Survival analysis of cervical cancer using stratified Cox regression
NASA Astrophysics Data System (ADS)
Purnami, S. W.; Inayati, K. D.; Sari, N. W. Wulan; Chosuvivatwong, V.; Sriplung, H.
2016-04-01
Cervical cancer is one of the mostly widely cancer cause of the women death in the world including Indonesia. Most cervical cancer patients come to the hospital already in an advanced stadium. As a result, the treatment of cervical cancer becomes more difficult and even can increase the death's risk. One of parameter that can be used to assess successfully of treatment is the probability of survival. This study raises the issue of cervical cancer survival patients at Dr. Soetomo Hospital using stratified Cox regression based on six factors such as age, stadium, treatment initiation, companion disease, complication, and anemia. Stratified Cox model is used because there is one independent variable that does not satisfy the proportional hazards assumption that is stadium. The results of the stratified Cox model show that the complication variable is significant factor which influent survival probability of cervical cancer patient. The obtained hazard ratio is 7.35. It means that cervical cancer patient who has complication is at risk of dying 7.35 times greater than patient who did not has complication. While the adjusted survival curves showed that stadium IV had the lowest probability of survival.
Factors Associated with Methadone Treatment Duration: A Cox Regression Analysis
Peng, Ching-Yi; Chao, En; Lee, Tony Szu-Hsien
2015-01-01
This study examined retention rates and associated predictors of methadone maintenance treatment (MMT) duration among 128 newly admitted patients in Taiwan. A semi-structured questionnaire was used to obtain demographic and drug use history. Daily records of methadone taken and test results for HIV, HCV, and morphine toxicology were taken from a computerized medical registry. Cox regression analyses were performed to examine factors associated with MMT duration. MMT retention rates were 80.5%, 68.8%, 53.9%, and 41.4% for 3, 6, 12, and 18 months, respectively. Excluding 38 patients incarcerated during the study period, retention rates were 81.1%, 73.3%, 61.1%, and 48.9% for 3 months, 6 months, 12 months, and 18 months, respectively. No participant seroconverted to HIV and 1 died during the 18-months follow-up. Results showed that being female, imprisonment, a longer distance from house to clinic, having a lower methadone dose after 30 days, being HCV positive, and in the New Taipei city program predicted early patient dropout. The findings suggest favorable MMT outcomes of HIV seroincidence and mortality. Results indicate that the need to minimize travel distance and to provide programs that meet women’s requirements justify expansion of MMT clinics in Taiwan. PMID:25875531
Mizukami, Akira; Matsue, Yuya; Naruse, Yoshihisa; Kowase, Shinya; Kurosaki, Kenji; Suzuki, Makoto; Matsumura, Akihiko; Nogami, Akihiko; Aonuma, Kazutaka; Hashimoto, Yuji
2016-09-01
The presented data were obtained from 982 consecutive patients receiving their first pacemaker implantation with right ventricular (RV) lead placement between January 2008 and December 2013 at two centers in Japan. Patients were divided into RV apical and septal pacing groups. Data of Kaplan-Meier survival analysis and Cox regression analysis are presented. Refer to the research article "Implications of right ventricular septal pacing for medium-term prognosis: propensity-matched analysis" (Mizukami et al., in press) [1] for further interpretation and discussion. PMID:27570808
Simultaneous confidence bands for Cox regression from semiparametric random censorship.
Mondal, Shoubhik; Subramanian, Sundarraman
2016-01-01
Cox regression is combined with semiparametric random censorship models to construct simultaneous confidence bands (SCBs) for subject-specific survival curves. Simulation results are presented to compare the performance of the proposed SCBs with the SCBs that are based only on standard Cox. The new SCBs provide correct empirical coverage and are more informative. The proposed SCBs are illustrated with two real examples. An extension to handle missing censoring indicators is also outlined.
Simultaneous confidence bands for Cox regression from semiparametric random censorship.
Mondal, Shoubhik; Subramanian, Sundarraman
2016-01-01
Cox regression is combined with semiparametric random censorship models to construct simultaneous confidence bands (SCBs) for subject-specific survival curves. Simulation results are presented to compare the performance of the proposed SCBs with the SCBs that are based only on standard Cox. The new SCBs provide correct empirical coverage and are more informative. The proposed SCBs are illustrated with two real examples. An extension to handle missing censoring indicators is also outlined. PMID:25691289
Vaeth, Michael; Skovlund, Eva
2004-06-15
For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination.
Cox regression methods for two-stage randomization designs.
Lokhnygina, Yuliya; Helterbrand, Jeffrey D
2007-06-01
Two-stage randomization designs (TSRD) are becoming increasingly common in oncology and AIDS clinical trials as they make more efficient use of study participants to examine therapeutic regimens. In these designs patients are initially randomized to an induction treatment, followed by randomization to a maintenance treatment conditional on their induction response and consent to further study treatment. Broader acceptance of TSRDs in drug development may hinge on the ability to make appropriate intent-to-treat type inference within this design framework as to whether an experimental induction regimen is better than a standard induction regimen when maintenance treatment is fixed. Recently Lunceford, Davidian, and Tsiatis (2002, Biometrics 58, 48-57) introduced an inverse probability weighting based analytical framework for estimating survival distributions and mean restricted survival times, as well as for comparing treatment policies at landmarks in the TSRD setting. In practice Cox regression is widely used and in this article we extend the analytical framework of Lunceford et al. (2002) to derive a consistent estimator for the log hazard in the Cox model and a robust score test to compare treatment policies. Large sample properties of these methods are derived, illustrated via a simulation study, and applied to a TSRD clinical trial. PMID:17425633
Iuliano, Antonella; Occhipinti, Annalisa; Angelini, Claudia; De Feis, Italia; Lió, Pietro
2016-01-01
International initiatives such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are collecting multiple datasets at different genome-scales with the aim of identifying novel cancer biomarkers and predicting survival of patients. To analyze such data, several statistical methods have been applied, among them Cox regression models. Although these models provide a good statistical framework to analyze omic data, there is still a lack of studies that illustrate advantages and drawbacks in integrating biological information and selecting groups of biomarkers. In fact, classical Cox regression algorithms focus on the selection of a single biomarker, without taking into account the strong correlation between genes. Even though network-based Cox regression algorithms overcome such drawbacks, such network-based approaches are less widely used within the life science community. In this article, we aim to provide a clear methodological framework on the use of such approaches in order to turn cancer research results into clinical applications. Therefore, we first discuss the rationale and the practical usage of three recently proposed network-based Cox regression algorithms (i.e., Net-Cox, AdaLnet, and fastcox). Then, we show how to combine existing biological knowledge and available data with such algorithms to identify networks of cancer biomarkers and to estimate survival of patients. Finally, we describe in detail a new permutation-based approach to better validate the significance of the selection in terms of cancer gene signatures and pathway/networks identification. We illustrate the proposed methodology by means of both simulations and real case studies. Overall, the aim of our work is two-fold. Firstly, to show how network-based Cox regression models can be used to integrate biological knowledge (e.g., multi-omics data) for the analysis of survival data. Secondly, to provide a clear methodological and computational approach for
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…
Xu, Haoming; Moni, Mohammad Ali; Liò, Pietro
2015-12-01
In cancer genomics, gene expression levels provide important molecular signatures for all types of cancer, and this could be very useful for predicting the survival of cancer patients. However, the main challenge of gene expression data analysis is high dimensionality, and microarray is characterised by few number of samples with large number of genes. To overcome this problem, a variety of penalised Cox proportional hazard models have been proposed. We introduce a novel network regularised Cox proportional hazard model and a novel multiplex network model to measure the disease comorbidities and to predict survival of the cancer patient. Our methods are applied to analyse seven microarray cancer gene expression datasets: breast cancer, ovarian cancer, lung cancer, liver cancer, renal cancer and osteosarcoma. Firstly, we applied a principal component analysis to reduce the dimensionality of original gene expression data. Secondly, we applied a network regularised Cox regression model on the reduced gene expression datasets. By using normalised mutual information method and multiplex network model, we predict the comorbidities for the liver cancer based on the integration of diverse set of omics and clinical data, and we find the diseasome associations (disease-gene association) among different cancers based on the identified common significant genes. Finally, we evaluated the precision of the approach with respect to the accuracy of survival prediction using ROC curves. We report that colon cancer, liver cancer and renal cancer share the CXCL5 gene, and breast cancer, ovarian cancer and renal cancer share the CCND2 gene. Our methods are useful to predict survival of the patient and disease comorbidities more accurately and helpful for improvement of the care of patients with comorbidity. Software in Matlab and R is available on our GitHub page: https://github.com/ssnhcom/NetworkRegularisedCox.git. PMID:26611766
Estimation of treatment effects based on possibly misspecified Cox regression.
Hattori, Satoshi; Henmi, Masayuki
2012-10-01
In randomized clinical trials, a treatment effect on a time-to-event endpoint is often estimated by the Cox proportional hazards model. The maximum partial likelihood estimator does not make sense if the proportional hazard assumption is violated. Xu and O'Quigley (Biostatistics 1:423-439, 2000) proposed an estimating equation, which provides an interpretable estimator for the treatment effect under model misspecification. Namely it provides a consistent estimator for the log-hazard ratio among the treatment groups if the model is correctly specified, and it is interpreted as an average log-hazard ratio over time even if misspecified. However, the method requires the assumption that censoring is independent of treatment group, which is more restricted than that for the maximum partial likelihood estimator and is often violated in practice. In this paper, we propose an alternative estimating equation. Our method provides an estimator of the same property as that of Xu and O'Quigley under the usual assumption for the maximum partial likelihood estimation. We show that our estimator is consistent and asymptotically normal, and derive a consistent estimator of the asymptotic variance. If the proportional hazards assumption holds, the efficiency of the estimator can be improved by applying the covariate adjustment method based on the semiparametric theory proposed by Lu and Tsiatis (Biometrika 95:679-694, 2008). PMID:22527680
Mortality Prediction in ICUs Using A Novel Time-Slicing Cox Regression Method
Wang, Yuan; Chen, Wenlin; Heard, Kevin; Kollef, Marin H.; Bailey, Thomas C.; Cui, Zhicheng; He, Yujie; Lu, Chenyang; Chen, Yixin
2015-01-01
Over the last few decades, machine learning and data mining have been increasingly used for clinical prediction in ICUs. However, there is still a huge gap in making full use of the time-series data generated from ICUs. Aiming at filling this gap, we propose a novel approach entitled Time Slicing Cox regression (TS-Cox), which extends the classical Cox regression into a classification method on multi-dimensional time-series. Unlike traditional classifiers such as logistic regression and support vector machines, our model not only incorporates the discriminative features derived from the time-series, but also naturally exploits the temporal orders of these features based on a Cox-like function. Empirical evaluation on MIMIC-II database demonstrates the efficacy of the TS-Cox model. Our TS-Cox model outperforms all other baseline models by a good margin in terms of AUC_PR, sensitivity and PPV, which indicates that TS-Cox may be a promising tool for mortality prediction in ICUs. PMID:26958269
Mortality Prediction in ICUs Using A Novel Time-Slicing Cox Regression Method.
Wang, Yuan; Chen, Wenlin; Heard, Kevin; Kollef, Marin H; Bailey, Thomas C; Cui, Zhicheng; He, Yujie; Lu, Chenyang; Chen, Yixin
2015-01-01
Over the last few decades, machine learning and data mining have been increasingly used for clinical prediction in ICUs. However, there is still a huge gap in making full use of the time-series data generated from ICUs. Aiming at filling this gap, we propose a novel approach entitled Time Slicing Cox regression (TS-Cox), which extends the classical Cox regression into a classification method on multi-dimensional time-series. Unlike traditional classifiers such as logistic regression and support vector machines, our model not only incorporates the discriminative features derived from the time-series, but also naturally exploits the temporal orders of these features based on a Cox-like function. Empirical evaluation on MIMIC-II database demonstrates the efficacy of the TS-Cox model. Our TS-Cox model outperforms all other baseline models by a good margin in terms of AUC_PR, sensitivity and PPV, which indicates that TS-Cox may be a promising tool for mortality prediction in ICUs.
Non-Asymptotic Oracle Inequalities for the High-Dimensional Cox Regression via Lasso
Kong, Shengchun; Nan, Bin
2013-01-01
We consider finite sample properties of the regularized high-dimensional Cox regression via lasso. Existing literature focuses on linear models or generalized linear models with Lipschitz loss functions, where the empirical risk functions are the summations of independent and identically distributed (iid) losses. The summands in the negative log partial likelihood function for censored survival data, however, are neither iid nor Lipschitz.We first approximate the negative log partial likelihood function by a sum of iid non-Lipschitz terms, then derive the non-asymptotic oracle inequalities for the lasso penalized Cox regression using pointwise arguments to tackle the difficulties caused by lacking iid Lipschitz losses. PMID:24516328
Selective cyclooxygenase-2 (COX-2) inhibitors used for preventing or regressing cancer.
de Souza Pereira, Ricardo
2009-06-01
The current use of antineoplastic drugs in human therapy causes a substancial number of toxic or side effects which consequently lead to a reduction of the amount of drug to be administered, and in some cases to discontinuation of the therapy. A reduction of the amount of drug to be administered or discontinuation of the therapy causes an increase in primary tumour growth and/or the occurrence of tumour metastases. For this reason, the development of new anti-cancer drugs with lower side effects is necessary. This review gives a general idea about the origins of cancer and the importance of cyclooxygenase-2 (COX-2) in oncogenesis. Evidence from clinical and preclinical studies indicates that COX-2-derived prostaglandins participate in carcinogenesis, inflammation, immune response suppression, apoptosis inhibition, angiogenesis, and tumour cell invasion and metastasis. The recent anti-tumour drugs are based on tests of known selective COX-2 inhibitors and on the drawing and synthesis of new potent derivatives. Maybe, this can be the way to obtain new anti-tumour drugs with very low collateral effects. Selective COX-2 inhibitors are being mixtured with new anti-cancer drugs in order to obtain better results in the regression of cancers. Some natural products are selective COX-2 inhibitors and have anti-inflammatory and anti-cancer properties. The relevant patents are discussed.
Cox regression with missing covariate data using a modified partial likelihood method.
Martinussen, Torben; Holst, Klaus K; Scheike, Thomas H
2016-10-01
Missing covariate values is a common problem in survival analysis. In this paper we propose a novel method for the Cox regression model that is close to maximum likelihood but avoids the use of the EM-algorithm. It exploits that the observed hazard function is multiplicative in the baseline hazard function with the idea being to profile out this function before carrying out the estimation of the parameter of interest. In this step one uses a Breslow type estimator to estimate the cumulative baseline hazard function. We focus on the situation where the observed covariates are categorical which allows us to calculate estimators without having to assume anything about the distribution of the covariates. We show that the proposed estimator is consistent and asymptotically normal, and derive a consistent estimator of the variance-covariance matrix that does not involve any choice of a perturbation parameter. Moderate sample size performance of the estimators is investigated via simulation and by application to a real data example.
Cox regression with missing covariate data using a modified partial likelihood method.
Martinussen, Torben; Holst, Klaus K; Scheike, Thomas H
2016-10-01
Missing covariate values is a common problem in survival analysis. In this paper we propose a novel method for the Cox regression model that is close to maximum likelihood but avoids the use of the EM-algorithm. It exploits that the observed hazard function is multiplicative in the baseline hazard function with the idea being to profile out this function before carrying out the estimation of the parameter of interest. In this step one uses a Breslow type estimator to estimate the cumulative baseline hazard function. We focus on the situation where the observed covariates are categorical which allows us to calculate estimators without having to assume anything about the distribution of the covariates. We show that the proposed estimator is consistent and asymptotically normal, and derive a consistent estimator of the variance-covariance matrix that does not involve any choice of a perturbation parameter. Moderate sample size performance of the estimators is investigated via simulation and by application to a real data example. PMID:26493471
Modern Regression Discontinuity Analysis
ERIC Educational Resources Information Center
Bloom, Howard S.
2012-01-01
This article provides a detailed discussion of the theory and practice of modern regression discontinuity (RD) analysis for estimating the effects of interventions or treatments. Part 1 briefly chronicles the history of RD analysis and summarizes its past applications. Part 2 explains how in theory an RD analysis can identify an average effect of…
Xiao, Yongling; Abrahamowicz, Michal
2010-03-30
We propose two bootstrap-based methods to correct the standard errors (SEs) from Cox's model for within-cluster correlation of right-censored event times. The cluster-bootstrap method resamples, with replacement, only the clusters, whereas the two-step bootstrap method resamples (i) the clusters, and (ii) individuals within each selected cluster, with replacement. In simulations, we evaluate both methods and compare them with the existing robust variance estimator and the shared gamma frailty model, which are available in statistical software packages. We simulate clustered event time data, with latent cluster-level random effects, which are ignored in the conventional Cox's model. For cluster-level covariates, both proposed bootstrap methods yield accurate SEs, and type I error rates, and acceptable coverage rates, regardless of the true random effects distribution, and avoid serious variance under-estimation by conventional Cox-based standard errors. However, the two-step bootstrap method over-estimates the variance for individual-level covariates. We also apply the proposed bootstrap methods to obtain confidence bands around flexible estimates of time-dependent effects in a real-life analysis of cluster event times.
Survival Prognostic Factors of Male Breast Cancer in Southern Iran: a LASSO-Cox Regression Approach.
Shahraki, Hadi Raeisi; Salehi, Alireza; Zare, Najaf
2015-01-01
We used to LASSO-Cox method for determining prognostic factors of male breast cancer survival and showed the superiority of this method compared to Cox proportional hazard model in low sample size setting. In order to identify and estimate exactly the relative hazard of the most important factors effective for the survival duration of male breast cancer, the LASSO-Cox method has been used. Our data includes the information of male breast cancer patients in Fars province, south of Iran, from 1989 to 2008. Cox proportional hazard and LASSO-Cox models were fitted for 20 classified variables. To reduce the impact of missing data, the multiple imputation method was used 20 times through the Markov chain Mont Carlo method and the results were combined with Rubin's rules. In 50 patients, the age at diagnosis was 59.6 (SD=12.8) years with a minimum of 34 and maximum of 84 years and the mean of survival time was 62 months. Three, 5 and 10 year survival were 92%, 77% and 26%, respectively. Using the LASSO-Cox method led to eliminating 8 low effect variables and also decreased the standard error by 2.5 to 7 times. The relative efficiency of LASSO-Cox method compared with the Cox proportional hazard method was calculated as 22.39. The19 years follow of male breast cancer patients show that the age, having a history of alcohol use, nipple discharge, laterality, histological grade and duration of symptoms were the most important variables that have played an effective role in the patient's survival. In such situations, estimating the coefficients by LASSO-Cox method will be more efficient than the Cox's proportional hazard method. PMID:26434910
Tarpey, Thaddeus; Petkova, Eva
2010-07-01
Finite mixture models have come to play a very prominent role in modelling data. The finite mixture model is predicated on the assumption that distinct latent groups exist in the population. The finite mixture model therefore is based on a categorical latent variable that distinguishes the different groups. Often in practice distinct sub-populations do not actually exist. For example, disease severity (e.g. depression) may vary continuously and therefore, a distinction of diseased and not-diseased may not be based on the existence of distinct sub-populations. Thus, what is needed is a generalization of the finite mixture's discrete latent predictor to a continuous latent predictor. We cast the finite mixture model as a regression model with a latent Bernoulli predictor. A latent regression model is proposed by replacing the discrete Bernoulli predictor by a continuous latent predictor with a beta distribution. Motivation for the latent regression model arises from applications where distinct latent classes do not exist, but instead individuals vary according to a continuous latent variable. The shapes of the beta density are very flexible and can approximate the discrete Bernoulli distribution. Examples and a simulation are provided to illustrate the latent regression model. In particular, the latent regression model is used to model placebo effect among drug treated subjects in a depression study. PMID:20625443
Multiple linear regression analysis
NASA Technical Reports Server (NTRS)
Edwards, T. R.
1980-01-01
Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
NASA Technical Reports Server (NTRS)
Kattan, Michael W.; Hess, Kenneth R.; Kattan, Michael W.
1998-01-01
New computationally intensive tools for medical survival analyses include recursive partitioning (also called CART) and artificial neural networks. A challenge that remains is to better understand the behavior of these techniques in effort to know when they will be effective tools. Theoretically they may overcome limitations of the traditional multivariable survival technique, the Cox proportional hazards regression model. Experiments were designed to test whether the new tools would, in practice, overcome these limitations. Two datasets in which theory suggests CART and the neural network should outperform the Cox model were selected. The first was a published leukemia dataset manipulated to have a strong interaction that CART should detect. The second was a published cirrhosis dataset with pronounced nonlinear effects that a neural network should fit. Repeated sampling of 50 training and testing subsets was applied to each technique. The concordance index C was calculated as a measure of predictive accuracy by each technique on the testing dataset. In the interaction dataset, CART outperformed Cox (P less than 0.05) with a C improvement of 0.1 (95% Cl, 0.08 to 0.12). In the nonlinear dataset, the neural network outperformed the Cox model (P less than 0.05), but by a very slight amount (0.015). As predicted by theory, CART and the neural network were able to overcome limitations of the Cox model. Experiments like these are important to increase our understanding of when one of these new techniques will outperform the standard Cox model. Further research is necessary to predict which technique will do best a priori and to assess the magnitude of superiority.
Precision Efficacy Analysis for Regression.
ERIC Educational Resources Information Center
Brooks, Gordon P.
When multiple linear regression is used to develop a prediction model, sample size must be large enough to ensure stable coefficients. If the derivation sample size is inadequate, the model may not predict well for future subjects. The precision efficacy analysis for regression (PEAR) method uses a cross- validity approach to select sample sizes…
Devarajan, Karthik; Ebrahimi, Nader
2010-01-01
The assumption of proportional hazards (PH) fundamental to the Cox PH model sometimes may not hold in practice. In this paper, we propose a generalization of the Cox PH model in terms of the cumulative hazard function taking a form similar to the Cox PH model, with the extension that the baseline cumulative hazard function is raised to a power function. Our model allows for interaction between covariates and the baseline hazard and it also includes, for the two sample problem, the case of two Weibull distributions and two extreme value distributions differing in both scale and shape parameters. The partial likelihood approach can not be applied here to estimate the model parameters. We use the full likelihood approach via a cubic B-spline approximation for the baseline hazard to estimate the model parameters. A semi-automatic procedure for knot selection based on Akaike’s Information Criterion is developed. We illustrate the applicability of our approach using real-life data. PMID:21076652
Box-Cox transformation of firm size data in statistical analysis
NASA Astrophysics Data System (ADS)
Chen, Ting Ting; Takaishi, Tetsuya
2014-03-01
Firm size data usually do not show the normality that is often assumed in statistical analysis such as regression analysis. In this study we focus on two firm size data: the number of employees and sale. Those data deviate considerably from a normal distribution. To improve the normality of those data we transform them by the Box-Cox transformation with appropriate parameters. The Box-Cox transformation parameters are determined so that the transformed data best show the kurtosis of a normal distribution. It is found that the two firm size data transformed by the Box-Cox transformation show strong linearity. This indicates that the number of employees and sale have the similar property as a firm size indicator. The Box-Cox parameters obtained for the firm size data are found to be very close to zero. In this case the Box-Cox transformations are approximately a log-transformation. This suggests that the firm size data we used are approximately log-normal distributions.
Gene identification and survival prediction with Lp Cox regression and novel similarity measure.
Liu, Zhenqiu; Jiang, Feng
2009-01-01
In this paper, Cox's proportional hazards model with Lp penalty method is developed for simultaneous gene selection and survival prediction. Lp penalty shrinks coefficients and produces some coefficients that are exactly zero, and therefore can be used to identify survival related downstream genes. We also define a novel similarity measure to hunt the regulatory genes that their gene expression changes may be low but they are highly correlated with the selected genes. Experimental results with gene expression data demonstrate that the proposed procedures can be used for identifying important gene clusters that are related to time to death due to cancer and for building parsimonious model for predicting the survival of future patients.
Covariate analysis of survival data: a small-sample study of Cox's model
Johnson, M.E.; Tolley, H.D.; Bryson, M.C.; Goldman, A.S.
1982-09-01
Cox's proportional-hazards model is frequently used to adjust for covariate effects in survival-data analysis. The small-sample performances of the maximum partial likelihood estimators of the regression parameters in a two-covariate hazard function model are evaluated with respect to bias, variance, and power in hypothesis tests. Previous Monte Carlo work on the two-sample problem is reviewed.
Comparing the importance of prognostic factors in Cox and logistic regression using SAS.
Heinze, Georg; Schemper, Michael
2003-06-01
Two SAS macro programs are presented that evaluate the relative importance of prognostic factors in the proportional hazards regression model and in the logistic regression model. The importance of a prognostic factor is quantified by the proportion of variation in the outcome attributable to this factor. For proportional hazards regression, the program %RELIMPCR uses the recently proposed measure V to calculate the proportion of explained variation (PEV). For the logistic model, the R(2) measure based on squared raw residuals is used by the program %RELIMPLR. Both programs are able to compute marginal and partial PEV, to compare PEVs of factors, of groups of factors, and even to compare PEVs of different models. The programs use a bootstrap resampling scheme to test differences of the PEVs of different factors. Confidence limits for P-values are provided. The programs further allow to base the computation of PEV on models with shrinked or bias-corrected parameter estimates. The SAS macros are freely available at www.akh-wien.ac.at/imc/biometrie/relimp
Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions.
Fan, Ruzong; Wang, Yifan; Yan, Qi; Ding, Ying; Weeks, Daniel E; Lu, Zhaohui; Ren, Haobo; Cook, Richard J; Xiong, Momiao; Swaroop, Anand; Chew, Emily Y; Chen, Wei
2016-02-01
Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, here we develop Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT), which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example. PMID:26782979
Regression Analysis by Example. 5th Edition
ERIC Educational Resources Information Center
Chatterjee, Samprit; Hadi, Ali S.
2012-01-01
Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…
Dewi, Lestari
2016-01-01
Introduction: The enzyme cyclooxygenase (COX) is an enzyme that catalyzes the formation of one of the mediators of inflammation, the prostaglandins. Inhibition of COX allegedly can improve inflammation-induced pathological conditions. Aim: The purpose of the present study was to evaluate the potential of Sargassum sp. components, Fucoidan and alginate, as COX inhibitors. Material and methods: The study was conducted by means of a computational (in silico) method. It was performed in two main stages, the docking between COX-1 and COX-2 with Fucoidan, alginate and aspirin (for comparison) and the analysis of the amount of interactions formed and the residues directly involved in the process of interaction. Results: Our results showed that both Fucoidan and alginate had an excellent potential as inhibitors of COX-1 and COX-2. Fucoidan had a better potential as an inhibitor of COX than alginate. COX inhibition was expected to provide a more favorable effect on inflammation-related pathological conditions. Conclusion: The active compounds Fucoidan and alginate derived from Sargassum sp. were suspected to possess a good potential as inhibitors of COX-1 and COX-2. PMID:27594740
Ternès, Nils; Rotolo, Federico; Michiels, Stefan
2016-07-10
Correct selection of prognostic biomarkers among multiple candidates is becoming increasingly challenging as the dimensionality of biological data becomes higher. Therefore, minimizing the false discovery rate (FDR) is of primary importance, while a low false negative rate (FNR) is a complementary measure. The lasso is a popular selection method in Cox regression, but its results depend heavily on the penalty parameter λ. Usually, λ is chosen using maximum cross-validated log-likelihood (max-cvl). However, this method has often a very high FDR. We review methods for a more conservative choice of λ. We propose an empirical extension of the cvl by adding a penalization term, which trades off between the goodness-of-fit and the parsimony of the model, leading to the selection of fewer biomarkers and, as we show, to the reduction of the FDR without large increase in FNR. We conducted a simulation study considering null and moderately sparse alternative scenarios and compared our approach with the standard lasso and 10 other competitors: Akaike information criterion (AIC), corrected AIC, Bayesian information criterion (BIC), extended BIC, Hannan and Quinn information criterion (HQIC), risk information criterion (RIC), one-standard-error rule, adaptive lasso, stability selection, and percentile lasso. Our extension achieved the best compromise across all the scenarios between a reduction of the FDR and a limited raise of the FNR, followed by the AIC, the RIC, and the adaptive lasso, which performed well in some settings. We illustrate the methods using gene expression data of 523 breast cancer patients. In conclusion, we propose to apply our extension to the lasso whenever a stringent FDR with a limited FNR is targeted. Copyright © 2016 John Wiley & Sons, Ltd.
Lee, Paul H
2016-01-01
Healthy adults are advised to perform at least 150 min of moderate-intensity physical activity weekly, but this advice is based on studies using self-reports of questionable validity. This study examined the dose-response relationship of accelerometer-measured physical activity and sedentary behaviors on all-cause mortality using segmented Cox regression to empirically determine the break-points of the dose-response relationship. Data from 7006 adult participants aged 18 or above in the National Health and Nutrition Examination Survey waves 2003-2004 and 2005-2006 were included in the analysis and linked with death certificate data using a probabilistic matching approach in the National Death Index through December 31, 2011. Physical activity and sedentary behavior were measured using ActiGraph model 7164 accelerometer over the right hip for 7 consecutive days. Each minute with accelerometer count <100; 1952-5724; and ≥5725 were classified as sedentary, moderate-intensity physical activity, and vigorous-intensity physical activity, respectively. Segmented Cox regression was used to estimate the hazard ratio (HR) of time spent in sedentary behaviors, moderate-intensity physical activity, and vigorous-intensity physical activity and all-cause mortality, adjusted for demographic characteristics, health behaviors, and health conditions. Data were analyzed in 2016. During 47,119 person-year of follow-up, 608 deaths occurred. Each additional hour per day of sedentary behaviors was associated with a HR of 1.15 (95% CI 1.01, 1.31) among participants who spend at least 10.9 h per day on sedentary behaviors, and each additional minute per day spent on moderate-intensity physical activity was associated with a HR of 0.94 (95% CI 0.91, 0.96) among participants with daily moderate-intensity physical activity ≤14.1 min. Associations of moderate physical activity and sedentary behaviors on all-cause mortality were independent of each other. To conclude, evidence from this
Regression analysis of cytopathological data
Whittemore, A.S.; McLarty, J.W.; Fortson, N.; Anderson, K.
1982-12-01
Epithelial cells from the human body are frequently labelled according to one of several ordered levels of abnormality, ranging from normal to malignant. The label of the most abnormal cell in a specimen determines the score for the specimen. This paper presents a model for the regression of specimen scores against continuous and discrete variables, as in host exposure to carcinogens. Application to data and tests for adequacy of model fit are illustrated using sputum specimens obtained from a cohort of former asbestos workers.
Regression Analysis: Legal Applications in Institutional Research
ERIC Educational Resources Information Center
Frizell, Julie A.; Shippen, Benjamin S., Jr.; Luna, Andrew L.
2008-01-01
This article reviews multiple regression analysis, describes how its results should be interpreted, and instructs institutional researchers on how to conduct such analyses using an example focused on faculty pay equity between men and women. The use of multiple regression analysis will be presented as a method with which to compare salaries of…
Regression Analysis and the Sociological Imagination
ERIC Educational Resources Information Center
De Maio, Fernando
2014-01-01
Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.
Box-Cox Mixed Logit Model for Travel Behavior Analysis
NASA Astrophysics Data System (ADS)
Orro, Alfonso; Novales, Margarita; Benitez, Francisco G.
2010-09-01
To represent the behavior of travelers when they are deciding how they are going to get to their destination, discrete choice models, based on the random utility theory, have become one of the most widely used tools. The field in which these models were developed was halfway between econometrics and transport engineering, although the latter now constitutes one of their principal areas of application. In the transport field, they have mainly been applied to mode choice, but also to the selection of destination, route, and other important decisions such as the vehicle ownership. In usual practice, the most frequently employed discrete choice models implement a fixed coefficient utility function that is linear in the parameters. The principal aim of this paper is to present the viability of specifying utility functions with random coefficients that are nonlinear in the parameters, in applications of discrete choice models to transport. Nonlinear specifications in the parameters were present in discrete choice theory at its outset, although they have seldom been used in practice until recently. The specification of random coefficients, however, began with the probit and the hedonic models in the 1970s, and, after a period of apparent little practical interest, has burgeoned into a field of intense activity in recent years with the new generation of mixed logit models. In this communication, we present a Box-Cox mixed logit model, original of the authors. It includes the estimation of the Box-Cox exponents in addition to the parameters of the random coefficients distribution. Probability of choose an alternative is an integral that will be calculated by simulation. The estimation of the model is carried out by maximizing the simulated log-likelihood of a sample of observed individual choices between alternatives. The differences between the predictions yielded by models that are inconsistent with real behavior have been studied with simulation experiments.
Commonality Analysis for the Regression Case.
ERIC Educational Resources Information Center
Murthy, Kavita
Commonality analysis is a procedure for decomposing the coefficient of determination (R superscript 2) in multiple regression analyses into the percent of variance in the dependent variable associated with each independent variable uniquely, and the proportion of explained variance associated with the common effects of predictors in various…
Method for nonlinear exponential regression analysis
NASA Technical Reports Server (NTRS)
Junkin, B. G.
1972-01-01
Two computer programs developed according to two general types of exponential models for conducting nonlinear exponential regression analysis are described. Least squares procedure is used in which the nonlinear problem is linearized by expanding in a Taylor series. Program is written in FORTRAN 5 for the Univac 1108 computer.
Multiple Regression Analysis and Automatic Interaction Detection.
ERIC Educational Resources Information Center
Koplyay, Janos B.
The Automatic Interaction Detector (AID) is discussed as to its usefulness in multiple regression analysis. The algorithm of AID-4 is a reversal of the model building process; it starts with the ultimate restricted model, namely, the whole group as a unit. By a unique splitting process maximizing the between sum of squares for the categories of…
Using Regression Analysis: A Guided Tour.
ERIC Educational Resources Information Center
Shelton, Fred Ames
1987-01-01
Discusses the use and interpretation of multiple regression analysis with computer programs and presents a flow chart of the process. A general explanation of the flow chart is provided, followed by an example showing the development of a linear equation which could be used in estimating manufacturing overhead cost. (Author/LRW)
In -silico molecular docking analysis of prodigiosin and cycloprodigiosin as COX-2 inhibitors.
Krishna, Pabba Shiva; Vani, Kompally; Prasad, Metuku Ram; Samatha, Burra; Bindu, Nidadavolu Shesha Venkata Sathya Siva Surya Laxmi Hima; Charya, Maringanti Alaha Singara; Reddy Shetty, Prakasham
2013-12-01
Prodigiosin and cycloprodigiosin are tripyrrole red pigmented compounds with medical importance for their anticancer property. In the present investigation, molecular docking studies were performed for both prodigiosin and cycloprodigiosins to evaluate the in- silico anti-inflammatory activity against Cycloxigenase-2 (COX-2) protein as model compound and the data compared with rofecoxib and celcoxid. Cycloprodigiosin showed higher initial potential, initial RMS gradient and potential energy values compared to prodigiosin. Analysis of COX-2 protein and ligand binding revealed that cyclprodigiosin interacted with COX-2 protein amino acid residues of Tyr(324), Phe(487) and Arg(89) while prodigiosin interaction was observed with two amino acids i.e. Leu(321) and Tyr(324). The computational ligand binding interaction suggested > 45% higher fitness score value for prodigiosin to that of cycloprodigiosin with COX-2 protein while the standard compounds rofecoxib and celecoxid revealed fitness score of 44 and 62, respectively. The prodigiosin ligand revealed the best fitness score compared with the standard drug rofecoxib suggesting the prodigiosin could be effective as the potential inhibitor compound against COX-2 protein and can be evaluated as anti-inflammatory drug molecule using clinical trials.
Leffondré, Karen; Jager, Kitty J; Boucquemont, Julie; Stel, Vianda S; Heinze, Georg
2014-10-01
Regression models are being used to quantify the effect of an exposure on an outcome, while adjusting for potential confounders. While the type of regression model to be used is determined by the nature of the outcome variable, e.g. linear regression has to be applied for continuous outcome variables, all regression models can handle any kind of exposure variables. However, some fundamentals of representation of the exposure in a regression model and also some potential pitfalls have to be kept in mind in order to obtain meaningful interpretation of results. The objective of this educational paper was to illustrate these fundamentals and pitfalls, using various multiple regression models applied to data from a hypothetical cohort of 3000 patients with chronic kidney disease. In particular, we illustrate how to represent different types of exposure variables (binary, categorical with two or more categories and continuous), and how to interpret the regression coefficients in linear, logistic and Cox models. We also discuss the linearity assumption in these models, and show how wrongly assuming linearity may produce biased results and how flexible modelling using spline functions may provide better estimates.
A method for nonlinear exponential regression analysis
NASA Technical Reports Server (NTRS)
Junkin, B. G.
1971-01-01
A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.
Regression Analysis Of Zernike Polynomials Part II
NASA Astrophysics Data System (ADS)
Grey, Louis D.
1989-01-01
In an earlier paper entitled "Regression Analysis of Zernike Polynomials, Proceedings of SPIE, Vol. 18, pp. 392-398, the least squares fitting process of Zernike polynomials was examined from the point of view of linear statistical regression theory. Among the topics discussed were measures for determining how good the fit was, tests for the underlying assumptions of normality and constant variance, the treatment of outliers, the analysis of residuals and the computation of confidence intervals for the coefficients. The present paper is a continuation of the earlier paper and concerns applications of relatively new advances in certain areas of statistical theory made possible by the advent of the high speed computer. Among these are: 1. Jackknife - A technique for improving the accuracy of any statistical estimate. 2. Bootstrap - Increasing the accuracy of an estimate by generating new samples of data from some given set. 3. Cross-validation - The division of a data set into two halves, the first half of which is used to fit the model and the second half to see how well the fitted model predicts the data. The exposition is mainly by examples.
Wang, Zhi-Ming; Liu, Jie; Liu, Hong-Bo; Ye, Ming; Zhang, Yu-Fei; Yang, Dong-Sheng
2014-01-01
Background. The prognostic significance of COX2 for survival of patients with oral cancer remains controversial. Thus, the meta-analysis was performed in order to identify COX2 expression impact on prognosis of oral cancer. Method. Relevant literatures were searched using the following electronic databases without any language restrictions: Web of Science, the Cochrane Library Database, PubMed, EMBASE, CINAHL, and CBM. Version 12.0 STATA software (Stata Corporation, College Station, Texas, USA) was used for the current meta-analysis. Odds ratios (ORs) and hazard ratios (HRs) with their corresponding 95% confidence interval (95% CI) were also calculated to clarify the correlation between COX2 expression and prognosis of oral cancer. Results. Final analysis of 979 oral cancer patients from 12 clinical cohort studies was performed. The meta-analysis results show that COX2 expression in cancer tissues was significantly higher than those in normal and benign tissues (all P < 0.05). Combined HR of COX2 suggests that positive COX2 expression has a shorter overall survival (OS) than those of negative COX2 expression (P < 0.05). Conclusion. The meta-analysis study shows that elevated COX2 expression may be associated with the pathogenesis of oral cancer and with a worse prognosis in oral cancer patients. PMID:25028647
Mariani, L; Coradini, D; Biganzoli, E; Boracchi, P; Marubini, E; Pilotti, S; Salvadori, B; Silvestrini, R; Veronesi, U; Zucali, R; Rilke, F
1997-06-01
The purpose of the present study was to assess prognostic factor for metachronous contralateral recurrence of breast cancer (CBC). Two factors were of particular interest, namely estrogen (ER) and progesterone (PgR) receptors assayed with the biochemical method in primary tumor tissue. Information was obtained from a prospective clinical database for 1763 axillary node-negative women who had received curative surgery, mostly of the conservative type, and followed-up for a median of 82 months. The analysis was performed based on both a standard (linear) Cox model and an artificial neural network (ANN) extension of this model proposed by Faraggi and Simon. Furthermore, to assess the prognostic importance of the factors considered, model predictive ability was computed. In agreement with already published studies, the results of our analysis confirmed the prognostic role of age at surgery, histology, and primary tumor site, in that young patients (< or = 45 years) with tumors of lobular histology or located at inner/central mammary quadrants were at greater risk of developing CBC. ER and PgR were also shown to have a prognostic role. Their effect, however, was not simple in relation to the presence of interactions between ER and age, and between PgR and histology. In fact, ER appeared to play a protective role in young patients, whereas the opposite was true in older women. Higher levels of PgR implied a greater hazard of CBC occurrence in infiltrating duct carcinoma or tumors with an associated extensive intraductal component, and a lower hazard in infiltrating lobular carcinoma or other histotypes. In spite of the above findings, the predictive value of both the standard and ANN Cox models was relatively low, thus suggesting an intrinsic limitation of the prognostic variables considered, rather than their suboptimal modeling. Research for better prognostic variables should therefore continue.
A rotor optimization using regression analysis
NASA Technical Reports Server (NTRS)
Giansante, N.
1984-01-01
The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.
Sliced Inverse Regression for Time Series Analysis
NASA Astrophysics Data System (ADS)
Chen, Li-Sue
1995-11-01
In this thesis, general nonlinear models for time series data are considered. A basic form is x _{t} = f(beta_sp{1} {T}X_{t-1},beta_sp {2}{T}X_{t-1},... , beta_sp{k}{T}X_ {t-1},varepsilon_{t}), where x_{t} is an observed time series data, X_{t } is the first d time lag vector, (x _{t},x_{t-1},... ,x _{t-d-1}), f is an unknown function, beta_{i}'s are unknown vectors, varepsilon_{t }'s are independent distributed. Special cases include AR and TAR models. We investigate the feasibility applying SIR/PHD (Li 1990, 1991) (the sliced inverse regression and principal Hessian methods) in estimating beta _{i}'s. PCA (Principal component analysis) is brought in to check one critical condition for SIR/PHD. Through simulation and a study on 3 well -known data sets of Canadian lynx, U.S. unemployment rate and sunspot numbers, we demonstrate how SIR/PHD can effectively retrieve the interesting low-dimension structures for time series data.
Using Dominance Analysis to Determine Predictor Importance in Logistic Regression
ERIC Educational Resources Information Center
Azen, Razia; Traxel, Nicole
2009-01-01
This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…
Choi, In-Wook; Kim, Hwang-Yong; Quan, Juan-Hua; Ryu, Jae-Gee; Sun, Rubing; Lee, Young-Ha
2015-10-01
Fascioliasis, a food-borne trematode zoonosis, is a disease primarily in cattle and sheep and occasionally in humans. Water dropwort (Oenanthe javanica), an aquatic perennial herb, is a common second intermediate host of Fasciola, and the fresh stems and leaves are widely used as a seasoning in the Korean diet. However, no information regarding Fasciola species contamination in water dropwort is available. Here, we collected 500 samples of water dropwort in 3 areas in Korea during February and March 2015, and the water dropwort contamination of Fasciola species was monitored by DNA sequencing analysis of the Fasciola hepatica and Fasciola gigantica specific mitochondrial cytochrome c oxidase subunit 1 (cox1) and nuclear ribosomal internal transcribed spacer 2 (ITS-2). Among the 500 samples assessed, the presence of F. hepatica cox1 and 1TS-2 markers were detected in 2 samples, and F. hepatica contamination was confirmed by sequencing analysis. The nucleotide sequences of cox1 PCR products from the 2 F. hepatica-contaminated samples were 96.5% identical to the F. hepatica cox1 sequences in GenBank, whereas F. gigantica cox1 sequences were 46.8% similar with the sequence detected from the cox1 positive samples. However, F. gigantica cox1 and ITS-2 markers were not detected by PCR in the 500 samples of water dropwort. Collectively, in this survey of the water dropwort contamination with Fasciola species, very low prevalence of F. hepatica contamination was detected in the samples.
Choi, In-Wook; Kim, Hwang-Yong; Quan, Juan-Hua; Ryu, Jae-Gee; Sun, Rubing; Lee, Young-Ha
2015-01-01
Fascioliasis, a food-borne trematode zoonosis, is a disease primarily in cattle and sheep and occasionally in humans. Water dropwort (Oenanthe javanica), an aquatic perennial herb, is a common second intermediate host of Fasciola, and the fresh stems and leaves are widely used as a seasoning in the Korean diet. However, no information regarding Fasciola species contamination in water dropwort is available. Here, we collected 500 samples of water dropwort in 3 areas in Korea during February and March 2015, and the water dropwort contamination of Fasciola species was monitored by DNA sequencing analysis of the Fasciola hepatica and Fasciola gigantica specific mitochondrial cytochrome c oxidase subunit 1 (cox1) and nuclear ribosomal internal transcribed spacer 2 (ITS-2). Among the 500 samples assessed, the presence of F. hepatica cox1 and 1TS-2 markers were detected in 2 samples, and F. hepatica contamination was confirmed by sequencing analysis. The nucleotide sequences of cox1 PCR products from the 2 F. hepatica-contaminated samples were 96.5% identical to the F. hepatica cox1 sequences in GenBank, whereas F. gigantica cox1 sequences were 46.8% similar with the sequence detected from the cox1 positive samples. However, F. gigantica cox1 and ITS-2 markers were not detected by PCR in the 500 samples of water dropwort. Collectively, in this survey of the water dropwort contamination with Fasciola species, very low prevalence of F. hepatica contamination was detected in the samples. PMID:26537044
Giganti, Mark J.; Luz, Paula M.; Caro-Vega, Yanink; Cesar, Carina; Padgett, Denis; Koenig, Serena; Echevarria, Juan; McGowan, Catherine C.; Shepherd, Bryan E.
2015-01-01
Abstract Many studies of HIV/AIDS aggregate data from multiple cohorts to improve power and generalizability. There are several analysis approaches to account for cross-cohort heterogeneity; we assessed how different approaches can impact results from an HIV/AIDS study investigating predictors of mortality. Using data from 13,658 HIV-infected patients starting antiretroviral therapy from seven Latin American and Caribbean cohorts, we illustrate the assumptions of seven readily implementable approaches to account for across cohort heterogeneity with Cox proportional hazards models, and we compare hazard ratio estimates across approaches. As a sensitivity analysis, we modify cohort membership to generate specific heterogeneity conditions. Hazard ratio estimates varied slightly between the seven analysis approaches, but differences were not clinically meaningful. Adjusted hazard ratio estimates for the association between AIDS at treatment initiation and death varied from 2.00 to 2.20 across approaches that accounted for heterogeneity; the adjusted hazard ratio was estimated as 1.73 in analyses that ignored across cohort heterogeneity. In sensitivity analyses with more extreme heterogeneity, we noted a slightly greater distinction between approaches. Despite substantial heterogeneity between cohorts, the impact of the specific approach to account for heterogeneity was minimal in our case study. Our results suggest that it is important to account for across cohort heterogeneity in analyses, but that the specific technique for addressing heterogeneity may be less important. Because of their flexibility in accounting for cohort heterogeneity, we prefer stratification or meta-analysis methods, but we encourage investigators to consider their specific study conditions and objectives. PMID:25647087
Giganti, Mark J; Luz, Paula M; Caro-Vega, Yanink; Cesar, Carina; Padgett, Denis; Koenig, Serena; Echevarria, Juan; McGowan, Catherine C; Shepherd, Bryan E
2015-05-01
Many studies of HIV/AIDS aggregate data from multiple cohorts to improve power and generalizability. There are several analysis approaches to account for cross-cohort heterogeneity; we assessed how different approaches can impact results from an HIV/AIDS study investigating predictors of mortality. Using data from 13,658 HIV-infected patients starting antiretroviral therapy from seven Latin American and Caribbean cohorts, we illustrate the assumptions of seven readily implementable approaches to account for across cohort heterogeneity with Cox proportional hazards models, and we compare hazard ratio estimates across approaches. As a sensitivity analysis, we modify cohort membership to generate specific heterogeneity conditions. Hazard ratio estimates varied slightly between the seven analysis approaches, but differences were not clinically meaningful. Adjusted hazard ratio estimates for the association between AIDS at treatment initiation and death varied from 2.00 to 2.20 across approaches that accounted for heterogeneity; the adjusted hazard ratio was estimated as 1.73 in analyses that ignored across cohort heterogeneity. In sensitivity analyses with more extreme heterogeneity, we noted a slightly greater distinction between approaches. Despite substantial heterogeneity between cohorts, the impact of the specific approach to account for heterogeneity was minimal in our case study. Our results suggest that it is important to account for across cohort heterogeneity in analyses, but that the specific technique for addressing heterogeneity may be less important. Because of their flexibility in accounting for cohort heterogeneity, we prefer stratification or meta-analysis methods, but we encourage investigators to consider their specific study conditions and objectives.
Xiao, Zengming; Wu, Hao; Wu, Yang
2013-01-01
Background Numerous studies examining the relationship between Cyclooxygenase-2 (COX-2) immunoexpression and clinical outcome in osteosarcoma patients have yielded inconclusive results. Methods We accordingly conducted a meta-analysis of 9 studies (442 patients) that evaluated the correlation between COX-2 immunoexpression and clinical prognosis (death). Pooled odds ratios (OR) and risk ratios (RR) with 95% confidence intervals (95% CI) were calculated using the random-effects or fixed-effects model. Results Meta–analysis showed no significant association between COX-2 positivity and age, gender, tumor location, histology, stage, metastasis or 90% necrosis. Conversely, COX-2 immunoexpression was associated with overall survival rate (RR=2.12; 95% CI: 1.10–3.74; P=0.009) and disease-free survival rate (RR=1.63; 95% CI: 1.17–2.28; P=0.004) at 2 years. Sensitivity analysis performed by omitting low quality studies showed that the pooled results were stable. Conclusions COX-2 positivity was associated with a lower 2-year overall survival rate and disease-free survival rate. COX-2 expression change is an independent prognostic factor in patients with osteosarcoma. PMID:24358237
Crager, Michael R.; Tang, Gong
2015-01-01
We propose a method for assessing an individual patient’s risk of a future clinical event using clinical trial or cohort data and Cox proportional hazards regression, combining the information from several studies using meta-analysis techniques. The method combines patient-specific estimates of the log cumulative hazard across studies, weighting by the relative precision of the estimates, using either fixed- or random-effects meta-analysis calculations. Risk assessment can be done for any future patient using a few key summary statistics determined once and for all from each study. Generalizations of the method to logistic regression and linear models are immediate. We evaluate the methods using simulation studies and illustrate their application using real data. PMID:26664111
Genetic analysis of the DNA recognition sequence of the P2 Cox protein.
Cores de Vries, G; Wu, X S; Haggård-Ljungquist, E
1991-01-01
The Cox protein of temperate Escherichia coli phage P2 is involved in three important biological processes: (i) excision of the integrated prophage genome (G. Lindahl and M. Sunshine, Virology 49:180-187, 1972), (ii) transcriptional repression of the P2 Pc promoter, which controls the expression of the immunity repressor C and the integrase (S. Saha, E. Haggård-Ljungquist, and K. Nordström, EMBO J. 6:3191-3199, 1987), and (iii) transcriptional activation of the late PII promoter of the unrelated satellite phage P4 (S. Saha, E. Haggård-Ljungquist, and K. Nordström, Proc. Natl. Acad. Sci. USA 86:3973-3977, 1989). A comparison of the DNA regions protected by Cox from DNaseI degradation has revealed a presumptive Cox recognition sequence (Saha et al., Proc. Natl. Acad. Sci. USA). The binding region of Cox in the P2 Pc promoter contains three presumptive recognition sequences, "Cox boxes," located in tandem. P2 vir3 and P2 vir24 are virulent deletion mutants unable to plate on Cox-producing strains, most likely because the deletions locate the new early promoters too close to the Cox-binding region (Saha et al., EMBO J.). In this report, spontaneous P2 vir3 and vir24 mutants, no longer sensitive to repression by the Cox protein, have been isolated. These mutants plate with equal efficiency on strains with or without a Cox-producing plasmid, and they have been named cor for cox resistance. Three types are recognized; the four P2 vir3 cor mutants have a 1-base deletion in the first Cox box, while the P2 vir24 cor mutants were of two types; four have a base substitution in the first Cox box, and one has a base substitution in the second Cox box. The effect of the Cox protein on the mutated P2 vir3 and vir24 promoters was analyzed in vivo by using fusions to a promoterless cat (chloramphenicol acetyltransferase) gene. The activities of the P2 vir3 and vir24 early promoters, as opposed to the wild-type early Pe promoter, are drastically reduced by the Cox protein, and
Molecular docking analysis of known flavonoids as duel COX-2 inhibitors in the context of cancer.
Dash, Raju; Uddin, Mir Muhammad Nasir; Hosen, S M Zahid; Rahim, Zahed Bin; Dinar, Abu Mansur; Kabir, Mohammad Shah Hafez; Sultan, Ramiz Ahmed; Islam, Ashekul; Hossain, Md Kamrul
2015-01-01
Cyclooxygenase-2 (COX-2) catalyzed synthesis of prostaglandin E2 and it associates with tumor growth, infiltration, and metastasis in preclinical experiments. Known inhibitors against COX-2 exhibit toxicity. Therefore, it is of interest to screen natural compounds like flavanoids against COX-2. Molecular docking using 12 known flavanoids against COX-2 by FlexX and of ArgusLab were performed. All compounds showed a favourable binding energy of >-10 KJ/mol in FlexX and > -8 kcal/mol in ArgusLab. However, this data requires in vitro and in vivo verification for further consideration. PMID:26770028
Molecular docking analysis of known flavonoids as duel COX-2 inhibitors in the context of cancer
Dash, Raju; Uddin, Mir Muhammad Nasir; Hosen, S.M. Zahid; Rahim, Zahed Bin; Dinar, Abu Mansur; Kabir, Mohammad Shah Hafez; Sultan, Ramiz Ahmed; Islam, Ashekul; Hossain, Md Kamrul
2015-01-01
Cyclooxygenase-2 (COX-2) catalyzed synthesis of prostaglandin E2 and it associates with tumor growth, infiltration, and metastasis in preclinical experiments. Known inhibitors against COX-2 exhibit toxicity. Therefore, it is of interest to screen natural compounds like flavanoids against COX-2. Molecular docking using 12 known flavanoids against COX-2 by FlexX and of ArgusLab were performed. All compounds showed a favourable binding energy of >-10 KJ/mol in FlexX and > -8 kcal/mol in ArgusLab. However, this data requires in vitro and in vivo verification for further consideration. PMID:26770028
Applying Regression Analysis to Problems in Institutional Research.
ERIC Educational Resources Information Center
Bohannon, Tom R.
1988-01-01
Regression analysis is one of the most frequently used statistical techniques in institutional research. Principles of least squares, model building, residual analysis, influence statistics, and multi-collinearity are described and illustrated. (Author/MSE)
ERIC Educational Resources Information Center
Hecht, Jeffrey B.
The analysis of regression residuals and detection of outliers are discussed, with emphasis on determining how deviant an individual data point must be to be considered an outlier and the impact that multiple suspected outlier data points have on the process of outlier determination and treatment. Only bivariate (one dependent and one independent)…
Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro
2012-11-01
Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan.
General Nature of Multicollinearity in Multiple Regression Analysis.
ERIC Educational Resources Information Center
Liu, Richard
1981-01-01
Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)
The Precision Efficacy Analysis for Regression Sample Size Method.
ERIC Educational Resources Information Center
Brooks, Gordon P.; Barcikowski, Robert S.
The general purpose of this study was to examine the efficiency of the Precision Efficacy Analysis for Regression (PEAR) method for choosing appropriate sample sizes in regression studies used for precision. The PEAR method, which is based on the algebraic manipulation of an accepted cross-validity formula, essentially uses an effect size to…
Regression Commonality Analysis: A Technique for Quantitative Theory Building
ERIC Educational Resources Information Center
Nimon, Kim; Reio, Thomas G., Jr.
2011-01-01
When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…
3D Regression Heat Map Analysis of Population Study Data.
Klemm, Paul; Lawonn, Kai; Glaßer, Sylvia; Niemann, Uli; Hegenscheid, Katrin; Völzke, Henry; Preim, Bernhard
2016-01-01
Epidemiological studies comprise heterogeneous data about a subject group to define disease-specific risk factors. These data contain information (features) about a subject's lifestyle, medical status as well as medical image data. Statistical regression analysis is used to evaluate these features and to identify feature combinations indicating a disease (the target feature). We propose an analysis approach of epidemiological data sets by incorporating all features in an exhaustive regression-based analysis. This approach combines all independent features w.r.t. a target feature. It provides a visualization that reveals insights into the data by highlighting relationships. The 3D Regression Heat Map, a novel 3D visual encoding, acts as an overview of the whole data set. It shows all combinations of two to three independent features with a specific target disease. Slicing through the 3D Regression Heat Map allows for the detailed analysis of the underlying relationships. Expert knowledge about disease-specific hypotheses can be included into the analysis by adjusting the regression model formulas. Furthermore, the influences of features can be assessed using a difference view comparing different calculation results. We applied our 3D Regression Heat Map method to a hepatic steatosis data set to reproduce results from a data mining-driven analysis. A qualitative analysis was conducted on a breast density data set. We were able to derive new hypotheses about relations between breast density and breast lesions with breast cancer. With the 3D Regression Heat Map, we present a visual overview of epidemiological data that allows for the first time an interactive regression-based analysis of large feature sets with respect to a disease. PMID:26529689
3D Regression Heat Map Analysis of Population Study Data.
Klemm, Paul; Lawonn, Kai; Glaßer, Sylvia; Niemann, Uli; Hegenscheid, Katrin; Völzke, Henry; Preim, Bernhard
2016-01-01
Epidemiological studies comprise heterogeneous data about a subject group to define disease-specific risk factors. These data contain information (features) about a subject's lifestyle, medical status as well as medical image data. Statistical regression analysis is used to evaluate these features and to identify feature combinations indicating a disease (the target feature). We propose an analysis approach of epidemiological data sets by incorporating all features in an exhaustive regression-based analysis. This approach combines all independent features w.r.t. a target feature. It provides a visualization that reveals insights into the data by highlighting relationships. The 3D Regression Heat Map, a novel 3D visual encoding, acts as an overview of the whole data set. It shows all combinations of two to three independent features with a specific target disease. Slicing through the 3D Regression Heat Map allows for the detailed analysis of the underlying relationships. Expert knowledge about disease-specific hypotheses can be included into the analysis by adjusting the regression model formulas. Furthermore, the influences of features can be assessed using a difference view comparing different calculation results. We applied our 3D Regression Heat Map method to a hepatic steatosis data set to reproduce results from a data mining-driven analysis. A qualitative analysis was conducted on a breast density data set. We were able to derive new hypotheses about relations between breast density and breast lesions with breast cancer. With the 3D Regression Heat Map, we present a visual overview of epidemiological data that allows for the first time an interactive regression-based analysis of large feature sets with respect to a disease.
Combined survival analysis of cardiac patients by a Cox PH model and a Markov chain.
Shauly, Michal; Rabinowitz, Gad; Gilutz, Harel; Parmet, Yisrael
2011-10-01
The control and treatment of dyslipidemia is a major public health challenge, particularly for patients with coronary heart diseases. In this paper we propose a framework for survival analysis of patients who had a major cardiac event, focusing on assessment of the effect of changing LDL-cholesterol level and statins consumption on survival. This framework includes a Cox PH model and a Markov chain, and combines their results into reinforced conclusions regarding the factors that affect survival time. We prospectively studied 2,277 cardiac patients, and the results show high congruence between the Markov model and the PH model; both evidence that diabetes, history of stroke, peripheral vascular disease and smoking significantly increase hazard rate and reduce survival time. On the other hand, statin consumption is correlated with a lower hazard rate and longer survival time in both models. The role of such a framework in understanding the therapeutic behavior of patients and implementing effective secondary and primary prevention of heart diseases is discussed here. PMID:21735134
NASA Astrophysics Data System (ADS)
Ahn, Kuk-Hyun; Palmer, Richard
2016-09-01
Despite wide use of regression-based regional flood frequency analysis (RFFA) methods, the majority are based on either ordinary least squares (OLS) or generalized least squares (GLS). This paper proposes 'spatial proximity' based RFFA methods using the spatial lagged model (SLM) and spatial error model (SEM). The proposed methods are represented by two frameworks: the quantile regression technique (QRT) and parameter regression technique (PRT). The QRT develops prediction equations for flooding quantiles in average recurrence intervals (ARIs) of 2, 5, 10, 20, and 100 years whereas the PRT provides prediction of three parameters for the selected distribution. The proposed methods are tested using data incorporating 30 basin characteristics from 237 basins in Northeastern United States. Results show that generalized extreme value (GEV) distribution properly represents flood frequencies in the study gages. Also, basin area, stream network, and precipitation seasonality are found to be the most effective explanatory variables in prediction modeling by the QRT and PRT. 'Spatial proximity' based RFFA methods provide reliable flood quantile estimates compared to simpler methods. Compared to the QRT, the PRT may be recommended due to its accuracy and computational simplicity. The results presented in this paper may serve as one possible guidepost for hydrologists interested in flood analysis at ungaged sites.
Linear regression analysis of survival data with missing censoring indicators.
Wang, Qihua; Dinse, Gregg E
2011-04-01
Linear regression analysis has been studied extensively in a random censorship setting, but typically all of the censoring indicators are assumed to be observed. In this paper, we develop synthetic data methods for estimating regression parameters in a linear model when some censoring indicators are missing. We define estimators based on regression calibration, imputation, and inverse probability weighting techniques, and we prove all three estimators are asymptotically normal. The finite-sample performance of each estimator is evaluated via simulation. We illustrate our methods by assessing the effects of sex and age on the time to non-ambulatory progression for patients in a brain cancer clinical trial. PMID:20559722
Regression Model Optimization for the Analysis of Experimental Data
NASA Technical Reports Server (NTRS)
Ulbrich, N.
2009-01-01
A candidate math model search algorithm was developed at Ames Research Center that determines a recommended math model for the multivariate regression analysis of experimental data. The search algorithm is applicable to classical regression analysis problems as well as wind tunnel strain gage balance calibration analysis applications. The algorithm compares the predictive capability of different regression models using the standard deviation of the PRESS residuals of the responses as a search metric. This search metric is minimized during the search. Singular value decomposition is used during the search to reject math models that lead to a singular solution of the regression analysis problem. Two threshold dependent constraints are also applied. The first constraint rejects math models with insignificant terms. The second constraint rejects math models with near-linear dependencies between terms. The math term hierarchy rule may also be applied as an optional constraint during or after the candidate math model search. The final term selection of the recommended math model depends on the regressor and response values of the data set, the user s function class combination choice, the user s constraint selections, and the result of the search metric minimization. A frequently used regression analysis example from the literature is used to illustrate the application of the search algorithm to experimental data.
Consalvi, Sara; Poce, Giovanna; Ragno, Rino; Sabatino, Manuela; La Motta, Concettina; Sartini, Stefania; Calderone, Vincenzo; Martelli, Alma; Ghelardini, Carla; Di Cesare Mannelli, Lorenzo; Biava, Mariangela
2016-08-19
Herein we report the synthesis, biological evaluation, and docking analysis of a class of cyclooxygenase-2 (COX-2) inhibitors with nitric oxide (NO)-releasing properties. In an earlier study, a number of selective COX-2 inhibitors/NO donors were developed by conjugating a diarylpyrrole scaffold endowed with selective COX-2 inhibitory properties with various nitrooxyalkyl side chains such as esters, α-amino esters, amides, α-amino amides, ethers, β-amino ethers, inverse esters, and amides. These candidates were found to have high in vitro potencies (COX-2 inhibition at 10 μm: ≥96 %), great efficacy in determining NO-vasorelaxing responses, and good antinociceptive activity in an abdominal writhing test. Among the compounds synthesized in the present work, derivative 2 b [2-(2-(1-(3-fluorophenyl)-2-methyl-5-(4-sulfamoylphenyl)-1H-pyrrol-3-yl)acetamido)ethyl nitrate] showed particularly outstanding activity, with efficacy similar to that of celecoxib even at very low concentrations. PMID:27229194
Background stratified Poisson regression analysis of cohort data
Langholz, Bryan
2012-01-01
Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as ‘nuisance’ variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this ‘conditional’ regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models. PMID:22193911
Background stratified Poisson regression analysis of cohort data.
Richardson, David B; Langholz, Bryan
2012-03-01
Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models. PMID:22193911
Mignemi, Nicholas A.; Itani, Doha M.; Fasig, John H.; Keedy, Vicki L.; Hande, Kenneth R.; Whited, Brent W.; Homlar, Kelly C.; Correa, Hernan; Coffin, Cheryl M.; Black, Jennifer O.; Yi, Yajun; Halpern, Jennifer L.; Holt, Ginger E.; Schwartz, Herbert S.; Schoenecker, Jonathan G.; Cates, Justin M. M.
2014-01-01
Summary Despite reports of sex steroid receptor and COX2 expression in desmoid-type fibromatosis, responses to single agent therapy with anti-estrogens and nonsteroidal anti-inflammatory drugs are unpredictable. Perhaps combination pharmacotherapy might be more effective in desmoid tumors that co-express these targets. Clearly, a further understanding of the signaling pathways deregulated in desmoid tumors is essential for development of targeted molecular therapy. Transforming growth factor-β (TGFβ) and bone morphogenetic proteins (BMPs) are important regulators of fibroblast proliferation and matrix deposition, but little is known about the TGFβ superfamily in fibromatosis. A tissue microarray representing 27 desmoid tumors was constructed; 14 samples of healing scar and 6 samples of normal fibrous tissue were included for comparison. Expression of selected receptors and activated downstream transcription factors of TGFβ family signaling pathways, β-catenin, sex steroid hormone receptors and COX2 were assessed by immunohistochemistry; patterns of co-expression were explored via correlational statistical analyses. In addition to β-catenin, immunoreactivity for phosphorylated SMAD2/3 (indicative of active TGFβ signaling) and COX2 was significantly increased in desmoid tumors compared to healing scar and quiescent fibrous tissue. Low levels of phosphorylated SMAD1/5/8 were detected in only a minority of cases. TGFβ receptor type 1 and androgen receptor were expressed in both desmoid tumors and scar, but not in fibrous tissue. Estrogen receptor-β was present in all cases studied. TGFβ signaling appears to be activated in desmoid-type fibromatosis and phosphorylated SMAD2/3 and COX2 immunoreactivity may be of diagnostic utility in these tumors. Given the frequency of androgen receptor, estrogen receptor-β and COX2 co-expression in desmoid tumors, further assessment of the efficacy of combination pharmacotherapy using hormonal agonists/antagonists together
Joint regression analysis and AMMI model applied to oat improvement
NASA Astrophysics Data System (ADS)
Oliveira, A.; Oliveira, T. A.; Mejza, S.
2012-09-01
In our work we present an application of some biometrical methods useful in genotype stability evaluation, namely AMMI model, Joint Regression Analysis (JRA) and multiple comparison tests. A genotype stability analysis of oat (Avena Sativa L.) grain yield was carried out using data of the Portuguese Plant Breeding Board, sample of the 22 different genotypes during the years 2002, 2003 and 2004 in six locations. In Ferreira et al. (2006) the authors state the relevance of the regression models and of the Additive Main Effects and Multiplicative Interactions (AMMI) model, to study and to estimate phenotypic stability effects. As computational techniques we use the Zigzag algorithm to estimate the regression coefficients and the agricolae-package available in R software for AMMI model analysis.
Time series analysis using semiparametric regression on oil palm production
NASA Astrophysics Data System (ADS)
Yundari, Pasaribu, U. S.; Mukhaiyar, U.
2016-04-01
This paper presents semiparametric kernel regression method which has shown its flexibility and easiness in mathematical calculation, especially in estimating density and regression function. Kernel function is continuous and it produces a smooth estimation. The classical kernel density estimator is constructed by completely nonparametric analysis and it is well reasonable working for all form of function. Here, we discuss about parameter estimation in time series analysis. First, we consider the parameters are exist, then we use nonparametrical estimation which is called semiparametrical. The selection of optimum bandwidth is obtained by considering the approximation of Mean Integrated Square Root Error (MISE).
Analysis of Sting Balance Calibration Data Using Optimized Regression Models
NASA Technical Reports Server (NTRS)
Ulbrich, N.; Bader, Jon B.
2010-01-01
Calibration data of a wind tunnel sting balance was processed using a candidate math model search algorithm that recommends an optimized regression model for the data analysis. During the calibration the normal force and the moment at the balance moment center were selected as independent calibration variables. The sting balance itself had two moment gages. Therefore, after analyzing the connection between calibration loads and gage outputs, it was decided to choose the difference and the sum of the gage outputs as the two responses that best describe the behavior of the balance. The math model search algorithm was applied to these two responses. An optimized regression model was obtained for each response. Classical strain gage balance load transformations and the equations of the deflection of a cantilever beam under load are used to show that the search algorithm s two optimized regression models are supported by a theoretical analysis of the relationship between the applied calibration loads and the measured gage outputs. The analysis of the sting balance calibration data set is a rare example of a situation when terms of a regression model of a balance can directly be derived from first principles of physics. In addition, it is interesting to note that the search algorithm recommended the correct regression model term combinations using only a set of statistical quality metrics that were applied to the experimental data during the algorithm s term selection process.
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.
Quantile Regression with Censored Data
ERIC Educational Resources Information Center
Lin, Guixian
2009-01-01
The Cox proportional hazards model and the accelerated failure time model are frequently used in survival data analysis. They are powerful, yet have limitation due to their model assumptions. Quantile regression offers a semiparametric approach to model data with possible heterogeneity. It is particularly powerful for censored responses, where the…
Regression Analysis: Instructional Resource for Cost/Managerial Accounting
ERIC Educational Resources Information Center
Stout, David E.
2015-01-01
This paper describes a classroom-tested instructional resource, grounded in principles of active learning and a constructivism, that embraces two primary objectives: "demystify" for accounting students technical material from statistics regarding ordinary least-squares (OLS) regression analysis--material that students may find obscure or…
Analysis of Sting Balance Calibration Data Using Optimized Regression Models
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert; Bader, Jon B.
2009-01-01
Calibration data of a wind tunnel sting balance was processed using a search algorithm that identifies an optimized regression model for the data analysis. The selected sting balance had two moment gages that were mounted forward and aft of the balance moment center. The difference and the sum of the two gage outputs were fitted in the least squares sense using the normal force and the pitching moment at the balance moment center as independent variables. The regression model search algorithm predicted that the difference of the gage outputs should be modeled using the intercept and the normal force. The sum of the two gage outputs, on the other hand, should be modeled using the intercept, the pitching moment, and the square of the pitching moment. Equations of the deflection of a cantilever beam are used to show that the search algorithm s two recommended math models can also be obtained after performing a rigorous theoretical analysis of the deflection of the sting balance under load. The analysis of the sting balance calibration data set is a rare example of a situation when regression models of balance calibration data can directly be derived from first principles of physics and engineering. In addition, it is interesting to see that the search algorithm recommended the same regression models for the data analysis using only a set of statistical quality metrics.
COX-2-765G>C Polymorphism Increases the Risk of Cancer: A Meta-Analysis
Zhang, Xiao-wei; Hua, Rui-xi; Guo, Wei-jian
2013-01-01
Background Chronic inflammation has been regarded as an important mechanism in carcinogenesis. Inflammation-associated genetic variants have been highly associated with cancer risk. Polymorphisms in the gene cyclooxygenase-2 (COX-2), a pro-inflammation factor, have been suggested to alter the risk of multiple tumors, but the findings of various studies are not consistent. Methods A literature search through February 2013 was performed using PubMed, EMBASE, and CNKI databases. We used odds ratios (ORs) with confidence intervals (CIs) of 95% to assess the strength of the association between the COX-2-765G>C polymorphism and cancer risk in a random-effect model. We also assessed heterogeneity and publication bias. Results In total, 65 articles with 29,487 cancer cases and 39,212 non-cancer controls were included in this meta-analysis. The pooled OR (95% CIs) in the co-dominant model (GC vs. GG) was 1.11 (1.02–1.22), and in the dominant model ((CC+GC) vs. GG), the pooled OR was 1.12 (1.02–1.23). In the subgroup analysis, stratified by cancer type and race, significant associations were found between the-765 C allele and higher risk for gastric cancer, leukemia, pancreatic cancer, and cancer in the Asian population. Conclusion In summary, the COX-2-765 C allele was related to increased cancer susceptibility, especially gastric cancer and cancer in the Asian population. PMID:24023834
Parra, Edwin Roger; Lin, Flavia; Martins, Vanessa; Rangel, Maristela Peres; Capelozzi, Vera Luiza
2013-01-01
OBJECTIVE: To study the expression of COX-1 and COX-2 in the remodeled lung in systemic sclerosis (SSc) and idiopathic pulmonary fibrosis (IPF) patients, correlating that expression with patient survival. METHODS: We examined open lung biopsy specimens from 24 SSc patients and 30 IPF patients, using normal lung tissue as a control. The histological patterns included fibrotic nonspecific interstitial pneumonia (NSIP) in SSc patients and usual interstitial pneumonia (UIP) in IPF patients. We used immunohistochemistry and histomorphometry to evaluate the expression of COX-1 and COX-2 in alveolar septa, vessels, and bronchioles. We then correlated that expression with pulmonary function test results and evaluated its impact on patient survival. RESULTS: The expression of COX-1 and COX-2 in alveolar septa was significantly higher in IPF-UIP and SSc-NSIP lung tissue than in the control tissue. No difference was found between IPF-UIP and SSc-NSIP tissue regarding COX-1 and COX-2 expression. Multivariate analysis based on the Cox regression model showed that the factors associated with a low risk of death were younger age, high DLCO/alveolar volume, IPF, and high COX-1 expression in alveolar septa, whereas those associated with a high risk of death were advanced age, low DLCO/alveolar volume, SSc (with NSIP), and low COX-1 expression in alveolar septa. CONCLUSIONS: Our findings suggest that strategies aimed at preventing low COX-1 synthesis will have a greater impact on SSc, whereas those aimed at preventing high COX-2 synthesis will have a greater impact on IPF. However, prospective randomized clinical trials are needed in order to confirm that. PMID:24473763
Principal regression analysis and the index leverage effect
NASA Astrophysics Data System (ADS)
Reigneron, Pierre-Alain; Allez, Romain; Bouchaud, Jean-Philippe
2011-09-01
We revisit the index leverage effect, that can be decomposed into a volatility effect and a correlation effect. We investigate the latter using a matrix regression analysis, that we call ‘Principal Regression Analysis' (PRA) and for which we provide some analytical (using Random Matrix Theory) and numerical benchmarks. We find that downward index trends increase the average correlation between stocks (as measured by the most negative eigenvalue of the conditional correlation matrix), and makes the market mode more uniform. Upward trends, on the other hand, also increase the average correlation between stocks but rotates the corresponding market mode away from uniformity. There are two time scales associated to these effects, a short one on the order of a month (20 trading days), and a longer time scale on the order of a year. We also find indications of a leverage effect for sectorial correlations as well, which reveals itself in the second and third mode of the PRA.
Robust regression applied to fractal/multifractal analysis.
NASA Astrophysics Data System (ADS)
Portilla, F.; Valencia, J. L.; Tarquis, A. M.; Saa-Requejo, A.
2012-04-01
Fractal and multifractal are concepts that have grown increasingly popular in recent years in the soil analysis, along with the development of fractal models. One of the common steps is to calculate the slope of a linear fit commonly using least squares method. This shouldn't be a special problem, however, in many situations using experimental data the researcher has to select the range of scales at which is going to work neglecting the rest of points to achieve the best linearity that in this type of analysis is necessary. Robust regression is a form of regression analysis designed to circumvent some limitations of traditional parametric and non-parametric methods. In this method we don't have to assume that the outlier point is simply an extreme observation drawn from the tail of a normal distribution not compromising the validity of the regression results. In this work we have evaluated the capacity of robust regression to select the points in the experimental data used trying to avoid subjective choices. Based on this analysis we have developed a new work methodology that implies two basic steps: • Evaluation of the improvement of linear fitting when consecutive points are eliminated based on R p-value. In this way we consider the implications of reducing the number of points. • Evaluation of the significance of slope difference between fitting with the two extremes points and fitted with the available points. We compare the results applying this methodology and the common used least squares one. The data selected for these comparisons are coming from experimental soil roughness transect and simulated based on middle point displacement method adding tendencies and noise. The results are discussed indicating the advantages and disadvantages of each methodology. Acknowledgements Funding provided by CEIGRAM (Research Centre for the Management of Agricultural and Environmental Risks) and by Spanish Ministerio de Ciencia e Innovación (MICINN) through project no
2012-01-01
Background Evidence is accumulating that chronic inflammation may have an important role in prostate cancer (PCa). The COX-2 polymorphism rs2745557 (+202 C/T) has been extensively investigated as a potential risk factor for PCa, but the results have thus far been inconclusive. This meta-analysis was performed to derive a more precise estimation of the association. Methods A comprehensive search was conducted to identify all case-control studies of COX-2 rs2745557 polymorphism and PCa risk. We used odds ratios (ORs) to assess the strength of the association, and 95% confidence intervals (CIs) give a sense of the precision of the estimate. Statistical analyses were performed by Review Manage, version 5.0 and Stata 10.0. Results A total of 8 available studies were considered in the present meta-analysis, with 11356 patients and 11641 controls for rs2745557. When all groups were pooled, there was no evidence that rs2745557 had significant association with PCa under co-dominant, recessive, over-dominant, and allelic models. However, our analysis suggested that rs2745557 was associated with a lower PCa risk under dominant model in overall population (OR = 0.85, 95%CI = 0.74-0.97, P = 0.02). When stratifying for race, there was a significant association between rs2745557 polymorphism and lower PCa risk in dominant model comparison in the subgroup of Caucasians (OR = 0.86, 95%CI = 0.75-0.99, P = 0.04), but not in co-dominant, recessive, over-dominant and allelic comparisons. Conclusion Based on our meta-analysis, COX-2 rs2745557 was associated with a lower PCa risk under dominant model in Caucasians. PMID:22435969
Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.
Zhang, Futao; Xie, Dan; Liang, Meimei; Xiong, Momiao
2016-04-01
To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI's Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.
Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.
Zhang, Futao; Xie, Dan; Liang, Meimei; Xiong, Momiao
2016-04-01
To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI's Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes. PMID:27104857
Poisson Regression Analysis of Illness and Injury Surveillance Data
Frome E.L., Watkins J.P., Ellis E.D.
2012-12-12
The Department of Energy (DOE) uses illness and injury surveillance to monitor morbidity and assess the overall health of the work force. Data collected from each participating site include health events and a roster file with demographic information. The source data files are maintained in a relational data base, and are used to obtain stratified tables of health event counts and person time at risk that serve as the starting point for Poisson regression analysis. The explanatory variables that define these tables are age, gender, occupational group, and time. Typical response variables of interest are the number of absences due to illness or injury, i.e., the response variable is a count. Poisson regression methods are used to describe the effect of the explanatory variables on the health event rates using a log-linear main effects model. Results of fitting the main effects model are summarized in a tabular and graphical form and interpretation of model parameters is provided. An analysis of deviance table is used to evaluate the importance of each of the explanatory variables on the event rate of interest and to determine if interaction terms should be considered in the analysis. Although Poisson regression methods are widely used in the analysis of count data, there are situations in which over-dispersion occurs. This could be due to lack-of-fit of the regression model, extra-Poisson variation, or both. A score test statistic and regression diagnostics are used to identify over-dispersion. A quasi-likelihood method of moments procedure is used to evaluate and adjust for extra-Poisson variation when necessary. Two examples are presented using respiratory disease absence rates at two DOE sites to illustrate the methods and interpretation of the results. In the first example the Poisson main effects model is adequate. In the second example the score test indicates considerable over-dispersion and a more detailed analysis attributes the over-dispersion to extra
2014-01-01
Background A detailed analysis of the expression of 440 cancer-related genes was performed after the combined treatment of medulloblastoma cells with all-trans retinoic acid (ATRA) and inhibitors of lipoxygenases (LOX) and cyclooxygenases (COX). The combinations of retinoids and celecoxib as a COX-2 inhibitor were reported to be effective in some regimens of metronomic therapy of relapsed solid tumors with poor prognosis. Our previous findings on neuroblastoma cells using expression profiling showed that LOX/COX inhibitors have the capability of enhancing the differentiating action of ATRA. Presented study focused on the continuation of our previous work to confirm the possibility of enhancing ATRA-induced cell differentiation in these cell lines via the application of LOX/COX inhibitors. This study provides more detailed information concerning the mechanisms of the enhancement of the ATRA-induced differentiation of medulloblastoma cells. Methods The Daoy and D283 Med medulloblastoma cell lines were chosen for this study. Caffeic acid (an inhibitor of 5-LOX) and celecoxib (an inhibitor on COX-2) were used in combined treatment with ATRA. The expression profiling was performed using Human Cancer Oligo GEArray membranes, and the most promising results were verified using RT-PCR. Results The expression profiling of the selected cancer-related genes clearly confirmed that the differentiating effects of ATRA should be enhanced via its combined administration with caffeic acid or celecoxib. This effect was detected in both cell lines. An increased expression of the genes that encoded the proteins participating in induced differentiation and cytoskeleton remodeling was detected in both cell lines in a concentration-dependent manner. This effect was also observed for the CDKN1A gene encoding the p21 protein, which is an important regulator of the cell cycle, and for the genes encoding proteins that are associated with proteasome activity. Furthermore, our results showed
Four cases of Taenia saginata infection with an analysis of COX1 gene.
Cho, Jaeeun; Jung, Bong-Kwang; Lim, Hyemi; Kim, Min-Jae; Yooyen, Thanapon; Lee, Dongmin; Eom, Keeseon S; Shin, Eun-Hee; Chai, Jong-Yil
2014-02-01
Human taeniases had been not uncommon in the Republic of Korea (=Korea) until the 1980s. The prevalence decreased and a national survey in 2004 revealed no Taenia egg positive cases. However, a subsequent national survey in 2012 showed 0.04% (10 cases) prevalence of Taenia spp. eggs suggesting its resurgence in Korea. We recently encountered 4 cases of Taenia saginata infection who had symptoms of taeniasis that included discharge of proglottids. We obtained several proglottids from each case. Because the morphological features of T. saginata are almost indistinguishable from those of Taenia asiatica, molecular analyses using the PCR-RFLP and DNA sequencing of the cytochrome c oxidase subunit 1 (cox1) were performed to identify the species. The PCR-RFLP patterns of all of the 4 specimens were consistent with T. saginata, and the cox1 gene sequence showed 99.8-100% identity with that of T. saginata reported previously from Korea, Japan, China, and Cambodia. All of the 4 patients had the history of travel abroad but its relation with contracting taeniasis was unclear. Our findings may suggest resurgence of T. saginata infection among people in Korea. PMID:24623887
de Pedro, María; Baeza, Sara; Escudero, María-Teresa; Dierssen-Sotos, Trinidad; Gómez-Acebo, Inés; Pollán, Marina; Llorca, Javier
2015-01-01
Evidence on non-steroidal anti-inflammatory drugs (NSAID) use and breast cancer risk shows a slightly protective effect of these drugs, but previous studies lack randomized clinical trial results and present high heterogeneity in exposure measurement. This systematic review and meta-analysis widens the knowledge about NSAID use and breast cancer risk, updating the information from the last meta-analysis, focusing on evidence on specific effects of COX-2 inhibitors and differential expression patterns of hormonal receptors. A PubMed-database search was conducted to include all entries published with the keywords "BREAST CANCER NSAID ANTI-INFLAMMATORY" until 10/24/2013 providing original results from cohort studies, case-control studies, or randomized clinical trials with at least one reported relative risk (RR) or odds ratio (OR) on the association between any NSAID use and incidence of invasive breast cancer. This resulted in 49 publications, from which the information was retrieved about type of study, exposure characteristics, breast cancer characteristics, and breast cancer-NSAID association. Meta-analyses were performed separately for case-control and cohort studies and for different hormone-receptor status. NSAID use reduced invasive breast cancer risk by about 20 %. A similar effect was found for aspirin, acetaminophen, COX-2 inhibitors and, to a lesser extent, ibuprofen. The effect of aspirin was similar in preventing hormone-receptor-positive breast cancer. This meta-analysis suggests a slightly protective effect of NSAIDs-especially aspirin and COX-2 inhibitors- against breast cancer, which seems to be restricted to ER/PR+tumors.
Multivariate concentration determination using principal component regression with residual analysis
Keithley, Richard B.; Heien, Michael L.; Wightman, R. Mark
2009-01-01
Data analysis is an essential tenet of analytical chemistry, extending the possible information obtained from the measurement of chemical phenomena. Chemometric methods have grown considerably in recent years, but their wide use is hindered because some still consider them too complicated. The purpose of this review is to describe a multivariate chemometric method, principal component regression, in a simple manner from the point of view of an analytical chemist, to demonstrate the need for proper quality-control (QC) measures in multivariate analysis and to advocate the use of residuals as a proper QC method. PMID:20160977
Bayesian residual analysis for beta-binomial regression models
NASA Astrophysics Data System (ADS)
Pires, Rubiane Maria; Diniz, Carlos Alberto Ribeiro
2012-10-01
The beta-binomial regression model is an alternative model to the sum of any sequence of equicorrelated binary variables with common probability of success p. In this work a Bayesian perspective of this model is presented considering different link functions and different correlation structures. A general Bayesian residual analysis for this model, a issue which is often neglected in Bayesian analysis, using the residuals based on the predicted values obtained by the conditional predictive ordinate [1], the residuals based on the posterior distribution of the model parameters [2] and the Bayesian deviance residual [3] are presented in order to check the assumptions in the model.
Analysis of regression methods for solar activity forecasting
NASA Technical Reports Server (NTRS)
Lundquist, C. A.; Vaughan, W. W.
1979-01-01
The paper deals with the potential use of the most recent solar data to project trends in the next few years. Assuming that a mode of solar influence on weather can be identified, advantageous use of that knowledge presumably depends on estimating future solar activity. A frequently used technique for solar cycle predictions is a linear regression procedure along the lines formulated by McNish and Lincoln (1949). The paper presents a sensitivity analysis of the behavior of such regression methods relative to the following aspects: cycle minimum, time into cycle, composition of historical data base, and unnormalized vs. normalized solar cycle data. Comparative solar cycle forecasts for several past cycles are presented as to these aspects of the input data. Implications for the current cycle, No. 21, are also given.
A regressed phase analysis for coupled joint systems.
Wininger, Michael
2011-01-01
This study aims to address shortcomings of the relative phase analysis, a widely used method for assessment of coupling among joints of the lower limb. Goniometric data from 15 individuals with spastic diplegic cerebral palsy were recorded from the hip and knee joints during ambulation on a flat surface, and from a single healthy individual with no known motor impairment, over at least 10 gait cycles. The minimum relative phase (MRP) revealed substantial disparity in the timing and severity of the instance of maximum coupling, depending on which reference frame was selected: MRP(knee-hip) differed from MRP(hip-knee) by 16.1±14% of gait cycle and 50.6±77% difference in scale. Additionally, several relative phase portraits contained discontinuities which may contribute to error in phase feature extraction. These vagaries can be attributed to the predication of relative phase analysis on a transformation into the velocity-position phase plane, and the extraction of phase angle by the discontinuous arc-tangent operator. Here, an alternative phase analysis is proposed, wherein kinematic data is transformed into a profile of joint coupling across the entire gait cycle. By comparing joint velocities directly via a standard linear regression in the velocity-velocity phase plane, this regressed phase analysis provides several key advantages over relative phase analysis including continuity, commutativity between reference frames, and generalizability to many-joint systems.
Epistasis analysis for quantitative traits by functional regression model.
Zhang, Futao; Boerwinkle, Eric; Xiong, Momiao
2014-06-01
The critical barrier in interaction analysis for rare variants is that most traditional statistical methods for testing interactions were originally designed for testing the interaction between common variants and are difficult to apply to rare variants because of their prohibitive computational time and poor ability. The great challenges for successful detection of interactions with next-generation sequencing (NGS) data are (1) lack of methods for interaction analysis with rare variants, (2) severe multiple testing, and (3) time-consuming computations. To meet these challenges, we shift the paradigm of interaction analysis between two loci to interaction analysis between two sets of loci or genomic regions and collectively test interactions between all possible pairs of SNPs within two genomic regions. In other words, we take a genome region as a basic unit of interaction analysis and use high-dimensional data reduction and functional data analysis techniques to develop a novel functional regression model to collectively test interactions between all possible pairs of single nucleotide polymorphisms (SNPs) within two genome regions. By intensive simulations, we demonstrate that the functional regression models for interaction analysis of the quantitative trait have the correct type 1 error rates and a much better ability to detect interactions than the current pairwise interaction analysis. The proposed method was applied to exome sequence data from the NHLBI's Exome Sequencing Project (ESP) and CHARGE-S study. We discovered 27 pairs of genes showing significant interactions after applying the Bonferroni correction (P-values < 4.58 × 10(-10)) in the ESP, and 11 were replicated in the CHARGE-S study.
Spectral Regression Discriminant Analysis for Hyperspectral Image Classification
NASA Astrophysics Data System (ADS)
Pan, Y.; Wu, J.; Huang, H.; Liu, J.
2012-08-01
Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for Hyperspectral Image Classification. The manifold learning methods are popular for dimensionality reduction, such as Locally Linear Embedding, Isomap, and Laplacian Eigenmap. However, a disadvantage of many manifold learning methods is that their computations usually involve eigen-decomposition of dense matrices which is expensive in both time and memory. In this paper, we introduce a new dimensionality reduction method, called Spectral Regression Discriminant Analysis (SRDA). SRDA casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. It can make efficient use of data points to discover the intrinsic discriminant structure in the data. Experimental results on Washington DC Mall and AVIRIS Indian Pines hyperspectral data sets demonstrate the effectiveness of the proposed method.
Risk factors for mortality after bereavement: a logistic regression analysis
Bowling, Ann; Charlton, John
1987-01-01
A national sample of elderly widowed people was followed up for six years. Excess mortality was found for men aged 75 years and over in the first six months of bereavement compared with men of the same age in the general population. Logistic regression analysis, controlling for age and sex together, demonstrated that the best independent predictors of mortality among the elderly widowed were: interviewer assessment of low happiness level; interviewer assessed and self-reported problems with nerves and depression; and lack of telephone contacts. The general practitioner is well placed to assess levels of depression and unhappiness among the widowed and to check that they have adequate social support. PMID:3503942
FRATS: Functional Regression Analysis of DTI Tract Statistics
Zhu, Hongtu; Styner, Martin; Tang, Niansheng; Liu, Zhexing; Lin, Weili; Gilmore, John H.
2010-01-01
Diffusion tensor imaging (DTI) provides important information on the structure of white matter fiber bundles as well as detailed tissue properties along these fiber bundles in vivo. This paper presents a functional regression framework, called FRATS, for the analysis of multiple diffusion properties along fiber bundle as functions in an infinite dimensional space and their association with a set of covariates of interest, such as age, diagnostic status and gender, in real applications. The functional regression framework consists of four integrated components: the local polynomial kernel method for smoothing multiple diffusion properties along individual fiber bundles, a functional linear model for characterizing the association between fiber bundle diffusion properties and a set of covariates, a global test statistic for testing hypotheses of interest, and a resampling method for approximating the p-value of the global test statistic. The proposed methodology is applied to characterizing the development of five diffusion properties including fractional anisotropy, mean diffusivity, and the three eigenvalues of diffusion tensor along the splenium of the corpus callosum tract and the right internal capsule tract in a clinical study of neurodevelopment. Significant age and gestational age effects on the five diffusion properties were found in both tracts. The resulting analysis pipeline can be used for understanding normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles. PMID:20335089
Evaluating Geographically Weighted Regression Models for Environmental Chemical Risk Analysis
Czarnota, Jenna; Wheeler, David C; Gennings, Chris
2015-01-01
In the evaluation of cancer risk related to environmental chemical exposures, the effect of many correlated chemicals on disease is often of interest. The relationship between correlated environmental chemicals and health effects is not always constant across a study area, as exposure levels may change spatially due to various environmental factors. Geographically weighted regression (GWR) has been proposed to model spatially varying effects. However, concerns about collinearity effects, including regression coefficient sign reversal (ie, reversal paradox), may limit the applicability of GWR for environmental chemical risk analysis. A penalized version of GWR, the geographically weighted lasso, has been proposed to remediate the collinearity effects in GWR models. Our focus in this study was on assessing through a simulation study the ability of GWR and GWL to correctly identify spatially varying chemical effects for a mixture of correlated chemicals within a study area. Our results showed that GWR suffered from the reversal paradox, while GWL overpenalized the effects for the chemical most strongly related to the outcome. PMID:25983546
Regression analysis exploring teacher impact on student FCI post scores
NASA Astrophysics Data System (ADS)
Mahadeo, Jonathan V.; Manthey, Seth R.; Brewe, Eric
2013-01-01
High School Modeling Workshops are designed to improve high school physics teachers' understanding of physics and how to teach using the Modeling method. The basic assumption is that the teacher plays a critical role in their students' physics education. This study investigated teacher impacts on students' Force Concept Inventory scores, (FCI), with the hopes of identifying quantitative differences between teachers. This study examined student FCI scores from 18 teachers with at least a year of teaching high school physics. This data was then evaluated using a General Linear Model (GLM), which allowed for a regression equation to be fitted to the data. This regression equation was used to predict student post FCI scores, based on: teacher ID, student pre FCI score, gender, and representation. The results show 12 out of 18 teachers significantly impact their student post FCI scores. The GLM further revealed that of the 12 teachers only five have a positive impact on student post FCI scores. Given these differences among teachers it is our intention to extend our analysis to investigate pedagogical differences between them.
Nonparametric survival analysis using Bayesian Additive Regression Trees (BART).
Sparapani, Rodney A; Logan, Brent R; McCulloch, Robert E; Laud, Purushottam W
2016-07-20
Bayesian additive regression trees (BART) provide a framework for flexible nonparametric modeling of relationships of covariates to outcomes. Recently, BART models have been shown to provide excellent predictive performance, for both continuous and binary outcomes, and exceeding that of its competitors. Software is also readily available for such outcomes. In this article, we introduce modeling that extends the usefulness of BART in medical applications by addressing needs arising in survival analysis. Simulation studies of one-sample and two-sample scenarios, in comparison with long-standing traditional methods, establish face validity of the new approach. We then demonstrate the model's ability to accommodate data from complex regression models with a simulation study of a nonproportional hazards scenario with crossing survival functions and survival function estimation in a scenario where hazards are multiplicatively modified by a highly nonlinear function of the covariates. Using data from a recently published study of patients undergoing hematopoietic stem cell transplantation, we illustrate the use and some advantages of the proposed method in medical investigations. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26854022
Least Principal Components Analysis (LPCA): An Alternative to Regression Analysis.
ERIC Educational Resources Information Center
Olson, Jeffery E.
Often, all of the variables in a model are latent, random, or subject to measurement error, or there is not an obvious dependent variable. When any of these conditions exist, an appropriate method for estimating the linear relationships among the variables is Least Principal Components Analysis. Least Principal Components are robust, consistent,…
Estimation of crown closure from AVIRIS data using regression analysis
NASA Technical Reports Server (NTRS)
Staenz, K.; Williams, D. J.; Truchon, M.; Fritz, R.
1993-01-01
Crown closure is one of the input parameters used for forest growth and yield modelling. Preliminary work by Staenz et al. indicates that imaging spectrometer data acquired with sensors such as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) have some potential for estimating crown closure on a stand level. The objectives of this paper are: (1) to establish a relationship between AVIRIS data and the crown closure derived from aerial photography of a forested test site within the Interior Douglas Fir biogeoclimatic zone in British Columbia, Canada; (2) to investigate the impact of atmospheric effects and the forest background on the correlation between AVIRIS data and crown closure estimates; and (3) to improve this relationship using multiple regression analysis.
A Visual Analytics Approach for Correlation, Classification, and Regression Analysis
Steed, Chad A; SwanII, J. Edward; Fitzpatrick, Patrick J.; Jankun-Kelly, T.J.
2013-01-01
New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today s increasing complex, multivariate data sets. In this paper, a visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today s data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. This chapter provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.
Moderated regression analysis and Likert scales: too coarse for comfort.
Russell, C J; Bobko, P
1992-06-01
One of the most commonly accepted models of relationships among three variables in applied industrial and organizational psychology is the simple moderator effect. However, many authors have expressed concern over the general lack of empirical support for interaction effects reported in the literature. We demonstrate in the current sample that use of a continuous, dependent-response scale instead of a discrete, Likert-type scale, causes moderated regression analysis effect sizes to increase an average of 93%. We suggest that use of relatively coarse Likert scales to measure fine dependent responses causes information loss that, although varying widely across subjects, greatly reduces the probability of detecting true interaction effects. Specific recommendations for alternate research strategies are made. PMID:1601825
A Visual Analytics Approach for Correlation, Classification, and Regression Analysis
Steed, Chad A; SwanII, J. Edward; Fitzpatrick, Patrick J.; Jankun-Kelly, T.J.
2012-02-01
New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today's increasing complex, multivariate data sets. In this paper, a novel visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today's data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. The current work provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.
Spatial regression analysis of traffic crashes in Seoul.
Rhee, Kyoung-Ah; Kim, Joon-Ki; Lee, Young-ihn; Ulfarsson, Gudmundur F
2016-06-01
Traffic crashes can be spatially correlated events and the analysis of the distribution of traffic crash frequency requires evaluation of parameters that reflect spatial properties and correlation. Typically this spatial aspect of crash data is not used in everyday practice by planning agencies and this contributes to a gap between research and practice. A database of traffic crashes in Seoul, Korea, in 2010 was developed at the traffic analysis zone (TAZ) level with a number of GIS developed spatial variables. Practical spatial models using available software were estimated. The spatial error model was determined to be better than the spatial lag model and an ordinary least squares baseline regression. A geographically weighted regression model provided useful insights about localization of effects. The results found that an increased length of roads with speed limit below 30 km/h and a higher ratio of residents below age of 15 were correlated with lower traffic crash frequency, while a higher ratio of residents who moved to the TAZ, more vehicle-kilometers traveled, and a greater number of access points with speed limit difference between side roads and mainline above 30 km/h all increased the number of traffic crashes. This suggests, for example, that better control or design for merging lower speed roads with higher speed roads is important. A key result is that the length of bus-only center lanes had the largest effect on increasing traffic crashes. This is important as bus-only center lanes with bus stop islands have been increasingly used to improve transit times. Hence the potential negative safety impacts of such systems need to be studied further and mitigated through improved design of pedestrian access to center bus stop islands. PMID:26994374
Inferring gene expression dynamics via functional regression analysis
Müller, Hans-Georg; Chiou, Jeng-Min; Leng, Xiaoyan
2008-01-01
Background Temporal gene expression profiles characterize the time-dynamics of expression of specific genes and are increasingly collected in current gene expression experiments. In the analysis of experiments where gene expression is obtained over the life cycle, it is of interest to relate temporal patterns of gene expression associated with different developmental stages to each other to study patterns of long-term developmental gene regulation. We use tools from functional data analysis to study dynamic changes by relating temporal gene expression profiles of different developmental stages to each other. Results We demonstrate that functional regression methodology can pinpoint relationships that exist between temporary gene expression profiles for different life cycle phases and incorporates dimension reduction as needed for these high-dimensional data. By applying these tools, gene expression profiles for pupa and adult phases are found to be strongly related to the profiles of the same genes obtained during the embryo phase. Moreover, one can distinguish between gene groups that exhibit relationships with positive and others with negative associations between later life and embryonal expression profiles. Specifically, we find a positive relationship in expression for muscle development related genes, and a negative relationship for strictly maternal genes for Drosophila, using temporal gene expression profiles. Conclusion Our findings point to specific reactivation patterns of gene expression during the Drosophila life cycle which differ in characteristic ways between various gene groups. Functional regression emerges as a useful tool for relating gene expression patterns from different developmental stages, and avoids the problems with large numbers of parameters and multiple testing that affect alternative approaches. PMID:18226220
Zhao, Qiu-jiong; Bai, Shao-cong; Cheng, Cheng; Tao, Ben-zhang; Wang, Le-kai; Liang, Shuang; Yin, Ling; Hang, Xing-yi; Shang, Ai-jia
2016-01-01
Copy number variations have been found in patients with neural tube abnormalities. In this study, we performed genome-wide screening using high-resolution array-based comparative genomic hybridization in three children with tethered spinal cord syndrome and two healthy parents. Of eight copy number variations, four were non-polymorphic. These non-polymorphic copy number variations were associated with Angelman and Prader-Willi syndromes, and microcephaly. Gene function enrichment analysis revealed that COX8C, a gene associated with metabolic disorders of the nervous system, was located in the copy number variation region of Patient 1. Our results indicate that array-based comparative genomic hybridization can be used to diagnose tethered spinal cord syndrome. Our results may help determine the pathogenesis of tethered spinal cord syndrome and prevent occurrence of this disease.
Zhao, Qiu-jiong; Bai, Shao-cong; Cheng, Cheng; Tao, Ben-zhang; Wang, Le-kai; Liang, Shuang; Yin, Ling; Hang, Xing-yi; Shang, Ai-jia
2016-01-01
Copy number variations have been found in patients with neural tube abnormalities. In this study, we performed genome-wide screening using high-resolution array-based comparative genomic hybridization in three children with tethered spinal cord syndrome and two healthy parents. Of eight copy number variations, four were non-polymorphic. These non-polymorphic copy number variations were associated with Angelman and Prader-Willi syndromes, and microcephaly. Gene function enrichment analysis revealed that COX8C, a gene associated with metabolic disorders of the nervous system, was located in the copy number variation region of Patient 1. Our results indicate that array-based comparative genomic hybridization can be used to diagnose tethered spinal cord syndrome. Our results may help determine the pathogenesis of tethered spinal cord syndrome and prevent occurrence of this disease. PMID:27651783
Zhao, Qiu-Jiong; Bai, Shao-Cong; Cheng, Cheng; Tao, Ben-Zhang; Wang, Le-Kai; Liang, Shuang; Yin, Ling; Hang, Xing-Yi; Shang, Ai-Jia
2016-08-01
Copy number variations have been found in patients with neural tube abnormalities. In this study, we performed genome-wide screening using high-resolution array-based comparative genomic hybridization in three children with tethered spinal cord syndrome and two healthy parents. Of eight copy number variations, four were non-polymorphic. These non-polymorphic copy number variations were associated with Angelman and Prader-Willi syndromes, and microcephaly. Gene function enrichment analysis revealed that COX8C, a gene associated with metabolic disorders of the nervous system, was located in the copy number variation region of Patient 1. Our results indicate that array-based comparative genomic hybridization can be used to diagnose tethered spinal cord syndrome. Our results may help determine the pathogenesis of tethered spinal cord syndrome and prevent occurrence of this disease. PMID:27651783
Standardized Regression Coefficients as Indices of Effect Sizes in Meta-Analysis
ERIC Educational Resources Information Center
Kim, Rae Seon
2011-01-01
When conducting a meta-analysis, it is common to find many collected studies that report regression analyses, because multiple regression analysis is widely used in many fields. Meta-analysis uses effect sizes drawn from individual studies as a means of synthesizing a collection of results. However, indices of effect size from regression analyses…
Risk factors for temporomandibular disorder: Binary logistic regression analysis
Magalhães, Bruno G.; de-Sousa, Stéphanie T.; de Mello, Victor V C.; da-Silva-Barbosa, André C.; de-Assis-Morais, Mariana P L.; Barbosa-Vasconcelos, Márcia M V.
2014-01-01
Objectives: To analyze the influence of socioeconomic and demographic factors (gender, economic class, age and marital status) on the occurrence of temporomandibular disorder. Study Design: One hundred individuals from urban areas in the city of Recife (Brazil) registered at Family Health Units was examined using Axis I of the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) which addresses myofascial pain and joint problems (disc displacement, arthralgia, osteoarthritis and oesteoarthrosis). The Brazilian Economic Classification Criteria (CCEB) was used for the collection of socioeconomic and demographic data. Then, it was categorized as Class A (high social class), Classes B/C (middle class) and Classes D/E (very poor social class). The results were analyzed using Pearson’s chi-square test for proportions, Fisher’s exact test, nonparametric Mann-Whitney test and Binary logistic regression analysis. Results: None of the participants belonged to Class A, 72% belonged to Classes B/C and 28% belonged to Classes D/E. The multivariate analysis revealed that participants from Classes D/E had a 4.35-fold greater chance of exhibiting myofascial pain and 11.3-fold greater chance of exhibiting joint problems. Conclusions: Poverty is a important condition to exhibit myofascial pain and joint problems. Key words:Temporomandibular joint disorders, risk factors, prevalence. PMID:24316706
Integrated analysis of incidence, progression, regression and disappearance probabilities
Huang, Guan-Hua
2008-01-01
Background Age-related maculopathy (ARM) is a leading cause of vision loss in people aged 65 or older. ARM is distinctive in that it is a disease which can transition through incidence, progression, regression and disappearance. The purpose of this study is to develop methodologies for studying the relationship of risk factors with different transition probabilities. Methods Our framework for studying this relationship includes two different analytical approaches. In the first approach, one can define, model and estimate the relationship between each transition probability and risk factors separately. This approach is similar to constraining a population to a certain disease status at the baseline, and then analyzing the probability of the constrained population to develop a different status. While this approach is intuitive, one risks losing available information while at the same time running into the problem of insufficient sample size. The second approach specifies a transition model for analyzing such a disease. This model provides the conditional probability of a current disease status based upon a previous status, and can therefore jointly analyze all transition probabilities. Throughout the paper, an analysis to determine the birth cohort effect on ARM is used as an illustration. Results and conclusion This study has found parallel separate and joint analyses to be more enlightening than any analysis in isolation. By implementing both approaches, one can obtain more reliable and more efficient results. PMID:18577235
Mixed-effects Poisson regression analysis of adverse event reports
Gibbons, Robert D.; Segawa, Eisuke; Karabatsos, George; Amatya, Anup K.; Bhaumik, Dulal K.; Brown, C. Hendricks; Kapur, Kush; Marcus, Sue M.; Hur, Kwan; Mann, J. John
2008-01-01
SUMMARY A new statistical methodology is developed for the analysis of spontaneous adverse event (AE) reports from post-marketing drug surveillance data. The method involves both empirical Bayes (EB) and fully Bayes estimation of rate multipliers for each drug within a class of drugs, for a particular AE, based on a mixed-effects Poisson regression model. Both parametric and semiparametric models for the random-effect distribution are examined. The method is applied to data from Food and Drug Administration (FDA)’s Adverse Event Reporting System (AERS) on the relationship between antidepressants and suicide. We obtain point estimates and 95 per cent confidence (posterior) intervals for the rate multiplier for each drug (e.g. antidepressants), which can be used to determine whether a particular drug has an increased risk of association with a particular AE (e.g. suicide). Confidence (posterior) intervals that do not include 1.0 provide evidence for either significant protective or harmful associations of the drug and the adverse effect. We also examine EB, parametric Bayes, and semiparametric Bayes estimators of the rate multipliers and associated confidence (posterior) intervals. Results of our analysis of the FDA AERS data revealed that newer antidepressants are associated with lower rates of suicide adverse event reports compared with older antidepressants. We recommend improvements to the existing AERS system, which are likely to improve its public health value as an early warning system. PMID:18404622
An Effect Size for Regression Predictors in Meta-Analysis
ERIC Educational Resources Information Center
Aloe, Ariel M.; Becker, Betsy Jane
2012-01-01
A new effect size representing the predictive power of an independent variable from a multiple regression model is presented. The index, denoted as r[subscript sp], is the semipartial correlation of the predictor with the outcome of interest. This effect size can be computed when multiple predictor variables are included in the regression model…
Analysis of retirement income adequacy using quantile regression: A case study in Malaysia
NASA Astrophysics Data System (ADS)
Alaudin, Ros Idayuwati; Ismail, Noriszura; Isa, Zaidi
2015-09-01
Quantile regression is a statistical analysis that does not restrict attention to the conditional mean and therefore, permitting the approximation of the whole conditional distribution of a response variable. Quantile regression is a robust regression to outliers compared to mean regression models. In this paper, we demonstrate how quantile regression approach can be used to analyze the ratio of projected wealth to needs (wealth-needs ratio) during retirement.
Fast nonlinear regression method for CT brain perfusion analysis.
Bennink, Edwin; Oosterbroek, Jaap; Kudo, Kohsuke; Viergever, Max A; Velthuis, Birgitta K; de Jong, Hugo W A M
2016-04-01
Although computed tomography (CT) perfusion (CTP) imaging enables rapid diagnosis and prognosis of ischemic stroke, current CTP analysis methods have several shortcomings. We propose a fast nonlinear regression method with a box-shaped model (boxNLR) that has important advantages over the current state-of-the-art method, block-circulant singular value decomposition (bSVD). These advantages include improved robustness to attenuation curve truncation, extensibility, and unified estimation of perfusion parameters. The method is compared with bSVD and with a commercial SVD-based method. The three methods were quantitatively evaluated by means of a digital perfusion phantom, described by Kudo et al. and qualitatively with the aid of 50 clinical CTP scans. All three methods yielded high Pearson correlation coefficients ([Formula: see text]) with the ground truth in the phantom. The boxNLR perfusion maps of the clinical scans showed higher correlation with bSVD than the perfusion maps from the commercial method. Furthermore, it was shown that boxNLR estimates are robust to noise, truncation, and tracer delay. The proposed method provides a fast and reliable way of estimating perfusion parameters from CTP scans. This suggests it could be a viable alternative to current commercial and academic methods. PMID:27413770
Kammarnjesadakul, Patcharee; Palaga, Tanapat; Sritunyalucksana, Kallaya; Mendoza, Leonel; Krajaejun, Theerapong; Vanittanakom, Nongnuch; Tongchusak, Songsak; Denduangboripant, Jessada; Chindamporn, Ariya
2011-04-01
To investigate the phylogenetic relationship among Pythium insidiosum isolates in Thailand, we investigated the genomic DNA of 31 P. insidiosum strains isolated from humans and environmental sources from Thailand, and two from North and Central America. We used PCR to amplify the partial COX II DNA coding sequences and the ITS regions of these isolates. The nucleotide sequences of both amplicons were analyzed by the Bioedit program. Phylogenetic analysis using genetic distance method with Neighbor Joining (NJ) approach was performed using the MEGA4 software. Additional sequences of three other Pythium species, Phytophthora sojae and Lagenidium giganteum were employed as outgroups. The sizes of the COX II amplicons varied from 558-564 bp, whereas the ITS products varied from approximately 871-898 bp. Corrected sequence divergences with Kimura 2-parameter model calculated for the COX II and the ITS DNA sequences ranged between 0.0000-0.0608 and 0.0000-0.2832, respectively. Phylogenetic analysis using both the COX II and the ITS DNA sequences showed similar trees, where we found three sister groups (A(TH), B(TH), and C(TH)) among P. insidiosum strains. All Thai isolates from clinical cases and environmental sources were placed in two separated sister groups (B(TH) and C(TH)), whereas the Americas isolates were grouped into A(TH.) Although the phylogenetic tree based on both regions showed similar distribution, the COX II phylogenetic tree showed higher resolution than the one using the ITS sequences. Our study indicates that COX II gene is the better of the two alternatives to study the phylogenetic relationships among P. insidiosum strains. PMID:20818919
Prognostic models in coronary artery disease: Cox and network approaches
Mora, Antonio; Sicari, Rosa; Cortigiani, Lauro; Carpeggiani, Clara; Picano, Eugenio; Capobianco, Enrico
2015-01-01
Predictive assessment of the risk of developing cardiovascular diseases is usually provided by computational approaches centred on Cox models. The complex interdependence structure underlying clinical data patterns can limit the performance of Cox analysis and complicate the interpretation of results, thus calling for complementary and integrative methods. Prognostic models are proposed for studying the risk associated with patients with known or suspected coronary artery disease (CAD) undergoing vasodilator stress echocardiography, an established technique for CAD detection and prognostication. In order to complement standard Cox models, network inference is considered a possible solution to quantify the complex relationships between heterogeneous data categories. In particular, a mutual information network is designed to explore the paths linking patient-associated variables to endpoint events, to reveal prognostic factors and to identify the best possible predictors of death. Data from a prospective, multicentre, observational study are available from a previous study, based on 4313 patients (2532 men; 64±11 years) with known (n=1547) or suspected (n=2766) CAD, who underwent high-dose dipyridamole (0.84 mg kg−1 over 6 min) stress echocardiography with coronary flow reserve (CFR) evaluation of left anterior descending (LAD) artery by Doppler. The overall mortality was the only endpoint analysed by Cox models. The estimated connectivity between clinical variables assigns a complementary value to the proposed network approach in relation to the established Cox model, for instance revealing connectivity paths. Depending on the use of multiple metrics, the constraints of regression analysis in measuring the association strength among clinical variables can be relaxed, and identification of communities and prognostic paths can be provided. On the basis of evidence from various model comparisons, we show in this CAD study that there may be characteristic
Prognostic models in coronary artery disease: Cox and network approaches.
Mora, Antonio; Sicari, Rosa; Cortigiani, Lauro; Carpeggiani, Clara; Picano, Eugenio; Capobianco, Enrico
2015-02-01
Predictive assessment of the risk of developing cardiovascular diseases is usually provided by computational approaches centred on Cox models. The complex interdependence structure underlying clinical data patterns can limit the performance of Cox analysis and complicate the interpretation of results, thus calling for complementary and integrative methods. Prognostic models are proposed for studying the risk associated with patients with known or suspected coronary artery disease (CAD) undergoing vasodilator stress echocardiography, an established technique for CAD detection and prognostication. In order to complement standard Cox models, network inference is considered a possible solution to quantify the complex relationships between heterogeneous data categories. In particular, a mutual information network is designed to explore the paths linking patient-associated variables to endpoint events, to reveal prognostic factors and to identify the best possible predictors of death. Data from a prospective, multicentre, observational study are available from a previous study, based on 4313 patients (2532 men; 64±11 years) with known (n=1547) or suspected (n=2766) CAD, who underwent high-dose dipyridamole (0.84 mg kg(-1) over 6 min) stress echocardiography with coronary flow reserve (CFR) evaluation of left anterior descending (LAD) artery by Doppler. The overall mortality was the only endpoint analysed by Cox models. The estimated connectivity between clinical variables assigns a complementary value to the proposed network approach in relation to the established Cox model, for instance revealing connectivity paths. Depending on the use of multiple metrics, the constraints of regression analysis in measuring the association strength among clinical variables can be relaxed, and identification of communities and prognostic paths can be provided. On the basis of evidence from various model comparisons, we show in this CAD study that there may be characteristic
Hu, Yao-Dong; Pang, Hui-Zhong; Li, De-Sheng; Ling, Shan-Shan; Lan, Dan; Wang, Ye; Zhu, Yun; Li, Di-Yan; Wei, Rong-Ping; Zhang, He-Min; Wang, Cheng-Dong
2016-11-01
As the rate-limiting enzyme of the mitochondrial respiratory chain, cytochrome c oxidase (COX) plays a crucial role in biological metabolism. "Living fossil" giant panda (Ailuropoda melanoleuca) is well-known for its special bamboo diet. In an effort to explore functional variation of COX1 in the energy metabolism behind giant panda's low-energy bamboo diet, we looked at genetic variation of COX1 gene in giant panda, and tested for its selection effect. In 1545 base pairs of the gene from 15 samples, 9 positions were variable and 1 mutation leaded to an amino acid sequence change. COX1 gene produces six haplotypes, nucleotide (pi), haplotype diversity (Hd). In addition, the average number of nucleotide differences (k) is 0.001629±0.001036, 0.8083±0.0694 and 2.517, respectively. Also, dN/dS ratio is significantly below 1. These results indicated that giant panda had a low population genetic diversity, and an obvious purifying selection of the COX1 gene which reduces synthesis of ATP determines giant panda's low-energy bamboo diet. Phylogenetic trees based on the COX1 gene were constructed to demonstrate that giant panda is the sister group of other Ursidae.
Hu, Yao-Dong; Pang, Hui-Zhong; Li, De-Sheng; Ling, Shan-Shan; Lan, Dan; Wang, Ye; Zhu, Yun; Li, Di-Yan; Wei, Rong-Ping; Zhang, He-Min; Wang, Cheng-Dong
2016-11-01
As the rate-limiting enzyme of the mitochondrial respiratory chain, cytochrome c oxidase (COX) plays a crucial role in biological metabolism. "Living fossil" giant panda (Ailuropoda melanoleuca) is well-known for its special bamboo diet. In an effort to explore functional variation of COX1 in the energy metabolism behind giant panda's low-energy bamboo diet, we looked at genetic variation of COX1 gene in giant panda, and tested for its selection effect. In 1545 base pairs of the gene from 15 samples, 9 positions were variable and 1 mutation leaded to an amino acid sequence change. COX1 gene produces six haplotypes, nucleotide (pi), haplotype diversity (Hd). In addition, the average number of nucleotide differences (k) is 0.001629±0.001036, 0.8083±0.0694 and 2.517, respectively. Also, dN/dS ratio is significantly below 1. These results indicated that giant panda had a low population genetic diversity, and an obvious purifying selection of the COX1 gene which reduces synthesis of ATP determines giant panda's low-energy bamboo diet. Phylogenetic trees based on the COX1 gene were constructed to demonstrate that giant panda is the sister group of other Ursidae. PMID:27421668
Regression analysis of technical parameters affecting nuclear power plant performances
Ghazy, R.; Ricotti, M. E.; Trueco, P.
2012-07-01
Since the 80's many studies have been conducted in order to explicate good and bad performances of commercial nuclear power plants (NPPs), but yet no defined correlation has been found out to be totally representative of plant operational experience. In early works, data availability and the number of operating power stations were both limited; therefore, results showed that specific technical characteristics of NPPs were supposed to be the main causal factors for successful plant operation. Although these aspects keep on assuming a significant role, later studies and observations showed that other factors concerning management and organization of the plant could instead be predominant comparing utilities operational and economic results. Utility quality, in a word, can be used to summarize all the managerial and operational aspects that seem to be effective in determining plant performance. In this paper operational data of a consistent sample of commercial nuclear power stations, out of the total 433 operating NPPs, are analyzed, mainly focusing on the last decade operational experience. The sample consists of PWR and BWR technology, operated by utilities located in different countries, including U.S. (Japan)) (France)) (Germany)) and Finland. Multivariate regression is performed using Unit Capability Factor (UCF) as the dependent variable; this factor reflects indeed the effectiveness of plant programs and practices in maximizing the available electrical generation and consequently provides an overall indication of how well plants are operated and maintained. Aspects that may not be real causal factors but which can have a consistent impact on the UCF, as technology design, supplier, size and age, are included in the analysis as independent variables. (authors)
Quantile regression provides a fuller analysis of speed data.
Hewson, Paul
2008-03-01
Considerable interest already exists in terms of assessing percentiles of speed distributions, for example monitoring the 85th percentile speed is a common feature of the investigation of many road safety interventions. However, unlike the mean, where t-tests and ANOVA can be used to provide evidence of a statistically significant change, inference on these percentiles is much less common. This paper examines the potential role of quantile regression for modelling the 85th percentile, or any other quantile. Given that crash risk may increase disproportionately with increasing relative speed, it may be argued these quantiles are of more interest than the conditional mean. In common with the more usual linear regression, quantile regression admits a simple test as to whether the 85th percentile speed has changed following an intervention in an analogous way to using the t-test to determine if the mean speed has changed by considering the significance of parameters fitted to a design matrix. Having briefly outlined the technique and briefly examined an application with a widely published dataset concerning speed measurements taken around the introduction of signs in Cambridgeshire, this paper will demonstrate the potential for quantile regression modelling by examining recent data from Northamptonshire collected in conjunction with a "community speed watch" programme. Freely available software is used to fit these models and it is hoped that the potential benefits of using quantile regression methods when examining and analysing speed data are demonstrated.
Li, Qing; Liu, Liu; Liu, Yanling; Zhou, Huirong; Yang, Zhi; Yuan, Keng; Min, Weiping
2015-01-01
The correlationship between COX-2 gene polymorphisms and breast cancer has been wildly studied, but the results remain controversial. Hence, the present meta-analysis aimed to investigate the association between COX-2 SNPs (rs5275, rs20417, rs689466, rs5277, rs2206593) and risk of breast cancer. Data were collected from PubMed, Embase and China National Knowledge Infrastructure. Summary odds ratio (OR) with 95 % confidence interval (CI) was applied to assess the relationship. Heterogeneity test, sensitivity analysis and publication bias test were also performed. There were 17 articles that contained 19 studies in this research. Fourteen case-control studies with 15,007 breast cancer cases and 20,005 controls were concerning rs5275 polymorphism, and 8 case-control studies with 10,216 cases and 12,839 controls were about rs20417 polymorphism. Other three polymorphisms (rs689466, rs2206593, rs5277) were studied in 5, 3 and 3 studies, respectively. COX-2-rs20417 CC genotype was significantly associated with increased risk of breast cancer when comparing to G allele [ORs were 1.231 (1.050-1.444) for CC vs. GG, P = 0.01, 1.223 (1.045-1.432) for CC vs. G carrier, P = 0.01]. Furthermore, the results of the subgroup analysis by ethnicity suggested that C allele significantly contributed to the risk of breast cancer for Asians [1.459 (1.182-1.802) for GC vs. GG, 1.472 (1.201-1.805) for C carrier vs. GG]. However, no association was found for rs5275, rs689466, rs5277 and rs2206593 in all comparison modes. This meta-analysis indicated that the COX-2 rs20417 polymorphism contributed to genetic susceptibility of breast cancer. In contrast, COX-2 rs5275, rs689466, rs2206593 and rs5277 polymorphisms might be not associated with the risk of breast cancer. PMID:25433948
Multiple regression for physiological data analysis: the problem of multicollinearity.
Slinker, B K; Glantz, S A
1985-07-01
Multiple linear regression, in which several predictor variables are related to a response variable, is a powerful statistical tool for gaining quantitative insight into complex in vivo physiological systems. For these insights to be correct, all predictor variables must be uncorrelated. However, in many physiological experiments the predictor variables cannot be precisely controlled and thus change in parallel (i.e., they are highly correlated). There is a redundancy of information about the response, a situation called multicollinearity, that leads to numerical problems in estimating the parameters in regression equations; the parameters are often of incorrect magnitude or sign or have large standard errors. Although multicollinearity can be avoided with good experimental design, not all interesting physiological questions can be studied without encountering multicollinearity. In these cases various ad hoc procedures have been proposed to mitigate multicollinearity. Although many of these procedures are controversial, they can be helpful in applying multiple linear regression to some physiological problems.
DFT analysis and spectral characteristics of Celecoxib a potent COX-2 inhibitor
NASA Astrophysics Data System (ADS)
Vijayakumar, B.; Kannappan, V.; Sathyanarayanamoorthi, V.
2016-10-01
Extensive quantum mechanical studies are carried out on Celecoxib (CXB), a new generation drug to understand the vibrational and electronic spectral characteristics of the molecule. The vibrational frequencies of CXB are computed by HF and B3LYP methods with 6-311++G (d, p) basis set. The theoretical scaled vibrational frequencies have been assigned and they agreed satisfactorily with experimental FT-IR and Raman frequencies. The theoretical maximum wavelength of absorption of CXB are calculated in water and ethanol by TD-DFT method and these values are compared with experimentally determined λmax values. The spectral and Natural bonds orbital (NBO) analysis in conjunction with spectral data established the presence of intra molecular interactions such as mesomeric, hyperconjugative and steric effects in CXB. The electron density at various positions and reactivity descriptors of CXB indicate that the compound functions as a nucleophile and establish that aromatic ring system present in the molecule is the site of drug action. Electronic distribution and HOMO - LUMO energy values of CXB are discussed in terms of intra-molecular interactions. Computed values of Mulliken charges and thermodynamic properties of CXB are reported.
Exact Analysis of Squared Cross-Validity Coefficient in Predictive Regression Models
ERIC Educational Resources Information Center
Shieh, Gwowen
2009-01-01
In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference…
Technology Transfer Automated Retrieval System (TEKTRAN)
Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...
Measuring Habituation in Infants: An Approach Using Regression Analysis.
ERIC Educational Resources Information Center
Ashmead, Daniel H.; Davis, DeFord L.
1996-01-01
Used computer simulations to examine effectiveness of different criteria for measuring infant visual habituation. Found that a criterion based on fitting a second-order polynomial regression function to looking-time data produced more accurate estimation of looking times and higher power for detecting novelty effects than did the traditional…
Teaching Quantitative Literacy through a Regression Analysis of Exam Performance
ERIC Educational Resources Information Center
Lindner, Andrew M.
2012-01-01
Quantitative literacy is increasingly essential for both informed citizenship and a variety of careers. Though regression is one of the most common methods in quantitative sociology, it is rarely taught until late in students' college careers. In this article, the author describes a classroom-based activity introducing students to regression…
Remedial Education and Student Achievement: A Regression-Discontinuity Analysis.
ERIC Educational Resources Information Center
Jacob, Brian A.; Lefgren, Lars
This study used a regression discontinuity design to examine the causal effect of summer school and grade retention on student achievement. In 1996, the Chicago Public Schools instituted an accountability policy that tied summer school attendance and promotional decisions to performance on standardized tests, which created a highly non-linear…
Some Applied Research Concerns Using Multiple Linear Regression Analysis.
ERIC Educational Resources Information Center
Newman, Isadore; Fraas, John W.
The intention of this paper is to provide an overall reference on how a researcher can apply multiple linear regression in order to utilize the advantages that it has to offer. The advantages and some concerns expressed about the technique are examined. A number of practical ways by which researchers can deal with such concerns as…
Growth in Mathematics Achievement: Analysis with Classification and Regression Trees
ERIC Educational Resources Information Center
Ma, Xin
2005-01-01
A recently developed statistical technique, often referred to as classification and regression trees (CART), holds great potential for researchers to discover how student-level (and school-level) characteristics interactively affect growth in mathematics achievement. CART is a host of advanced statistical methods that statistically cluster…
NASA Technical Reports Server (NTRS)
Parsons, Vickie s.
2009-01-01
The request to conduct an independent review of regression models, developed for determining the expected Launch Commit Criteria (LCC) External Tank (ET)-04 cycle count for the Space Shuttle ET tanking process, was submitted to the NASA Engineering and Safety Center NESC on September 20, 2005. The NESC team performed an independent review of regression models documented in Prepress Regression Analysis, Tom Clark and Angela Krenn, 10/27/05. This consultation consisted of a peer review by statistical experts of the proposed regression models provided in the Prepress Regression Analysis. This document is the consultation's final report.
HIGH RESOLUTION FOURIER ANALYSIS WITH AUTO-REGRESSIVE LINEAR PREDICTION
Barton, J.; Shirley, D.A.
1984-04-01
Auto-regressive linear prediction is adapted to double the resolution of Angle-Resolved Photoemission Extended Fine Structure (ARPEFS) Fourier transforms. Even with the optimal taper (weighting function), the commonly used taper-and-transform Fourier method has limited resolution: it assumes the signal is zero beyond the limits of the measurement. By seeking the Fourier spectrum of an infinite extent oscillation consistent with the measurements but otherwise having maximum entropy, the errors caused by finite data range can be reduced. Our procedure developed to implement this concept adapts auto-regressive linear prediction to extrapolate the signal in an effective and controllable manner. Difficulties encountered when processing actual ARPEFS data are discussed. A key feature of this approach is the ability to convert improved measurements (signal-to-noise or point density) into improved Fourier resolution.
A New Approach in Regression Analysis for Modeling Adsorption Isotherms
Onjia, Antonije E.
2014-01-01
Numerous regression approaches to isotherm parameters estimation appear in the literature. The real insight into the proper modeling pattern can be achieved only by testing methods on a very big number of cases. Experimentally, it cannot be done in a reasonable time, so the Monte Carlo simulation method was applied. The objective of this paper is to introduce and compare numerical approaches that involve different levels of knowledge about the noise structure of the analytical method used for initial and equilibrium concentration determination. Six levels of homoscedastic noise and five types of heteroscedastic noise precision models were considered. Performance of the methods was statistically evaluated based on median percentage error and mean absolute relative error in parameter estimates. The present study showed a clear distinction between two cases. When equilibrium experiments are performed only once, for the homoscedastic case, the winning error function is ordinary least squares, while for the case of heteroscedastic noise the use of orthogonal distance regression or Margart's percent standard deviation is suggested. It was found that in case when experiments are repeated three times the simple method of weighted least squares performed as well as more complicated orthogonal distance regression method. PMID:24672394
Regression analysis of correlated ordinal data using orthogonalized residuals.
Perin, J; Preisser, J S; Phillips, C; Qaqish, B
2014-12-01
Semi-parametric regression models for the joint estimation of marginal mean and within-cluster pairwise association parameters are used in a variety of settings for population-averaged modeling of multivariate categorical outcomes. Recently, a formulation of alternating logistic regressions based on orthogonalized, marginal residuals has been introduced for correlated binary data. Unlike the original procedure based on conditional residuals, its covariance estimator is invariant to the ordering of observations within clusters. In this article, the orthogonalized residuals method is extended to model correlated ordinal data with a global odds ratio, and shown in a simulation study to be more efficient and less biased with regards to estimating within-cluster association parameters than an existing extension to ordinal data of alternating logistic regressions based on conditional residuals. Orthogonalized residuals are used to estimate a model for three correlated ordinal outcomes measured repeatedly in a longitudinal clinical trial of an intervention to improve recovery of patients' perception of altered sensation following jaw surgery.
COX7AR is a Stress-inducible Mitochondrial COX Subunit that Promotes Breast Cancer Malignancy.
Zhang, Kezhong; Wang, Guohui; Zhang, Xuebao; Hüttemann, Philipp P; Qiu, Yining; Liu, Jenney; Mitchell, Allison; Lee, Icksoo; Zhang, Chao; Lee, Jin-Sook; Pecina, Petr; Wu, Guojun; Yang, Zeng-Quan; Hüttemann, Maik; Grossman, Lawrence I
2016-01-01
Cytochrome c oxidase (COX), the terminal enzyme of the mitochondrial respiratory chain, plays a key role in regulating mitochondrial energy production and cell survival. COX subunit VIIa polypeptide 2-like protein (COX7AR) is a novel COX subunit that was recently found to be involved in mitochondrial supercomplex assembly and mitochondrial respiration activity. Here, we report that COX7AR is expressed in high energy-demanding tissues, such as brain, heart, liver, and aggressive forms of human breast cancer cells. Under cellular stress that stimulates energy metabolism, COX7AR is induced and incorporated into the mitochondrial COX complex. Functionally, COX7AR promotes cellular energy production in human mammary epithelial cells. Gain- and loss-of-function analysis demonstrates that COX7AR is required for human breast cancer cells to maintain higher rates of proliferation, clone formation, and invasion. In summary, our study revealed that COX7AR is a stress-inducible mitochondrial COX subunit that facilitates human breast cancer malignancy. These findings have important implications in the understanding and treatment of human breast cancer and the diseases associated with mitochondrial energy metabolism. PMID:27550821
COX7AR is a Stress-inducible Mitochondrial COX Subunit that Promotes Breast Cancer Malignancy
Zhang, Kezhong; Wang, Guohui; Zhang, Xuebao; Hüttemann, Philipp P.; Qiu, Yining; Liu, Jenney; Mitchell, Allison; Lee, Icksoo; Zhang, Chao; Lee, Jin-sook; Pecina, Petr; Wu, Guojun; Yang, Zeng-quan; Hüttemann, Maik; Grossman, Lawrence I.
2016-01-01
Cytochrome c oxidase (COX), the terminal enzyme of the mitochondrial respiratory chain, plays a key role in regulating mitochondrial energy production and cell survival. COX subunit VIIa polypeptide 2-like protein (COX7AR) is a novel COX subunit that was recently found to be involved in mitochondrial supercomplex assembly and mitochondrial respiration activity. Here, we report that COX7AR is expressed in high energy-demanding tissues, such as brain, heart, liver, and aggressive forms of human breast cancer cells. Under cellular stress that stimulates energy metabolism, COX7AR is induced and incorporated into the mitochondrial COX complex. Functionally, COX7AR promotes cellular energy production in human mammary epithelial cells. Gain- and loss-of-function analysis demonstrates that COX7AR is required for human breast cancer cells to maintain higher rates of proliferation, clone formation, and invasion. In summary, our study revealed that COX7AR is a stress-inducible mitochondrial COX subunit that facilitates human breast cancer malignancy. These findings have important implications in the understanding and treatment of human breast cancer and the diseases associated with mitochondrial energy metabolism. PMID:27550821
Striker, Lora K.; Medalie, Laura
1997-01-01
This report provides the results of a detailed Level II analysis of scour potential at structure MORETH00010021 on Town Highway 1 crossing Cox Brook, Moretown, Vermont (figures 1–8). A Level II study is a basic engineering analysis of the site, including a quantitative analysis of stream stability and scour (U.S. Department of Transportation, 1993). Results of a Level I scour investigation also are included in Appendix E of this report. A Level I investigation provides a qualitative geomorphic characterization of the study site. Information on the bridge, gleaned from Vermont Agency of Transportation (VTAOT) files, was compiled prior to conducting Level I and Level II analyses and is found in Appendix D. The site is in the Green Mountain section of the New England physiographic province in north-central Vermont. The 2.85-mi2 drainage area is in a predominantly rural and forested basin. In the vicinity of the study site, the surface cover is predominantly forested. In the study area, Cox Brook has an incised, sinuous channel with a slope of approximately 0.02 ft/ft, an average channel top width of 23 ft and an average bank height of 4 ft. The channel bed material ranges from gravel to cobble with a median grain size (D50) of 47.5 mm (0.156 ft). The geomorphic assessment at the time of the Level I and Level II site visit on July 18, 1996, indicated that the reach was stable. The Town Highway 1 crossing of Cox Brook is a 29-ft-long, two-lane bridge consisting of one 27-foot steel-beam span (Vermont Agency of Transportation, written communication, October 13, 1995). The opening length of the structure parallel to the bridge face is 24.8 ft. The bridge is supported by vertical, concrete abutments with wingwalls. The channel is skewed approximately 60 degrees to the opening while the measured opening-skew-to-roadway is 40 degrees. A scour hole 1.0 ft deeper than the mean thalweg depth was observed along the left abutment downstream during the Level I assessment. The
S-PLUS Library For Nonlinear Bayesian Regression Analysis
Heasler, Patrick G. ); Anderson, Kevin K. ); Hylden, Jeff L. )
2002-09-25
This document describes a library of Splus functions used for nonlinear Bayesian regression in general and IR estimation in particular. This library has been developed to solve a general class of problems described by the nonlinear regression model: Y = F (beta,data)+ E where Y represents a vector of measurements, and F(beta,data) represents a Splus function that has been constructed to describe the measurements. The function F(beta,data) depends upon beta, a vector of parameters to be estimated, while data$ is an Splus object containing any other information needed by the model. The errors, E, are assumed to be independent, normal, unbiased and to have known standard deviations of stdev(E) = sd.E. The components in beta are split into two groups; estimation parameters and nuisance parameters. The Bayesian prior on the estimation parameters will generally be non-informative, while the prior on the nuisance parameters will be constructed to reflect the information we have about them. We hope an extended beta distribution is general enough to adequately represent the information we have on them. While we expect these functions to be improved and revised, this library is mature enough to be used without major modification.
Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis
ERIC Educational Resources Information Center
Williams, Ryan T.
2012-01-01
Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use…
Regression analysis of reported earthquake precursors. I. Presentation of data
NASA Astrophysics Data System (ADS)
Niazi, Mansour
1984-11-01
Around 700 reported precursors of about 350 earthquakes, including the negative observations, have been compiled in 11 categories with 31 subdivisions. The data base is subjected to an initial sorting and screening by imposing three restrictions on the ranges of main shock magnitude ( M≥4.0), precursory time ( t≤20 years), and the epicentral distance of observation points ( X m≤4.100.3 M ). Of the 31 subcategories of precursory phenomena, 18 with 9 data points or more are independently studied by regressing their precursory times against magnitude. The preliminary results tend to classify the precursors into three groups: 1. The precursors which show weak or no correlation between time and the magnitude of the eventual main shock. Examples of this group are foreshocks and precursory tilt. 2. The precursors which show clear scaling with magnitude. These include seismic velocity ratio ( V p/Vs), travel time delay, duration of seismic quiescence, and, to some degree, the variation of b-value, and anomalous seismicity. 3. The precursors which display clustering of precursory times around a mean value, which differs for different precursors from a few hours to a few years. Examples include the conductivity rate, geoelectric current and potential, strain, water well level, geochemical anomalies, change of focal mechanism, and the enhancement of seismicity reported only for larger earthquakes. Some of the precursors in this category, such as leveling changes and the occurrence of microseismicity, show bimodal patterns of precursory times and may partially be coseismic. In addition, each category with a sufficient number of reported estimates of distance and signal amplitude is subjected to multiple linear regression. The usefulness of these regressions at this stage appears to be limited to specifying which of the parameters shows a more significant correlation. Standard deviations of residuals of precursory time against magnitude are generally reduced when
Logistic regression analysis of cadmium-induced renal abnormalities
Ellis, K.J.; Yuen, K.; Cohn, S.H.
1986-02-01
Cases of renal dysfunction associated with cadium exposure have been reported in Belgium, Great Britian, Japan, United States, and Sweden. Indirect estimates of body burden were often based on the measurement of environmental exposure conditions or on tissue concentrations in urine, blood, saliva, or hair clippings. More recently, however, the direct in vivo assessment of liver and kidney cadmium burden in humans has provided additional data. Sufficient data on humans does exist, however, to make reasonable estimates of the increased risk for cadmium-induced renal dysfunction. In the present paper, a linear logistic regression model has been developed on the basis of liver and kidney cadmium burden. These relationships are discussed with respect to the concept of a critical concentration for the renal cortex. 14 refs., 3 figs., 2 tabs.
An improved multiple linear regression and data analysis computer program package
NASA Technical Reports Server (NTRS)
Sidik, S. M.
1972-01-01
NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.
Gogoi, Dhrubajyoti; Bezbaruah, Rajib Lochan; Bordoloi, Manabjyoti; Sarmah, Rajeev; Bora, Tarun Chandra
2012-01-01
Litsea spp of Laural family are traditionally used as herbal medicine for treating inflammation including gastroenterologia, oedema and rheumatic arthritis. Therefore, it is of interest to investigate and understand the molecular principles for such actions. Here, we have illustrated the binding of thirteen Litsea derived biologically active compounds against the inflammation associated target COX (cyclo-oxygenase) -2 enzymes. We compared the binding information of these compounds with a selected number of already known COX-2 inhibitors. The comparison reflected that some of these compounds such as linderol, catechin, 6'-hydroxy-2',3',4' - trimethoxy-chalcone and litseaone have better or equivalent binding features compared to already known inhibitory compounds namely celecoxib, acetylsalicylic acid, rofecoxib. Therefore, all these small compounds reported from plant Litsea spp were found to possess potential medicinal values with anti-inflammatory properties.
Schübel, U; Kraut, M; Mörsdorf, G; Meyer, O
1995-01-01
The CO dehydrogenase structural genes (cox) and orf4 are clustered in the transcriptional order coxM--> coxS--> coxL--> orf4 on the 128-kb megaplasmid pHCG3 of the carboxidotroph Oligotropha carboxidovorans OM5. Sequence analysis suggested association of molybdopterin cytosine dinucleotide and flavin adenine dinucleotide with CoxL and of the [2Fe-2S] clusters with CoxS. PMID:7721710
A Hypothesis Verification Method Using Regression Tree for Semiconductor Yield Analysis
NASA Astrophysics Data System (ADS)
Tsuda, Hidetaka; Shirai, Hidehiro; Terabe, Masahiro; Hashimoto, Kazuo; Shinohara, Ayumi
Several researchers have reported the regression tree analysis for semiconductor yield. However, the scope of these analyses is restricted by the difficulty involved in applying the regression tree analysis to a small number of samples with many attributes. It is often observed that splitting attributes in the route node do not indicate the hypothesized causes of failure. We propose a method for verifying the hypothesized causes of failure, which reduces the number of verification hypotheses. Our method involves selecting sets of analysis data with the same cause of failure, extracting the hypothesis by applying the regression tree analysis separately to each set of analysis data, and merging and sorting attributes according to the t value. The results of an experiment conducted in a real environment show that the proposed method helps in widening the scope of applicability of the regression tree analysis for semiconductor yield.
Robust analysis of trends in noisy tokamak confinement data using geodesic least squares regression
NASA Astrophysics Data System (ADS)
Verdoolaege, G.; Shabbir, A.; Hornung, G.
2016-11-01
Regression analysis is a very common activity in fusion science for unveiling trends and parametric dependencies, but it can be a difficult matter. We have recently developed the method of geodesic least squares (GLS) regression that is able to handle errors in all variables, is robust against data outliers and uncertainty in the regression model, and can be used with arbitrary distribution models and regression functions. We here report on first results of application of GLS to estimation of the multi-machine scaling law for the energy confinement time in tokamaks, demonstrating improved consistency of the GLS results compared to standard least squares.
Analysis of Differential Item Functioning (DIF) Using Hierarchical Logistic Regression Models.
ERIC Educational Resources Information Center
Swanson, David B.; Clauser, Brian E.; Case, Susan M.; Nungester, Ronald J.; Featherman, Carol
2002-01-01
Outlines an approach to differential item functioning (DIF) analysis using hierarchical linear regression that makes it possible to combine results of logistic regression analyses across items to identify consistent sources of DIF, to quantify the proportion of explained variation in DIF coefficients, and to compare the predictive accuracy of…
Development of a User Interface for a Regression Analysis Software Tool
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert Manfred; Volden, Thomas R.
2010-01-01
An easy-to -use user interface was implemented in a highly automated regression analysis tool. The user interface was developed from the start to run on computers that use the Windows, Macintosh, Linux, or UNIX operating system. Many user interface features were specifically designed such that a novice or inexperienced user can apply the regression analysis tool with confidence. Therefore, the user interface s design minimizes interactive input from the user. In addition, reasonable default combinations are assigned to those analysis settings that influence the outcome of the regression analysis. These default combinations will lead to a successful regression analysis result for most experimental data sets. The user interface comes in two versions. The text user interface version is used for the ongoing development of the regression analysis tool. The official release of the regression analysis tool, on the other hand, has a graphical user interface that is more efficient to use. This graphical user interface displays all input file names, output file names, and analysis settings for a specific software application mode on a single screen which makes it easier to generate reliable analysis results and to perform input parameter studies. An object-oriented approach was used for the development of the graphical user interface. This choice keeps future software maintenance costs to a reasonable limit. Examples of both the text user interface and graphical user interface are discussed in order to illustrate the user interface s overall design approach.
Advanced GIS Exercise: Predicting Rainfall Erosivity Index Using Regression Analysis
ERIC Educational Resources Information Center
Post, Christopher J.; Goddard, Megan A.; Mikhailova, Elena A.; Hall, Steven T.
2006-01-01
Graduate students from a variety of agricultural and natural resource fields are incorporating geographic information systems (GIS) analysis into their graduate research, creating a need for teaching methodologies that help students understand advanced GIS topics for use in their own research. Graduate-level GIS exercises help students understand…
A Noncentral "t" Regression Model for Meta-Analysis
ERIC Educational Resources Information Center
Camilli, Gregory; de la Torre, Jimmy; Chiu, Chia-Yi
2010-01-01
In this article, three multilevel models for meta-analysis are examined. Hedges and Olkin suggested that effect sizes follow a noncentral "t" distribution and proposed several approximate methods. Raudenbush and Bryk further refined this model; however, this procedure is based on a normal approximation. In the current research literature, this…
ERIC Educational Resources Information Center
Dolan, Conor V.; Wicherts, Jelte M.; Molenaar, Peter C. M.
2004-01-01
We consider the question of how variation in the number and reliability of indicators affects the power to reject the hypothesis that the regression coefficients are zero in latent linear regression analysis. We show that power remains constant as long as the coefficient of determination remains unchanged. Any increase in the number of indicators…
NASA Astrophysics Data System (ADS)
Nishidate, Izumi; Wiswadarma, Aditya; Hase, Yota; Tanaka, Noriyuki; Maeda, Takaaki; Niizeki, Kyuichi; Aizu, Yoshihisa
2011-08-01
In order to visualize melanin and blood concentrations and oxygen saturation in human skin tissue, a simple imaging technique based on multispectral diffuse reflectance images acquired at six wavelengths (500, 520, 540, 560, 580 and 600nm) was developed. The technique utilizes multiple regression analysis aided by Monte Carlo simulation for diffuse reflectance spectra. Using the absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are deduced numerically in advance, while oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments with human skin of the human hand during upper limb occlusion and of the inner forearm exposed to UV irradiation demonstrated the ability of the method to evaluate physiological reactions of human skin tissue.
Barresi, Vincenza; Trovato-Salinaro, Angela; Spampinato, Giorgia; Musso, Nicolò; Castorina, Sergio; Rizzarelli, Enrico; Condorelli, Daniele Filippo
2016-08-01
Copper homeostasis and distribution is strictly regulated by a network of transporters and intracellular chaperones encoded by a group of genes collectively known as copper homeostasis genes (CHGs). In this work, analysis of The Cancer Genome Atlas database for somatic point mutations in colorectal cancer revealed that inactivating mutations are absent or extremely rare in CHGs. Using oligonucleotide microarrays, we found a strong increase in mRNA levels of the membrane copper transporter 1 protein [CTR1; encoded by the solute carrier family 31 member 1 gene (SLC31A1 gene)] in our series of colorectal carcinoma samples. CTR1 is the main copper influx transporter and changes in its expression are able to induce modifications of cellular copper accumulation. The increased SLC31A1 mRNA level is accompanied by a parallel increase in transcript levels for copper efflux pump ATP7A, copper metabolism Murr1 domain containing 1 (COMMD1), the cytochrome C oxidase assembly factors [synthesis of cytochrome c oxidase 1 (SCO1) and cytochrome c oxidase copper chaperone 11 (COX11)], the cupric reductase six transmembrane epithelial antigen of the prostate (STEAP3), and the metal-regulatory transcription factors (MTF1, MTF2) and specificity protein 1 (SP1). The significant correlation between SLC31A1,SCO1, and COX11 mRNA levels suggests that this transcriptional upregulation might be part of a coordinated program of gene regulation. Transcript-level upregulation of SLC31A1,SCO1, and COX11 was also confirmed by the analysis of different colon carcinoma cell lines (Caco-2, HT116, HT29) and cancer cell lines of different tissue origin (MCF7, PC3). Finally, exon-level expression analysis of SLC31A1 reveals differential expression of alternative transcripts in colorectal cancer and normal colonic mucosa.
Telmo, C; Lousada, J; Moreira, N
2010-06-01
The gross calorific value (GCV), proximate, ultimate and chemical analysis of debark wood in Portugal were studied, for future utilization in wood pellets industry and the results compared with CEN/TS 14961. The relationship between GCV, ultimate and chemical analysis were determined by multiple regression stepwise backward. The treatment between hardwoods-softwoods did not result in significant statistical differences for proximate, ultimate and chemical analysis. Significant statistical differences were found in carbon for National (hardwoods-softwoods) and (National-tropical) hardwoods in volatile matter, fixed carbon, carbon and oxygen and also for chemical analysis in National (hardwoods-softwoods) for F and (National-tropical) hardwoods for Br. GCV was highly positively related to C (0.79 * * *) and negatively to O (-0.71 * * *). The final independent variables of the model were (C, O, S, Zn, Ni, Br) with R(2)=0.86; F=27.68 * * *. The hydrogen did not contribute statistically to the energy content.
Deng, Yangyang; Parajuli, Prem B.
2011-08-10
Evaluation of economic feasibility of a bio-gasification facility needs understanding of its unit cost under different production capacities. The objective of this study was to evaluate the unit cost of syngas production at capacities from 60 through 1800Nm 3/h using an economic model with three regression analysis techniques (simple regression, reciprocal regression, and log-log regression). The preliminary result of this study showed that reciprocal regression analysis technique had the best fit curve between per unit cost and production capacity, with sum of error squares (SES) lower than 0.001 and coefficient of determination of (R 2) 0.996. The regression analysis techniques determined the minimum unit cost of syngas production for micro-scale bio-gasification facilities of $0.052/Nm 3, under the capacity of 2,880 Nm 3/h. The results of this study suggest that to reduce cost, facilities should run at a high production capacity. In addition, the contribution of this technique could be the new categorical criterion to evaluate micro-scale bio-gasification facility from the perspective of economic analysis.
Regression Models for Demand Reduction based on Cluster Analysis of Load Profiles
Yamaguchi, Nobuyuki; Han, Junqiao; Ghatikar, Girish; Piette, Mary Ann; Asano, Hiroshi; Kiliccote, Sila
2009-06-28
This paper provides new regression models for demand reduction of Demand Response programs for the purpose of ex ante evaluation of the programs and screening for recruiting customer enrollment into the programs. The proposed regression models employ load sensitivity to outside air temperature and representative load pattern derived from cluster analysis of customer baseline load as explanatory variables. The proposed models examined their performances from the viewpoint of validity of explanatory variables and fitness of regressions, using actual load profile data of Pacific Gas and Electric Company's commercial and industrial customers who participated in the 2008 Critical Peak Pricing program including Manual and Automated Demand Response.
Zhang, Hong-guang; Lu, Jian-gang
2016-02-01
Abstract To overcome the problems of significant difference among samples and nonlinearity between the property and spectra of samples in spectral quantitative analysis, a local regression algorithm is proposed in this paper. In this algorithm, net signal analysis method(NAS) was firstly used to obtain the net analyte signal of the calibration samples and unknown samples, then the Euclidean distance between net analyte signal of the sample and net analyte signal of calibration samples was calculated and utilized as similarity index. According to the defined similarity index, the local calibration sets were individually selected for each unknown sample. Finally, a local PLS regression model was built on each local calibration sets for each unknown sample. The proposed method was applied to a set of near infrared spectra of meat samples. The results demonstrate that the prediction precision and model complexity of the proposed method are superior to global PLS regression method and conventional local regression algorithm based on spectral Euclidean distance.
ERIC Educational Resources Information Center
Barringer, Mary S.
Researchers are becoming increasingly aware of the advantages of using multiple regression as opposed to analysis of variance (ANOVA) or analysis of covariance (ANCOVA). Multiple regression is more versatile and does not force the researcher to throw away variance by categorizing intervally scaled data. Polynomial regression analysis offers the…
NASA Astrophysics Data System (ADS)
Noor, Nor Fashihah Mohd; Adnan, Farah Adibah; Saad, Syafawati Ab.; Zakaria, Haslina Binti; Yazid, Nornadia Binti Mohd; Jalil, Mohd Faizal Ab; Kamarudzaman, Ain Nihla
2014-07-01
This paper presents the application of regression analysis in measuring the relationship between rainfall area and Timah Tasoh reservoir level. The trend lines of the rainfall data on a monthly basis were checked. The data collected from the year 2007 until 2012 were run using JMP software to obtain the regression model. Result showed that Wang Kelian has high humidity in Perlis and significantly has contributed to high level of Timah Tasoh reservoir.
Uleberg, Kai-Erik; Øvestad, Irene Tveiterås; Munk, Ane Cecilie; van Diermen, Bianca; Gudlaugsson, Einar; Janssen, Emiel A. M.; Hjelle, Anne; Baak, Jan P. A.
2014-01-01
Regression of cervical intraepithelial neoplasia (CIN) 2-3 to CIN 1 or less is associated with immune response as demonstrated by immunohistochemistry in formaldehyde-fixed paraffin-embedded (FFPE) biopsies. Proteomic analysis of water-soluble proteins in supernatants of biopsy samples with LC-MS (LTQ-Orbitrap) was used to identify proteins predictive of CIN2-3 lesions regression. CIN2-3 in the biopsies and persistence (CIN2-3) or regression (≤CIN1) in follow-up cone biopsies was validated histologically by two experienced pathologists. In a learning set of 20 CIN2-3 (10 regressions and 10 persistence cases), supernatants were depleted of seven high abundance proteins prior to unidimensional LC-MS/MS protein analysis. Mean protein concentration was 0.81 mg/mL (range: 0.55–1.14). Multivariate statistical methods were used to identify proteins that were able to discriminate between regressive and persistent CIN2-3. The findings were validated in an independent test set of 20 CIN2-3 (10 regressions and 10 persistence cases). Multistep identification criteria identified 165 proteins. In the learning set, zinc finger protein 441 and phospholipase D6 independently discriminated between regressive and persistent CIN2-3 lesions and correctly classified all 20 patients. Nine regression and all persistence cases were correctly classified in the validation set. Zinc finger protein 441 and phospholipase D6 in supernatant samples detected by LTQ-Orbitrap can predict regression of CIN2-3. PMID:25018881
Hawkey, C J
2005-01-01
The role of selective cyclooxygenase (COX)-2 inhibitors in medical practice has become controversial since evidence emerged that their use is associated with an increased risk of myocardial infarction. Selective COX-2 inhibitors were seen as successor to non-selective non-steroidal anti-inflammatory drugs, in turn successors to aspirin. The importance of pain relief means that such drugs have always attracted attention. The fact that they work through inhibition of cyclooxygenase, are widespread, and have multiple effects also means that adverse effects that were unanticipated (even though predictable) have always emerged. In this paper I therefore present an historical perspective so that the lessons of the past may be applied to the present. PMID:16227351
Regression Analysis of Physician Distribution to Identify Areas of Need: Some Preliminary Findings.
ERIC Educational Resources Information Center
Morgan, Bruce B.; And Others
A regression analysis was conducted of factors that help to explain the variance in physician distribution and which identify those factors that influence the maldistribution of physicians. Models were developed for different geographic areas to determine the most appropriate unit of analysis for the Western Missouri Area Health Education Center…
Criteria for the use of regression analysis for remote sensing of sediment and pollutants
NASA Technical Reports Server (NTRS)
Whitlock, C. H.; Kuo, C. Y.; Lecroy, S. R. (Principal Investigator)
1982-01-01
Data analysis procedures for quantification of water quality parameters that are already identified and are known to exist within the water body are considered. The liner multiple-regression technique was examined as a procedure for defining and calibrating data analysis algorithms for such instruments as spectrometers and multispectral scanners.
Modeling of retardance in ferrofluid with Taguchi-based multiple regression analysis
NASA Astrophysics Data System (ADS)
Lin, Jing-Fung; Wu, Jyh-Shyang; Sheu, Jer-Jia
2015-03-01
The citric acid (CA) coated Fe3O4 ferrofluids are prepared by a co-precipitation method and the magneto-optical retardance property is measured by a Stokes polarimeter. Optimization and multiple regression of retardance in ferrofluids are executed by combining Taguchi method and Excel. From the nine tests for four parameters, including pH of suspension, molar ratio of CA to Fe3O4, volume of CA, and coating temperature, influence sequence and excellent program are found. Multiple regression analysis and F-test on the significance of regression equation are performed. It is found that the model F value is much larger than Fcritical and significance level P <0.0001. So it can be concluded that the regression model has statistically significant predictive ability. Substituting excellent program into equation, retardance is obtained as 32.703°, higher than the highest value in tests by 11.4%.
2014-01-01
Background and aim Altered glucose metabolism, oxidative stress, lipid levels and inflammatory markers are important risk factors in diabetes, cardiovascular, and many other diseases. Cocoa has been shown to exert antioxidant and anti-inflammatory effects. The aim of this study is twofold: to assess the effect of Cocoa on the lipid profile and peroxidation in addition to the inflammatory markers in type 2 diabetic patients, and to represent a virtual model of probable action mechanism of observed clinical effects of Cocoa consumption using in silico analysis and bioinformatics data. Methods One hundred subjects with type 2 diabetes were included in a randomized clinical control trial. Fifty treatment subjects received 10 grams cocoa powder and 10 grams milk powder dissolved in 250 ml of boiling water, and the other fifty control subjects received only 10 grams milk powder dissolved in 250 ml boiling water. Both groups were on the mentioned regimen twice daily for 6 weeks. Blood samples were obtained prior to Cocoa consumption and 6 weeks after intervention. Serum lipids and lipoproteins profile, malondialdehyde and inflammatory markers including tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and high sensitive C-reactive protein (hs-CRP) were measured. For statistical analysis two independent and paired samples t-test and linear regression were used. Bioinformatics and virtual analysis were performed using string data base and Molegro virtual software. Results Cocoa consumption lowered blood cholesterol,triglyceride, LDL-cholesterol, and TNF-α, hs-CRP, IL-6 significantly (P < 0.01). The results showed that the levels of HDL-cholesterol decreased significantly (P < 0.05) but Cocoa inhibited lipid peroxidation in treatment group than control group (P < 0.0001). Virtual analysis showed that the most frequent Cocoa ingredients, (+)-Catechin and (−)-Epicatechin, can dock to the enzyme COX-2. Conclusion These data support the beneficial effect
Factor analysis and multiple regression between topography and precipitation on Jeju Island, Korea
NASA Astrophysics Data System (ADS)
Um, Myoung-Jin; Yun, Hyeseon; Jeong, Chang-Sam; Heo, Jun-Haeng
2011-11-01
SummaryIn this study, new factors that influence precipitation were extracted from geographic variables using factor analysis, which allow for an accurate estimation of orographic precipitation. Correlation analysis was also used to examine the relationship between nine topographic variables from digital elevation models (DEMs) and the precipitation in Jeju Island. In addition, a spatial analysis was performed in order to verify the validity of the regression model. From the results of the correlation analysis, it was found that all of the topographic variables had a positive correlation with the precipitation. The relations between the variables also changed in accordance with a change in the precipitation duration. However, upon examining the correlation matrix, no significant relationship between the latitude and the aspect was found. According to the factor analysis, eight topographic variables (latitude being the exception) were found to have a direct influence on the precipitation. Three factors were then extracted from the eight topographic variables. By directly comparing the multiple regression model with the factors (model 1) to the multiple regression model with the topographic variables (model 3), it was found that model 1 did not violate the limits of statistical significance and multicollinearity. As such, model 1 was considered to be appropriate for estimating the precipitation when taking into account the topography. In the study of model 1, the multiple regression model using factor analysis was found to be the best method for estimating the orographic precipitation on Jeju Island.
Regression Model Term Selection for the Analysis of Strain-Gage Balance Calibration Data
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert Manfred; Volden, Thomas R.
2010-01-01
The paper discusses the selection of regression model terms for the analysis of wind tunnel strain-gage balance calibration data. Different function class combinations are presented that may be used to analyze calibration data using either a non-iterative or an iterative method. The role of the intercept term in a regression model of calibration data is reviewed. In addition, useful algorithms and metrics originating from linear algebra and statistics are recommended that will help an analyst (i) to identify and avoid both linear and near-linear dependencies between regression model terms and (ii) to make sure that the selected regression model of the calibration data uses only statistically significant terms. Three different tests are suggested that may be used to objectively assess the predictive capability of the final regression model of the calibration data. These tests use both the original data points and regression model independent confirmation points. Finally, data from a simplified manual calibration of the Ames MK40 balance is used to illustrate the application of some of the metrics and tests to a realistic calibration data set.
Trend Analysis of Cancer Mortality and Incidence in Panama, Using Joinpoint Regression Analysis
Politis, Michael; Higuera, Gladys; Chang, Lissette Raquel; Gomez, Beatriz; Bares, Juan; Motta, Jorge
2015-01-01
Abstract Cancer is one of the leading causes of death worldwide and its incidence is expected to increase in the future. In Panama, cancer is also one of the leading causes of death. In 1964, a nationwide cancer registry was started and it was restructured and improved in 2012. The aim of this study is to utilize Joinpoint regression analysis to study the trends of the incidence and mortality of cancer in Panama in the last decade. Cancer mortality was estimated from the Panamanian National Institute of Census and Statistics Registry for the period 2001 to 2011. Cancer incidence was estimated from the Panamanian National Cancer Registry for the period 2000 to 2009. The Joinpoint Regression Analysis program, version 4.0.4, was used to calculate trends by age-adjusted incidence and mortality rates for selected cancers. Overall, the trend of age-adjusted cancer mortality in Panama has declined over the last 10 years (−1.12% per year). The cancers for which there was a significant increase in the trend of mortality were female breast cancer and ovarian cancer; while the highest increases in incidence were shown for breast cancer, liver cancer, and prostate cancer. Significant decrease in the trend of mortality was evidenced for the following: prostate cancer, lung and bronchus cancer, and cervical cancer; with respect to incidence, only oral and pharynx cancer in both sexes had a significant decrease. Some cancers showed no significant trends in incidence or mortality. This study reveals contrasting trends in cancer incidence and mortality in Panama in the last decade. Although Panama is considered an upper middle income nation, this study demonstrates that some cancer mortality trends, like the ones seen in cervical and lung cancer, behave similarly to the ones seen in high income countries. In contrast, other types, like breast cancer, follow a pattern seen in countries undergoing a transition to a developed economy with its associated lifestyle, nutrition, and
Trend Analysis of Cancer Mortality and Incidence in Panama, Using Joinpoint Regression Analysis
Politis, Michael; Higuera, Gladys; Chang, Lissette Raquel; Gomez, Beatriz; Bares, Juan; Motta, Jorge
2015-01-01
Abstract Cancer is one of the leading causes of death worldwide and its incidence is expected to increase in the future. In Panama, cancer is also one of the leading causes of death. In 1964, a nationwide cancer registry was started and it was restructured and improved in 2012. The aim of this study is to utilize Joinpoint regression analysis to study the trends of the incidence and mortality of cancer in Panama in the last decade. Cancer mortality was estimated from the Panamanian National Institute of Census and Statistics Registry for the period 2001 to 2011. Cancer incidence was estimated from the Panamanian National Cancer Registry for the period 2000 to 2009. The Joinpoint Regression Analysis program, version 4.0.4, was used to calculate trends by age-adjusted incidence and mortality rates for selected cancers. Overall, the trend of age-adjusted cancer mortality in Panama has declined over the last 10 years (−1.12% per year). The cancers for which there was a significant increase in the trend of mortality were female breast cancer and ovarian cancer; while the highest increases in incidence were shown for breast cancer, liver cancer, and prostate cancer. Significant decrease in the trend of mortality was evidenced for the following: prostate cancer, lung and bronchus cancer, and cervical cancer; with respect to incidence, only oral and pharynx cancer in both sexes had a significant decrease. Some cancers showed no significant trends in incidence or mortality. This study reveals contrasting trends in cancer incidence and mortality in Panama in the last decade. Although Panama is considered an upper middle income nation, this study demonstrates that some cancer mortality trends, like the ones seen in cervical and lung cancer, behave similarly to the ones seen in high income countries. In contrast, other types, like breast cancer, follow a pattern seen in countries undergoing a transition to a developed economy with its associated lifestyle, nutrition, and
Khalil, Mohamed H.; Shebl, Mostafa K.; Kosba, Mohamed A.; El-Sabrout, Karim; Zaki, Nesma
2016-01-01
Aim: This research was conducted to determine the most affecting parameters on hatchability of indigenous and improved local chickens’ eggs. Materials and Methods: Five parameters were studied (fertility, early and late embryonic mortalities, shape index, egg weight, and egg weight loss) on four strains, namely Fayoumi, Alexandria, Matrouh, and Montazah. Multiple linear regression was performed on the studied parameters to determine the most influencing one on hatchability. Results: The results showed significant differences in commercial and scientific hatchability among strains. Alexandria strain has the highest significant commercial hatchability (80.70%). Regarding the studied strains, highly significant differences in hatching chick weight among strains were observed. Using multiple linear regression analysis, fertility made the greatest percent contribution (71.31%) to hatchability, and the lowest percent contributions were made by shape index and egg weight loss. Conclusion: A prediction of hatchability using multiple regression analysis could be a good tool to improve hatchability percentage in chickens.
Khalil, Mohamed H.; Shebl, Mostafa K.; Kosba, Mohamed A.; El-Sabrout, Karim; Zaki, Nesma
2016-01-01
Aim: This research was conducted to determine the most affecting parameters on hatchability of indigenous and improved local chickens’ eggs. Materials and Methods: Five parameters were studied (fertility, early and late embryonic mortalities, shape index, egg weight, and egg weight loss) on four strains, namely Fayoumi, Alexandria, Matrouh, and Montazah. Multiple linear regression was performed on the studied parameters to determine the most influencing one on hatchability. Results: The results showed significant differences in commercial and scientific hatchability among strains. Alexandria strain has the highest significant commercial hatchability (80.70%). Regarding the studied strains, highly significant differences in hatching chick weight among strains were observed. Using multiple linear regression analysis, fertility made the greatest percent contribution (71.31%) to hatchability, and the lowest percent contributions were made by shape index and egg weight loss. Conclusion: A prediction of hatchability using multiple regression analysis could be a good tool to improve hatchability percentage in chickens. PMID:27651666
A multiple additive regression tree analysis of three exposure measures during Hurricane Katrina.
Curtis, Andrew; Li, Bin; Marx, Brian D; Mills, Jacqueline W; Pine, John
2011-01-01
This paper analyses structural and personal exposure to Hurricane Katrina. Structural exposure is measured by flood height and building damage; personal exposure is measured by the locations of 911 calls made during the response. Using these variables, this paper characterises the geography of exposure and also demonstrates the utility of a robust analytical approach in understanding health-related challenges to disadvantaged populations during recovery. Analysis is conducted using a contemporary statistical approach, a multiple additive regression tree (MART), which displays considerable improvement over traditional regression analysis. By using MART, the percentage of improvement in R-squares over standard multiple linear regression ranges from about 62 to more than 100 per cent. The most revealing finding is the modelled verification that African Americans experienced disproportionate exposure in both structural and personal contexts. Given the impact of exposure to health outcomes, this finding has implications for understanding the long-term health challenges facing this population.
Isolating the Effects of Training Using Simple Regression Analysis: An Example of the Procedure.
ERIC Educational Resources Information Center
Waugh, C. Keith
This paper provides a case example of simple regression analysis, a forecasting procedure used to isolate the effects of training from an identified extraneous variable. This case example focuses on results of a three-day sales training program to improve bank loan officers' knowledge, skill-level, and attitude regarding solicitation and sale of…
Ultrasound-enhanced bioscouring of greige cotton: regression analysis of process factors
Technology Transfer Automated Retrieval System (TEKTRAN)
Process factors of enzyme concentration, time, power and frequency were investigated for ultrasound-enhanced bioscouring of greige cotton. A fractional factorial experimental design and subsequent regression analysis of the process factors were employed to determine the significance of each factor a...
ERIC Educational Resources Information Center
Schulz, Wolfram
Differences in student knowledge about democracy, institutions, and citizenship and students skills in interpreting political communication were studied through multilevel regression analysis of results from the second International Education Association (IEA) Study. This study provides data on 14-year-old students from 28 countries in Europe,…
Multiple Logistic Regression Analysis of Cigarette Use among High School Students
ERIC Educational Resources Information Center
Adwere-Boamah, Joseph
2011-01-01
A binary logistic regression analysis was performed to predict high school students' cigarette smoking behavior from selected predictors from 2009 CDC Youth Risk Behavior Surveillance Survey. The specific target student behavior of interest was frequent cigarette use. Five predictor variables included in the model were: a) race, b) frequency of…
Using Refined Regression Analysis To Assess The Ecological Services Of Restored Wetlands
A hierarchical approach to regression analysis of wetland water treatment was conducted to determine which factors are the most appropriate for characterizing wetlands of differing structure and function. We used this approach in an effort to identify the types and characteristi...
Declining Bias and Gender Wage Discrimination? A Meta-Regression Analysis
ERIC Educational Resources Information Center
Jarrell, Stephen B.; Stanley, T. D.
2004-01-01
The meta-regression analysis reveals that there is a strong tendency for discrimination estimates to fall and wage discrimination exist against the woman. The biasing effect of researchers' gender of not correcting for selection bias has weakened and changes in labor market have made it less important.
Factor Regression Analysis: A New Method for Weighting Predictors. Final Report.
ERIC Educational Resources Information Center
Curtis, Ervin W.
The optimum weighting of variables to predict a dependent-criterion variable is an important problem in nearly all of the social and natural sciences. Although the predominant method, multiple regression analysis (MR), yields optimum weights for the sample at hand, these weights are not generally optimum in the population from which the sample was…
Catching up with Harvard: Results from Regression Analysis of World Universities League Tables
ERIC Educational Resources Information Center
Li, Mei; Shankar, Sriram; Tang, Kam Ki
2011-01-01
This paper uses regression analysis to test if the universities performing less well according to Shanghai Jiao Tong University's world universities league tables are able to catch up with the top performers, and to identify national and institutional factors that could affect this catching up process. We have constructed a dataset of 461…
Family Background Variables as Instruments for Education in Income Regressions: A Bayesian Analysis
ERIC Educational Resources Information Center
Hoogerheide, Lennart; Block, Joern H.; Thurik, Roy
2012-01-01
The validity of family background variables instrumenting education in income regressions has been much criticized. In this paper, we use data from the 2004 German Socio-Economic Panel and Bayesian analysis to analyze to what degree violations of the strict validity assumption affect the estimation results. We show that, in case of moderate direct…
ERIC Educational Resources Information Center
Bates, Reid A.; Holton, Elwood F., III; Burnett, Michael F.
1999-01-01
A case study of learning transfer demonstrates the possible effect of influential observation on linear regression analysis. A diagnostic method that tests for violation of assumptions, multicollinearity, and individual and multiple influential observations helps determine which observation to delete to eliminate bias. (SK)
What Satisfies Students?: Mining Student-Opinion Data with Regression and Decision Tree Analysis
ERIC Educational Resources Information Center
Thomas, Emily H.; Galambos, Nora
2004-01-01
To investigate how students' characteristics and experiences affect satisfaction, this study uses regression and decision tree analysis with the CHAID algorithm to analyze student-opinion data. A data mining approach identifies the specific aspects of students' university experience that most influence three measures of general satisfaction. The…
Comparing Predictors in Multivariate Regression Models: An Extension of Dominance Analysis
ERIC Educational Resources Information Center
Azen, Razia; Budescu, David V.
2006-01-01
Dominance analysis (DA) is a method used to compare the relative importance of predictors in multiple regression. DA determines the dominance of one predictor over another by comparing their additional R[squared] contributions across all subset models. In this article DA is extended to multivariate models by identifying a minimal set of criteria…
A use of regression analysis in acoustical diagnostics of gear drives
NASA Technical Reports Server (NTRS)
Balitskiy, F. Y.; Genkin, M. D.; Ivanova, M. A.; Kobrinskiy, A. A.; Sokolova, A. G.
1973-01-01
A study is presented of components of the vibration spectrum as the filtered first and second harmonics of the tooth frequency which permits information to be obtained on the physical characteristics of the vibration excitation process, and an approach to be made to comparison of models of the gearing. Regression analysis of two random processes has shown a strong dependence of the second harmonic on the first, and independence of the first from the second. The nature of change in the regression line, with change in loading moment, gives rise to the idea of a variable phase shift between the first and second harmonics.
Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen
2014-01-01
It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models. PMID:24574916
Cirulli, N; Ballini, A; Cantore, S; Farronato, D; Inchingolo, F; Dipalma, G; Gatto, M R; Alessandri Bonetti, G
2015-01-01
Mixed dentition analysis forms a critical aspect of early orthodontic treatment. In fact an accurate space analysis is one of the important criteria in determining whether the treatment plan may involve serial extraction, guidance of eruption, space maintenance, space regaining or just periodic observation of the patients. The aim of the present study was to calculate linear regression equations in mixed dentition space analysis, measuring 230 dental casts mesiodistal tooth widths, obtained from southern Italian patients (118 females, 112 males, mean age 15±3 years). Students t-test or Wilcoxon test for independent and paired samples were used to determine right/left side and male/female differences. On the basis of the sum of the mesiodistal diameters of the 4 mandibular incisors as predictors for the sum of the widths of the canines and premolars in the mandibular mixed dentition, a new linear regression equation was found: y = 0.613x+7.294 (r= 0.701) for both genders in a southern Italian population. To better estimate the size of leeway space, a new regression equation was found to calculate the mesiodistal size of the second premolar using the sum of the four mandibular incisors, canine and first premolar as a predictor. The equation is y = 0.241x+1.224 (r= 0.732). In conclusion, new regression equations were derived for a southern Italian population.
Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen
2014-01-01
It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models. PMID:24574916
Cirulli, N; Ballini, A; Cantore, S; Farronato, D; Inchingolo, F; Dipalma, G; Gatto, M R; Alessandri Bonetti, G
2015-01-01
Mixed dentition analysis forms a critical aspect of early orthodontic treatment. In fact an accurate space analysis is one of the important criteria in determining whether the treatment plan may involve serial extraction, guidance of eruption, space maintenance, space regaining or just periodic observation of the patients. The aim of the present study was to calculate linear regression equations in mixed dentition space analysis, measuring 230 dental casts mesiodistal tooth widths, obtained from southern Italian patients (118 females, 112 males, mean age 15±3 years). Students t-test or Wilcoxon test for independent and paired samples were used to determine right/left side and male/female differences. On the basis of the sum of the mesiodistal diameters of the 4 mandibular incisors as predictors for the sum of the widths of the canines and premolars in the mandibular mixed dentition, a new linear regression equation was found: y = 0.613x+7.294 (r= 0.701) for both genders in a southern Italian population. To better estimate the size of leeway space, a new regression equation was found to calculate the mesiodistal size of the second premolar using the sum of the four mandibular incisors, canine and first premolar as a predictor. The equation is y = 0.241x+1.224 (r= 0.732). In conclusion, new regression equations were derived for a southern Italian population. PMID:26122245
Forecasting Model for IPTV Service in Korea Using Bootstrap Ridge Regression Analysis
NASA Astrophysics Data System (ADS)
Lee, Byoung Chul; Kee, Seho; Kim, Jae Bum; Kim, Yun Bae
The telecom firms in Korea are taking new step to prepare for the next generation of convergence services, IPTV. In this paper we described our analysis on the effective method for demand forecasting about IPTV broadcasting. We have tried according to 3 types of scenarios based on some aspects of IPTV potential market and made a comparison among the results. The forecasting method used in this paper is the multi generation substitution model with bootstrap ridge regression analysis.
Greensmith, David J
2014-01-01
Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow.
Inhibition of cyclooxygenase (COX)-2 affects endothelial progenitor cell proliferation
Colleselli, Daniela; Bijuklic, Klaudija; Mosheimer, Birgit A.; Kaehler, Christian M. . E-mail: C.M.Kaehler@uibk.ac.at
2006-09-10
Growing evidence indicates that inducible cyclooxygenase-2 (COX-2) is involved in the pathogenesis of inflammatory disorders and various types of cancer. Endothelial progenitor cells recruited from the bone marrow have been shown to be involved in the formation of new vessels in malignancies and discussed for being a key point in tumour progression and metastasis. However, until now, nothing is known about an interaction between COX and endothelial progenitor cells (EPC). Expression of COX-1 and COX-2 was detected by semiquantitative RT-PCR and Western blot. Proliferation kinetics, cell cycle distribution and rate of apoptosis were analysed by MTT test and FACS analysis. Further analyses revealed an implication of Akt phosphorylation and caspase-3 activation. Both COX-1 and COX-2 expression can be found in bone-marrow-derived endothelial progenitor cells in vitro. COX-2 inhibition leads to a significant reduction in proliferation of endothelial progenitor cells by an increase in apoptosis and cell cycle arrest. COX-2 inhibition leads further to an increased cleavage of caspase-3 protein and inversely to inhibition of Akt activation. Highly proliferating endothelial progenitor cells can be targeted by selective COX-2 inhibition in vitro. These results indicate that upcoming therapy strategies in cancer patients targeting COX-2 may be effective in inhibiting tumour vasculogenesis as well as angiogenic processes.
NASA Technical Reports Server (NTRS)
Rummler, D. R.
1976-01-01
The results are presented of investigations to apply regression techniques to the development of methodology for creep-rupture data analysis. Regression analysis techniques are applied to the explicit description of the creep behavior of materials for space shuttle thermal protection systems. A regression analysis technique is compared with five parametric methods for analyzing three simulated and twenty real data sets, and a computer program for the evaluation of creep-rupture data is presented.
Sun, Xiaoyan; Manatunga, Amita; Marcus, Michele
2015-01-01
Summary In many observational longitudinal studies, the outcome of interest presents a skewed distribution, is subject to censoring due to detection limit or other reasons, and is observed at irregular times that may follow a outcome-dependent pattern. In this work, we consider quantile regression modeling of such longitudinal data, because quantile regression is generally robust in handling skewed and censored outcomes and is flexible to accommodate dynamic covariate-outcome relationships. Specifically, we study a longitudinal quantile regression model that specifies covariate effects on the marginal quantiles of the longitudinal outcome. Such a model is easy to interpret and can accommodate dynamic outcome profile changes over time. We propose estimation and inference procedures that can appropriately account for censoring and irregular outcome-dependent follow-up. Our proposals can be readily implemented based on existing software for quantile regression. We establish the asymptotic properties of the proposed estimator, including uniform consistency and weak convergence. Extensive simulations suggest good finite-sample performance of the new method. We also present an analysis of data from a long-term study of a population exposed to Polybrominated Biphenyls (PBB), which uncovers an inhomogeneous PBB elimination pattern that would not be detected by traditional longitudinal data analysis. PMID:26237289
The estimation of Aerosol Optical Depth in eastern China based on regression analysis
NASA Astrophysics Data System (ADS)
Wang, Jing; Shi, Runhe; Liu, Chaoshun; Zhou, Cong
2015-09-01
The atmospheric pollution and air quality issues are getting worse in China, the formation mechanism of aerosols and their environment effects attracted more and more attention. Aerosol Optical Depth (AOD) is one of the most important parameters which can indicate the atmospheric turbidity and aerosol load. High-quality AOD data are significant for the study in the atmospheric environment (i.e., air quality). This paper used MODIS/Terra AOD in 2008 to improve the coverage of MODIS/Aqua AOD, which was based on linear regression analysis model. RMSE between estimation value and AquaAOD detected through satellite is 0.132. The average value of test data was 0.812. The average of regression result was 0.807. It showed that the regression model between AODTerra and AODAqua worked well. Also, we built two sets of estimation models (MODIS AOD and OMI AOD) through stepwise regression analysis model. One is using OMI AOD and meteorological elements to estimate MODIS AOD. The value of RMSE was 0.113, which represents 13.916% of the average(R2=0.782). The other one is using MODIS AOD and meteorological elements to estimate OMI AOD. RMSE of the model is 0.132, which represents 18.182% of the average (R2=0.726).
Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic Algorithms
2014-01-01
On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets. PMID:25110755
Sun, Xiaoyan; Peng, Limin; Manatunga, Amita; Marcus, Michele
2016-03-01
In many observational longitudinal studies, the outcome of interest presents a skewed distribution, is subject to censoring due to detection limit or other reasons, and is observed at irregular times that may follow a outcome-dependent pattern. In this work, we consider quantile regression modeling of such longitudinal data, because quantile regression is generally robust in handling skewed and censored outcomes and is flexible to accommodate dynamic covariate-outcome relationships. Specifically, we study a longitudinal quantile regression model that specifies covariate effects on the marginal quantiles of the longitudinal outcome. Such a model is easy to interpret and can accommodate dynamic outcome profile changes over time. We propose estimation and inference procedures that can appropriately account for censoring and irregular outcome-dependent follow-up. Our proposals can be readily implemented based on existing software for quantile regression. We establish the asymptotic properties of the proposed estimator, including uniform consistency and weak convergence. Extensive simulations suggest good finite-sample performance of the new method. We also present an analysis of data from a long-term study of a population exposed to polybrominated biphenyls (PBB), which uncovers an inhomogeneous PBB elimination pattern that would not be detected by traditional longitudinal data analysis.
A deformation analysis method of stepwise regression for bridge deflection prediction
NASA Astrophysics Data System (ADS)
Shen, Yueqian; Zeng, Ying; Zhu, Lei; Huang, Teng
2015-12-01
Large-scale bridges are among the most important infrastructures whose safe conditions concern people's daily activities and life safety. Monitoring of large-scale bridges is crucial since deformation might have occurred. How to obtain the deformation information and then judge the safe conditions are the key and difficult problems in bridge deformation monitoring field. Deflection is the important index for evaluation of bridge safety. This paper proposes a forecasting modeling of stepwise regression analysis. Based on the deflection monitoring data of Yangtze River Bridge, the main factors influenced deflection deformation is chiefly studied. Authors use the monitoring data to forecast the deformation value of a bridge deflection at different time from the perspective of non-bridge structure, and compared to the forecasting of gray relational analysis based on linear regression. The result show that the accuracy and reliability of stepwise regression analysis is high, which provides the scientific basis to the bridge operation management. And above all, the ideas of this research provide and effective method for bridge deformation analysis.
Morrison, D G; Humes, P E; Keith, N K; Godke, R A
1985-03-01
Data from 131 calvings of Chianina crossbred cows (2 to 5 yr old) bred to Chianina bulls were used to compare stepwise multiple regression analysis (RA) and stepwise, two-group discriminant analysis (DA) for predicting dystocia. Variables (21) studied in relation to dystocia included both prebreeding and precalving cow and calf effects. Calving was categorized as either unassisted or assisted without regard to the severity of dystocia. During this study, 30 (22.9%) assisted births occurred. All variables were standardized to a mean of zero and a variance of one before statistical analyses. Models were developed based on precalving variables and with both precalving and postcalving variables with both RA and DA. Average discriminant scores (centroids) were different (P less than .01) between assisted and unassisted cows. Significant precalving DA variables were cow age and precalving pelvic height. This model correctly predicted 26 of 30 (86.7%) of the occurrences of dystocia. Significant precalving RA variables were prebreeding pelvic width and precalving pelvic height. The amount of variation accounted for by these two factors was 31.5%. Calf birth weight, calf chest depth, calf height, precalving pelvic area, cow age and precalving cow weight were selected by DA for use in the combined precalving and postcalving prediction model. Calf birth weight was 58% more important than either pelvic size or cow age. Percentage correctly classified with this model was 87.4. Significant postcalving variables selected by RA in order of importance were prebreeding pelvic width, calf birth weight and calf shoulder width (R2 = .399).(ABSTRACT TRUNCATED AT 250 WORDS)
Dimitriu, G; Poiata, Antonia; Tuchiluş, Cristina; Buiuc, D
2006-01-01
Linezolid is a new synthetic antibiotic belonging to the oxazolidinone class, available for the therapy of gram-positive infections, caused by methicillin-resistant staphylococci, vancomycin-resistant enterococci and penicillin-resistant pneumococci. The aim of the study was to determine the in vitro activity of linezolid against staphylococci strains and also to determine the relationship between the minimum inhibitory concentration (MIC) and inhibition zone diameter by calculating the regression analysis. We tested one hundred S. aureus isolates, obtained from healthy persons (naso-pharyngeal swabs) during 2005 year. The antibiotic susceptibility of strains was determined by disk diffusion standardized method and by agar dilution method using a multipoint inoculator. The relationship between the diameter of the inhibition zone produced by a linezolid disc impregnated with a fixed amount (30 eg) was determined by regression performed with the least squares method, considering the log2 of the minimum inhibitory concentrations (MICs) as the independent variable and the zone diameter as the dependent variable. The MIC values expressed in logarithmic form are plotted against inhibition zone diameter (arithmetic scale) of the same strain. The activity of linezolid against staphylococci was very good, with MIC 90 of 1 mg/l. All strains were fully sensitive. The regression line for linezolid passes through a continuous series of points that all are approximately located on the a straight line. For each of the MIC values the differences result no greater than 23 mm in diameter sizes were registered. Regression equation was y= -0.188x + 8.048. In conclusion, the regression line analysis calculated for linezolid, demonstrates a significant correlation between MIC values and the inhibition zone diameters obtained by a 30 mg disc.
Forecasting municipal solid waste generation using prognostic tools and regression analysis.
Ghinea, Cristina; Drăgoi, Elena Niculina; Comăniţă, Elena-Diana; Gavrilescu, Marius; Câmpean, Teofil; Curteanu, Silvia; Gavrilescu, Maria
2016-11-01
For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction. PMID:27454099
Forecasting municipal solid waste generation using prognostic tools and regression analysis.
Ghinea, Cristina; Drăgoi, Elena Niculina; Comăniţă, Elena-Diana; Gavrilescu, Marius; Câmpean, Teofil; Curteanu, Silvia; Gavrilescu, Maria
2016-11-01
For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction.
Use of generalized ordered logistic regression for the analysis of multidrug resistance data.
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.
Genetic analysis of tolerance to infections using random regressions: a simulation study.
Kause, Antti
2011-08-01
Tolerance to infections is the ability of a host to limit the impact of a given pathogen burden on host performance. This simulation study demonstrated the merit of using random regressions to estimate unbiased genetic variances for tolerance slope and its genetic correlations with other traits, which could not be obtained using the previously implemented statistical methods. Genetic variance in tolerance was estimated as genetic variance in regression slopes of host performance along an increasing pathogen burden level. Random regressions combined with covariance functions allowed genetic variance for host performance to be estimated at any point along the pathogen burden trajectory, providing a novel means to analyse infection-induced changes in genetic variation of host performance. Yet, the results implied that decreasing family size as well as a non-zero environmental or genetic correlation between initial host performance before infection and pathogen burden led to biased estimates for tolerance genetic variance. In both cases, genetic correlation between tolerance slope and host performance in a pathogen-free environment became artificially negative, implying a genetic trade-off when it did not exist. Moreover, recording a normally distributed pathogen burden as a threshold trait is not a realistic way of obtaining unbiased estimates for tolerance genetic variance. The results show that random regressions are suitable for the genetic analysis of tolerance, given suitable data structure collected either under field or experimental conditions. PMID:21767462
Repeated-measures regression designs and analysis for environmental effects monitoring programs
NASA Astrophysics Data System (ADS)
Paine, Michael D.; Skinner, Marc A.; Kilgour, Bruce W.; DeBlois, Elisabeth M.; Tracy, Ellen
2014-12-01
This paper provides a general overview of repeated-measures (RM) regression designs and analysis for marine monitoring programs, in support of sediment chemistry, particle size and benthic macroinvertebrate community analyses provided as part of this series. In RM regression designs, the same n replicates (usually stations in monitoring programs) are re-sampled (i.e., repeatedly measured) at t>1 Times (usually years). The stations provide variation in the predictor, or X variables. In the Terra Nova environmental effects monitoring (EEM) program, n=48 stations were sampled in each of t=7 years from 2000 to 2010. Two distance measures from five drill centres (sources of drilling wastes) were fixed predictor variables. RM regression designs are rarely used in environmental monitoring programs, but are often suitable and would be appropriate if applied to data from many monitoring programs. For the Terra Nova EEM program, carry-over effects, or persistent and usually small-scale variations among stations unrelated to distance, were strong for most sediment quality variables. Whenever natural carry-over effects are strong, RM designs and analysis will usually be more powerful and suitable than alternative approaches to the analysis.
Non-Stationary Hydrologic Frequency Analysis using B-Splines Quantile Regression
NASA Astrophysics Data System (ADS)
Nasri, B.; St-Hilaire, A.; Bouezmarni, T.; Ouarda, T.
2015-12-01
Hydrologic frequency analysis is commonly used by engineers and hydrologists to provide the basic information on planning, design and management of hydraulic structures and water resources system under the assumption of stationarity. However, with increasing evidence of changing climate, it is possible that the assumption of stationarity would no longer be valid and the results of conventional analysis would become questionable. In this study, we consider a framework for frequency analysis of extreme flows based on B-Splines quantile regression, which allows to model non-stationary data that have a dependence on covariates. Such covariates may have linear or nonlinear dependence. A Markov Chain Monte Carlo (MCMC) algorithm is used to estimate quantiles and their posterior distributions. A coefficient of determination for quantiles regression is proposed to evaluate the estimation of the proposed model for each quantile level. The method is applied on annual maximum and minimum streamflow records in Ontario, Canada. Climate indices are considered to describe the non-stationarity in these variables and to estimate the quantiles in this case. The results show large differences between the non-stationary quantiles and their stationary equivalents for annual maximum and minimum discharge with high annual non-exceedance probabilities. Keywords: Quantile regression, B-Splines functions, MCMC, Streamflow, Climate indices, non-stationarity.
Moreno-Betancur, Margarita; Rey, Grégoire; Latouche, Aurélien
2015-06-01
Competing risks arise in the analysis of failure times when there is a distinction between different causes of failure. In many studies, it is difficult to obtain complete cause of failure information for all individuals. Thus, several authors have proposed strategies for semi-parametric modeling of competing risks when some causes of failure are missing under the missing at random (MAR) assumption. As many authors have stressed, while semi-parametric models are convenient, fully-parametric regression modeling of the cause-specific hazards (CSH) and cumulative incidence functions (CIF) may be of interest for prediction and is likely to contribute towards a fuller understanding of the time-dynamics of the competing risks mechanism. We propose a so-called "direct likelihood" approach for fitting fully-parametric regression models for these two functionals under MAR. The MAR assumption not being verifiable from the observed data, we propose an approach for performing sensitivity analyses to assess the robustness of inferences to departures from this assumption. The method relies on so-called "pattern-mixture models" from the missing data literature and was evaluated in a simulation study. This sensitivity analysis approach is applicable to various competing risks regression models (fully-parametric or semi-parametric, for the CSH or the CIF). We illustrate the proposed methods with the analysis of a breast cancer clinical trial, including suggestions for ad hoc graphical goodness-of-fit assessments under MAR.
Length bias correction in gene ontology enrichment analysis using logistic regression.
Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H
2012-01-01
When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible. PMID:23056249
NASA Astrophysics Data System (ADS)
Baghi, Quentin; Métris, Gilles; Bergé, Joël; Christophe, Bruno; Touboul, Pierre; Rodrigues, Manuel
2015-03-01
The analysis of physical measurements often copes with highly correlated noises and interruptions caused by outliers, saturation events, or transmission losses. We assess the impact of missing data on the performance of linear regression analysis involving the fit of modeled or measured time series. We show that data gaps can significantly alter the precision of the regression parameter estimation in the presence of colored noise, due to the frequency leakage of the noise power. We present a regression method that cancels this effect and estimates the parameters of interest with a precision comparable to the complete data case, even if the noise power spectral density (PSD) is not known a priori. The method is based on an autoregressive fit of the noise, which allows us to build an approximate generalized least squares estimator approaching the minimal variance bound. The method, which can be applied to any similar data processing, is tested on simulated measurements of the MICROSCOPE space mission, whose goal is to test the weak equivalence principle (WEP) with a precision of 1 0-15. In this particular context the signal of interest is the WEP violation signal expected to be found around a well defined frequency. We test our method with different gap patterns and noise of known PSD and find that the results agree with the mission requirements, decreasing the uncertainty by a factor of 60 with respect to ordinary least squares methods. We show that it also provides a test of significance to assess the uncertainty of the measurement.
Yao, Yan; Wang, Chang-yue; Liu, Hui-jun; Tang, Jian-bin; Cai, Jin-hui; Wang, Jing-jun
2015-07-01
Forest bio-fuel, a new type renewable energy, has attracted increasing attention as a promising alternative. In this study, a new method called Sparse Partial Least Squares Regression (SPLS) is used to construct the proximate analysis model to analyze the fuel characteristics of sawdust combining Near Infrared Spectrum Technique. Moisture, Ash, Volatile and Fixed Carbon percentage of 80 samples have been measured by traditional proximate analysis. Spectroscopic data were collected by Nicolet NIR spectrometer. After being filtered by wavelet transform, all of the samples are divided into training set and validation set according to sample category and producing area. SPLS, Principle Component Regression (PCR), Partial Least Squares Regression (PLS) and Least Absolute Shrinkage and Selection Operator (LASSO) are presented to construct prediction model. The result advocated that SPLS can select grouped wavelengths and improve the prediction performance. The absorption peaks of the Moisture is covered in the selected wavelengths, well other compositions have not been confirmed yet. In a word, SPLS can reduce the dimensionality of complex data sets and interpret the relationship between spectroscopic data and composition concentration, which will play an increasingly important role in the field of NIR application. PMID:26717741
NASA Astrophysics Data System (ADS)
Urrutia, J. D.; Bautista, L. A.; Baccay, E. B.
2014-04-01
The aim of this study was to develop mathematical models for estimating earthquake casualties such as death, number of injured persons, affected families and total cost of damage. To quantify the direct damages from earthquakes to human beings and properties given the magnitude, intensity, depth of focus, location of epicentre and time duration, the regression models were made. The researchers formulated models through regression analysis using matrices and used α = 0.01. The study considered thirty destructive earthquakes that hit the Philippines from the inclusive years 1968 to 2012. Relevant data about these said earthquakes were obtained from Philippine Institute of Volcanology and Seismology. Data on damages and casualties were gathered from the records of National Disaster Risk Reduction and Management Council. The mathematical models made are as follows: This study will be of great value in emergency planning, initiating and updating programs for earthquake hazard reductionin the Philippines, which is an earthquake-prone country.
Analysis of ontogenetic spectra of populations of plants and lichens via ordinal regression
NASA Astrophysics Data System (ADS)
Sofronov, G. Yu.; Glotov, N. V.; Ivanov, S. M.
2015-03-01
Ontogenetic spectra of plants and lichens tend to vary across the populations. This means that if several subsamples within a sample (or a population) were collected, then the subsamples would not be homogeneous. Consequently, the statistical analysis of the aggregated data would not be correct, which could potentially lead to false biological conclusions. In order to take into account the heterogeneity of the subsamples, we propose to use ordinal regression, which is a type of generalized linear regression. In this paper, we study the populations of cowberry Vaccinium vitis-idaea L. and epiphytic lichens Hypogymnia physodes (L.) Nyl. and Pseudevernia furfuracea (L.) Zopf. We obtain estimates for the proportions of between-sample variability in the total variability of the ontogenetic spectra of the populations.
Alados, C.L.; Pueyo, Y.; Giner, M.L.; Navarro, T.; Escos, J.; Barroso, F.; Cabezudo, B.; Emlen, J.M.
2003-01-01
We studied the effect of grazing on the degree of regression of successional vegetation dynamic in a semi-arid Mediterranean matorral. We quantified the spatial distribution patterns of the vegetation by fractal analyses, using the fractal information dimension and spatial autocorrelation measured by detrended fluctuation analyses (DFA). It is the first time that fractal analysis of plant spatial patterns has been used to characterize the regressive ecological succession. Plant spatial patterns were compared over a long-term grazing gradient (low, medium and heavy grazing pressure) and on ungrazed sites for two different plant communities: A middle dense matorral of Chamaerops and Periploca at Sabinar-Romeral and a middle dense matorral of Chamaerops, Rhamnus and Ulex at Requena-Montano. The two communities differed also in the microclimatic characteristics (sea oriented at the Sabinar-Romeral site and inland oriented at the Requena-Montano site). The information fractal dimension increased as we moved from a middle dense matorral to discontinuous and scattered matorral and, finally to the late regressive succession, at Stipa steppe stage. At this stage a drastic change in the fractal dimension revealed a change in the vegetation structure, accurately indicating end successional vegetation stages. Long-term correlation analysis (DFA) revealed that an increase in grazing pressure leads to unpredictability (randomness) in species distributions, a reduction in diversity, and an increase in cover of the regressive successional species, e.g. Stipa tenacissima L. These comparisons provide a quantitative characterization of the successional dynamic of plant spatial patterns in response to grazing perturbation gradient. ?? 2002 Elsevier Science B.V. All rights reserved.
Bareth, Bettina; Dennerlein, Sven; Mick, David U.; Nikolov, Miroslav; Urlaub, Henning
2013-01-01
Cox1, the core subunit of the cytochrome c oxidase, receives two heme a cofactors during assembly of the 13-subunit enzyme complex. However, at which step of the assembly process and how heme is inserted into Cox1 have remained an enigma. Shy1, the yeast SURF1 homolog, has been implicated in heme transfer to Cox1, whereas the heme a synthase, Cox15, catalyzes the final step of heme a synthesis. Here we performed a comprehensive analysis of cytochrome c oxidase assembly intermediates containing Shy1. Our analyses suggest that Cox15 displays a role in cytochrome c oxidase assembly, which is independent of its functions as the heme a synthase. Cox15 forms protein complexes with Shy1 and also associates with Cox1-containing complexes independently of Shy1 function. These findings indicate that Shy1 does not serve as a mobile heme carrier between the heme a synthase and maturing Cox1 but rather cooperates with Cox15 for heme transfer and insertion in early assembly intermediates of cytochrome c oxidase. PMID:23979592
Application of artificial neural network to fMRI regression analysis.
Misaki, Masaya; Miyauchi, Satoru
2006-01-15
We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.
Robust best linear estimation for regression analysis using surrogate and instrumental variables
Wang, C. Y.
2012-01-01
We investigate methods for regression analysis when covariates are measured with errors. In a subset of the whole cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies the classical measurement error model, but it may not have repeated measurements. In addition to the surrogate variables that are available among the subjects in the calibration sample, we assume that there is an instrumental variable (IV) that is available for all study subjects. An IV is correlated with the unobserved true exposure variable and hence can be useful in the estimation of the regression coefficients. We propose a robust best linear estimator that uses all the available data, which is the most efficient among a class of consistent estimators. The proposed estimator is shown to be consistent and asymptotically normal under very weak distributional assumptions. For Poisson or linear regression, the proposed estimator is consistent even if the measurement error from the surrogate or IV is heteroscedastic. Finite-sample performance of the proposed estimator is examined and compared with other estimators via intensive simulation studies. The proposed method and other methods are applied to a bladder cancer case–control study. PMID:22285992
Oil and gas pipeline construction cost analysis and developing regression models for cost estimation
NASA Astrophysics Data System (ADS)
Thaduri, Ravi Kiran
In this study, cost data for 180 pipelines and 136 compressor stations have been analyzed. On the basis of the distribution analysis, regression models have been developed. Material, Labor, ROW and miscellaneous costs make up the total cost of a pipeline construction. The pipelines are analyzed based on different pipeline lengths, diameter, location, pipeline volume and year of completion. In a pipeline construction, labor costs dominate the total costs with a share of about 40%. Multiple non-linear regression models are developed to estimate the component costs of pipelines for various cross-sectional areas, lengths and locations. The Compressor stations are analyzed based on the capacity, year of completion and location. Unlike the pipeline costs, material costs dominate the total costs in the construction of compressor station, with an average share of about 50.6%. Land costs have very little influence on the total costs. Similar regression models are developed to estimate the component costs of compressor station for various capacities and locations.
Regression Analysis of Stage Variability for West-Central Florida Lakes
Sacks, Laura A.; Ellison, Donald L.; Swancar, Amy
2008-01-01
The variability in a lake's stage depends upon many factors, including surface-water flows, meteorological conditions, and hydrogeologic characteristics near the lake. An understanding of the factors controlling lake-stage variability for a population of lakes may be helpful to water managers who set regulatory levels for lakes. The goal of this study is to determine whether lake-stage variability can be predicted using multiple linear regression and readily available lake and basin characteristics defined for each lake. Regressions were evaluated for a recent 10-year period (1996-2005) and for a historical 10-year period (1954-63). Ground-water pumping is considered to have affected stage at many of the 98 lakes included in the recent period analysis, and not to have affected stage at the 20 lakes included in the historical period analysis. For the recent period, regression models had coefficients of determination (R2) values ranging from 0.60 to 0.74, and up to five explanatory variables. Standard errors ranged from 21 to 37 percent of the average stage variability. Net leakage was the most important explanatory variable in regressions describing the full range and low range in stage variability for the recent period. The most important explanatory variable in the model predicting the high range in stage variability was the height over median lake stage at which surface-water outflow would occur. Other explanatory variables in final regression models for the recent period included the range in annual rainfall for the period and several variables related to local and regional hydrogeology: (1) ground-water pumping within 1 mile of each lake, (2) the amount of ground-water inflow (by category), (3) the head gradient between the lake and the Upper Floridan aquifer, and (4) the thickness of the intermediate confining unit. Many of the variables in final regression models are related to hydrogeologic characteristics, underscoring the importance of ground
NASA Astrophysics Data System (ADS)
Liu, Pudong; Shi, Runhe; Wang, Hong; Bai, Kaixu; Gao, Wei
2014-10-01
Leaf pigments are key elements for plant photosynthesis and growth. Traditional manual sampling of these pigments is labor-intensive and costly, which also has the difficulty in capturing their temporal and spatial characteristics. The aim of this work is to estimate photosynthetic pigments at large scale by remote sensing. For this purpose, inverse model were proposed with the aid of stepwise multiple linear regression (SMLR) analysis. Furthermore, a leaf radiative transfer model (i.e. PROSPECT model) was employed to simulate the leaf reflectance where wavelength varies from 400 to 780 nm at 1 nm interval, and then these values were treated as the data from remote sensing observations. Meanwhile, simulated chlorophyll concentration (Cab), carotenoid concentration (Car) and their ratio (Cab/Car) were taken as target to build the regression model respectively. In this study, a total of 4000 samples were simulated via PROSPECT with different Cab, Car and leaf mesophyll structures as 70% of these samples were applied for training while the last 30% for model validation. Reflectance (r) and its mathematic transformations (1/r and log (1/r)) were all employed to build regression model respectively. Results showed fair agreements between pigments and simulated reflectance with all adjusted coefficients of determination (R2) larger than 0.8 as 6 wavebands were selected to build the SMLR model. The largest value of R2 for Cab, Car and Cab/Car are 0.8845, 0.876 and 0.8765, respectively. Meanwhile, mathematic transformations of reflectance showed little influence on regression accuracy. We concluded that it was feasible to estimate the chlorophyll and carotenoids and their ratio based on statistical model with leaf reflectance data.
Spatial regression analysis on 32 years of total column ozone data
NASA Astrophysics Data System (ADS)
Knibbe, J. S.; van der A, R. J.; de Laat, A. T. J.
2014-08-01
Multiple-regression analyses have been performed on 32 years of total ozone column data that was spatially gridded with a 1 × 1.5° resolution. The total ozone data consist of the MSR (Multi Sensor Reanalysis; 1979-2008) and 2 years of assimilated SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) ozone data (2009-2010). The two-dimensionality in this data set allows us to perform the regressions locally and investigate spatial patterns of regression coefficients and their explanatory power. Seasonal dependencies of ozone on regressors are included in the analysis. A new physically oriented model is developed to parameterize stratospheric ozone. Ozone variations on nonseasonal timescales are parameterized by explanatory variables describing the solar cycle, stratospheric aerosols, the quasi-biennial oscillation (QBO), El Niño-Southern Oscillation (ENSO) and stratospheric alternative halogens which are parameterized by the effective equivalent stratospheric chlorine (EESC). For several explanatory variables, seasonally adjusted versions of these explanatory variables are constructed to account for the difference in their effect on ozone throughout the year. To account for seasonal variation in ozone, explanatory variables describing the polar vortex, geopotential height, potential vorticity and average day length are included. Results of this regression model are compared to that of a similar analysis based on a more commonly applied statistically oriented model. The physically oriented model provides spatial patterns in the regression results for each explanatory variable. The EESC has a significant depleting effect on ozone at mid- and high latitudes, the solar cycle affects ozone positively mostly in the Southern Hemisphere, stratospheric aerosols affect ozone negatively at high northern latitudes, the effect of QBO is positive and negative in the tropics and mid- to high latitudes, respectively, and ENSO affects ozone negatively
NASA Astrophysics Data System (ADS)
Goovaerts, Pierre
2013-06-01
Analyzing temporal trends in health outcomes can provide a more comprehensive picture of the burden of a disease like cancer and generate new insights about the impact of various interventions. In the United States such an analysis is increasingly conducted using joinpoint regression outside a spatial framework, which overlooks the existence of significant variation among U.S. counties and states with regard to the incidence of cancer. This paper presents several innovative ways to account for space in joinpoint regression: (1) prior filtering of noise in the data by binomial kriging and use of the kriging variance as measure of reliability in weighted least-square regression, (2) detection of significant boundaries between adjacent counties based on tests of parallelism of time trends and confidence intervals of annual percent change of rates, and (3) creation of spatially compact groups of counties with similar temporal trends through the application of hierarchical cluster analysis to the results of boundary analysis. The approach is illustrated using time series of proportions of prostate cancer late-stage cases diagnosed yearly in every county of Florida since 1980s. The annual percent change (APC) in late-stage diagnosis and the onset years for significant declines vary greatly across Florida. Most counties with non-significant average APC are located in the north-western part of Florida, known as the Panhandle, which is more rural than other parts of Florida. The number of significant boundaries peaked in the early 1990s when prostate-specific antigen (PSA) test became widely available, a temporal trend that suggests the existence of geographical disparities in the implementation and/or impact of the new screening procedure, in particular as it began available.
NASA Technical Reports Server (NTRS)
Waller, M. C.
1976-01-01
An electro-optical device called an oculometer which tracks a subject's lookpoint as a time function has been used to collect data in a real-time simulation study of instrument landing system (ILS) approaches. The data describing the scanning behavior of a pilot during the instrument approaches have been analyzed by use of a stepwise regression analysis technique. A statistically significant correlation between pilot workload, as indicated by pilot ratings, and scanning behavior has been established. In addition, it was demonstrated that parameters derived from the scanning behavior data can be combined in a mathematical equation to provide a good representation of pilot workload.
KINETIC ANALYSIS OF HIGH-NITROGEN ENERGETIC MATERIALS USING MULTIVARIATE NONLINEAR REGRESSION
Campbell, M. S.; Rabie, R. L.; Diaz-Acosta, I.; Pulay, P.
2001-01-01
New high-nitrogen energetic materials were synthesized by Hiskey and Naud. J. Opfermann reported a new tool for finding the probable model of the complex reactions using multivariate non-linear regression analysis of DSC and TGA data from several measurements run at different heating rates. This study is to take the kinetic parameters from the different steps and discover which reaction step is responsible for the runaway reaction by comparing predicted results from the Frank-Kamenetsckii equation with the critical temperature found experimentally using the modified Henkin test.
Hofland, G.S.; Barton, C.C.
1990-10-01
The computer program FREQFIT is designed to perform regression and statistical chi-squared goodness of fit analysis on one-dimensional or two-dimensional data. The program features an interactive user dialogue, numerous help messages, an option for screen or line printer output, and the flexibility to use practically any commercially available graphics package to create plots of the program`s results. FREQFIT is written in Microsoft QuickBASIC, for IBM-PC compatible computers. A listing of the QuickBASIC source code for the FREQFIT program, a user manual, and sample input data, output, and plots are included. 6 refs., 1 fig.
NASA Astrophysics Data System (ADS)
Mandal, Nilrudra; Doloi, Biswanath; Mondal, Biswanath
2016-01-01
In the present study, an attempt has been made to apply the Taguchi parameter design method and regression analysis for optimizing the cutting conditions on surface finish while machining AISI 4340 steel with the help of the newly developed yttria based Zirconia Toughened Alumina (ZTA) inserts. These inserts are prepared through wet chemical co-precipitation route followed by powder metallurgy process. Experiments have been carried out based on an orthogonal array L9 with three parameters (cutting speed, depth of cut and feed rate) at three levels (low, medium and high). Based on the mean response and signal to noise ratio (SNR), the best optimal cutting condition has been arrived at A3B1C1 i.e. cutting speed is 420 m/min, depth of cut is 0.5 mm and feed rate is 0.12 m/min considering the condition smaller is the better approach. Analysis of Variance (ANOVA) is applied to find out the significance and percentage contribution of each parameter. The mathematical model of surface roughness has been developed using regression analysis as a function of the above mentioned independent variables. The predicted values from the developed model and experimental values are found to be very close to each other justifying the significance of the model. A confirmation run has been carried out with 95 % confidence level to verify the optimized result and the values obtained are within the prescribed limit.
Neck-focused panic attacks among Cambodian refugees; a logistic and linear regression analysis.
Hinton, Devon E; Chhean, Dara; Pich, Vuth; Um, Khin; Fama, Jeanne M; Pollack, Mark H
2006-01-01
Consecutive Cambodian refugees attending a psychiatric clinic were assessed for the presence and severity of current--i.e., at least one episode in the last month--neck-focused panic. Among the whole sample (N=130), in a logistic regression analysis, the Anxiety Sensitivity Index (ASI; odds ratio=3.70) and the Clinician-Administered PTSD Scale (CAPS; odds ratio=2.61) significantly predicted the presence of current neck panic (NP). Among the neck panic patients (N=60), in the linear regression analysis, NP severity was significantly predicted by NP-associated flashbacks (beta=.42), NP-associated catastrophic cognitions (beta=.22), and CAPS score (beta=.28). Further analysis revealed the effect of the CAPS score to be significantly mediated (Sobel test [Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182]) by both NP-associated flashbacks and catastrophic cognitions. In the care of traumatized Cambodian refugees, NP severity, as well as NP-associated flashbacks and catastrophic cognitions, should be specifically assessed and treated.
Correlated versus uncorrelated frailty Cox models: A comparison of different estimation procedures.
Elghafghuf, Adel; Stryhn, Henrik
2016-09-01
In many studies in medicine, including clinical trials and epidemiological investigations, data are clustered into groups such as health centers or herds in veterinary medicine. Such data are usually analyzed by hierarchical regression models to account for possible variation between groups. When such variation is large, it is of potential interest to explore whether additionally the effect of a within-group predictor varies between groups. In survival analysis, this may be investigated by including two frailty terms at group level in a Cox proportional hazards model. Several estimation methods have been proposed to estimate this type of frailty Cox models. We review four of these methods, apply them to real data from veterinary medicine, and compare them using a simulation study. PMID:27273127
Selenium Exposure and Cancer Risk: an Updated Meta-analysis and Meta-regression
Cai, Xianlei; Wang, Chen; Yu, Wanqi; Fan, Wenjie; Wang, Shan; Shen, Ning; Wu, Pengcheng; Li, Xiuyang; Wang, Fudi
2016-01-01
The objective of this study was to investigate the associations between selenium exposure and cancer risk. We identified 69 studies and applied meta-analysis, meta-regression and dose-response analysis to obtain available evidence. The results indicated that high selenium exposure had a protective effect on cancer risk (pooled OR = 0.78; 95%CI: 0.73–0.83). The results of linear and nonlinear dose-response analysis indicated that high serum/plasma selenium and toenail selenium had the efficacy on cancer prevention. However, we did not find a protective efficacy of selenium supplement. High selenium exposure may have different effects on specific types of cancer. It decreased the risk of breast cancer, lung cancer, esophageal cancer, gastric cancer, and prostate cancer, but it was not associated with colorectal cancer, bladder cancer, and skin cancer. PMID:26786590
Jalal, Hawre; Goldhaber-Fiebert, Jeremy D; Kuntz, Karen M
2015-07-01
Decision makers often desire both guidance on the most cost-effective interventions given current knowledge and also the value of collecting additional information to improve the decisions made (i.e., from value of information [VOI] analysis). Unfortunately, VOI analysis remains underused due to the conceptual, mathematical, and computational challenges of implementing Bayesian decision-theoretic approaches in models of sufficient complexity for real-world decision making. In this study, we propose a novel practical approach for conducting VOI analysis using a combination of probabilistic sensitivity analysis, linear regression metamodeling, and unit normal loss integral function--a parametric approach to VOI analysis. We adopt a linear approximation and leverage a fundamental assumption of VOI analysis, which requires that all sources of prior uncertainties be accurately specified. We provide examples of the approach and show that the assumptions we make do not induce substantial bias but greatly reduce the computational time needed to perform VOI analysis. Our approach avoids the need to analytically solve or approximate joint Bayesian updating, requires only one set of probabilistic sensitivity analysis simulations, and can be applied in models with correlated input parameters. PMID:25840900
Jalal, Hawre; Goldhaber-Fiebert, Jeremy D.; Kuntz, Karen M.
2016-01-01
Decision makers often desire both guidance on the most cost-effective interventions given current knowledge and also the value of collecting additional information to improve the decisions made [i.e., from value of information (VOI) analysis]. Unfortunately, VOI analysis remains underutilized due to the conceptual, mathematical and computational challenges of implementing Bayesian decision theoretic approaches in models of sufficient complexity for real-world decision making. In this study, we propose a novel practical approach for conducting VOI analysis using a combination of probabilistic sensitivity analysis, linear regression metamodeling, and unit normal loss integral function – a parametric approach to VOI analysis. We adopt a linear approximation and leverage a fundamental assumption of VOI analysis which requires that all sources of prior uncertainties be accurately specified. We provide examples of the approach and show that the assumptions we make do not induce substantial bias but greatly reduce the computational time needed to perform VOI analysis. Our approach avoids the need to analytically solve or approximate joint Bayesian updating, requires only one set of probabilistic sensitivity analysis simulations, and can be applied in models with correlated input parameters. PMID:25840900
Measuring treatment and scale bias effects by linear regression in the analysis of OHI-S scores.
Moore, B J
1977-05-01
A linear regression model is presented for estimating unbiased treatment effects from OHI-S scores. An example is given to illustrate an analysis and to compare results of an unbiased regression estimator with those based on a biased simple difference estimator.
NASA Astrophysics Data System (ADS)
Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.
2013-06-01
This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.
Regression analysis of growth responses to water depth in three wetland plant species
Sorrell, Brian K.; Tanner, Chris C.; Brix, Hans
2012-01-01
Background and aims Plant species composition in wetlands and on lakeshores often shows dramatic zonation, which is frequently ascribed to differences in flooding tolerance. This study compared the growth responses to water depth of three species (Phormium tenax, Carex secta and Typha orientalis) differing in depth preferences in wetlands, using non-linear and quantile regression analyses to establish how flooding tolerance can explain field zonation. Methodology Plants were established for 8 months in outdoor cultures in waterlogged soil without standing water, and then randomly allocated to water depths from 0 to 0.5 m. Morphological and growth responses to depth were followed for 54 days before harvest, and then analysed by repeated-measures analysis of covariance, and non-linear and quantile regression analysis (QRA), to compare flooding tolerances. Principal results Growth responses to depth differed between the three species, and were non-linear. Phormium tenax growth decreased rapidly in standing water >0.25 m depth, C. secta growth increased initially with depth but then decreased at depths >0.30 m, accompanied by increased shoot height and decreased shoot density, and T. orientalis was unaffected by the 0- to 0.50-m depth range. In P. tenax the decrease in growth was associated with a decrease in the number of leaves produced per ramet and in C. secta the effect of water depth was greatest for the tallest shoots. Allocation patterns were unaffected by depth. Conclusions The responses are consistent with the principle that zonation in the field is primarily structured by competition in shallow water and by physiological flooding tolerance in deep water. Regression analyses, especially QRA, proved to be powerful tools in distinguishing genuine phenotypic responses to water depth from non-phenotypic variation due to size and developmental differences. PMID:23259044
Selection of Higher Order Regression Models in the Analysis of Multi-Factorial Transcription Data
Prazeres da Costa, Olivia; Hoffman, Arthur; Rey, Johannes W.; Mansmann, Ulrich
2014-01-01
Introduction Many studies examine gene expression data that has been obtained under the influence of multiple factors, such as genetic background, environmental conditions, or exposure to diseases. The interplay of multiple factors may lead to effect modification and confounding. Higher order linear regression models can account for these effects. We present a new methodology for linear model selection and apply it to microarray data of bone marrow-derived macrophages. This experiment investigates the influence of three variable factors: the genetic background of the mice from which the macrophages were obtained, Yersinia enterocolitica infection (two strains, and a mock control), and treatment/non-treatment with interferon-γ. Results We set up four different linear regression models in a hierarchical order. We introduce the eruption plot as a new practical tool for model selection complementary to global testing. It visually compares the size and significance of effect estimates between two nested models. Using this methodology we were able to select the most appropriate model by keeping only relevant factors showing additional explanatory power. Application to experimental data allowed us to qualify the interaction of factors as either neutral (no interaction), alleviating (co-occurring effects are weaker than expected from the single effects), or aggravating (stronger than expected). We find a biologically meaningful gene cluster of putative C2TA target genes that appear to be co-regulated with MHC class II genes. Conclusions We introduced the eruption plot as a tool for visual model comparison to identify relevant higher order interactions in the analysis of expression data obtained under the influence of multiple factors. We conclude that model selection in higher order linear regression models should generally be performed for the analysis of multi-factorial microarray data. PMID:24658540
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.
Poisson regression analysis of mortality among male workers at a thorium-processing plant
Liu, Zhiyuan; Lee, Tze-San; Kotek, T.J.
1991-12-31
Analyses of mortality among a cohort of 3119 male workers employed between 1915 and 1973 at a thorium-processing plant were updated to the end of 1982. Of the whole group, 761 men were deceased and 2161 men were still alive, while 197 men were lost to follow-up. A total of 250 deaths was added to the 511 deaths observed in the previous study. The standardized mortality ratio (SMR) for all causes of death was 1.12 with 95% confidence interval (CI) of 1.05-1.21. The SMRs were also significantly increased for all malignant neoplasms (SMR = 1.23, 95% CI = 1.04-1.43) and lung cancer (SMR = 1.36, 95% CI = 1.02-1.78). Poisson regression analysis was employed to evaluate the joint effects of job classification, duration of employment, time since first employment, age and year at first employment on mortality of all malignant neoplasms and lung cancer. A comparison of internal and external analyses with the Poisson regression model was also conducted and showed no obvious difference in fitting the data on lung cancer mortality of the thorium workers. The results of the multivariate analysis showed that there was no significant effect of all the study factors on mortality due to all malignant neoplasms and lung cancer. Therefore, further study is needed for the former thorium workers.
NASA Astrophysics Data System (ADS)
Buck, J. A.; Underhill, P. R.; Morelli, J.; Krause, T. W.
2016-02-01
Nuclear steam generators (SGs) are a critical component for ensuring safe and efficient operation of a reactor. Life management strategies are implemented in which SG tubes are regularly inspected by conventional eddy current testing (ECT) and ultrasonic testing (UT) technologies to size flaws, and safe operating life of SGs is predicted based on growth models. ECT, the more commonly used technique, due to the rapidity with which full SG tube wall inspection can be performed, is challenged when inspecting ferromagnetic support structure materials in the presence of magnetite sludge and multiple overlapping degradation modes. In this work, an emerging inspection method, pulsed eddy current (PEC), is being investigated to address some of these particular inspection conditions. Time-domain signals were collected by an 8 coil array PEC probe in which ferromagnetic drilled support hole diameter, depth of rectangular tube frets and 2D tube off-centering were varied. Data sets were analyzed with a modified principal components analysis (MPCA) to extract dominant signal features. Multiple linear regression models were applied to MPCA scores to size hole diameter as well as size rectangular outer diameter tube frets. Models were improved through exploratory factor analysis, which was applied to MPCA scores to refine selection for regression models inputs by removing nonessential information.
Prognosis of conservatively treated patients with Pott's paraplegia: logistic regression analysis
Kalita, J; Misra, U; Mandal, S; Srivastava, M
2005-01-01
Methods: The study included 43 patients with Pott's paraplegia, managed conservatively. The diagnosis of Pott's spine was based on clinical, magnetic resonance imaging, and computed tomography or ultrasound guided aspiration biopsy. All patients were examined clinically, and motor evoked potentials (MEPs) to lower limbs and tibial somatosensory evoked potentials (SEP) were recorded. Outcome at six months was defined as good or poor. For evaluating predictors of outcome, 15 clinical, investigative, and evoked potential variables were analysed, using multiple logistic regression analysis. Results: The age range of the patients was 16–70 years, and 22 were female. Mild spasticity with hyperreflexia only was seen in 13 patients. In the remaining, weakness was severe in eight, and moderate and mild in 11 patients each. Twenty patients had loss of joint position sensation. MEP and SEP were abnormal in 19 and 18 patients, respectively. On multiple regression analysis, the best model predicting six month outcome included power, paraplegia score, SEP, and MEP. Conclusion: Patients with Pott's paraplegia are likely to recover completely by six months if they have mild weakness, lower paraplegia score and normal SEPs and MEPs. PMID:15897514
Error analysis of leaf area estimates made from allometric regression models
NASA Technical Reports Server (NTRS)
Feiveson, A. H.; Chhikara, R. S.
1986-01-01
Biological net productivity, measured in terms of the change in biomass with time, affects global productivity and the quality of life through biochemical and hydrological cycles and by its effect on the overall energy balance. Estimating leaf area for large ecosystems is one of the more important means of monitoring this productivity. For a particular forest plot, the leaf area is often estimated by a two-stage process. In the first stage, known as dimension analysis, a small number of trees are felled so that their areas can be measured as accurately as possible. These leaf areas are then related to non-destructive, easily-measured features such as bole diameter and tree height, by using a regression model. In the second stage, the non-destructive features are measured for all or for a sample of trees in the plots and then used as input into the regression model to estimate the total leaf area. Because both stages of the estimation process are subject to error, it is difficult to evaluate the accuracy of the final plot leaf area estimates. This paper illustrates how a complete error analysis can be made, using an example from a study made on aspen trees in northern Minnesota. The study was a joint effort by NASA and the University of California at Santa Barbara known as COVER (Characterization of Vegetation with Remote Sensing).
Regression analysis of overdispersed correlated count data with subject specific covariates.
Solis-Trapala, I L; Farewell, V T
2005-08-30
A robust likelihood approach for the analysis of overdispersed correlated count data that takes into account cluster varying covariates is proposed. We emphasise two characteristics of the proposed method: That the correlation structure satisfies the constraints on the second moments and that the estimation of the correlation structure guarantees consistent estimates of the regression coefficients. In addition we extend the mean specification to include within- and between-cluster effects. The method is illustrated through the analysis of data from two studies. In the first study, cross-sectional count data from a randomised controlled trial are analysed to evaluate the efficacy of a communication skills training programme. The second study involves longitudinal count data which represent counts of damaged hand joints in patients with psoriatic arthritis. Motivated by this study, we generalize our model to accommodate for a subpopulation of patients who are not susceptible to the development of damaged hand joints.
Frequency-domain nonlinear regression algorithm for spectral analysis of broadband SFG spectroscopy.
He, Yuhan; Wang, Ying; Wang, Jingjing; Guo, Wei; Wang, Zhaohui
2016-03-01
The resonant spectral bands of the broadband sum frequency generation (BB-SFG) spectra are often distorted by the nonresonant portion and the lineshapes of the laser pulses. Frequency domain nonlinear regression (FDNLR) algorithm was proposed to retrieve the first-order polarization induced by the infrared pulse and to improve the analysis of SFG spectra through simultaneous fitting of a series of time-resolved BB-SFG spectra. The principle of FDNLR was presented, and the validity and reliability were tested by the analysis of the virtual and measured SFG spectra. The relative phase, dephasing time, and lineshapes of the resonant vibrational SFG bands can be retrieved without any preset assumptions about the SFG bands and the incident laser pulses. PMID:26974068
NASA Astrophysics Data System (ADS)
Păniţă, Ovidiu
2015-09-01
In the years 2012-2014 on Banu-Maracine DRS there were tested an assortment of 25 isogenic lines of wheat (Triticum aestivum ssp.vulgare), the analyzed characters being the number of seeds/spike, seeds weight/spike (g), no. of spikes/m2, weight of a thousand seeds (WTS) (g) and no. of emerged plants/m2. Based on recorded data and statistical processing of those, they were identified a numbers of links between these characters. Also available regression models were identified between some of the studied characters. Based on component analysis, no. of seeds/spike and seeds weight/spike are components that influence in excess of 88% variance analysis, a total of seven genotypes with positive scores for both factors.
Modeling age-of-onset: Cox model with latent major gene effects
Li, H.; Thompson, E.A.
1994-09-01
Analysis of age-of-onset is a key factor in the segregation and linkage analysis of complex genetic traits, but is complicated by the censoring of unaffected individuals. Most previous work has used parametric distributional assumptions, but it is hard to characterize the distribution of age-of-onset by a single distribution. Other approaches discretize age-of-onset and use logistic regression to model incidence; this approach does not use the information fully. Frailty models have been used for age-of-oset in the biostatistics literature, but these models do not lend themselves to modeling the correlations due to genetic effects which segregate within a family. Here, we propose use of the Cox model with latent major gene effects; conditional on the major genotypes, Cox`s proportional hazards model is used for age-of-onset for each individual. This is a semiparametric model; we do not specify the baseline hazard function. Likelihood analysis of such models is restricted by the difficulty in evaluating of maximizing the likelihood, especially when data are available for some of the members of an extended pedigree. Markov chain Monte Carlo permits genotypic configurations to be realized from the posterior distributions given a current model and the observed data. Hence methods for likelihood analysis can be developed: Monte Carlo EM is used for estimation of the parameters and their variance-covariance matrix. Markers and observed covariates are easily incorporated into this analysis. We present the model, methods for likelihood analysis and the results of a simulation study. The results are comparable with those based on a Cox model with known genotypic dependence in a pedigree. An early-onset Alzheimer`s pedigree and some breast cancer pedigrees have been used as real data examples. Some possible extensions are also discussed.
Huang, Dong; Cabral, Ricardo; De la Torre, Fernando
2016-02-01
Discriminative methods (e.g., kernel regression, SVM) have been extensively used to solve problems such as object recognition, image alignment and pose estimation from images. These methods typically map image features ( X) to continuous (e.g., pose) or discrete (e.g., object category) values. A major drawback of existing discriminative methods is that samples are directly projected onto a subspace and hence fail to account for outliers common in realistic training sets due to occlusion, specular reflections or noise. It is important to notice that existing discriminative approaches assume the input variables X to be noise free. Thus, discriminative methods experience significant performance degradation when gross outliers are present. Despite its obvious importance, the problem of robust discriminative learning has been relatively unexplored in computer vision. This paper develops the theory of robust regression (RR) and presents an effective convex approach that uses recent advances on rank minimization. The framework applies to a variety of problems in computer vision including robust linear discriminant analysis, regression with missing data, and multi-label classification. Several synthetic and real examples with applications to head pose estimation from images, image and video classification and facial attribute classification with missing data are used to illustrate the benefits of RR. PMID:26761740
Modelling and analysis of turbulent datasets using Auto Regressive Moving Average processes
NASA Astrophysics Data System (ADS)
Faranda, Davide; Pons, Flavio Maria Emanuele; Dubrulle, Bérengère; Daviaud, François; Saint-Michel, Brice; Herbert, Éric; Cortet, Pierre-Philippe
2014-10-01
We introduce a novel way to extract information from turbulent datasets by applying an Auto Regressive Moving Average (ARMA) statistical analysis. Such analysis goes well beyond the analysis of the mean flow and of the fluctuations and links the behavior of the recorded time series to a discrete version of a stochastic differential equation which is able to describe the correlation structure in the dataset. We introduce a new index Υ that measures the difference between the resulting analysis and the Obukhov model of turbulence, the simplest stochastic model reproducing both Richardson law and the Kolmogorov spectrum. We test the method on datasets measured in a von Kármán swirling flow experiment. We found that the ARMA analysis is well correlated with spatial structures of the flow, and can discriminate between two different flows with comparable mean velocities, obtained by changing the forcing. Moreover, we show that the Υ is highest in regions where shear layer vortices are present, thereby establishing a link between deviations from the Kolmogorov model and coherent structures. These deviations are consistent with the ones observed by computing the Hurst exponents for the same time series. We show that some salient features of the analysis are preserved when considering global instead of local observables. Finally, we analyze flow configurations with multistability features where the ARMA technique is efficient in discriminating different stability branches of the system.
Modelling and analysis of turbulent datasets using Auto Regressive Moving Average processes
Faranda, Davide Dubrulle, Bérengère; Daviaud, François; Pons, Flavio Maria Emanuele; Saint-Michel, Brice; Herbert, Éric; Cortet, Pierre-Philippe
2014-10-15
We introduce a novel way to extract information from turbulent datasets by applying an Auto Regressive Moving Average (ARMA) statistical analysis. Such analysis goes well beyond the analysis of the mean flow and of the fluctuations and links the behavior of the recorded time series to a discrete version of a stochastic differential equation which is able to describe the correlation structure in the dataset. We introduce a new index Υ that measures the difference between the resulting analysis and the Obukhov model of turbulence, the simplest stochastic model reproducing both Richardson law and the Kolmogorov spectrum. We test the method on datasets measured in a von Kármán swirling flow experiment. We found that the ARMA analysis is well correlated with spatial structures of the flow, and can discriminate between two different flows with comparable mean velocities, obtained by changing the forcing. Moreover, we show that the Υ is highest in regions where shear layer vortices are present, thereby establishing a link between deviations from the Kolmogorov model and coherent structures. These deviations are consistent with the ones observed by computing the Hurst exponents for the same time series. We show that some salient features of the analysis are preserved when considering global instead of local observables. Finally, we analyze flow configurations with multistability features where the ARMA technique is efficient in discriminating different stability branches of the system.
P300 Amplitude in Alzheimer's Disease: A Meta-Analysis and Meta-Regression.
Hedges, Dawson; Janis, Rebecca; Mickelson, Stephen; Keith, Cierra; Bennett, David; Brown, Bruce L
2016-01-01
Alzheimer's disease accounts for 60% of all dementia. Numerous biomarkers have been developed that can help in making an early diagnosis. The P300 is an event-related potential that may be abnormal in Alzheimer's disease. Given the possible association between P300 amplitude and Alzheimer's disease and the need for biomarkers in early Alzheimer's disease, the main purpose of this meta-analysis and meta-regression was to characterize P300 amplitude in probable Alzheimer's disease compared to healthy controls. Using online search engines, we identified peer-reviewed articles containing amplitude measures for the P300 in response to a visual or auditory oddball stimulus in subjects with Alzheimer's disease and in a healthy control group and pooled effect sizes for differences in P300 amplitude between Alzheimer's disease and control groups to obtain summary effect sizes. We also used meta-regression to determine whether age, sex, educational attainment, or dementia severity affected the association between P300 amplitude and Alzheimer's disease. Twenty articles containing a total of 646 subjects met inclusion and exclusion criteria. The overall effect size from all electrode locations was 1.079 (95% confidence interval=0.745-1.412, P<.001). The pooled effect sizes for the Cz, Fz, and Pz locations were 1.226 (P<.001), 0.724 (P=.0007), and 1.430 (P<.001), respectively. Meta-regression showed an association between amplitude and educational attainment, but no association between amplitude and age, sex, and dementia severity. In conclusion, P300 amplitude is smaller in subjects with Alzheimer's disease than in healthy controls.
The Impact of Outliers on Net-Benefit Regression Model in Cost-Effectiveness Analysis.
Wen, Yu-Wen; Tsai, Yi-Wen; Wu, David Bin-Chia; Chen, Pei-Fen
2013-01-01
Ordinary least square (OLS) in regression has been widely used to analyze patient-level data in cost-effectiveness analysis (CEA). However, the estimates, inference and decision making in the economic evaluation based on OLS estimation may be biased by the presence of outliers. Instead, robust estimation can remain unaffected and provide result which is resistant to outliers. The objective of this study is to explore the impact of outliers on net-benefit regression (NBR) in CEA using OLS and to propose a potential solution by using robust estimations, i.e. Huber M-estimation, Hampel M-estimation, Tukey's bisquare M-estimation, MM-estimation and least trimming square estimation. Simulations under different outlier-generating scenarios and an empirical example were used to obtain the regression estimates of NBR by OLS and five robust estimations. Empirical size and empirical power of both OLS and robust estimations were then compared in the context of hypothesis testing. Simulations showed that the five robust approaches compared with OLS estimation led to lower empirical sizes and achieved higher empirical powers in testing cost-effectiveness. Using real example of antiplatelet therapy, the estimated incremental net-benefit by OLS estimation was lower than those by robust approaches because of outliers in cost data. Robust estimations demonstrated higher probability of cost-effectiveness compared to OLS estimation. The presence of outliers can bias the results of NBR and its interpretations. It is recommended that the use of robust estimation in NBR can be an appropriate method to avoid such biased decision making. PMID:23840378
Bode, Manuela; Woellhaf, Michael W.; Bohnert, Maria; van der Laan, Martin; Sommer, Frederik; Jung, Martin; Zimmermann, Richard; Schroda, Michael; Herrmann, Johannes M.
2015-01-01
Members of the twin Cx9C protein family constitute the largest group of proteins in the intermembrane space (IMS) of mitochondria. Despite their conserved nature and their essential role in the biogenesis of the respiratory chain, the molecular function of twin Cx9C proteins is largely unknown. We performed a SILAC-based quantitative proteomic analysis to identify interaction partners of the conserved twin Cx9C protein Cox19. We found that Cox19 interacts in a dynamic manner with Cox11, a copper transfer protein that facilitates metalation of the Cu(B) center of subunit 1 of cytochrome c oxidase. The interaction with Cox11 is critical for the stable accumulation of Cox19 in mitochondria. Cox19 consists of a helical hairpin structure that forms a hydrophobic surface characterized by two highly conserved tyrosine-leucine dipeptides. These residues are essential for Cox19 function and its specific binding to a cysteine-containing sequence in Cox11. Our observations suggest that an oxidative modification of this cysteine residue of Cox11 stimulates Cox19 binding, pointing to a redox-regulated interplay of Cox19 and Cox11 that is critical for copper transfer in the IMS and thus for biogenesis of cytochrome c oxidase. PMID:25926683
Fu, Yuan-Yuan; Wang, Ji-Hua; Yang, Gui-Jun; Song, Xiao-Yu; Xu, Xin-Gang; Feng, Hai-Kuan
2013-05-01
The major limitation of using existing vegetation indices for crop biomass estimation is that it approaches a saturation level asymptotically for a certain range of biomass. In order to resolve this problem, band depth analysis and partial least square regression (PLSR) were combined to establish winter wheat biomass estimation model in the present study. The models based on the combination of band depth analysis and PLSR were compared with the models based on common vegetation indexes from the point of view of estimation accuracy, subsequently. Band depth analysis was conducted in the visible spectral domain (550-750 nm). Band depth, band depth ratio (BDR), normalized band depth index, and band depth normalized to area were utilized to represent band depth information. Among the calibrated estimation models, the models based on the combination of band depth analysis and PLSR reached higher accuracy than those based on the vegetation indices. Among them, the combination of BDR and PLSR got the highest accuracy (R2 = 0.792, RMSE = 0.164 kg x m(-2)). The results indicated that the combination of band depth analysis and PLSR could well overcome the saturation problem and improve the biomass estimation accuracy when winter wheat biomass is large.
Improved Regression Analysis of Temperature-Dependent Strain-Gage Balance Calibration Data
NASA Technical Reports Server (NTRS)
Ulbrich, N.
2015-01-01
An improved approach is discussed that may be used to directly include first and second order temperature effects in the load prediction algorithm of a wind tunnel strain-gage balance. The improved approach was designed for the Iterative Method that fits strain-gage outputs as a function of calibration loads and uses a load iteration scheme during the wind tunnel test to predict loads from measured gage outputs. The improved approach assumes that the strain-gage balance is at a constant uniform temperature when it is calibrated and used. First, the method introduces a new independent variable for the regression analysis of the balance calibration data. The new variable is designed as the difference between the uniform temperature of the balance and a global reference temperature. This reference temperature should be the primary calibration temperature of the balance so that, if needed, a tare load iteration can be performed. Then, two temperature{dependent terms are included in the regression models of the gage outputs. They are the temperature difference itself and the square of the temperature difference. Simulated temperature{dependent data obtained from Triumph Aerospace's 2013 calibration of NASA's ARC-30K five component semi{span balance is used to illustrate the application of the improved approach.
A cautionary note on the use of EESC-based regression analysis for ozone trend studies
NASA Astrophysics Data System (ADS)
Kuttippurath, J.; Bodeker, G. E.; Roscoe, H. K.; Nair, P. J.
2015-01-01
Equivalent effective stratospheric chlorine (EESC) construct of ozone regression models attributes ozone changes to EESC changes using a single value of the sensitivity of ozone to EESC over the whole period. Using space-based total column ozone (TCO) measurements, and a synthetic TCO time series constructed such that EESC does not fall below its late 1990s maximum, we demonstrate that the EESC-based estimates of ozone changes in the polar regions (70-90°) after 2000 may, falsely, suggest an EESC-driven increase in ozone over this period. An EESC-based regression of our synthetic "failed Montreal Protocol with constant EESC" time series suggests a positive TCO trend that is statistically significantly different from zero over 2001-2012 when, in fact, no recovery has taken place. Our analysis demonstrates that caution needs to be exercised when using explanatory variables, with a single fit coefficient, fitted to the entire data record, to interpret changes in only part of the record.
Yu, Rongqin; Geddes, John R; Fazel, Seena
2012-10-01
The risk of antisocial outcomes in individuals with personality disorder (PD) remains uncertain. The authors synthesize the current evidence on the risks of antisocial behavior, violence, and repeat offending in PD, and they explore sources of heterogeneity in risk estimates through a systematic review and meta-regression analysis of observational studies comparing antisocial outcomes in personality disordered individuals with controls groups. Fourteen studies examined risk of antisocial and violent behavior in 10,007 individuals with PD, compared with over 12 million general population controls. There was a substantially increased risk of violent outcomes in studies with all PDs (random-effects pooled odds ratio [OR] = 3.0, 95% CI = 2.6 to 3.5). Meta-regression revealed that antisocial PD and gender were associated with higher risks (p = .01 and .07, respectively). The odds of all antisocial outcomes were also elevated. Twenty-five studies reported the risk of repeat offending in PD compared with other offenders. The risk of a repeat offense was also increased (fixed-effects pooled OR = 2.4, 95% CI = 2.2 to 2.7) in offenders with PD. The authors conclude that although PD is associated with antisocial outcomes and repeat offending, the risk appears to differ by PD category, gender, and whether individuals are offenders or not.
Gayou, Olivier; Das, Shiva K; Zhou, Su-Min; Marks, Lawrence B; Parda, David S; Miften, Moyed
2008-12-01
A given outcome of radiotherapy treatment can be modeled by analyzing its correlation with a combination of dosimetric, physiological, biological, and clinical factors, through a logistic regression fit of a large patient population. The quality of the fit is measured by the combination of the predictive power of this particular set of factors and the statistical significance of the individual factors in the model. We developed a genetic algorithm (GA), in which a small sample of all the possible combinations of variables are fitted to the patient data. New models are derived from the best models, through crossover and mutation operations, and are in turn fitted. The process is repeated until the sample converges to the combination of factors that best predicts the outcome. The GA was tested on a data set that investigated the incidence of lung injury in NSCLC patients treated with 3DCRT. The GA identified a model with two variables as the best predictor of radiation pneumonitis: the V30 (p=0.048) and the ongoing use of tobacco at the time of referral (p=0.074). This two-variable model was confirmed as the best model by analyzing all possible combinations of factors. In conclusion, genetic algorithms provide a reliable and fast way to select significant factors in logistic regression analysis of large clinical studies.
Gayou, Olivier; Das, Shiva K.; Zhou, Su-Min; Marks, Lawrence B.; Parda, David S.; Miften, Moyed
2008-01-01
A given outcome of radiotherapy treatment can be modeled by analyzing its correlation with a combination of dosimetric, physiological, biological, and clinical factors, through a logistic regression fit of a large patient population. The quality of the fit is measured by the combination of the predictive power of this particular set of factors and the statistical significance of the individual factors in the model. We developed a genetic algorithm (GA), in which a small sample of all the possible combinations of variables are fitted to the patient data. New models are derived from the best models, through crossover and mutation operations, and are in turn fitted. The process is repeated until the sample converges to the combination of factors that best predicts the outcome. The GA was tested on a data set that investigated the incidence of lung injury in NSCLC patients treated with 3DCRT. The GA identified a model with two variables as the best predictor of radiation pneumonitis: the V30 (p=0.048) and the ongoing use of tobacco at the time of referral (p=0.074). This two-variable model was confirmed as the best model by analyzing all possible combinations of factors. In conclusion, genetic algorithms provide a reliable and fast way to select significant factors in logistic regression analysis of large clinical studies. PMID:19175102
Combining regression analysis and air quality modelling to predict benzene concentration levels
NASA Astrophysics Data System (ADS)
Vlachokostas, Ch.; Achillas, Ch.; Chourdakis, E.; Moussiopoulos, N.
2011-05-01
State of the art epidemiological research has found consistent associations between traffic-related air pollution and various outcomes, such as respiratory symptoms and premature mortality. However, many urban areas are characterised by the absence of the necessary monitoring infrastructure, especially for benzene (C 6H 6), which is a known human carcinogen. The use of environmental statistics combined with air quality modelling can be of vital importance in order to assess air quality levels of traffic-related pollutants in an urban area in the case where there are no available measurements. This paper aims at developing and presenting a reliable approach, in order to forecast C 6H 6 levels in urban environments, demonstrated for Thessaloniki, Greece. Multiple stepwise regression analysis is used and a strong statistical relationship is detected between C 6H 6 and CO. The adopted regression model is validated in order to depict its applicability and representativeness. The presented results demonstrate that the adopted approach is capable of capturing C 6H 6 concentration trends and should be considered as complementary to air quality monitoring.
An innovative land use regression model incorporating meteorology for exposure analysis.
Su, Jason G; Brauer, Michael; Ainslie, Bruce; Steyn, Douw; Larson, Timothy; Buzzelli, Michael
2008-02-15
The advent of spatial analysis and geographic information systems (GIS) has led to studies of chronic exposure and health effects based on the rationale that intra-urban variations in ambient air pollution concentrations are as great as inter-urban differences. Such studies typically rely on local spatial covariates (e.g., traffic, land use type) derived from circular areas (buffers) to predict concentrations/exposures at receptor sites, as a means of averaging the annual net effect of meteorological influences (i.e., wind speed, wind direction and insolation). This is the approach taken in the now popular land use regression (LUR) method. However spatial studies of chronic exposures and temporal studies of acute exposures have not been adequately integrated. This paper presents an innovative LUR method implemented in a GIS environment that reflects both temporal and spatial variability and considers the role of meteorology. The new source area LUR integrates wind speed, wind direction and cloud cover/insolation to estimate hourly nitric oxide (NO) and nitrogen dioxide (NO(2)) concentrations from land use types (i.e., road network, commercial land use) and these concentrations are then used as covariates to regress against NO and NO(2) measurements at various receptor sites across the Vancouver region and compared directly with estimates from a regular LUR. The results show that, when variability in seasonal concentration measurements is present, the source area LUR or SA-LUR model is a better option for concentration estimation.
Ren, Y Y; Zhou, L C; Yang, L; Liu, P Y; Zhao, B W; Liu, H X
2016-09-01
The paper highlights the use of the logistic regression (LR) method in the construction of acceptable statistically significant, robust and predictive models for the classification of chemicals according to their aquatic toxic modes of action. Essentials accounting for a reliable model were all considered carefully. The model predictors were selected by stepwise forward discriminant analysis (LDA) from a combined pool of experimental data and chemical structure-based descriptors calculated by the CODESSA and DRAGON software packages. Model predictive ability was validated both internally and externally. The applicability domain was checked by the leverage approach to verify prediction reliability. The obtained models are simple and easy to interpret. In general, LR performs much better than LDA and seems to be more attractive for the prediction of the more toxic compounds, i.e. compounds that exhibit excess toxicity versus non-polar narcotic compounds and more reactive compounds versus less reactive compounds. In addition, model fit and regression diagnostics was done through the influence plot which reflects the hat-values, studentized residuals, and Cook's distance statistics of each sample. Overdispersion was also checked for the LR model. The relationships between the descriptors and the aquatic toxic behaviour of compounds are also discussed. PMID:27653817
NASA Astrophysics Data System (ADS)
Simms, Laura E.; Engebretson, Mark J.; Pilipenko, Viacheslav; Reeves, Geoffrey D.; Clilverd, Mark
2016-04-01
The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF Bz, Dst, and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the prediction of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). A path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current (Dst), AE, and wave activity.
Imai, Chisato; Hashizume, Masahiro
2015-01-01
Background: Time series analysis is suitable for investigations of relatively direct and short-term effects of exposures on outcomes. In environmental epidemiology studies, this method has been one of the standard approaches to assess impacts of environmental factors on acute non-infectious diseases (e.g. cardiovascular deaths), with conventionally generalized linear or additive models (GLM and GAM). However, the same analysis practices are often observed with infectious diseases despite of the substantial differences from non-infectious diseases that may result in analytical challenges. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, systematic review was conducted to elucidate important issues in assessing the associations between environmental factors and infectious diseases using time series analysis with GLM and GAM. Published studies on the associations between weather factors and malaria, cholera, dengue, and influenza were targeted. Findings: Our review raised issues regarding the estimation of susceptible population and exposure lag times, the adequacy of seasonal adjustments, the presence of strong autocorrelations, and the lack of a smaller observation time unit of outcomes (i.e. daily data). These concerns may be attributable to features specific to infectious diseases, such as transmission among individuals and complicated causal mechanisms. Conclusion: The consequence of not taking adequate measures to address these issues is distortion of the appropriate risk quantifications of exposures factors. Future studies should pay careful attention to details and examine alternative models or methods that improve studies using time series regression analysis for environmental determinants of infectious diseases. PMID:25859149
NASA Astrophysics Data System (ADS)
Lorenzetti, G.; Foresta, A.; Palleschi, V.; Legnaioli, S.
2009-09-01
The recent development of mobile instrumentation, specifically devoted to in situ analysis and study of museum objects, allows the acquisition of many LIBS spectra in very short time. However, such large amount of data calls for new analytical approaches which would guarantee a prompt analysis of the results obtained. In this communication, we will present and discuss the advantages of statistical analytical methods, such as Partial Least Squares Multiple Regression algorithms vs. the classical calibration curve approach. PLS algorithms allows to obtain in real time the information on the composition of the objects under study; this feature of the method, compared to the traditional off-line analysis of the data, is extremely useful for the optimization of the measurement times and number of points associated with the analysis. In fact, the real time availability of the compositional information gives the possibility of concentrating the attention on the most `interesting' parts of the object, without over-sampling the zones which would not provide useful information for the scholars or the conservators. Some example on the applications of this method will be presented, including the studies recently performed by the researcher of the Applied Laser Spectroscopy Laboratory on museum bronze objects.
Melanin and blood concentration in human skin studied by multiple regression analysis: experiments
NASA Astrophysics Data System (ADS)
Shimada, M.; Yamada, Y.; Itoh, M.; Yatagai, T.
2001-09-01
Knowledge of the mechanism of human skin colour and measurement of melanin and blood concentration in human skin are needed in the medical and cosmetic fields. The absorbance spectrum from reflectance at the visible wavelength of human skin increases under several conditions such as a sunburn or scalding. The change of the absorbance spectrum from reflectance including the scattering effect does not correspond to the molar absorption spectrum of melanin and blood. The modified Beer-Lambert law is applied to the change in the absorbance spectrum from reflectance of human skin as the change in melanin and blood is assumed to be small. The concentration of melanin and blood was estimated from the absorbance spectrum reflectance of human skin using multiple regression analysis. Estimated concentrations were compared with the measured one in a phantom experiment and this method was applied to in vivo skin.
Gad, R S; Parab, J S; Naik, G M
2010-11-01
Multivariate system spectroscopic model plays important role in understanding chemometrics of ensemble under study. Here in this manuscript we discuss various approaches of modeling of spectroscopic system and demonstrate how Lorentz oscillator can be used to model any general spectroscopic system. Chemometric studies require customized templates design for the corresponding variants participating in ensemble, which generates the characteristic matrix of the ensemble under study. The typical biological system that resembles human blood tissue consisting of five major constituents i.e., alanine, urea, lactate, glucose, ascorbate; has been tested on the model. The model was validated using three approaches, namely, root mean square error (RMSE) analysis in the range of ±5% confidence interval, clerk gird error plot, and RMSE versus percent noise level study. Also the model was tested across various template sizes (consisting of samples ranging from 10 up to 1000) to ascertain the validity of partial least squares regression. The model has potential in understanding the chemometrics of proteomics pathways.
NASA Astrophysics Data System (ADS)
Gad, R. S.; Parab, J. S.; Naik, G. M.
2010-11-01
Multivariate system spectroscopic model plays important role in understanding chemometrics of ensemble under study. Here in this manuscript we discuss various approaches of modeling of spectroscopic system and demonstrate how Lorentz oscillator can be used to model any general spectroscopic system. Chemometric studies require customized templates design for the corresponding variants participating in ensemble, which generates the characteristic matrix of the ensemble under study. The typical biological system that resembles human blood tissue consisting of five major constituents i.e., alanine, urea, lactate, glucose, ascorbate; has been tested on the model. The model was validated using three approaches, namely, root mean square error (RMSE) analysis in the range of ±5% confidence interval, clerk gird error plot, and RMSE versus percent noise level study. Also the model was tested across various template sizes (consisting of samples ranging from 10 up to 1000) to ascertain the validity of partial least squares regression. The model has potential in understanding the chemometrics of proteomics pathways.
Analysis of time-dependent covariates in a regressive relative survival model.
Giorgi, Roch; Gouvernet, Joanny
2005-12-30
Relative survival is a method for assessing prognostic factors for disease-specific mortality. However, most relative survival models assume that the effect of covariate on disease-specific mortality is fixed-in-time, which may not hold in some studies and requires adapted modelling. We propose an extension of the Esteve et al. regressive relative survival model that uses the counting process approach to accommodate time-dependent effect of a predictor's on disease-specific mortality. This approach had shown its robustness, and the properties of the counting process give a simple and attractive computational solution to model time-dependent covariates. Our approach is illustrated with the data from the Stanford Heart Transplant Study and with data from a hospital-based study on invasive breast cancer. Advantages of modelling time-dependent covariates in relative survival analysis are discussed.
Chuang, Chun-Ling; Chang, Peng-Chan; Lin, Rong-Ho
2011-10-01
As changes in the medical environment and policies on national health insurance coverage have triggered tremendous impacts on the business performance and financial management of medical institutions, effective management becomes increasingly crucial for hospitals to enhance competitiveness and to strive for sustainable development. The study accordingly aims at evaluating hospital operational efficiency for better resource allocation and cost effectiveness. Several data envelopment analysis (DEA)-based models were first compared, and the DEA-artificial neural network (ANN) model was identified as more capable than the DEA and DEA-assurance region (AR) models of measuring operational efficiency and recognizing the best-performing hospital. The classification and regression tree (CART) efficiency model was then utilized to extract rules for improving resource allocation of medical institutions. PMID:20878210
Sparse regression analysis of task-relevant information distribution in the brain
NASA Astrophysics Data System (ADS)
Rish, Irina; Cecchi, Guillermo A.; Heuton, Kyle; Baliki, Marwan N.; Apkarian, A. Vania
2012-02-01
One of key topics in fMRI analysis is discovery of task-related brain areas. We focus on predictive accuracy as a better relevance measure than traditional univariate voxel activations that miss important multivariate voxel interactions. We use sparse regression (more specifically, the Elastic Net1) to learn predictive models simultaneously with selection of predictive voxel subsets, and to explore transition from task-relevant to task-irrelevant areas. Exploring the space of sparse solutions reveals a much wider spread of task-relevant information in the brain than it is typically suggested by univariate correlations. This happens for several tasks we considered, and is most noticeable in case of complex tasks such as pain rating; however, for certain simpler tasks, a clear separation between a small subset of relevant voxels and the rest of the brain is observed even with multivariate approach to measuring relevance.
Ghorashi, Seyed Ali; Tavassoli, Mousa; Peters, Andrew; Shamsi, Shokoofeh; Hajipour, Naser
2016-01-01
The phylogenetic relationships among seven Linguatula serrata (L. serrata) isolates collected from cattle, goats, sheep, dogs and camels in different geographical locations of Iran were investigated using partial 18S ribosomal RNA (rRNA) and partial mitochondrial cytochrome c oxidase subunit 1 (cox1) gene sequences. The nucleotide sequences were analysed in order to determine the phylogenetic relationships between the isolates. Higher sequence diversity and intraspecies variation was observed in the cox1 gene compared to 18S rRNA sequences. Phylogenetic analysis of the cox1 gene placed all L. serrata isolates in a sister clade to L. arctica. The Mantel regression analysis revealed no association between genetic variations and host species or geographical location, perhaps due to the small sample size. However, genetic variations between L. serrata isolates in Iran and those isolated in other parts of the world may exist and could reveal possible evolutionary relationships.
Ghorashi, Seyed Ali; Tavassoli, Mousa; Peters, Andrew; Shamsi, Shokoofeh; Hajipour, Naser
2016-01-01
The phylogenetic relationships among seven Linguatula serrata (L. serrata) isolates collected from cattle, goats, sheep, dogs and camels in different geographical locations of Iran were investigated using partial 18S ribosomal RNA (rRNA) and partial mitochondrial cytochrome c oxidase subunit 1 (cox1) gene sequences. The nucleotide sequences were analysed in order to determine the phylogenetic relationships between the isolates. Higher sequence diversity and intraspecies variation was observed in the cox1 gene compared to 18S rRNA sequences. Phylogenetic analysis of the cox1 gene placed all L. serrata isolates in a sister clade to L. arctica. The Mantel regression analysis revealed no association between genetic variations and host species or geographical location, perhaps due to the small sample size. However, genetic variations between L. serrata isolates in Iran and those isolated in other parts of the world may exist and could reveal possible evolutionary relationships. PMID:27149706
Poisson regression analysis of the mortality among a cohort of World War II nuclear industry workers
Frome, E.L.; Cragle, D.L.; McLain, R.W. )
1990-08-01
A historical cohort mortality study was conducted among 28,008 white male employees who had worked for at least 1 month in Oak Ridge, Tennessee, during World War II. The workers were employed at two plants that were producing enriched uranium and a research and development laboratory. Vital status was ascertained through 1980 for 98.1% of the cohort members and death certificates were obtained for 96.8% of the 11,671 decedents. A modified version of the traditional standardized mortality ratio (SMR) analysis was used to compare the cause-specific mortality experience of the World War II workers with the U.S. white male population. An SMR and a trend statistic were computed for each cause-of-death category for the 30-year interval from 1950 to 1980. The SMR for all causes was 1.11, and there was a significant upward trend of 0.74% per year. The excess mortality was primarily due to lung cancer and diseases of the respiratory system. Poisson regression methods were used to evaluate the influence of duration of employment, facility of employment, socioeconomic status, birth year, period of follow-up, and radiation exposure on cause-specific mortality. Maximum likelihood estimates of the parameters in a main-effects model were obtained to describe the joint effects of these six factors on cause-specific mortality of the World War II workers. We show that these multivariate regression techniques provide a useful extension of conventional SMR analysis and illustrate their effective use in a large occupational cohort study.
Menon, Prashanthi; Podolsky, Irina; Feig, Jonathan E.; Aderem, Alan; Fisher, Edward A.; Gold, Elizabeth S.
2014-01-01
We report the first systems biology investigation of regulators controlling arterial plaque macrophage transcriptional changes in response to lipid lowering in vivo in two distinct mouse models of atherosclerosis regression. Transcriptome measurements from plaque macrophages from the Reversa mouse were integrated with measurements from an aortic transplant-based mouse model of plaque regression. Functional relevance of the genes detected as differentially expressed in plaque macrophages in response to lipid lowering in vivo was assessed through analysis of gene functional annotations, overlap with in vitro foam cell studies, and overlap of associated eQTLs with human atherosclerosis/CAD risk SNPs. To identify transcription factors that control plaque macrophage responses to lipid lowering in vivo, we used an integrative strategy – leveraging macrophage epigenomic measurements – to detect enrichment of transcription factor binding sites upstream of genes that are differentially expressed in plaque macrophages during regression. The integrated analysis uncovered eight transcription factor binding site elements that were statistically overrepresented within the 5′ regulatory regions of genes that were upregulated in plaque macrophages in the Reversa model under maximal regression conditions and within the 5′ regulatory regions of genes that were upregulated in the aortic transplant model during regression. Of these, the TCF/LEF binding site was present in promoters of upregulated genes related to cell motility, suggesting that the canonical Wnt signaling pathway may be activated in plaque macrophages during regression. We validated this network-based prediction by demonstrating that β-catenin expression is higher in regressing (vs. control group) plaques in both regression models, and we further demonstrated that stimulation of canonical Wnt signaling increases macrophage migration in vitro. These results suggest involvement of canonical Wnt signaling in
The Arabidopsis COX11 Homolog is Essential for Cytochrome c Oxidase Activity.
Radin, Ivan; Mansilla, Natanael; Rödel, Gerhard; Steinebrunner, Iris
2015-01-01
Members of the ubiquitous COX11 (cytochrome c oxidase 11) protein family are involved in copper delivery to the COX complex. In this work, we characterize the Arabidopsis thaliana COX11 homolog (encoded by locus At1g02410). Western blot analyses and confocal microscopy identified Arabidopsis COX11 as an integral mitochondrial protein. Despite sharing high sequence and structural similarities, the Arabidopsis COX11 is not able to functionally replace the Saccharomyces cerevisiae COX11 homolog. Nevertheless, further analysis confirmed the hypothesis that Arabidopsis COX11 is essential for COX activity. Disturbance of COX11 expression through knockdown (KD) or overexpression (OE) affected COX activity. In KD lines, the activity was reduced by ~50%, resulting in root growth inhibition, smaller rosettes and leaf curling. In OE lines, the reduction was less pronounced (~80% of the wild type), still resulting in root growth inhibition. Additionally, pollen germination was impaired in COX11 KD and OE plants. This effect on pollen germination can only partially be attributed to COX deficiency and may indicate a possible auxiliary role of COX11 in ROS metabolism. In agreement with its role in energy production, the COX11 promoter is highly active in cells and tissues with high-energy demand for example shoot and root meristems, or vascular tissues of source and sink organs. In COX11 KD lines, the expression of the plasma-membrane copper transporter COPT2 and of several copper chaperones was altered, indicative of a retrograde signaling pathway pertinent to copper homeostasis. Based on our data, we postulate that COX11 is a mitochondrial chaperone, which plays an important role for plant growth and pollen germination as an essential COX complex assembly factor. PMID:26734017
The Arabidopsis COX11 Homolog is Essential for Cytochrome c Oxidase Activity
Radin, Ivan; Mansilla, Natanael; Rödel, Gerhard; Steinebrunner, Iris
2015-01-01
Members of the ubiquitous COX11 (cytochrome c oxidase 11) protein family are involved in copper delivery to the COX complex. In this work, we characterize the Arabidopsis thaliana COX11 homolog (encoded by locus At1g02410). Western blot analyses and confocal microscopy identified Arabidopsis COX11 as an integral mitochondrial protein. Despite sharing high sequence and structural similarities, the Arabidopsis COX11 is not able to functionally replace the Saccharomyces cerevisiae COX11 homolog. Nevertheless, further analysis confirmed the hypothesis that Arabidopsis COX11 is essential for COX activity. Disturbance of COX11 expression through knockdown (KD) or overexpression (OE) affected COX activity. In KD lines, the activity was reduced by ~50%, resulting in root growth inhibition, smaller rosettes and leaf curling. In OE lines, the reduction was less pronounced (~80% of the wild type), still resulting in root growth inhibition. Additionally, pollen germination was impaired in COX11 KD and OE plants. This effect on pollen germination can only partially be attributed to COX deficiency and may indicate a possible auxiliary role of COX11 in ROS metabolism. In agreement with its role in energy production, the COX11 promoter is highly active in cells and tissues with high-energy demand for example shoot and root meristems, or vascular tissues of source and sink organs. In COX11 KD lines, the expression of the plasma-membrane copper transporter COPT2 and of several copper chaperones was altered, indicative of a retrograde signaling pathway pertinent to copper homeostasis. Based on our data, we postulate that COX11 is a mitochondrial chaperone, which plays an important role for plant growth and pollen germination as an essential COX complex assembly factor. PMID:26734017
Zeng, Fangfang; Li, Zhongtao; Yu, Xiaoling; Zhou, Linuo
2013-01-01
Background This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches. Methods and Materials We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses. Conclusion The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset. PMID:23940593
COX-2 and PPAR-γ confer cannabidiol-induced apoptosis of human lung cancer cells.
Ramer, Robert; Heinemann, Katharina; Merkord, Jutta; Rohde, Helga; Salamon, Achim; Linnebacher, Michael; Hinz, Burkhard
2013-01-01
The antitumorigenic mechanism of cannabidiol is still controversial. This study investigates the role of COX-2 and PPAR-γ in cannabidiol's proapoptotic and tumor-regressive action. In lung cancer cell lines (A549, H460) and primary cells from a patient with lung cancer, cannabidiol elicited decreased viability associated with apoptosis. Apoptotic cell death by cannabidiol was suppressed by NS-398 (COX-2 inhibitor), GW9662 (PPAR-γ antagonist), and siRNA targeting COX-2 and PPAR-γ. Cannabidiol-induced apoptosis was paralleled by upregulation of COX-2 and PPAR-γ mRNA and protein expression with a maximum induction of COX-2 mRNA after 8 hours and continuous increases of PPAR-γ mRNA when compared with vehicle. In response to cannabidiol, tumor cell lines exhibited increased levels of COX-2-dependent prostaglandins (PG) among which PGD(2) and 15-deoxy-Δ(12,14)-PGJ(2) (15d-PGJ(2)) caused a translocation of PPAR-γ to the nucleus and induced a PPAR-γ-dependent apoptotic cell death. Moreover, in A549-xenografted nude mice, cannabidiol caused upregulation of COX-2 and PPAR-γ in tumor tissue and tumor regression that was reversible by GW9662. Together, our data show a novel proapoptotic mechanism of cannabidiol involving initial upregulation of COX-2 and PPAR-γ and a subsequent nuclear translocation of PPAR-γ by COX-2-dependent PGs.
NASA Technical Reports Server (NTRS)
Jolly, William H.
1992-01-01
Relationships defining the ballistic limit of Space Station Freedom's (SSF) dual wall protection systems have been determined. These functions were regressed from empirical data found in Marshall Space Flight Center's (MSFC) Hypervelocity Impact Testing Summary (HITS) for the velocity range between three and seven kilometers per second. A stepwise linear least squares regression was used to determine the coefficients of several expressions that define a ballistic limit surface. Using statistical significance indicators and graphical comparisons to other limit curves, a final set of expressions is recommended for potential use in Probability of No Critical Flaw (PNCF) calculations for Space Station. The three equations listed below represent the mean curves for normal, 45 degree, and 65 degree obliquity ballistic limits, respectively, for a dual wall protection system consisting of a thin 6061-T6 aluminum bumper spaced 4.0 inches from a .125 inches thick 2219-T87 rear wall with multiple layer thermal insulation installed between the two walls. Normal obliquity is d(sub c) = 1.0514 v(exp 0.2983 t(sub 1)(exp 0.5228). Forty-five degree obliquity is d(sub c) = 0.8591 v(exp 0.0428) t(sub 1)(exp 0.2063). Sixty-five degree obliquity is d(sub c) = 0.2824 v(exp 0.1986) t(sub 1)(exp -0.3874). Plots of these curves are provided. A sensitivity study on the effects of using these new equations in the probability of no critical flaw analysis indicated a negligible increase in the performance of the dual wall protection system for SSF over the current baseline. The magnitude of the increase was 0.17 percent over 25 years on the MB-7 configuration run with the Bumper II program code.
Tillyer, C R; Gobin, P T; Ray, A K; Rimanova, H
1992-07-01
We undertook a retrospective analysis of the monthly test rejection rates and the monthly external quality assessment scheme performance indices for our laboratory's two automated analysers, and examined the association of these variables with measures of laboratory workload, manpower, staff training, instrument servicing, seasonal and temporal factors and changes of calibration, method and assigned internal quality control values. Using multiple linear regression and stepwise multiple linear regression, we found that test rejection rates differed significantly between instruments, and were highest on the instrument performing the widest variety and lowest volume of tests. On that instrument, rejection rates were significantly associated with the introduction of new staff and laboratory manpower levels, and also showed a highly significant trend upwards over the study period, independent of the effects of the other variables examined. External quality assessment scheme performance indices showed small trends over the study period. They were not related to the test rejection rates on either analyser but also showed a significant association with the introduction of new staff and a small but significant association with laboratory workload. We conclude that the training and introduction of new staff and decreased laboratory manpower levels may significantly increase the level of test rejection, and adherence to appropriate quality control protocols effectively maintains the quality of the laboratory's results, but may not be completely successful in filtering out the effects of some assignable causes of variation in test results. It is suggested that clinical laboratories use the statistical approach adopted here to identify factors which may be adversely affecting quality performance and running costs and to provide evidence that quality control procedures are both cost- and quality-effective.
Xi, Shaoyan; Zhang, Tian; Dong, Jun; Cai, Muyan; Wang, Chengtao; Zhang, Huizhong; Zhou, Tongchong; Gao, Yuanhong; Wen, Bixiu
2016-01-01
Purpose To investigate predictive value of APAF-1 and COX-2 expression in pathologic complete response (pCR) for patients with rectal adenocarcinoma (RAC) who were treated with neoadjuvant chemoradiotherapy (neo-CRT) followed by total mesorectal excision (TME). Materials and Methods Immunohistochemistry assay was used to detect expression of APAF-1 and COX-2 in paraffin-wax embedded tissues obtained before neo-CRT for patients with RAC. A 5-point tumor-regression grade (TRG) based on the ratio of residual tumor to fibrosis according to Dworak's scoring system was used to assess neo-CRT response. The relationship between expression of APAF-1 and COX-2 genes and pCR was explored. Results pCR (TRG4) was observed in 23 patients (28.0%). pCR were more likely to be achieved for those with APAF-1 over-expression or lower expression of COX-2. pCR rate in patients with combination of high APAF-1 and low COX-2 expression was 56.0%, significantly higher than those with other combination of APAF1 and COX-2 expression. Multivariate analysis showed that over-expression of APAF-1 and suppressed expression of COX-2 were independent predictive factors for pCR. Conclusion Immunohistochemical evaluation of APAF-1 and COX-2 expression on pretreatment specimen may be used to predict pCR to neo-CRT in patients with RAC. The potential of the markers in monitoring pCR patient merits further investigation. PMID:27153549
Bayesian random threshold estimation in a Cox proportional hazards cure model.
Zhao, Lili; Feng, Dai; Bellile, Emily L; Taylor, Jeremy M G
2014-02-20
In this paper, we develop a Bayesian approach to estimate a Cox proportional hazards model that allows a threshold in the regression coefficient, when some fraction of subjects are not susceptible to the event of interest. A data augmentation scheme with latent binary cure indicators is adopted to simplify the Markov chain Monte Carlo implementation. Given the binary cure indicators, the Cox cure model reduces to a standard Cox model and a logistic regression model. Furthermore, the threshold detection problem reverts to a threshold problem in a regular Cox model. The baseline cumulative hazard for the Cox model is formulated non-parametrically using counting processes with a gamma process prior. Simulation studies demonstrate that the method provides accurate point and interval estimates. Application to a data set of oropharynx cancer patients suggests a significant threshold in age at diagnosis such that the effect of gender on disease-specific survival changes after the threshold.
Wilson, Andrew J.; Fadare, Oluwole; Beeghly-Fadiel, Alicia; Son, Deok-Soo; Liu, Qi; Zhao, Shilin; Saskowski, Jeanette; Uddin, Md. Jashim; Daniel, Cristina; Crews, Brenda; Lehmann, Brian D.; Pietenpol, Jennifer A.; Crispens, Marta A.; Marnett, Lawrence J.; Khabele, Dineo
2015-01-01
Cyclooxygenase-1 (COX-1) is implicated in ovarian cancer. However, patterns of COX expression and function have been unclear and controversial. In this report, patterns of COX-1 and COX-2 gene expression were obtained from RNA-seq data through The Cancer Genome Atlas. Our analysis revealed markedly higher COX-1 mRNA expression than COX-2 in high-grade serous ovarian cancers (HGSOC) and higher COX-1 expression in HGSOC tumors than 10 other tumor types. High expression of COX-1 in HGSOC tumors was confirmed in an independent tissue microarray. In contrast, lower or similar expression of COX-1 compared to COX-2 was observed in endometrioid, mucinous and clear cell tumors. Stable COX-1 knockdown in HGSOC-representative OVCAR-3 ovarian cancer cells reduced gene expression in multiple pro-tumorigenic pathways. Functional cell viability, clonogenicity, and migration/invasion assays were consistent with transcriptomic changes. These effects were reversed by stable over-expression of COX-1 in SKOV-3 cells. Our results demonstrate a distinct pattern of COX-1 over-expression in HGSOC tumors and strong association of COX-1 with multiple pro-tumorigenic pathways in ovarian cancer cells. These findings provide additional insight into the role of COX-1 in human ovarian cancer and support further development of methods to selectively target COX-1 in the management of HGSOC tumors. PMID:25972361
Generalized Multilevel Function-on-Scalar Regression and Principal Component Analysis
Goldsmith, Jeff; Zipunnikov, Vadim; Schrack, Jennifer
2015-01-01
Summary This manuscript considers regression models for generalized, multilevel functional responses: functions are generalized in that they follow an exponential family distribution and multilevel in that they are clustered within groups or subjects. This data structure is increasingly common across scientific domains and is exemplified by our motivating example, in which binary curves indicating physical activity or inactivity are observed for nearly six hundred subjects over five days. We use a generalized linear model to incorporate scalar covariates into the mean structure, and decompose subject-specific and subject-day-specific deviations using multilevel functional principal components analysis. Thus, functional fixed effects are estimated while accounting for within-function and within-subject correlations, and major directions of variability within and between subjects are identified. Fixed effect coefficient functions and principal component basis functions are estimated using penalized splines; model parameters are estimated in a Bayesian framework using Stan, a programming language that implements a Hamiltonian Monte Carlo sampler. Simulations designed to mimic the application have good estimation and inferential properties with reasonable computation times for moderate datasets, in both cross-sectional and multilevel scenarios; code is publicly available. In the application we identify effects of age and BMI on the time-specific change in probability of being active over a twenty-four hour period; in addition, the principal components analysis identifies the patterns of activity that distinguish subjects and days within subjects. PMID:25620473
Binary Logistic Regression Analysis of Foramen Magnum Dimensions for Sex Determination.
Kamath, Venkatesh Gokuldas; Asif, Muhammed; Shetty, Radhakrishna; Avadhani, Ramakrishna
2015-01-01
Purpose. The structural integrity of foramen magnum is usually preserved in fire accidents and explosions due to its resistant nature and secluded anatomical position and this study attempts to determine its sexing potential. Methods. The sagittal and transverse diameters and area of foramen magnum of seventy-two skulls (41 male and 31 female) from south Indian population were measured. The analysis was done using Student's t-test, linear correlation, histogram, Q-Q plot, and Binary Logistic Regression (BLR) to obtain a model for sex determination. The predicted probabilities of BLR were analysed using Receiver Operating Characteristic (ROC) curve. Result. BLR analysis and ROC curve revealed that the predictability of the dimensions in sexing the crania was 69.6% for sagittal diameter, 66.4% for transverse diameter, and 70.3% for area of foramen. Conclusion. The sexual dimorphism of foramen magnum dimensions is established. However, due to considerable overlapping of male and female values, it is unwise to singularly rely on the foramen measurements. However, considering the high sex predictability percentage of its dimensions in the present study and the studies preceding it, the foramen measurements can be used to supplement other sexing evidence available so as to precisely ascertain the sex of the skeleton. PMID:26346917
Kumar, Abhinesh; Sawant, Krutika K
2014-01-01
The present investigation deals with development of anastrozole-loaded PLGA nanoparticles (NPs) as an alternate to conventional cancer therapy. The NPs were prepared by nanoprecipitation method and optimized using multiple regression analysis. Independent variables included drug:polymer ratio (X1), polymer concentration in organic phase (X2) and surfactant concentration in aqueous phase (X3) while dependent variables were percentage drug entrapment (PDE) and particle size (PS). Results of desirability criteria, check point analysis and normalized error were considered for selecting the formulation with highest PDE and lowest PS. Prepared NPs were characterized for zeta potential, transmission electron microscopy (TEM), differential scanning calorimetry (DSC) and in vitro drug release studies. DSC and TEM studies indicated absence of any drug-polymer interaction and spherical nature of NPs, respectively. In vitro drug release showed biphasic pattern exhibiting Fickian diffusion-based release mechanism. This delivery system of anastrozole is expected to reduce the side effects associated with the conventional cancer therapy by reducing dosing frequency.
Binary Logistic Regression Analysis of Foramen Magnum Dimensions for Sex Determination
Kamath, Venkatesh Gokuldas; Asif, Muhammed; Shetty, Radhakrishna; Avadhani, Ramakrishna
2015-01-01
Purpose. The structural integrity of foramen magnum is usually preserved in fire accidents and explosions due to its resistant nature and secluded anatomical position and this study attempts to determine its sexing potential. Methods. The sagittal and transverse diameters and area of foramen magnum of seventy-two skulls (41 male and 31 female) from south Indian population were measured. The analysis was done using Student's t-test, linear correlation, histogram, Q-Q plot, and Binary Logistic Regression (BLR) to obtain a model for sex determination. The predicted probabilities of BLR were analysed using Receiver Operating Characteristic (ROC) curve. Result. BLR analysis and ROC curve revealed that the predictability of the dimensions in sexing the crania was 69.6% for sagittal diameter, 66.4% for transverse diameter, and 70.3% for area of foramen. Conclusion. The sexual dimorphism of foramen magnum dimensions is established. However, due to considerable overlapping of male and female values, it is unwise to singularly rely on the foramen measurements. However, considering the high sex predictability percentage of its dimensions in the present study and the studies preceding it, the foramen measurements can be used to supplement other sexing evidence available so as to precisely ascertain the sex of the skeleton. PMID:26346917
NASA Astrophysics Data System (ADS)
Rajab, Jasim Mohammed; Jafri, Mohd. Zubir Mat; Lim, Hwee San; Abdullah, Khiruddin
2012-10-01
This study encompasses air surface temperature (AST) modeling in the lower atmosphere. Data of four atmosphere pollutant gases (CO, O3, CH4, and H2O) dataset, retrieved from the National Aeronautics and Space Administration Atmospheric Infrared Sounder (AIRS), from 2003 to 2008 was employed to develop a model to predict AST value in the Malaysian peninsula using the multiple regression method. For the entire period, the pollutants were highly correlated (R=0.821) with predicted AST. Comparisons among five stations in 2009 showed close agreement between the predicted AST and the observed AST from AIRS, especially in the southwest monsoon (SWM) season, within 1.3 K, and for in situ data, within 1 to 2 K. The validation results of AST with AST from AIRS showed high correlation coefficient (R=0.845 to 0.918), indicating the model's efficiency and accuracy. Statistical analysis in terms of β showed that H2O (0.565 to 1.746) tended to contribute significantly to high AST values during the northeast monsoon season. Generally, these results clearly indicate the advantage of using the satellite AIRS data and a correlation analysis study to investigate the impact of atmospheric greenhouse gases on AST over the Malaysian peninsula. A model was developed that is capable of retrieving the Malaysian peninsulan AST in all weather conditions, with total uncertainties ranging between 1 and 2 K.
Generalized multilevel function-on-scalar regression and principal component analysis.
Goldsmith, Jeff; Zipunnikov, Vadim; Schrack, Jennifer
2015-06-01
This manuscript considers regression models for generalized, multilevel functional responses: functions are generalized in that they follow an exponential family distribution and multilevel in that they are clustered within groups or subjects. This data structure is increasingly common across scientific domains and is exemplified by our motivating example, in which binary curves indicating physical activity or inactivity are observed for nearly 600 subjects over 5 days. We use a generalized linear model to incorporate scalar covariates into the mean structure, and decompose subject-specific and subject-day-specific deviations using multilevel functional principal components analysis. Thus, functional fixed effects are estimated while accounting for within-function and within-subject correlations, and major directions of variability within and between subjects are identified. Fixed effect coefficient functions and principal component basis functions are estimated using penalized splines; model parameters are estimated in a Bayesian framework using Stan, a programming language that implements a Hamiltonian Monte Carlo sampler. Simulations designed to mimic the application have good estimation and inferential properties with reasonable computation times for moderate datasets, in both cross-sectional and multilevel scenarios; code is publicly available. In the application we identify effects of age and BMI on the time-specific change in probability of being active over a 24-hour period; in addition, the principal components analysis identifies the patterns of activity that distinguish subjects and days within subjects.
A factor analysis-multiple regression model for source apportionment of suspended particulate matter
NASA Astrophysics Data System (ADS)
Okamoto, Shin'ichi; Hayashi, Masayuki; Nakajima, Masaomi; Kainuma, Yasutaka; Shiozawa, Kiyoshige
A factor analysis-multiple regression (FA-MR) model has been used for a source apportionment study in the Tokyo metropolitan area. By a varimax rotated factor analysis, five source types could be identified: refuse incineration, soil and automobile, secondary particles, sea salt and steel mill. Quantitative estimations using the FA-MR model corresponded to the calculated contributing concentrations determined by using a weighted least-squares CMB model. However, the source type of refuse incineration identified by the FA-MR model was similar to that of biomass burning, rather than that produced by an incineration plant. The estimated contributions of sea salt and steel mill by the FA-MR model contained those of other sources, which have the same temporal variation of contributing concentrations. This symptom was caused by a multicollinearity problem. Although this result shows the limitation of the multivariate receptor model, it gives useful information concerning source types and their distribution by comparing with the results of the CMB model. In the Tokyo metropolitan area, the contributions from soil (including road dust), automobile, secondary particles and refuse incineration (biomass burning) were larger than industrial contributions: fuel oil combustion and steel mill. However, since vanadium is highly correlated with SO 42- and other secondary particle related elements, a major portion of secondary particles is considered to be related to fuel oil combustion.
Coelho, Lúcia H G; Gutz, Ivano G R
2006-03-15
A chemometric method for analysis of conductometric titration data was introduced to extend its applicability to lower concentrations and more complex acid-base systems. Auxiliary pH measurements were made during the titration to assist the calculation of the distribution of protonable species on base of known or guessed equilibrium constants. Conductivity values of each ionized or ionizable species possibly present in the sample were introduced in a general equation where the only unknown parameters were the total concentrations of (conjugated) bases and of strong electrolytes not involved in acid-base equilibria. All these concentrations were adjusted by a multiparametric nonlinear regression (NLR) method, based on the Levenberg-Marquardt algorithm. This first conductometric titration method with NLR analysis (CT-NLR) was successfully applied to simulated conductometric titration data and to synthetic samples with multiple components at concentrations as low as those found in rainwater (approximately 10 micromol L(-1)). It was possible to resolve and quantify mixtures containing a strong acid, formic acid, acetic acid, ammonium ion, bicarbonate and inert electrolyte with accuracy of 5% or better.
Risky decision making in Attention-Deficit/Hyperactivity Disorder: A meta-regression analysis.
Dekkers, Tycho J; Popma, Arne; Agelink van Rentergem, Joost A; Bexkens, Anika; Huizenga, Hilde M
2016-04-01
ADHD has been associated with various forms of risky real life decision making, for example risky driving, unsafe sex and substance abuse. However, results from laboratory studies on decision making deficits in ADHD have been inconsistent, probably because of between study differences. We therefore performed a meta-regression analysis in which 37 studies (n ADHD=1175; n Control=1222) were included, containing 52 effect sizes. The overall analysis yielded a small to medium effect size (standardized mean difference=.36, p<.001, 95% CI [.22, .51]), indicating that groups with ADHD showed more risky decision making than control groups. There was a trend for a moderating influence of co-morbid Disruptive Behavior Disorders (DBD): studies including more participants with co-morbid DBD had larger effect sizes. No moderating influence of co-morbid internalizing disorders, age or task explicitness was found. These results indicate that ADHD is related to increased risky decision making in laboratory settings, which tended to be more pronounced if ADHD is accompanied by DBD. We therefore argue that risky decision making should have a more prominent role in research on the neuropsychological and -biological mechanisms of ADHD, which can be useful in ADHD assessment and intervention. PMID:26978323
VanEngelsdorp, Dennis; Speybroeck, Niko; Evans, Jay D; Nguyen, Bach Kim; Mullin, Chris; Frazier, Maryann; Frazier, Jim; Cox-Foster, Diana; Chen, Yanping; Tarpy, David R; Haubruge, Eric; Pettis, Jeffrey S; Saegerman, Claude
2010-10-01
Colony collapse disorder (CCD), a syndrome whose defining trait is the rapid loss of adult worker honey bees, Apis mellifera L., is thought to be responsible for a minority of the large overwintering losses experienced by U.S. beekeepers since the winter 2006-2007. Using the same data set developed to perform a monofactorial analysis (PloS ONE 4: e6481, 2009), we conducted a classification and regression tree (CART) analysis in an attempt to better understand the relative importance and interrelations among different risk variables in explaining CCD. Fifty-five exploratory variables were used to construct two CART models: one model with and one model without a cost of misclassifying a CCD-diagnosed colony as a non-CCD colony. The resulting model tree that permitted for misclassification had a sensitivity and specificity of 85 and 74%, respectively. Although factors measuring colony stress (e.g., adult bee physiological measures, such as fluctuating asymmetry or mass of head) were important discriminating values, six of the 19 variables having the greatest discriminatory value were pesticide levels in different hive matrices. Notably, coumaphos levels in brood (a miticide commonly used by beekeepers) had the highest discriminatory value and were highest in control (healthy) colonies. Our CART analysis provides evidence that CCD is probably the result of several factors acting in concert, making afflicted colonies more susceptible to disease. This analysis highlights several areas that warrant further attention, including the effect of sublethal pesticide exposure on pathogen prevalence and the role of variability in bee tolerance to pesticides on colony survivorship.
PUMA: A Unified Framework for Penalized Multiple Regression Analysis of GWAS Data
Hoffman, Gabriel E.; Logsdon, Benjamin A.; Mezey, Jason G.
2013-01-01
Penalized Multiple Regression (PMR) can be used to discover novel disease associations in GWAS datasets. In practice, proposed PMR methods have not been able to identify well-supported associations in GWAS that are undetectable by standard association tests and thus these methods are not widely applied. Here, we present a combined algorithmic and heuristic framework for PUMA (Penalized Unified Multiple-locus Association) analysis that solves the problems of previously proposed methods including computational speed, poor performance on genome-scale simulated data, and identification of too many associations for real data to be biologically plausible. The framework includes a new minorize-maximization (MM) algorithm for generalized linear models (GLM) combined with heuristic model selection and testing methods for identification of robust associations. The PUMA framework implements the penalized maximum likelihood penalties previously proposed for GWAS analysis (i.e. Lasso, Adaptive Lasso, NEG, MCP), as well as a penalty that has not been previously applied to GWAS (i.e. LOG). Using simulations that closely mirror real GWAS data, we show that our framework has high performance and reliably increases power to detect weak associations, while existing PMR methods can perform worse than single marker testing in overall performance. To demonstrate the empirical value of PUMA, we analyzed GWAS data for type 1 diabetes, Crohns's disease, and rheumatoid arthritis, three autoimmune diseases from the original Wellcome Trust Case Control Consortium. Our analysis replicates known associations for these diseases and we discover novel etiologically relevant susceptibility loci that are invisible to standard single marker tests, including six novel associations implicating genes involved in pancreatic function, insulin pathways and immune-cell function in type 1 diabetes; three novel associations implicating genes in pro- and anti-inflammatory pathways in Crohn's disease; and one
Regression Analysis of Combined Gene Expression Regulation in Acute Myeloid Leukemia
Li, Yue; Liang, Minggao; Zhang, Zhaolei
2014-01-01
Gene expression is a combinatorial function of genetic/epigenetic factors such as copy number variation (CNV), DNA methylation (DM), transcription factors (TF) occupancy, and microRNA (miRNA) post-transcriptional regulation. At the maturity of microarray/sequencing technologies, large amounts of data measuring the genome-wide signals of those factors became available from Encyclopedia of DNA Elements (ENCODE) and The Cancer Genome Atlas (TCGA). However, there is a lack of an integrative model to take full advantage of these rich yet heterogeneous data. To this end, we developed RACER (Regression Analysis of Combined Expression Regulation), which fits the mRNA expression as response using as explanatory variables, the TF data from ENCODE, and CNV, DM, miRNA expression signals from TCGA. Briefly, RACER first infers the sample-specific regulatory activities by TFs and miRNAs, which are then used as inputs to infer specific TF/miRNA-gene interactions. Such a two-stage regression framework circumvents a common difficulty in integrating ENCODE data measured in generic cell-line with the sample-specific TCGA measurements. As a case study, we integrated Acute Myeloid Leukemia (AML) data from TCGA and the related TF binding data measured in K562 from ENCODE. As a proof-of-concept, we first verified our model formalism by 10-fold cross-validation on predicting gene expression. We next evaluated RACER on recovering known regulatory interactions, and demonstrated its superior statistical power over existing methods in detecting known miRNA/TF targets. Additionally, we developed a feature selection procedure, which identified 18 regulators, whose activities clustered consistently with cytogenetic risk groups. One of the selected regulators is miR-548p, whose inferred targets were significantly enriched for leukemia-related pathway, implicating its novel role in AML pathogenesis. Moreover, survival analysis using the inferred activities identified C-Fos as a potential AML
ERIC Educational Resources Information Center
Kaplan, David
2005-01-01
This article considers the problem of estimating dynamic linear regression models when the data are generated from finite mixture probability density function where the mixture components are characterized by different dynamic regression model parameters. Specifically, conventional linear models assume that the data are generated by a single…
CATEGORICAL REGRESSION ANALYSIS OF ACUTE INHALATION TOXICITY DATA FOR HYDROGEN SULFIDE
Categorical regression is one of the tools offered by the U.S. EPA for derivation of acute reference exposures (AREs), which are dose-response assessments for acute exposures to inhaled chemicals. Categorical regression is used as a meta-analytical technique to calculate probabi...
Beyond Multiple Regression: Using Commonality Analysis to Better Understand R[superscript 2] Results
ERIC Educational Resources Information Center
Warne, Russell T.
2011-01-01
Multiple regression is one of the most common statistical methods used in quantitative educational research. Despite the versatility and easy interpretability of multiple regression, it has some shortcomings in the detection of suppressor variables and for somewhat arbitrarily assigning values to the structure coefficients of correlated…
Schmitz, J M; Claus, D; Neundörfer, B; Handwerker, H O
1995-01-01
Three algorithms for assessment of respiratory sinus arrhythmia (RSA) have been evaluated: cross-correlation function, histogram analysis and regression plot. The algorithms were tested experimentally in a group of 11 subjects. A cross-correlation function with a high time resolution (1 ms) was used for investigation of the time lag between instantaneous heart rate and respiration (CTL). This time lag was not affected by the breathing rate in a range of 8 to 29 breaths per minute. A mathematical model of CTL compared with experimental results indicates that respiratory sinus arrhythmia is probably modulated directly by the respiratory network in the brainstem rather than by a baroreflex in the range of breathing rate investigated. Histogram analysis reflects the impact of inspiration and expiration on respiratory sinus arrhythmia. For this purpose heart rate changes were separated into two distributions (inspiration-expiration). The result value (U-VAL) of the Mann-Whitney U-test reflects the impact of respiration on heart rate variability. Regression analysis of heart rate versus respiration shows that the heart rate increase is more closely coupled to inspiration than the heart rate decrease to expiration. Both, CTL and U-VAL are thought to be useful parameters for clinical investigation of RSA.
NASA Astrophysics Data System (ADS)
Kirsanov, Dmitry; Panchuk, Vitaly; Goydenko, Alexander; Khaydukova, Maria; Semenov, Valentin; Legin, Andrey
2015-11-01
This study addresses the problem of simultaneous quantitative analysis of six lanthanides (Ce, Pr, Nd, Sm, Eu, Gd) in mixed solutions by two different X-ray fluorescence techniques: energy-dispersive (EDX) and total reflection (TXRF). Concentration of each lanthanide was varied in the range 10- 6-10- 3 mol/L, low values being around the detection limit of the method. This resulted in XRF spectra with very poor signal to noise ratio and overlapping bands in case of EDX, while only the latter problem was observed for TXRF. It was shown that ordinary least squares approach in numerical calibration fails to provide for reasonable precision in quantification of individual lanthanides. Partial least squares (PLS) regression was able to circumvent spectral inferiorities and yielded adequate calibration models for both techniques with RMSEP (root mean squared error of prediction) values around 10- 5 mol/L. It was demonstrated that comparatively simple and inexpensive EDX method is capable of ensuring the similar precision to more sophisticated TXRF, when the spectra are treated by PLS.
Savescu, Roxana Florenta; Laba, Marian
2016-06-01
This paper highlights the statistical methodology used in a dissection experiment carried out in Romania to calibrate and standardize two classification devices, OptiGrade PRO (OGP) and Fat-o-Meat'er (FOM). One hundred forty-five carcasses were measured using the two probes and dissected according to the European reference method. To derive prediction formulas for each device, multiple linear regression analysis was performed on the relationship between the reference lean meat percentage and the back fat and muscle thicknesses, using the ordinary least squares technique. The root mean squared error of prediction calculated using the leave-one-out cross validation met European Commission (EC) requirements. The application of the new prediction equations reduced the gap between the lean meat percentage measured with the OGP and FOM from 2.43% (average for the period Q3/2006-Q2/2008) to 0.10% (average for the period Q3/2008-Q4/2014), providing the basis for a fair payment system for the pig producers. PMID:26835835
Screening houses for vapor intrusion risks: a multiple regression analysis approach.
Johnston, Jill E; Gibson, Jacqueline MacDonald
2013-06-01
The migration of chlorinated volatile organic compounds from groundwater to indoor air-known as vapor intrusion-can be an important exposure pathway at hazardous waste sites. Because sampling indoor air at every potentially affected home is often logistically infeasible, screening tools are needed to help identify at-risk homes. Currently, the U.S. Environmental Protection Agency (EPA) uses a simple screening approach that employs a generic vapor "attenuation factor," the ratio of the indoor air pollutant concentration to the pollutant concentration in the soil gas directly above the groundwater table. At every potentially affected home above contaminated groundwater, the EPA assumes the vapor attenuation factor is less than 1/1000--that is, that the indoor air concentration will not exceed 1/1000 times the soil-gas concentration immediately above groundwater. This paper reports on a screening-level model that improves on the EPA approach by considering environmental, contaminant, and household characteristics. The model is based on an analysis of the EPA's vapor intrusion database, which contains almost 2,400 indoor air and corresponding subsurface concentration samples collected in 15 states. We use the site data to develop a multilevel regression model for predicting the vapor attenuation factor. We find that the attenuation factor varies significantly with soil type, depth to groundwater, season, household foundation type, and contaminant molecular weight. The resulting model decreases the rate of false negatives compared to EPA's screening approach.
Predicting pesticide removal efficacy of vegetated filter strips: A meta-regression analysis.
Chen, Huajin; Grieneisen, Michael L; Zhang, Minghua
2016-04-01
Vegetated Filter Strips (VFS's) are widely used for alleviating agricultural pesticide loadings to surface water bodies. However, effective tools are lacking to quantify the performance of VFS's in reducing off-site pesticide transport. In this study, we applied meta-regression to develop a model for predicting VFS pesticide retention efficiency based on hydrologic responses of VFS's, incoming pollutant characteristics and the interaction within and between these two factor groups (R(2)=0.83). In cross-validation analysis, our model (Q(2)=0.81) outperformed the existing pesticide retention module of VFSMOD (Q(2)=0.72) by explicitly accounting for interaction effect and the categorical effect of pesticide adsorption properties. Based on the 181 data points studied, infiltration had a leading, positive influence on pesticide retention, followed by sedimentation and interaction between the two. Interaction between infiltration and pesticide adsorption properties was also prominent, as the influence of infiltration was significantly lower for strongly adsorbed pesticides. In addition, the clay content of incoming sediment was negatively associated with pesticide retention. Our model is not only valuable in predicting VFS performance, but also provides a quantitative characterization of the interacting VFS processes, thereby facilitating a deeper understanding of the underlying mechanisms.
NASA Astrophysics Data System (ADS)
Elnasir, Selma; Shamsuddin, Siti Mariyam; Farokhi, Sajad
2015-01-01
Palm vein recognition (PVR) is a promising new biometric that has been applied successfully as a method of access control by many organizations, which has even further potential in the field of forensics. The palm vein pattern has highly discriminative features that are difficult to forge because of its subcutaneous position in the palm. Despite considerable progress and a few practical issues, providing accurate palm vein readings has remained an unsolved issue in biometrics. We propose a robust and more accurate PVR method based on the combination of wavelet scattering (WS) with spectral regression kernel discriminant analysis (SRKDA). As the dimension of WS generated features is quite large, SRKDA is required to reduce the extracted features to enhance the discrimination. The results based on two public databases-PolyU Hyper Spectral Palmprint public database and PolyU Multi Spectral Palmprint-show the high performance of the proposed scheme in comparison with state-of-the-art methods. The proposed approach scored a 99.44% identification rate and a 99.90% verification rate [equal error rate (EER)=0.1%] for the hyperspectral database and a 99.97% identification rate and a 99.98% verification rate (EER=0.019%) for the multispectral database.
A New Global Regression Analysis Method for the Prediction of Wind Tunnel Model Weight Corrections
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert Manfred; Bridge, Thomas M.; Amaya, Max A.
2014-01-01
A new global regression analysis method is discussed that predicts wind tunnel model weight corrections for strain-gage balance loads during a wind tunnel test. The method determines corrections by combining "wind-on" model attitude measurements with least squares estimates of the model weight and center of gravity coordinates that are obtained from "wind-off" data points. The method treats the least squares fit of the model weight separate from the fit of the center of gravity coordinates. Therefore, it performs two fits of "wind- off" data points and uses the least squares estimator of the model weight as an input for the fit of the center of gravity coordinates. Explicit equations for the least squares estimators of the weight and center of gravity coordinates are derived that simplify the implementation of the method in the data system software of a wind tunnel. In addition, recommendations for sets of "wind-off" data points are made that take typical model support system constraints into account. Explicit equations of the confidence intervals on the model weight and center of gravity coordinates and two different error analyses of the model weight prediction are also discussed in the appendices of the paper.
Tahsin, Subrina; Chang, Ni-Bin
2016-02-01
Stormwater wet detention ponds have been a commonly employed best management practice for stormwater management throughout the world for many years. In the past, the trophic state index values have been used to evaluate seasonal changes in water quality and rank lakes within a region or between several regions; yet, to date, there is no similar index for stormwater wet detention ponds. This study aimed to develop a new multivariate trophic state index (MTSI) suitable for conducting a rapid eutrophication assessment of stormwater wet detention ponds under uncertainty with respect to three typical physical and chemical properties. Six stormwater wet detention ponds in Florida were selected for demonstration of the new MTSI with respect to total phosphorus (TP), total nitrogen (TN), and Secchi disk depth (SDD) as cognitive assessment metrics to sense eutrophication potential collectively and inform the environmental impact holistically. Due to the involvement of multiple endogenous variables (i.e., TN, TP, and SDD) for the eutrophication assessment simultaneously under uncertainty, fuzzy synthetic evaluation was applied to first standardize and synchronize the sources of uncertainty in the decision analysis. The ordered probit regression model was then formulated for assessment based on the concept of MTSI with the inputs from the fuzzy synthetic evaluation. It is indicative that the severe eutrophication condition is present during fall, which might be due to frequent heavy summer storm events contributing to high-nutrient inputs in these six ponds. PMID:26733470
Hou, J
1989-01-01
Cixian county, one of the high-risk counties of esophageal cancer in the world, has a standardized mortality of 142.19/10(5) population, 1969-1971. The incidence of esophageal cancer had dropped year by year from 1974 to 1982. The significance of the incidence tendency was studied. The results are highly significant (P less than 0.001). The causative factors of esophageal cancer including five independent variables: X1 (number of people taking sanitized water), X2 (number of people on pickled Chinese cabbage), X3 (annual output of fruit), X4 (annual output of fresh vegetable) and X5 (annual output of sweet potato) and one dependent variable Y (morbidity of esophageal cancer) were studied by correlative analysis and multiple stepwise regression. Three correlative factors (X1, X2, and X5) with significant effect on the esophageal cancer were selected from the five suspected factors. The result indicated that taking sanitized water, reducing the number of people on pickled Chinese cabbage, changing the structure of food and keeping the nutrient balance, might decrease the incidence of esophageal cancer. PMID:2789130
Aoyama, Shigeru; Kino, Koji; Kobayashi, Jyunji; Yoshimasu, Hidemi; Amagasa, Teruo
2005-06-01
This study compares temporomandibular joint dysfunction (TMD) symptoms before and after bilateral sagittal split ramus osteotomy, and identifies predictive factors for the postoperative TMD symptoms by assessing the adjusted odds ratio using multiple logistic regression analysis. A consecutive series of 37 cases treated only with bilateral sagittal split ramus osteotomy were evaluated. New postoperative TMD symptoms appeared in 9 cases, preoperative TMD symptoms disappeared in 6 cases, and TMD symptoms were unchanged in 5 cases. The median period until the interincisal opening range attained 40 mm was 5 months (range, from 2 to 15 months). Age was a positive factor in patients with postoperative TMD symptoms, with an odds ratio of 1.43 (95 percent confidence interval, from 1.05 to 1.93). In addition, the maximum value of the bilateral setback distance of more than 9 mm was a positive factor of 6.95 (95 percent confidence interval, from 1.06 to 45.42). We concluded that surgical correction in skeletal malocclusion may affect temporomandibular joint dysfunction symptoms. PMID:16187616
Anomalous particle pinch and scaling of vin/D based on transport analysis and multiple regression
NASA Astrophysics Data System (ADS)
Becker, G.; Kardaun, O.
2007-01-01
Predictions of density profiles in current tokamaks and ITER require a validated scaling relation for vin/D where vin is the anomalous inward drift velocity and D is the anomalous diffusion coefficient. Transport analysis is necessary for determining the anomalous particle pinch from measured density profiles and for separating the impact of particle sources. A set of discharges in ASDEX Upgrade, DIII-D, JET and ASDEX is analysed using a special version of the 1.5-D BALDUR transport code. Profiles of ρsvin/D with ρs the effective separatrix radius, five other dimensionless parameters and many further quantities in the confinement zone are compiled, resulting in the dataset VIND1.dat, which covers a wide parameter range. Weighted multiple regression is applied to the ASDEX Upgrade subset which leads to a two-term scaling \\rho _sv_in ({x'}) /D ({x'}) =0.0432 [ { ({L_{T_{\\rme}} ({ \\bar {x}'}) / \\rho _s}) ^{-2.58}+7.13 \\, U_L^{1.55} \
Dai, Wensheng; Wu, Jui-Yu; Lu, Chi-Jie
2014-01-01
Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.
Magura, Stephen; Cleland, Charles M.; Tonigan, J. Scott
2013-01-01
Objective: The objective of the study is to determine whether Alcoholics Anonymous (AA) participation leads to reduced drinking and problems related to drinking within Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity), an existing national alcoholism treatment data set. Method: The method used is structural equation modeling of panel data with cross-lagged partial regression coefficients. The main advantage of this technique for the analysis of AA outcomes is that potential reciprocal causation between AA participation and drinking behavior can be explicitly modeled through the specification of finite causal lags. Results: For the outpatient subsample (n = 952), the results strongly support the hypothesis that AA attendance leads to increases in alcohol abstinence and reduces drinking/problems, whereas a causal effect in the reverse direction is unsupported. For the aftercare subsample (n = 774), the results are not as clear but also suggest that AA attendance leads to better outcomes. Conclusions: Although randomized controlled trials are the surest means of establishing causal relations between interventions and outcomes, such trials are rare in AA research for practical reasons. The current study successfully exploited the multiple data waves in Project MATCH to examine evidence of causality between AA participation and drinking outcomes. The study obtained unique statistical results supporting the effectiveness of AA primarily in the context of primary outpatient treatment for alcoholism. PMID:23490566
Tahsin, Subrina; Chang, Ni-Bin
2016-02-01
Stormwater wet detention ponds have been a commonly employed best management practice for stormwater management throughout the world for many years. In the past, the trophic state index values have been used to evaluate seasonal changes in water quality and rank lakes within a region or between several regions; yet, to date, there is no similar index for stormwater wet detention ponds. This study aimed to develop a new multivariate trophic state index (MTSI) suitable for conducting a rapid eutrophication assessment of stormwater wet detention ponds under uncertainty with respect to three typical physical and chemical properties. Six stormwater wet detention ponds in Florida were selected for demonstration of the new MTSI with respect to total phosphorus (TP), total nitrogen (TN), and Secchi disk depth (SDD) as cognitive assessment metrics to sense eutrophication potential collectively and inform the environmental impact holistically. Due to the involvement of multiple endogenous variables (i.e., TN, TP, and SDD) for the eutrophication assessment simultaneously under uncertainty, fuzzy synthetic evaluation was applied to first standardize and synchronize the sources of uncertainty in the decision analysis. The ordered probit regression model was then formulated for assessment based on the concept of MTSI with the inputs from the fuzzy synthetic evaluation. It is indicative that the severe eutrophication condition is present during fall, which might be due to frequent heavy summer storm events contributing to high-nutrient inputs in these six ponds.
Dai, Wensheng
2014-01-01
Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting. PMID:25165740
Air pollution and acute respiratory diseases in children: regression analysis of morbidity data.
Biesiada, M; Zejda, J E; Skiba, M
2000-01-01
The aim of this study was to investigate the relationship between acute respiratory diseases and the air quality in the urban area of the Upper Silesian Industrial Zone during autumn and winter with special emphasis on temporal variability in the air concentrations of pollutants. The survey was carried out in 5 primary care units in Chorzów where the morbidity data on the selected respiratory diseases were collected from 1 November 1992 to 31 March 1993. The air pollution data were obtained from the monitoring station, being a part of the Sanitary and Epidemiological Station Network. Regression analysis with mean values of concentrations of air pollutants as explanatory variables revealed a positive effect of combined suspended particulate matter and SO2 concentration on the increased prevalence of bronchitis and bronchiolitis. Similar and even stronger effect was observed at the level of temporal variability coefficients of the air pollutants. A hypothesis that temporal variability of the air concentration of pollutants might be a more relevant factor for determining the prevalence of respiratory diseases than simple mean values of the pollutant concentrations is very interesting worthy of further investigations.
The basis function regression in pharmaceutical analysis. Theory and example of application.
Komsta, Lukasz; Skibiński, Robert; Paryło, Marta; Dudek, Karolina
2008-08-01
The BFR (Basis Function Regression) is an interesting alternative to common techniques (such as PCR or PLS) in chemometrics. It is based on projecting the spectral information onto some number of equally spaced spline bases, than obtaining integrals of resulted curves. Existing references show that in certain cases it can reduce almost twice the RMSEP values. As this technique is not so popular in chemometrics nor applied in pharmaceutical analysis, it is desirable to present its theoretical considerations and implementation (with example MATLAB/Octave code). As an illustrative example we present the chemometric model for content recognition of a tablet (12 possible compounds in binary or ternary combinations) from the UV spectrum of its methanolic extract. The BFR technique gave lowest prediction error and the estimators obtained have more meritorical meaning than in case of PCR, PLS and other techniques used for comparison. In our opinion this technique should be considered in any chemometric approach as it often shows better performance. PMID:18450403
An, Xin; Xu, Shuo; Zhang, Lu-Da; Su, Shi-Guang
2009-01-01
In the present paper, on the basis of LS-SVM algorithm, we built a multiple dependent variables LS-SVM (MLS-SVM) regression model whose weights can be optimized, and gave the corresponding algorithm. Furthermore, we theoretically explained the relationship between MLS-SVM and LS-SVM. Sixty four broomcorn samples were taken as experimental material, and the sample ratio of modeling set to predicting set was 51 : 13. We first selected randomly and uniformly five weight groups in the interval [0, 1], and then in the way of leave-one-out (LOO) rule determined one appropriate weight group and parameters including penalizing parameters and kernel parameters in the model according to the criterion of the minimum of average relative error. Then a multiple dependent variables quantitative analysis model was built with NIR spectrum and simultaneously analyzed three chemical constituents containing protein, lysine and starch. Finally, the average relative errors between actual values and predicted ones by the model of three components for the predicting set were 1.65%, 6.47% and 1.37%, respectively, and the correlation coefficients were 0.9940, 0.8392 and 0.8825, respectively. For comparison, LS-SVM was also utilized, for which the average relative errors were 1.68%, 6.25% and 1.47%, respectively, and the correlation coefficients were 0.9941, 0.8310 and 0.8800, respectively. It is obvious that MLS-SVM algorithm is comparable to LS-SVM algorithm in modeling analysis performance, and both of them can give satisfying results. The result shows that the model with MLS-SVM algorithm is capable of doing multi-components NIR quantitative analysis synchronously. Thus MLS-SVM algorithm offers a new multiple dependent variables quantitative analysis approach for chemometrics. In addition, the weights have certain effect on the prediction performance of the model with MLS-SVM, which is consistent with our intuition and is validated in this study. Therefore, it is necessary to optimize
Wagner, Philippe; Ghith, Nermin; Leckie, George
2016-01-01
Background and Aim Many multilevel logistic regression analyses of “neighbourhood and health” focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that distinguishes between “specific” (measures of association) and “general” (measures of variance) contextual effects. Performing two empirical examples we illustrate the methodology, interpret the results and discuss the implications of this kind of analysis in public health. Methods We analyse 43,291 individuals residing in 218 neighbourhoods in the city of Malmö, Sweden in 2006. We study two individual outcomes (psychotropic drug use and choice of private vs. public general practitioner, GP) for which the relative importance of neighbourhood as a source of individual variation differs substantially. In Step 1 of the analysis, we evaluate the OR and the area under the receiver operating characteristic (AUC) curve for individual-level covariates (i.e., age, sex and individual low income). In Step 2, we assess general contextual effects using the AUC. Finally, in Step 3 the OR for a specific neighbourhood characteristic (i.e., neighbourhood income) is interpreted jointly with the proportional change in variance (i.e., PCV) and the proportion of ORs in the opposite direction (POOR) statistics. Results For both outcomes, information on individual characteristics (Step 1) provide a low discriminatory accuracy (AUC = 0.616 for psychotropic drugs; = 0.600 for choosing a private GP). Accounting for neighbourhood of residence (Step 2) only improved the AUC for choosing a private GP (+0.295 units). High neighbourhood income (Step 3) was strongly associated to choosing a private GP (OR = 3.50) but the PCV was only 11% and the POOR 33%. Conclusion Applying an innovative stepwise multilevel analysis, we observed that, in Malmö, the neighbourhood context per se had a negligible
NASA Astrophysics Data System (ADS)
Gizaw, Mesgana Seyoum; Gan, Thian Yew
2016-07-01
Regional Flood Frequency Analysis (RFFA) is a statistical method widely used to estimate flood quantiles of catchments with limited streamflow data. In addition, to estimate the flood quantile of ungauged sites, there could be only a limited number of stations with complete dataset are available from hydrologically similar, surrounding catchments. Besides traditional regression based RFFA methods, recent applications of machine learning algorithms such as the artificial neural network (ANN) have shown encouraging results in regional flood quantile estimations. Another novel machine learning technique that is becoming widely applicable in the hydrologic community is the Support Vector Regression (SVR). In this study, an RFFA model based on SVR was developed to estimate regional flood quantiles for two study areas, one with 26 catchments located in southeastern British Columbia (BC) and another with 23 catchments located in southern Ontario (ON), Canada. The SVR-RFFA model for both study sites was developed from 13 sets of physiographic and climatic predictors for the historical period. The Ef (Nash Sutcliffe coefficient) and R2 of the SVR-RFFA model was about 0.7 when estimating flood quantiles of 10, 25, 50 and 100 year return periods which indicate satisfactory model performance in both study areas. In addition, the SVR-RFFA model also performed well based on other goodness-of-fit statistics such as BIAS (mean bias) and BIASr (relative BIAS). If the amount of data available for training RFFA models is limited, the SVR-RFFA model was found to perform better than an ANN based RFFA model, and with significantly lower median CV (coefficient of variation) of the estimated flood quantiles. The SVR-RFFA model was then used to project changes in flood quantiles over the two study areas under the impact of climate change using the RCP4.5 and RCP8.5 climate projections of five Coupled Model Intercomparison Project (CMIP5) GCMs (Global Climate Models) for the 2041
NASA Astrophysics Data System (ADS)
Pradhan, Biswajeet
Recently, in 2006 and 2007 heavy monsoons rainfall have triggered floods along Malaysia's east coast as well as in southern state of Johor. The hardest hit areas are along the east coast of peninsular Malaysia in the states of Kelantan, Terengganu and Pahang. The city of Johor was particularly hard hit in southern side. The flood cost nearly billion ringgit of property and many lives. The extent of damage could have been reduced or minimized if an early warning system would have been in place. This paper deals with flood susceptibility analysis using logistic regression model. We have evaluated the flood susceptibility and the effect of flood-related factors along the Kelantan river basin using the Geographic Information System (GIS) and remote sensing data. Previous flooded areas were extracted from archived radarsat images using image processing tools. Flood susceptibility mapping was conducted in the study area along the Kelantan River using radarsat imagery and then enlarged to 1:25,000 scales. Topographical, hydrological, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence flood occurrence were: topographic slope, topographic aspect, topographic curvature, DEM and distance from river drainage, all from the topographic database; flow direction, flow accumulation, extracted from hydrological database; geology and distance from lineament, taken from the geologic database; land use from SPOT satellite images; soil texture from soil database; and the vegetation index value from SPOT satellite images. Flood susceptible areas were analyzed and mapped using the probability-logistic regression model. Results indicate that flood prone areas can be performed at 1:25,000 which is comparable to some conventional flood hazard map scales. The flood prone areas delineated on these maps correspond to areas that would be inundated by significant flooding
A Bayesian ridge regression analysis of congestion's impact on urban expressway safety.
Shi, Qi; Abdel-Aty, Mohamed; Lee, Jaeyoung
2016-03-01
With the rapid growth of traffic in urban areas, concerns about congestion and traffic safety have been heightened. This study leveraged both Automatic Vehicle Identification (AVI) system and Microwave Vehicle Detection System (MVDS) installed on an expressway in Central Florida to explore how congestion impacts the crash occurrence in urban areas. Multiple congestion measures from the two systems were developed. To ensure more precise estimates of the congestion's effects, the traffic data were aggregated into peak and non-peak hours. Multicollinearity among traffic parameters was examined. The results showed the presence of multicollinearity especially during peak hours. As a response, ridge regression was introduced to cope with this issue. Poisson models with uncorrelated random effects, correlated random effects, and both correlated random effects and random parameters were constructed within the Bayesian framework. It was proven that correlated random effects could significantly enhance model performance. The random parameters model has similar goodness-of-fit compared with the model with only correlated random effects. However, by accounting for the unobserved heterogeneity, more variables were found to be significantly related to crash frequency. The models indicated that congestion increased crash frequency during peak hours while during non-peak hours it was not a major crash contributing factor. Using the random parameter model, the three congestion measures were compared. It was found that all congestion indicators had similar effects while Congestion Index (CI) derived from MVDS data was a better congestion indicator for safety analysis. Also, analyses showed that the segments with higher congestion intensity could not only increase property damage only (PDO) crashes, but also more severe crashes. In addition, the issues regarding the necessity to incorporate specific congestion indicator for congestion's effects on safety and to take care of the
A Bayesian ridge regression analysis of congestion's impact on urban expressway safety.
Shi, Qi; Abdel-Aty, Mohamed; Lee, Jaeyoung
2016-03-01
With the rapid growth of traffic in urban areas, concerns about congestion and traffic safety have been heightened. This study leveraged both Automatic Vehicle Identification (AVI) system and Microwave Vehicle Detection System (MVDS) installed on an expressway in Central Florida to explore how congestion impacts the crash occurrence in urban areas. Multiple congestion measures from the two systems were developed. To ensure more precise estimates of the congestion's effects, the traffic data were aggregated into peak and non-peak hours. Multicollinearity among traffic parameters was examined. The results showed the presence of multicollinearity especially during peak hours. As a response, ridge regression was introduced to cope with this issue. Poisson models with uncorrelated random effects, correlated random effects, and both correlated random effects and random parameters were constructed within the Bayesian framework. It was proven that correlated random effects could significantly enhance model performance. The random parameters model has similar goodness-of-fit compared with the model with only correlated random effects. However, by accounting for the unobserved heterogeneity, more variables were found to be significantly related to crash frequency. The models indicated that congestion increased crash frequency during peak hours while during non-peak hours it was not a major crash contributing factor. Using the random parameter model, the three congestion measures were compared. It was found that all congestion indicators had similar effects while Congestion Index (CI) derived from MVDS data was a better congestion indicator for safety analysis. Also, analyses showed that the segments with higher congestion intensity could not only increase property damage only (PDO) crashes, but also more severe crashes. In addition, the issues regarding the necessity to incorporate specific congestion indicator for congestion's effects on safety and to take care of the
2010-01-01
Background Incidence of liver hydatid cyst (LHC) rupture ranged 15%-40% of all cases and most of them concern the bile duct tree. Patients with biliocystic communication (BCC) had specific clinic and therapeutic aspect. The purpose of this study was to determine witch patients with LHC may develop BCC using classification and regression tree (CART) analysis Methods A retrospective study of 672 patients with liver hydatid cyst treated at the surgery department "A" at Ibn Sina University Hospital, Rabat Morocco. Four-teen risk factors for BCC occurrence were entered into CART analysis to build an algorithm that can predict at the best way the occurrence of BCC. Results Incidence of BCC was 24.5%. Subgroups with high risk were patients with jaundice and thick pericyst risk at 73.2% and patients with thick pericyst, with no jaundice 36.5 years and younger with no past history of LHC risk at 40.5%. Our developed CART model has sensitivity at 39.6%, specificity at 93.3%, positive predictive value at 65.6%, a negative predictive value at 82.6% and accuracy of good classification at 80.1%. Discriminating ability of the model was good 82%. Conclusion we developed a simple classification tool to identify LHC patients with high risk BCC during a routine clinic visit (only on clinical history and examination followed by an ultrasonography). Predictive factors were based on pericyst aspect, jaundice, age, past history of liver hydatidosis and morphological Gharbi cyst aspect. We think that this classification can be useful with efficacy to direct patients at appropriated medical struct's. PMID:20398342
Determinants for changing the treatment of COPD: a regression analysis from a clinical audit
López-Campos, Jose Luis; Abad Arranz, María; Calero Acuña, Carmen; Romero Valero, Fernando; Ayerbe García, Ruth; Hidalgo Molina, Antonio; Aguilar Perez-Grovas, Ricardo I; García Gil, Francisco; Casas Maldonado, Francisco; Caballero Ballesteros, Laura; Sánchez Palop, María; Pérez-Tejero, Dolores; Segado, Alejandro; Calvo Bonachera, Jose; Hernández Sierra, Bárbara; Doménech, Adolfo; Arroyo Varela, Macarena; González Vargas, Francisco; Cruz Rueda, Juan J
2016-01-01
Introduction This study is an analysis of a pilot COPD clinical audit that evaluated adherence to guidelines for patients with COPD in a stable disease phase during a routine visit in specialized secondary care outpatient clinics in order to identify the variables associated with the decision to step-up or step-down pharmacological treatment. Methods This study was a pilot clinical audit performed at hospital outpatient respiratory clinics in the region of Andalusia, Spain (eight provinces with over eight million inhabitants), in which 20% of centers in the area (catchment population 3,143,086 inhabitants) were invited to participate. Treatment changes were evaluated in terms of the number of prescribed medications and were classified as step-up, step-down, or no change. Three backward stepwise binominal multivariate logistic regression analyses were conducted to evaluate variables associated with stepping up, stepping down, and inhaled corticosteroids discontinuation. Results The present analysis evaluated 565 clinical records (91%) of the complete audit. Of those records, 366 (64.8%) cases saw no change in pharmacological treatment, while 99 patients (17.5%) had an increase in the number of drugs, 55 (9.7%) had a decrease in the number of drugs, and 45 (8.0%) noted a change to other medication for a similar therapeutic scheme. Exacerbations were the main factor in stepping up treatment, as were the symptoms themselves. In contrast, rather than symptoms, doctors used forced expiratory volume in 1 second and previous treatment with long-term antibiotics or inhaled corticosteroids as the key determinants to stepping down treatment. Conclusion The majority of doctors did not change the prescription. When changes were made, a number of related factors were noted. Future trials must evaluate whether these therapeutic changes impact clinically relevant outcomes at follow-up. PMID:27330285
Regression Analysis of Top of Descent Location for Idle-thrust Descents
NASA Technical Reports Server (NTRS)
Stell, Laurel; Bronsvoort, Jesper; McDonald, Greg
2013-01-01
In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. The independent variables cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also recorded or computed post-operations. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajec- tory parameters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowl- edge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace. In particular, a model for TOD location that is linear in the independent variables would enable decision support tool human-machine interfaces for which a kinetic approach would be computationally too slow.
Integrative analysis of multiple diverse omics datasets by sparse group multitask regression.
Lin, Dongdong; Zhang, Jigang; Li, Jingyao; He, Hao; Deng, Hong-Wen; Wang, Yu-Ping
2014-01-01
A variety of high throughput genome-wide assays enable the exploration of genetic risk factors underlying complex traits. Although these studies have remarkable impact on identifying susceptible biomarkers, they suffer from issues such as limited sample size and low reproducibility. Combining individual studies of different genetic levels/platforms has the promise to improve the power and consistency of biomarker identification. In this paper, we propose a novel integrative method, namely sparse group multitask regression, for integrating diverse omics datasets, platforms, and populations to identify risk genes/factors of complex diseases. This method combines multitask learning with sparse group regularization, which will: (1) treat the biomarker identification in each single study as a task and then combine them by multitask learning; (2) group variables from all studies for identifying significant genes; (3) enforce sparse constraint on groups of variables to overcome the "small sample, but large variables" problem. We introduce two sparse group penalties: sparse group lasso and sparse group ridge in our multitask model, and provide an effective algorithm for each model. In addition, we propose a significance test for the identification of potential risk genes. Two simulation studies are performed to evaluate the performance of our integrative method by comparing it with conventional meta-analysis method. The results show that our sparse group multitask method outperforms meta-analysis method significantly. In an application to our osteoporosis studies, 7 genes are identified as significant genes by our method and are found to have significant effects in other three independent studies for validation. The most significant gene SOD2 has been identified in our previous osteoporosis study involving the same expression dataset. Several other genes such as TREML2, HTR1E, and GLO1 are shown to be novel susceptible genes for osteoporosis, as confirmed from other
Nakasone, Yutaka Ikeda, Osamu; Yamashita, Yasuyuki; Kudoh, Kouichi; Shigematsu, Yoshinori; Harada, Kazunori
2007-09-15
We applied multivariate analysis to the clinical findings in patients with acute gastrointestinal (GI) hemorrhage and compared the relationship between these findings and angiographic evidence of extravasation. Our study population consisted of 46 patients with acute GI bleeding. They were divided into two groups. In group 1 we retrospectively analyzed 41 angiograms obtained in 29 patients (age range, 25-91 years; average, 71 years). Their clinical findings including the shock index (SI), diastolic blood pressure, hemoglobin, platelet counts, and age, which were quantitatively analyzed. In group 2, consisting of 17 patients (age range, 21-78 years; average, 60 years), we prospectively applied statistical analysis by a logistics regression model to their clinical findings and then assessed 21 angiograms obtained in these patients to determine whether our model was useful for predicting the presence of angiographic evidence of extravasation. On 18 of 41 (43.9%) angiograms in group 1 there was evidence of extravasation; in 3 patients it was demonstrated only by selective angiography. Factors significantly associated with angiographic visualization of extravasation were the SI and patient age. For differentiation between cases with and cases without angiographic evidence of extravasation, the maximum cutoff point was between 0.51 and 0.0.53. Of the 21 angiograms obtained in group 2, 13 (61.9%) showed evidence of extravasation; in 1 patient it was demonstrated only on selective angiograms. We found that in 90% of the cases, the prospective application of our model correctly predicted the angiographically confirmed presence or absence of extravasation. We conclude that in patients with GI hemorrhage, angiographic visualization of extravasation is associated with the pre-embolization SI. Patients with a high SI value should undergo study to facilitate optimal treatment planning.
Pineda, Silvia; Real, Francisco X; Kogevinas, Manolis; Carrato, Alfredo; Chanock, Stephen J; Malats, Núria; Van Steen, Kristel
2015-12-01
Omics data integration is becoming necessary to investigate the genomic mechanisms involved in complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological processes. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we propose a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. This was applied with penalized regression methods (LASSO and ENET) when exploring relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whose expression levels were significantly associated with both SNPs and CPGs. Importantly, 36 (75%) of them were replicated in an independent data set (TCGA) and the performance of the proposed method was checked with a simulation study. We further support our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexible and easy to implement when analyzing several types of omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complex genetic mechanisms involved in disease
Zhang, Man; Liu, Xu-Hua; He, Xiong-Kui; Zhang, Lu-Da; Zhao, Long-Lian; Li, Jun-Hui
2010-05-01
In the present paper, taking 66 wheat samples for testing materials, ridge regression technology in near-infrared (NIR) spectroscopy quantitative analysis was researched. The NIR-ridge regression model for determination of protein content was established by NIR spectral data of 44 wheat samples to predict the protein content of the other 22 samples. The average relative error was 0.015 18 between the predictive results and Kjeldahl's values (chemical analysis values). And the predictive results were compared with those values derived through partial least squares (PLS) method, showing that ridge regression method was deserved to be chosen for NIR spectroscopy quantitative analysis. Furthermore, in order to reduce the disturbance to predictive capacity of the quantitative analysis model resulting from irrelevant information, one effective way is to screen the wavelength information. In order to select the spectral information with more content information and stronger relativity with the composition or the nature of the samples to improve the model's predictive accuracy, ridge regression was used to select wavelength information in this paper. The NIR-ridge regression model was established with the spectral information at 4 wavelength points, which were selected from 1 297 wavelength points, to predict the protein content of the 22 samples. The average relative error was 0.013 7 and the correlation coefficient reached 0.981 7 between the predictive results and Kjeldahl's values. The results showed that ridge regression was able to screen the essential wavelength information from a large amount of spectral information. It not only can simplify the model and effectively reduce the disturbance resulting from collinearity information, but also has practical significance for designing special NIR analysis instrument for analyzing specific component in some special samples. PMID:20672604
Zhang, Man; Liu, Xu-Hua; He, Xiong-Kui; Zhang, Lu-Da; Zhao, Long-Lian; Li, Jun-Hui
2010-05-01
In the present paper, taking 66 wheat samples for testing materials, ridge regression technology in near-infrared (NIR) spectroscopy quantitative analysis was researched. The NIR-ridge regression model for determination of protein content was established by NIR spectral data of 44 wheat samples to predict the protein content of the other 22 samples. The average relative error was 0.015 18 between the predictive results and Kjeldahl's values (chemical analysis values). And the predictive results were compared with those values derived through partial least squares (PLS) method, showing that ridge regression method was deserved to be chosen for NIR spectroscopy quantitative analysis. Furthermore, in order to reduce the disturbance to predictive capacity of the quantitative analysis model resulting from irrelevant information, one effective way is to screen the wavelength information. In order to select the spectral information with more content information and stronger relativity with the composition or the nature of the samples to improve the model's predictive accuracy, ridge regression was used to select wavelength information in this paper. The NIR-ridge regression model was established with the spectral information at 4 wavelength points, which were selected from 1 297 wavelength points, to predict the protein content of the 22 samples. The average relative error was 0.013 7 and the correlation coefficient reached 0.981 7 between the predictive results and Kjeldahl's values. The results showed that ridge regression was able to screen the essential wavelength information from a large amount of spectral information. It not only can simplify the model and effectively reduce the disturbance resulting from collinearity information, but also has practical significance for designing special NIR analysis instrument for analyzing specific component in some special samples.
NASA Astrophysics Data System (ADS)
Yang, Jianhong; Yi, Cancan; Xu, Jinwu; Ma, Xianghong
2015-05-01
A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.
NASA Astrophysics Data System (ADS)
Denli, H. H.; Koc, Z.
2015-12-01
Estimation of real properties depending on standards is difficult to apply in time and location. Regression analysis construct mathematical models which describe or explain relationships that may exist between variables. The problem of identifying price differences of properties to obtain a price index can be converted into a regression problem, and standard techniques of regression analysis can be used to estimate the index. Considering regression analysis for real estate valuation, which are presented in real marketing process with its current characteristics and quantifiers, the method will help us to find the effective factors or variables in the formation of the value. In this study, prices of housing for sale in Zeytinburnu, a district in Istanbul, are associated with its characteristics to find a price index, based on information received from a real estate web page. The associated variables used for the analysis are age, size in m2, number of floors having the house, floor number of the estate and number of rooms. The price of the estate represents the dependent variable, whereas the rest are independent variables. Prices from 60 real estates have been used for the analysis. Same price valued locations have been found and plotted on the map and equivalence curves have been drawn identifying the same valued zones as lines.
Regression analysis to predict growth performance from dietary net energy in growing-finishing pigs.
Nitikanchana, S; Dritz, S S; Tokach, M D; DeRouchey, J M; Goodband, R D; White, B J
2015-06-01
Data from 41 trials with multiple energy levels (285 observations) were used in a meta-analysis to predict growth performance based on dietary NE concentration. Nutrient and energy concentrations in all diets were estimated using the NRC ingredient library. Predictor variables examined for best fit models using Akaike information criteria included linear and quadratic terms of NE, BW, CP, standardized ileal digestible (SID) Lys, crude fiber, NDF, ADF, fat, ash, and their interactions. The initial best fit models included interactions between NE and CP or SID Lys. After removal of the observations that fed SID Lys below the suggested requirement, these terms were no longer significant. Including dietary fat in the model with NE and BW significantly improved the G:F prediction model, indicating that NE may underestimate the influence of fat on G:F. The meta-analysis indicated that, as long as diets are adequate for other nutrients (i.e., Lys), dietary NE is adequate to predict changes in ADG across different dietary ingredients and conditions. The analysis indicates that ADG increases with increasing dietary NE and BW but decreases when BW is above 87 kg. The G:F ratio improves with increasing dietary NE and fat but decreases with increasing BW. The regression equations were then evaluated by comparing the actual and predicted performance of 543 finishing pigs in 2 trials fed 5 dietary treatments, included 3 different levels of NE by adding wheat middlings, soybean hulls, dried distillers grains with solubles (DDGS; 8 to 9% oil), or choice white grease (CWG) to a corn-soybean meal-based diet. Diets were 1) 30% DDGS, 20% wheat middlings, and 4 to 5% soybean hulls (low energy); 2) 20% wheat middlings and 4 to 5% soybean hulls (low energy); 3) a corn-soybean meal diet (medium energy); 4) diet 2 supplemented with 3.7% CWG to equalize the NE level to diet 3 (medium energy); and 5) a corn-soybean meal diet with 3.7% CWG (high energy). Only small differences were observed
NASA Astrophysics Data System (ADS)
Middlebrook, A. M.; Murphy, D. M.; Lee, S.; Lee, S.; Lee, S.; Thomson, D. S.; Thomson, D. S.
2001-12-01
During the Atlanta Supersites project in August 1999, the PALMS (Particle Analysis by Laser Mass Spectrometry) instrument collected over 500,000 individual particle spectra. The Atlanta data were originally analyzed by examining combinations of peaks and relative peak areas [Lee et al., 2001a,b], and a wide range of particle components such as sulfate, nitrate, mineral species, metals, organic species, and elemental carbon were detected. To further study the dataset, a classification program using regression tree analysis was developed and applied. Spectral data were compressed into a lower resolution spectrum (every 0.25 mass units) of the raw data and a list of peak areas (every mass unit). Each spectrum started as a normalized classification vector by itself. If the dot product of two classification vectors was within a certain threshold, they were combined into a new classification. The new classification vector was a normalized running average of the classifications being combined. In subsequent steps, the threshold for combining classifications was continuously lowered until a reasonable number of classifications remained. After the final iteration, each spectrum was compared individually with the entire set of classification vectors. Classifications were also combined manually. The classification results from the Atlanta data are generally consistent with those determined by peak identification. However, the classification program identified specific patterns in the mass spectra that were not found by peak identification and generated new particle types. Furthermore, rare particle types that may affect human health were studied in more detail. A description of the classification program as well as the results for the Atlanta data will be presented. Lee, S.-H., D. M. Murphy, D. S. Thomson, and A. M. Middlebrook, Chemical components of single particles measured with particle analysis by laser mass spectrometry (PALMS) during the Atlanta Supersites Project
NASA Astrophysics Data System (ADS)
Fushimi, Akihiro; Kawashima, Hiroto; Kajihara, Hideo
Understanding the contribution of each emission source of air pollutants to ambient concentrations is important to establish effective measures for risk reduction. We have developed a source apportionment method based on an atmospheric dispersion model and multiple linear regression analysis (MLR) in conjunction with ambient concentrations simultaneously measured at points in a grid network. We used a Gaussian plume dispersion model developed by the US Environmental Protection Agency called the Industrial Source Complex model (ISC) in the method. Our method does not require emission amounts or source profiles. The method was applied to the case of benzene in the vicinity of the Keiyo Central Coastal Industrial Complex (KCCIC), one of the biggest industrial complexes in Japan. Benzene concentrations were simultaneously measured from December 2001 to July 2002 at sites in a grid network established in the KCCIC and the surrounding residential area. The method was used to estimate benzene emissions from the factories in the KCCIC and from automobiles along a section of a road, and then the annual average contribution of the KCCIC to the ambient concentrations was estimated based on the estimated emissions. The estimated contributions of the KCCIC were 65% inside the complex, 49% at 0.5-km sites, 35% at 1.5-km sites, 20% at 3.3-km sites, and 9% at a 5.6-km site. The estimated concentrations agreed well with the measured values. The estimated emissions from the factories and the road were slightly larger than those reported in the first Pollutant Release and Transfer Register (PRTR). These results support the reliability of our method. This method can be applied to other chemicals or regions to achieve reasonable source apportionments.
Expert Involvement Predicts mHealth App Downloads: Multivariate Regression Analysis of Urology Apps
Osório, Luís; Cavadas, Vitor; Fraga, Avelino; Carrasquinho, Eduardo; Cardoso de Oliveira, Eduardo; Castelo-Branco, Miguel; Roobol, Monique J
2016-01-01
Background Urological mobile medical (mHealth) apps are gaining popularity with both clinicians and patients. mHealth is a rapidly evolving and heterogeneous field, with some urology apps being downloaded over 10,000 times and others not at all. The factors that contribute to medical app downloads have yet to be identified, including the hypothetical influence of expert involvement in app development. Objective The objective of our study was to identify predictors of the number of urology app downloads. Methods We reviewed urology apps available in the Google Play Store and collected publicly available data. Multivariate ordinal logistic regression evaluated the effect of publicly available app variables on the number of apps being downloaded. Results Of 129 urology apps eligible for study, only 2 (1.6%) had >10,000 downloads, with half having ≤100 downloads and 4 (3.1%) having none at all. Apps developed with expert urologist involvement (P=.003), optional in-app purchases (P=.01), higher user rating (P<.001), and more user reviews (P<.001) were more likely to be installed. App cost was inversely related to the number of downloads (P<.001). Only data from the Google Play Store and the developers’ websites, but not other platforms, were publicly available for analysis, and the level and nature of expert involvement was not documented. Conclusions The explicit participation of urologists in app development is likely to enhance its chances to have a higher number of downloads. This finding should help in the design of better apps and further promote urologist involvement in mHealth. Official certification processes are required to ensure app quality and user safety. PMID:27421338
Zhang, Yiwei; Pan, Wei
2015-03-01
Genome-wide association studies (GWAS) have been established as a major tool to identify genetic variants associated with complex traits, such as common diseases. However, GWAS may suffer from false positives and false negatives due to confounding population structures, including known or unknown relatedness. Another important issue is unmeasured environmental risk factors. Among many methods for adjusting for population structures, two approaches stand out: one is principal component regression (PCR) based on principal component analysis, which is perhaps the most popular due to its early appearance, simplicity, and general effectiveness; the other is based on a linear mixed model (LMM) that has emerged recently as perhaps the most flexible and effective, especially for samples with complex structures as in model organisms. As shown previously, the PCR approach can be regarded as an approximation to an LMM; such an approximation depends on the number of the top principal components (PCs) used, the choice of which is often difficult in practice. Hence, in the presence of population structure, the LMM appears to outperform the PCR method. However, due to the different treatments of fixed vs. random effects in the two approaches, we show an advantage of PCR over LMM: in the presence of an unknown but spatially confined environmental confounder (e.g., environmental pollution or lifestyle), the PCs may be able to implicitly and effectively adjust for the confounder whereas the LMM cannot. Accordingly, to adjust for both population structures and nongenetic confounders, we propose a hybrid method combining the use and, thus, strengths of PCR and LMM. We use real genotype data and simulated phenotypes to confirm the above points, and establish the superior performance of the hybrid method across all scenarios.
Regression analysis of time trends in perinatal mortality in Germany 1980-1993.
Scherb, H; Weigelt, E; Brüske-Hohlfeld, I
2000-02-01
Numerous investigations have been carried out on the possible impact of the Chernobyl accident on the prevalence of anomalies at birth and on perinatal mortality. In many cases the studies were aimed at the detection of differences of pregnancy outcome measurements between regions or time periods. Most authors conclude that there is no evidence of a detrimental physical effect on congenital anomalies or other outcomes of pregnancy following the accident. In this paper, we report on statistical analyses of time trends of perinatal mortality in Germany. Our main intention is to investigate whether perinatal mortality, as reflected in official records, was increased in 1987 as a possible effect of the Chernobyl accident. We show that, in Germany as a whole, there was a significantly elevated perinatal mortality proportion in 1987 as compared to the trend function. The increase is 4.8% (p = 0.0046) of the expected perinatal death proportion for 1987. Even more pronounced levels of 8.2% (p = 0. 0458) and 8.5% (p = 0.0702) may be found in the higher contaminated areas of the former German Democratic Republic (GDR), including West Berlin, and of Bavaria, respectively. To investigate the impact of statistical models on results, we applied three standard regression techniques. The observed significant increase in 1987 is independent of the statistical model used. Stillbirth proportions show essentially the same behavior as perinatal death proportions, but the results for all of Germany are nonsignificant due to the smaller numbers involved. Analysis of the association of stillbirth proportions with the (137)Cs deposition on a district level in Bavaria discloses a significant relationship. Our results are in contrast to those of many analyses of the health consequences of the Chernobyl accident and contradict the present radiobiologic knowledge. As we are dealing with highly aggregated data, other causes or artifacts may explain the observed effects. Hence, the findings
Ilic, Milena; Ilic, Irena
2014-01-01
Background Limited data on mortality from malignant lymphatic and hematopoietic neoplasms have been published for Serbia. Methods The study covered population of Serbia during the 1991–2010 period. Mortality trends were assessed using the joinpoint regression analysis. Results Trend for overall death rates from malignant lymphoid and haematopoietic neoplasms significantly decreased: by −2.16% per year from 1991 through 1998, and then significantly increased by +2.20% per year for the 1998–2010 period. The growth during the entire period was on average +0.8% per year (95% CI 0.3 to 1.3). Mortality was higher among males than among females in all age groups. According to the comparability test, mortality trends from malignant lymphoid and haematopoietic neoplasms in men and women were parallel (final selected model failed to reject parallelism, P = 0.232). Among younger Serbian population (0–44 years old) in both sexes: trends significantly declined in males for the entire period, while in females 15–44 years of age mortality rates significantly declined only from 2003 onwards. Mortality trend significantly increased in elderly in both genders (by +1.7% in males and +1.5% in females in the 60–69 age group, and +3.8% in males and +3.6% in females in the 70+ age group). According to the comparability test, mortality trend for Hodgkin's lymphoma differed significantly from mortality trends for all other types of malignant lymphoid and haematopoietic neoplasms (P<0.05). Conclusion Unfavourable mortality trend in Serbia requires targeted intervention for risk factors control, early diagnosis and modern therapy. PMID:25333862
Spalj, Stjepan; Spalj, Vedrana Tudor; Ivanković, Luida; Plancak, Darije
2014-03-01
The aim of this study was to explore the patterns of oral health-related risk behaviours in relation to dental status, attitudes, motivation and knowledge among Croatian adolescents. The assessment was conducted in the sample of 750 male subjects - military recruits aged 18-28 in Croatia using the questionnaire and clinical examination. Mean number of decayed, missing and filled teeth (DMFT) and Significant Caries Index (SIC) were calculated. Multiple logistic regression models were crated for analysis. Although models of risk behaviours were statistically significant their explanatory values were quite low. Five of them--rarely toothbrushing, not using hygiene auxiliaries, rarely visiting dentist, toothache as a primary reason to visit dentist, and demand for tooth extraction due to toothache--had the highest explanatory values ranging from 21-29% and correctly classified 73-89% of subjects. Toothache as a primary reason to visit dentist, extraction as preferable therapy when toothache occurs, not having brushing education in school and frequent gingival bleeding were significantly related to population with high caries experience (DMFT > or = 14 according to SiC) producing Odds ratios of 1.6 (95% CI 1.07-2.46), 2.1 (95% CI 1.29-3.25), 1.8 (95% CI 1.21-2.74) and 2.4 (95% CI 1.21-2.74) respectively. DMFT> or = 14 model had low explanatory value of 6.5% and correctly classified 83% of subjects. It can be concluded that oral health-related risk behaviours are interrelated. Poor association was seen between attitudes concerning oral health and oral health-related risk behaviours, indicating insufficient motivation to change lifestyle and habits. Self-reported oral hygiene habits were not strongly related to dental status.
Liu, Bilan; Qiu, Xing; Zhu, Tong; Tian, Wei; Hu, Rui; Ekholm, Sven; Schifitto, Giovanni; Zhong, Jianhui
2016-01-01
Quantitative measurement of localized longitudinal changes in brain abnormalities at an individual level may offer critical information for disease diagnosis and treatment. The voxel-wise permutation-based method SPREAD/iSPREAD, which combines resampling and spatial regression of neighboring voxels, provides an effective and robust method for detecting subject-specific longitudinal changes within the whole brain, especially for longitudinal studies with a limited number of scans. As an extension of SPREAD/iSPREAD, we present a general method that facilitates analysis of serial Diffusion Tensor Imaging (DTI) measurements (with more than two time points) for testing localized changes in longitudinal studies. Two types of voxel-level test statistics (model-free test statistics, which measure intra-subject variability across time, and test statistics based on general linear model that incorporate specific lesion evolution models) were estimated and tested against the null hypothesis among groups of DTI data across time. The implementation and utility of the proposed statistical method were demonstrated by both Monte Carlo simulations and applications on clinical DTI data from human brain in vivo. By a design of test statistics based on the disease progression model, it was possible to apportion the true significant voxels attributed to the disease progression and those caused by underlying anatomical differences that cannot be explained by the model, which led to improvement in false positive (FP) control in the results. Extension of the proposed method to include other diseases or drug effect models, as well as the feasibility of global statistics, was discussed. The proposed statistical method can be extended to a broad spectrum of longitudinal studies with carefully designed test statistics, which helps to detect localized changes at the individual level.
Zhang, Kun; Huang, Feifei; Chen, Jie; Cai, Qingqing; Wang, Tong; Zou, Rong; Zuo, Zhiyi; Wang, Jingfeng; Huang, Hui
2014-11-01
Overweight and obesity are associated with adverse cardiovascular outcomes. However, the role of overweight and obesity in left ventricular hypertrophy (LVH) of hypertensive patients is controversial. The aim of the current meta-analysis was to evaluate the influence of overweight and obesity on LVH regression in the hypertensive population.Twenty-eight randomized controlled trials comprising 2403 hypertensive patients (mean age range: 43.8-66.7 years) were identified. Three groups were divided according to body mass index: normal weight, overweight, and obesity groups.Compared with the normal-weight group, LVH regression in the overweight and obesity groups was more obvious with less reduction of systolic blood pressure after antihypertensive therapies (P < 0.001). The renin-angiotensin system inhibitor was the most effective in regressing LVH in overweight and obese hypertensive patients (19.27 g/m, 95% confidence interval [15.25, 23.29], P < 0.001), followed by β-blockers, calcium channel blockers, and diuretics. In the stratified analysis based on blood pressure measurement methods and age, more significant LVH regression was found in 24-h ambulatory blood pressure monitoring (ABPM) group and in relatively young patients (40-60 years' old) group (P < 0.01).Overweight and obesity are independent risk factors for LVH in hypertensive patients. Intervention at an early age and monitoring by ABPM may facilitate therapy-induced LVH regression in overweight and obese hypertensive patients.
Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert M.
2013-01-01
A new regression model search algorithm was developed that may be applied to both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The algorithm is a simplified version of a more complex algorithm that was originally developed for the NASA Ames Balance Calibration Laboratory. The new algorithm performs regression model term reduction to prevent overfitting of data. It has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a regression model search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression model. Therefore, the simplified algorithm is not intended to replace the original algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new search algorithm.
ERIC Educational Resources Information Center
Fraas, John W.; Newman, Isadore
1996-01-01
In a conjoint-analysis consumer-preference study, researchers must determine whether the product factor estimates, which measure consumer preferences, should be calculated and interpreted for each respondent or collectively. Multiple regression models can determine whether to aggregate data by examining factor-respondent interaction effects. This…
ERIC Educational Resources Information Center
Muller, Veronica; Brooks, Jessica; Tu, Wei-Mo; Moser, Erin; Lo, Chu-Ling; Chan, Fong
2015-01-01
Purpose: The main objective of this study was to determine the extent to which physical and cognitive-affective factors are associated with fibromyalgia (FM) fatigue. Method: A quantitative descriptive design using correlation techniques and multiple regression analysis. The participants consisted of 302 members of the National Fibromyalgia &…
ERIC Educational Resources Information Center
Thomas, Emily H.; Galambos, Nora
To investigate how students' characteristics and experiences affect satisfaction, this study used regression and decision-tree analysis with the CHAID algorithm to analyze student opinion data from a sample of 1,783 college students. A data-mining approach identifies the specific aspects of students' university experience that most influence three…
ERIC Educational Resources Information Center
Brabant, Marie-Eve; Hebert, Martine; Chagnon, Francois
2013-01-01
This study explored the clinical profiles of 77 female teenager survivors of sexual abuse and examined the association of abuse-related and personal variables with suicidal ideations. Analyses revealed that 64% of participants experienced suicidal ideations. Findings from classification and regression tree analysis indicated that depression,…
Significant drivers of the virtual water trade evaluated with a multivariate regression analysis
NASA Astrophysics Data System (ADS)
Tamea, Stefania; Laio, Francesco; Ridolfi, Luca
2014-05-01
International trade of food is vital for the food security of many countries, which rely on trade to compensate for an agricultural production insufficient to feed the population. At the same time, food trade has implications on the distribution and use of water resources, because through the international trade of food commodities, countries virtually displace the water used for food production, known as "virtual water". Trade thus implies a network of virtual water fluxes from exporting to importing countries, which has been estimated to displace more than 2 billions of m3 of water per year, or about the 2% of the annual global precipitation above land. It is thus important to adequately identify the dynamics and the controlling factors of the virtual water trade in that it supports and enables the world food security. Using the FAOSTAT database of international trade and the virtual water content available from the Water Footprint Network, we reconstructed 25 years (1986-2010) of virtual water fluxes. We then analyzed the dependence of exchanged fluxes on a set of major relevant factors, that includes: population, gross domestic product, arable land, virtual water embedded in agricultural production and dietary consumption, and geographical distance between countries. Significant drivers have been identified by means of a multivariate regression analysis, applied separately to the export and import fluxes of each country; temporal trends are outlined and the relative importance of drivers is assessed by a commonality analysis. Results indicate that population, gross domestic product and geographical distance are the major drivers of virtual water fluxes, with a minor (but non-negligible) contribution given by the agricultural production of exporting countries. Such drivers have become relevant for an increasing number of countries throughout the years, with an increasing variance explained by the distance between countries and a decreasing role of the gross
Nagatsuka, Kazuyuki; Miyata, Shigeki; Kada, Akiko; Kawamura, Atsushi; Nakagawara, Jyoji; Furui, Eisuke; Takiuchi, Shin; Taomoto, Katsushi; Kario, Kazuomi; Uchiyama, Shinichiro; Saito, Kozue; Nagao, Takehiko; Kitagawa, Kazuo; Hosomi, Naohisa; Tanaka, Keiji; Kaikita, Koichi; Katayama, Yasuo; Abumiya, Takeo; Nakane, Hiroshi; Wada, Hideo; Hattori, Akira; Kimura, Kazumi; Isshiki, Takaaki; Nishikawa, Masakatsu; Yamawaki, Takemori; Yonemoto, Naohiro; Okada, Hiromi; Ogawa, Hisao; Minematsu, Kazuo; Miyata, Toshiyuki
2016-08-01
Several studies have indicated that approximately 25 % of patients treated with aspirin exhibit high on-treatment platelet reactivity (HTPR), which is potentially associated with cardiovascular events (CVEs). However, this association is still controversial, since the mechanisms by which HTPR contributes to CVEs remain unclear and a no standardised definition of HTPR has been established. To determine whether HTPR is associated with CVE recurrence and what type of assay would best predict CVE recurrence, we conducted a multicentre prospective cohort study of 592 stable cardiovascular outpatients treated with aspirin monotherapy for secondary prevention. Their HTPR was determined by arachidonic acid- or collagen-induced aggregation assays using two different agonist concentrations. Residual cyclooxygenase (COX)-1 activity was assessed by measuring serum thromboxane (TX)B2 or urinary 11-dehydro TXB2. Shear-induced platelet thrombus formation was also examined. We followed all patients for two years to evaluate how these seven indexes were related to the recurrence of CVEs (cerebral infarction, transient ischaemic attack, myocardial infarction, unstable angina, revascularisation, other arterial thrombosis, or cardiovascular death). Of 583 patients eligible for the analysis, CVEs occurred in 69 (11.8 %). A Cox regression model identified several classical risk factors associated with CVEs. However, neither HTPR nor high residual COX-1 activity was significantly associated with CVEs, even by applying cut-off values suggested in previous reports or a receiver-operating characteristic analysis. In conclusion, recurrence of CVEs occurred independently of HTPR and residual COX-1 activity. Thus, our findings do not support the use of platelet or COX-1 functional testing for predicting clinical outcomes in stable cardiovascular patients. PMID:27098431
Solving Logistic Regression with Group Cardinality Constraints for Time Series Analysis
Zhang, Yong; Pohl, Kilian M.
2016-01-01
We propose an algorithm to distinguish 3D+t images of healthy from diseased subjects by solving logistic regression based on cardinality constrained, group sparsity. This method reduces the risk of overfitting by providing an elegant solution to identifying anatomical regions most impacted by disease. It also ensures that consistent identification across the time series by grouping each image feature across time and counting the number of non-zero groupings. While popular in medical imaging, group cardinality constrained problems are generally solved by relaxing counting with summing over the groupings. We instead solve the original problem by generalizing a penalty decomposition algorithm, which alternates between minimizing a logistic regression function with a regularizer based on the Frobenius norm and enforcing sparsity. Applied to 86 cine MRIs of healthy cases and subjects with Tetralogy of Fallot (TOF), our method correctly identifies regions impacted by TOF and obtains a statistically significant higher classification accuracy than logistic regression without and relaxed grouped sparsity constraint.
NASA Technical Reports Server (NTRS)
Gentry, R. C.; Rodgers, E.; Steranka, J.; Shenk, W. E.
1978-01-01
A regression technique was developed to forecast 24 hour changes of the maximum winds for weak (maximum winds less than or equal to 65 Kt) and strong (maximum winds greater than 65 Kt) tropical cyclones by utilizing satellite measured equivalent blackbody temperatures around the storm alone and together with the changes in maximum winds during the preceding 24 hours and the current maximum winds. Independent testing of these regression equations shows that the mean errors made by the equations are lower than the errors in forecasts made by the peristence techniques.
Development of LACIE CCEA-1 weather/wheat yield models. [regression analysis
NASA Technical Reports Server (NTRS)
Strommen, N. D.; Sakamoto, C. M.; Leduc, S. K.; Umberger, D. E. (Principal Investigator)
1979-01-01
The advantages and disadvantages of the casual (phenological, dynamic, physiological), statistical regression, and analog approaches to modeling for grain yield are examined. Given LACIE's primary goal of estimating wheat production for the large areas of eight major wheat-growing regions, the statistical regression approach of correlating historical yield and climate data offered the Center for Climatic and Environmental Assessment the greatest potential return within the constraints of time and data sources. The basic equation for the first generation wheat-yield model is given. Topics discussed include truncation, trend variable, selection of weather variables, episodic events, strata selection, operational data flow, weighting, and model results.
JT-60 configuration parameters for feedback control determined by regression analysis
NASA Astrophysics Data System (ADS)
Matsukawa, Makoto; Hosogane, Nobuyuki; Ninomiya, Hiromasa
1991-12-01
The stepwise regression procedure was applied to obtain measurement formulas for equilibrium parameters used in the feedback control of JT-60. This procedure automatically selects variables necessary for the measurements, and selects a set of variables which are not likely to be picked up by physical considerations. Regression equations with stable and small multicollinearity were obtained and it was experimentally confirmed that the measurement formulas obtained through this procedure were accurate enough to be applicable to the feedback control of plasma configurations in JT-60.
2013-01-01
Background A tandem technique of hard equipment is often used for the chemical analysis of a single cell to first isolate and then detect the wanted identities. The first part is the separation of wanted chemicals from the bulk of a cell; the second part is the actual detection of the important identities. To identify the key structural modifications around ligand binding, the present study aims to develop a counterpart of tandem technique for cheminformatics. A statistical regression and its outliers act as a computational technique for separation. Results A PPARγ (peroxisome proliferator-activated receptor gamma) agonist cellular system was subjected to such an investigation. Results show that this tandem regression-outlier analysis, or the prioritization of the context equations tagged with features of the outliers, is an effective regression technique of cheminformatics to detect key structural modifications, as well as their tendency of impact to ligand binding. Conclusions The key structural modifications around ligand binding are effectively extracted or characterized out of cellular reactions. This is because molecular binding is the paramount factor in such ligand cellular system and key structural modifications around ligand binding are expected to create outliers. Therefore, such outliers can be captured by this tandem regression-outlier analysis. PMID:23627990
Huntley, J D; Gould, R L; Liu, K; Smith, M; Howard, R J
2015-01-01
Objectives To review the efficacy of cognitive interventions on improving general cognition in dementia. Method Online literature databases and trial registers, previous systematic reviews and leading journals were searched for relevant randomised controlled trials. A systematic review, random-effects meta-analyses and meta-regression were conducted. Cognitive interventions were categorised as: cognitive stimulation (CS), involving a range of social and cognitive activities to stimulate multiple cognitive domains; cognitive training (CT), involving repeated practice of standardised tasks targeting a specific cognitive function; cognitive rehabilitation (CR), which takes a person-centred approach to target impaired function; or mixed CT and stimulation (MCTS). Separate analyses were conducted for general cognitive outcome measures and for studies using ‘active’ (designed to control for non-specific therapeutic effects) and non-active (minimal or no intervention) control groups. Results 33 studies were included. Significant positive effect sizes (Hedges’ g) were found for CS with the mini-mental state examination (MMSE) (g=0.51, 95% CI 0.29 to 0.69; p<0.001) compared to non-active controls and (g=0.35, 95% CI 0.06 to 0.65; p=0.019) compared to active controls. Significant benefit was also seen with the Alzheimer's disease Assessment Scale-Cognition (ADAS-Cog) (g=−0.26, 95% CI −0.445 to −0.08; p=0.005). There was no evidence that CT or MCTS produced significant improvements on general cognition outcomes and not enough CR studies for meta-analysis. The lowest accepted minimum clinically important difference was reached in 11/17 CS studies for the MMSE, but only 2/9 studies for the ADAS-Cog. Additionally, 95% prediction intervals suggested that although statistically significant, CS may not lead to benefits on the ADAS-Cog in all clinical settings. Conclusions CS improves scores on MMSE and ADAS-Cog in dementia, but benefits on the ADAS-Cog are generally
Genetic analysis of carcass traits in beef cattle using random regression models.
Englishby, T M; Banos, G; Moore, K L; Coffey, M P; Evans, R D; Berry, D P
2016-04-01
Livestock mature at different rates depending, in part, on their genetic merit; therefore, the optimal age at slaughter for progeny of certain sires may differ. The objective of the present study was to examine sire-level genetic profiles for carcass weight, carcass conformation, and carcass fat in cattle of multiple beef and dairy breeds, including crossbreeds. Slaughter records from 126,214 heifers and 124,641 steers aged between 360 and 1,200 d and from 86,089 young bulls aged between 360 and 720 d were used in the analysis; animals were from 15,127 sires. Variance components for each trait across age at slaughter were generated using sire random regression models that included quadratic polynomials for fixed and random effects; heterogeneous residual variances were assumed across ages. Heritability estimates across genders ranged from 0.08 (±0.02) to 0.34 (±0.02) for carcass weight, from 0.24 (±0.02) to 0.42 (±0.01) for conformation, and from 0.16 (±0.03) to 0.40 (±0.02) for fat score. Genetic correlations within each trait across ages weakened as the interval between ages compared lengthened but were all >0.64, suggesting a similar genetic background for each trait across different ages. Eigenvalues and eigenfunctions of the additive genetic covariance matrix revealed genetic variability among animals in their growth profiles for carcass traits, although most of the genetic variability was associated with the height of the growth profile. At the same age, a positive genetic correlation (0.60 to 0.78; SE ranged from 0.01 to 0.04) existed between carcass weight and conformation, whereas negative genetic correlations existed between fatness and both conformation (-0.46 to 0.08; SE ranged from 0.02 to 0.09) and carcass weight (-0.48 to -0.16; SE ranged from 0.02 to 0.14) at the same age. The estimated genetic parameters in the present study indicate genetic variability in the growth trajectory in cattle, which can be exploited through breeding programs and
NASA Astrophysics Data System (ADS)
Tomczyk, Aleksandra; Ewertowski, Marek; White, Piran; Kasprzak, Leszek
2016-04-01
The dual role of many Protected Natural Areas in providing benefits for both conservation and recreation poses challenges for management. Although recreation-based damage to ecosystems can occur very quickly, restoration can take many years. The protection of conservation interests at the same as providing for recreation requires decisions to be made about how to prioritise and direct management actions. Trails are commonly used to divert visitors from the most important areas of a site, but high visitor pressure can lead to increases in trail width and a concomitant increase in soil erosion. Here we use detailed field data on condition of recreational trails in Gorce National Park, Poland, as the basis for a regression tree analysis to determine the factors influencing trail deterioration, and link specific trail impacts with environmental, use related and managerial factors. We distinguished 12 types of trails, characterised by four levels of degradation: (1) trails with an acceptable level of degradation; (2) threatened trails; (3) damaged trails; and (4) heavily damaged trails. Damaged trails were the most vulnerable of all trails and should be prioritised for appropriate conservation and restoration. We also proposed five types of monitoring of recreational trail conditions: (1) rapid inventory of negative impacts; (2) monitoring visitor numbers and variation in type of use; (3) change-oriented monitoring focusing on sections of trail which were subjected to changes in type or level of use or subjected to extreme weather events; (4) monitoring of dynamics of trail conditions; and (5) full assessment of trail conditions, to be carried out every 10-15 years. The application of the proposed framework can enhance the ability of Park managers to prioritise their trail management activities, enhancing trail conditions and visitor safety, while minimising adverse impacts on the conservation value of the ecosystem. A.M.T. was supported by the Polish Ministry of
Buston, Peter M; Elith, Jane
2011-05-01
1. Central questions of behavioural and evolutionary ecology are what factors influence the reproductive success of dominant breeders and subordinate nonbreeders within animal societies? A complete understanding of any society requires that these questions be answered for all individuals. 2. The clown anemonefish, Amphiprion percula, forms simple societies that live in close association with sea anemones, Heteractis magnifica. Here, we use data from a well-studied population of A. percula to determine the major predictors of reproductive success of dominant pairs in this species. 3. We analyse the effect of multiple predictors on four components of reproductive success, using a relatively new technique from the field of statistical learning: boosted regression trees (BRTs). BRTs have the potential to model complex relationships in ways that give powerful insight. 4. We show that the reproductive success of dominant pairs is unrelated to the presence, number or phenotype of nonbreeders. This is consistent with the observation that nonbreeders do not help or hinder breeders in any way, confirming and extending the results of a previous study. 5. Primarily, reproductive success is negatively related to male growth and positively related to breeding experience. It is likely that these effects are interrelated because males that grow a lot have little breeding experience. These effects are indicative of a trade-off between male growth and parental investment. 6. Secondarily, reproductive success is positively related to female growth and size. In this population, female size is positively related to group size and anemone size, also. These positive correlations among traits likely are caused by variation in site quality and are suggestive of a silver-spoon effect. 7. Noteworthily, whereas reproductive success is positively related to female size, it is unrelated to male size. This observation provides support for the size advantage hypothesis for sex change: both
Witt, Katrina; van Dorn, Richard; Fazel, Seena
2013-01-01
Background Previous reviews on risk and protective factors for violence in psychosis have produced contrasting findings. There is therefore a need to clarify the direction and strength of association of risk and protective factors for violent outcomes in individuals with psychosis. Method We conducted a systematic review and meta-analysis using 6 electronic databases (CINAHL, EBSCO, EMBASE, Global Health, PsycINFO, PUBMED) and Google Scholar. Studies were identified that reported factors associated with violence in adults diagnosed, using DSM or ICD criteria, with schizophrenia and other psychoses. We considered non-English language studies and dissertations. Risk and protective factors were meta-analysed if reported in three or more primary studies. Meta-regression examined sources of heterogeneity. A novel meta-epidemiological approach was used to group similar risk factors into one of 10 domains. Sub-group analyses were then used to investigate whether risk domains differed for studies reporting severe violence (rather than aggression or hostility) and studies based in inpatient (rather than outpatient) settings. Findings There were 110 eligible studies reporting on 45,533 individuals, 8,439 (18.5%) of whom were violent. A total of 39,995 (87.8%) were diagnosed with schizophrenia, 209 (0.4%) were diagnosed with bipolar disorder, and 5,329 (11.8%) were diagnosed with other psychoses. Dynamic (or modifiable) risk factors included hostile behaviour, recent drug misuse, non-adherence with psychological therapies (p values<0.001), higher poor impulse control scores, recent substance misuse, recent alcohol misuse (p values<0.01), and non-adherence with medication (p value <0.05). We also examined a number of static factors, the strongest of which were criminal history factors. When restricting outcomes to severe violence, these associations did not change materially. In studies investigating inpatient violence, associations differed in strength but not direction
ERIC Educational Resources Information Center
Wiley, Kristofor R.
2013-01-01
Many of the social and emotional needs that have historically been associated with gifted students have been questioned on the basis of recent empirical evidence. Research on the topic, however, is often limited by sample size, selection bias, or definition. This study addressed these limitations by applying linear regression methodology to data…
ERIC Educational Resources Information Center
Luna, Andrew L.; Brennan, Kelly A.
2009-01-01
This study uses a regression model to determine if a significant difference exists between the actual budget allocation that an academic department received and the model's predicted budget allocation for that same department. Budget data from a Southeastern Master's/Comprehensive state university were used as the dependent variable, and the…
Using Robust Variance Estimation to Combine Multiple Regression Estimates with Meta-Analysis
ERIC Educational Resources Information Center
Williams, Ryan
2013-01-01
The purpose of this study was to explore the use of robust variance estimation for combining commonly specified multiple regression models and for combining sample-dependent focal slope estimates from diversely specified models. The proposed estimator obviates traditionally required information about the covariance structure of the dependent…
Multiple Regression Analysis of Factors that May Influence Middle School Science Scores
ERIC Educational Resources Information Center
Glover, Judith
2012-01-01
The purpose of this quantitative multiple regression study was to determine whether a relationship existed between Maryland State Assessment (MSA) reading scores, MSA math scores, gender, ethnicity, age, and MSA science scores. Also examined was if MSA reading scores, MSA math scores, gender, ethnicity, and age can be used in combination or alone…
ERIC Educational Resources Information Center
Baylor, Carolyn; Yorkston, Kathryn; Bamer, Alyssa; Britton, Deanna; Amtmann, Dagmar
2010-01-01
Purpose: To explore variables associated with self-reported communicative participation in a sample (n = 498) of community-dwelling adults with multiple sclerosis (MS). Method: A battery of questionnaires was administered online or on paper per participant preference. Data were analyzed using multiple linear backward stepwise regression. The…
Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert Manfred
2013-01-01
A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.
ERIC Educational Resources Information Center
Jennings, Earl
1995-01-01
This textbook is intended for a course in applied regression for upper-division undergraduates and first-year graduate students and is designed to require only mathematics at a high school level. Teachers should find it a useful resource. (SLD)
ERIC Educational Resources Information Center
Cohen, Ayala; Nahum-Shani, Inbal; Doveh, Etti
2010-01-01
In their seminal paper, Edwards and Parry (1993) presented the polynomial regression as a better alternative to applying difference score in the study of congruence. Although this method is increasingly applied in congruence research, its complexity relative to other methods for assessing congruence (e.g., difference score methods) was one of the…
Multiple linear regression models are often used to predict levels of fecal indicator bacteria (FIB) in recreational swimming waters based on independent variables (IVs) such as meteorologic, hydrodynamic, and water-quality measures. The IVs used for these analyses are traditiona...
Computation of major solute concentrations and loads in German rivers using regression analysis.
Steele, T.D.
1980-01-01
Regression functions between concentrations of several inorganic solutes and specific conductance and between specific conductance and stream discharge were derived from intermittent samples collected for 2 rivers in West Germany. These functions, in conjunction with daily records of streamflow, were used to determine monthly and annual solute loadings. -from Author
ERIC Educational Resources Information Center
Cohen, Ira L.; Liu, Xudong; Hudson, Melissa; Gillis, Jennifer; Cavalari, Rachel N. S.; Romanczyk, Raymond G.; Karmel, Bernard Z.; Gardner, Judith M.
2016-01-01
In order to improve discrimination accuracy between Autism Spectrum Disorder (ASD) and similar neurodevelopmental disorders, a data mining procedure, Classification and Regression Trees (CART), was used on a large multi-site sample of PDD Behavior Inventory (PDDBI) forms on children with and without ASD. Discrimination accuracy exceeded 80%,…
On the Usefulness of a Multilevel Logistic Regression Approach to Person-Fit Analysis
ERIC Educational Resources Information Center
Conijn, Judith M.; Emons, Wilco H. M.; van Assen, Marcel A. L. M.; Sijtsma, Klaas
2011-01-01
The logistic person response function (PRF) models the probability of a correct response as a function of the item locations. Reise (2000) proposed to use the slope parameter of the logistic PRF as a person-fit measure. He reformulated the logistic PRF model as a multilevel logistic regression model and estimated the PRF parameters from this…
NASA Astrophysics Data System (ADS)
Keat, Sim Chong; Chun, Beh Boon; San, Lim Hwee; Jafri, Mohd Zubir Mat
2015-04-01
Climate change due to carbon dioxide (CO2) emissions is one of the most complex challenges threatening our planet. This issue considered as a great and international concern that primary attributed from different fossil fuels. In this paper, regression model is used for analyzing the causal relationship among CO2 emissions based on the energy consumption in Malaysia using time series data for the period of 1980-2010. The equations were developed using regression model based on the eight major sources that contribute to the CO2 emissions such as non energy, Liquefied Petroleum Gas (LPG), diesel, kerosene, refinery gas, Aviation Turbine Fuel (ATF) and Aviation Gasoline (AV Gas), fuel oil and motor petrol. The related data partly used for predict the regression model (1980-2000) and partly used for validate the regression model (2001-2010). The results of the prediction model with the measured data showed a high correlation coefficient (R2=0.9544), indicating the model's accuracy and efficiency. These results are accurate and can be used in early warning of the population to comply with air quality standards.
A Latent Class Regression Analysis of Men's Conformity to Masculine Norms and Psychological Distress
ERIC Educational Resources Information Center
Wong, Y. Joel; Owen, Jesse; Shea, Munyi
2012-01-01
How are specific dimensions of masculinity related to psychological distress in specific groups of men? To address this question, the authors used latent class regression to assess the optimal number of latent classes that explained differential relationships between conformity to masculine norms and psychological distress in a racially diverse…
Risk Factors of Falls in Community-Dwelling Older Adults: Logistic Regression Tree Analysis
ERIC Educational Resources Information Center
Yamashita, Takashi; Noe, Douglas A.; Bailer, A. John
2012-01-01
Purpose of the Study: A novel logistic regression tree-based method was applied to identify fall risk factors and possible interaction effects of those risk factors. Design and Methods: A nationally representative sample of American older adults aged 65 years and older (N = 9,592) in the Health and Retirement Study 2004 and 2006 modules was used.…
Watanabe, K I; Ohama, T
2001-01-01
In the unicellular green alga, Chlamydomonas reinhardtii, cytochrome oxidase subunit 2 (cox2) and 3 (cox3) genes are missing from the mitochondrial genome. We isolated and sequenced a BAC clone that carries the whole cox3 gene and its corresponding cDNA. Almost the entire cox2 gene and its cDNA were also determined. Comparison of the genomic and the corresponding cDNA sequences revealed that the cox3 gene contains as many as nine spliceosomal introns and that cox2 bears six introns. Putative mitochondria targeting signals were predicted at each N terminal of the cox genes. These spliceosomal introns were typical GT-AG-type introns, which are very common not only in Chlamydomonas nuclear genes but also in diverse eukaryotic taxa. We found no particular distinguishing features in the cox introns. Comparative analysis of these genes with the various mitochondrial genes showed that 8 of the 15 introns were interrupting the conserved mature protein coding segments, while the other 7 introns were located in the N-terminal target peptide regions. Phylogenetic analysis of the evolutionary position of C. reinhardtii in Chlorophyta was carried out and the existence of the cox2 and cox3 genes in the mitochondrial genome was superimposed in the tree. This analysis clearly shows that these cox genes were relocated during the evolution of Chlorophyceae. It is apparent that long before the estimated period of relocation of these mitochondrial genes, the cytosol had lost the splicing ability for group II introns. Therefore, at least eight introns located in the mature protein coding region cannot be the direct descendant of group II introns. Here, we conclude that the presence of these introns is due to the invasion of spliceosomal introns, which occurred during the evolution of Chlorophyceae. This finding provides concrete evidence supporting the "intron-late" model, which rests largely on the mobility of spliceosomal introns. PMID:11675593
Criteria for the use of regression analysis for remote sensing of sediment and pollutants
NASA Technical Reports Server (NTRS)
Whitlock, C. H.; Kuo, C. Y.; Lecroy, S. R.
1982-01-01
An examination of limitations, requirements, and precision of the linear multiple-regression technique for quantification of marine environmental parameters is conducted. Both environmental and optical physics conditions have been defined for which an exact solution to the signal response equations is of the same form as the multiple regression equation. Various statistical parameters are examined to define a criteria for selection of an unbiased fit when upwelled radiance values contain error and are correlated with each other. Field experimental data are examined to define data smoothing requirements in order to satisfy the criteria of Daniel and Wood (1971). Recommendations are made concerning improved selection of ground-truth locations to maximize variance and to minimize physical errors associated with the remote sensing experiment.
Lee, Soo Min; Lee, Jae-Won
2014-11-01
In this study, the optimal conditions for biomass torrefaction were determined by comparing the gain of energy content to the weight loss of biomass from the final products. Torrefaction experiments were performed at temperatures ranging from 220 to 280°C using 20-80min reaction times. Polynomial regression models ranging from the 1st to the 3rd order were used to determine a relationship between the severity factor (SF) and calorific value or weight loss. The intersection of two regression models for calorific value and weight loss was determined and assumed to be the optimized SF. The optimized SFs on each biomass ranged from 6.056 to 6.372. Optimized torrefaction conditions were determined at various reaction times of 15, 30, and 60min. The average optimized temperature was 248.55°C in the studied biomass when torrefaction was performed for 60min.
Predicting Student Success on the Texas Chemistry STAAR Test: A Logistic Regression Analysis
ERIC Educational Resources Information Center
Johnson, William L.; Johnson, Annabel M.; Johnson, Jared
2012-01-01
Background: The context is the new Texas STAAR end-of-course testing program. Purpose: The authors developed a logistic regression model to predict who would pass-or-fail the new Texas chemistry STAAR end-of-course exam. Setting: Robert E. Lee High School (5A) with an enrollment of 2700 students, Tyler, Texas. Date of the study was the 2011-2012…
Sanford, Ward E.; Nelms, David L.; Pope, Jason P.; Selnick, David L.
2012-01-01
This study by the U.S. Geological Survey, prepared in cooperation with the Virginia Department of Environmental Quality, quantifies the components of the hydrologic cycle across the Commonwealth of Virginia. Long-term, mean fluxes were calculated for precipitation, surface runoff, infiltration, total evapotranspiration (ET), riparian ET, recharge, base flow (or groundwater discharge) and net total outflow. Fluxes of these components were first estimated on a number of real-time-gaged watersheds across Virginia. Specific conductance was used to distinguish and separate surface runoff from base flow. Specific-conductance data were collected every 15 minutes at 75 real-time gages for approximately 18 months between March 2007 and August 2008. Precipitation was estimated for 1971–2000 using PRISM climate data. Precipitation and temperature from the PRISM data were used to develop a regression-based relation to estimate total ET. The proportion of watershed precipitation that becomes surface runoff was related to physiographic province and rock type in a runoff regression equation. Component flux estimates from the watersheds were transferred to flux estimates for counties and independent cities using the ET and runoff regression equations. Only 48 of the 75 watersheds yielded sufficient data, and data from these 48 were used in the final runoff regression equation. The base-flow proportion for the 48 watersheds averaged 72 percent using specific conductance, a value that was substantially higher than the 61 percent average calculated using a graphical-separation technique (the USGS program PART). Final results for the study are presented as component flux estimates for all counties and independent cities in Virginia.
GIS-assisted regression analysis to identify sources of selenium in streams
See, Randolph B.; Naftz, David L.; Qualls, Charles L.
1992-01-01
Using a geographic information system, a regression model has been developed to identify and to assess potential sources of selenium in the Kendrick Reclamation Project Area, Wyoming. A variety of spatially distributed factors was examined to determine which factors are most likely to affect selenium discharge in tributaries to the North Platte River. Areas of Upper Cretaceous Cody Shale and Quaternary alluvial deposits and irrigated land, length of irrigation canals, and boundaries of hydrologic subbasins of the major tributaries to the North Platte River were digitized and stored in a geographic information system. Selenium concentrations in samples of soil, plant material, ground water, and surface water were determined and evaluated. The location of all sampling sites was digitized and stored in the geographic information system, together with the selenium concentrations in samples. A regression model was developed using stepwise multiple regression of median selenium discharges on the physical and chemical characteristics of hydrologic subbasins. Results indicate that the intensity of irrigation in a hydrologic subbasin, as determined by area of irrigated land and length of irrigation delivery canals, accounts for the largest variation in median selenium discharges among subbasins. Tributaries draining hydrologic subbasins with greater intensity of irrigation result in greater selenium discharges to the North Platte River than do tributaries draining subbasins with lesser intensity of irrigation.
Capacitance Regression Modelling Analysis on Latex from Selected Rubber Tree Clones
NASA Astrophysics Data System (ADS)
Rosli, A. D.; Hashim, H.; Khairuzzaman, N. A.; Mohd Sampian, A. F.; Baharudin, R.; Abdullah, N. E.; Sulaiman, M. S.; Kamaru'zzaman, M.
2015-11-01
This paper investigates the capacitance regression modelling performance of latex for various rubber tree clones, namely clone 2002, 2008, 2014 and 3001. Conventionally, the rubber tree clones identification are based on observation towards tree features such as shape of leaf, trunk, branching habit and pattern of seeds texture. The former method requires expert persons and very time-consuming. Currently, there is no sensing device based on electrical properties that can be employed to measure different clones from latex samples. Hence, with a hypothesis that the dielectric constant of each clone varies, this paper discusses the development of a capacitance sensor via Capacitance Comparison Bridge (known as capacitance sensor) to measure an output voltage of different latex samples. The proposed sensor is initially tested with 30ml of latex sample prior to gradually addition of dilution water. The output voltage and capacitance obtained from the test are recorded and analyzed using Simple Linear Regression (SLR) model. This work outcome infers that latex clone of 2002 has produced the highest and reliable linear regression line with determination coefficient of 91.24%. In addition, the study also found that the capacitive elements in latex samples deteriorate if it is diluted with higher volume of water.
Unification of regression-based methods for the analysis of natural selection.
Morrissey, Michael B; Sakrejda, Krzysztof
2013-07-01
Regression analyses are central to characterization of the form and strength of natural selection in nature. Two common analyses that are currently used to characterize selection are (1) least squares-based approximation of the individual relative fitness surface for the purpose of obtaining quantitatively useful selection gradients, and (2) spline-based estimation of (absolute) fitness functions to obtain flexible inference of the shape of functions by which fitness and phenotype are related. These two sets of methodologies are often implemented in parallel to provide complementary inferences of the form of natural selection. We unify these two analyses, providing a method whereby selection gradients can be obtained for a given observed distribution of phenotype and characterization of a function relating phenotype to fitness. The method allows quantitatively useful selection gradients to be obtained from analyses of selection that adequately model nonnormal distributions of fitness, and provides unification of the two previously separate regression-based fitness analyses. We demonstrate the method by calculating directional and quadratic selection gradients associated with a smooth regression-based generalized additive model of the relationship between neonatal survival and the phenotypic traits of gestation length and birth mass in humans.
NASA Astrophysics Data System (ADS)
Schlechtingen, Meik; Ferreira Santos, Ilmar
2011-07-01
This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a normal behavior model is compared to two artificial neural network based approaches, which are a full signal reconstruction and an autoregressive normal behavior model. Based on a real time series containing two generator bearing damages the capabilities of identifying the incipient fault prior to the actual failure are investigated. The period after the first bearing damage is used to develop the three normal behavior models. The developed or trained models are used to investigate how the second damage manifests in the prediction error. Furthermore the full signal reconstruction and the autoregressive approach are applied to further real time series containing gearbox bearing damages and stator temperature anomalies. The comparison revealed all three models being capable of detecting incipient faults. However, they differ in the effort required for model development and the remaining operational time after first indication of damage. The general nonlinear neural network approaches outperform the regression model. The remaining seasonality in the regression model prediction error makes it difficult to detect abnormality and leads to increased alarm levels and thus a shorter remaining operational period. For the bearing damages and the stator anomalies under investigation the full signal reconstruction neural network gave the best fault visibility and thus led to the highest confidence level.
Gmur, Stephan; Vogt, Daniel; Zabowski, Darlene; Moskal, L. Monika
2012-01-01
The characterization of soil attributes using hyperspectral sensors has revealed patterns in soil spectra that are known to respond to mineral composition, organic matter, soil moisture and particle size distribution. Soil samples from different soil horizons of replicated soil series from sites located within Washington and Oregon were analyzed with the FieldSpec Spectroradiometer to measure their spectral signatures across the electromagnetic range of 400 to 1,000 nm. Similarity rankings of individual soil samples reveal differences between replicate series as well as samples within the same replicate series. Using classification and regression tree statistical methods, regression trees were fitted to each spectral response using concentrations of nitrogen, carbon, carbonate and organic matter as the response variables. Statistics resulting from fitted trees were: nitrogen R2 0.91 (p < 0.01) at 403, 470, 687, and 846 nm spectral band widths, carbonate R2 0.95 (p < 0.01) at 531 and 898 nm band widths, total carbon R2 0.93 (p < 0.01) at 400, 409, 441 and 907 nm band widths, and organic matter R2 0.98 (p < 0.01) at 300, 400, 441, 832 and 907 nm band widths. Use of the 400 to 1,000 nm electromagnetic range utilizing regression trees provided a powerful, rapid and inexpensive method for assessing nitrogen, carbon, carbonate and organic matter for upper soil horizons in a nondestructive method. PMID:23112620
iNOS signaling interacts with COX-2 pathway in colonic fibroblasts.
Zhu, Yingting; Zhu, Min; Lance, Peter
2012-10-01
COX-2 and iNOS are two major inflammatory mediators implicated in colorectal inflammation and cancer. Previously, the role of colorectal fibroblasts involved in regulation of COX-2 and iNOS expression was largely ignored. In addition, the combined interaction of COX-2 and iNOS signalings and their significance in the progression of colorectal inflammation and cancer within the fibroblasts have received little investigation. To address those issues, we investigated the role of colonic fibroblasts in the regulation of COX-2 and iNOS gene expression, and explored possible mechanisms of interaction between COX-2 and iNOS signalings using a colonic CCD-18Co fibroblast line and LPS, a potential stimulator of COX-2 and iNOS. Our results clearly demonstrated that LPS activated COX-2 gene expression and enhanced PGE(2) production, stimulated iNOS gene expression and promoted NO production in the fibroblasts. Interestingly, activation of COX-2 signaling by LPS was not involved in activation of iNOS signaling, while activation of iNOS signaling by LPS contributed in part to activation of COX-2 signaling. Further analysis indicated that PKC plays a major role in the activation and interaction of COX-2 and iNOS signalings induced by LPS in the fibroblasts. PMID:22683859
Analysis of extreme drinking in patients with alcohol dependence using Pareto regression.
Das, Sourish; Harel, Ofer; Dey, Dipak K; Covault, Jonathan; Kranzler, Henry R
2010-05-20
We developed a novel Pareto regression model with an unknown shape parameter to analyze extreme drinking in patients with Alcohol Dependence (AD). We used the generalized linear model (GLM) framework and the log-link to include the covariate information through the scale parameter of the generalized Pareto distribution. We proposed a Bayesian method based on Ridge prior and Zellner's g-prior for the regression coefficients. Simulation study indicated that the proposed Bayesian method performs better than the existing likelihood-based inference for the Pareto regression.We examined two issues of importance in the study of AD. First, we tested whether a single nucleotide polymorphism within GABRA2 gene, which encodes a subunit of the GABA(A) receptor, and that has been associated with AD, influences 'extreme' alcohol intake and second, the efficacy of three psychotherapies for alcoholism in treating extreme drinking behavior. We found an association between extreme drinking behavior and GABRA2. We also found that, at baseline, men with a high-risk GABRA2 allele had a significantly higher probability of extreme drinking than men with no high-risk allele. However, men with a high-risk allele responded to the therapy better than those with two copies of the low-risk allele. Women with high-risk alleles also responded to the therapy better than those with two copies of the low-risk allele, while women who received the cognitive behavioral therapy had better outcomes than those receiving either of the other two therapies. Among men, motivational enhancement therapy was the best for the treatment of the extreme drinking behavior.
ERIC Educational Resources Information Center
Matson, Johnny L.; Kozlowski, Alison M.
2010-01-01
Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…
Chi, Peter; Aras, Radha; Martin, Katie; Favero, Carlita
2016-05-15
Fetal Alcohol Spectrum Disorders (FASD) collectively describes the constellation of effects resulting from human alcohol consumption during pregnancy. Even with public awareness, the incidence of FASD is estimated to be upwards of 5% in the general population and is becoming a global health problem. The physical, cognitive, and behavioral impairments of FASD are recapitulated in animal models. Recently rodent models utilizing voluntary drinking paradigms have been developed that accurately reflect moderate consumption, which makes up the majority of FASD cases. The range in severity of FASD characteristics reflects the frequency, dose, developmental timing, and individual susceptibility to alcohol exposure. As most rodent models of FASD use C57BL/6 mice, there is a need to expand the stocks of mice studied in order to more fully understand the complex neurobiology of this disorder. To that end, we allowed pregnant Swiss Webster mice to voluntarily drink ethanol via the drinking in the dark (DID) paradigm throughout their gestation period. Ethanol exposure did not alter gestational outcomes as determined by no significant differences in maternal weight gain, maternal liquid consumption, litter size, or pup weight at birth or weaning. Despite seemingly normal gestation, ethanol-exposed offspring exhibit significantly altered timing to achieve developmental milestones (surface righting, cliff aversion, and open field traversal), as analyzed through mixed-effects Cox proportional hazards models. These results confirm Swiss Webster mice as a viable option to study the incidence and causes of ethanol-induced neurobehavioral alterations during development. Future studies in our laboratory will investigate the brain regions and molecules responsible for these behavioral changes.
Ordinal logistic regression analysis on the nutritional status of children in KarangKitri village
NASA Astrophysics Data System (ADS)
Ohyver, Margaretha; Yongharto, Kimmy Octavian
2015-09-01
Ordinal logistic regression is a statistical technique that can be used to describe the relationship between ordinal response variable with one or more independent variables. This method has been used in various fields including in the health field. In this research, ordinal logistic regression is used to describe the relationship between nutritional status of children with age, gender, height, and family status. Nutritional status of children in this research is divided into over nutrition, well nutrition, less nutrition, and malnutrition. The purpose for this research is to describe the characteristics of children in the KarangKitri Village and to determine the factors that influence the nutritional status of children in the KarangKitri village. There are three things that obtained from this research. First, there are still children who are not categorized as well nutritional status. Second, there are children who come from sufficient economic level which include in not normal status. Third, the factors that affect the nutritional level of children are age, family status, and height.
NASA Technical Reports Server (NTRS)
Patnaik, Surya N.; Guptill, James D.; Hopkins, Dale A.; Lavelle, Thomas M.
2000-01-01
The NASA Engine Performance Program (NEPP) can configure and analyze almost any type of gas turbine engine that can be generated through the interconnection of a set of standard physical components. In addition, the code can optimize engine performance by changing adjustable variables under a set of constraints. However, for engine cycle problems at certain operating points, the NEPP code can encounter difficulties: nonconvergence in the currently implemented Powell's optimization algorithm and deficiencies in the Newton-Raphson solver during engine balancing. A project was undertaken to correct these deficiencies. Nonconvergence was avoided through a cascade optimization strategy, and deficiencies associated with engine balancing were eliminated through neural network and linear regression methods. An approximation-interspersed cascade strategy was used to optimize the engine's operation over its flight envelope. Replacement of Powell's algorithm by the cascade strategy improved the optimization segment of the NEPP code. The performance of the linear regression and neural network methods as alternative engine analyzers was found to be satisfactory. This report considers two examples-a supersonic mixed-flow turbofan engine and a subsonic waverotor-topped engine-to illustrate the results, and it discusses insights gained from the improved version of the NEPP code.
NASA Astrophysics Data System (ADS)
Shortridge, J.; Guikema, S.; Zaitchik, B. F.
2015-12-01
In the past decade, machine-learning methods for empirical rainfall-runoff modeling have seen extensive development. However, the majority of research has focused on a small number of methods, such as artificial neural networks, while not considering other approaches for non-parametric regression that have been developed in recent years. These methods may be able to achieve comparable predictive accuracy to ANN's and more easily provide physical insights into the system of interest through evaluation of covariate influence. Additionally, these methods could provide a straightforward, computationally efficient way of evaluating climate change impacts in basins where data to support physical hydrologic models is limited. In this paper, we use multiple regression and machine-learning approaches to predict monthly streamflow in five highly-seasonal rivers in the highlands of Ethiopia. We find that generalized additive models, random forests, and cubist models achieve better predictive accuracy than ANNs in many basins assessed and are also able to outperform physical models developed for the same region. We discuss some challenges that could hinder the use of such models for climate impact assessment, such as biases resulting from model formulation and prediction under extreme climate conditions, and suggest methods for preventing and addressing these challenges. Finally, we demonstrate how predictor variable influence can be assessed to provide insights into the physical functioning of data-sparse watersheds.
Geddes, John; Freemantle, Nick; Harrison, Paul; Bebbington, Paul
2000-01-01
Objective To develop an evidence base for recommendations on the use of atypical antipsychotics for patients with schizophrenia. Design Systematic overview and meta-regression analyses of randomised controlled trials, as a basis for formal development of guidelines. Subjects 12 649 patients in 52 randomised trials comparing atypical antipsychotics (amisulpride, clozapine, olanzapine, quetiapine, risperidone, and sertindole) with conventional antipsychotics (usually haloperidol or chlorpromazine) or alternative atypical antipsychotics. Main outcome measures Overall symptom scores. Rate of drop out (as a proxy for tolerability) and of side effects, notably extrapyramidal side effects. Results For both symptom reduction and drop out, there was substantial heterogeneity between the results of trials, including those evaluating the same atypical antipsychotic and comparator drugs. Meta-regression suggested that dose of conventional antipsychotic explained the heterogeneity. When the dose was ⩽12 mg/day of haloperidol (or equivalent), atypical antipsychotics had no benefits in terms of efficacy or overall tolerability, but they still caused fewer extrapyramidal side effects. Conclusions There is no clear evidence that atypical antipsychotics are more effective or are better tolerated than conventional antipsychotics. Conventional antipsychotics should usually be used in the initial treatment of an episode of schizophrenia unless the patient has previously not responded to these drugs or has unacceptable extrapyramidal side effects. PMID:11099280
Zero-inflated models for regression analysis of count data: a study of growth and development.
Cheung, Yin Bin
2002-05-30
Poisson regression is widely used in medical studies, and can be extended to negative binomial regression to allow for heterogeneity. When there is an excess number of zero counts, a useful approach is to used a mixture model with a proportion P of subjects not at risk, and a proportion of 1--P at-risk subjects who take on outcome values following a Poisson or negative binomial distribution. Covariate effects can be incorporated into both components of the models. In child assessment, fine motor development is often measured by test items that involve a process of imitation and a process of fine motor exercise. One such developmental milestone is 'building a tower of cubes'. This study analyses the impact of foetal growth and postnatal somatic growth on this milestone, operationalized as the number of cubes and measured around the age of 22 months. It is shown that the two aspects of early growth may have different implications for imitation and fine motor dexterity. The usual approach of recording and analysing the milestone as a binary outcome, such as whether the child can build a tower of three cubes, may leave out important information.
Recurrent events and the exploding Cox model
Gjessing, Håkon K.; Røysland, Kjetil; Pena, Edsel A.; Aalen, Odd O.
2014-01-01
Counting process models have played an important role in survival and event history analysis for more than 30 years. Nevertheless, almost all models that are being used have a very simple structure. Analyzing recurrent events invites the application of more complex models with dynamic covariates. We discuss how to define valid models in such a setting. One has to check carefully that a suggested model is well defined as a stochastic process. We give conditions for this to hold. Some detailed discussion is presented in relation to a Cox type model, where the exponential structure combined with feedback lead to an exploding model. In general, counting process models with dynamic covariates can be formulated to avoid explosions. In particular, models with a linear feedback structure do not explode, making them useful tools in general modeling of recurrent events. PMID:20625827
ERIC Educational Resources Information Center
Phillips, Gary W.
The usefulness of path analysis as a means of better understanding various linear models is demonstrated. First, two linear models are presented in matrix form using linear structural relations (LISREL) notation. The two models, regression and factor analysis, are shown to be identical although the research question and data matrix to which these…
Fakayode, Sayo O; Mitchell, Breanna S; Pollard, David A
2014-08-01
Accurate understanding of analyte boiling points (BP) is of critical importance in gas chromatographic (GC) separation and crude oil refinery operation in petrochemical industries. This study reported the first combined use of GC separation and partial-least-square (PLS1) multivariate regression analysis of petrochemical structural activity relationship (SAR) for accurate BP determination of two commercially available (D3710 and MA VHP) calibration gas mix samples. The results of the BP determination using PLS1 multivariate regression were further compared with the results of traditional simulated distillation method of BP determination. The developed PLS1 regression was able to correctly predict analytes BP in D3710 and MA VHP calibration gas mix samples, with a root-mean-square-%-relative-error (RMS%RE) of 6.4%, and 10.8% respectively. In contrast, the overall RMS%RE of 32.9% and 40.4%, respectively obtained for BP determination in D3710 and MA VHP using a traditional simulated distillation method were approximately four times larger than the corresponding RMS%RE of BP prediction using MRA, demonstrating the better predictive ability of MRA. The reported method is rapid, robust, and promising, and can be potentially used routinely for fast analysis, pattern recognition, and analyte BP determination in petrochemical industries.
Mokhtari, Mehdi; Miri, Mohammad; Nikoonahad, Ali; Jalilian, Ali; Naserifar, Razi; Ghaffari, Hamid Reza; Kazembeigi, Farogh
2016-11-01
The aim of this study was to investigate the impact of the environmental factors on cutaneous leishmaniasis (CL) prevalence and morbidity in Ilam province, western Iran, as a known endemic area for this disease. Accurate locations of 3237 CL patients diagnosed from 2013 to 2015, their demographic information, and data of 17 potentially predictive environmental variables (PPEVs) were prepared to be used in Geographic Information System (GIS) and Land-Use Regression (LUR) analysis. The prevalence, risk, and predictive risk maps were provided using Inverse Distance Weighting (IDW) model in GIS software. Regression analysis was used to determine how environmental variables affect on CL prevalence. All maps and regression models were developed based on the annual and three-year average of the CL prevalence. The results showed that there was statistically significant relationship (P value≤0.05) between CL prevalence and 11 (64%) PPEVs which were elevation, population, rainfall, temperature, urban land use, poorland, dry farming, inceptisol and aridisol soils, and forest and irrigated lands. The highest probability of the CL prevalence was predicted in the west of the study area and frontier with Iraq. An inverse relationship was found between CL prevalence and environmental factors, including elevation, covering soil, rainfall, agricultural irrigation, and elevation while this relation was positive for temperature, urban land use, and population density. Environmental factors were found to be an important predictive variables for CL prevalence and should be considered in management strategies for CL control. PMID:27496622
Mokhtari, Mehdi; Miri, Mohammad; Nikoonahad, Ali; Jalilian, Ali; Naserifar, Razi; Ghaffari, Hamid Reza; Kazembeigi, Farogh
2016-11-01
The aim of this study was to investigate the impact of the environmental factors on cutaneous leishmaniasis (CL) prevalence and morbidity in Ilam province, western Iran, as a known endemic area for this disease. Accurate locations of 3237 CL patients diagnosed from 2013 to 2015, their demographic information, and data of 17 potentially predictive environmental variables (PPEVs) were prepared to be used in Geographic Information System (GIS) and Land-Use Regression (LUR) analysis. The prevalence, risk, and predictive risk maps were provided using Inverse Distance Weighting (IDW) model in GIS software. Regression analysis was used to determine how environmental variables affect on CL prevalence. All maps and regression models were developed based on the annual and three-year average of the CL prevalence. The results showed that there was statistically significant relationship (P value≤0.05) between CL prevalence and 11 (64%) PPEVs which were elevation, population, rainfall, temperature, urban land use, poorland, dry farming, inceptisol and aridisol soils, and forest and irrigated lands. The highest probability of the CL prevalence was predicted in the west of the study area and frontier with Iraq. An inverse relationship was found between CL prevalence and environmental factors, including elevation, covering soil, rainfall, agricultural irrigation, and elevation while this relation was positive for temperature, urban land use, and population density. Environmental factors were found to be an important predictive variables for CL prevalence and should be considered in management strategies for CL control.
Fakayode, Sayo O; Mitchell, Breanna S; Pollard, David A
2014-08-01
Accurate understanding of analyte boiling points (BP) is of critical importance in gas chromatographic (GC) separation and crude oil refinery operation in petrochemical industries. This study reported the first combined use of GC separation and partial-least-square (PLS1) multivariate regression analysis of petrochemical structural activity relationship (SAR) for accurate BP determination of two commercially available (D3710 and MA VHP) calibration gas mix samples. The results of the BP determination using PLS1 multivariate regression were further compared with the results of traditional simulated distillation method of BP determination. The developed PLS1 regression was able to correctly predict analytes BP in D3710 and MA VHP calibration gas mix samples, with a root-mean-square-%-relative-error (RMS%RE) of 6.4%, and 10.8% respectively. In contrast, the overall RMS%RE of 32.9% and 40.4%, respectively obtained for BP determination in D3710 and MA VHP using a traditional simulated distillation method were approximately four times larger than the corresponding RMS%RE of BP prediction using MRA, demonstrating the better predictive ability of MRA. The reported method is rapid, robust, and promising, and can be potentially used routinely for fast analysis, pattern recognition, and analyte BP determination in petrochemical industries. PMID:24881546
Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D.; Hood, Darryl B.; Skelton, Tyler
2014-01-01
The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire. PMID:23395953
Ghosh, Debarchana; Manson, Steven M
2008-01-01
In this paper, we present a hybrid approach, robust principal component geographically weighted regression (RPCGWR), in examining urbanization as a function of both extant urban land use and the effect of social and environmental factors in the Twin Cities Metropolitan Area (TCMA) of Minnesota. We used remotely sensed data to treat urbanization via the proxy of impervious surface. We then integrated two different methods, robust principal component analysis (RPCA) and geographically weighted regression (GWR) to create an innovative approach to model urbanization. The RPCGWR results show significant spatial heterogeneity in the relationships between proportion of impervious surface and the explanatory factors in the TCMA. We link this heterogeneity to the "sprawling" nature of urban land use that has moved outward from the core Twin Cities through to their suburbs and exurbs.
Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D; Hood, Darryl B; Skelton, Tyler
2013-02-01
The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire.
Kang, Seung-Wan; Byun, Gukdo; Park, Hun-Joon
2014-12-01
This paper presents empirical research into the relationship between leader-follower value congruence in social responsibility and the level of ethical satisfaction for employees in the workplace. 163 dyads were analyzed, each consisting of a team leader and an employee working at a large manufacturing company in South Korea. Following current methodological recommendations for congruence research, polynomial regression and response surface modeling methodologies were used to determine the effects of value congruence. Results indicate that leader-follower value congruence in social responsibility was positively related to the ethical satisfaction of employees. Furthermore, employees' ethical satisfaction was stronger when aligned with a leader with high social responsibility. The theoretical and practical implications are discussed.
Prediction of elemental creep. [steady state and cyclic data from regression analysis
NASA Technical Reports Server (NTRS)
Davis, J. W.; Rummler, D. R.
1975-01-01
Cyclic and steady-state creep tests were performed to provide data which were used to develop predictive equations. These equations, describing creep as a function of stress, temperature, and time, were developed through the use of a least squares regression analyses computer program for both the steady-state and cyclic data sets. Comparison of the data from the two types of tests, revealed that there was no significant difference between the cyclic and steady-state creep strains for the L-605 sheet under the experimental conditions investigated (for the same total time at load). Attempts to develop a single linear equation describing the combined steady-state and cyclic creep data resulted in standard errors of estimates higher than obtained for the individual data sets. A proposed approach to predict elemental creep in metals uses the cyclic creep equation and a computer program which applies strain and time hardening theories of creep accumulation.
Linear regression models and k-means clustering for statistical analysis of fNIRS data.
Bonomini, Viola; Zucchelli, Lucia; Re, Rebecca; Ieva, Francesca; Spinelli, Lorenzo; Contini, Davide; Paganoni, Anna; Torricelli, Alessandro
2015-02-01
We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets.
Linear regression models and k-means clustering for statistical analysis of fNIRS data
Bonomini, Viola; Zucchelli, Lucia; Re, Rebecca; Ieva, Francesca; Spinelli, Lorenzo; Contini, Davide; Paganoni, Anna; Torricelli, Alessandro
2015-01-01
We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets. PMID:25780751
NASA Astrophysics Data System (ADS)
Zhang, Mei; Fei, Yetai; Sheng, Li; Ma, Xiushui; Yang, Hong-tao
2008-12-01
The reasons why the coordinate measuring machine (CMM) dynamic error exists are complicate. And there are many elements which influence the error. So it is hard to build an accurate model. For the sake of attaining a model which not only avoided analyzing complex error sources and the interactions among them, but also solved the multiple colinearity among the variables. This paper adopted the Partial Least-Squares Regression (PLSR) to build model. The model takes 3D coordinates (X, Y, Z) and the moving velocity as the independent variable and takes the CMM dynamic error value as the dependent variable. The experimental results show that the model can be easily explained. At the same time the results show the magnitude and direction of the independent variable influencing the dependent variable.
Siordia, Carlos; Saenz, Joseph; Tom, Sarah E.
2014-01-01
Type II diabetes is a growing health problem in the United States. Understanding geographic variation in diabetes prevalence will inform where resources for management and prevention should be allocated. Investigations of the correlates of diabetes prevalence have largely ignored how spatial nonstationarity might play a role in the macro-level distribution of diabetes. This paper introduces the reader to the concept of spatial nonstationarity—variance in statistical relationships as a function of geographical location. Since spatial nonstationarity means different predictors can have varying effects on model outcomes, we make use of a geographically weighed regression to calculate correlates of diabetes as a function of geographic location. By doing so, we demonstrate an exploratory example in which the diabetes-poverty macro-level statistical relationship varies as a function of location. In particular, we provide evidence that when predicting macro-level diabetes prevalence, poverty is not always positively associated with diabetes PMID:25414731
Cohen, Ira L; Liu, Xudong; Hudson, Melissa; Gillis, Jennifer; Cavalari, Rachel N S; Romanczyk, Raymond G; Karmel, Bernard Z; Gardner, Judith M
2016-09-01
In order to improve discrimination accuracy between Autism Spectrum Disorder (ASD) and similar neurodevelopmental disorders, a data mining procedure, Classification and Regression Trees (CART), was used on a large multi-site sample of PDD Behavior Inventory (PDDBI) forms on children with and without ASD. Discrimination accuracy exceeded 80 %, generalized to an independent validation set, and generalized across age groups and sites, and agreed well with ADOS classifications. Parent PDDBIs yielded better results than teacher PDDBIs but, when CART predictions agreed across informants, sensitivity increased. Results also revealed three subtypes of ASD: minimally verbal, verbal, and atypical; and two, relatively common subtypes of non-ASD children: social pragmatic problems and good social skills. These subgroups corresponded to differences in behavior profiles and associated bio-medical findings. PMID:27318809
Exposure to diesel exhaust upregulates COX-2 expression in ApoE knockout mice
Bai, Ni; Tranfield, Erin M.; Kavanagh, Terrance J.; Kaufman, Joel D.; Rosenfeld, Michael E.; van Eeden, Stephan F.
2015-01-01
Introduction We have shown that diesel exhaust (DE) inhalation caused progression of atherosclerosis; however, the mechanisms are not fully understood. We hypothesize that exposure to DE upregulates cyclooxygenase (COX) expression and activity, which could play a role in DE-induced atherosclerosis. Methods ApoE knockout mice (30-week old) fed with regular chow were exposed to DE (at 200 μg/m3 of particulate matter) or filtered air (control) for 7 weeks (6 h/day, 5 days/week). The protein and mRNA expression of COX-1 and COX-2 were evaluated by immunohistochemistry analysis and quantitative real-time PCR, respectively. To examine COX activity, thoracic aortae were mounted in a wire myograph, and phenylephrine (PE)-stimulated vasoconstriction was measured with and without the presence of COX antagonists (indomethacin). COX-2 activity was further assessed by urine 2,3-dinor-6-keto PGF1α level, a major metabolite of prostacyclin I2 (PGI2). Results Immunohistochemistry analysis demonstrates that DE exposure enhanced COX-2 expression in both thoracic aorta (p < 0.01) and aortic root (p < 0.03), with no modification of COX-1 expression. The increased COX-2 expression was positively correlated with smooth muscle cell content in aortic lesions (R2 = 0.4081, p < 0.008). The fractional changes of maximal vasoconstriction in the presence of indomethacin was attenuated by 3-fold after DE exposure (p < 0.02). Urine 2,3-dinor-6-keto PGF1α level was 15-fold higher in DE group than the control (p < 0.007). The mRNA expression of COX-2 (p < 0.006) and PGI synthase (p < 0.02), but not COX-1, was significantly augmented after DE exposure. Conclusion We show that DE inhalation enhanced COX-2 expression, which is also associated with phenotypic changes of aortic lesion. PMID:22746401
Monsalve, Irene F.; Pérez, Alejandro; Molinaro, Nicola
2014-01-01
During language comprehension, semantic contextual information is used to generate expectations about upcoming items. This has been commonly studied through the N400 event-related potential (ERP), as a measure of facilitated lexical retrieval. However, the associative relationships in multi-word expressions (MWE) may enable the generation of a categorical expectation, leading to lexical retrieval before target word onset. Processing of the target word would thus reflect a target-identification mechanism, possibly indexed by a P3 ERP component. However, given their time overlap (200–500 ms post-stimulus onset), differentiating between N400/P3 ERP responses (averaged over multiple linguistically variable trials) is problematic. In the present study, we analyzed EEG data from a previous experiment, which compared ERP responses to highly expected words that were placed either in a MWE or a regular non-fixed compositional context, and to low predictability controls. We focused on oscillatory dynamics and regression analyses, in order to dissociate between the two contexts by modeling the electrophysiological response as a function of item-level parameters. A significant interaction between word position and condition was found in the regression model for power in a theta range (~7–9 Hz), providing evidence for the presence of qualitative differences between conditions. Power levels within this band were lower for MWE than compositional contexts when the target word appeared later on in the sentence, confirming that in the former lexical retrieval would have taken place before word onset. On the other hand, gamma-power (~50–70 Hz) was also modulated by predictability of the item in all conditions, which is interpreted as an index of a similar “matching” sub-step for both types of contexts, binding an expected representation and the external input. PMID:25161630
Gender roles and binge drinking among Latino emerging adults: a latent class regression analysis.
Vaughan, Ellen L; Wong, Y Joel; Middendorf, Katharine G
2014-09-01
Gender roles are often cited as a culturally specific predictor of drinking among Latino populations. This study used latent class regression to test the relationships between gender roles and binge drinking in a sample of Latino emerging adults. Participants were Latino emerging adults who participated in Wave III of the National Longitudinal Study of Adolescent Health (N = 2,442). A subsample of these participants (n = 660) completed the Bem Sex Role Inventory--Short. We conducted latent class regression using 3 dimensions of gender roles (femininity, social masculinity, and personal masculinity) to predict binge drinking. Results indicated a 3-class solution. In Class 1, the protective personal masculinity class, personal masculinity (e.g., being a leader, defending one's own beliefs) was associated with a reduction in the odds of binge drinking. In Class 2, the nonsignificant class, gender roles were not related to binge drinking. In Class 3, the mixed masculinity class, personal masculinity was associated with a reduction in the odds of binge drinking, whereas social masculinity (e.g., forceful, dominant) was associated with an increase in the odds of binge drinking. Post hoc analyses found that females, those born outside the United States, and those with greater English language usage were at greater odds of being in Class 1 (vs. Class 2). Males, those born outside the United States, and those with greater Spanish language usage were at greater odds of being in Class 3 (vs. Class 2). Directions for future research and implications for practice with Latino emerging adults are discussed.
Fenske, Nora; Burns, Jacob; Hothorn, Torsten; Rehfuess, Eva A.
2013-01-01
Background Most attempts to address undernutrition, responsible for one third of global child deaths, have fallen behind expectations. This suggests that the assumptions underlying current modelling and intervention practices should be revisited. Objective We undertook a comprehensive analysis of the determinants of child stunting in India, and explored whether the established focus on linear effects of single risks is appropriate. Design Using cross-sectional data for children aged 0–24 months from the Indian National Family Health Survey for 2005/2006, we populated an evidence-based diagram of immediate, intermediate and underlying determinants of stunting. We modelled linear, non-linear, spatial and age-varying effects of these determinants using additive quantile regression for four quantiles of the Z-score of standardized height-for-age and logistic regression for stunting and severe stunting. Results At least one variable within each of eleven groups of determinants was significantly associated with height-for-age in the 35% Z-score quantile regression. The non-modifiable risk factors child age and sex, and the protective factors household wealth, maternal education and BMI showed the largest effects. Being a twin or multiple birth was associated with dramatically decreased height-for-age. Maternal age, maternal BMI, birth order and number of antenatal visits influenced child stunting in non-linear ways. Findings across the four quantile and two logistic regression models were largely comparable. Conclusions Our analysis confirms the multifactorial nature of child stunting. It emphasizes the need to pursue a systems-based approach and to consider non-linear effects, and suggests that differential effects across the height-for-age distribution do not play a major role. PMID:24223839
NASA Technical Reports Server (NTRS)
Smith, Timothy D.; Steffen, Christopher J., Jr.; Yungster, Shaye; Keller, Dennis J.
1998-01-01
The all rocket mode of operation is shown to be a critical factor in the overall performance of a rocket based combined cycle (RBCC) vehicle. An axisymmetric RBCC engine was used to determine specific impulse efficiency values based upon both full flow and gas generator configurations. Design of experiments methodology was used to construct a test matrix and multiple linear regression analysis was used to build parametric models. The main parameters investigated in this study were: rocket chamber pressure, rocket exit area ratio, injected secondary flow, mixer-ejector inlet area, mixer-ejector area ratio, and mixer-ejector length-to-inlet diameter ratio. A perfect gas computational fluid dynamics analysis, using both the Spalart-Allmaras and k-omega turbulence models, was performed with the NPARC code to obtain values of vacuum specific impulse. Results from the multiple linear regression analysis showed that for both the full flow and gas generator configurations increasing mixer-ejector area ratio and rocket area ratio increase performance, while increasing mixer-ejector inlet area ratio and mixer-ejector length-to-diameter ratio decrease performance. Increasing injected secondary flow increased performance for the gas generator analysis, but was not statistically significant for the full flow analysis. Chamber pressure was found to be not statistically significant.
Schümberg, Katharina; Polyakova, Maryna; Steiner, Johann; Schroeter, Matthias L
2016-01-01
S100B has been linked to glial pathology in several psychiatric disorders. Previous studies found higher S100B serum levels in patients with schizophrenia compared to healthy controls, and a number of covariates influencing the size of this effect have been proposed in the literature. Here, we conducted a meta-analysis and meta-regression analysis on alterations of serum S100B in schizophrenia in comparison with healthy control subjects. The meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to guarantee a high quality and reproducibility. With strict inclusion criteria 19 original studies could be included in the quantitative meta-analysis, comprising a total of 766 patients and 607 healthy control subjects. The meta-analysis confirmed higher values of the glial serum marker S100B in schizophrenia if compared with control subjects. Meta-regression analyses revealed significant effects of illness duration and clinical symptomatology, in particular the total score of the Positive and Negative Syndrome Scale (PANSS), on serum S100B levels in schizophrenia. In sum, results confirm glial pathology in schizophrenia that is modulated by illness duration and related to clinical symptomatology. Further studies are needed to investigate mechanisms and mediating factors related to these findings.
The Cox3p assembly module of yeast cytochrome oxidase
Su, Chen-Hsien; McStay, Gavin P.; Tzagoloff, Alexander
2014-01-01
Yeast cytochrome oxidase (COX) was previously inferred to assemble from three modules, each containing one of the three mitochondrially encoded subunits and a different subset of the eight nuclear gene products that make up this respiratory complex. Pull-down assays of pulse-labeled mitochondria enabled us to characterize Cox3p subassemblies that behave as COX precursors and contain Cox4p, Cox7p, and Cox13p. Surprisingly, Cox4p is a constituent of two other complexes, one of which was previously proposed to be an intermediate of Cox1p biogenesis. This suggests that Cox4p, which contacts Cox1p and Cox3p in the holoenzyme, can be incorporated into COX by two alternative pathways. In addition to subunits of COX, some Cox3p intermediates contain Rcf1p, a protein associated with the supercomplex that stabilizes the interaction of COX with the bc1 (ubiquinol-cytochrome c reductase) complex. Finally, our results indicate that although assembly of the Cox1p module is not contingent on the presence of Cox3p, the converse is not true, as none of the Cox3p subassemblies were detected in a mutant blocked in translation of Cox1p. These studies support our proposal that Cox3p and Cox1p are separate assembly modules with unique compositions of ancillary factors and subunits derived from the nuclear genome. PMID:24478450
Wax, Y
1992-07-01
In epidemiologic studies, two forms of collinear relationships between the intake of major nutrients, high correlations, and the relative homogeneity of the diet, can yield unstable and not easily interpreted regression estimates for the effect of diet on disease risk. This paper presents tools for assessing the magnitude and source of the corresponding collinear relationships among the estimated coefficients for relative risk regression models. I show how to extend three tools (condition indices, variance decomposition proportions, and standard inflation factors) for diagnosing collinearity in standard regression models to likelihood and partial likelihood estimation for logistic and proportional hazards models. This extension is based on the analogue role of the information matrix in such analyses and the cross-product matrix in the standard linear model. I apply the methodology to relative risk models that relate crude intakes (on the log scale) and nutrient densities to breast cancer cases in the NHANES-I follow-up study. The three diagnostic tools provide complementary evidence of the existence of a strong collinearity in all models that is due largely to homogeneity of the population with respect to our risk scale for the crude intakes. The analysis suggests that the non-significant relative risks for the crude intakes in these models may be due to their involvement in collinear relationships, while the nonsignificant relative risks for the nutrient densities are far less affected by multicollinearity.
Wang, Chong; Sun, Qun; Wahab, Magd Abdel; Zhang, Xingyu; Xu, Limin
2015-09-01
Rotary cup brushes mounted on each side of a road sweeper undertake heavy debris removal tasks but the characteristics have not been well known until recently. A Finite Element (FE) model that can analyze brush deformation and predict brush characteristics have been developed to investigate the sweeping efficiency and to assist the controller design. However, the FE model requires large amount of CPU time to simulate each brush design and operating scenario, which may affect its applications in a real-time system. This study develops a mathematical regression model to summarize the FE modeled results. The complex brush load characteristic curves were statistically analyzed to quantify the effects of cross-section, length, mounting angle, displacement and rotational speed etc. The data were then fitted by a multiple variable regression model using the maximum likelihood method. The fitted results showed good agreement with the FE analysis results and experimental results, suggesting that the mathematical regression model may be directly used in a real-time system to predict characteristics of different brushes under varying operating conditions. The methodology may also be used in the design and optimization of rotary brush tools.
Zhao, Rui-Na; Zhang, Bo; Yang, Xiao; Jiang, Yu-Xin; Lai, Xing-Jian; Zhang, Xiao-Yan
2015-12-01
The purpose of the study described here was to determine specific characteristics of thyroid microcarcinoma (TMC) and explore the value of contrast-enhanced ultrasound (CEUS) combined with conventional ultrasound (US) in the diagnosis of TMC. Characteristics of 63 patients with TMC and 39 with benign sub-centimeter thyroid nodules were retrospectively analyzed. Multivariate logistic regression analysis was performed to determine independent risk factors. Four variables were included in the logistic regression models: age, shape, blood flow distribution and enhancement pattern. The area under the receiver operating characteristic curve was 0.919. With 0.113 selected as the cutoff value, sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 90.5%, 82.1%, 89.1%, 84.2% and 87.3%, respectively. Independent risk factors for TMC determined with the combination of CEUS and conventional US were age, shape, blood flow distribution and enhancement pattern. Age was negatively correlated with malignancy, whereas shape, blood flow distribution and enhancement pattern were positively correlated. The logistic regression model involving CEUS and conventional US was found to be effective in the diagnosis of sub-centimeter thyroid nodules.
Environmental sensitivity in relation to size and sex in birds: meta-regression analysis.
Jones, Kristopher S; Nakagawa, Shinichi; Sheldon, Ben C
2009-07-01
Studies of animals often report a greater sensitivity of one sex to poor rearing environments. However, it is unclear whether size differences associated with sex, sex itself, or other factors are responsible for differences in performance. While the greater nutritional requirement of the larger sex is a plausible explanation for increased sensitivity (i.e., size-dependent vulnerability), other hypotheses suggest that size-independent traits may have effects on the fitness of offspring (i.e., sex-dependent vulnerability). For example, the heterogametic sex may be more vulnerable to expression of sex-linked recessives in poor environments, or sex-specific phenotypes may have negative effects (e.g., increased testosterone in males). We examined support for these hypotheses through the use of meta-analytic techniques based on the published literature on avian species. Our results revealed small, nonsignificant effects for hypotheses of size- and sex-dependent susceptibilities alone. Application of a multivariate meta-analytic technique (meta-regression) suggests a joint influence of sexual size dimorphism and clutch size in explaining sex-specific patterns of vulnerability. These findings suggest that none of the proposed hypotheses tested here on their own can sufficiently explain the observed patterns and that additional factors must be considered in order to explain the diversity of patterns of sex-specific sensitivity observed in the literature.
Jamali, Jamshid; Roustaei, Narges; Ayatollahi, Seyyed Mohammad Taghi; Sadeghi, Erfan
2015-01-01
Background: Mental health is one of the most important dimensions of life and its quality. Minor Psychiatric Disorder as a type of mental health problem is prevalent among health workers. Nursing is considered to be one of the most stressful occupations. Objectives: This study aimed to evaluate the prevalence of minor psychiatric disorder and its associated factors among nurses in southern Iran. Patients and Methods: A cross-sectional study was carried out on 771 nurses working in 20 cities of Bushehr and Fars provinces in southern Iran. Participants were recruited through multi-stage sampling during 2014. The General Health Questionnaire (GHQ-12) was used for screening of minor psychiatric disorder in nurses. Latent Class Regression was used to analyze the data. Results: The prevalence of minor psychiatric disorder among nurses was estimated to be 27.5%. Gender and sleep disorders were significant factors in determining the level of minor psychiatric disorder (P Values of 0.04 and < 0.001, respectively). Female nurses were 20% more likely than males to be classified into the minor psychiatric disorder group. Conclusions: The results of this study provide information about the prevalence of minor psychiatric disorder among nurses, and factors, which affect the prevalence of such disorders. These findings can be used in strategic planning processes to improve nurses’ mental health. PMID:26339670
Multiple regression and principal components analysis of puberty and growth in cattle.
Baker, J F; Stewart, T S; Long, C R; Cartwright, T C
1988-09-01
Multiple regression and principal components analyses were employed to examine relationships among pubertal and growth characters. Records used were from 424 bulls and 475 heifers produced by a diallel mating of Angus, Brahman, Hereford, Holstein and Jersey breeds. Characters studied were age, weight and height at puberty and measurements of weight and hip height from 9 to 21 mo of age; pelvic measurements of heifers also were included. Measurements of weight and height near 1 yr of age were related most highly to pubertal age, weight adn height. Larger size near 1 yr of age was associated with younger, larger animals at puberty. Growth rate was associated with pubertal characters before, but not after, adjustment for effects of breed-type. Principal components of the variation of pubertal and growth characters among animals were strongly related to both weight and height. The majority of the variation among breed-types was due to height. Characteristic vectors of principal components describing the variation of bulls and heifers were strikingly similar. The variance-covariance structure of pubertal characters was essentially the same for both sexes even though the mean values of the characters differed. PMID:3170369
Stevens, F. J.; Bobrovnik, S. A.; Biosciences Division; Palladin Inst. Biochemistry
2007-12-01
Physiological responses of the adaptive immune system are polyclonal in nature whether induced by a naturally occurring infection, by vaccination to prevent infection or, in the case of animals, by challenge with antigen to generate reagents of research or commercial significance. The composition of the polyclonal responses is distinct to each individual or animal and changes over time. Differences exist in the affinities of the constituents and their relative proportion of the responsive population. In addition, some of the antibodies bind to different sites on the antigen, whereas other pairs of antibodies are sterically restricted from concurrent interaction with the antigen. Even if generation of a monoclonal antibody is the ultimate goal of a project, the quality of the resulting reagent is ultimately related to the characteristics of the initial immune response. It is probably impossible to quantitatively parse the composition of a polyclonal response to antigen. However, molecular regression allows further parameterization of a polyclonal antiserum in the context of certain simplifying assumptions. The antiserum is described as consisting of two competing populations of high- and low-affinity and unknown relative proportions. This simple model allows the quantitative determination of representative affinities and proportions. These parameters may be of use in evaluating responses to vaccines, to evaluating continuity of antibody production whether in vaccine recipients or animals used for the production of antisera, or in optimizing selection of donors for the production of monoclonal antibodies.
Kang, Gumin; Lee, Kwangchil; Park, Haesung; Lee, Jinho; Jung, Youngjean; Kim, Kyoungsik; Son, Boongho; Park, Hyoungkuk
2010-06-15
Mixed hydrofluoric and nitric acids are widely used as a good etchant for the pickling process of stainless steels. The cost reduction and the procedure optimization in the manufacturing process can be facilitated by optically detecting the concentration of the mixed acids. In this work, we developed a novel method which allows us to obtain the concentrations of hydrofluoric acid (HF) and nitric acid (HNO(3)) mixture samples with high accuracy. The experiments were carried out for the mixed acids which consist of the HF (0.5-3wt%) and the HNO(3) (2-12wt%) at room temperature. Fourier Transform Raman spectroscopy has been utilized to measure the concentration of the mixed acids HF and HNO(3), because the mixture sample has several strong Raman bands caused by the vibrational mode of each acid in this spectrum. The calibration of spectral data has been performed using the partial least squares regression method which is ideal for local range data treatment. Several figures of merit (FOM) were calculated using the concept of net analyte signal (NAS) to evaluate performance of our methodology.
The effects of invertebrate herbivores on plant population growth: a meta-regression analysis.
Katz, Daniel S W
2016-09-01
Over the last two decades, an increasing number of studies have quantified the effects of herbivory on plant populations using stage-structured population models and integral projection models, allowing for the calculation of plant population growth rates (λ) with and without herbivory. In this paper, I assembled 29 studies and conducted a meta-regression to determine the importance of invertebrate herbivores to population growth rates (λ) while accounting for missing data. I found that invertebrate herbivory often induced important reductions in plant population growth rates (with herbivory, λ was 1.08 ± 0.36; without herbivory, λ was 1.28 ± 0.58). This relationship tended to be weaker for seed predation than for other types of herbivory, except when seed predation rates were very high. Even so, the amount by which studies reduced herbivory was a poor predictor of differences in population growth rates-which strongly cautions against using measured herbivory rates as a proxy for the impact of herbivores. Herbivory reduced plant population growth rates significantly more when potential growth rates were high, which helps to explain why there was less variation in actual population growth rates than in potential population growth rates. The synthesis of these studies also shows the need for future studies to report variance in estimates of λ and to quantify how λ varies as a function of plant density.
Frost, G; Harding, A-H; Darnton, A; McElvenny, D; Morgan, D
2008-09-01
The asbestos industry has shifted from manufacture to stripping/removal work. The aim of this study was to investigate early indications of mortality among removal workers. The study population consisted of 31 302 stripping/removal workers in the Great Britain Asbestos Survey, followed up to December 2005. Relative risks (RR) for causes of death with elevated standardised mortality ratios (SMR) and sufficient deaths were obtained from Poisson regression. Risk factors considered included dust suppression technique, type of respirator used, hours spent stripping, smoking status and exposure length. Deaths were elevated for all causes (SMR 123, 95% CI 119-127, n=985), all cancers including lung cancer, mesothelioma, and circulatory disease. There were no significant differences between suppression techniques and respirator types. Spending more than 40 h per week stripping rather than less than 10, increased mortality risk from all causes (RR 1.4, 95% CI 1.2-1.7), circulatory disease and ischaemic heart disease. Elevated mesothelioma risks were observed for those first exposed at young ages or exposed for more than 30 years. This study is a first step in assessing long-term mortality of asbestos removal workers in relation to working practices and asbestos exposure. Further follow-up will allow the impact of recent regulations to be assessed.
NASA Astrophysics Data System (ADS)
Snyder, Carolyn W.
2016-09-01
Statistical challenges often preclude comparisons among different sea surface temperature (SST) reconstructions over the past million years. Inadequate consideration of uncertainty can result in misinterpretation, overconfidence, and biased conclusions. Here I apply Bayesian hierarchical regressions to analyze local SST responsiveness to climate changes for 54 SST reconstructions from across the globe over the past million years. I develop methods to account for multiple sources of uncertainty, including the quantification of uncertainty introduced from absolute dating into interrecord comparisons. The estimates of local SST responsiveness explain 64% (62% to 77%, 95% interval) of the total variation within each SST reconstruction with a single number. There is remarkable agreement between SST proxy methods, with the exception of Mg/Ca proxy methods estimating muted responses at high latitudes. The Indian Ocean exhibits a muted response in comparison to other oceans. I find a stable estimate of the proposed "universal curve" of change in local SST responsiveness to climate changes as a function of sin2(latitude) over the past 400,000 years: SST change at 45°N/S is larger than the average tropical response by a factor of 1.9 (1.5 to 2.6, 95% interval) and explains 50% (35% to 58%, 95% interval) of the total variation between each SST reconstruction. These uncertainty and statistical methods are well suited for application across paleoclimate and environmental data series intercomparisons.
The effects of invertebrate herbivores on plant population growth: a meta-regression analysis.
Katz, Daniel S W
2016-09-01
Over the last two decades, an increasing number of studies have quantified the effects of herbivory on plant populations using stage-structured population models and integral projection models, allowing for the calculation of plant population growth rates (λ) with and without herbivory. In this paper, I assembled 29 studies and conducted a meta-regression to determine the importance of invertebrate herbivores to population growth rates (λ) while accounting for missing data. I found that invertebrate herbivory often induced important reductions in plant population growth rates (with herbivory, λ was 1.08 ± 0.36; without herbivory, λ was 1.28 ± 0.58). This relationship tended to be weaker for seed predation than for other types of herbivory, except when seed predation rates were very high. Even so, the amount by which studies reduced herbivory was a poor predictor of differences in population growth rates-which strongly cautions against using measured herbivory rates as a proxy for the impact of herbivores. Herbivory reduced plant population growth rates significantly more when potential growth rates were high, which helps to explain why there was less variation in actual population growth rates than in potential population growth rates. The synthesis of these studies also shows the need for future studies to report variance in estimates of λ and to quantify how λ varies as a function of plant density. PMID:27017603
A stochastic regression model for general trend analysis of longitudinal continuous data.
Chao, Wei-Hsiung; Chen, Su-Hua
2009-08-01
A predictive continuous time model is developed for continuous panel data to assess the effect of time-varying covariates on the general direction of the movement of a continuous response that fluctuates over time. This is accomplished by reparameterizing the infinitesimal mean of an Ornstein-Uhlenbeck processes in terms of its equilibrium mean and a drift parameter, which assesses the rate that the process reverts to its equilibrium mean. The equilibrium mean is modeled as a linear predictor of covariates. This model can be viewed as a continuous time first-order autoregressive regression model with time-varying lag effects of covariates and the response, which is more appropriate for unequally spaced panel data than its discrete time analog. Both maximum likelihood and quasi-likelihood approaches are considered for estimating the model parameters and their performances are compared through simulation studies. The simpler quasi-likelihood approach is suggested because it yields an estimator that is of high efficiency relative to the maximum likelihood estimator and it yields a variance estimator that is robust to the diffusion assumption of the model. To illustrate the proposed model, an application to diastolic blood pressure data from a follow-up study on cardiovascular diseases is presented. Missing observations are handled naturally with this model.
Kang, Gumin; Lee, Kwangchil; Park, Haesung; Lee, Jinho; Jung, Youngjean; Kim, Kyoungsik; Son, Boongho; Park, Hyoungkuk
2010-06-15
Mixed hydrofluoric and nitric acids are widely used as a good etchant for the pickling process of stainless steels. The cost reduction and the procedure optimization in the manufacturing process can be facilitated by optically detecting the concentration of the mixed acids. In this work, we developed a novel method which allows us to obtain the concentrations of hydrofluoric acid (HF) and nitric acid (HNO(3)) mixture samples with high accuracy. The experiments were carried out for the mixed acids which consist of the HF (0.5-3wt%) and the HNO(3) (2-12wt%) at room temperature. Fourier Transform Raman spectroscopy has been utilized to measure the concentration of the mixed acids HF and HNO(3), because the mixture sample has several strong Raman bands caused by the vibrational mode of each acid in this spectrum. The calibration of spectral data has been performed using the partial least squares regression method which is ideal for local range data treatment. Several figures of merit (FOM) were calculated using the concept of net analyte signal (NAS) to evaluate performance of our methodology. PMID:20441916
Lewis, Kristin Nicole; Heckman, Bernadette Davantes; Himawan, Lina
2011-08-01
Growth mixture modeling (GMM) identified latent groups based on treatment outcome trajectories of headache disability measures in patients in headache subspecialty treatment clinics. Using a longitudinal design, 219 patients in headache subspecialty clinics in 4 large cities throughout Ohio provided data on their headache disability at pretreatment and 3 follow-up assessments. GMM identified 3 treatment outcome trajectory groups: (1) patients who initiated treatment with elevated disability levels and who reported statistically significant reductions in headache disability (high-disability improvers; 11%); (2) patients who initiated treatment with elevated disability but who reported no reductions in disability (high-disability nonimprovers; 34%); and (3) patients who initiated treatment with moderate disability and who reported statistically significant reductions in headache disability (moderate-disability improvers; 55%). Based on the final multinomial logistic regression model, a dichotomized treatment appointment attendance variable was a statistically significant predictor for differentiating high-disability improvers from high-disability nonimprovers. Three-fourths of patients who initiated treatment with elevated disability levels did not report reductions in disability after 5 months of treatment with new preventive pharmacotherapies. Preventive headache agents may be most efficacious for patients with moderate levels of disability and for patients with high disability levels who attend all treatment appointments.
Nicoară, Simona D.; Ştefănuţ, Anne C.; Nascutzy, Constanta; Zaharie, Gabriela C.; Toader, Laura E.; Drugan, Tudor C.
2016-01-01
Background Retinopathy is a serious complication related to prematurity and a leading cause of childhood blindness. The aggressive posterior form of retinopathy of prematurity (APROP) has a worse anatomical and functional outcome following laser therapy, as compared with the classic form of the disease. The main outcome measures are the APROP regression rate, structural outcomes, and complications associated with intravitreal bevacizumab (IVB) versus laser photocoagulation in APROP. Material/Methods This is a retrospective case series that includes infants with APROP who received either IVB or laser photocoagulation and had a follow-up of at least 60 weeks (for the laser photocoagulation group) and 80 weeks (for the IVB group). In the first group, laser photocoagulation of the retina was carried out and in the second group, 1 bevacizumab injection was administered intravitreally. The following parameters were analyzed in each group: sex, gestational age, birth weight, postnatal age and postmenstrual age at treatment, APROP regression, sequelae, and complications. Statistical analysis was performed using Microsoft Excel and IBM SPSS (version 23.0). Results The laser photocoagulation group consisted of 6 premature infants (12 eyes) and the IVB group consisted of 17 premature infants (34 eyes). Within the laser photocoagulation group, the evolution was favorable in 9 eyes (75%) and unfavorable in 3 eyes (25%). Within the IVB group, APROP regressed in 29 eyes (85.29%) and failed to regress in 5 eyes (14.71%). These differences are statistically significant, as proved by the McNemar test (P<0.001). Conclusions The IVB group had a statistically significant better outcome compared with the laser photocoagulation group, in APROP in our series. PMID:27062023
Nicoară, Simona D; Ștefănuţ, Anne C; Nascutzy, Constanta; Zaharie, Gabriela C; Toader, Laura E; Drugan, Tudor C
2016-04-10
BACKGROUND Retinopathy is a serious complication related to prematurity and a leading cause of childhood blindness. The aggressive posterior form of retinopathy of prematurity (APROP) has a worse anatomical and functional outcome following laser therapy, as compared with the classic form of the disease. The main outcome measures are the APROP regression rate, structural outcomes, and complications associated with intravitreal bevacizumab (IVB) versus laser photocoagulation in APROP. MATERIAL AND METHODS This is a retrospective case series that includes infants with APROP who received either IVB or laser photocoagulation and had a follow-up of at least 60 weeks (for the laser photocoagulation group) and 80 weeks (for the IVB group). In the first group, laser photocoagulation of the retina was carried out and in the second group, 1 bevacizumab injection was administered intravitreally. The following parameters were analyzed in each group: sex, gestational age, birth weight, postnatal age and postmenstrual age at treatment, APROP regression, sequelae, and complications. Statistical analysis was performed using Microsoft Excel and IBM SPSS (version 23.0). RESULTS The laser photocoagulation group consisted of 6 premature infants (12 eyes) and the IVB group consisted of 17 premature infants (34 eyes). Within the laser photocoagulation group, the evolution was favorable in 9 eyes (75%) and unfavorable in 3 eyes (25%). Within the IVB group, APROP regressed in 29 eyes (85.29%) and failed to regress in 5 eyes (14.71%). These differences are statistically significant, as proved by the McNemar test (P<0.001). CONCLUSIONS The IVB group had a statistically significant better outcome compared with the laser photocoagulation group, in APROP in our series.
Gerber, Samuel; Rubel, Oliver; Bremer, Peer -Timo; Pascucci, Valerio; Whitaker, Ross T.
2012-01-19
This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduces a quality-of-fit-driven decomposition of the domain. The emphasis in this work is on a topologically meaningful segmentation. Thus, the proposed regression approach is based on a segmentation induced by a discrete approximation of the Morse–Smale complex. This yields a segmentation with partitions corresponding to regions of the function with a single minimum and maximum that are often well approximated by a linear model. This approach yields regression models that are amenable to interpretation and have good predictive capacity. Typically, regression estimates are quantified by their geometrical accuracy. For the proposed regression, an important aspect is the quality of the segmentation itself. Thus, this article introduces a new criterion that measures the topological accuracy of the estimate. The topological accuracy provides a complementary measure to the classical geometrical error measures and is very sensitive to overfitting. The Morse–Smale regression is compared to state-of-the-art approaches in terms of geometry and topology and yields comparable or improved fits in many cases. Finally, a detailed study on climate-simulation data demonstrates the application of the Morse–Smale regression. Supplementary Materials are available online and contain an implementation of the proposed approach in the R package msr, an analysis and simulations on the stability of the Morse–Smale complex approximation, and additional tables for the climate-simulation study.
NASA Technical Reports Server (NTRS)
Alston, D. W.
1981-01-01
The considered research had the objective to design a statistical model that could perform an error analysis of curve fits of wind tunnel test data using analysis of variance and regression analysis techniques. Four related subproblems were defined, and by solving each of these a solution to the general research problem was obtained. The capabilities of the evolved true statistical model are considered. The least squares fit is used to determine the nature of the force, moment, and pressure data. The order of the curve fit is increased in order to delete the quadratic effect in the residuals. The analysis of variance is used to determine the magnitude and effect of the error factor associated with the experimental data.
Fiumera, Heather L; Dunham, Maitreya J; Saracco, Scott A; Butler, Christine A; Kelly, Jessica A; Fox, Thomas D
2009-06-01
Members of the Oxa1/YidC/Alb3 family of protein translocases are essential for assembly of energy-transducing membrane complexes. In Saccharomyces cerevisiae, Oxa1 and its paralog, Cox18, are required for assembly of Cox2, a mitochondrially encoded subunit of cytochrome c oxidase. Oxa1 is known to be required for cotranslational export of the Cox2 N-terminal domain across the inner mitochondrial membrane, while Cox18 is known to be required for post-translational export of the Cox2 C-tail domain. We find that overexpression of Oxa1 does not compensate for the absence of Cox18 at the level of respiratory growth. However, it does promote some translocation of the Cox2 C-tail domain across the inner membrane and causes increased accumulation of Cox2, which remains unassembled. This result suggests that Cox18 not only translocates the C-tail, but also must deliver it in a distinct state competent for cytochrome oxidase assembly. We identified respiring mutants from a cox18Delta strain overexpressing OXA1, whose respiratory growth requires overexpression of OXA1. The recessive nuclear mutations allow some assembly of Cox2 into cytochrome c oxidase. After failing to identify these mutations by methods based on transformation, we successfully located them to MGR1 and MGR3 by comparative hybridization to whole-genome tiling arrays and microarray-assisted bulk segregant analysis followed by linkage mapping. While Mgr1 and Mgr3 are known to associate with the Yme1 mitochondrial inner membrane i-AAA protease and to participate in membrane protein degradation, their absence does not appear to stabilize Cox2 under these conditions. Instead, Yme1 probably chaperones the folding and/or assembly of Oxa1-exported Cox2 in the absence of Mrg1 or Mgr3, since respiratory growth and cytochrome c oxidase assembly in a cox18 mgr3 double-mutant strain overexpressing OXA1 is YME1 dependent.
Fiumera, Heather L.; Dunham, Maitreya J.; Saracco, Scott A.; Butler, Christine A.; Kelly, Jessica A.; Fox, Thomas D.
2009-01-01
Members of the Oxa1/YidC/Alb3 family of protein translocases are essential for assembly of energy-transducing membrane complexes. In Saccharomyces cerevisiae, Oxa1 and its paralog, Cox18, are required for assembly of Cox2, a mitochondrially encoded subunit of cytochrome c oxidase. Oxa1 is known to be required for cotranslational export of the Cox2 N-terminal domain across the inner mitochondrial membrane, while Cox18 is known to be required for post-translational export of the Cox2 C-tail domain. We find that overexpression of Oxa1 does not compensate for the absence of Cox18 at the level of respiratory growth. However, it does promote some translocation of the Cox2 C-tail domain across the inner membrane and causes increased accumulation of Cox2, which remains unassembled. This result suggests that Cox18 not only translocates the C-tail, but also must deliver it in a distinct state competent for cytochrome oxidase assembly. We identified respiring mutants from a cox18Δ strain overexpressing OXA1, whose respiratory growth requires overexpression of OXA1. The recessive nuclear mutations allow some assembly of Cox2 into cytochrome c oxidase. After failing to identify these mutations by methods based on transformation, we successfully located them to MGR1 and MGR3 by comparative hybridization to whole-genome tiling arrays and microarray-assisted bulk segregant analysis followed by linkage mapping. While Mgr1 and Mgr3 are known to associate with the Yme1 mitochondrial inner membrane i-AAA protease and to participate in membrane protein degradation, their absence does not appear to stabilize Cox2 under these conditions. Instead, Yme1 probably chaperones the folding and/or assembly of Oxa1-exported Cox2 in the absence of Mrg1 or Mgr3, since respiratory growth and cytochrome c oxidase assembly in a cox18 mgr3 double-mutant strain overexpressing OXA1 is YME1 dependent. PMID:19307606
Bener, A; Hussain, S J; Al-Malki, M a; Shotar, M M; Al-Said, M F; Jadaan, K S
2010-03-01
Smeed's equation is a widely used model for prediction of traffic fatalities but has been inadequate for use in developing countries. We applied regression analysis to time-series data on vehicles, exponential models for fatality prediction, producing an average absolute error of 20.9% for Qatar, 10.9% for population and traffic fatalities in the United Arab Emirates (UAE), Jordan and Qatar. The data were fitted to Jordan and 5.5% for the UAE. We found a strong linear relationship between gross domestic product and fatality rate.
Regression Analysis of Long-Term Profile Ozone Data Set from BUV Instruments
NASA Technical Reports Server (NTRS)
Stolarski, Richard S.
2005-01-01
We have produced a profile merged ozone data set (MOD) based on the SBUV/SBUV2 series of nadir-viewing satellite backscatter instruments, covering the period from November 1978 - December 2003. In 2004, data from the Nimbus 7 SBUV and NOAA 9, ll, and 16 SBUV/2 instruments were reprocessed using the Version 8 (V8) algorithm and most recent calibrations. More recently, data from the Nimbus 4 BUT instrument, which was operational from 1970 - 1977, were also reprocessed using the V8 algorithm. As part of the V8 profile calibration, the Nimbus 7 and NOAA 9 (1993-1997 only) instrument calibrations have been adjusted to match the NOAA 11 calibration, which was established based on comparisons with SSBUV shuttle flight data. Differences between NOAA 11, Nimbus 7 and NOAA 9 profile zonal means are within plus or minus 5% at all levels when averaged over the respective periods of data overlap. NOAA 16 SBUV/2 data have insufficient overlap with NOAA 11, so its calibration is based on pre-flight information. Mean differences over 4 months of overlap are within plus or minus 7%. Given the level of agreement between the data sets, we simply average the ozone values during periods of instrument overlap to produce the MOD profile data set. Initial comparisons of coincident matches of N4 BUV and Arosa Umkehr data show mean differences of 0.5 (0.5)% at 30km; 7.5 (0.5)% at 35 km; and 11 (0.7)% at 40 km, where the number in parentheses is the standard error of the mean. In this study, we use the MOD profile data set (1978-2003) to estimate the change in profile ozone due to changing stratospheric chlorine levels. We use a standard linear regression model with proxies for the seasonal cycle, solar cycle, QBO, and ozone trend. To account for the non-linearity of stratospheric chlorine levels since the late 1990s, we use a time series of Effective Chlorine, defined as the global average of Chlorine + 50 * Bromine at 1 hPa, as the trend proxy. The Effective Chlorine data are taken from
NASA Astrophysics Data System (ADS)
Ghasemi, Jahan B.; Zolfonoun, Ehsan
2013-11-01
A new multicomponent analysis method, based on principal component analysis-multivariate adaptive regression splines (PC-MARS) is proposed for the determination of dialkyltin compounds. In Tween-20 micellar media, dimethyl and dibutyltin react with morin to give fluorescent complexes with the maximum emission peaks at 527 and 520 nm, respectively. The spectrofluorimetric matrix data, before building the MARS models, were subjected to principal component analysis and decomposed to PC scores as starting points for the MARS algorithm. The algorithm classifies the calibration data into several groups, in each a regression line or hyperplane is fitted. Performances of the proposed methods were tested in term of root mean square errors of prediction (RMSEP), using synthetic solutions. The results show the strong potential of PC-MARS, as a multivariate calibration method, to be applied to spectral data for multicomponent determinations. The effect of different experimental parameters on the performance of the method were studied and discussed. The prediction capability of the proposed method compared with GC-MS method for determination of dimethyltin and/or dibutyltin.
Kitsantas, Panagiota
2009-01-01
Objective to be addressed The purpose of this study was to investigate the structural and organizational factors that contribute to the availability and increased capacity for substance abuse treatment programs in correctional settings. We used Classification and Regression Tree statistical procedures to identify how multi-level data can explain the variability in availability and capacity of substance abuse treatment programs in jails and probation/parole offices. Methods The data for this study combined the National Criminal Justice Treatment Practices survey (NCJTP) and the 2000 Census. The NCJTP survey was a nationally representative sample of correctional administrators for jails and probation/parole agencies. The sample size included 295 substance abuse treatment programs that were classified according to the intensity of their services: high, medium, and low. The independent variables included jurisdictional-level structural variables, attributes of the correctional administrators, and program and service delivery characteristics of the correctional agency. Results The two most important variables in predicting the availability of all three types of services were stronger working relationships with other organizations and the adoption of a standardized substance abuse screening tool by correctional agencies. For high and medium intensive programs, the capacity increased when an organizational learning strategy was used by administrators and the organization used a substance abuse screening tool. Implications on advancing treatment practices in correctional settings are discussed, including further work to test theories on how to better understand access to intensive treatment services. This study presents the first phase of understanding capacity-related issues regarding treatment programs offered in correctional settings. PMID:19395204
Binary logistic regression analysis of hard palate dimensions for sexing human crania
Asif, Muhammed; Shetty, Radhakrishna; Avadhani, Ramakrishna
2016-01-01
Sex determination is the preliminary step in every forensic investigation and the hard palate assumes significance in cranial sexing in cases involving burns and explosions due to its resistant nature and secluded location. This study analyzes the sexing potential of incisive foramen to posterior nasal spine length, palatine process of maxilla length, horizontal plate of palatine bone length and transverse length between the greater palatine foramina. The study deviates from the conventional method of measuring the maxillo-alveolar length and breadth as the dimensions considered in this study are more heat resistant and useful in situations with damaged alveolar margins. The study involves 50 male and 50 female adult dry skulls of Indian ethnic group. The dimensions measured were statistically analyzed using Student's t test, binary logistic regression and receiver operating characteristic curve. It was observed that the incisive foramen to posterior nasal spine length is a definite sex marker with sex predictability of 87.2%. The palatine process of maxilla length with 66.8% sex predictability and the horizontal plate of palatine bone length with 71.9% sex predictability cannot be relied upon as definite sex markers. The transverse length between the greater palatine foramina is statistically insignificant in sexing crania (P=0.318). Considering a significant overlap of values in both the sexes the palatal dimensions singularly cannot be relied upon for sexing. Nevertheless, considering the high sex predictability of incisive foramen to posterior nasal spine length this dimension can definitely be used to supplement other sexing evidence available to precisely conclude the cranial sex. PMID:27382518
A regression approach to the analysis of serial peak flow among fuel oil ash exposed workers.
Hauser, R; Daskalakis, C; Christiani, D C
1996-10-01
We investigated the association between exposure to fuel oil ash and acute airway obstruction in 31 boilermakers and 31 utility workers during the overhaul of a large oil-fired boiler. Air flow was assessed with self-recorded serial peak expiratory flow rate measurements (PEFR) using a mini-Wright meter. Exposure to thoracic particulates with an aerodynamic diameter of 10 gm or smaller (PM10) was assessed using personal sampling devices and detailed work diaries. All subjects were male, with an average age of 43 yr, and an average of 18 yr at their current trade. Average PM10 exposure on work days was 2.75 mg/m3 for boilermakers and 0.57 mg/m3 for utility workers. Three daily PEFR measurements (start-of-shift, end-of-shift, and bed-time) were analyzed simultaneously, using Huber linear regression. After adjustment for job title, welder status, age, height, smoking, and weld-years, for each mg/m3 increase in PM10, the estimated decline in PEFR was 13.2 L/min (p = 0.008) for end-of-shift, 9.9 L/min (p = 0.045) for bed-time, and 6.6 L/min (p = 0.26) for start-of-shift of the following day. This decline of the exposure effect over the 24-h period that follows was statistically significant (p = 0.004). No other factors were found to significantly modify the effect of exposure. Our results suggest that occupational exposure to fuel oil ash is associated with significant acute decrements in peak flow. PMID:8887594
LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS
Almquist, Zack W.; Butts, Carter T.
2015-01-01
Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach. PMID:26120218
A regression approach to the analysis of serial peak flow among fuel oil ash exposed workers.
Hauser, R; Daskalakis, C; Christiani, D C
1996-10-01
We investigated the association between exposure to fuel oil ash and acute airway obstruction in 31 boilermakers and 31 utility workers during the overhaul of a large oil-fired boiler. Air flow was assessed with self-recorded serial peak expiratory flow rate measurements (PEFR) using a mini-Wright meter. Exposure to thoracic particulates with an aerodynamic diameter of 10 gm or smaller (PM10) was assessed using personal sampling devices and detailed work diaries. All subjects were male, with an average age of 43 yr, and an average of 18 yr at their current trade. Average PM10 exposure on work days was 2.75 mg/m3 for boilermakers and 0.57 mg/m3 for utility workers. Three daily PEFR measurements (start-of-shift, end-of-shift, and bed-time) were analyzed simultaneously, using Huber linear regression. After adjustment for job title, welder status, age, height, smoking, and weld-years, for each mg/m3 increase in PM10, the estimated decline in PEFR was 13.2 L/min (p = 0.008) for end-of-shift, 9.9 L/min (p = 0.045) for bed-time, and 6.6 L/min (p = 0.26) for start-of-shift of the following day. This decline of the exposure effect over the 24-h period that follows was statistically significant (p = 0.004). No other factors were found to significantly modify the effect of exposure. Our results suggest that occupational exposure to fuel oil ash is associated with significant acute decrements in peak flow.
Maternal heavy alcohol use and toddler behavior problems: a fixed effects regression analysis.
Knudsen, Ann Kristin; Ystrom, Eivind; Skogen, Jens Christoffer; Torgersen, Leila
2015-10-01
Using data from the longitudinal Norwegian Mother and Child Cohort Study, the aims of the current study were to examine associations between postnatal maternal heavy alcohol use and toddler behavior problems, taking both observed and unobserved confounding factors into account by employing fixed effects regression models. Postnatal maternal heavy alcohol use (defined as drinking alcohol 4 or more times a week, or drinking 7 units or more per alcohol use episode) and toddler internalizing and externalizing behavior problems were assessed when the toddlers were aged 18 and 36 months. Maternal psychopathology, civil status and negative life events last year were included as time-variant covariates. Maternal heavy alcohol use was associated with toddler internalizing and externalizing behavior problems (p < 0.001) in the population when examined with generalized estimating equation models. The associations disappeared when observed and unobserved sources of confounding were taken into account in the fixed effects models [(p = 0.909 for externalizing behaviors (b = 0.002, SE = 0.021), p = 0.928 for internalizing behaviors (b = 0.002, SE = 0.023)], with an even further reduction of the estimates with the inclusion of time-variant confounders. No causal effect was found between postnatal maternal heavy alcohol use and toddler behavior problems. Increased levels of behavior problems among toddlers of heavy drinking mothers should therefore be attributed to other adverse characteristics associated with these mothers, toddlers and families. This should be taken into account when interventions aimed at at-risk families identified by maternal heavy alcohol use are planned and conducted.
Garrido, M; Larrechi, M S; Rius, F X
2004-12-01
The present study investigates the relationship between the changes in complex viscosity and near-infrared spectra. Principal component regression analysis is applied to a near-infrared data set obtained from the in situ monitoring of the curing of diglycidyl ether of bisphenol A with the diamine 4,4'-diaminodiphenylmethane. The values of complex viscosity obtained by dynamic mechanical analysis during the cure process were used as a reference. The near-infrared spectra recorded throughout the reaction, unlike the univariate data analysis at some wavelengths of the spectra, contain a sufficient amount of information to estimate the complex viscosity. The relationship found was high and the results demonstrate the quality of the fitted model. Also, a simple user-friendly procedure for applying the model, focused on the user, is shown.
THE COX-MAZE IV PROCEDURE: PREDICTORS OF LATE RECURRENCE
Damiano, Ralph J.; Schwartz, Forrest H.; Bailey, Marci S.; Maniar, Hersh S.; Munfakh, Nabil A.; Moon, Marc R.; Schuessler, Richard B.
2010-01-01
Objectives The Cox-Maze III procedure(CMP) achieved high cure rates and became the surgical gold standard for the treatment of atrial fibrillation(AF). Due to its invasiveness, a more simplified ablation-assisted procedure(CMP-IV) has been performed at our institution since January, 2002. The study examined multiple preoperative and perioperative variables to determine predictors of late recurrence. Methods Data were collected prospectively on 282 patients who underwent the CMP-IV from January 2002 through December 2009. Forty-two percent of patients had paroxysmal and 58% had either persistent or long-standing persistent AF. All patients were available for follow-up. Follow-up included ECGs in all patients. Since 2006, 24 hour holter monitoring was obtained in 94% of patients at 3, 6 and 12 months. Data were analyzed by logistic regression analysis at 12 months with 13 preoperative and perioperative variables used as co-variants. Results Sixty-six percent of patients had a concomitant procedure. Following an ablation-assisted CMP, the freedom from AF was 89%, 93%, and 89% at 3, 6, and 12 months, respectively. The freedom from both AF and antiarrhythmic drugs was 63%, 79%, and 78% at 3, 6, and 12 months. The risk factors for AF recurrence at one year were enlarged left atrial(LA) diameter(p=0.027), failure to isolate the entire posterior left atrium(p=0.022), and early atrial tachyarrhythmias (ATAs)(p=0.010). Conclusions The CMP-IV has a high success rate at one year, even with improved follow-up and stricter definitions of failure. In patients with large LA, there may be a need for more extensive size reduction or expanded lesion sets. PMID:21168019
Meekes, Joost; Braams, Olga B; Braun, Kees P J; Jennekens-Schinkel, Aag; van Rijen, Peter C; Alpherts, Willem C J; Hendriks, Marc P H; van Nieuwenhuizen, Onno
2014-07-01
Visual memory is vulnerable to epilepsy surgery in adults, but studies in children suggest no change or small improvements. We investigated visual memory after epilepsy surgery, both group-wise and in individual children, using two techniques to assess change: 1) repeated measures analysis of variance (ANOVA) and 2) an empirically based technique for detecting cognitive change [standardized regression-based (SRB) analysis]. A prospective cohort consisting of 21 children completed comprehensive assessments of memory both before surgery (T0) and 6 (T1), 12 (T2), and 24 months (T3) after surgery. For each patient, two age- and gender-matched controls were assessed with the same tests at the same intervals. Repeated measures ANOVA replicated the results of previous studies reporting no change or minor improvements after surgery. However, group analysis of SRB results eliminated virtually all improvements, indicating that the ANOVA results were confounded by practice effects. Standardized regression-based group results showed that in fact patients scored lower after surgery than would be predicted based on their presurgical performance. Analysis of individual SRB results showed that per visual memory measure, an average of 18% of patients obtained a significantly negative SRB score, whereas, on average, only 2% obtained a significantly positive SRB score. At T3, the number of significantly negative SRB scores outweighed the number of significantly positive SRB scores in 62% of patients. There were no clear associations of clinical variables (including side and site of surgery and postsurgical seizure freedom) with memory outcome. The present analysis revealed that given their individual presurgical functioning, many children obtained disappointing results on some visual memory tests after epilepsy surgery. Comparison of the SRB analysis with ANOVA results emphasizes the importance of empirically based techniques for detecting cognitive effects of epilepsy surgery in
Meekes, Joost; Braams, Olga B; Braun, Kees P J; Jennekens-Schinkel, Aag; van Rijen, Peter C; Alpherts, Willem C J; Hendriks, Marc P H; van Nieuwenhuizen, Onno
2014-07-01
Visual memory is vulnerable to epilepsy surgery in adults, but studies in children suggest no change or small improvements. We investigated visual memory after epilepsy surgery, both group-wise and in individual children, using two techniques to assess change: 1) repeated measures analysis of variance (ANOVA) and 2) an empirically based technique for detecting cognitive change [standardized regression-based (SRB) analysis]. A prospective cohort consisting of 21 children completed comprehensive assessments of memory both before surgery (T0) and 6 (T1), 12 (T2), and 24 months (T3) after surgery. For each patient, two age- and gender-matched controls were assessed with the same tests at the same intervals. Repeated measures ANOVA replicated the results of previous studies reporting no change or minor improvements after surgery. However, group analysis of SRB results eliminated virtually all improvements, indicating that the ANOVA results were confounded by practice effects. Standardized regression-based group results showed that in fact patients scored lower after surgery than would be predicted based on their presurgical performance. Analysis of individual SRB results showed that per visual memory measure, an average of 18% of patients obtained a significantly negative SRB score, whereas, on average, only 2% obtained a significantly positive SRB score. At T3, the number of significantly negative SRB scores outweighed the number of significantly positive SRB scores in 62% of patients. There were no clear associations of clinical variables (including side and site of surgery and postsurgical seizure freedom) with memory outcome. The present analysis revealed that given their individual presurgical functioning, many children obtained disappointing results on some visual memory tests after epilepsy surgery. Comparison of the SRB analysis with ANOVA results emphasizes the importance of empirically based techniques for detecting cognitive effects of epilepsy surgery in
ERIC Educational Resources Information Center
Berenson, Mark L.
2013-01-01
There is consensus in the statistical literature that severe departures from its assumptions invalidate the use of regression modeling for purposes of inference. The assumptions of regression modeling are usually evaluated subjectively through visual, graphic displays in a residual analysis but such an approach, taken alone, may be insufficient…
A systematic review and meta-regression analysis of prophylactic gabapentin for postoperative pain.
Doleman, B; Heinink, T P; Read, D J; Faleiro, R J; Lund, J N; Williams, J P
2015-10-01
We searched MEDLINE, Embase, CINAHL, AMED and CENTRAL databases until December 2014 and included 133 randomised controlled trials of peri-operative gabapentin vs placebo. Gabapentin reduced mean (95% CI) 24-h morphine-equivalent consumption by 8.44 (7.26-9.62) mg, p < 0.001, whereas more specific reductions in morphine equivalents were predicted (R(2) = 90%, p < 0.001) by the meta-regression equation: 3.73 + (-0.378 × control morphine consumption (mg)) + (-0.0023 × gabapentin dose (mg)) + (-1.917 × anaesthetic type), where 'anaesthetic type' is '1' for general anaesthesia and '0' for spinal anaesthesia. The type of surgery was not independently associated with gabapentin effect. Gabapentin reduced postoperative pain scores on a 10-point scale at 1 h, 2 h, 6 h, 12 h and 24 h by a mean (95% CI) of: 1.68 (1.35-2.01); 1.21 (0.88-1.55); 1.28 (0.98-1.57); 1.12 (0.91-1.33); and 0.71 (0.56-0.87), respectively, p < 0.001 for all. The risk ratios (95% CI) for postoperative nausea, vomiting, pruritus and sedation with gabapentin were: 0.78 (0.69-0.87), 0.67 (0.59-0.76), 0.64 (0.51-0.80) and 1.18 (1.09-1.28), respectively, p < 0.001 for all. Gabapentin reduced pre-operative anxiety and increased patient satisfaction on a 10-point scale by a mean (95% CI) of 1.52 (0.78-2.26) points and 0.89 (0.22-1.57) points, p < 0.001 and p = 0.01, respectively. All the effects of gabapentin may have been overestimated by statistically significant small study effects.
Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Wang, Xuchen
2016-02-01
Hyperspectral estimation of soil organic matter (SOM) in coal mining regions is an important tool for enhancing fertilization in soil restoration programs. The correlation--partial least squares regression (PLSR) method effectively solves the information loss problem of correlation--multiple linear stepwise regression, but results of the correlation analysis must be optimized to improve precision. This study considers the relationship between spectral reflectance and SOM based on spectral reflectance curves of soil samples collected from coal mining regions. Based on the major absorption troughs in the 400-1006 nm spectral range, PLSR analysis was performed using 289 independent bands of the second derivative (SDR) with three levels and measured SOM values. A wavelet-correlation-PLSR (W-C-PLSR) model was then constructed. By amplifying useful information that was previously obscured by noise, the W-C-PLSR model was optimal for estimating SOM content, with smaller prediction errors in both calibration (R(2) = 0.970, root mean square error (RMSEC) = 3.10, and mean relative error (MREC) = 8.75) and validation (RMSEV = 5.85 and MREV = 14.32) analyses, as compared with other models. Results indicate that W-C-PLSR has great potential to estimate SOM in coal mining regions.
El-Ansary, Afaf
2016-01-01
This work demonstrates data of multiple regression analysis between nine biomarkers related to glutamate excitotoxicity and impaired detoxification as two mechanisms recently recorded as autism phenotypes. The presented data was obtained by measuring a panel of markers in 20 autistic patients aged 3–15 years and 20 age and gender matching healthy controls. Levels of GSH, glutathione status (GSH/GSSG), glutathione reductase (GR), glutathione-s-transferase (GST), thioredoxin (Trx), thioredoxin reductase (TrxR) and peroxidoxins (Prxs I and III), glutamate, glutamine, glutamate/glutamine ratio glutamate dehydrogenase (GDH) in plasma and mercury (Hg) in red blood cells were determined in both groups. In Multiple regression analysis, R2 values which describe the proportion or percentage of variance in the dependent variable attributed to the variance in the independent variables together were calculated. Moreover, β coefficients values which show the direction either positive or negative and the contribution of the independent variable relative to the other independent variables in explaining the variation of the dependent variable were determined. A panel of inter-related markers was recorded. This paper contains data related to and supporting research articles currently published entitled “Mechanism of nitrogen metabolism-related parameters and enzyme activities in the pathophysiology of autism” [1], “Novel metabolic biomarkers related to sulfur-dependent detoxification pathways in autistic patients of Saudi Arabia [2], and “A key role for an impaired detoxification mechanism in the etiology and severity of autism spectrum disorders” [3]. PMID:26933667
Lamm, Steven H; Ferdosi, Hamid; Dissen, Elisabeth K; Li, Ji; Ahn, Jaeil
2015-12-07
High levels (> 200 µg/L) of inorganic arsenic in drinking water are known to be a cause of human lung cancer, but the evidence at lower levels is uncertain. We have sought the epidemiological studies that have examined the dose-response relationship between arsenic levels in drinking water and the risk of lung cancer over a range that includes both high and low levels of arsenic. Regression analysis, based on six studies identified from an electronic search, examined the relationship between the log of the relative risk and the log of the arsenic exposure over a range of 1-1000 µg/L. The best-fitting continuous meta-regression model was sought and found to be a no-constant linear-quadratic analysis where both the risk and the exposure had been logarithmically transformed. This yielded both a statistically significant positive coefficient for the quadratic term and a statistically significant negative coefficient for the linear term. Sub-analyses by study design yielded results that were similar for both ecological studies and non-ecological studies. Statistically significant X-intercepts consistently found no increased level of risk at approximately 100-150 µg/L arsenic.
El-Ansary, Afaf
2016-06-01
This work demonstrates data of multiple regression analysis between nine biomarkers related to glutamate excitotoxicity and impaired detoxification as two mechanisms recently recorded as autism phenotypes. The presented data was obtained by measuring a panel of markers in 20 autistic patients aged 3-15 years and 20 age and gender matching healthy controls. Levels of GSH, glutathione status (GSH/GSSG), glutathione reductase (GR), glutathione-s-transferase (GST), thioredoxin (Trx), thioredoxin reductase (TrxR) and peroxidoxins (Prxs I and III), glutamate, glutamine, glutamate/glutamine ratio glutamate dehydrogenase (GDH) in plasma and mercury (Hg) in red blood cells were determined in both groups. In Multiple regression analysis, R (2) values which describe the proportion or percentage of variance in the dependent variable attributed to the variance in the independent variables together were calculated. Moreover, β coefficients values which show the direction either positive or negative and the contribution of the independent variable relative to the other independent variables in explaining the variation of the dependent variable were determined. A panel of inter-related markers was recorded. This paper contains data related to and supporting research articles currently published entitled "Mechanism of nitrogen metabolism-related parameters and enzyme activities in the pathophysiology of autism" [1], "Novel metabolic biomarkers related to sulfur-dependent detoxification pathways in autistic patients of Saudi Arabia [2], and "A key role for an impaired detoxification mechanism in the etiology and severity of autism spectrum disorders" [3]. PMID:26933667
Hu, Meng; Clark, Kelsey L; Gong, Xiajing; Noudoost, Behrad; Li, Mingyao; Moore, Tirin; Liang, Hualou
2015-06-10
Inferotemporal (IT) neurons are known to exhibit persistent, stimulus-selective activity during the delay period of object-based working memory tasks. Frontal eye field (FEF) neurons show robust, spatially selective delay period activity during memory-guided saccade tasks. We present a copula regression paradigm to examine neural interaction of these two types of signals between areas IT and FEF of the monkey during a working memory task. This paradigm is based on copula models that can account for both marginal distribution over spiking activity of individual neurons within each area and joint distribution over ensemble activity of neurons between areas. Considering the popular GLMs as marginal models, we developed a general and flexible likelihood framework that uses the copula to integrate separate GLMs into a joint regression analysis. Such joint analysis essentially leads to a multivariate analog of the marginal GLM theory and hence efficient model estimation. In addition, we show that Granger causality between spike trains can be readily assessed via the likelihood ratio statistic. The performance of this method is validated by extensive simulations, and compared favorably to the widely used GLMs. When applied to spiking activity of simultaneously recorded FEF and IT neurons during working memory task, we observed significant Granger causality influence from FEF to IT, but not in the opposite direction, suggesting the role of the FEF in the selection and retention of visual information during working memory. The copula model has the potential to provide unique neurophysiological insights about network properties of the brain. PMID:26063909
Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Wang, Xuchen
2016-02-01
Hyperspectral estimation of soil organic matter (SOM) in coal mining regions is an important tool for enhancing fertilization in soil restoration programs. The correlation--partial least squares regression (PLSR) method effectively solves the information loss problem of correlation--multiple linear stepwise regression, but results of the correlation analysis must be optimized to improve precision. This study considers the relationship between spectral reflectance and SOM based on spectral reflectance curves of soil samples collected from coal mining regions. Based on the major absorption troughs in the 400-1006 nm spectral range, PLSR analysis was performed using 289 independent bands of the second derivative (SDR) with three levels and measured SOM values. A wavelet-correlation-PLSR (W-C-PLSR) model was then constructed. By amplifying useful information that was previously obscured by noise, the W-C-PLSR model was optimal for estimating SOM content, with smaller prediction errors in both calibration (R(2) = 0.970, root mean square error (RMSEC) = 3.10, and mean relative error (MREC) = 8.75) and validation (RMSEV = 5.85 and MREV = 14.32) analyses, as compared with other models. Results indicate that W-C-PLSR has great potential to estimate SOM in coal mining regions. PMID:26780416
Lamm, Steven H.; Ferdosi, Hamid; Dissen, Elisabeth K.; Li, Ji; Ahn, Jaeil
2015-01-01
High levels (> 200 µg/L) of inorganic arsenic in drinking water are known to be a cause of human lung cancer, but the evidence at lower levels is uncertain. We have sought the epidemiological studies that have examined the dose-response relationship between arsenic levels in drinking water and the risk of lung cancer over a range that includes both high and low levels of arsenic. Regression analysis, based on six studies identified from an electronic search, examined the relationship between the log of the relative risk and the log of the arsenic exposure over a range of 1–1000 µg/L. The best-fitting continuous meta-regression model was sought and found to be a no-constant linear-quadratic analysis where both the risk and the exposure had been logarithmically transformed. This yielded both a statistically significant positive coefficient for the quadratic term and a statistically significant negative coefficient for the linear term. Sub-analyses by study design yielded results that were similar for both ecological studies and non-ecological studies. Statistically significant X-intercepts consistently found no increased level of risk at approximately 100–150 µg/L arsenic. PMID:26690190
Stepwise Regression Analysis of MDOE Balance Calibration Data Acquired at DNW
NASA Technical Reports Server (NTRS)
DeLoach, RIchard; Philipsen, Iwan
2007-01-01
This paper reports a comparison of two experiment design methods applied in the calibration of a strain-gage balance. One features a 734-point test matrix in which loads are varied systematically according to a method commonly applied in aerospace research and known in the literature of experiment design as One Factor At a Time (OFAT) testing. Two variations of an alternative experiment design were also executed on the same balance, each with different features of an MDOE experiment design. The Modern Design of Experiments (MDOE) is an integrated process of experiment design, execution, and analysis applied at NASA's Langley Research Center to achieve significant reductions in cycle time, direct operating cost, and experimental uncertainty in aerospace research generally and in balance calibration experiments specifically. Personnel in the Instrumentation and Controls Department of the German Dutch Wind Tunnels (DNW) have applied MDOE methods to evaluate them in the calibration of a balance using an automated calibration machine. The data have been sent to Langley Research Center for analysis and comparison. This paper reports key findings from this analysis. The chief result is that a 100-point calibration exploiting MDOE principles delivered quality comparable to a 700+ point OFAT calibration with significantly reduced cycle time and attendant savings in direct and indirect costs. While the DNW test matrices implemented key MDOE principles and produced excellent results, additional MDOE concepts implemented in balance calibrations at Langley Research Center are also identified and described.
2013-01-01
Background In recent years, there has been growing interest in measuring the efficiency of hospitals in Iran and several studies have been conducted on the topic. The main objective of this paper was to review studies in the field of hospital efficiency and examine the estimated technical efficiency (TE) of Iranian hospitals. Methods Persian and English databases were searched for studies related to measuring hospital efficiency in Iran. Ordinary least squares (OLS) regression models were applied for statistical analysis. The PRISMA guidelines were followed in the search process. Results A total of 43 efficiency scores from 29 studies were retrieved and used to approach the research question. Data envelopment analysis was the principal frontier efficiency method in the estimation of efficiency scores. The pooled estimate of mean TE was 0.846 (±0.134). There was a considerable variation in the efficiency scores between the different studies performed in Iran. There were no differences in efficiency scores between data envelopment analysis (DEA) and stochastic frontier analysis (SFA) techniques. The reviewed studies are generally similar and suffer from similar methodological deficiencies, such as no adjustment for case mix and quality of care differences. The results of OLS regression revealed that studies that included more variables and more heterogeneous hospitals generally reported higher TE. Larger sample size was associated with reporting lower TE. Conclusions The features of frontier-based techniques had a profound impact on the efficiency scores among Iranian hospital studies. These studies suffer from major methodological deficiencies and were of sub-optimal quality, limiting their validity and reliability. It is suggested that improving data collection and processing in Iranian hospital databases may have a substantial impact on promoting the quality of research in this field. PMID:23945011
Cyclooxygenase (COX) Inhibitors and the Newborn Kidney
Smith, Francine G.; Wade, Andrew W.; Lewis, Megan L.; Qi, Wei
2012-01-01
This review summarizes our current understanding of the role of cyclo-oxygenase inhibitors (COXI) in influencing the structural development as well as the function of the developing kidney. COXI administered either during pregnancy or after birth can influence kidney development including nephronogenesis, and can decrease renal perfusion and ultrafiltration potentially leading to acute kidney injury in the newborn period. To date, which COX isoform (COX-1 or COX-2) plays a more important role in during fetal development and influences kidney function early in life is not known, though evidence points to a predominant role for COX-2. Clinical implications of the use of COXI in pregnancy and in the newborn infant are also evaluated herein, with specific reference to the potential effects of COXI on nephronogenesis as well as newborn kidney function. PMID:24281306
Levy, Jonathan I; Clougherty, Jane E; Baxter, Lisa K; Houseman, E Andres; Paciorek, Christopher J
2010-12-01
Previous studies have identified associations between traffic exposures and a variety of adverse health effects, but many of these studies relied on proximity measures rather than measured or modeled concentrations of specific air pollutants, complicating interpretability of the findings. An increasing number of studies have used land-use regression (LUR) or other techniques to model small-scale variability in concentrations of specific air pollutants. However, these studies have generally considered a limited number of pollutants, focused on outdoor concentrations (or indoor concentrations of ambient origin) when indoor concentrations are better proxies for personal exposures, and have not taken full advantage of statistical methods for source apportionment that may have provided insight about the structure of the LUR models and the interpretability of model results. Given these issues, the primary objective of our study was to determine predictors of indoor and outdoor residential concentrations of multiple traffic-related air pollutants within an urban area, based on a combination of central site monitoring data; geographic information system (GIS) covariates reflecting traffic and other outdoor sources; questionnaire data reflecting indoor sources and activities that affect ventilation rates; and factor-analytic methods to better infer source contributions. As part of a prospective birth cohort study assessing asthma etiology in urban Boston, we collected indoor and/or outdoor 3-to-4 day samples of nitrogen dioxide (NO2) and fine particulate matter with an aerodynamic diameter or = 2.5 pm (PM2.5) at 44 residences during multiple seasons of the year from 2003 through 2005. We performed reflectance analysis, x-ray fluorescence spectroscopy (XRF), and high-resolution inductively coupled plasma-mass spectrometry (ICP-MS) on particle filters to estimate the concentrations of elemental carbon (EC), trace elements, and water-soluble metals, respectively. We derived
Levy, Jonathan I; Clougherty, Jane E; Baxter, Lisa K; Houseman, E Andres; Paciorek, Christopher J
2010-12-01
Previous studies have identified associations between traffic exposures and a variety of adverse health effects, but many of these studies relied on proximity measures rather than measured or modeled concentrations of specific air pollutants, complicating interpretability of the findings. An increasing number of studies have used land-use regression (LUR) or other techniques to model small-scale variability in concentrations of specific air pollutants. However, these studies have generally considered a limited number of pollutants, focused on outdoor concentrations (or indoor concentrations of ambient origin) when indoor concentrations are better proxies for personal exposures, and have not taken full advantage of statistical methods for source apportionment that may have provided insight about the structure of the LUR models and the interpretability of model results. Given these issues, the primary objective of our study was to determine predictors of indoor and outdoor residential concentrations of multiple traffic-related air pollutants within an urban area, based on a combination of central site monitoring data; geographic information system (GIS) covariates reflecting traffic and other outdoor sources; questionnaire data reflecting indoor sources and activities that affect ventilation rates; and factor-analytic methods to better infer source contributions. As part of a prospective birth cohort study assessing asthma etiology in urban Boston, we collected indoor and/or outdoor 3-to-4 day samples of nitrogen dioxide (NO2) and fine particulate matter with an aerodynamic diameter or = 2.5 pm (PM2.5) at 44 residences during multiple seasons of the year from 2003 through 2005. We performed reflectance analysis, x-ray fluorescence spectroscopy (XRF), and high-resolution inductively coupled plasma-mass spectrometry (ICP-MS) on particle filters to estimate the concentrations of elemental carbon (EC), trace elements, and water-soluble metals, respectively. We derived
Olvera, Hector A; Garcia, Mario; Li, Wen-Whai; Yang, Hongling; Amaya, Maria A; Myers, Orrin; Burchiel, Scott W; Berwick, Marianne; Pingitore, Nicholas E
2012-05-15
The use of land-use regression (LUR) techniques for modeling small-scale variations of intraurban air pollution has been increasing in the last decade. The most appealing feature of LUR techniques is the economical monitoring requirements. In this study, principal component analysis (PCA) was employed to optimize an LUR model for PM2.5. The PM2.5 monitoring network consisted of 13 sites, which constrained the regression model to a maximum of one independent variable. An optimized surrogate of vehicle emissions was produced by PCA and employed as the predictor variable in the model. The vehicle emissions surrogate consisted of a linear combination of several traffic variables (e.g., vehicle miles traveled, speed, traffic demand, road length, and time) obtained from a road network used for traffic modeling. The vehicle-emissions surrogate produced by the PCA had a predictive capacity greater (R2=.458) than the traffic variable, Traffic Demand summarized for a 1 km buffer, with best predictive capacity (R2=.341). The PCA-based method employed in this study was effective at increasing the fit of an ordinary LUR model by optimizing the utilization of a PM2.5 dataset from small-n monitoring network. In general, the method used can contribute to LUR techniques in two major ways: 1) by improving the predictive power of the input variable, by substituting a principal component for a single variable and 2) by creating an orthogonal set of predictor variables, and thus fulfilling the no colinearity assumption of the linear regression methods. The proposed PCA method, should be universally applicable to LUR methods and will expand their economical attractiveness.
Stauffer, Melissa E; Weisenfluh, Lauren; Morrison, Alan
2013-01-01
Background Triglyceride levels were found to be independently predictive of the development of primary coronary heart disease in epidemiologic studies. The objective of this study was to determine whether triglyceride levels were predictive of cardiovascular events in randomized controlled trials (RCTs) of lipid-modifying drugs. Methods We performed a systematic review and meta-regression analysis of 40 RCTs of lipid-modifying drugs with cardiovascular events as an outcome. The log of the rate ratio of cardiovascular events (eg, coronary death or myocardial infarction) was plotted against the proportional difference between treatment and control groups in triglyceride and other lipid levels (high density lipoprotein cholesterol [HDL-C], low density lipoprotein cholesterol [LDL-C], and total cholesterol) for all trials and for trials of primary and secondary prevention populations. Linear regression was used to determine the statistical significance of the relationship between lipid values and cardiovascular events. Results The proportional difference in triglyceride levels was predictive of cardiovascular events in all trials (P=0.005 for the slope of the regression line; N=40) and in primary prevention trials (P=0.010; N=11), but not in secondary prevention trials (P=0.114; N=25). The proportional difference in HDL-C was not predictive of cardiovascular events in all trials (P=0.822; N=40), or in trials of primary (P=0.223; N=11) or secondary (P=0.487; N=25) prevention. LDL-C levels were predictive of cardiovascular events in both primary (P=0.002; N=11) and secondary (P<0.001; N=25) populations. Conclusions Changes in triglyceride levels were predictive of cardiovascular events in RCTs. This relationship was significant in primary prevention populations but not in secondary prevention populations. PMID:24204156
Jage, C R; Zipper, C E; Noble, R
2001-01-01
Use of successive alkalinity-producing systems (SAPS) for treatment of acidic mine drainage (AMD) has grown in recent years. However, inconsistent performance has hampered widespread acceptance of this technology. This research was conducted to determine the influence of system design and influent AMD chemistry on net alkalinity generation by SAPS. Monthly observations were obtained from eight SAPS cells in southern West Virginia and southwestern Virginia. Analysis of these data revealed strong, positive correlations between net alkalinity generation and three variables: the natural log of limestone residence time, influent dissolved Fe concentration, and influent non-Mn acidity. A statistical model was constructed to describe SAPS performance. Subsequent analysis of data obtained from five systems in western Pennsylvania (calibration data set) was used to reevaluate the model form, and the statistical model was adjusted using the combined data sets. Limestone residence time exhibited a strong, positive logarithmic correlation with net alkalinity generation, indicating net alkalinity generation occurs most rapidly within the first few hours of AMD-limestone contact and additional residence time yields diminishing gains in treatment. Influent Fe and non-Mn acidity concentrations both show strong positive linear relationships with net alkalinity generation, reflecting the increased solubility of limestone under acidic conditions. These relationships were present in the original and the calibration data sets, separately, and in the statistical model derived from the combined data set. In the combined data set, these three factors accounted for 68% of the variability in SAPS systems performance. PMID:11401248
Regression problems for magnitudes
NASA Astrophysics Data System (ADS)
Castellaro, S.; Mulargia, F.; Kagan, Y. Y.
2006-06-01
Least-squares linear regression is so popular that it is sometimes applied without checking whether its basic requirements are satisfied. In particular, in studying earthquake phenomena, the conditions (a) that the uncertainty on the independent variable is at least one order of magnitude smaller than the one on the dependent variable, (b) that both data and uncertainties are normally distributed and (c) that residuals are constant are at times disregarded. This may easily lead to wrong results. As an alternative to least squares, when the ratio between errors on the independent and the dependent variable can be estimated, orthogonal regression can be applied. We test the performance of orthogonal regression in its general form against Gaussian and non-Gaussian data and error distributions and compare it with standard least-square regression. General orthogonal regression is found to be superior or equal to the standard least squares in all the cases investigated and its use is recommended. We also compare the performance of orthogonal regression versus standard regression when, as often happens in the literature, the ratio between errors on the independent and the dependent variables cannot be estimated and is arbitrarily set to 1. We apply these results to magnitude scale conversion, which is a common problem in seismology, with important implications in seismic hazard evaluation, and analyse it through specific tests. Our analysis concludes that the commonly used standard regression may induce systematic errors in magnitude conversion as high as 0.3-0.4, and, even more importantly, this can introduce apparent catalogue incompleteness, as well as a heavy bias in estimates of the slope of the frequency-magnitude distributions. All this can be avoided by using the general orthogonal regression in magnitude conversions.
NASA Technical Reports Server (NTRS)
Gohil, B. S.; Hariharan, T. A.; Sharma, A. K.; Pandey, P. C.
1982-01-01
The 19.35 GHz and 22.235 GHz passive microwave radiometers (SAMIR) on board the Indian satellite Bhaskara have provided very useful data. From these data has been demonstrated the feasibility of deriving atmospheric and ocean surface parameters such as water vapor content, liquid water content, rainfall rate and ocean surface winds. Different approaches have been tried for deriving the atmospheric water content. The statistical and empirical methods have been used by others for the analysis of the Nimbus data. A simulation technique has been attempted for the first time for 19.35 GHz and 22.235 GHz radiometer data. The results obtained from three different methods are compared with radiosonde data. A case study of a tropical depression has been undertaken to demonstrate the capability of Bhaskara SAMIR data to show the variation of total water vapor and liquid water contents.
Factors linked to outcomes in sexually abused girls: a regression tree analysis.
Hébert, Martine; Collin-Vézina, Delphine; Daigneault, Isabelle; Parent, Nathalie; Tremblay, Caroline
2006-01-01
Children who report sexual abuse (SA) have been found to display a range of internalizing and externalizing behavior problems. In the present study, a tree-based analysis was used to derive models predicting the variability of internalizing and externalizing behavior problems as well as dissociation symptoms in SA girls. Participants were 150 girls aged 4 to 12 years referred to a specialized pediatric clinic after disclosure of SA. The potential predictors taken into account included sociodemographic and abuse-related variables as well as maternal and family characteristics. The models obtained point to prior abuse as a salient variable in predicting outcomes of SA girls. Implications for the treatment for children disclosing SA are discussed.
Lesterhuis, W. Joost; Rinaldi, Catherine; Jones, Anya; Rozali, Esdy N.; Dick, Ian M.; Khong, Andrea; Boon, Louis; Robinson, Bruce W.; Nowak, Anna K.; Bosco, Anthony; Lake, Richard A.
2015-01-01
Cancer immunotherapy has shown impressive results, but most patients do not respond. We hypothesized that the effector response in the tumour could be visualized as a complex network of interacting gene products and that by mapping this network we could predict effective pharmacological interventions. Here, we provide proof of concept for the validity of this approach in a murine mesothelioma model, which displays a dichotomous response to anti-CTLA4 immune checkpoint blockade. Network analysis of gene expression profiling data from responding versus non-responding tumours was employed to identify modules associated with response. Targeting the modules via selective modulation of hub genes or alternatively by using repurposed pharmaceuticals selected on the basis of their expression perturbation signatures dramatically enhanced the efficacy of CTLA4 blockade in this model. Our approach provides a powerful platform to repurpose drugs, and define contextually relevant novel therapeutic targets. PMID:26193793
NASA Astrophysics Data System (ADS)
Liu, Bilan; Qiu, Xing; Zhu, Tong; Tian, Wei; Hu, Rui; Ekholm, Sven; Schifitto, Giovanni; Zhong, Jianhui
2016-03-01
Subject-specific longitudinal DTI study is vital for investigation of pathological changes of lesions and disease evolution. Spatial Regression Analysis of Diffusion tensor imaging (SPREAD) is a non-parametric permutation-based statistical framework that combines spatial regression and resampling techniques to achieve effective detection of localized longitudinal diffusion changes within the whole brain at individual level without a priori hypotheses. However, boundary blurring and dislocation limit its sensitivity, especially towards detecting lesions of irregular shapes. In the present study, we propose an improved SPREAD (dubbed improved SPREAD, or iSPREAD) method by incorporating a three-dimensional (3D) nonlinear anisotropic diffusion filtering method, which provides edge-preserving image smoothing through a nonlinear scale space approach. The statistical inference based on iSPREAD was evaluated and compared with the original SPREAD method using both simulated and in vivo human brain data. Results demonstrated that the sensitivity and accuracy of the SPREAD method has been improved substantially by adapting nonlinear anisotropic filtering. iSPREAD identifies subject-specific longitudinal changes in the brain with improved sensitivity, accuracy, and enhanced statistical power, especially when the spatial correlation is heterogeneous among neighboring image pixels in DTI.
Liu, Bilan; Qiu, Xing; Zhu, Tong; Tian, Wei; Hu, Rui; Ekholm, Sven; Schifitto, Giovanni; Zhong, Jianhui
2016-03-21
Subject-specific longitudinal DTI study is vital for investigation of pathological changes of lesions and disease evolution. Spatial Regression Analysis of Diffusion tensor imaging (SPREAD) is a non-parametric permutation-based statistical framework that combines spatial regression and resampling techniques to achieve effective detection of localized longitudinal diffusion changes within the whole brain at individual level without a priori hypotheses. However, boundary blurring and dislocation limit its sensitivity, especially towards detecting lesions of irregular shapes. In the present study, we propose an improved SPREAD (dubbed improved SPREAD, or iSPREAD) method by incorporating a three-dimensional (3D) nonlinear anisotropic diffusion filtering method, which provides edge-preserving image smoothing through a nonlinear scale space approach. The statistical inference based on iSPREAD was evaluated and compared with the original SPREAD method using both simulated and in vivo human brain data. Results demonstrated that the sensitivity and accuracy of the SPREAD method has been improved substantially by adapting nonlinear anisotropic filtering. iSPREAD identifies subject-specific longitudinal changes in the brain with improved sensitivity, accuracy, and enhanced statistical power, especially when the spatial correlation is heterogeneous among neighboring image pixels in DTI. PMID:26948513
Lee, Yeonjung; Ha, Sun-Yong; Park, Hae-Kyung; Han, Myung-Soo; Shin, Kyung-Hoon
2015-04-01
To understand the factors controlling algal production in two lakes located on the Han River in South Korea, Lake Cheongpyeong and Lake Paldang, a principal component regression model study was conducted using environmental monitoring and primary productivity data. Although the two lakes were geographically close and located along the same river system, the main factors controlling primary productivity in each lake were different: hydraulic retention time and light conditions predominantly influenced algal productivity in Lake Cheongpyeong, while hydraulic retention time, chlorophyll a-specific productivity, and zooplankton grazing rate were most important in Lake Paldang. This investigation confirmed the utility of principal component regression analysis using environmental monitoring data for predicting complex biological processes such as primary productivity; in addition, the study also increased the understanding of explicit interactions between environmental variables. The findings obtained in this research will be useful for the adaptive management of water reservoirs. The results will also aid in the development of management strategies for water resources, thereby improving total environmental conservation.
Juhasz, Albert L; Weber, John; Smith, Euan
2011-12-15
A number of in vitro assays are available for the determination of arsenic (As) bioaccessibility and prediction of As relative bioavailability (RBA) to quantify exposure for site-specific risk assessment. These data are usually considered in isolation; however, meta analysis may provide predictive capabilities for source-specific As bioaccessibility and RBA. The objectives of this study were to predict As RBA using previously published in vivo/in vitro correlations and to assess the influence of As sources on As RBA independent of geographical location. Data representing 351 soils (classified based on As source) and 514 independent bioaccessibility values were retrieved from the literature for comparison. Arsenic RBA was predicted using published in vivo/in vitro regression models, and 90th and 95th percentiles were determined for each As source classification and in vitro methodology. Differences in predicted mean As RBA were observed among soils contaminated from different As sources and within source materials when various in vitro methodologies were utilized. However, when in vitro data were standardized by transforming SBRC intestinal, IVG, and PBET data to SBRC gastric phase values (through linear regression models), predicted As RBA values for As sources followed the order CCA posts ≥ herbicide/pesticide > mining/smelting > gossan soils with 95th percentiles for predicted As RBA of 78.0, 78.4, 67.0, and 23.7%, respectively.
Jansson, Bruce S; Nyamathi, Adeline; Heidemann, Gretchen; Duan, Lei; Kaplan, Charles
2015-01-01
Although literature documents the need for hospital social workers, nurses, and medical residents to engage in patient advocacy, little information exists about what predicts the extent they do so. This study aims to identify predictors of health professionals' patient advocacy engagement with respect to a broad range of patients' problems. A cross-sectional research design was employed with a sample of 94 social workers, 97 nurses, and 104 medical residents recruited from eight hospitals in Los Angeles. Bivariate correlations explored whether seven scales (Patient Advocacy Eagerness, Ethical Commitment, Skills, Tangible Support, Organizational Receptivity, Belief Other Professionals Engage, and Belief the Hospital Empowers Patients) were associated with patient advocacy engagement, measured by the validated Patient Advocacy Engagement Scale. Regression analysis examined whether these scales, when controlling for sociodemographic and setting variables, predicted patient advocacy engagement. While all seven predictor scales were significantly associated with patient advocacy engagement in correlational analyses, only Eagerness, Skills, and Belief the Hospital Empowers Patients predicted patient advocacy engagement in regression analyses. Additionally, younger professionals engaged in higher levels of patient advocacy than older professionals, and social workers engaged in greater patient advocacy than nurses. Limitations and the utility of these findings for acute-care hospitals are discussed. PMID:26317762
Jansson, Bruce S; Nyamathi, Adeline; Heidemann, Gretchen; Duan, Lei; Kaplan, Charles
2015-01-01
Although literature documents the need for hospital social workers, nurses, and medical residents to engage in patient advocacy, little information exists about what predicts the extent they do so. This study aims to identify predictors of health professionals' patient advocacy engagement with respect to a broad range of patients' problems. A cross-sectional research design was employed with a sample of 94 social workers, 97 nurses, and 104 medical residents recruited from eight hospitals in Los Angeles. Bivariate correlations explored whether seven scales (Patient Advocacy Eagerness, Ethical Commitment, Skills, Tangible Support, Organizational Receptivity, Belief Other Professionals Engage, and Belief the Hospital Empowers Patients) were associated with patient advocacy engagement, measured by the validated Patient Advocacy Engagement Scale. Regression analysis examined whether these scales, when controlling for sociodemographic and setting variables, predicted patient advocacy engagement. While all seven predictor scales were significantly associated with patient advocacy engagement in correlational analyses, only Eagerness, Skills, and Belief the Hospital Empowers Patients predicted patient advocacy engagement in regression analyses. Additionally, younger professionals engaged in higher levels of patient advocacy than older professionals, and social workers engaged in greater patient advocacy than nurses. Limitations and the utility of these findings for acute-care hospitals are discussed.
Silva, Ana Elisa Pereira; Freitas, Corina da Costa; Dutra, Luciano Vieira; Molento, Marcelo Beltrão
2016-02-15
Fasciola hepatica is the causative agent of fasciolosis, a disease that triggers a chronic inflammatory process in the liver affecting mainly ruminants and other animals including humans. In Brazil, F. hepatica occurs in larger numbers in the most Southern state of Rio Grande do Sul. The objective of this study was to estimate areas at risk using an eight-year (2002-2010) time series of climatic and environmental variables that best relate to the disease using a linear regression method to municipalities in the state of Rio Grande do Sul. The positivity index of the disease, which is the rate of infected animal per slaughtered animal, was divided into three risk classes: low, medium and high. The accuracy of the known sample classification on the confusion matrix for the low, medium and high rates produced by the estimated model presented values between 39 and 88% depending of the year. The regression analysis showed the importance of the time-based data for the construction of the model, considering the two variables of the previous year of the event (positivity index and maximum temperature). The generated data is important for epidemiological and parasite control studies mainly because F. hepatica is an infection that can last from months to years. PMID:26827853
NASA Astrophysics Data System (ADS)
Pevná, Hana; Jeníček, Michal
2014-05-01
Snow is the important component of hydrological cycle in the central Europe. Large quantity of water is accumulated as snow during winter period and this water runs off into rivers in relative short time during spring period. Increased risk of floods in central Europe exists namely in alpine and pre-alpine catchments which have the pluvio-nival flow regime. Research of snow accumulation and snowmelt processes is important for runoff forecast and reservoir management. The research is carried out in small mountain catchments in the Czech Republic. Experimental catchments are differing in elevation range, aspect, slope and type of vegetation cover. Automatic and field measurements of the snow depth and snow water equivalent (SWE) have been caring out at specific localities since 2008. Each locality is specified with elevation, aspect, slope and vegetation type (open area, clearing, young forest, sparse mature forest and dense mature forest). Measurements of snow depth and SWE are carried out at 19 localities both during snow accumulation and snow melt period. Data of snow depth and SWE were assessed using both simple statistical analysis and multiple regression and cluster analysis in order to describe the spatial distribution in snow accumulation and snowmelt. The correlation of SWE with vegetation type, elevation, aspect and slope was tested. The main findings of the research show that vegetation type has the most significant influence on the snowpack distribution and on the snow accumulation and snowmelt dynamics. Significant correlations were also proved for aspect (especially for southern slopes). The study completes similar results carried out in different study areas and climatic conditions but moreover it shows changes of importace of governing factors during snow accumulation and snowmelt periods. The results demonstrate a good applicability of cluster analysis and multiple regression for description of snowpack distribution.
Koizumi, Itsuro; Yamamoto, Shoichiro; Maekawa, Koji
2006-10-01
Isolation by distance is usually tested by the correlation of genetic and geographic distances separating all pairwise populations' combinations. However, this method can be significantly biased by only a few highly diverged populations and lose the information of individual population. To detect outlier populations and investigate the relative strengths of gene flow and genetic drift for each population, we propose a decomposed pairwise regression analysis. This analysis was applied to the well-described one-dimensional stepping-stone system of stream-dwelling Dolly Varden charr (Salvelinus malma). When genetic and geographic distances were plotted for all pairs of 17 tributary populations, the correlation was significant but weak (r(2) = 0.184). Seven outlier populations were determined based on the systematic bias of the regression residuals, followed by Akaike's information criteria. The best model, 10 populations included, showed a strong pattern of isolation by distance (r(2) = 0.758), suggesting equilibrium between gene flow and genetic drift in these populations. Each outlier population was also analysed by plotting pairwise genetic and geographic distances against the 10 nonoutlier populations, and categorized into one of the three patterns: strong genetic drift, genetic drift with a limited gene flow and a high level of gene flow. These classifications were generally consistent with a priori predictions for each population (physical barrier, population size, anthropogenic impacts). Combined the genetic analysis with field observations, Dolly Varden in this river appeared to form a mainland-island or source-sink metapopulation structure. The generality of the method will merit many types of spatial genetic analyses.
De la Cruz, Rolando; Meza, Cristian; Arribas-Gil, Ana; Carroll, Raymond J.
2016-01-01
Joint models for a wide class of response variables and longitudinal measurements consist on a mixed-effects model to fit longitudinal trajectories whose random effects enter as covariates in a generalized linear model for the primary response. They provide a useful way to assess association between these two kinds of data, which in clinical studies are often collected jointly on a series of individuals and may help understanding, for instance, the mechanisms of recovery of a certain disease or the efficacy of a given therapy. When a nonlinear mixed-effects model is used to fit the longitudinal trajectories, the existing estimation strategies based on likelihood approximations have been shown to exhibit some computational efficiency problems (De la Cruz et al., 2011). In this article we consider a Bayesian estimation procedure for the joint model with a nonlinear mixed-effects model for the longitudinal data and a generalized linear model for the primary response. The proposed prior structure allows for the implementation of an MCMC sampler. Moreover, we consider that the errors in the longitudinal model may be correlated. We apply our method to the analysis of hormone levels measured at the early stages of pregnancy that can be used to predict normal versus abnormal pregnancy outcomes. We also conduct a simulation study to assess the importance of modelling correlated errors and quantify the consequences of model misspecification. PMID:27274601
Regression Analysis of Long-term Profile Ozone Data Set from BUV Instruments
NASA Technical Reports Server (NTRS)
Frith, Stacey; Taylor, Steve; DeLand, Matt; Ahn, Chang-Woo; Stolarski, Richard S.
2005-01-01
We have produced a profile merged ozone data set (MOD) based on the SBUV/SBUV2 series of nadir-viewing satellite backscatter instruments, covering the period from November 1978 - December 2003. In 2004, data from the Nimbus 7 SBUV and NOAA 9,11, and 16 SBUV/2 instruments were reprocessed using the Version 8 (V8) algorithm and most recent calibrations. More recently, data from the Nimbus 4 BUV instrument, which operated from 1970 - 1977, were also reprocessed using the V8 algorithm. As part of the V8 profile calibration, the Nimbus 7 and NOAA 9 (1993-1997 only) instrument calibrations have been adjusted to match the NOAA 11 calibration, which was established from comparisons with SSBUV shuttle flight data. Given the level of agreement between the data sets, we simply average the ozone values during periods of instrument overlap to produce the MOD profile data set. We use statistical time-series analysis of the MOD profile data set (1978-2003) to estimate the change in profile ozone due to changing stratospheric chlorine levels. The Nimbus 4 BUV data offer an opportunity to test the physical properties of our statistical model. We extrapolate our statistical model fit backwards in time and compare to the Nimbus 4 data. We compare the statistics of the residuals from the fit for the Nimbus 4 period to those obtained from the 1978-2003 period over which the statistical model coefficients were estimated.
miR-101 inhibits glioma cell invasion via the downregulation of COX-2
Ma, Chunyang; Zheng, Chuanyi; Bai, Enqi; Yang, Kun
2016-01-01
Glioma is the most common type of primary tumor of the central nervous system. The present study aimed to demonstrate the role of miR-101 and cyclooxygenase-2 (COX-2) in the initiation and development of glioma. The expression of miR-101 and COX-2 in normal and malignant human glial cells and tissues was determined by western blotting and quantitative polymerase chain reaction analysis. The role of miR-101 on COX-2 expression was evaluated by a dual-luciferase reporter assay. The effects of miR-101 and COX-2 in glioma cell proliferation and invasion was verified by CCK-8 test and Transwell assays, respectively. The present study demonstrated that miR-101 expression was downregulated while COX-2 was upregulated in glioma tissues and cells. Furthermore, transfection of miR-101 significantly downregulated COX-2 expression in both U373 and U87 glioma cells. In addition, further experiments revealed that overexpression of miR-101 resulted in significant inhibition of the in vitro proliferation and migration of glioma cells, and the in vivo growth of established tumors. Direct downregulation of COX-2 by transfection with corresponding small interfering RNA also inhibited the proliferation and invasion of glioma cells. These results indicate that downregulation of miR-101 is involved in the initiation and development of glioma via COX-2 upregulation.
miR-101 inhibits glioma cell invasion via the downregulation of COX-2
Ma, Chunyang; Zheng, Chuanyi; Bai, Enqi; Yang, Kun
2016-01-01
Glioma is the most common type of primary tumor of the central nervous system. The present study aimed to demonstrate the role of miR-101 and cyclooxygenase-2 (COX-2) in the initiation and development of glioma. The expression of miR-101 and COX-2 in normal and malignant human glial cells and tissues was determined by western blotting and quantitative polymerase chain reaction analysis. The role of miR-101 on COX-2 expression was evaluated by a dual-luciferase reporter assay. The effects of miR-101 and COX-2 in glioma cell proliferation and invasion was verified by CCK-8 test and Transwell assays, respectively. The present study demonstrated that miR-101 expression was downregulated while COX-2 was upregulated in glioma tissues and cells. Furthermore, transfection of miR-101 significantly downregulated COX-2 expression in both U373 and U87 glioma cells. In addition, further experiments revealed that overexpression of miR-101 resulted in significant inhibition of the in vitro proliferation and migration of glioma cells, and the in vivo growth of established tumors. Direct downregulation of COX-2 by transfection with corresponding small interfering RNA also inhibited the proliferation and invasion of glioma cells. These results indicate that downregulation of miR-101 is involved in the initiation and development of glioma via COX-2 upregulation. PMID:27698824
Viscum album-Mediated COX-2 Inhibition Implicates Destabilization of COX-2 mRNA
Saha, Chaitrali; Hegde, Pushpa; Friboulet, Alain; Bayry, Jagadeesh; Kaveri, Srinivas V.
2015-01-01
Extensive use of Viscum album (VA) preparations in the complementary therapy of cancer and in several other human pathologies has led to an increasing number of cellular and molecular approaches to explore the mechanisms of action of VA. We have recently demonstrated that, VA preparations exert a potent anti-inflammatory effect by selectively down-regulating the COX-2-mediated cytokine-induced secretion of prostaglandin E2 (PGE2), one of the important molecular signatures of inflammatory reactions. In this study, we observed a significant down-regulation of COX-2 protein expression in VA-treated A549 cells however COX-2 mRNA levels were unaltered. Therefore, we hypothesized that VA induces destabilisation of COX-2 mRNA, thereby depleting the available functional COX-2 mRNA for the protein synthesis and for the subsequent secretion of PGE2. To address this question, we analyzed the molecular degradation of COX-2 protein and its corresponding mRNA in A549 cell line. Using cyclohexamide pulse chase experiment, we demonstrate that, COX-2 protein degradation is not affected by the treatment with VA whereas experiments on transcriptional blockade with actinomycin D, revealed a marked reduction in the half life of COX-2 mRNA due to its rapid degradation in the cells treated with VA compared to that in IL-1β-stimulated cells. These results thus demonstrate that VA-mediated inhibition of PGE2 implicates destabilization of COX-2 mRNA. PMID:25664986
NASA Astrophysics Data System (ADS)
Sethuramalingam, Prabhu; Vinayagam, Babu Kupusamy
2016-07-01
Carbon nanotube mixed grinding wheel is used in the grinding process to analyze the surface characteristics of AISI D2 tool steel material. Till now no work has been carried out using carbon nanotube based grinding wheel. Carbon nanotube based grinding wheel has excellent thermal conductivity and good mechanical properties which are used to improve the surface finish of the workpiece. In the present study, the multi response optimization of process parameters like surface roughness and metal removal rate of grinding process of single wall carbon nanotube (CNT) in mixed cutting fluids is undertaken using orthogonal array with grey relational analysis. Experiments are performed with designated grinding conditions obtained using the L9 orthogonal array. Based on the results of the grey relational analysis, a set of optimum grinding parameters is obtained. Using the analysis of variance approach the significant machining parameters are found. Empirical model for the prediction of output parameters has been developed using regression analysis and the results are compared empirically, for conditions of with and without CNT grinding wheel in grinding process.
Ying, Yung-Hsiang; Wu, Chin-Chih; Chang, Koyin
2013-01-01
To understand the impact of drinking and driving laws on drinking and driving fatality rates, this study explored the different effects these laws have on areas with varying severity rates for drinking and driving. Unlike previous studies, this study employed quantile regression analysis. Empirical results showed that policies based on local conditions must be used to effectively reduce drinking and driving fatality rates; that is, different measures should be adopted to target the specific conditions in various regions. For areas with low fatality rates (low quantiles), people’s habits and attitudes toward alcohol should be emphasized instead of transportation safety laws because “preemptive regulations” are more effective. For areas with high fatality rates (or high quantiles), “ex-post regulations” are more effective, and impact these areas approximately 0.01% to 0.05% more than they do areas with low fatality rates. PMID:24084673
2016-01-01
In today's world, Public expenditures on health are one of the most important issues for governments. These increased expenditures are putting pressure on public budgets. Therefore, health policy makers have focused on the performance of their health systems and many countries have introduced reforms to improve the performance of their health systems. This study investigates the most important determinants of healthcare efficiency for OECD countries using second stage approach for Bayesian Stochastic Frontier Analysis (BSFA). There are two steps in this study. First we measure 29 OECD countries' healthcare efficiency by BSFA using the data from the OECD Health Database. At second stage, we expose the multiple relationships between the healthcare efficiency and characteristics of healthcare systems across OECD countries using Bayesian beta regression. PMID:27118987
Li, Zhongwei; Xin, Yuezhen; Wang, Xun; Sun, Beibei; Xia, Shengyu; Li, Hui
2016-01-01
Phellinus is a kind of fungus and is known as one of the elemental components in drugs to avoid cancers. With the purpose of finding optimized culture conditions for Phellinus production in the laboratory, plenty of experiments focusing on single factor were operated and large scale of experimental data were generated. In this work, we use the data collected from experiments for regression analysis, and then a mathematical model of predicting Phellinus production is achieved. Subsequently, a gene-set based genetic algorithm is developed to optimize the values of parameters involved in culture conditions, including inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time, and rotation speed. These optimized values of the parameters have accordance with biological experimental results, which indicate that our method has a good predictability for culture conditions optimization. PMID:27610365
Sander, R.K.; Quagliano, J.R.; Fry, H.
1997-08-01
Until recently use of lasers for long path absorption measurements has relied on using differential absorption at two wavelengths to look for one species at a time in the atmosphere. With the advent of multi-line CO{sub 2} lasers it is now feasible to generate 30 to 40 lines in a rapid burst to look for spectra of all the chemical species that may be present. Measurements have been made under relatively constant meteorological conditions in a summertime desert environment with a multi-line tunable laser. Multivariate regression analysis of this data shows that the spectra can be accurately fit using a small number of spectral factors or eigenvectors of the time dependent spectral data matrix. The factors can be rationalized in terms of lidar system effects and atmospheric composition changes.
NASA Technical Reports Server (NTRS)
Barrett, C. A.
1985-01-01
Multiple linear regression analysis was used to determine an equation for estimating hot corrosion attack for a series of Ni base cast turbine alloys. The U transform (i.e., 1/sin (% A/100) to the 1/2) was shown to give the best estimate of the dependent variable, y. A complete second degree equation is described for the centered" weight chemistries for the elements Cr, Al, Ti, Mo, W, Cb, Ta, and Co. In addition linear terms for the minor elements C, B, and Zr were added for a basic 47 term equation. The best reduced equation was determined by the stepwise selection method with essentially 13 terms. The Cr term was found to be the most important accounting for 60 percent of the explained variability hot corrosion attack.
Duchateau, L; Kruska, R L; Perry, B D
1997-10-01
Large databases with multiple variables, selected because they are available and might provide an insight into establishing causal relationships, are often difficult to analyse and interpret because of multicollinearity. The objective of this study was to reduce the dimensionality of a multivariable spatial database of Zimbabwe, containing many environmental variables that were collected to predict the distribution of outbreaks of theileriosis (the tick-borne infection of cattle caused by Theileria parva and transmitted by the brown ear tick). Principal-component analysis and varimax rotation of the principal components were first used to select a reduced number of variables. The logistic-regression model was evaluated by appropriate goodness-of-fit tests.
Schut, Christina; Weik, Ulrike; Tews, Natalia; Gieler, Uwe; Deinzer, Renate; Kupfer, Jörg
2015-02-01
Even though it has been shown that stress and itch are associated in patients with atopic dermatitis (AD), it remains unclear whether this relationship occurs due to certain coping strategies being activated under stress. Therefore, this study investigates the role of coping as possible mediating factor between stress and itch in 31 patients with AD. Coping and itch were assessed by self-reported measures, while stress was measured both by a validated questionnaire and by a physiological stress marker, the postawakening cortisol. Using a regression and a mediation analysis, this study showed a relationship between perceived stress and itch (corrected R2 = 0.21), which was fully mediated by negative itch-related cognitions. 62.3% of the variance of itch intensity could be explained by negative itch-related cognitions. This finding helps to explain the positive effects of cognitive restructuring in the treatment of chronic itch.
NASA Astrophysics Data System (ADS)
Ravi, D.; Parammasivam, K. M.
2016-09-01
Numerical investigations were conducted on a turbine cascade, with end-wall cooling by a single row of cylindrical holes, inclined at 30°. The mainstream fluid was hot air and the coolant was CO2 gas. Based on the Reynolds number, the flow was turbulent at the inlet. The film hole row position, its pitch and blowing ratio was varied with five different values. Taguchi approach was used in designing a L25 orthogonal array (OA) for these parameters. The end-wall averaged film cooling effectiveness (bar η) was chosen as the quality characteristic. CFD analyses were carried out using Ansys Fluent on computational domains designed with inputs from OA. Experiments were conducted for one chosen OA configuration and the computational results were found to correlate well with experimental measurements. The responses from the CFD analyses were fed to the statistical tool to develop a correlation for bar η using regression analysis.
Albek, E.
1999-12-01
Chloride-discharge relationships at several stations on Turkish streams are investigated, both qualitatively and quantitatively, to identify natural and anthropogenic sources of chloride. Simple expressions are used to distinguish among sources. Linear regression analysis is conducted to estimate parameters of the models. Five groups of stations are distinguished respective to different sources of chloride and change of chloride concentration with stream discharge. Emphasis is placed on the identification of anthropogenic sources of chloride to aid in water pollution control strategies. The polluted Sakarya River and its primary tributary, the Porsuk Stream, are studied in detail to trace chloride behavior along the waterway and to assess the level of pollution from cities discharging to the streams. Among natural sources of chloride, evaporite sediment sources are examined in detail.