all these features were absent in the amorphous sucrose. Calibration models were constructed based on (1) peak area analysis and (2) partial least square regression analysis, with the latter giving the best LOD and LOQ of 0.76% and 2.3%, respectively. The potential for using THz spectroscopy, as a quantitative in-line tool for percent crystallinity in a range of complex systems such as conventional tablets and freeze-dried formulations, is suggested in this study.

Association between maternal smoking, gender, and cleft lip and palate.

PubMed

Martelli, Daniella Reis Barbosa; Coletta, Ricardo D; Oliveira, Eduardo A; Swerts, Mário Sérgio Oliveira; Rodrigues, Laíse A Mendes; Oliveira, Maria Christina; Martelli Júnior, Hercílio

2015-01-01

Cleft lip and/or palate (CL/P) represent the most common congenital anomalies of the face. To assess the relationship between maternal smoking, gender and CL/P. This is an epidemiological cross-sectional study. We interviewed 1519 mothers divided into two groups: mothers of children with CL/P (n=843) and mothers of children without CL/P (n=676). All mothers were classified as smoker or non-smoker subjects during the first trimester of pregnancy. To determine an association among maternal smoking, gender, and CL/P, odds ratios were calculated and the adjustment was made by a logistic regression model. An association between maternal smoking and the presence of cleft was observed. There was also a strong association between male gender and the presence of cleft (OR=3.51; 95% CI 2.83-4.37). By binary logistic regression analysis, it was demonstrated that both variables were independently associated with clefts. In a multivariate analysis, male gender and maternal smoking had a 2.5- and a 1.5-time greater chance of having a cleft, respectively. Our findings are consistent with a positive association between maternal smoking during pregnancy and CL/P in male gender. The results support the importance of smoking prevention and introduction of cessation programs among women with childbearing potential. Copyright © 2015 Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial. Published by Elsevier Editora Ltda. All rights reserved.

Alcohol use, risky sexual behavior, and condom possession among bar patrons.

PubMed

Chaney, Beth H; Vail-Smith, Karen; Martin, Ryan J; Cremeens-Matthews, Jennifer

2016-09-01

The current study seeks to: 1) assess the relationship between alcohol consumption and intentions to engage in unprotected sex in an uncontrolled environment, and 2) to identify if covariates (race, age, sex, breath alcohol content (BrAC), intentions to engage in sex, hazardous drinking rates) are significant predictors of condom possession during time of uncontrolled alcohol consumption. Data were collected from 917 bar patrons to assess alcohol use using the Alcohol Use Disorders Identification Test (AUDIT-C), BrAC levels, intentions to engage in risky sex, and condom possession. Correlational analysis and hierarchical binary logistic regression was conducted using SPSS. Correlational analyses indicated a negative relationship between AUDIT-C scores (r=-0.115, p=0.001), BrAC (r=-0.08, p=0.015), and intentions to use a condom. Over 70% of participants intended to use a condom if they engaged in sex; however, only 28.4% had a condom to use. The regression analysis indicated the predictive model (χ(2)=114.5, df=8, p<0.001) was statistically significant, and correctly classified 72.9% of those in possession of a condom. Alcohol consumption was associated with intentions to have unprotected sex; however, intentions to engage in protected sex and condom possession were higher for males and those with higher BrAC levels. Copyright © 2016 Elsevier Ltd. All rights reserved.

Association of salivary levels of the bone remodelling regulators sRANKL and OPG with periodontal clinical status.

PubMed

Tobón-Arroyave, Sergio I; Isaza-Guzmán, Diana M; Restrepo-Cadavid, Eliana M; Zapata-Molina, Sandra M; Martínez-Pabón, María C

2012-12-01

To determine the variations in salivary concentrations of sRANKL, osteoprotegerin (OPG) and its ratio, regarding the periodontal status. Ninety-seven chronic periodontitis (CP) subjects and 43 healthy controls were selected. Periodontal status was assessed based on full-mouth clinical periodontal measurements. sRANKL and OPG salivary levels were analysed by ELISA. The association between these analytes and its ratio with CP was analysed individually and adjusted for confounding using a binary logistic regression model. sRANKL and sRANKL/OPG ratio were increased, whereas OPG was decreased in CP compared with healthy controls subjects. Although univariate analysis revealed a positive association of sRANKL salivary levels ≥6 pg/ml, OPG salivary levels ≤131 pg/ml and sRANKL/OPG ratio ≥0.062 with CP, after logistic regression analysis only the latter parameter was strongly and independently associated with disease status. Confounding and interaction effects of ageing and smoking habit on sRANKL and OPG levels could be noted. Although salivary concentrations of sRANKL, OPG and its ratio may act as indicators of the amount/extent of periodontal breakdown, the mutual confounding and synergistic biological interactive effects related to ageing and smoking habit of the susceptible host may also promote the tissue destruction in CP. © 2012 John Wiley & Sons A/S.

Knowledge, awareness, and behaviors of endocrinologists and dentists for the relationship between diabetes and periodontitis.

PubMed

Lin, Hanxiao; Zhang, Hua; Yan, Yuxia; Liu, Duan; Zhang, Ruyi; Liu, Yeungyeung; Chen, Pei; Zhang, Jincai; Xuan, Dongying

2014-12-01

This study aimed to compare the opinions of dentists and endocrinologists regarding diabetes mellitus (DM) and periodontitis, and to investigate the possible effects on their practice. Cross-sectional data were collected from 297 endocrinologists and 134 dentists practicing in southern China using two separated questionnaires. Questions were close-ended or Likert-scaled. Statistical analyses were done by descriptive statistics, bivariate and binary logistic regression analysis. Compared with endocrinologists, dentists presented more favorable attitudes for the relationship of DM and periodontitis (P<0.001). 61.2% of dentists reported they would frequently refer patients with severe periodontitis for DM evaluation, while only 26.6% of endocrinologists reported they would frequently advise patients with DM to visit a dentist. Nearly all of the respondents (94.4%) agreed that the interdisciplinary collaboration should be strengthened. The logistic regression analysis exhibited that respondents with more favorable attitudes were more likely to advise a dental visit (P=0.003) or to screen for DM (P=0.006). Endocrinologists and dentists are not equally equipped with the knowledge about the relationship between DM and periodontitis, and there is a wide gap between their practice and the current evidence, especially for endocrinologists. It's urgent to take measures to develop the interdisciplinary education and collaboration among the health care providers. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

A Dimensionally Reduced Clustering Methodology for Heterogeneous Occupational Medicine Data Mining.

PubMed

Saâdaoui, Foued; Bertrand, Pierre R; Boudet, Gil; Rouffiac, Karine; Dutheil, Frédéric; Chamoux, Alain

2015-10-01

Clustering is a set of techniques of the statistical learning aimed at finding structures of heterogeneous partitions grouping homogenous data called clusters. There are several fields in which clustering was successfully applied, such as medicine, biology, finance, economics, etc. In this paper, we introduce the notion of clustering in multifactorial data analysis problems. A case study is conducted for an occupational medicine problem with the purpose of analyzing patterns in a population of 813 individuals. To reduce the data set dimensionality, we base our approach on the Principal Component Analysis (PCA), which is the statistical tool most commonly used in factorial analysis. However, the problems in nature, especially in medicine, are often based on heterogeneous-type qualitative-quantitative measurements, whereas PCA only processes quantitative ones. Besides, qualitative data are originally unobservable quantitative responses that are usually binary-coded. Hence, we propose a new set of strategies allowing to simultaneously handle quantitative and qualitative data. The principle of this approach is to perform a projection of the qualitative variables on the subspaces spanned by quantitative ones. Subsequently, an optimal model is allocated to the resulting PCA-regressed subspaces.

Advanced statistical analysis of Raman spectroscopic data for the identification of body fluid traces: semen and blood mixtures.

PubMed

Sikirzhytski, Vitali; Sikirzhytskaya, Aliaksandra; Lednev, Igor K

2012-10-10

Conventional confirmatory biochemical tests used in the forensic analysis of body fluid traces found at a crime scene are destructive and not universal. Recently, we reported on the application of near-infrared (NIR) Raman microspectroscopy for non-destructive confirmatory identification of pure blood, saliva, semen, vaginal fluid and sweat. Here we expand the method to include dry mixtures of semen and blood. A classification algorithm was developed for differentiating pure body fluids and their mixtures. The classification methodology is based on an effective combination of Support Vector Machine (SVM) regression (data selection) and SVM Discriminant Analysis of preprocessed experimental Raman spectra collected using an automatic mapping of the sample. This extensive cross-validation of the obtained results demonstrated that the detection limit of the minor contributor is as low as a few percent. The developed methodology can be further expanded to any binary mixture of complex solutions, including but not limited to mixtures of other body fluids. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

[Prevalence of smoking among Colombian adolescents].

PubMed

Martínez-Torres, Javier; Peñuela Epalza, Martha

2017-03-01

Cigarette smoking is considered the most important preventable public health problem in developed countries, especially among adolescents. To determine the prevalence of cigarette smoking and associated factors in high school adolescents, from a Colombian city. The self-administered global tobacco youth survey (GTYS) was answered by 831 teenagers aged 14 ± 2 years (54% females). For data analysis, proportions were calculated; for associations, binary and multivariable logistic regression was applied. Fourteen percent of respondents declared that they had consumed at least one cigarette during the last 30 days. The life-time prevalence of tobacco use was 27.1%. Being older than thirteen years old, father’s academic level and having a smoker mother were factors associated with smoking. The prevalence of smoking in these adolescents was high. Age over 13 years and a smoking mother were associated with the cigarette smoking.

Independent Life Skills among psychosocial care network users of Rio Grande do Sul, Brazil.

PubMed

Rodrigues, Cândida Garcia Sinott Silveira; Jardim, Vanda Maria da Rosa; Kantorski, Luciane Prado; Coimbra, Valeria Cristina Christello; Treichel, Carlos Alberto Dos Santos; Francchini, Beatriz; Bretanha, Andreia Ferreira; Neutzling, Aline Dos Santos

2016-08-01

This is a cross-sectional study that aims to identify the prevalence of lower independent living skills and their associations in 390 users of psychiatric community-based services in the state Rio Grande do Sul, Brazil. For tracing the outcome it was used the "scale Independent Living Skills Survey", adopting a cut-off value lower than 2. The crude and adjusted analyses were conducted on binary logistic regressions and they considered a hierarchical model developed through a systematic literature review. In adjusted analysis the level of the same variables were adjusted to each other and to previous levels. The statistical significance remained as a < 0.05 p-value. The prevalence of smaller independent living skills was 33% and their associations were: younger age; no partner; lower education; resident at SRT; diagnosis of schizophrenia and younger diagnosis.

The role of middle-class status in payday loan borrowing: a multivariate approach.

PubMed

Lim, Younghee; Bickham, Trey; Broussard, Julia; Dinecola, Cassie M; Gregory, Alethia; Weber, Brittany E

2014-10-01

Payday loans refer to small-dollar, high-interest, short-term loans usually extended to lower-income consumers. Despite much research to the contrary, the payday loan industry asserts that it primarily serves middle-class Americans. This article discusses the authors' investigation of the industry's claim, by analyzing data from a U.S. bankruptcy court serving a Southern district. Results of the multivariate binary logistic regression analysis showed that, controlling for various sociodemographic and economic variables, two middle-class indicators--home-ownership and annual income at or greater than the median income--are associated with a decreased likelihood of using payday loans. The article concludes with a discussion of the implications of the results for social work practice and advocacy in regard to financial capability, particularly asset development, income maintenance, and payday loan regulation.

Anisotropic composite human skull model and skull fracture validation against temporo-parietal skull fracture.

PubMed

Sahoo, Debasis; Deck, Caroline; Yoganandan, Narayan; Willinger, Rémy

2013-12-01

A composite material model for skull, taking into account damage is implemented in the Strasbourg University finite element head model (SUFEHM) in order to enhance the existing skull mechanical constitutive law. The skull behavior is validated in terms of fracture patterns and contact forces by reconstructing 15 experimental cases. The new SUFEHM skull model is capable of reproducing skull fracture precisely. The composite skull model is validated not only for maximum forces, but also for lateral impact against actual force time curves from PMHS for the first time. Skull strain energy is found to be a pertinent parameter to predict the skull fracture and based on statistical (binary logistical regression) analysis it is observed that 50% risk of skull fracture occurred at skull strain energy of 544.0mJ. © 2013 Elsevier Ltd. All rights reserved.

Binarity and Variable Stars in the Open Cluster NGC 2126

NASA Astrophysics Data System (ADS)

Chehlaeh, Nareemas; Mkrtichian, David; Kim, Seung-Lee; Lampens, Patricia; Komonjinda, Siramas; Kusakin, Anatoly; Glazunova, Ljudmila

2018-04-01

We present the results of an analysis of photometric time-series observations for NGC 2126 acquired at the Thai National Observatory (TNO) in Thailand and the Mount Lemmon Optical Astronomy Observatory (LOAO) in USA during the years 2004, 2013 and 2015. The main purpose is to search for new variable stars and to study the light curves of binary systems as well as the oscillation spectra of pulsating stars. NGC 2126 is an intermediate-age open cluster which has a population of stars inside the δ Scuti instability strip. Several variable stars are reported including three eclipsing binary stars, one of which is an eclipsing binary star with a pulsating component (V551 Aur). The Wilson-Devinney technique was used to analyze its light curves and to determine a new set of the system’s parameters. A frequency analysis of the eclipse-subtracted light curve was also performed. Eclipsing binaries which are members of open clusters are capable of delivering strong constraints on the cluster’s properties which are in turn useful for a pulsational analysis of their pulsating components. Therefore, high-resolution, high-quality spectra will be needed to derive accurate component radial velocities of the faint eclipsing binaries which are located in the field of NGC 2126. The new Devasthal Optical Telescope, suitably equipped, could in principle do this.

Selenium in irrigated agricultural areas of the western United States

USGS Publications Warehouse

Nolan, B.T.; Clark, M.L.

1997-01-01

A logistic regression model was developed to predict the likelihood that Se exceeds the USEPA chronic criterion for aquatic life (5 ??g/L) in irrigated agricultural areas of the western USA. Preliminary analysis of explanatory variables used in the model indicated that surface-water Se concentration increased with increasing dissolved solids (DS) concentration and with the presence of Upper Cretaceous, mainly marine sediment. The presence or absence of Cretaceous sediment was the major variable affecting Se concentration in surface-water samples from the National Irrigation Water Quality Program. Median Se concentration was 14 ??g/L in samples from areas underlain by Cretaceous sediments and < 1 ??g/L in samples from areas underlain by non-Cretaceous sediments. Wilcoxon rank sum tests indicated that elevated Se concentrations in samples from areas with Cretaceous sediments, irrigated areas, and from closed lakes and ponds were statistically significant. Spearman correlations indicated that Se was positively correlated with a binary geology variable (0.64) and DS (0.45). Logistic regression models indicated that the concentration of Se in surface water was almost certain to exceed the Environmental Protection Agency aquatic-life chronic criterion of 5 ??g/L when DS was greater than 3000 mg/L in areas with Cretaceous sediments. The 'best' logistic regression model correctly predicted Se exceedances and nonexceedances 84.4% of the time, and model sensitivity was 80.7%. A regional map of Cretaceous sediment showed the location of potential problem areas. The map and logistic regression model are tools that can be used to determine the potential for Se contamination of irrigated agricultural areas in the western USA.

Which sociodemographic factors are important on smoking behaviour of high school students? The contribution of classification and regression tree methodology in a broad epidemiological survey.

PubMed

Ozge, C; Toros, F; Bayramkaya, E; Camdeviren, H; Sasmaz, T

2006-08-01

The purpose of this study is to evaluate the most important sociodemographic factors on smoking status of high school students using a broad randomised epidemiological survey. Using in-class, self administered questionnaire about their sociodemographic variables and smoking behaviour, a representative sample of total 3304 students of preparatory, 9th, 10th, and 11th grades, from 22 randomly selected schools of Mersin, were evaluated and discriminative factors have been determined using appropriate statistics. In addition to binary logistic regression analysis, the study evaluated combined effects of these factors using classification and regression tree methodology, as a new statistical method. The data showed that 38% of the students reported lifetime smoking and 16.9% of them reported current smoking with a male predominancy and increasing prevalence by age. Second hand smoking was reported at a 74.3% frequency with father predominance (56.6%). The significantly important factors that affect current smoking in these age groups were increased by household size, late birth rank, certain school types, low academic performance, increased second hand smoking, and stress (especially reported as separation from a close friend or because of violence at home). Classification and regression tree methodology showed the importance of some neglected sociodemographic factors with a good classification capacity. It was concluded that, as closely related with sociocultural factors, smoking was a common problem in this young population, generating important academic and social burden in youth life and with increasing data about this behaviour and using new statistical methods, effective coping strategies could be composed.

Detecting phase separation of freeze-dried binary amorphous systems using pair-wise distribution function and multivariate data analysis.

PubMed

Chieng, Norman; Trnka, Hjalte; Boetker, Johan; Pikal, Michael; Rantanen, Jukka; Grohganz, Holger

2013-09-15

The purpose of this study is to investigate the use of multivariate data analysis for powder X-ray diffraction-pair-wise distribution function (PXRD-PDF) data to detect phase separation in freeze-dried binary amorphous systems. Polymer-polymer and polymer-sugar binary systems at various ratios were freeze-dried. All samples were analyzed by PXRD, transformed to PDF and analyzed by principal component analysis (PCA). These results were validated by differential scanning calorimetry (DSC) through characterization of glass transition of the maximally freeze-concentrate solute (Tg'). Analysis of PXRD-PDF data using PCA provides a more clear 'miscible' or 'phase separated' interpretation through the distribution pattern of samples on a score plot presentation compared to residual plot method. In a phase separated system, samples were found to be evenly distributed around the theoretical PDF profile. For systems that were miscible, a clear deviation of samples away from the theoretical PDF profile was observed. Moreover, PCA analysis allows simultaneous analysis of replicate samples. Comparatively, the phase behavior analysis from PXRD-PDF-PCA method was in agreement with the DSC results. Overall, the combined PXRD-PDF-PCA approach improves the clarity of the PXRD-PDF results and can be used as an alternative explorative data analytical tool in detecting phase separation in freeze-dried binary amorphous systems. Copyright © 2013 Elsevier B.V. All rights reserved.

Novel spectrophotometric determination of chloramphenicol and dexamethasone in the presence of non labeled interfering substances using univariate methods and multivariate regression model updating

NASA Astrophysics Data System (ADS)

Hegazy, Maha A.; Lotfy, Hayam M.; Rezk, Mamdouh R.; Omran, Yasmin Rostom

2015-04-01

Smart and novel spectrophotometric and chemometric methods have been developed and validated for the simultaneous determination of a binary mixture of chloramphenicol (CPL) and dexamethasone sodium phosphate (DSP) in presence of interfering substances without prior separation. The first method depends upon derivative subtraction coupled with constant multiplication. The second one is ratio difference method at optimum wavelengths which were selected after applying derivative transformation method via multiplying by a decoding spectrum in order to cancel the contribution of non labeled interfering substances. The third method relies on partial least squares with regression model updating. They are so simple that they do not require any preliminary separation steps. Accuracy, precision and linearity ranges of these methods were determined. Moreover, specificity was assessed by analyzing synthetic mixtures of both drugs. The proposed methods were successfully applied for analysis of both drugs in their pharmaceutical formulation. The obtained results have been statistically compared to that of an official spectrophotometric method to give a conclusion that there is no significant difference between the proposed methods and the official ones with respect to accuracy and precision.

Examining the Link Between Public Transit Use and Active Commuting

PubMed Central

Bopp, Melissa; Gayah, Vikash V.; Campbell, Matthew E.

2015-01-01

Background: An established relationship exists between public transportation (PT) use and physical activity. However, there is limited literature that examines the link between PT use and active commuting (AC) behavior. This study examines this link to determine if PT users commute more by active modes. Methods: A volunteer, convenience sample of adults (n = 748) completed an online survey about AC/PT patterns, demographic, psychosocial, community and environmental factors. t-test compared differences between PT riders and non-PT riders. Binary logistic regression analyses examined the effect of multiple factors on AC and a full logistic regression model was conducted to examine AC. Results: Non-PT riders (n = 596) reported less AC than PT riders. There were several significant relationships with AC for demographic, interpersonal, worksite, community and environmental factors when considering PT use. The logistic multivariate analysis for included age, number of children and perceived distance to work as negative predictors and PT use, feelings of bad weather and lack of on-street bike lanes as a barrier to AC, perceived behavioral control and spouse AC were positive predictors. Conclusions: This study revealed the complex relationship between AC and PT use. Further research should investigate how AC and public transit use are related. PMID:25898405

Propensity score matching of the gymnastics for diabetes mellitus using logistic regression

NASA Astrophysics Data System (ADS)

Otok, Bambang Widjanarko; Aisyah, Amalia; Purhadi, Andari, Shofi

2017-12-01

Diabetes Mellitus (DM) is a group of metabolic diseases with characteristics shows an abnormal blood glucose level occurring due to pancreatic insulin deficiency, decreased insulin effectiveness or both. The report from the ministry of health shows that DMs prevalence data of East Java province is 2.1%, while the DMs prevalence of Indonesia is only 1,5%. Given the high cases of DM in East Java, it needs the preventive action to control factors causing the complication of DM. This study aims to determine the combination factors causing the complication of DM to reduce the bias by confounding variables using Propensity Score Matching (PSM) with the method of propensity score estimation is binary logistic regression. The data used in this study is the medical record from As-Shafa clinic consisting of 6 covariates and health complication as response variable. The result of PSM analysis showed that there are 22 of 126 DMs patients attending gymnastics paired with patients who didnt attend to diabetes gymnastics. The Average Treatment of Treated (ATT) estimation results showed that the more patients who didnt attend to gymnastics, the more likely the risk for the patients having DMs complications.

Examining the link between public transit use and active commuting.

PubMed

Bopp, Melissa; Gayah, Vikash V; Campbell, Matthew E

2015-04-17

An established relationship exists between public transportation (PT) use and physical activity. However, there is limited literature that examines the link between PT use and active commuting (AC) behavior. This study examines this link to determine if PT users commute more by active modes. A volunteer, convenience sample of adults (n = 748) completed an online survey about AC/PT patterns, demographic, psychosocial, community and environmental factors. t-test compared differences between PT riders and non-PT riders. Binary logistic regression analyses examined the effect of multiple factors on AC and a full logistic regression model was conducted to examine AC. Non-PT riders (n = 596) reported less AC than PT riders. There were several significant relationships with AC for demographic, interpersonal, worksite, community and environmental factors when considering PT use. The logistic multivariate analysis for included age, number of children and perceived distance to work as negative predictors and PT use, feelings of bad weather and lack of on-street bike lanes as a barrier to AC, perceived behavioral control and spouse AC were positive predictors. This study revealed the complex relationship between AC and PT use. Further research should investigate how AC and public transit use are related.

Influence of ethnicity, geography and climate on the variation of stature among Indian populations.

PubMed

Bharati, Susmita; Mukherji, Dipak; Pal, Manoranjan; Som, Suparna; Kumar Adak, Dipak; Vasulu, T S; Bharati, Premananda

2010-12-01

This paper analyzes the variation in the mean stature of adult males of a variety of population groups in India and examines the influence of geographical, climatic and ethnic factors on it. A considerable variation in mean stature has been found with respect to these three attributes. Variation "between" ethnic groups compared with "within" ethnic groups was found to be much more than that of geographical and climatic zones. Scheduled Castes (SC) and Scheduled Tribes (ST) populations have much low average height than that of General Castes (GC). Climatically dry and semiarid zones have a tendency to have higher stature than in the Monsoon areas. The mean height has been found to be the highest in north India. It is closely followed by west India. An interesting feature is that as one goes towards east and south the mean height gradually decreases. It is the lowest in islands. The mean heights have been regressed on geographical, climatic and ethnic factors, after converting these factors into binary variables. The regression analysis has strengthened the findings, that there is a highly significant relationship between height and geographical, climatic and ethnic factors.

Bayesian inference for joint modelling of longitudinal continuous, binary and ordinal events.

PubMed

Li, Qiuju; Pan, Jianxin; Belcher, John

2016-12-01

In medical studies, repeated measurements of continuous, binary and ordinal outcomes are routinely collected from the same patient. Instead of modelling each outcome separately, in this study we propose to jointly model the trivariate longitudinal responses, so as to take account of the inherent association between the different outcomes and thus improve statistical inferences. This work is motivated by a large cohort study in the North West of England, involving trivariate responses from each patient: Body Mass Index, Depression (Yes/No) ascertained with cut-off score not less than 8 at the Hospital Anxiety and Depression Scale, and Pain Interference generated from the Medical Outcomes Study 36-item short-form health survey with values returned on an ordinal scale 1-5. There are some well-established methods for combined continuous and binary, or even continuous and ordinal responses, but little work was done on the joint analysis of continuous, binary and ordinal responses. We propose conditional joint random-effects models, which take into account the inherent association between the continuous, binary and ordinal outcomes. Bayesian analysis methods are used to make statistical inferences. Simulation studies show that, by jointly modelling the trivariate outcomes, standard deviations of the estimates of parameters in the models are smaller and much more stable, leading to more efficient parameter estimates and reliable statistical inferences. In the real data analysis, the proposed joint analysis yields a much smaller deviance information criterion value than the separate analysis, and shows other good statistical properties too. © The Author(s) 2014.

Spectroscopic observations of V443 Herculis - A symbiotic binary with a low mass white dwarf

NASA Technical Reports Server (NTRS)

Dobrzycka, Danuta; Kenyon, Scott J.; Mikolajewska, Joanna

1993-01-01

We present an analysis of new and existing photometric and spectroscopic observations of the symbiotic binary V443 Herculis. This binary system consists of a normal M5 giant and a hot compact star. These two objects have comparable luminosities: about 1500 solar for the M5 giant and about 1000 solar for the compact star. We identify three nebular regions in this binary: a small, highly ionized volume surrounding the hot component, a modestly ionized shell close to the red giant photosphere, and a less dense region of intermediate ionization encompassing both binary components. The system parameters for V443 Her suggest the hot component currently declines from a symbiotic nova eruption.

Mal-Xtract: Hidden Code Extraction using Memory Analysis

NASA Astrophysics Data System (ADS)

Lim, Charles; Syailendra Kotualubun, Yohanes; Suryadi; Ramli, Kalamullah

2017-01-01

Software packer has been used effectively to hide the original code inside a binary executable, making it more difficult for existing signature based anti malware software to detect malicious code inside the executable. A new method of written and rewritten memory section is introduced to to detect the exact end time of unpacking routine and extract original code from packed binary executable using Memory Analysis running in an software emulated environment. Our experiment results show that at least 97% of the original code from the various binary executable packed with different software packers could be extracted. The proposed method has also been successfully extracted hidden code from recent malware family samples.

DANCING IN THE DARK: NEW BROWN DWARF BINARIES FROM KERNEL PHASE INTERFEROMETRY

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

Pope, Benjamin; Tuthill, Peter; Martinache, Frantz, E-mail: bjsp@physics.usyd.edu.au, E-mail: p.tuthill@physics.usyd.edu.au, E-mail: frantz@naoj.org

2013-04-20

This paper revisits a sample of ultracool dwarfs in the solar neighborhood previously observed with the Hubble Space Telescope's NICMOS NIC1 instrument. We have applied a novel high angular resolution data analysis technique based on the extraction and fitting of kernel phases to archival data. This was found to deliver a dramatic improvement over earlier analysis methods, permitting a search for companions down to projected separations of {approx}1 AU on NIC1 snapshot images. We reveal five new close binary candidates and present revised astrometry on previously known binaries, all of which were recovered with the technique. The new candidate binariesmore » have sufficiently close separation to determine dynamical masses in a short-term observing campaign. We also present four marginal detections of objects which may be very close binaries or high-contrast companions. Including only confident detections within 19 pc, we report a binary fraction of at least #Greek Lunate Epsilon Symbol#{sub b} = 17.2{sub -3.7}{sup +5.7}%. The results reported here provide new insights into the population of nearby ultracool binaries, while also offering an incisive case study of the benefits conferred by the kernel phase approach in the recovery of companions within a few resolution elements of the point-spread function core.« less

Dielectric and spectroscopic study of binary mixture of Acrylonitrile with Chlorobenzene

NASA Astrophysics Data System (ADS)

Deshmukh, Snehal D.; Pattebahadur, K. L.; Mohod, A. G.; Undre, P. B.; Patil, S. S.; Khirade, P. W.

2018-05-01

In this paper, study of binary mixture of Acrylonitrile (ACN) with Chlorobenzene (CBZ) has been carried out at eleven concentrations at room temperature. The determined Dielectric Constant (ɛ0) Density (ρ) and Refractive index (nD) values of binary mixture are used to calculate the excess properties of mixture over the entire composition range and fitted to the Redlich-Kister equation. From the above parameters, intermolecular interaction and dynamics of molecules of binary mixture at molecular level are discussed. The Conformational analysis of the intermolecular interaction between Acrylonitrile and Chlorobenzene is supported by the FTIR spectra.

A unifying framework for marginalized random intercept models of correlated binary outcomes

PubMed Central

Swihart, Bruce J.; Caffo, Brian S.; Crainiceanu, Ciprian M.

2013-01-01

We demonstrate that many current approaches for marginal modeling of correlated binary outcomes produce likelihoods that are equivalent to the copula-based models herein. These general copula models of underlying latent threshold random variables yield likelihood-based models for marginal fixed effects estimation and interpretation in the analysis of correlated binary data with exchangeable correlation structures. Moreover, we propose a nomenclature and set of model relationships that substantially elucidates the complex area of marginalized random intercept models for binary data. A diverse collection of didactic mathematical and numerical examples are given to illustrate concepts. PMID:25342871

Analytic gravitational waveforms for generic precessing compact binaries

NASA Astrophysics Data System (ADS)

Chatziioannou, Katerina; Klein, Antoine; Cornish, Neil; Yunes, Nicolas

2017-01-01

Gravitational waves from compact binaries are subject to amplitude and phase modulations arising from interactions between the angular momenta of the system. Failure to account for such spin-precession effects in gravitational wave data analysis could hinder detection and completely ruin parameter estimation. In this talk I will describe the construction of closed-form, frequency-domain waveforms for fully-precessing, quasi-circular binary inspirals. The resulting waveforms can model spinning binaries of arbitrary spin magnitudes, spin orientations, and masses during the inspiral phase. I will also describe ongoing efforts to extend these inspiral waveforms to the merger and ringdown phases.

Randomizing world trade. II. A weighted network analysis

NASA Astrophysics Data System (ADS)

Squartini, Tiziano; Fagiolo, Giorgio; Garlaschelli, Diego

2011-10-01

Based on the misleading expectation that weighted network properties always offer a more complete description than purely topological ones, current economic models of the International Trade Network (ITN) generally aim at explaining local weighted properties, not local binary ones. Here we complement our analysis of the binary projections of the ITN by considering its weighted representations. We show that, unlike the binary case, all possible weighted representations of the ITN (directed and undirected, aggregated and disaggregated) cannot be traced back to local country-specific properties, which are therefore of limited informativeness. Our two papers show that traditional macroeconomic approaches systematically fail to capture the key properties of the ITN. In the binary case, they do not focus on the degree sequence and hence cannot characterize or replicate higher-order properties. In the weighted case, they generally focus on the strength sequence, but the knowledge of the latter is not enough in order to understand or reproduce indirect effects.

Chemical composition and binary mixture of human urinary stones using FT-Raman spectroscopy method.

PubMed

Selvaraju, R; Raja, A; Thiruppathi, G

2013-10-01

In the present study the human urinary stones were observed in their different chemical compositions of calcium oxalate monohydrate, calcium oxalate dihydrate, calcium phosphate, struvite (magnesium ammonium phosphate), uric acid, cystine, oxammite (ammonium oxalate monohydrate), natroxalate (sodium oxalate), glushinkite (magnesium oxalate dihydrate) and moolooite (copper oxalate) were analyzed using Fourier Transform-Raman (FT-Raman) spectroscopy. For the quantitative analysis, various human urinary stone samples are used for ratios calculation of binary mixtures compositions such as COM/COD, HAP/COD, HAP/COD, Uric acid/COM, uric acid/COD and uric acid/HAP. The calibration curve is used for further analysis of binary mixture of human urinary stones. For the binary mixture calculation the various intensities bands at 1462 cm(-1) (I(COM)), 1473 cm(-1) (I(COD)), 961 cm(-1) (I(HAP)) and 1282 cm(-1) (I(UA)) were used. Copyright © 2013 Elsevier B.V. All rights reserved.

OGLE-2016-BLG-1003: First Resolved Caustic-crossing Binary-source Event Discovered by Second-generation Microlensing Surveys

NASA Astrophysics Data System (ADS)

Jung, Y. K.; Udalski, A.; Bond, I. A.; Yee, J. C.; Gould, A.; Han, C.; Albrow, M. D.; Lee, C.-U.; Kim, S.-L.; Hwang, K.-H.; Chung, S.-J.; Ryu, Y.-H.; Shin, I.-G.; Zhu, W.; Cha, S.-M.; Kim, D.-J.; Lee, Y.; Park, B.-G.; Kim, H.-W.; Pogge, R. W.; KMTNet Collaboration; Skowron, J.; Szymański, M. K.; Poleski, R.; Mróz, P.; Kozłowski, S.; Pietrukowicz, P.; Soszyński, I.; Ulaczyk, K.; Pawlak, M.; OGLE Collaboration; Abe, F.; Bennett, D. P.; Barry, R.; Sumi, T.; Asakura, Y.; Bhattacharya, A.; Donachie, M.; Fukui, A.; Hirao, Y.; Itow, Y.; Koshimoto, N.; Li, M. C. A.; Ling, C. H.; Masuda, K.; Matsubara, Y.; Muraki, Y.; Nagakane, M.; Rattenbury, N. J.; Evans, P.; Sharan, A.; Sullivan, D. J.; Suzuki, D.; Tristram, P. J.; Yamada, T.; Yamada, T.; Yonehara, A.; MOA Collaboration

2017-06-01

We report the analysis of the first resolved caustic-crossing binary-source microlensing event OGLE-2016-BLG-1003. The event is densely covered by round-the-clock observations of three surveys. The light curve is characterized by two nested caustic-crossing features, which is unusual for typical caustic-crossing perturbations. From the modeling of the light curve, we find that the anomaly is produced by a binary source passing over a caustic formed by a binary lens. The result proves the importance of high-cadence and continuous observations, and the capability of second-generation microlensing experiments to identify such complex perturbations that are previously unknown. However, the result also raises the issues of the limitations of current analysis techniques for understanding lens systems beyond two masses and of determining the appropriate multiband observing strategy of survey experiments.

Observations, Analysis, and Spectroscopic Classification of HO Piscium: A Bright Shallow-Contact Binary with G- and M-Type Components

NASA Astrophysics Data System (ADS)

Samec, Ronald G.; Smith, Paul M.; Robb, Russell; Faulkner, Danny R.; Van Hamme, W.

2012-07-01

We present a spectrum and a photometric analysis of the newly discovered, high-amplitude, solar-type, eclipsing binary HO Piscium. A spectroscopic identification, a period study, q-search, and a simultaneous UBVRc Ic light-curve solution are presented. The spectra and our photometric solution indicate that HO Psc is a W-type W UMa shallow-contact (fill-out ˜8%) binary system. The primary component has a G6V spectral type with an apparently precontact spectral type of M2V for the secondary component. The small fill-out indicates that the system has not yet achieved thermal contact and thus has recently come into physical contact. This may mean that this solar-type binary system has not attained its ˜0.4 mass ratio via a long period of magnetic braking, as would normally be assumed.

Measuring Parameters of Massive Black Hole Binaries with Partially-Aligned Spins

NASA Technical Reports Server (NTRS)

Lang, Ryan N.; Hughes, Scott A.; Cornish, Neil J.

2010-01-01

It is important to understand how well the gravitational-wave observatory LISA can measure parameters of massive black hole binaries. It has been shown that including spin precession in the waveform breaks degeneracies and produces smaller expected parameter errors than a simpler, precession-free analysis. However, recent work has shown that gas in binaries can partially align the spins with the orbital angular momentum, thus reducing the precession effect. We show how this degrades the earlier results, producing more pessimistic errors in gaseous mergers. However, we then add higher harmonics to the signal model; these also break degeneracies, but they are not affected by the presence of gas. The harmonics often restore the errors in partially-aligned binaries to the same as, or better than/ those that are obtained for fully precessing binaries with no harmonics. Finally, we investigate what LISA measurements of spin alignment can tell us about the nature of gas around a binary,

Ethnicity and acculturation: do they predict weight status in a longitudinal study among Asian, Hispanic, and non-Hispanic White early adolescent females?

PubMed Central

Fialkowski, Marie K; Ettienne, Reynolette; Shvetsov, Yurii B; Rivera, Rebecca L; Van Loan, Marta D; Savaiano, Dennis A; Boushey, Carol J

2015-01-01

Background The prevalence of overweight and obesity among adolescents has increased over the past decade. Prevalence rates are disparate among certain racial and ethnic groups. This study sought to longitudinally examine the relationship between overweight status (≥85th percentile according to the Centers for Disease Control and Prevention growth charts) and ethnic group, as well as acculturation (generation and language spoken in the home) in a sample of adolescent females. Methods Asian (n=160), Hispanic (n=217), and non-Hispanic White (n=304) early adolescent girls participating in the multistate calcium intervention study with complete information on weight, ethnicity, and acculturation were included. Multiple methods of assessing longitudinal relationships (binary logistic regression model, linear regression model, Cox proportional-hazards regression analysis, and Kaplan–Meier survival analysis) were used to examine the relationship. Results The total proportion of girls overweight at baseline was 36%. When examining by ethnic group, the proportion varied with Hispanic girls having the highest percentage (46%) in comparison to their Asian (23%) and Non-Hispanic White (35%) counterparts. Although the total proportion of overweight was 36% at 18 months, the variation across the ethnic groups remained with the proportion of Hispanic girls becoming overweight (55%) being greater than their Asian (18%) and non-Hispanic White (34%) counterparts. However, regardless of the statistical approach used, there were no significant associations between overweight status and acculturation over time. Conclusion These unexpected results warrant further exploration into factors associated with overweight, especially among Hispanic girls, and further investigation of acculturation’s role is warranted. Identifying these risk factors will be important for developing targeted obesity prevention initiatives. PMID:25624775

Beyond the Binary: Differences in Eating Disorder Prevalence by Gender Identity in a Transgender Sample.

PubMed

Diemer, Elizabeth W; White Hughto, Jaclyn M; Gordon, Allegra R; Guss, Carly; Austin, S Bryn; Reisner, Sari L

2018-01-01

Purpose: To investigate whether the prevalence of eating disorders (EDs) differs across diverse gender identity groups in a transgender sample. Methods: Secondary analysis of data from Project VOICE, a cross-sectional study of stress and health among 452 transgender adults (ages 18-75 years) residing in Massachusetts. Age-adjusted logistic regression models were fit to compare the prevalence of self-reported lifetime EDs in female-to-male (FTM), male-to-female (MTF), and gender-nonconforming participants assigned male at birth (MBGNC) to gender-nonconforming participants assigned female at birth (FBGNC; referent). Results: The age-adjusted odds of self-reported ED in MTF participants were 0.14 times the odds of self-reported ED in FBGNC participants ( p =0.022). In FTM participants, the age-adjusted odds of self-reported ED were 0.46 times the odds of self-reported ED in FBGNC participants, a marginally significant finding ( p =0.068). No statistically significant differences in ED prevalence were found for MBGNC individuals. Conclusions: Gender nonconforming individuals assigned a female sex at birth appear to have heightened lifetime risk of EDs relative to MTF participants. Further research into specific biologic and psychosocial ED risk factors and gender-responsive intervention strategies are urgently needed. Training clinical providers and ensuring competency of treatment services beyond the gender binary will be vital to addressing this disparity.

Prevalence and determinants of cardiovascular disease risk factors among the residents of urban community housing projects in Malaysia

PubMed Central

2014-01-01

Objectives The objectives are to assess the prevalence and determinants of cardiovascular disease (CVD) risk factors among the residents of Community Housing Projects in metropolitan Kuala Lumpur, Malaysia. Method By using simple random sampling, we selected and surveyed 833 households which comprised of 3,722 individuals. Out of the 2,360 adults, 50.5% participated in blood sampling and anthropometric measurement sessions. Uni and bivariate data analysis and multivariate binary logistic regression were applied to identify demographic and socioeconomic determinants of the existence of having at least one CVD risk factor. Results As a Result, while obesity (54.8%), hypercholesterolemia (51.5%), and hypertension (39.3%) were the most common CVD risk factors among the low-income respondents, smoking (16.3%), diabetes mellitus (7.8%) and alcohol consumption (1.4%) were the least prevalent. Finally, the results from the multivariate binary logistic model illustrated that compared to the Malays, the Indians were 41% less likely to have at least one of the CVD risk factors (OR = 0.59; 95% CI: 0.37 - 0.93). Conclusion In Conclusion, the low-income individuals were at higher risk of developing CVDs. Prospective policies addressing preventive actions and increased awareness focusing on low-income communities are highly recommended and to consider age, gender, ethnic backgrounds, and occupation classes. PMID:25436515

Thin layer chromatography-densitometric determination of some non-sedating antihistamines in combination with pseudoephedrine or acetaminophen in synthetic mixtures and in pharmaceutical formulations.

PubMed

El-Kommos, Michael E; El-Gizawy, Samia M; Atia, Noha N; Hosny, Noha M

2014-03-01

The combination of certain non-sedating antihistamines (NSA) such as fexofenadine (FXD), ketotifen (KET) and loratadine (LOR) with pseudoephedrine (PSE) or acetaminophen (ACE) is widely used in the treatment of allergic rhinitis, conjunctivitis and chronic urticaria. A rapid, simple, selective and precise densitometric method was developed and validated for simultaneous estimation of six synthetic binary mixtures and their pharmaceutical dosage forms. The method employed thin layer chromatography aluminum plates precoated with silica gel G 60 F254 as the stationary phase. The mobile phases chosen for development gave compact bands for the mixtures FXD-PSE (I), KET-PSE (II), LOR-PSE (III), FXD-ACE (IV), KET-ACE (V) and LOR-ACE (VI) [Retardation factor (Rf ) values were (0.20, 0.32), (0.69, 0.34), (0.79, 0.13), (0.36, 0.70), (0.51, 0.30) and (0.76, 0.26), respectively]. Spectrodensitometric scanning integration was performed at 217, 218, 218, 233, 272 and 251 nm for the mixtures I-VI, respectively. The linear regression data for the calibration plots showed an excellent linear relationship. The method was validated for precision, accuracy, robustness and recovery. Limits of detection and quantitation were calculated. Statistical analysis proved that the method is reproducible and selective for the simultaneous estimation of these binary mixtures. Copyright © 2013 John Wiley & Sons, Ltd.

Beyond the Binary: Differences in Eating Disorder Prevalence by Gender Identity in a Transgender Sample

PubMed Central

Diemer, Elizabeth W.; White Hughto, Jaclyn M.; Gordon, Allegra R.; Guss, Carly; Austin, S. Bryn; Reisner, Sari L.

2018-01-01

Abstract Purpose: To investigate whether the prevalence of eating disorders (EDs) differs across diverse gender identity groups in a transgender sample. Methods: Secondary analysis of data from Project VOICE, a cross-sectional study of stress and health among 452 transgender adults (ages 18–75 years) residing in Massachusetts. Age-adjusted logistic regression models were fit to compare the prevalence of self-reported lifetime EDs in female-to-male (FTM), male-to-female (MTF), and gender-nonconforming participants assigned male at birth (MBGNC) to gender-nonconforming participants assigned female at birth (FBGNC; referent). Results: The age-adjusted odds of self-reported ED in MTF participants were 0.14 times the odds of self-reported ED in FBGNC participants (p=0.022). In FTM participants, the age-adjusted odds of self-reported ED were 0.46 times the odds of self-reported ED in FBGNC participants, a marginally significant finding (p=0.068). No statistically significant differences in ED prevalence were found for MBGNC individuals. Conclusions: Gender nonconforming individuals assigned a female sex at birth appear to have heightened lifetime risk of EDs relative to MTF participants. Further research into specific biologic and psychosocial ED risk factors and gender-responsive intervention strategies are urgently needed. Training clinical providers and ensuring competency of treatment services beyond the gender binary will be vital to addressing this disparity. PMID:29359198

[Overload in the informal caregivers of patients with multiple comorbidities in an urban area].

PubMed

Álvarez-Tello, Margarita; Casado-Mejía, Rosa; Ortega-Calvo, Manuel; Ruiz-Arias, Esperanza

2012-01-01

The aim of the study was, to determine the profile of the family caregiver of patients with multiple pathologies, identify factors associated with overload, and construct predictive models using items from the Caregiver Strain Index (CSI). A cross-sectional study of caregivers of patients with multiple comorbidities who attended an urban health centre. Data were collected from health records and questionnaires (Barthel index, Pfeiffer index, and CSI). Statistical analysis was performed using measures of central tendency and dispersion, and by building multivariate models with binary logistic regression with the CSI items as predictors (program R version 2.14.0). The sample included 67 caregivers, with a mean age of 64.69 years (standard deviation=12.71, median 62 years), of whom 74.6% were women, 35.8% were wives, and 32.8% were daughters. The level of dependence of the patients cared for was total/severe in 77.6%, and moderate in 12% (Barthel), and 47.8% had some level of cognitive impairment (Pfeiffer). A CSI equal or greater than 7 was seen in 47.8% of caregivers, identifying life problems in more than 40% of them such as, restriction of social life, physical exertion, discomfort with change, bad behaviour, personal and family emotional changes, and sleep disturbances. Item 4 of the CSI, analysing the social restriction, was the one that showed a greater significance in the predictive multivariate model. Item 12 (economic burden) was the most significant with age in patients with cognitive impairment. Women tend to take the role of caregiver at an earlier age than men in the urban environment studied, and items from CSI showed that items 4 (social restrictions) and 12 (economic burden) have more significance in the predictive models constructed with Binary Logistic Regression. Copyright © 2012 Elsevier España, S.L. All rights reserved.

Methods for estimation of radiation risk in epidemiological studies accounting for classical and Berkson errors in doses.

PubMed

Kukush, Alexander; Shklyar, Sergiy; Masiuk, Sergii; Likhtarov, Illya; Kovgan, Lina; Carroll, Raymond J; Bouville, Andre

2011-02-16

With a binary response Y, the dose-response model under consideration is logistic in flavor with pr(Y=1 | D) = R (1+R)(-1), R = λ(0) + EAR D, where λ(0) is the baseline incidence rate and EAR is the excess absolute risk per gray. The calculated thyroid dose of a person i is expressed as Dimes=fiQi(mes)/Mi(mes). Here, Qi(mes) is the measured content of radioiodine in the thyroid gland of person i at time t(mes), Mi(mes) is the estimate of the thyroid mass, and f(i) is the normalizing multiplier. The Q(i) and M(i) are measured with multiplicative errors Vi(Q) and ViM, so that Qi(mes)=Qi(tr)Vi(Q) (this is classical measurement error model) and Mi(tr)=Mi(mes)Vi(M) (this is Berkson measurement error model). Here, Qi(tr) is the true content of radioactivity in the thyroid gland, and Mi(tr) is the true value of the thyroid mass. The error in f(i) is much smaller than the errors in ( Qi(mes), Mi(mes)) and ignored in the analysis. By means of Parametric Full Maximum Likelihood and Regression Calibration (under the assumption that the data set of true doses has lognormal distribution), Nonparametric Full Maximum Likelihood, Nonparametric Regression Calibration, and by properly tuned SIMEX method we study the influence of measurement errors in thyroid dose on the estimates of λ(0) and EAR. The simulation study is presented based on a real sample from the epidemiological studies. The doses were reconstructed in the framework of the Ukrainian-American project on the investigation of Post-Chernobyl thyroid cancers in Ukraine, and the underlying subpolulation was artificially enlarged in order to increase the statistical power. The true risk parameters were given by the values to earlier epidemiological studies, and then the binary response was simulated according to the dose-response model.

Postoperative Delirium in Severely Burned Patients Undergoing Early Escharotomy: Incidence, Risk Factors, and Outcomes.

PubMed

Guo, Zhenggang; Liu, Jiabin; Li, Jia; Wang, Xiaoyan; Guo, Hui; Ma, Panpan; Su, Xiaojun; Li, Ping

The aim of this study is to investigate the incidence, related risk factors, and outcomes of postoperative delirium (POD) in severely burned patients undergoing early escharotomy. This study included 385 severely burned patients (injured <1 week; TBSA, 31-50% or 11-20%; American Society of Anesthesiologists physical status, II-IV) aged 18 to 65 years, who underwent early escharotomy between October 2014 and December 2015, and were selected by cluster sampling. The authors excluded patients with preoperative delirium or diagnosed dementia, depression, or cognitive dysfunction. Preoperative, perioperative, intraoperative, and postoperative information, such as demographic characteristics, vital signs, and health history were collected. The Confusion Assessment Method was used once daily for 5 days after surgery to identify POD. Stepwise binary logistic regression analysis was used to identify the risk factors for POD, t-tests, and χ tests were performed to compare the outcomes of patients with and without the condition. Fifty-six (14.55%) of the patients in the sample were diagnosed with POD. Stepwise binary logistic regression showed that the significant risk factors for POD in severely burned patients undergoing early escharotomy were advanced age (>50 years old), a history of alcohol consumption (>3/week), high American Society of Anesthesiologists classification (III or IV), time between injury and surgery (>2 days), number of previous escharotomies (>2), combined intravenous and inhalation anesthesia, no bispectral index applied, long duration surgery (>180 min), and intraoperative hypotension (mean arterial pressure < 55 mm Hg). On the basis of the different odds ratios, the authors established a weighted model. When the score of a patient's weighted odds ratios is more than 6, the incidence of POD increased significantly (P < .05). When the score of a patient's weighted odds ratios is more than 6, the incidence of POD increased significantly (P < .05). Further, POD was associated with more postoperative complications, including hepatic and renal function impairment and hypernatremia, as well as prolonged hospitalization, increased medical costs, and higher mortality.

Partial Least Squares with Structured Output for Modelling the Metabolomics Data Obtained from Complex Experimental Designs: A Study into the Y-Block Coding.

PubMed

Xu, Yun; Muhamadali, Howbeer; Sayqal, Ali; Dixon, Neil; Goodacre, Royston

2016-10-28

Partial least squares (PLS) is one of the most commonly used supervised modelling approaches for analysing multivariate metabolomics data. PLS is typically employed as either a regression model (PLS-R) or a classification model (PLS-DA). However, in metabolomics studies it is common to investigate multiple, potentially interacting, factors simultaneously following a specific experimental design. Such data often cannot be considered as a "pure" regression or a classification problem. Nevertheless, these data have often still been treated as a regression or classification problem and this could lead to ambiguous results. In this study, we investigated the feasibility of designing a hybrid target matrix Y that better reflects the experimental design than simple regression or binary class membership coding commonly used in PLS modelling. The new design of Y coding was based on the same principle used by structural modelling in machine learning techniques. Two real metabolomics datasets were used as examples to illustrate how the new Y coding can improve the interpretability of the PLS model compared to classic regression/classification coding.

Computation of elementary modes: a unifying framework and the new binary approach

PubMed Central

Gagneur, Julien; Klamt, Steffen

2004-01-01

Background Metabolic pathway analysis has been recognized as a central approach to the structural analysis of metabolic networks. The concept of elementary (flux) modes provides a rigorous formalism to describe and assess pathways and has proven to be valuable for many applications. However, computing elementary modes is a hard computational task. In recent years we assisted in a multiplication of algorithms dedicated to it. We require a summarizing point of view and a continued improvement of the current methods. Results We show that computing the set of elementary modes is equivalent to computing the set of extreme rays of a convex cone. This standard mathematical representation provides a unified framework that encompasses the most prominent algorithmic methods that compute elementary modes and allows a clear comparison between them. Taking lessons from this benchmark, we here introduce a new method, the binary approach, which computes the elementary modes as binary patterns of participating reactions from which the respective stoichiometric coefficients can be computed in a post-processing step. We implemented the binary approach in FluxAnalyzer 5.1, a software that is free for academics. The binary approach decreases the memory demand up to 96% without loss of speed giving the most efficient method available for computing elementary modes to date. Conclusions The equivalence between elementary modes and extreme ray computations offers opportunities for employing tools from polyhedral computation for metabolic pathway analysis. The new binary approach introduced herein was derived from this general theoretical framework and facilitates the computation of elementary modes in considerably larger networks. PMID:15527509

Two-dimensional PCA-based human gait identification

NASA Astrophysics Data System (ADS)

Chen, Jinyan; Wu, Rongteng

2012-11-01

It is very necessary to recognize person through visual surveillance automatically for public security reason. Human gait based identification focus on recognizing human by his walking video automatically using computer vision and image processing approaches. As a potential biometric measure, human gait identification has attracted more and more researchers. Current human gait identification methods can be divided into two categories: model-based methods and motion-based methods. In this paper a two-Dimensional Principal Component Analysis and temporal-space analysis based human gait identification method is proposed. Using background estimation and image subtraction we can get a binary images sequence from the surveillance video. By comparing the difference of two adjacent images in the gait images sequence, we can get a difference binary images sequence. Every binary difference image indicates the body moving mode during a person walking. We use the following steps to extract the temporal-space features from the difference binary images sequence: Projecting one difference image to Y axis or X axis we can get two vectors. Project every difference image in the difference binary images sequence to Y axis or X axis difference binary images sequence we can get two matrixes. These two matrixes indicate the styles of one walking. Then Two-Dimensional Principal Component Analysis(2DPCA) is used to transform these two matrixes to two vectors while at the same time keep the maximum separability. Finally the similarity of two human gait images is calculated by the Euclidean distance of the two vectors. The performance of our methods is illustrated using the CASIA Gait Database.

Risk stratification personalised model for prediction of life-threatening ventricular tachyarrhythmias in patients with chronic heart failure.

PubMed

Frolov, Alexander Vladimirovich; Vaikhanskaya, Tatjana Gennadjevna; Melnikova, Olga Petrovna; Vorobiev, Anatoly Pavlovich; Guel, Ludmila Michajlovna

2017-01-01

The development of prognostic factors of life-threatening ventricular tachyarrhythmias (VTA) and sudden cardiac death (SCD) continues to maintain its priority and relevance in cardiology. The development of a method of personalised prognosis based on multifactorial analysis of the risk factors associated with life-threatening heart rhythm disturbances is considered a key research and clinical task. To design a prognostic and mathematical model to define personalised risk for life-threatening VTA in patients with chronic heart failure (CHF). The study included 240 patients with CHF (mean-age of 50.5 ± 12.1 years; left ventricular ejection fraction 32.8 ± 10.9%; follow-up period 36.8 ± 5.7 months). The participants received basic therapy for heart failure. The elec-trocardiogram (ECG) markers of myocardial electrical instability were assessed including microvolt T-wave alternans, heart rate turbulence, heart rate deceleration, and QT dispersion. Additionally, echocardiography and Holter monitoring (HM) were performed. The cardiovascular events were considered as primary endpoints, including SCD, paroxysmal ventricular tachycardia/ventricular fibrillation (VT/VF) based on HM-ECG data, and data obtained from implantable device interrogation (CRT-D, ICD) as well as appropriated shocks. During the follow-up period, 66 (27.5%) subjects with CHF showed adverse arrhythmic events, including nine SCD events and 57 VTAs. Data from a stepwise discriminant analysis of cumulative ECG-markers of myocardial electrical instability were used to make a mathematical model of preliminary VTA risk stratification. Uni- and multivariate Cox logistic regression analysis were performed to define an individualised risk stratification model of SCD/VTA. A binary logistic regression model demonstrated a high prognostic significance of discriminant function with a classification sensitivity of 80.8% and specificity of 99.1% (F = 31.2; c2 = 143.2; p < 0.0001). The method of personalised risk stratification using Cox logistic regression allows correct classification of more than 93.9% of CHF cases. A robust body of evidence concerning logistic regression prognostic significance to define VTA risk allows inclusion of this method into the algorithm of subsequent control and selection of the optimal treatment modality to treat patients with CHF.

Low-mass Pre-He White Dwarf Stars in Kepler Eclipsing Binaries with Multi-periodic Pulsations

NASA Astrophysics Data System (ADS)

Zhang, X. B.; Fu, J. N.; Liu, N.; Luo, C. Q.; Ren, A. B.

2017-12-01

We report the discovery of two thermally bloated low-mass pre-He white dwarfs (WDs) in two eclipsing binaries, KIC 10989032 and KIC 8087799. Based on the Kepler long-cadence photometry, we determined comprehensive photometric solutions of the two binary systems. The light curve analysis reveals that KIC 10989032 is a partially eclipsed detached binary system containing a probable low-mass WD with the temperature of about 10,300 K. Having a WD with the temperature of about 13,300, KKIC 8087799 is typical of an EL CVn system. By utilizing radial velocity measurements available for the A-type primary star of KIC 10989032, the mass and radius of the WD component are determined to be 0.24+/- 0.02 {M}⊙ and 0.50+/- 0.01 {R}⊙ , respectively. The values of mass and radius of the WD in KIC 8087799 are estimated as 0.16 ± 0.02 M ⊙ and 0.21 ± 0.01 R ⊙, respectively, according to the effective temperature and mean density of the A-type star derived from the photometric solution. We therefore introduce KIC 10989032 and KIC 8087799 as the eleventh and twelfth dA+WD eclipsing binaries in the Kepler field. Moreover, both binaries display marked multi-periodic pulsations superimposed on binary effects. A preliminary frequency analysis is applied to the light residuals when subtracting the synthetic eclipsing light curves from the observations, revealing that the light pulsations of the two systems are both due to the δ Sct-type primaries. We hence classify KIC 10989032 and KIC 8087799 as two WD+δ Sct binaries.

Measuring Close Binary Stars with Speckle Interferometry

DTIC Science & Technology

2014-09-01

extra effort to be measured. One method of observing such binary star systems is to use adaptive optics to correct the atmospheric blur in real-time...simplicity, and with no loss in generalization, this analysis will be reduced to one dimension . From equation (4), it can be seen that the frequency (u...the binary pair are systematically too large , due to the displacement of the minima of the fringes by the atmospheric OTF, when left uncorrected

The Impact of Additional Weekdays of Active Commuting to School on Children Achieving a Criterion of 300+ Minutes of Moderate-to-Vigorous Physical Activity

ERIC Educational Resources Information Center

Daly-Smith, Andy J. W.; McKenna, Jim; Radley, Duncan; Long, Jonathan

2011-01-01

Objective: To investigate the value of additional days of active commuting for meeting a criterion of 300+ minutes of moderate-to-vigorous physical activity (MVPA; 60+ mins/day x 5) during the school week. Methods: Based on seven-day diaries supported by teachers, binary logistic regression analyses were used to predict achievement of MVPA…

Extramural vascular invasion and response to neoadjuvant chemoradiotherapy in rectal cancer: Influence of the CpG island methylator phenotype

PubMed Central

Williamson, Jeremy Stuart; Jones, Huw Geraint; Williams, Namor; Griffiths, Anthony Paul; Jenkins, Gareth; Beynon, John; Harris, Dean Anthony

2017-01-01

AIM To identify whether CpG island methylator phenotype (CIMP) is predictive of response to neoadjuvant chemoradiotherapy (NACRT) and outcomes in rectal cancer. METHODS Patients undergoing NACRT and surgical resection for rectal cancer in a tertiary referral centre between 2002-2011 were identified. Pre-treatment tumour biopsies were analysed for CIMP status (high, intermediate or low) using methylation specific PCR. KRAS and BRAF status were also determined using pyrosequencing analysis. Clinical information was extracted from case records and cancer services databases. Response to radiotherapy was measured by tumour regression scores determined upon histological examination of the resected specimen. The relationship between these molecular features, response to NACRT and oncological outcomes were analysed. RESULTS There were 160 patients analysed with a median follow-up time of 46.4 mo. Twenty-one (13%) patients demonstrated high levels of CIMP methylation (CIMP-H) and this was significantly associated with increased risk of extramural vascular invasion (EMVI) compared with CIMP-L [8/21 (38%) vs 15/99 (15%), P = 0.028]. CIMP status was not related to tumour regression after radiotherapy or survival, however EMVI was significantly associated with adverse survival (P < 0.001). Intermediate CIMP status was significantly associated with KRAS mutation (P = 0.01). There were 14 (9%) patients with a pathological complete response (pCR) compared to 116 (73%) patients having no or minimal regression after neoadjuvant chemoradiotherapy. Those patients with pCR had median survival of 106 mo compared to 65.8 mo with minimal regression, although this was not statistically significant (P = 0.26). Binary logistic regression analysis of the relationship between EMVI and other prognostic features revealed, EMVI positivity was associated with poor overall survival, advanced “T” stage and CIMP-H but not nodal status, age, sex, KRAS mutation status and presence of local or systemic recurrence. CONCLUSION We report a novel association of pre-treatment characterisation of CIMP-H with EMVI status which has prognostic implications and is not readily detectable on pre-treatment histological examination. PMID:28567185

Extramural vascular invasion and response to neoadjuvant chemoradiotherapy in rectal cancer: Influence of the CpG island methylator phenotype.

PubMed

Williamson, Jeremy Stuart; Jones, Huw Geraint; Williams, Namor; Griffiths, Anthony Paul; Jenkins, Gareth; Beynon, John; Harris, Dean Anthony

2017-05-15

To identify whether CpG island methylator phenotype (CIMP) is predictive of response to neoadjuvant chemoradiotherapy (NACRT) and outcomes in rectal cancer. Patients undergoing NACRT and surgical resection for rectal cancer in a tertiary referral centre between 2002-2011 were identified. Pre-treatment tumour biopsies were analysed for CIMP status (high, intermediate or low) using methylation specific PCR. KRAS and BRAF status were also determined using pyrosequencing analysis. Clinical information was extracted from case records and cancer services databases. Response to radiotherapy was measured by tumour regression scores determined upon histological examination of the resected specimen. The relationship between these molecular features, response to NACRT and oncological outcomes were analysed. There were 160 patients analysed with a median follow-up time of 46.4 mo. Twenty-one (13%) patients demonstrated high levels of CIMP methylation (CIMP-H) and this was significantly associated with increased risk of extramural vascular invasion (EMVI) compared with CIMP-L [8/21 (38%) vs 15/99 (15%), P = 0.028]. CIMP status was not related to tumour regression after radiotherapy or survival, however EMVI was significantly associated with adverse survival ( P < 0.001). Intermediate CIMP status was significantly associated with KRAS mutation ( P = 0.01). There were 14 (9%) patients with a pathological complete response (pCR) compared to 116 (73%) patients having no or minimal regression after neoadjuvant chemoradiotherapy. Those patients with pCR had median survival of 106 mo compared to 65.8 mo with minimal regression, although this was not statistically significant ( P = 0.26). Binary logistic regression analysis of the relationship between EMVI and other prognostic features revealed, EMVI positivity was associated with poor overall survival, advanced "T" stage and CIMP-H but not nodal status, age, sex, KRAS mutation status and presence of local or systemic recurrence. We report a novel association of pre-treatment characterisation of CIMP-H with EMVI status which has prognostic implications and is not readily detectable on pre-treatment histological examination.

Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection

USGS Publications Warehouse

Jarnevich, Catherine S.; Talbert, Marian; Morisette, Jeffrey T.; Aldridge, Cameron L.; Brown, Cynthia; Kumar, Sunil; Manier, Daniel; Talbert, Colin; Holcombe, Tracy R.

2017-01-01

Evaluating the conditions where a species can persist is an important question in ecology both to understand tolerances of organisms and to predict distributions across landscapes. Presence data combined with background or pseudo-absence locations are commonly used with species distribution modeling to develop these relationships. However, there is not a standard method to generate background or pseudo-absence locations, and method choice affects model outcomes. We evaluated combinations of both model algorithms (simple and complex generalized linear models, multivariate adaptive regression splines, Maxent, boosted regression trees, and random forest) and background methods (random, minimum convex polygon, and continuous and binary kernel density estimator (KDE)) to assess the sensitivity of model outcomes to choices made. We evaluated six questions related to model results, including five beyond the common comparison of model accuracy assessment metrics (biological interpretability of response curves, cross-validation robustness, independent data accuracy and robustness, and prediction consistency). For our case study with cheatgrass in the western US, random forest was least sensitive to background choice and the binary KDE method was least sensitive to model algorithm choice. While this outcome may not hold for other locations or species, the methods we used can be implemented to help determine appropriate methodologies for particular research questions.

The Mantel-Haenszel procedure revisited: models and generalizations.

PubMed

Fidler, Vaclav; Nagelkerke, Nico

2013-01-01

Several statistical methods have been developed for adjusting the Odds Ratio of the relation between two dichotomous variables X and Y for some confounders Z. With the exception of the Mantel-Haenszel method, commonly used methods, notably binary logistic regression, are not symmetrical in X and Y. The classical Mantel-Haenszel method however only works for confounders with a limited number of discrete strata, which limits its utility, and appears to have no basis in statistical models. Here we revisit the Mantel-Haenszel method and propose an extension to continuous and vector valued Z. The idea is to replace the observed cell entries in strata of the Mantel-Haenszel procedure by subject specific classification probabilities for the four possible values of (X,Y) predicted by a suitable statistical model. For situations where X and Y can be treated symmetrically we propose and explore the multinomial logistic model. Under the homogeneity hypothesis, which states that the odds ratio does not depend on Z, the logarithm of the odds ratio estimator can be expressed as a simple linear combination of three parameters of this model. Methods for testing the homogeneity hypothesis are proposed. The relationship between this method and binary logistic regression is explored. A numerical example using survey data is presented.

The Mantel-Haenszel Procedure Revisited: Models and Generalizations

PubMed Central

Fidler, Vaclav; Nagelkerke, Nico

2013-01-01

Several statistical methods have been developed for adjusting the Odds Ratio of the relation between two dichotomous variables X and Y for some confounders Z. With the exception of the Mantel-Haenszel method, commonly used methods, notably binary logistic regression, are not symmetrical in X and Y. The classical Mantel-Haenszel method however only works for confounders with a limited number of discrete strata, which limits its utility, and appears to have no basis in statistical models. Here we revisit the Mantel-Haenszel method and propose an extension to continuous and vector valued Z. The idea is to replace the observed cell entries in strata of the Mantel-Haenszel procedure by subject specific classification probabilities for the four possible values of (X,Y) predicted by a suitable statistical model. For situations where X and Y can be treated symmetrically we propose and explore the multinomial logistic model. Under the homogeneity hypothesis, which states that the odds ratio does not depend on Z, the logarithm of the odds ratio estimator can be expressed as a simple linear combination of three parameters of this model. Methods for testing the homogeneity hypothesis are proposed. The relationship between this method and binary logistic regression is explored. A numerical example using survey data is presented. PMID:23516463

Kepler eclipsing binary stars. IV. Precise eclipse times for close binaries and identification of candidate three-body systems

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

Conroy, Kyle E.; Stassun, Keivan G.; Prša, Andrej

2014-02-01

We present a catalog of precise eclipse times and analysis of third-body signals among 1279 close binaries in the latest Kepler Eclipsing Binary Catalog. For these short-period binaries, Kepler's 30 minute exposure time causes significant smearing of light curves. In addition, common astrophysical phenomena such as chromospheric activity, as well as imperfections in the light curve detrending process, can create systematic artifacts that may produce fictitious signals in the eclipse timings. We present a method to measure precise eclipse times in the presence of distorted light curves, such as in contact and near-contact binaries which exhibit continuously changing light levelsmore » in and out of eclipse. We identify 236 systems for which we find a timing variation signal compatible with the presence of a third body. These are modeled for the light travel time effect and the basic properties of the third body are derived. This study complements J. A. Orosz et al. (in preparation), which focuses on eclipse timing variations of longer period binaries with flat out-of-eclipse regions. Together, these two papers provide comprehensive eclipse timings for all binaries in the Kepler Eclipsing Binary Catalog, as an ongoing resource freely accessible online to the community.« less

THE EFFECT OF UNRESOLVED BINARIES ON GLOBULAR CLUSTER PROPER-MOTION DISPERSION PROFILES

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

Bianchini, P.; Norris, M. A.; Ven, G. van de

2016-03-20

High-precision kinematic studies of globular clusters (GCs) require an accurate knowledge of all possible sources of contamination. Among other sources, binary stars can introduce systematic biases in the kinematics. Using a set of Monte Carlo cluster simulations with different concentrations and binary fractions, we investigate the effect of unresolved binaries on proper-motion dispersion profiles, treating the simulations like Hubble Space Telescope proper-motion samples. Since GCs evolve toward a state of partial energy equipartition, more-massive stars lose energy and decrease their velocity dispersion. As a consequence, on average, binaries have a lower velocity dispersion, since they are more-massive kinematic tracers. Wemore » show that, in the case of clusters with high binary fractions (initial binary fractions of 50%) and high concentrations (i.e., closer to energy equipartition), unresolved binaries introduce a color-dependent bias in the velocity dispersion of main-sequence stars of the order of 0.1–0.3 km s{sup −1} (corresponding to 1%−6% of the velocity dispersion), with the reddest stars having a lower velocity dispersion, due to the higher fraction of contaminating binaries. This bias depends on the ability to distinguish binaries from single stars, on the details of the color–magnitude diagram and the photometric errors. We apply our analysis to the HSTPROMO data set of NGC 7078 (M15) and show that no effect ascribable to binaries is observed, consistent with the low binary fraction of the cluster. Our work indicates that binaries do not significantly bias proper-motion velocity-dispersion profiles, but should be taken into account in the error budget of kinematic analyses.« less

Predictors of depressive symptoms in older Japanese primiparas at 1 month post-partum: A risk-stratified analysis.

PubMed

Iwata, Hiroko; Mori, Emi; Tsuchiya, Miyako; Sakajo, Akiko; Maehara, Kunie; Ozawa, Harumi; Morita, Akiko; Maekawa, Tomoko; Aoki, Kyoko; Tamakoshi, Koji

2016-01-01

Older maternal age has become more common in Japan. Studies suggest that older maternal age and primiparity are associated with post-partum depression. The present study aimed to identify predictors of post-partum depression in older Japanese primiparas at 1 month post-partum. Participants were 479 primiparas aged 35 years and over, drawn from a prospective cohort study. Data were collected using self-report questionnaires. Depression was measured with the Japanese version of the Edinburgh Postnatal Depression Scale. Stepwise logistic regression analysis was conducted on binary outcome variables of depression at 1 month post-partum, along with a stratified analysis based on the risk status of depression. Five predictors were identified: (i) the depression score during hospital stay; (ii) financial burden; (iii) dissatisfaction with appraisal support; (iv) physical burden in daily life; and (v) concerns about infant caretaking. Stratified analysis identified dissatisfaction with instrumental support in the low-risk group, and the Child-care Value Scale score as unique predictors in the high-risk group. These results highlight the importance of early assessment of depressive symptoms and the provision of continuous care. © 2015 Japan Academy of Nursing Science.

Predicting allergic contact dermatitis: a hierarchical structure activity relationship (SAR) approach to chemical classification using topological and quantum chemical descriptors

NASA Astrophysics Data System (ADS)

Basak, Subhash C.; Mills, Denise; Hawkins, Douglas M.

2008-06-01

A hierarchical classification study was carried out based on a set of 70 chemicals—35 which produce allergic contact dermatitis (ACD) and 35 which do not. This approach was implemented using a regular ridge regression computer code, followed by conversion of regression output to binary data values. The hierarchical descriptor classes used in the modeling include topostructural (TS), topochemical (TC), and quantum chemical (QC), all of which are based solely on chemical structure. The concordance, sensitivity, and specificity are reported. The model based on the TC descriptors was found to be the best, while the TS model was extremely poor.

Association Between Socio-Demographic Background and Self-Esteem of University Students.

PubMed

Haq, Muhammad Ahsan Ul

2016-12-01

The purpose of this study was to scrutinize self-esteem of university students and explore association of self-esteem with academic achievement, gender and other factors. A sample of 346 students was selected from Punjab University, Lahore Pakistan. Rosenberg self-esteem scale with demographic variables was used for data collection. Besides descriptive statistics, binary logistic regression and t test were used for analysing the data. Significant gender difference was observed, self-esteem was significantly higher in males than females. Logistic regression indicates that age, medium of instruction, family income, student monthly expenditures, GPA and area of residence has direct effect on self-esteem; while number of siblings showed an inverse effect.

glmnetLRC f/k/a lrc package: Logistic Regression Classification

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

2016-06-09

Methods for fitting and predicting logistic regression classifiers (LRC) with an arbitrary loss function using elastic net or best subsets. This package adds additional model fitting features to the existing glmnet and bestglm R packages. This package was created to perform the analyses described in Amidan BG, Orton DJ, LaMarche BL, et al. 2014. Signatures for Mass Spectrometry Data Quality. Journal of Proteome Research. 13(4), 2215-2222. It makes the model fitting available in the glmnet and bestglm packages more general by identifying optimal model parameters via cross validation with an customizable loss function. It also identifies the optimal threshold formore » binary classification.« less

Investigation of intermolecular interaction of binary mixture of acrylonitrile with bromobenzene

NASA Astrophysics Data System (ADS)

Deshmukh, S. D.; Pattebahadur, K. L.; Mohod, A. G.; Patil, S. S.; Khirade, P. W.

2018-04-01

In this paper, study of binary mixture of Acrylonitrile (ACN)with Bromobenzene(BB) has been carried out at eleven concentrations at room temperature. The determined density(ρ) and refractive index (nD) values of binary mixture are used to calculate the excess properties of mixture over the entire composition range. The aforesaid parameters are used to calculate excess parameters and fitted to the Redlich-Kister equation to determine the bj coefficients. From the above parameters, intermolecular interaction and dynamics of molecules of binary mixture at molecular level are discussed. The Conformational analysis of the intermolecular interaction between Acrylonitrile and Bromobenzene is supported by the FTIR spectra.

A model of the evaporation of binary-fuel clusters of drops

NASA Technical Reports Server (NTRS)

Harstad, K.; Bellan, J.

1991-01-01

A formulation has been developed to describe the evaporation of dense or dilute clusters of binary-fuel drops. The binary fuel is assumed to be made of a solute and a solvent whose volatility is much lower than that of the solute. Convective flow effects, inducing a circulatory motion inside the drops, are taken into account, as well as turbulence external to the cluster volume. Results obtained with this model show that, similar to the conclusions for single isolated drops, the evaporation of the volatile is controlled by liquid mass diffusion when the cluster is dilute. In contrast, when the cluster is dense, the evaporation of the volatile is controlled by surface layer stripping, that is, by the regression rate of the drop, which is in fact controlled by the evaporation rate of the solvent. These conclusions are in agreement with existing experimental observations. Parametric studies show that these conclusions remain valid with changes in ambient temperature, initial slip velocity between drops and gas, initial drop size, initial cluster size, initial liquid mass fraction of the solute, and various combinations of solvent and solute. The implications of these results for computationally intensive combustor calculations are discussed.

Gastroesophageal pressure gradients in gastroesophageal reflux disease: relations with hiatal hernia, body mass index, and esophageal acid exposure.

PubMed

de Vries, Durk R; van Herwaarden, Margot A; Smout, André J P M; Samsom, Melvin

2008-06-01

The roles of intragastric pressure (IGP), intraesophageal pressure (IEP), gastroesophageal pressure gradient (GEPG), and body mass index (BMI) in the pathophysiology of gastroesophageal reflux disease (GERD) and hiatal hernia (HH) are only partly understood. In total, 149 GERD patients underwent stationary esophageal manometry, 24-h pH-metry, and endoscopy. One hundred three patients had HH. Linear regression analysis showed that each kilogram per square meter of BMI caused a 0.047-kPa increase in inspiratory IGP (95% confidence interval [CI] 0.026-0.067) and a 0.031-kPa increase in inspiratory GEPG (95% CI 0.007-0.055). Each kilogram per square meter of BMI caused expiratory IGP to increase with 0.043 kPa (95% CI 0.025-0.060) and expiratory IEP with 0.052 kPa (95% CI 0.027-0.077). Each added year of age caused inspiratory IEP to decrease by 0.008 kPa (95% CI -0.015-0.001) and inspiratory GEPG to increase by 0.008 kPa (95% CI 0.000-0.015). In binary logistic regression analysis, HH was predicted by inspiratory and expiratory IGP (odds ratio [OR] 2.93 and 2.62, respectively), inspiratory and expiratory GEPG (OR 3.19 and 2.68, respectively), and BMI (OR 1.72/5 kg/m(2)). In linear regression analysis, HH caused an average 5.09% increase in supine acid exposure (95% CI 0.96-9.22) and an average 3.46% increase in total acid exposure (95% CI 0.82-6.09). Each added year of age caused an average 0.10% increase in upright acid exposure and a 0.09% increase in total acid exposure (95% CI 0.00-0.20 and 0.00-0.18). BMI predicts IGP, inspiratory GEPG, and expiratory IEP. Age predicts inspiratory IEP and GEPG. Presence of HH is predicted by IGP, GEPG, and BMI. GEPG is not associated with acid exposure.

Multivariable Regression Analysis in Schistosoma mansoni-Infected Individuals in the Sudan Reveals Unique Immunoepidemiological Profiles in Uninfected, egg+ and Non-egg+ Infected Individuals.

PubMed

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

2016-05-01

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

Instability of a solidifying binary mixture

NASA Technical Reports Server (NTRS)

Antar, B. N.

1982-01-01

An analysis is performed on the stability of a solidifying binary mixture due to surface tension variation of the free liquid surface. The basic state solution is obtained numerically as a nonstationary function of time. Due to the time dependence of the basic state, the stability analysis is of the global type which utilizes a variational technique. Also due to the fact that the basic state is a complex function of both space and time, the stability analysis is performed through numerical means.

Contamination of RR Lyrae stars from Binary Evolution Pulsators

NASA Astrophysics Data System (ADS)

Karczmarek, Paulina; Pietrzyński, Grzegorz; Belczyński, Krzysztof; Stępień, Kazimierz; Wiktorowicz, Grzegorz; Iłkiewicz, Krystian

2016-06-01

Binary Evolution Pulsator (BEP) is an extremely low-mass member of a binary system, which pulsates as a result of a former mass transfer to its companion. BEP mimics RR Lyrae-type pulsations but has different internal structure and evolution history. We present possible evolution channels to produce BEPs, and evaluate the contamination value, i.e. how many objects classified as RR Lyrae stars can be undetected BEPs. In this analysis we use population synthesis code StarTrack.

Calculating Mass Diffusion in High-Pressure Binary Fluids

NASA Technical Reports Server (NTRS)

Bellan, Josette; Harstad, Kenneth

2004-01-01

A comprehensive mathematical model of mass diffusion has been developed for binary fluids at high pressures, including critical and supercritical pressures. Heretofore, diverse expressions, valid for limited parameter ranges, have been used to correlate high-pressure binary mass-diffusion-coefficient data. This model will likely be especially useful in the computational simulation and analysis of combustion phenomena in diesel engines, gas turbines, and liquid rocket engines, wherein mass diffusion at high pressure plays a major role.

LUT observations of the mass-transferring binary AI Dra

NASA Astrophysics Data System (ADS)

Liao, Wenping; Qian, Shengbang; Li, Linjia; Zhou, Xiao; Zhao, Ergang; Liu, Nianping

2016-06-01

Complete UV band light curve of the eclipsing binary AI Dra was observed with the Lunar-based Ultraviolet Telescope (LUT) in October 2014. It is very useful to adopt this continuous and uninterrupted light curve to determine physical and orbital parameters of the binary system. Photometric solutions of the spot model are obtained by using the W-D (Wilson and Devinney) method. It is confirmed that AI Dra is a semi-detached binary with secondary component filling its critical Roche lobe, which indicates that a mass transfer from the secondary component to the primary one should happen. Orbital period analysis based on all available eclipse times suggests a secular period increase and two cyclic variations. The secular period increase was interpreted by mass transfer from the secondary component to the primary one at a rate of 4.12 ×10^{-8}M_{⊙}/yr, which is in agreement with the photometric solutions. Two cyclic oscillations were due to light travel-time effect (LTTE) via the presence of two cool stellar companions in a near 2:1 mean-motion resonance. Both photometric solutions and orbital period analysis confirm that AI Dra is a mass-transferring binary, the massive primary is filling 69 % of its critical Roche lobe. After the primary evolves to fill the critical Roche lobe, the mass transfer will be reversed and the binary will evolve into a contact configuration.

Improving the analysis of composite endpoints in rare disease trials.

PubMed

McMenamin, Martina; Berglind, Anna; Wason, James M S

2018-05-22

Composite endpoints are recommended in rare diseases to increase power and/or to sufficiently capture complexity. Often, they are in the form of responder indices which contain a mixture of continuous and binary components. Analyses of these outcomes typically treat them as binary, thus only using the dichotomisations of continuous components. The augmented binary method offers a more efficient alternative and is therefore especially useful for rare diseases. Previous work has indicated the method may have poorer statistical properties when the sample size is small. Here we investigate small sample properties and implement small sample corrections. We re-sample from a previous trial with sample sizes varying from 30 to 80. We apply the standard binary and augmented binary methods and determine the power, type I error rate, coverage and average confidence interval width for each of the estimators. We implement Firth's adjustment for the binary component models and a small sample variance correction for the generalized estimating equations, applying the small sample adjusted methods to each sub-sample as before for comparison. For the log-odds treatment effect the power of the augmented binary method is 20-55% compared to 12-20% for the standard binary method. Both methods have approximately nominal type I error rates. The difference in response probabilities exhibit similar power but both unadjusted methods demonstrate type I error rates of 6-8%. The small sample corrected methods have approximately nominal type I error rates. On both scales, the reduction in average confidence interval width when using the adjusted augmented binary method is 17-18%. This is equivalent to requiring a 32% smaller sample size to achieve the same statistical power. The augmented binary method with small sample corrections provides a substantial improvement for rare disease trials using composite endpoints. We recommend the use of the method for the primary analysis in relevant rare disease trials. We emphasise that the method should be used alongside other efforts in improving the quality of evidence generated from rare disease trials rather than replace them.

Management Options and Outcomes for Neonatal Hypoplastic Left Heart Syndrome in the Early Twenty-First Century.

PubMed

Kane, Jason M; Canar, Jeff; Kalinowski, Valerie; Johnson, Tricia J; Hoehn, K Sarah

2016-02-01

Without surgical treatment, neonatal hypoplastic left heart syndrome (HLHS) mortality in the first year of life exceeds 90 % and, in spite of improved surgical outcomes, many families still opt for non-surgical management. The purpose of this study was to investigate trends in neonatal HLHS management and to identify characteristics of patients who did not undergo surgical palliation. Neonates with HLHS were identified from a serial cross-sectional analysis using the Healthcare Cost and Utilization Project's Kids' Inpatient Database from 2000 to 2012. The primary analysis compared children undergoing surgical palliation to those discharged alive without surgery using a binary logistic regression model. Multivariate logistic regression was conducted to determine factors associated with treatment choice. A total of 1750 patients underwent analysis. Overall hospital mortality decreased from 35.3 % in 2000 to 22.9 % in 2012. The percentage of patients undergoing comfort care discharge without surgery also decreased from 21.2 to 14.8 %. After controlling for demographics and comorbidities, older patients at presentation were less likely to undergo surgery (OR 0.93, 0.91-0.96), and patients in 2012 were more likely to undergo surgery compared to those in prior years (OR 1.5, 1.1-2.1). Discharge without surgical intervention is decreasing with a 30 % reduction between 2000 and 2012. Given the improvement in surgical outcomes, further dialogue about ethical justification of non-operative comfort or palliative care is warranted. In the meantime, clinicians should present families with surgical outcome data and recommend intervention, while supporting their option to refuse.

Preoperatively staging liver fibrosis using noninvasive method in Hepatitis B virus-infected hepatocellular carcinoma patients

PubMed Central

Gao, Hengyi; Zhu, Feng; Wang, Min; Zhang, Hang; Ye, Dawei; Yang, Jiayin; Jiang, Li; Liu, Chang; Qin, Renyi; Yan, Lunan; Xiao, Guangqin

2017-01-01

Background Advanced liver fibrosis can result in serious complications (even patient’s death) after partial hepatectomy. Preoperatively percutaneous liver biopsy is an invasive and expensive method to assess liver fibrosis. We aim to establish a noninvasive model, on the basis of preoperative biomarkers, to predict liver fibrosis in hepatocellular carcinoma (HCC) patients with hepatitis B virus (HBV) infection. Methods The HBV-infected liver cancer patients who had received hepatectomy were retrospectively and prospectively enrolled in this study. Univariate analysis was used to compare the variables of the patients with mild to moderate liver fibrosis and with severe liver fibrosis. The significant factors were selected into binary logistic regression analysis. Factors determined to be significant were used to establish a noninvasive model. Then the diagnostic accuracy of this novel model was examined based on sensitivity, specificity and area under the receiver-operating characteristic curve (AUC). Results This study included 2,176 HBV-infected HCC patients who had undergone partial hepatectomy (1,682 retrospective subjects and 494 prospective subjects). Regression analysis indicated that total bilirubin and prothrombin time had positive correlation with liver fibrosis. It also demonstrated that blood platelet count and fibrinogen had negative correlation with liver fibrosis. The AUC values of the model based on these four factors for predicting significant fibrosis, advanced fibrosis and cirrhosis were 0.79-0.83, 0.83-0.85 and 0.85-0.88, respectively. Conclusion The results showed that this novel preoperative model was an excellent noninvasive method for assessing liver fibrosis in HBV-infected HCC patients. PMID:28008144

Efficacy of pulsed dye laser treatment for common warts is not influenced by the causative HPV type: a prospective study.

PubMed

Fichman, Yoseph; Levi, Assi; Hodak, Emmilia; Halachmi, Shlomit; Mazor, Sigal; Wolf, Dana; Caplan, Orit; Lapidoth, Moshe

2018-05-01

Verruca vulgaris (VV) is a prevalent skin condition caused by various subtypes of human papilloma virus (HPV). The most common causes of non-genital lesions are HPV types 2 and 4, and to a lesser extent types 1, 3, 26, 29, and 57. Although numerous therapeutic modalities exist, none is universally effective or without adverse events (AE). Pulsed dye laser (PDL) is a favorable option due to its observed efficacy and relatively low AE rate. However, it is not known which verrucae are most likely to respond to PDL, or whether the causative viral subtype influences this response. The objective of this prospective blinded study was to assess whether the HPV subtype was predictive of response to PDL. For that matter, 26 verrucae from 26 immunocompetent patients were biopsied prior to treatment by PDL. HPV coding sequences were isolated and genotyped using PCR analysis. Patients were treated by PDL (595 nm wavelength, 5 mm spot size, 1.5 ms pulse duration, 12 J/cm 2 fluence) once a month for up to 6 months, and clinical response was assessed. Binary logistic regression analysis and linear logistic regression analysis were used in order to evaluate statistical significance. Different types of HPV were identified in 22 of 26 tissue samples. Response to treatment did not correlate with HPV type, age, or gender. As no association between HPV type and response to PDL therapy could be established, it is therefore equally effective for all HPV types and remains a favorable treatment option for all VV.

CHILDHOOD DEPRESSION. Exploring the association between family violence and other psychosocial factors in low-income Brazilian schoolchildren

PubMed Central

2012-01-01

Background Childhood depression affects the morbidity, mortality and life functions of children. Individual, family and environmental factors have been documented as psychosocial risk factors for childhood depression, especially family violence, which results in inadequate support, low family cohesion and poor communication. This study investigates the association between psychosocial depression factors in low-income schoolchildren and reveals the potential trouble spots, highlighting several forms of violence that take place within the family context. Methods The study was based on a cross-sectional analysis of 464 schoolchildren aged between 6 and 10, selected by random sampling from a city in the state of Rio de Janeiro, Brazil. Socio-economic, family and individual variables were investigated on the strength of the caregivers’ information and organized in blocks for analysis. A binary logistic regression model was applied, according to hierarchical blocks. Results The final hierarchical regression analysis showed that the following variables are potential psychosocial factors associated with depression in childhood: average/poor relationship with the father (OR 3.24, 95% CI 1.32-7.94), high frequency of victimization by psychological violence (humiliation) (OR 6.13, 95% CI 2.06-18.31), parental divorce (OR 2.89, 95% CI 1.14-7.32) and externalizing behavior problems (OR 3.53 IC 1.51-8.23). Conclusions The results point to multiple determinants of depressive behavior in children, as well as the potential contribution of psychological family violence. The study also reveals potential key targets for early intervention, especially for children from highly vulnerable families. PMID:22776354

Dancing in the Dark: New Brown Dwarf Binaries from Kernel Phase Interferometry

NASA Astrophysics Data System (ADS)

Pope, Benjamin; Martinache, Frantz; Tuthill, Peter

2013-04-01

This paper revisits a sample of ultracool dwarfs in the solar neighborhood previously observed with the Hubble Space Telescope's NICMOS NIC1 instrument. We have applied a novel high angular resolution data analysis technique based on the extraction and fitting of kernel phases to archival data. This was found to deliver a dramatic improvement over earlier analysis methods, permitting a search for companions down to projected separations of ~1 AU on NIC1 snapshot images. We reveal five new close binary candidates and present revised astrometry on previously known binaries, all of which were recovered with the technique. The new candidate binaries have sufficiently close separation to determine dynamical masses in a short-term observing campaign. We also present four marginal detections of objects which may be very close binaries or high-contrast companions. Including only confident detections within 19 pc, we report a binary fraction of at least \\epsilon _b = 17.2^{+5.7}_{-3.7} %. The results reported here provide new insights into the population of nearby ultracool binaries, while also offering an incisive case study of the benefits conferred by the kernel phase approach in the recovery of companions within a few resolution elements of the point-spread function core. Based on observations performed with the NASA/ESA Hubble Space Telescope. The Hubble observations are associated with proposal ID 10143 and 10879 and were obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555.

Organic–inorganic binary mixture matrix for comprehensive laser-desorption ionization mass spectrometric analysis and imaging of medium-size molecules including phospholipids, glycerolipids, and oligosaccharides

DOE PAGES

Feenstra, Adam D.; Ames Lab., Ames, IA; O'Neill, Kelly C.; ...

2016-10-13

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is a widely adopted, versatile technique, especially in high-throughput analysis and imaging. However, matrix-dependent selectivity of analytes is often a severe limitation. In this work, a mixture of organic 2,5-dihydroxybenzoic acid and inorganic Fe 3O 4 nanoparticles is developed as a binary MALDI matrix to alleviate the well-known issue of triacylglycerol (TG) ion suppression by phosphatidylcholine (PC). In application to lipid standards and maize seed cross-sections, the binary matrix not only dramatically reduced the ion suppression of TG, but also efficiently desorbed and ionized a wide variety of lipids such as cationic PC, anionicmore » phosphatidylethanolamine (PE) and phosphatidylinositol (PI), and neutral digalactosyldiacylglycerol (DGDG). The binary matrix was also very efficient for large polysaccharides, which were not detected by either of the individual matrices. As a result, the usefulness of the binary matrix is demonstrated in MS imaging of maize seed sections, successfully visualizing diverse medium-size molecules and acquiring high-quality MS/MS spectra for these compounds.« less

The Binary Pulsar: Gravity Waves Exist.

ERIC Educational Resources Information Center

Will, Clifford

1987-01-01

Reviews the history of pulsars generally and the 1974 discovery of the binary pulsar by Joe Taylor and Russell Hulse specifically. Details the data collection and analysis used by Taylor and Hulse. Uses this discussion as support for Albert Einstein's theory of gravitational waves. (CW)

Kepler AutoRegressive Planet Search

NASA Astrophysics Data System (ADS)

Feigelson, Eric

NASA's Kepler mission is the source of more exoplanets than any other instrument, but the discovery depends on complex statistical analysis procedures embedded in the Kepler pipeline. A particular challenge is mitigating irregular stellar variability without loss of sensitivity to faint periodic planetary transits. This proposal presents a two-stage alternative analysis procedure. First, parametric autoregressive ARFIMA models, commonly used in econometrics, remove most of the stellar variations. Second, a novel matched filter is used to create a periodogram from which transit-like periodicities are identified. This analysis procedure, the Kepler AutoRegressive Planet Search (KARPS), is confirming most of the Kepler Objects of Interest and is expected to identify additional planetary candidates. The proposed research will complete application of the KARPS methodology to the prime Kepler mission light curves of 200,000: stars, and compare the results with Kepler Objects of Interest obtained with the Kepler pipeline. We will then conduct a variety of astronomical studies based on the KARPS results. Important subsamples will be extracted including Habitable Zone planets, hot super-Earths, grazing-transit hot Jupiters, and multi-planet systems. Groundbased spectroscopy of poorly studied candidates will be performed to better characterize the host stars. Studies of stellar variability will then be pursued based on KARPS analysis. The autocorrelation function and nonstationarity measures will be used to identify spotted stars at different stages of autoregressive modeling. Periodic variables with folded light curves inconsistent with planetary transits will be identified; they may be eclipsing or mutually-illuminating binary star systems. Classification of stellar variables with KARPS-derived statistical properties will be attempted. KARPS procedures will then be applied to archived K2 data to identify planetary transits and characterize stellar variability.

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.

An Astrometric Analysis of eta Carinae’s Eruptive History Using HST WF/PC2 and ACS Observations

DTIC Science & Technology

2007-07-11

Std Z39-18 to address the question of binarity. Based on an astrometric analysis of the data, binary reflex motion is detected in the primary and, by...Measurement Results 96 5.1 Primary Luminosity and Mass . . . . . . . . . . . . . . . . . . . . . . 96 5.2 Secondary Mass and Luminosity...Binary Models . . . . . . . . . . . 100 5.5 Primary –Secondary Distance . . . . . . . . . . . . . . . . . . . . . . . 102 5.6 Periastron passage

Milk consumption and lactose intolerance in adults.

PubMed

Qiao, Rong; Huang, ChengYu; Du, HuiZhang; Zeng, Guo; Li, Ling; Ye, Sheng

2011-10-01

To investigate relations between milk consumption and lactose intolerance (LI) in adults and to explore the effect of milk consumption on lactase activity. Total of 182 subjects aged 20-70 years were recruited and interviewed by questionnaires, and their accumulative cow's milk intake (AMI) was calculated. LI was evaluated by hydrogen breath test (HBT). A negative correlation was found between AMI and severity of observed LI symptom (r=-0.2884; P<0.05). Binary logistic regression analysis showed a negative correlation between LI and duration and frequency of milk consumption (OR, 0.317 and 0.465, respectively; both P<0.05) and a positive correlation between LI and amount of milk consumed per sitting (OR, 6.337; P<0.05). LI is related to various milk consumption behaviors. Most Chinese adults with LI may tolerate moderate milk consumption <160 mL. Copyright © 2011 The Editorial Board of Biomedical and Environmental Sciences. Published by Elsevier B.V. All rights reserved.

Bayley Scales of Infant Development Screening Test-Gross Motor Subtest: efficacy in determining need for services.

PubMed

Jackson, Barbara J; Needelman, Howard; Roberts, Holly; Willet, Sandy; McMorris, Carol

2012-01-01

To identify the efficacy of the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III), Screening Test-Gross Motor Subtest (GMS) in identifying infants who are accepted for early intervention services. This retrospective study included 93 infants with a neonatal intensive care experience who participated in a 6-month developmental assessment follow-up visit. All infants were examined using the BSID-III Screening Test-GMS and the Alberta Infant Motor Scale. A binary logical regression analysis was used to determine the best predictors of acceptance status in this sample. The BSID-III Screening Test-GMS accounted for a significant portion of the variance in acceptance status. The results suggest that the BSID-III Screening Test-GMS has great applicability for transdisciplinary/interdisciplinary teams as it effectively identified children who were eligible for early intervention.

Spectral analysis of white ash response to emerald ash borer infestations

NASA Astrophysics Data System (ADS)

Calandra, Laura

The emerald ash borer (EAB) (Agrilus planipennis Fairmaire) is an invasive insect that has killed over 50 million ash trees in the US. The goal of this research was to establish a method to identify ash trees infested with EAB using remote sensing techniques at the leaf-level and tree crown level. First, a field-based study at the leaf-level used the range of spectral bands from the WorldView-2 sensor to determine if there was a significant difference between EAB-infested white ash (Fraxinus americana) and healthy leaves. Binary logistic regression models were developed using individual and combinations of wavelengths; the most successful model included 545 and 950 nm bands. The second half of this research employed imagery to identify healthy and EAB-infested trees, comparing pixel- and object-based methods by applying an unsupervised classification approach and a tree crown delineation algorithm, respectively. The pixel-based models attained the highest overall accuracies.

[Characteristics present in secondary school students who do not smoke and who have no intention to smoke].

PubMed

Lima-Serrano, Marta; Martínez-Montilla, José Manuel; Vargas-Martínez, Ana Magdalena; Zafra-Agea, José Antonio; Lima-Rodríguez, Joaquín Salvador

2018-02-27

To know the variables present in primary and secondary school students who do not smoke or intend to smoke from a positive health model. Cross-sectional study with 482 students from Andalusia and Catalonia using a validated questionnaire (ESFA and PASE project). Binary logistic regression analysis was performed. Those who did not intend to smoke viewed smoking unfavourably and had high self-efficacy (p <0.001). In non-consumers, the most associated variables were attitude, social model (p <0.001), and self-efficacy (p =0.005). The results show motivational factors present in students who do not smoke and do not intend to do so. Attitude and self-efficacy are strongly associated with intention and behaviour. This information might be useful for developing positive health promotion strategies from a salutogenesis approach. Copyright © 2018 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Central nervous system drug consumption depending on the time between symptom onset and the diagnosis of Alzheimer's disease: an analysis by the Registry of Dementias of Girona.

PubMed

Calvó-Perxas, Laia; López-Pousa, Secundino; Vilalta-Franch, Joan; Turró-Garriga, Oriol; Blankenburg, Michael; Febrer, Laia; Flaqué, Margarida; Vallmajó, Natàlia; Aguirregomozcorta, Maria; Genís, David; Casas, Isabel; Perkal, Héctor; Coromina, Joan; Garre-Olmo, Josep

2012-01-01

To describe central nervous system (CNS) drug consumption patterns depending on the time to diagnosis of Alzheimer's disease (AD), and to check whether the cases diagnosed later are associated with greater severity and consuming more CNS drugs. Cross-sectional study using 952 cases of the Registry of Dementias of Girona. A binary logistic regression was used to detect variables associated with the use of CNS drugs depending on the time to diagnosis. CNS drugs were consumed by 95.8% of the AD patients. Only antipsychotics presented a statistically significant increase in the frequency of prescription to patients with longer time elapsed from symptom onset to AD diagnosis. Longer time elapsed from the onset of symptoms to the diagnosis resulted in increased probability of antipsychotic consumption. Copyright © 2012 S. Karger AG, Basel.

Family violence exposure and associated risk factors for child PTSD in a Mexican sample.

PubMed

Erolin, Kara S; Wieling, Elizabeth; Parra, R Elizabeth Aguilar

2014-06-01

This study was undertaken in an effort to help illuminate the deleterious effects of traumatic stress on children and families in Mexico. Rates of exposure to traumatic events, family and community violence, and posttraumatic stress disorder (PTSD) were investigated in 87 school-age children and their mothers. Binary logistic regression analysis was performed to examine potential family and ecological risk factors for the presence of child PTSD. A total of 51 children (58.6%) reported an event that met the DSM-IV A criteria, and 36 children (41.4%; 20 boys and 16 girls) met criteria for full PTSD. Traumatic exposure in this sample was considerable, particularly intense, and chronic as a result of interpersonal violence in the home and community. Results support the need for preventive systemic interventions targeting the individual level, parent-child dyadic level, and the larger cultural and community context. Published by Elsevier Ltd.

[Analysis of population survey for determining the factors associated with the control diabetes mellitus in Mexico].

PubMed

Hernández-Romieu, Alfonso Claudio; Elnecavé-Olaiz, Alejandro; Huerta-Uribe, Nidia; Reynoso-Noverón, Nancy

2011-01-01

Determine the influence of nutritional counseling, exercise, access to social healthcare and drugs, and the quality of medical care on the control of diabetics. The information and blood samples were obtained in 2005. Glycemic control was defined as good if HbA1c was ≤7.0%, poor from 7.01%-9.50% and very poor if HbA1c >9.5%. Binary logistic regression models were used to determine the association of these factors with HbA1c>9.5%. Thirty percent of the patients with a medical diagnosis of diabetes had adequate metabolic control. Nutritional guidance was associated with an increase in the degree of control. A majority of diabetics have poor or very poor glycemic control. Strengthening the quality of and access to medical care for these patients is urgently needed.

Catastrophic Health Expenditure Among Colorectal Cancer Patients and Families: A Case of Malaysia.

PubMed

Azzani, Meram; Yahya, Abqariyah; Roslani, April Camilla; Su, Tin Tin

2017-09-01

This study aimed to estimate the cost of colorectal cancer (CRC) management and to explore the prevalence and determinants of catastrophic health expenditure (CHE) among CRC patients and their families arising from the costs of CRC management. Data were collected prospectively from 138 CRC patients. Patients were interviewed by using a structured questionnaire at the time of the diagnosis, then at 6 months and 12 months following diagnosis. Simple descriptive methods and multivariate binary logistic regression were used in the analysis. The mean cost of managing CRC was RM8306.9 (US$2595.9), and 47.8% of patients' families experienced CHE. The main determinants of CHE were the economic status of the family and the likelihood of the patient undergoing surgery. The results of this study strongly suggest that stakeholders and policy makers should provide individuals with financial protection against the consequences of cancer, a costly illness that often requires prolonged treatment.

Global Patterns in the Implementation of Payments for Environmental Services

PubMed Central

Ezzine-de-Blas, Driss; Wunder, Sven; Ruiz-Pérez, Manuel; Moreno-Sanchez, Rocio del Pilar

2016-01-01

Assessing global tendencies and impacts of conditional payments for environmental services (PES) programs is challenging because of their heterogeneity, and scarcity of comparative studies. This meta-study systematizes 55 PES schemes worldwide in a quantitative database. Using categorical principal component analysis to highlight clustering patterns, we reconfirm frequently hypothesized differences between public and private PES schemes, but also identify diverging patterns between commercial and non-commercial private PES vis-à-vis their service focus, area size, and market orientation. When do these PES schemes likely achieve significant environmental additionality? Using binary logistical regression, we find additionality to be positively influenced by three theoretically recommended PES ‘best design’ features: spatial targeting, payment differentiation, and strong conditionality, alongside some contextual controls (activity paid for and implementation time elapsed). Our results thus stress the preeminence of customized design over operational characteristics when assessing what determines the outcomes of PES implementation. PMID:26938065

Correcting Velocity Dispersions of Dwarf Spheroidal Galaxies for Binary Orbital Motion

NASA Astrophysics Data System (ADS)

Minor, Quinn E.; Martinez, Greg; Bullock, James; Kaplinghat, Manoj; Trainor, Ryan

2010-10-01

We show that the measured velocity dispersions of dwarf spheroidal galaxies from about 4 to 10 km s-1 are unlikely to be inflated by more than 30% due to the orbital motion of binary stars and demonstrate that the intrinsic velocity dispersions can be determined to within a few percent accuracy using two-epoch observations with 1-2 yr as the optimal time interval. The crucial observable is the threshold fraction—the fraction of stars that show velocity changes larger than a given threshold between measurements. The threshold fraction is tightly correlated with the dispersion introduced by binaries, independent of the underlying binary fraction and distribution of orbital parameters. We outline a simple procedure to correct the velocity dispersion to within a few percent accuracy by using the threshold fraction and provide fitting functions for this method. We also develop a methodology for constraining properties of binary populations from both single- and two-epoch velocity measurements by including the binary velocity distribution in a Bayesian analysis.

Influence of protein size on surface-enhanced Raman scattering (SERS) spectra in binary protein mixtures.

PubMed

Avci, Ertug; Culha, Mustafa

2014-01-01

The size-dependent interactions of eight blood proteins with silver nanoparticles (AgNPs) in their binary mixtures were investigated using surface-enhanced Raman scattering (SERS). Principal component analysis (PCA) was performed on the SERS spectra of each binary mixture, and the differentiation ability of the mixtures was tested. It was found that the effect of relative concentration change on the SERS spectra of the binary mixtures of small proteins could be detected using PCA. However, this change was not observed with the binary mixtures of large proteins. This study demonstrated that the relative interactions of the smaller proteins with an average size of 50 nm AgNPs smaller than the large proteins could be monitored, and this information can be used for the detection of proteins in protein mixtures.

Evidence for a planetary mass third body orbiting the binary star KIC 5095269

NASA Astrophysics Data System (ADS)

Getley, A. K.; Carter, B.; King, R.; O'Toole, S.

2017-07-01

In this paper, we report the evidence for a planetary mass body orbiting the close binary star KIC 5095269. This detection arose from a search for eclipse timing variations amongst the more than 2000 eclipsing binaries observed by Kepler. Light curve and periodic eclipse time variations have been analysed using systemic and a custom Binary Eclipse Timings code based on the Transit Analysis Package which indicates a 7.70 ± 0.08MJup object orbiting every 237.7 ± 0.1 d around a 1.2 M⊙ primary and a 0.51 M⊙ secondary in an 18.6 d orbit. A dynamical integration over 107 yr suggests a stable orbital configuration. Radial velocity observations are recommended to confirm the properties of the binary star components and the planetary mass of the companion.

Choosing appropriate analysis methods for cluster randomised cross-over trials with a binary outcome.

PubMed

Morgan, Katy E; Forbes, Andrew B; Keogh, Ruth H; Jairath, Vipul; Kahan, Brennan C

2017-01-30

In cluster randomised cross-over (CRXO) trials, clusters receive multiple treatments in a randomised sequence over time. In such trials, there is usual correlation between patients in the same cluster. In addition, within a cluster, patients in the same period may be more similar to each other than to patients in other periods. We demonstrate that it is necessary to account for these correlations in the analysis to obtain correct Type I error rates. We then use simulation to compare different methods of analysing a binary outcome from a two-period CRXO design. Our simulations demonstrated that hierarchical models without random effects for period-within-cluster, which do not account for any extra within-period correlation, performed poorly with greatly inflated Type I errors in many scenarios. In scenarios where extra within-period correlation was present, a hierarchical model with random effects for cluster and period-within-cluster only had correct Type I errors when there were large numbers of clusters; with small numbers of clusters, the error rate was inflated. We also found that generalised estimating equations did not give correct error rates in any scenarios considered. An unweighted cluster-level summary regression performed best overall, maintaining an error rate close to 5% for all scenarios, although it lost power when extra within-period correlation was present, especially for small numbers of clusters. Results from our simulation study show that it is important to model both levels of clustering in CRXO trials, and that any extra within-period correlation should be accounted for. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Near-Earth asteroid satellite spins under spin-orbit coupling

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

Naidu, Shantanu P.; Margot, Jean-Luc

We develop a fourth-order numerical integrator to simulate the coupled spin and orbital motions of two rigid bodies having arbitrary mass distributions under the influence of their mutual gravitational potential. We simulate the dynamics of components in well-characterized binary and triple near-Earth asteroid systems and use surface of section plots to map the possible spin configurations of the satellites. For asynchronous satellites, the analysis reveals large regions of phase space where the spin state of the satellite is chaotic. For synchronous satellites, we show that libration amplitudes can reach detectable values even for moderately elongated shapes. The presence of chaoticmore » regions in the phase space has important consequences for the evolution of binary asteroids. It may substantially increase spin synchronization timescales, explain the observed fraction of asychronous binaries, delay BYORP-type evolution, and extend the lifetime of binaries. The variations in spin rate due to large librations also affect the analysis and interpretation of light curve and radar observations.« less

Which sociodemographic factors are important on smoking behaviour of high school students? The contribution of classification and regression tree methodology in a broad epidemiological survey

PubMed Central

Özge, C; Toros, F; Bayramkaya, E; Çamdeviren, H; Şaşmaz, T

2006-01-01

Background The purpose of this study is to evaluate the most important sociodemographic factors on smoking status of high school students using a broad randomised epidemiological survey. Methods Using in‐class, self administered questionnaire about their sociodemographic variables and smoking behaviour, a representative sample of total 3304 students of preparatory, 9th, 10th, and 11th grades, from 22 randomly selected schools of Mersin, were evaluated and discriminative factors have been determined using appropriate statistics. In addition to binary logistic regression analysis, the study evaluated combined effects of these factors using classification and regression tree methodology, as a new statistical method. Results The data showed that 38% of the students reported lifetime smoking and 16.9% of them reported current smoking with a male predominancy and increasing prevalence by age. Second hand smoking was reported at a 74.3% frequency with father predominance (56.6%). The significantly important factors that affect current smoking in these age groups were increased by household size, late birth rank, certain school types, low academic performance, increased second hand smoking, and stress (especially reported as separation from a close friend or because of violence at home). Classification and regression tree methodology showed the importance of some neglected sociodemographic factors with a good classification capacity. Conclusions It was concluded that, as closely related with sociocultural factors, smoking was a common problem in this young population, generating important academic and social burden in youth life and with increasing data about this behaviour and using new statistical methods, effective coping strategies could be composed. PMID:16891446

Binary partition tree analysis based on region evolution and its application to tree simplification.

PubMed

Lu, Huihai; Woods, John C; Ghanbari, Mohammed

2007-04-01

Pyramid image representations via tree structures are recognized methods for region-based image analysis. Binary partition trees can be applied which document the merging process with small details found at the bottom levels and larger ones close to the root. Hindsight of the merging process is stored within the tree structure and provides the change histories of an image property from the leaf to the root node. In this work, the change histories are modelled by evolvement functions and their second order statistics are analyzed by using a knee function. Knee values show the reluctancy of each merge. We have systematically formulated these findings to provide a novel framework for binary partition tree analysis, where tree simplification is demonstrated. Based on an evolvement function, for each upward path in a tree, the tree node associated with the first reluctant merge is considered as a pruning candidate. The result is a simplified version providing a reduced solution space and still complying with the definition of a binary tree. The experiments show that image details are preserved whilst the number of nodes is dramatically reduced. An image filtering tool also results which preserves object boundaries and has applications for segmentation.

Binary Population and Spectral Synthesis Version 2.1: Construction, Observational Verification, and New Results

NASA Astrophysics Data System (ADS)

Eldridge, J. J.; Stanway, E. R.; Xiao, L.; McClelland, L. A. S.; Taylor, G.; Ng, M.; Greis, S. M. L.; Bray, J. C.

2017-11-01

The Binary Population and Spectral Synthesis suite of binary stellar evolution models and synthetic stellar populations provides a framework for the physically motivated analysis of both the integrated light from distant stellar populations and the detailed properties of those nearby. We present a new version 2.1 data release of these models, detailing the methodology by which Binary Population and Spectral Synthesis incorporates binary mass transfer and its effect on stellar evolution pathways, as well as the construction of simple stellar populations. We demonstrate key tests of the latest Binary Population and Spectral Synthesis model suite demonstrating its ability to reproduce the colours and derived properties of resolved stellar populations, including well-constrained eclipsing binaries. We consider observational constraints on the ratio of massive star types and the distribution of stellar remnant masses. We describe the identification of supernova progenitors in our models, and demonstrate a good agreement to the properties of observed progenitors. We also test our models against photometric and spectroscopic observations of unresolved stellar populations, both in the local and distant Universe, finding that binary models provide a self-consistent explanation for observed galaxy properties across a broad redshift range. Finally, we carefully describe the limitations of our models, and areas where we expect to see significant improvement in future versions.

OGLE-2016-BLG-1469L: Microlensing Binary Composed of Brown Dwarfs

NASA Astrophysics Data System (ADS)

Han, C.; Udalski, A.; Sumi, T.; Gould, A.; Albrow, M. D.; Chung, S.-J.; Jung, Y. K.; Ryu, Y.-H.; Shin, I.-G.; Yee, J. C.; Zhu, W.; Cha, S.-M.; Kim, S.-L.; Kim, D.-J.; Lee, C.-U.; Lee, Y.; Park, B.-G.; KMTNet Collaboration; Soszyński, I.; Mróz, P.; Pietrukowicz, P.; Szymański, M. K.; Skowron, J.; Poleski, R.; Kozłowski, S.; Ulaczyk, K.; Pawlak, M.; OGLE Collaboration; Abe, F.; Asakura, Y.; Bennett, D. P.; Bond, I. A.; Bhattacharya, A.; Donachie, M.; Freeman, M.; Fukui, A.; Hirao, Y.; Itow, Y.; Koshimoto, N.; Li, M. C. A.; Ling, C. H.; Masuda, K.; Matsubara, Y.; Muraki, Y.; Nagakane, M.; Ohnishi, K.; Oyokawa, H.; Rattenbury, N. J.; Saito, To.; Sharan, A.; Sullivan, D. J.; Suzuki, D.; Tristram, P. J.; Yamada, T.; Yamada, T.; Yonehara, A.; Barry, R.; MOA Collaboration

2017-07-01

We report the discovery of a binary composed of two brown dwarfs, based on the analysis of the microlensing event OGLE-2016-BLG-1469. Thanks to the detection of both finite-source and microlens-parallax effects, we are able to measure both the masses {M}1˜ 0.05 {M}⊙ and {M}2˜ 0.01 {M}⊙ , and the distance {D}{{L}}˜ 4.5 {kpc}, as well as the projected separation {a}\\perp ˜ 0.33 au. This is the third brown-dwarf binary detected using the microlensing method, demonstrating the usefulness of microlensing in detecting field brown-dwarf binaries with separations of less than 1 au.

Collisional redistribution of radiation. II - The effects of degeneracy on the equations of motion for the density matrix. III - The equation of motion for the correlation function and the scattered spectrum

NASA Technical Reports Server (NTRS)

Burnett, K.; Cooper, J.

1980-01-01

The effect of correlations between an absorber atom and perturbers in the binary-collision approximation are applied to degenerate atomic systems. A generalized absorption profile which specifies the final state of the atom after an absorption event is related to the total intensities of Rayleigh scattering and fluorescence from the atom. It is suggested that additional dynamical information to that obtainable from ordinary absorption experiments is required in order to describe redistributed atomic radiation. The scattering of monochromatic radiation by a degenerate atom is computed in a binary-collision approximation; an equation of motion is derived for the correlation function which is valid outside the quantum-regression regime. Solutions are given for the weak-field conditions in terms of generalized absorption and emission profiles that depend on the indices of the atomic multipoles.

The As-Cu-Ni System: A Chemical Thermodynamic Model for Ancient Recycling

NASA Astrophysics Data System (ADS)

Sabatini, Benjamin J.

2015-12-01

This article is the first thermodynamically reasoned ancient metal system assessment intended for use by archaeologists and archaeometallurgists to aid in the interpretation of remelted/recycled copper alloys composed of arsenic and copper, and arsenic, copper, and nickel. These models are meant to fulfill two main purposes: first, to be applied toward the identification of progressive and regressive temporal changes in artifact chemistry that would have occurred due to recycling, and second, to provide thermodynamic insight into why such metal combinations existed in antiquity. Built on well-established thermodynamics, these models were created using a combination of custom-written software and published binary thermodynamic systems data adjusted to within the boundary conditions of 1200°C and 1 atm. Using these parameters, the behavior of each element and their likelihood of loss in the binaries As-Cu, As-Ni, Cu-Ni, and ternary As-Cu-Ni, systems, under assumed ancient furnace conditions, was determined.

Global optimization of small bimetallic Pd-Co binary nanoalloy clusters: a genetic algorithm approach at the DFT level.

PubMed

Aslan, Mikail; Davis, Jack B A; Johnston, Roy L

2016-03-07

The global optimisation of small bimetallic PdCo binary nanoalloys are systematically investigated using the Birmingham Cluster Genetic Algorithm (BCGA). The effect of size and composition on the structures, stability, magnetic and electronic properties including the binding energies, second finite difference energies and mixing energies of Pd-Co binary nanoalloys are discussed. A detailed analysis of Pd-Co structural motifs and segregation effects is also presented. The maximal mixing energy corresponds to Pd atom compositions for which the number of mixed Pd-Co bonds is maximised. Global minimum clusters are distinguished from transition states by vibrational frequency analysis. HOMO-LUMO gap, electric dipole moment and vibrational frequency analyses are made to enable correlation with future experiments.

Childhood growth and development associated with need for full-time special education at school age.

PubMed

Mannerkoski, Minna; Aberg, Laura; Hoikkala, Marianne; Sarna, Seppo; Kaski, Markus; Autti, Taina; Heiskala, Hannu

2009-01-01

To explore how growth measurements and attainment of developmental milestones in early childhood reflect the need for full-time special education (SE). After stratification in this population-based study, 900 pupils in full-time SE groups (age-range 7-16 years, mean 12 years 8 months) at three levels and 301 pupils in mainstream education (age-range 7-16, mean 12 years 9 months) provided data on height and weight from birth to age 7 years and head circumference to age 1 year. Developmental screening was evaluated from age 1 month to 48 months. Statistical methods included a general linear model (growth measurements), binary logistic regression analysis (odds ratios for growth), and multinomial logistic regression analysis (odds ratios for developmental milestones). At 1 year, a 1 standard deviation score (SDS) decrease in height raised the probability of SE placement by 40%, and a 1 SDS decrease in head size by 28%. In developmental screening, during the first months of life the gross motor milestones, especially head support, differentiated the children at levels 0-3. Thereafter, the fine motor milestones and those related to speech and social skills became more important. Children whose growth is mildly impaired, though in the normal range, and who fail to attain certain developmental milestones have an increased probability for SE and thus a need for special attention when toddlers age. Similar to the growth curves, these children seem to have consistent developmental curves (patterns).

RISK FACTORS FOR CONTACT LENS INDUCED PAPILLARY CONJUNCTIVITIS ASSOCIATED WITH SILICONE HYDROGEL CONTACT LENS WEAR

PubMed Central

Tagliaferri, Angela; Love, Thomas E.; Szczotka-Flynn, Loretta

2014-01-01

BACKGROUND Contact lens induced papillary conjunctivitis (CLPC) continues to be a major cause of dropout during contact lens extended wear. This retrospective study explores risk factors for the development of CLPC during silicone hydrogel lens extended wear. METHODS Data from 205 subjects enrolled in the Longitudinal Analysis of Silicone Hydrogel Contact Lens (LASH) study wearing lotrafilcon A silicone hydrogel lenses for up to 30 days of continuous wear were used to determine risk factors for CLPC in this secondary analysis of the main cohort. The main covariates of interest included substantial lens-associated bacterial bioburden, and topographically determined lens base curve-to-cornea fitting relationships. Additional covariates of interest included history of prior adverse events, time of year, race, education level, gender and other subject demographics. Statistical analyses included univariate logistic regression to assess the impact of potential risk factors on the binary CLPC outcome, and Cox proportional hazards regression to describe the impact of those factors on time-to-CLPC diagnosis. RESULTS Across 12 months of follow-up, 52 subjects (25%) experienced CLPC. No associations were found between CLPC development and the presence of bacterial bioburden, lens-to-cornea fitting relationships, history of prior adverse events, gender or race. CLPC development followed the same seasonal trends as the local peaks in environmental allergans. CONCLUSIONS Lens fit and biodeposits, in the form of lens associated bacterial bioburden, were not associated with the development of CLPC during extended wear with lotrafilcon A silicone hydrogel lenses. PMID:24681609

Child sex tourism - prevalence of and risk factors for its use in a German community sample.

PubMed

Koops, Thula; Turner, Daniel; Neutze, Janina; Briken, Peer

2017-04-20

To investigate the prevalence of child sex tourism (CST) in a large German community sample, and to compare those who made use of CST with other child sexual abusers regarding established characteristics and risk factors for child sexual abuse. Adult German men were recruited through a German market research panel and questioned by means of an anonymous online survey. Group assignment was accomplished based on information on previous sexual contacts with children and previous use of CST. Characteristics and risk factors were compared between the groups using t- and Chi-square tests. Binary logistic regression analysis was performed to predict CST. Data collection was conducted in 2013, data analysis in January 2015. Out of 8718 men, 36 (0.4%) reported CST use. The CST group differed from the nonCST group (n = 96; 1.1%) with regard to pedophilic sexual and antisocial behaviors as well as own experiences of sexual abuse. Social difficulties, pedophilic sexual interests, and hypersexuality were not distinct features in the CST group. Own experiences of sexual abuse, child prostitution use, and previous conviction for a violent offense predicted CST in a logistic regression model. This study is a first step to gain insight into the prevalence and characteristics of men using CST. Findings could help to augment prevention strategies against commercial forms of sexual abuse in developed as well as in developing countries by fostering the knowledge about the characteristics of perpetrators.

Factors associated with multidimensional aspect of post-stroke fatigue in acute stroke period.

PubMed

Mutai, Hitoshi; Furukawa, Tomomi; Houri, Ayumi; Suzuki, Akihito; Hanihara, Tokiji

2017-04-01

Post-stroke fatigue (PSF) is a frequent and distressing consequence of stroke, and can be both acute and long lasting. We aimed to investigate multidimensional aspects of acute PSF and to determine the clinical factors relevant to acute PSF. We collected data of 101 patients admitted to the hospital for acute stroke. PSF was assessed using the Multidimensional Fatigue Inventory within 2 weeks of stroke. Measures included Mini-Mental State Examination, Hospital Anxiety and Depression Scale, and Functional Independence Measure. Stroke character, lesion location, and clinical variables that potentially influence PSF were also collected. The prevalence of pathological fatigue is 56.4% within 2 weeks of stroke. Binary logistic regression analysis revealed that anxiety was the only predictor for presence of PSF (OR=1.32, 95% CI: 1.13-1.53, P<0.001). Multivariate stepwise regression analysis showed anxiety, right lesion side, thalamus, and/or brainstem were independently associated with general fatigue, right lesion side, depression, diabetes mellitus, and anxiety with physical fatigue, depression with reduced activity, depression, and BMI with reduced motivation, depression, and diabetes mellitus with mental fatigue. PSF was highly prevalent in the acute phase, and specific factors including lesion location (right side lesion, thalamic and brainstem lesion), anxiety, and depression were independently associated with multidimensional aspects of PSF. Further study is needed to elucidate how specific structural lesions and anxiety symptoms relate to the development of early fatigue following stroke. Copyright © 2016 Elsevier B.V. All rights reserved.

Analysis of Risk Factor for the Development of Chronic Subdural Hematoma in Patients with Traumatic Subdural Hygroma.

PubMed

Ahn, Jun Hyong; Jun, Hyo Sub; Kim, Ji Hee; Oh, Jae Keun; Song, Joon Ho; Chang, In Bok

2016-11-01

Although a high incidence of chronic subdural hematoma (CSDH) following traumatic subdural hygroma (SDG) has been reported, no study has evaluated risk factors for the development of CSDH. Therefore, we analyzed the risk factors contributing to formation of CSDH in patients with traumatic SDG. We retrospectively reviewed patients admitted to Hallym University Hospital with traumatic head injury from January 2004 through December 2013. A total of 45 patients with these injuries in which traumatic SDG developed during the follow-up period were analyzed. All patients were divided into two groups based on the development of CSDH, and the associations between the development of CSDH and independent variables were investigated. Thirty-one patients suffered from bilateral SDG, whereas 14 had unilateral SDG. Follow-up computed tomography scans revealed regression of SDG in 25 of 45 patients (55.6%), but the remaining 20 patients (44.4%) suffered from transition to CSDH. Eight patients developed bilateral CSDH, and 12 patients developed unilateral CSDH. Hemorrhage-free survival rates were significantly lower in the male and bilateral SDG group (log-rank test; p =0.043 and p =0.013, respectively). Binary logistic regression analysis revealed male (OR, 7.68; 95% CI 1.18-49.78; p =0.033) and bilateral SDG (OR, 8.04; 95% CI 1.41-45.7; p =0.019) were significant risk factors for development of CSDH. The potential to evolve into CSDH should be considered in patients with traumatic SDG, particularly male patients with bilateral SDG.

Factors affecting medication adherence in community-managed patients with hypertension based on the principal component analysis: evidence from Xinjiang, China.

PubMed

Zhang, Yuji; Li, Xiaoju; Mao, Lu; Zhang, Mei; Li, Ke; Zheng, Yinxia; Cui, Wangfei; Yin, Hongpo; He, Yanli; Jing, Mingxia

2018-01-01

The analysis of factors affecting the nonadherence to antihypertensive medications is important in the control of blood pressure among patients with hypertension. The purpose of this study was to assess the relationship between factors and medication adherence in Xinjiang community-managed patients with hypertension based on the principal component analysis. A total of 1,916 community-managed patients with hypertension, selected randomly through a multi-stage sampling, participated in the survey. Self-designed questionnaires were used to classify the participants as either adherent or nonadherent to their medication regimen. A principal component analysis was used in order to eliminate the correlation between factors. Factors related to nonadherence were analyzed by using a χ 2 -test and a binary logistic regression model. This study extracted nine common factors, with a cumulative variance contribution rate of 63.6%. Further analysis revealed that the following variables were significantly related to nonadherence: severity of disease, community management, diabetes, and taking traditional medications. Community management plays an important role in improving the patients' medication-taking behavior. Regular medication regimen instruction and better community management services through community-level have the potential to reduce nonadherence. Mild hypertensive patients should be monitored by community health care providers.

Gravitational waves: search results, data analysis and parameter estimation: Amaldi 10 Parallel session C2.

PubMed

Astone, Pia; Weinstein, Alan; Agathos, Michalis; Bejger, Michał; Christensen, Nelson; Dent, Thomas; Graff, Philip; Klimenko, Sergey; Mazzolo, Giulio; Nishizawa, Atsushi; Robinet, Florent; Schmidt, Patricia; Smith, Rory; Veitch, John; Wade, Madeline; Aoudia, Sofiane; Bose, Sukanta; Calderon Bustillo, Juan; Canizares, Priscilla; Capano, Colin; Clark, James; Colla, Alberto; Cuoco, Elena; Da Silva Costa, Carlos; Dal Canton, Tito; Evangelista, Edgar; Goetz, Evan; Gupta, Anuradha; Hannam, Mark; Keitel, David; Lackey, Benjamin; Logue, Joshua; Mohapatra, Satyanarayan; Piergiovanni, Francesco; Privitera, Stephen; Prix, Reinhard; Pürrer, Michael; Re, Virginia; Serafinelli, Roberto; Wade, Leslie; Wen, Linqing; Wette, Karl; Whelan, John; Palomba, C; Prodi, G

The Amaldi 10 Parallel Session C2 on gravitational wave (GW) search results, data analysis and parameter estimation included three lively sessions of lectures by 13 presenters, and 34 posters. The talks and posters covered a huge range of material, including results and analysis techniques for ground-based GW detectors, targeting anticipated signals from different astrophysical sources: compact binary inspiral, merger and ringdown; GW bursts from intermediate mass binary black hole mergers, cosmic string cusps, core-collapse supernovae, and other unmodeled sources; continuous waves from spinning neutron stars; and a stochastic GW background. There was considerable emphasis on Bayesian techniques for estimating the parameters of coalescing compact binary systems from the gravitational waveforms extracted from the data from the advanced detector network. This included methods to distinguish deviations of the signals from what is expected in the context of General Relativity.

Likelihood-Based Random-Effect Meta-Analysis of Binary Events.

PubMed

Amatya, Anup; Bhaumik, Dulal K; Normand, Sharon-Lise; Greenhouse, Joel; Kaizar, Eloise; Neelon, Brian; Gibbons, Robert D

2015-01-01

Meta-analysis has been used extensively for evaluation of efficacy and safety of medical interventions. Its advantages and utilities are well known. However, recent studies have raised questions about the accuracy of the commonly used moment-based meta-analytic methods in general and for rare binary outcomes in particular. The issue is further complicated for studies with heterogeneous effect sizes. Likelihood-based mixed-effects modeling provides an alternative to moment-based methods such as inverse-variance weighted fixed- and random-effects estimators. In this article, we compare and contrast different mixed-effect modeling strategies in the context of meta-analysis. Their performance in estimation and testing of overall effect and heterogeneity are evaluated when combining results from studies with a binary outcome. Models that allow heterogeneity in both baseline rate and treatment effect across studies have low type I and type II error rates, and their estimates are the least biased among the models considered.

Period Variations of the Eclipsing Binary Systems T LMi and VX Lac

NASA Astrophysics Data System (ADS)

Yılmaz, M.; İzci, D. D.; Gümüş, D.; Özavci, İ.; Selam, S. O.

2015-07-01

We present a period analysis of the two Algol-type eclipsing binary systems T LMi and VX Lac using all available times of minimum in the literature, as well as new minima obtained at the Ankara University Kreiken Observatory. The period analysis of T LMi suggests mass transfer between the components and also a third body that is dynamically bound to the binary system. The analysis of VX Lac also suggests mass transfer between the components, and the presence of a third and a fourth body under the assumption of a Light-Time Effect. In addition, the periodic variation of VX Lac was examined under the hypothesis of magnetic activity, and the corresponding parameters were derived. We report here the orbital parameters for both systems, along with the ones related to mass transfer, and those for the third and fourth bodies.

Gravitational Waves: Search Results, Data Analysis and Parameter Estimation. Amaldi 10 Parallel Session C2

NASA Technical Reports Server (NTRS)

Astone, Pia; Weinstein, Alan; Agathos, Michalis; Bejger, Michal; Christensen, Nelson; Dent, Thomas; Graff, Philip; Klimenko, Sergey; Mazzolo, Giulio; Nishizawa, Atsushi

2015-01-01

The Amaldi 10 Parallel Session C2 on gravitational wave(GW) search results, data analysis and parameter estimation included three lively sessions of lectures by 13 presenters, and 34 posters. The talks and posters covered a huge range of material, including results and analysis techniques for ground-based GW detectors, targeting anticipated signals from different astrophysical sources: compact binary inspiral, merger and ringdown; GW bursts from intermediate mass binary black hole mergers, cosmic string cusps, core-collapse supernovae, and other unmodeled sources; continuous waves from spinning neutron stars; and a stochastic GW background. There was considerable emphasis on Bayesian techniques for estimating the parameters of coalescing compact binary systems from the gravitational waveforms extracted from the data from the advanced detector network. This included methods to distinguish deviations of the signals from what is expected in the context of General Relativity.

QTest: Quantitative Testing of Theories of Binary Choice.

PubMed

Regenwetter, Michel; Davis-Stober, Clintin P; Lim, Shiau Hong; Guo, Ying; Popova, Anna; Zwilling, Chris; Cha, Yun-Shil; Messner, William

2014-01-01

The goal of this paper is to make modeling and quantitative testing accessible to behavioral decision researchers interested in substantive questions. We provide a novel, rigorous, yet very general, quantitative diagnostic framework for testing theories of binary choice. This permits the nontechnical scholar to proceed far beyond traditionally rather superficial methods of analysis, and it permits the quantitatively savvy scholar to triage theoretical proposals before investing effort into complex and specialized quantitative analyses. Our theoretical framework links static algebraic decision theory with observed variability in behavioral binary choice data. The paper is supplemented with a custom-designed public-domain statistical analysis package, the QTest software. We illustrate our approach with a quantitative analysis using published laboratory data, including tests of novel versions of "Random Cumulative Prospect Theory." A major asset of the approach is the potential to distinguish decision makers who have a fixed preference and commit errors in observed choices from decision makers who waver in their preferences.

Application of wavelet and Fuorier transforms as powerful alternatives for derivative spectrophotometry in analysis of binary mixtures: A comparative study

NASA Astrophysics Data System (ADS)

Hassan, Said A.; Abdel-Gawad, Sherif A.

2018-02-01

Two signal processing methods, namely, Continuous Wavelet Transform (CWT) and the second was Discrete Fourier Transform (DFT) were introduced as alternatives to the classical Derivative Spectrophotometry (DS) in analysis of binary mixtures. To show the advantages of these methods, a comparative study was performed on a binary mixture of Naltrexone (NTX) and Bupropion (BUP). The methods were compared by analyzing laboratory prepared mixtures of the two drugs. By comparing performance of the three methods, it was proved that CWT and DFT methods are more efficient and advantageous in analysis of mixtures with overlapped spectra than DS. The three signal processing methods were adopted for the quantification of NTX and BUP in pure and tablet forms. The adopted methods were validated according to the ICH guideline where accuracy, precision and specificity were found to be within appropriate limits.

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

Blaut, Arkadiusz; Babak, Stanislav; Krolak, Andrzej

We present data analysis methods used in the detection and estimation of parameters of gravitational-wave signals from the white dwarf binaries in the mock LISA data challenge. Our main focus is on the analysis of challenge 3.1, where the gravitational-wave signals from more than 6x10{sup 7} Galactic binaries were added to the simulated Gaussian instrumental noise. The majority of the signals at low frequencies are not resolved individually. The confusion between the signals is strongly reduced at frequencies above 5 mHz. Our basic data analysis procedure is the maximum likelihood detection method. We filter the data through the template bankmore » at the first step of the search, then we refine parameters using the Nelder-Mead algorithm, we remove the strongest signal found and we repeat the procedure. We detect reliably and estimate parameters accurately of more than ten thousand signals from white dwarf binaries.« less

NEUROBEHAVIORAL EVALUATIONS OF BINARY AND TERTIARY MIXTURES OF CHEMICALS: LESSIONS LEARNING.

EPA Science Inventory

The classical approach to the statistical analysis of binary chemical mixtures is to construct full dose-response curves for one compound in the presence of a range of doses of the second compound (isobolographic analyses). For interaction studies using more than two chemicals, ...

A possible additional body in eclipsing binary system HS 2231+2441

NASA Astrophysics Data System (ADS)

Vidmachenko, A. P.; Shliakhetska, Ya. O.; Romanyuk, Ya. O.

2016-12-01

Analysis of the light curves of eclipsing binary systems HS 2231+2441, obtained with the 36-cm telescope, is made. In processing the photometric data on eclipses by method of timing, obtained evidence for the existence of a third body in the system.

Applicability of refractometry for fast routine checking of hospital preparations.

PubMed

Hendrickx, Stijn; Verón, Aurora Monteagudo; Van Schepdael, Ann; Adams, Erwin

2016-04-30

Quality control of hospital pharmacy formulations is of the utmost importance to ensure constant quality and to avoid potential mistakes before administration to the patient. In this study we investigated the applicability of refractometry as a fast, inexpensive and easy-to-use quality control measurement. Refractive indices (RI) of a multitude of different hospital formulations with varying concentrations of active compound were measured. The samples consisted of a number of binary aqueous solutions (one compound in water), complex aqueous solutions (multiple compounds in water or in a constant matrix), two suspensions and one emulsion. For all these formulations, linear regression analysis was performed, quality control limits determined and accuracy and repeatability were checked. Subsequently, actual hospital pharmacy samples were analyzed to check whether they were within the specified limits. For both binary and complex aqueous formulations, repeatability was good and a linear correlation for all samples could be observed on condition that the concentration of the active compound was sufficiently high. The refractometer was not sensitive enough for solutions of folic acid and levothyroxine, which had too low a concentration of active compound. Due to lack of homogeneity and light scattering, emulsions and suspensions do not seem suitable for quality control by refractometry. A mathematical equation was generated to predict the refractive index of an aqueous solution containing clonidine HCl as active compound. Values calculated from the equation were compared with measured values and deviations of all samples were found to be lower than 1.3%. In order to use refractometry in a hospital pharmacy for quality control of multicomponent samples, additional intermediate measurements would be required, to overcome the fact that refractometry is not compound specific. In conclusion, we found that refractometry could potentially be useful for daily, fast quality measurements of relatively concentrated binary and more complex aqueous solutions in the hospital pharmacy. Copyright © 2016 Elsevier B.V. All rights reserved.

Frontal lobe function and behavioral changes in amyotrophic lateral sclerosis: a study from Southwest China.

PubMed

Wei, QianQian; Chen, XuePing; Zheng, ZhenZhen; Huang, Rui; Guo, XiaoYan; Cao, Bei; Zhao, Bi; Shang, Hui-Fang

2014-12-01

Despite growing interest, the frequency and characteristics of frontal lobe functional and behavioral deficits in Chinese people with amyotrophic lateral sclerosis (ALS), as well as their impact on the survival of ALS patients, remain unknown. The Chinese version of the frontal assessment battery (FAB) and frontal behavioral inventory (FBI) were used to evaluate 126 sporadic ALS patients and 50 healthy controls. The prevalence of frontal lobe dysfunction was 32.5%. The most notable impairment domain of the FAB was lexical fluency (30.7%). The binary logistic regression model revealed that an onset age older than 45 years (OR 5.976, P = 0.002) and a lower educational level (OR 0.858, P = 0.002) were potential determinants of an abnormal FAB. Based on the FBI score, 46.0% of patients showed varied degrees of frontal behavioral changes. The most common impaired neurobehavioral domains were irritability (25.4%), logopenia (20.6%) and apathy (19.0%). The binary logistic regression model revealed that the ALS Functional Rating Scale-Revised scale score (OR 0.127, P = 0.001) was a potential determinant of an abnormal FBI. Frontal functional impairment and the severity of frontal behavioral changes were not associated with the survival status or the progression of ALS by the cox proportional hazard model and multivariate regression analyses, respectively. Frontal lobe dysfunction and frontal behavioral changes are common in Chinese ALS patients. Frontal lobe dysfunction may be related to the onset age and educational level. The severity of frontal behavioral changes may be associated with the ALSFRS-R. However, the frontal functional impairment and the frontal behavioral changes do not worsen the progression or survival of ALS.

Pattern formation in binary colloidal assemblies: hidden symmetries in a kaleidoscope of structures.

PubMed

Lotito, Valeria; Zambelli, Tomaso

2018-06-10

In this study we present a detailed investigation of the morphology of binary colloidal structures formed by self-assembly at air/water interface of particles of two different sizes, with a size ratio such that the larger particles do not retain a hexagonal arrangement in the binary assembly. While the structure and symmetry of binary mixtures in which such hexagonal order is preserved has been thoroughly scrutinized, binary colloids in the regime of non-preservation of the hexagonal order have not been examined with the same level of detail due also to the difficulty in finding analysis tools suitable to recognize hidden symmetries in seemingly amorphous and disordered arrangements. For this purpose, we resorted to a combination of different analysis tools based on computational geometry and computational topology in order to get a comprehensive picture of the morphology of the assemblies. By carrying out an extensive investigation of binary assemblies in this regime with variable concentration of smaller particles with respect to larger particles, we identify the main patterns that coexist in the apparently disordered assemblies and detect transitions in the symmetries upon increase in the number of small particles. As the concentration of small particles increases, large particle arrangements become more dilute and a transition from hexagonal to rhombic and square symmetries occurs, accompanied also by an increase in clusters of small particles; the relative weight of each specific symmetry can be controlled by varying the composition of the assemblies. The demonstration of the possibility to control the morphology of apparently disordered binary colloidal assemblies by varying experimental conditions and the definition of a route for the investigation of disordered assemblies are precious for future studies of complex colloidal patterns to understand self-assembly mechanisms and to tailor physical properties of colloidal assemblies.

Photometric and Spectroscopic Analysis for the Determination of Physical Parameters of an Eclipsing Binary Star System

NASA Astrophysics Data System (ADS)

Reid, Piper

2013-01-01

A binary star system is a pair of stars that are bound together by gravity. Most of the stars that we see in the night sky are members of multiple star systems. A system of stars where one star passes in front of the other (as observed from Earth) on a periodic basis is called an eclipsing binary. Eclipsing binaries can have very short rotational periods and in all cases these pairs of stars are so far away that they can only be resolved from Earth as a single point of light. The interaction of the two stars serves to produce physical phenomena that can be observed and used to study stellar properties. By careful data collection and analysis is it possible for an amateur astronomer using commercial, low cost equipment (including a home built spectroscope) to gather photometric (brightness versus time) and spectroscopic (brightness versus wavelength) data, analyze the data, and calculate the physical properties of a binary star system? Using a CCD camera, tracking mount and telescope photometric data of BB Pegasi was collected and a light curve produced. 57 Cygni was also studied using a spectroscope, tracking mount and telescope to prove that Doppler shift of Hydrogen Balmer absorption lines can be used to determine radial velocity. The orbital period, orbital velocity, radius of each star, separation of the two stars and mass of each star was calculated for the eclipsing binary BB Pegasi using photometric and spectroscopic data and Kepler’s 3rd Law. These data were then compared to published data. By careful use of consumer grade astronomical equipment it is possible for an amateur astronomer to determine an array of physical parameters of a distant binary star system from a suburban setting.

Photometric Analysis and Modeling of Five Mass-Transferring Binary Systems

NASA Astrophysics Data System (ADS)

Geist, Emily; Beaky, Matthew; Jamison, Kate

2018-01-01

In overcontact eclipsing binary systems, both stellar components have overfilled their Roche lobes, resulting in a dumbbell-shaped shared envelope. Mass transfer is common in overcontact binaries, which can be observed as a slow change on the rotation period of the system.We studied five overcontact eclipsing binary systems with evidence of period change, and thus likely mass transfer between the components, identified by Nelson (2014): V0579 Lyr, KN Vul, V0406 Lyr, V2240 Cyg, and MS Her. We used the 31-inch NURO telescope at Lowell Observatory in Flagstaff, Arizona to obtain images in B,V,R, and I filters for V0579 Lyr, and the 16-inch Meade LX200GPS telescope with attached SBIG ST-8XME CCD camera at Juniata College in Huntingdon, Pennsylvania to image KN Vul, V0406 Lyr, V2240 Cyg, and MS Her, also in B,V,R, and I.After data reduction, we created light curves for each of the systems and modeled the eclipsing binaries using the BinaryMaker3 and PHOEBE programs to determine their fundamental physical parameters for the first time. Complete light curves and preliminary models for each of these neglected eclipsing binary systems will be presented.

Computer vision syndrome among computer office workers in a developing country: an evaluation of prevalence and risk factors.

PubMed

Ranasinghe, P; Wathurapatha, W S; Perera, Y S; Lamabadusuriya, D A; Kulatunga, S; Jayawardana, N; Katulanda, P

2016-03-09

Computer vision syndrome (CVS) is a group of visual symptoms experienced in relation to the use of computers. Nearly 60 million people suffer from CVS globally, resulting in reduced productivity at work and reduced quality of life of the computer worker. The present study aims to describe the prevalence of CVS and its associated factors among a nationally-representative sample of Sri Lankan computer workers. Two thousand five hundred computer office workers were invited for the study from all nine provinces of Sri Lanka between May and December 2009. A self-administered questionnaire was used to collect socio-demographic data, symptoms of CVS and its associated factors. A binary logistic regression analysis was performed in all patients with 'presence of CVS' as the dichotomous dependent variable and age, gender, duration of occupation, daily computer usage, pre-existing eye disease, not using a visual display terminal (VDT) filter, adjusting brightness of screen, use of contact lenses, angle of gaze and ergonomic practices knowledge as the continuous/dichotomous independent variables. A similar binary logistic regression analysis was performed in all patients with 'severity of CVS' as the dichotomous dependent variable and other continuous/dichotomous independent variables. Sample size was 2210 (response rate-88.4%). Mean age was 30.8 ± 8.1 years and 50.8% of the sample were males. The 1-year prevalence of CVS in the study population was 67.4%. Female gender (OR: 1.28), duration of occupation (OR: 1.07), daily computer usage (1.10), pre-existing eye disease (OR: 4.49), not using a VDT filter (OR: 1.02), use of contact lenses (OR: 3.21) and ergonomics practices knowledge (OR: 1.24) all were associated with significantly presence of CVS. The duration of occupation (OR: 1.04) and presence of pre-existing eye disease (OR: 1.54) were significantly associated with the presence of 'severe CVS'. Sri Lankan computer workers had a high prevalence of CVS. Female gender, longer duration of occupation, higher daily computer usage, pre-existing eye disease, not using a VDT filter, use of contact lenses and higher ergonomics practices knowledge all were associated with significantly with the presence of CVS. The factors associated with the severity of CVS were the duration of occupation and presence of pre-existing eye disease.

Predictive Factors for Differentiating Between Septic Arthritis and Lyme Disease of the Knee in Children.

PubMed

Baldwin, Keith D; Brusalis, Christopher M; Nduaguba, Afamefuna M; Sankar, Wudbhav N

2016-05-04

Differentiating between septic arthritis and Lyme disease of the knee in endemic areas can be challenging and has major implications for patient management. The purpose of this study was to identify a prediction rule to differentiate septic arthritis from Lyme disease in children presenting with knee pain and effusion. We retrospectively reviewed the records of patients younger than 18 years of age with knee effusions who underwent arthrocentesis at our institution from 2005 to 2013. Patients with either septic arthritis (positive joint fluid culture or synovial white blood-cell count of >60,000 white blood cells/mm(3) with negative Lyme titer) or Lyme disease (positive Lyme immunoglobulin G on Western blot analysis) were included. To avoid misclassification bias, undiagnosed knee effusions and joints with both a positive culture and positive Lyme titers were excluded. Historical, clinical, and laboratory data were compared between groups to identify variables for comparison. Binary logistic regression analysis was used to identify independent predictive variables. One hundred and eighty-nine patients were studied: 23 with culture-positive septic arthritis, 26 with culture-negative septic arthritis, and 140 with Lyme disease. Multivariate binary logistic regression identified pain with short arc motion, history of fever reported by the patient or a family member, C-reactive protein of >4 mg/L, and age younger than 2 years as independent predictive factors for septic arthritis. A simpler model was developed that showed that the risk of septic arthritis with none of these factors was 2%, with 1 of these factors was 18%, with 2 of these factors was 45%, with 3 of these factors was 84%, or with all 4 of these factors was 100%. Although septic arthritis of the knee and Lyme monoarthritis share common features that can make them difficult to distinguish clinically, the presence of pain with short arc motion, C-reactive protein of >4.0 mg/L, patient-reported history of fever, and age younger than 2 years were independent predictive factors of septic arthritis in pediatric patients. The more factors that are present, the higher the risk of having septic arthritis. Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence. Copyright © 2016 by The Journal of Bone and Joint Surgery, Incorporated.

Does migration 'pay off' for foreign-born migrant health workers? An exploratory analysis using the global WageIndicator dataset.

PubMed

de Vries, Daniel H; Steinmetz, Stephanie; Tijdens, Kea G

2016-06-24

This study used the global WageIndicator web survey to answer the following research questions: (RQ1) What are the migration patterns of health workers? (RQ2) What are the personal and occupational drivers of migration? (RQ3) Are foreign-born migrant health workers discriminated against in their destination countries? Of the unweighted data collected in 2006-2014 from health workers aged 15-64 in paid employment, 7.9 % were on migrants (N = 44,394; 36 countries). To answer RQ1, binary logistic regression models were applied to the full sample. To answer RQ2, binary logistic regression was used to compare data on migrants with that on native respondents from the same source countries, a condition met by only four African countries (N = 890) and five Latin American countries (N = 6356). To answer RQ3, a multilevel analysis was applied to the full sample to take into account the nested structure of the data (N = 33,765 individual observations nested within 31 countries). RQ1: 57 % migrated to a country where the same language is spoken, 33 % migrated to neighbouring countries and 21 % migrated to former colonizing countries. Women and nurses migrated to neighbouring countries, nurses and older and highly educated workers to former colonizing countries and highly educated health workers and medical doctors to countries that have a language match. RQ2: In the African countries, nurses more often out-migrated compared to other health workers; in the Latin American countries, this is the case for doctors. Out-migrated health workers earn more and work fewer hours than comparable workers in source countries, but only Latin American health workers reported a higher level of life satisfaction. RQ3: We did not detect discrimination against migrants with respect to wages and occupational status. However, there seems to be a small wage premium for the group of migrants in other healthcare occupations. Except doctors, migrant health workers reported a lower level of life satisfaction. Migration generally seems to 'pay off' in terms of work and labour conditions, although accrued benefits are not equal for all cadres, regions and routes. Because the WageIndicator survey is a voluntary survey, these findings are exploratory rather than representative.

The first orbital solution for the massive colliding-wind binary HD 93162 (≡WR 25)

NASA Astrophysics Data System (ADS)

Gamen, R.; Gosset, E.; Morrell, N.; Niemela, V.; Sana, H.; Nazé, Y.; Rauw, G.; Barbá, R.; Solivella, G.

2006-12-01

Context: Since the discovery, with the EINSTEIN satellite, of strong X-ray emission associated with HD 93162 (≡WR 25), this object has been predicted to be a colliding-wind binary system. However, radial-velocity variations that would prove the suspected binary nature have yet to be found. Aims: We spectroscopically monitored this object to investigate its possible variability to address this discordance. Methods: We compiled the largest available radial-velocity data set for this star to look for variations that might be due to binary motion. We derived radial velocities from spectroscopic data acquired mainly between 1994 and 2006, and searched these radial velocities for periodicities using different numerical methods. Results: For the first time, periodic radial-velocity variations are detected. Our analysis definitively shows that the Wolf-Rayet star WR 25 is an eccentric binary system with a probable period of about 208 days.

Accuracy of binary black hole waveform models for aligned-spin binaries

NASA Astrophysics Data System (ADS)

Kumar, Prayush; Chu, Tony; Fong, Heather; Pfeiffer, Harald P.; Boyle, Michael; Hemberger, Daniel A.; Kidder, Lawrence E.; Scheel, Mark A.; Szilagyi, Bela

2016-05-01

Coalescing binary black holes are among the primary science targets for second generation ground-based gravitational wave detectors. Reliable gravitational waveform models are central to detection of such systems and subsequent parameter estimation. This paper performs a comprehensive analysis of the accuracy of recent waveform models for binary black holes with aligned spins, utilizing a new set of 84 high-accuracy numerical relativity simulations. Our analysis covers comparable mass binaries (mass-ratio 1 ≤q ≤3 ), and samples independently both black hole spins up to a dimensionless spin magnitude of 0.9 for equal-mass binaries and 0.85 for unequal mass binaries. Furthermore, we focus on the high-mass regime (total mass ≳50 M⊙ ). The two most recent waveform models considered (PhenomD and SEOBNRv2) both perform very well for signal detection, losing less than 0.5% of the recoverable signal-to-noise ratio ρ , except that SEOBNRv2's efficiency drops slightly for both black hole spins aligned at large magnitude. For parameter estimation, modeling inaccuracies of the SEOBNRv2 model are found to be smaller than systematic uncertainties for moderately strong GW events up to roughly ρ ≲15 . PhenomD's modeling errors are found to be smaller than SEOBNRv2's, and are generally irrelevant for ρ ≲20 . Both models' accuracy deteriorates with increased mass ratio, and when at least one black hole spin is large and aligned. The SEOBNRv2 model shows a pronounced disagreement with the numerical relativity simulation in the merger phase, for unequal masses and simultaneously both black hole spins very large and aligned. Two older waveform models (PhenomC and SEOBNRv1) are found to be distinctly less accurate than the more recent PhenomD and SEOBNRv2 models. Finally, we quantify the bias expected from all four waveform models during parameter estimation for several recovered binary parameters: chirp mass, mass ratio, and effective spin.

Effects of spatial location and household wealth on health insurance subscription among women in Ghana.

PubMed

Kumi-Kyereme, Akwasi; Amo-Adjei, Joshua

2013-06-17

This study compares ownership of health insurance among Ghanaian women with respect to wealth status and spatial location. We explore the overarching research question by employing geographic and proxy means targeting through interactive analysis of wealth status and spatial issues. The paper draws on the 2008 Ghana Demographic and Health Survey. Bivariate descriptive analysis coupled with binary logistic regression estimation technique was used to analyse the data. By wealth status, the likelihood of purchasing insurance was significantly higher among respondents from the middle, richer and richest households compared to the poorest (reference category) and these differences widened more profoundly in the Northern areas after interacting wealth with zone of residence. Among women at the bottom of household wealth (poorest and poorer), there were no statistically significant differences in insurance subscription in all the areas. The results underscore the relevance of geographic and proxy means targeting in identifying populations who may be need of special interventions as part of the efforts to increase enrolment as well as means of social protection against the vulnerable.

Effects of spatial location and household wealth on health insurance subscription among women in Ghana

PubMed Central

2013-01-01

Background This study compares ownership of health insurance among Ghanaian women with respect to wealth status and spatial location. We explore the overarching research question by employing geographic and proxy means targeting through interactive analysis of wealth status and spatial issues. Methods The paper draws on the 2008 Ghana Demographic and Health Survey. Bivariate descriptive analysis coupled with binary logistic regression estimation technique was used to analyse the data. Results By wealth status, the likelihood of purchasing insurance was significantly higher among respondents from the middle, richer and richest households compared to the poorest (reference category) and these differences widened more profoundly in the Northern areas after interacting wealth with zone of residence. Among women at the bottom of household wealth (poorest and poorer), there were no statistically significant differences in insurance subscription in all the areas. Conclusions The results underscore the relevance of geographic and proxy means targeting in identifying populations who may be need of special interventions as part of the efforts to increase enrolment as well as means of social protection against the vulnerable. PMID:23768255

Factors influencing women's utilization of public health care services during childbirth in Malawi Public health facility utilization.

PubMed

Machira, Kennedy; Palamuleni, Martin

2017-06-01

Maternal mortality remains a public health challenge claiming many lives at the time of giving birth lives. However, there have been scanty studies investigating factors influencing women's use of public health facilities during childbirth. The aim of the study was to explore the factors associated with women choice of public health facility during childbirth. The study used 2010 Malawi Demographic Health Survey dataset and a binary logistics regression analysis to estimate the determinants influencing women's use of public health facilities at the time they give birth. Of 23020 women respondents, 8454(36.7%) chose to give birth in public health facilities. Multivariate analysis reported that frequency of antenatal care (ANC), birth order, women's education, wealth status and quality of care were the major predictors increasing women's choice to use public health facilities at childbirth. There is need to use multimedia approach to engage women on significance of utilizing public health facilities during childbirth and promote quality of care in facilities if their health outcome is to improve in Malawi.

Model parameter estimation approach based on incremental analysis for lithium-ion batteries without using open circuit voltage

NASA Astrophysics Data System (ADS)

Wu, Hongjie; Yuan, Shifei; Zhang, Xi; Yin, Chengliang; Ma, Xuerui

2015-08-01

To improve the suitability of lithium-ion battery model under varying scenarios, such as fluctuating temperature and SoC variation, dynamic model with parameters updated realtime should be developed. In this paper, an incremental analysis-based auto regressive exogenous (I-ARX) modeling method is proposed to eliminate the modeling error caused by the OCV effect and improve the accuracy of parameter estimation. Then, its numerical stability, modeling error, and parametric sensitivity are analyzed at different sampling rates (0.02, 0.1, 0.5 and 1 s). To identify the model parameters recursively, a bias-correction recursive least squares (CRLS) algorithm is applied. Finally, the pseudo random binary sequence (PRBS) and urban dynamic driving sequences (UDDSs) profiles are performed to verify the realtime performance and robustness of the newly proposed model and algorithm. Different sampling rates (1 Hz and 10 Hz) and multiple temperature points (5, 25, and 45 °C) are covered in our experiments. The experimental and simulation results indicate that the proposed I-ARX model can present high accuracy and suitability for parameter identification without using open circuit voltage.

Lymphatic vessel invasion and lymph node metastasis in patients with clinical stage I non-small cell lung cancer.

PubMed

Kang, Du-Young; Lee, Sungsoo

2014-09-01

The aim of this study was to investigate the association between the presence of lymphatic vessel invasion (LVI) in primary tumors and lymph node (LN) metastasis in clinical stage I non-small cell lung cancer (NSCLC) patients. A total of 76 patients who underwent complete resection for clinical stage I adenocarcinoma and squamous cell carcinoma were retrospectively examined. Tumors consisted of 51 cases of adenocarcinoma and 25 cases of squamous cell carcinoma as determined by histology. LN metastasis was detected in 24.4% (19/76) of patients. Factors associated with LN metastasis on univariate analysis included LVI (p < 0.001) and increased tumor dimensions (p < 0.05). Binary logistic regression analysis showed that the presence of LVI (p < 0.001) was the only predictor of LN metastasis. LVI is significantly associated with LN metastasis in patients with clinical stage I NSCLC. These findings may be helpful in determining the most appropriate operative strategy for patients if preoperative detection of LVI becomes feasible. Georg Thieme Verlag KG Stuttgart · New York.

Somatic symptoms and holistic thinking as major dimensions behind modern health worries.

PubMed

Köteles, Ferenc; Simor, Péter

2014-01-01

Modern health worries (MHWs) were related to somatic symptoms and to preference of holistic healing methods in previous studies. The study aimed to investigate the contribution of symptom-related and holism-related factors to MHWs. Participants (visitors of an Internet news portal; N = 16152; 64.1 % males) completed a questionnaire assessing MHWs, somatosensory amplification, somatic symptoms, positive and negative affect, spirituality, holistic health beliefs, and various aspects of health care utilization (both conventional and alternative). Exploratory factor analysis with oblique rotation revealed two independent dimensions ("Somatic symptom distress" and "Holism") MHWs were involved with factor loadings of 0.294 and 0.417, respectively. The existence of two factors was supported by the results of confirmatory factor analysis. No practically significant interaction between the two factors was found in binary logistic regression analysis. Positive and negative affect, somatosensory amplification, spirituality, and holistic health beliefs were positively connected, while self-rated health status was negatively connected to MHWs even after controlling for socio-demographic and treatment-related variables. Holistic thinking and symptom-related behavioral and psychological factors are independently associated with MHWs. Modern health worries can be conceptualized as symptom-related by-products of a holistic-spiritual worldview.